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diff --git a/Development/v4.x/backend/authorization/authorization_jobs.html b/Development/v4.x/backend/authorization/authorization_jobs.html
index fbf657cb..78ca29ed 100644
--- a/Development/v4.x/backend/authorization/authorization_jobs.html
+++ b/Development/v4.x/backend/authorization/authorization_jobs.html
@@ -1107,28 +1107,138 @@ Authorization table
Job Create Authorization Table
-These authorization permissions are configured directly in the create section of the job configuration.
They apply to the jobs endpoint POST:Jobs
-| Job Create Authorization | Endpoint Authentication | Endpoint Authentication Description | Instance Authentication | Instance Authentication Description |
-| --- | --- | --- | --- | --- |
-| #all | #all | any user can access this endpoint, both anonymous and authenticated | #all | Any user can create this instance of the job |
-| #admin | ADMINGROUPS | Only users belonging to any group listed in ADMIN_GROUPS will be able to access the endpoint | ADMIN_GROUPS | Only users belonging to any group listed in ADMIN_GROUPS will be able to create this instance of the job |
-| #authenticated | #user | any valid users can access the endpoint, independently from their groups | #user | any valid users can cretae this instance of the job |
-| #datasetPublic | #all | any user can access this endpoint, both anonymous and and authenticated | #datasetPublic | the job instance will be created only if all the datasets listed are public |
-| #datasetAccess | #user | any valid user can access this endpoint, independently from their groups | #datasetAccess | the job instance will be created only if the user has access to all the datasets listed |
-| #datasetOwner | #user | any valid user can access this endpoint, independently from their groups | #datasetOwner | the job instance will be created only if the user is part of all the datasets owner group |
-| GROUP | GROUP | only users that belongs to the specified group can access the endpoint | GROUP | the job instance will be created only if all the datasets listed belong to the group specified |
-| USER | USER | only the specified user can access the endpoint | #datasetOwner_ | the job instance will be created only if all the datasets listed are owned by any of the user's groups |
+These authorization permissions are configured directly in the create section of the job configuration.
They apply to the jobs endpoint POST:Jobs
+
+
+
+| Job Create Authorization |
+Endpoint Authentication |
+Endpoint Authentication Description |
+Instance Authentication |
+Instance Authentication Description |
+
+
+
+
+| #all |
+#all |
+any user can access this endpoint, both anonymous and authenticated |
+#all |
+Any user can create this instance of the job |
+
+
+| #admin |
+ADMIN_GROUPS |
+Only users belonging to any group listed in ADMIN_GROUPS will be able to access the endpoint |
+ADMIN_GROUPS |
+Only users belonging to any group listed in ADMIN_GROUPS will be able to create this instance of the job |
+
+
+| #authenticated |
+#user |
+any valid users can access the endpoint, independently from their groups |
+#user |
+any valid users can cretae this instance of the job |
+
+
+| #datasetPublic |
+#all |
+any user can access this endpoint, both anonymous and and authenticated |
+#datasetPublic |
+the job instance will be created only if all the datasets listed are public |
+
+
+| #datasetAccess |
+#user |
+any valid user can access this endpoint, independently from their groups |
+#datasetAccess |
+the job instance will be created only if the user has access to all the datasets listed |
+
+
+| #datasetOwner |
+#user |
+any valid user can access this endpoint, independently from their groups |
+#datasetOwner |
+the job instance will be created only if the user is part of all the datasets owner group |
+
+
+| GROUP |
+GROUP |
+only users that belongs to the specified group can access the endpoint |
+GROUP |
+the job instance will be created only if all the datasets listed belong to the group specified |
+
+
+| USER |
+USER |
+only the specified user can access the endpoint |
+#datasetOwner |
+the job instance will be created only if all the datasets listed are owned by any of the user's groups |
+
+
+
IMPORTANT: use option #all carefully, as it allows anybody to create a new job. It is mostly use for debuging and testing
Job Status Update Authorization Table
-These authorization permissions are configured directly in the update section of the job configuration.
They apply to the jobs endpoint POST:Jobs/statusUpdate
| Job Status Update Authorization | Endpoint Authentication | Endpoint Authentication Description | Instance Authentication | Instance Authentication Description |
-| --- | --- | --- | --- | --- |
-| #all | #all | any user can access this endpoint, both anonymous and authenticated | #all | Any user can update the status of this job instance |
-| #owner | #user | valid user can access the endpoint | #jobOwner | a user that belongs to the group listed as job owner can perform the update |
-| #admin | ADMINGROUPS | Only users belonging to any group listed in ADMINGROUPS will be able to access the endpoint | ADMINGROUPS | Only users belonging to any group listed in ADMIN_GROUPS are able to update the job status |
-| \ | \ | only users that belongs to the specified group can access the endpoint | \ | the job instance will be created only if all the datasets listed belong to the group specified |
-| \ | \__ | only the specified user can access the endpoint | #datasetOwner_ | the job instance will be created only if all the datasets listed are owned by any of the user's groups |
+These authorization permissions are configured directly in the update section of the job configuration.
They apply to the jobs endpoint POST:Jobs/statusUpdate
+
+
+
+| Job Status Update Authorization |
+Endpoint Authentication |
+Endpoint Authentication Description |
+Instance Authentication |
+Instance Authentication Description |
+
+
+
+
+| #all |
+#all |
+any user can access this endpoint, both anonymous and authenticated |
+#all |
+Any user can update the status of this job instance |
+
+
+| #owner |
+#user |
+valid user can access the endpoint |
+#jobOwner |
+a user that belongs to the group listed as job owner can perform the update |
+
+
+| #admin |
+ADMIN_GROUPS |
+Only users belonging to any group listed in ADMIN_GROUPS will be able to access the endpoint |
+ADMIN_GROUPS |
+Only users belonging to any group listed in ADMIN_GROUPS are able to update the job status |
+
+
+| GROUP |
+GROUP |
+only users that belongs to the specified group can access the endpoint |
+GROUP |
+the job instance will be created only if all the datasets listed belong to the group specified |
+
+
+| USER |
+USER |
+only the specified user can access the endpoint |
+#datasetOwner |
+the job instance will be created only if all the datasets listed are owned by any of the user's groups |
+
+
+
IMPORTANT: use option #all carefully, as it allows anybody to update the status of the job. It is mostly use for debuging and testing
-
+Job Authorization priority
+The endpoint authorization is the most permissive authorization across all the jobs defined.
+The priority between job create and status update authorization is as follow:
+
+graph LR;
+ all-->user;
+ user-->GROUP;
+ GROUP-->USER;
+ USER-->ADMIN_GROUPS;
+
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index 3388c848..c11218fc 100644
--- a/Development/v4.x/backend/authorization/authorization_origdatablocks.html
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index dbccdca4..f64b63f0 100644
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SciCat stores information about datasets, i.e. a collection of files, rather than single files. The persons responsible for adding new data to the data catalog do this, by defining these sets of files and jointly ingesting them to the system.\nPlease note that the data catalog does not store the file contents itself. It just stores the metadata , i.e. semantic information which is useful to understand the contents of the files contained in the dataset and the list of the filenames, their sizes etc.\nThe datasets can contain raw data , e.g. data coming from a measurement device or instrument and also derived data, which is data derived (calculated) from the raw data. \n"},"Users/Anonymous.html":{"url":"Users/Anonymous.html","title":"Anonymous View","keywords":"","body":"Anonymous View\nAs soon as you visit the starting page of the GUI application, and you are not yet logged in, you will see a list of datasets, which have been published, and can therefore be looked at anonymously:\nYou can filter the datasets by choosing the \"facets\" on the left navigation section (in the screenshot for the location field a value of \"V20\" was selected).\n\nFrom this list you can choose a particular dataset to see the details of this dataset\n\n"},"Users/Login.html":{"url":"Users/Login.html","title":"Login","keywords":"","body":"Login\nTo get access to all the data, for which you have read access, you first have to login. To login hit the \"Sign in\" Icon at the top right corner.\n\nThere are two types of account associated with the DataCatalog: Functional and User. A functional account will primarily be used by software and system administrators to deal with backups and other tasks.\nUser accounts are tied into the login system that is used by your institution, for example: Active Directory. You are able to log in to the system using the same credentials you use on that account. This process is called authenitication in IT tech terminology\nWhen you login as a user your user management system will assign groups to the logged in user. Each dataset is also assigned to one such group (via the so called ownerGroup field), and you can view the datasets only, if you are member of the corresponding group. The logic that defines, what parts of the data you can see, is called \"authorization\" in IT terminology\nAnother example login page from PSI is here\n\n"},"Users/Dashboard.html":{"url":"Users/Dashboard.html","title":"Dashboard","keywords":"","body":"Dashboard\nThe dashboard is the first page that you see after being logged in. It contains an overview of all datasets that you have access to.\n\nMenu access to different information pages\nYou can always navigate to other parts of the application, simply by clicking on the user icon on the top right corner \n\nFiltering Datasets\nYou can currently filter across 5 different fields:\n\nLocation (= field creationLocation)\nGroups (= field ownerGroup)\nType (=field type - e.g. raw data or derived data )\nKeywords =field keywords, the tags added to the datasets)\nStart - End Date ( = field createdAt, show datasets captured between the dates that you have set)\n\nThe text fields provide an auto completion, which becomes visible as you type. \nOne click on the date calendar selects the start date and a second selects the end date. Make sure you select 2 dates.\nIn the following screenshot the datasets are filterd by the condition ownerGroup=\"p17301\"\n\nSearching\nThe text field at the top of the navigation bar allows you to search the metadata for any word contained in the metadata (but not arbitrary substrings). The search starts automatically when to start to type in this textfield, so better type fast ;-) \nConfigure table columns\nThe cog wheel symbol on the top right allows to define the columns, that you want to see in the table\nView Details\nTo view a dataset simply click on it in the table and a more detailed view will load (this is covered in the next section)\n"},"Users/Dataset_Details.html":{"url":"Users/Dataset_Details.html","title":"Dataset Details","keywords":"","body":"Dataset Details View\nTo view a dataset simply click on it in the table in the dashboard and a more detailed view will load\n\nFrom here you can navigate to the Datafiles File Listing View as well to a view, where you can add image attachments\nYou can also navigate to a Jupyterhub instance, by clicking the orange button at the top of the page. This leads you (if configured) to a Jupyterhub site, where you can login in order to analyze the data in the meta data catalog. This is mainly meant as a way to pursue statistical analysis on the meta data.\nE.g. at PSI you will be given a set of predefined Jupyter notebooks, which retrieve the data contained in the data catalog by calling the data catalog interfaces (API server) and then provide pivot tables and graphs of the data, such as accumulated data over time etc. \n"},"Users/Dataset_Details_Files.html":{"url":"Users/Dataset_Details_Files.html","title":"Dataset Filelisting","keywords":"","body":"Dataset File Listing View\nTo view the files contained in a dataset go to the \"Datafiles tab\" in the dataset details view. This will then show the files\n\n"},"Users/Dataset_Attachments.html":{"url":"Users/Dataset_Attachments.html","title":"Dataset Attachments","keywords":"","body":"Dataset Attachments\nOn the dataset details page, you can click on the Attachments tab\n\nSimply follow the instructions to upload an image. The size is restricted to be below 16 MB however\nThis short video demonstrates how you can add an attachment to your dataset.\n"},"Users/View_Raw_Metadata.html":{"url":"Users/View_Raw_Metadata.html","title":"View Raw JSON data","keywords":"","body":"View Raw Metadata\nTo see all details of the stored metadata as a raw JSON format, just click the \"SHow Metadata\" button on the bottom of the dataset details page\n\n"},"Users/Edit_Scientific_Metadata.html":{"url":"Users/Edit_Scientific_Metadata.html","title":"Edit Scientific Metadata","keywords":"","body":"Edit Scientific Metadata\nIf enabled, fields in the scientific metadata can be modified and edited by the owner of the data by hitting the \"Edit\" Icon. The user can add,remove or change metadata fields, every change will create a new record in the databse with it's history.\n\nThis short video demonstrates how you can edit the metadata.\n"},"Users/Publishing.html":{"url":"Users/Publishing.html","title":"Dataset Publishing","keywords":"","body":"Publishing the data\nData that is known to the data catalog can be published. The publication workflow does the following:\n\nIt defines a set of datasets to be published\nIt assigns metadata relevant to the publication of the datasets, such as author, abstract etc\nIt assigns a digital object identifier DOI to the published data, which can e.g. be used to link from a journal article to the data\nIt makes the data publicly available by providing a landing page that describes the data.\nIt publishes the DOI to the worldwide DOI system , e.g. from Datacite\n\nSo the first step is to select the datasets, that should be published\n\nThen by hitting the \"Add to Cart\" button these datasets are available in the Cart. This step can be repeated to add further datasets. Once the selection is finished you open the Cart ( click on the Cart symbol and choose actions ) to see the selected datasets:\n\nHere you can still change your selection, remove datasets etc. Once this is finished simply hit the publish button. This leads you to the following screen:\n\nSome of these fields will be pre-filled with information derived from the proposal data, such as the abstract. Independent of the pre-filling you can change the contents as you like until you are satisfied. Then hit the publish button, which leads you to the resulting display page:\n\nInitially the status field is in state \"pending\". This means, the published data information has been stored, but not yet made public to the worldwide DOI system, and no landing page has been created yet. This gives the possibility, that (potentially another person) can have a look at the data and do further editing by hitting the Edit button:\n\nOnce this is finished one can hit the \"register\" button (not shown in previous screenshot, because already in state registered)to register the DOI and thus making the data public. The resulting public landing page for this data then looks somethink like this\n\nFinally you can have a look at all the published data by going to the Published Data menu item (again by clicking the user icon at the top right corner and choosing \"Published Data\"):\n\nThis short video demonstrates how you can add an attachment to your dataset and publish the data.\n"},"Users/Jobs.html":{"url":"Users/Jobs.html","title":"Archival and Retrieval Jobs","keywords":"","body":"Archival and Retrieval Jobs\nIf enabled by your SciCat operator you can submit Jobs to both archive data to permanent (tape) storage or retrieve it from tere\nThis short video demonstrates how you can archive your data to tape.\nArchiving\nTo start archive jobs you best select datasets, which are in an archivable state by using the appropriate radio button on top of the dataset table. The state archivable means, that there exist files for this dataset and the dataset has not yet been archived already.\n\nThen you select the datasets you want to have archived and hit the Archive button on the top right\n\nAfter a confirmation dialog a archive job will be submitted, which will take care e.g. of copying the data from disk to tape (status at PSI). It is up to the SciCat operator to define the detail action which will be triggerd by such a Job, see the Operator manual for details\nAs an alternative way of selecting the files you can first fill the Cart with the selected datasets, then go to the Cart to \"checkout\", and choose the proper action from the list of available actions, which could be archive, retrieve, publish etc\n\nRetrieval\nIn the case of dataset retrieval you do the same steps, just select Datasets in retrievable state and make your selection, then hit the Retrieve button. This will copy the data from tape to an intermediate central storage , from where you can copy the data to the final destination of your choice, using either a command line tool or a GUI or simply an rsync client (this describes the status at PSI, your Site may have decided to do something else)\nJob results\nYou can check the status of the Jobs submitted in the Jobs menu item , which you can select via the menu dropdown (click on the users icon on the top right, see dashboard info ). YOu can see both your own Jobs and other peoples jobs\n\nPolicy Configuration for Archiving and Retrieving\nYou can define for each proposal a default behaviour concerning the treatment of the data in case of archiving and retrieval. The following tables shows the settings for each proposal/pgroup\n\nMultiple of these settings can be edited simultaneously by first selecting the rows\n\nAnd then editing them:\n\n"},"Users/SampleData.html":{"url":"Users/SampleData.html","title":"Sample Data Entry","keywords":"","body":"Sample Data Entry\nThe sample data interface allows samples to be added by users. \n\n"},"Operator/":{"url":"Operator/","title":"Operator Guide","keywords":"","body":"Overview\nGetting SciCat up and running at your site should be rather straight forward for a test deployment. However turning into a production ready system may involve a bit more work, because different existing systems will need to be interfaced to SciCat\nUnderstanding the Components\nFor the subsequent sections it will be useful to have a \"helicopter\" overview of the various components that need to play together seamlessly. The following diagram shows these components and also shows potentially existing components at your site, that you would likely want to interface to SciCat. The specific diagram reflects essentially the situation at PSI as of Sept. 2020. Of course your situation may look different. The diagram should therefore be seen as an example , which you need to adapt to your situation.\n\nBackend\nAt the heart of the SciCat architecture there is the REST API server. This is a NodeJS application that uses the Loopback 3 framework to generate RESTful APIs from JSON files that define models (such as: Users, Datasets, Instruments etc). Following the Swagger/OpenAPI format SDKs can be generated in almost any language.\nThe persistence layer behind this API server is a MongoDB instance, i.e an open source, NoSQL, document-based database solution. The API server handles alll the bi-directional communication from the REST interface to the Database.\nThese two components together comprise the \"backend\" of the architecture.\nFrontend\nTo the REST server an arbitrary number of \"clients\" (frontends) can be connected. One of the most important clients is the web based GUI frontend. This allows to communicate with the data catalog in a user friendly way. It is based on the Angular (9+) technology and uses ngrx to communicate with the SciCat API and provide a searchable interface for datasets, as well as the option to carry out actions (i.e. archiving).\nIn addition to the GUI other clients exist, such as command line (CLI) clients (example exist written in GO and Python) or desktop based GUI applications based on Qt. The CLI tools are especially useful for automated workflows, e.g. to get the data into the data catalog. This process is termed \"ingestion\" of the data. But they can also be used to add the data manually, especially for derived data, since this part of the workflow is often not possible to automate, in particular in truly experimental setups.\nMessaging infrastructure\nAs a way to loosely couple external systems the API server can be connected to messaging system. In particular RabbitMQ (used at PSI) and Apache Kafka are in use. Such systems can e.g. be used to interface to an tape archive system. To add the specific business logic you can e.g. add your own scripting layer. At PSI however a Node-RED based solution proved to be a stable and flexible platform for this purpose. Node-RED is a A NodeJS based visual programming tool to handle flows of data from one source to another. The following shows the Nod-RED flow used for communicating job requests to the PSI archive system\n\nPublishing Server\nIn order to publish data you need to run a landing page server and you need to assign DOIs to your published data. Since the API server may be operated in an intranet, with no access to the internet the following architecture was chosen at PSI:\nAn OAI-PMH server is running in a DMZ connected to a local Mongo instance. At publication time the data from SciCat is pushed to the external OAI-PMH server.\nFrom this server the landing page server can fetch the information about the published data. Also external DOI systems connect to this OAI-PMH server to synchronize the data with the world wide DOI system.\nIf a user wants to download the full datasets of the published data, the data is copied from the internal file server to a https file server (acting as a cache file server) , which subsequently allows anonymous download of the data\nChat Server as electronic logbook\nTODO ESS\nExternal systems\nThe most important external system that you want to connect to is likely your local identity management (IDM) system, e.g. an LDAP or AD based solution. This is usually the source of both the authentication and the authorization information, the latter being realized by simple group membership of the users.\nAnother important external system that you may want to connect, is your local proposal handling system (such as DUO at PSI). This allows population of proposal metadata, such as proposal id, title, and additional access permissions. If you do not have such a system you can fill the Proposal Information also by hand, or just leave it empty. If you have such a system you need to write scripts which synchronize the data in these systems with the corresponding tables in the SciCat Database, i.e. the Proposal collection.\nFinally, external storage systems, in particular those dealing with storage on tape, are good candidates for external systems.\nThe SciCat architecture does not require any connection to the file contents, since it deals with metadata only. However, the GUI can link to storage systems, which offer to download data using the https protocol.\nUnderlying Infrastructure\nYou may or may not run the infrastructure as part of a Kubernetes cluster. E.g. at PSI the API server, the GUI application, RabbitMQ and the Node-RED instances are all deployed to a Kubernetes cluster, whereas the Mongo DB ist kept outside Kubernetes.\nKubernetes is not necessary to have, but can simplify operations. Also, the separation into internet and intranet zones can be defined as required. You can, of course, operate the whole infrastructure directly in internet accessible servers, if security policies permit.\n"},"Operator/Step_by_Step_Setup.html":{"url":"Operator/Step_by_Step_Setup.html","title":"Step by Step instruction","keywords":"","body":"Step by Step manual to Setup SciCat without Containers\nSetup Backend\nMongo\nThe first part is to setup a Mongo DB, if not yet existing. You will need at least version 3.4 . In the simplest case a simple\nyum install mongodb-server\n(on RedHat systems) or similar will suffice.\nA production ready setup however may require to setup a replicated DB server. Follow the Mongo DB Manuals in this case. E.g. at PSI we operate a 3 fold relicated Mongo DB server.\nRemark for Windows\nIt is possible to run a SciCat development and test instance on Windows.\nYou can use the Chocolatey package manager: https://chocolatey.org/\nchoco install mongodb –pre\nChocolatey starts the mongodb service automaticly. The default port is 21017.\nInstall MondoDB Shell\nchoco install mongodb-shell\nHint to add full text indexing\nMongo DB needs to work with Indices to speed up the queries. All of these index definitions are created automatically, with one exception. This exception is described here: the following command must be executed after the database has been created in MongoDB, i.e. after starting the backend once.\nLog into mongodb at the console, e.g. run \nIf not using Kubernetes\nmongo dacat\nin Windows cmd or powershell\nmongosh dacat\nor, if using Kubernetes\nkubectl exec -it mongo dacat\nand enter\ndb.Dataset.createIndex( { \"$**\": \"text\" } )\n\nNote: If the index is not created as above, frontend e2e test fails.\nAPI Server\nPrerequisites\nFirst you need to have node/npm installed\nnpm version 6 or higher\nNode version 10 or higher\nThe needed database will be created automatically when the API server starts. \nGet code\ngit clone https://github.com/SciCatProject/backend.git\ncd backend\ngit checkout master # (or develop)\nnpm install\nPlease note: the master branch is often quite behind the development, therefore to get all new features you may want to use the develop branch instead.\nSetup configuration\nThere are 5 configuration files that need to be adjusted to your situation. There are example configuration files for each of the 4 configuration files inside the server directory\n\nfunctionalAccounts.json : defines the functional accounts, such as admin, ingestor etc\ndatasources.json : defines the connection to the Mongo DB -sample datasources.json\nproviders.json : defines the authentication source for user accounts, e.g. how to link to your local LDAP/AD server\nconfig.local.js : defines the API server details, API root address, your local site name and policy settings for , the PID prefix for your datasets, the type of message broker to use, the connection to an email send server etc\ncomponent-config.json: defines the Express framework components, in particular if the explorer is enabled and optionally definition of the RabbitMQ topology, if used.\n\nAdjusting these settings to your infrastructure should be straight forward. In case you do not understand a setting just leave it at its default.\nNote for Windows\nCreate an .env file based on .env.example and use 127.0.0.1 instead of localhost\nMONGODB_URI=\"mongodb://127.0.0.1:27017/dacat\" \nEmail Notifications\nWhen jobs have been submitted successfully, the node-mailer package can send an email to the user that initiated.\nThe details are loaded into server/config.local.js and the following block provides an example:\nsmtpSettings: {\n host: 'HOSTPATH',\n port: 587,\n secure: false,\n auth: {user: DOMAIN\\\\USER', pass: 'PWD'}\n },\n smtpMessage: {\n from: 'gac-dacats@psi.ch',\n to: undefined,\n subject: '[SciCat]',\n text: undefined // can also set html key and this will override this\n }\nStart metadata server\nnpm run start\nTest functionality\nnpm run test\nYou can in addition test the API server using the \"explorer\", e.g if you run the instance locally point your browser to https://localhost:3000/explorer (note the explorer might be disabled for production environments, check the component-config.json file)\n\nYou can use this web interface to test all the available API end points. If you test endpoints, which are protected by authentication, you need to set an accessToken field on top of the explorer web page. You can login in frontend web UI and find the token under \"username->settings\".\nSetup GUI frontend\nThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.\nPrerequisites\nWhat things you need to install the software and how to install them\nAngular v8 or higher\nnpm version 6 or higher\nnode version 10 or higher\nInstalling\nUse git to clone the repository\ngit clone https://github.com/SciCatProject/frontend\ncd frontend\nFirst install the required modules\nnpm install\nThen build with the Angular CLI\nnpx ng build\nYou can deploy a test server with the following command:\nnpx ng serve\nRunning the unit tests\nTo run the unit tests, type:\nnpm test\nEnd to end tests\nTo run the end to end tests, type:\nnpm run e2e\nConfiguration\nYou can fine tune the features that you want to use in the GUI by setting the flags inside the src/environments folder. In this folder you find several example configuraion files used by the Sites. Please adjust to your needs. The angular.json file contains a section \"configurations\" which defines a set of prepared configurations, that you can choose from. This defines which files will be picked up at build time. The build system defaults to the environment.ts, but if you e.g. do ng build --env=development then environment.development.ts will be used instead. Here is an example configuration file\nThe most important configuration setting is the connection to the API server\nlbBaseURL: \"https://your-loopbak-api-server\",\nMany of the other settings are boolean flags, which allow you to switch on/off features of the GUI\nAgain, if in doubt, just leave the setting as defined in the available example environment files or leave them empty.\nOptional Extras\nConnecting to RabbitMQ\nSome extra configuration needs to be done in order to get Scicat talking to RabbitMQ. The rabbitmq mechanism is used to trigger asynchronous jobs\nthrough the archive and retrieve methods. \nSetting up RabbitMQ\nWhen using RabbitMQ with Scicat it needs to be configured initially, before the rest of the Scicat services are run. \nIt must be set up with a user before the backend is started, otherwise the backend cannot connect. \nIt is easy to set up a local rabbitmq server as a separate service before starting Scicat through docker compose.\nA basic docker compose for rabbitmq looks like:\nversion: \"3.8\"\nservices:\n local-rabbitmq:\n hostname: local-rabbitmq\n image: 'bitnami/rabbitmq:latest'\n labels:\n kompose.service.type: nodeport\n ports:\n - '4369:4369'\n - '5672:5672'\n - '25672:25672'\n - '15672:15672'\n volumes:\n - 'rabbitmq_data:/bitnami'\nvolumes:\n rabbitmq_data:\n driver: local\nThis set up gives you both a rabbitmq service and a Management Portal which can be useful for debugging.\nBefore connecting to the backend or the Management Portal a user with administration privileges needs to be configured manually which can be done via CLI. \nTo understand more about setting up rabbitmq please see the documentation at https://www.rabbitmq.com/cli.html.\nIf you have followed the set up with docker, the following commands can be executed in the docker container to set up initial users and the management portal.\n# To add the management plugin\nrabbitmq-plugins enable rabbitmq_management\n\n#to add a user\nrabbitmqctl add_user \nrabbitmqctl set_user_tags administrator\nrabbitmqctl set_permissions -p / '.*' '.*' '.*'\nOnce this is done you can log into the Management Portal hosted at http://localhost:15672. Any other users, exchanges , queues etc. can be configured manually.\nConnecting Scicat to RabbitMQ\nThe backend handles the connection to rabbitMQ through the component-config.local.json file. The backend will set up all the relative queues and exchanges in a running service, but the service must contain a user with administrative privileges\nbefore running the backend. A basic rabbitMQ set up in the component-config.local.json file looks like the following:\n\"topology\":{\n\"connection\":{\n\"uri\":\"amqp://my-admin-user:newpassword@local-rabbitmq\" # the uri of your service with your administrative user\n},\n\"exchanges\":[\n{\n\"name\":\"jobs.write\",\n\"type\":\"topic\",\n\"persistent\":true\n}\n],\n\"queues\":[\n{\n\"name\":\"client.jobs.write\",\n\"subscribe\":true\n}\n],\n\"bindings\":[\n{\n\"exchange\":\"jobs.write\",\n\"target\":\"client.jobs.write\",\n\"keys\":[\n\"jobqueue\"\n]\n}\n]\n}\n}\nThe exchange and the binding key must be named jobs.write and jobqueue respectively, the queue name can take any value. \nThe backend expects and exchange called jobs.write to publish jobs messages to bound with that key.\nUse in Practice\nIf you wish to have two Scicat services connecting to the same RabbitMQ server (e.g. scicat-dev and scicat-prod) you \ncould generate two queues bound to the jobs.write exchange. However these would be bound with the same key that\ncould cause cross talk. RabbitMQ allows you to set up any number of virtual hosts within the same server therefore it is\nbest to set up two vhosts (e.g. dev and prod) with the exchange, queues and bindings set as in the component-config.local.json file above.\n"},"Operator/Step_by_Step_Setup_Docker.html":{"url":"Operator/Step_by_Step_Setup_Docker.html","title":"Deploy with Docker Compose","keywords":"","body":"Setup SciCat with Docker Compose\nThe scicatlive repo defines a quick and convenient method for getting started. It sets up the Mongo DB , backend and frontend, all using docker containers, starts the containers and connects them , all with a single command. This is also a convenient method to contribute to the development of this project easily.\n"},"Operator/Step_by_Step_Setup_Kubernetes.html":{"url":"Operator/Step_by_Step_Setup_Kubernetes.html","title":"Deploy with Kubernetes","keywords":"","body":"Setup SciCat with Kubernetes\nThe procedure is described in the localdeploy repository\nIt uses a minikube based Kubernetes system as the main deployment platform. It should run both on Linux and Mac Systems and is a convenient way to get a full \"production like\" system running on your laptop.\n"},"Operator/Step_by_Step_Setup_Zip_Service.html":{"url":"Operator/Step_by_Step_Setup_Zip_Service.html","title":"Deploy Zip Service","keywords":"","body":"Step by Step manual to Setup the Zip Service\nSetup Zip Service\nThe zip service repository can be found here.\nTo install it, you first have to add a configuration file (local.config.json) to the source directory. A sample for this file can be found in CI/ESS or in the description of the repository.\nThen build the image and run it.\nJWT\nIn order for SciCat to authenticate against the zip service, the jwt secret has to be configured the same in both services. The jwt secret for the zip service can be configured in the file local.config.json. The configuration file containing the jwt secret for the SciCat backend can be found under the backend filepath:\n/home/node/app/server/config.json\nVolumes\nThe data volumes have to be mounted to the zip service in order for it to access the files, zip them and offer them for download. The filepath, stored in the OriginalDatablock and Datafile objects, have to match a valid path on the zip service.\n"},"Ingestor/":{"url":"Ingestor/","title":"Ingestor Guide","keywords":"","body":"Ingesting Data into SciCat\nThis manual collects various approaches for ingesting data\n\nIngest Manual from PSI : contains partly tools, which are not yet open sourced, but soon will\nIngest Instructions from ESS\nMinimalistic example via CURL\n\n"},"Ingestor/ingestManual.html":{"url":"Ingestor/ingestManual.html","title":"Ingest Manual PSI","keywords":"","body":"\nTable of Contents\n\n\n1. Overview and Concepts\n2. The Concept of Datasets\n3. Getting started\n4. Ingest\n\n4.1. Important Update since April 14th 2022\n4.2. Definition of input files\n4.3. Definition of metadata\n4.4. Special notes for the decentral use case\n\n4.4.1. For Windows\n4.4.2. For Linux.\n\n\n4.5. Summary of the different use cases\n\n\n5. Archive\n6. Retrieve\n\n6.1. First Step\n6.2. Second Step (for Linux)\n\n6.2.1. Standard commands\n6.2.2. Expert commands\n\n\n6.3. Second Step (for Windows)\n\n\n7. Ingest, Archive and Retrieve with QT desktop application SciCat\n\n7.1. Important Update since April 14th 2022:\n7.2. General considerations\n7.3. Getting started\n7.4. Login and permissions\n7.5. Pgroup selection\n7.6. Archiving\n7.7. Retrieval\n7.8. Settings\n\n\n8. Publish\n9. Cleanup and Retention\n10. Troubleshooting\n\n10.1. Locale error message\n10.2. Invalid certificate messages\n10.3. Long Running copy commands\n10.4. Kerberos tickets\n10.5. Instructions to set ACLS in AFS\n\n\n11. Appendix\n\n11.1. Installation of Tools\n\n11.1.1. Access to the SciCat GUI\n11.1.2. Loading datacatalog tools on Clusters\n11.1.3. (Non-standard Linux systems) Installing datacatalog tools\n11.1.4. Installation on Windows Systems\n11.1.5. Online work stations in beamline hutches\n\n\n11.2. Dataset limitations\n11.2.1. Size limitations\n11.2.2. SourceFolder and file names limitations\n11.3. Recommended file structure for raw datasets\n11.4. Metadata Field Definitions\n\n11.4.1. Metadata field definitions for datasets of type \"base\"\n11.4.2. Additional fields for type=\"raw\"\n11.4.3. Additional fields for type=\"derived\"\n\n\n11.5. About Scientific Values and Units\n11.6. Use Case Examples\n\n11.6.1. Use Case: Manual ingest using datasetIngestor program\n11.6.2. Use Case: Automated ingest of raw datasets from beamline or instruments\n11.6.3. Use Case: Ingest datasets stored on decentral systems\n11.6.4. Use Case: Ingest datasets from simulations/model calculations\n\n\n11.7. Policy settings and email notifications\n11.8. Analyzing Metadata Statistics\n\n11.8.1. Overview\n11.8.2. Getting started\n\n\n11.9. Access to the API (for script developers)\n11.10. Using datasetIngestor inside wrapper scripts (for developers)\n11.11. Ingestion of datasets which should never be published\n11.12. Retrieving proposal information\n11.13. Link to Group specific descriptions\n11.14. List of known creationLocation for raw data\n\n11.14.1. SLS\n11.14.2. Swissfel\n11.14.3. SINQ\n11.14.4. SmuS\n\n\n\n\n12. Update History of Ingest Manual\n\n\n\n\n\nOverview and Concepts\nPSI offers a Data Catalog Service for annotated long-term data storage\n, retrieval and publishing. The annotation information , i.e. metadata\nis stored in a central database to allow for fast query for the\ndata. The raw data itself is stored on the PetaByte Archive at the\nSwiss National Supercomputing Centre (CSCS). The Data Catalog and\nArchive is designed to be suitable for:\n\nRaw data generated by PSI instruments or simulations\nDerived data produced by processing the raw input data\nData required to reproduce PSI research and publications, e.g FAIR data\n\nAll data which are added to the data catalog must either not be\nclassified or have a classification level of \"normal\".\nYou are not allowed to add any personal or private data. You are not\nallowed to use the data catalog as a backup system. Data must come\nfrom scientific activities pursued at PSI. If data from external\npartner institutes should be stored, then this needs a dedicated\ncontract signed by the management.\nThe service is based on the catalog system SciCat, documented at\nhttps://scicatproject.github.io/ and\nhttps://github.com/SciCatProject/, which is an open source system that\nallows to ingest and retrieve datasets in different ways, matching the\nrequirements of the respective use cases. The use cases differ in the\nlevel of automation provided.\nData is always stored in terms of datasets, which you can think of as a\ncollection of files combined with administrativ and scientific metadata.\nThis manual describes how you can use this services by following the\nmain steps in the lifecycle of the data management:\n\nDefinition and ingestion of metadata\nArchiving of the datasets\nRetrieving of datasets\nPublishing of datasets\nRetention of datasets\n\nNote: as of today (June 2021) the services can be only be used from\nwithin the PSI intranet with the exception of the published data,\nwhich is by definition publicly available. Although the service itself\ncan be used from any operating system, the command line and\nGUI tools currently offered are available only for Linux and Windows\nplatforms.\nThe Concept of Datasets\nFor the following it is useful to have a better understanding of the\nconcept of a dataset. A dataset is a logical grouping of potentially\nmany files. It is up to the scientist to define datasets from the\nfiles. When defining datasets take the following conditions into\naccount\n\na dataset is the smallest unit for adding meta data\na dataset is the smallest unit for data handling (archiving and retrieval)\na dataset is the smallest unit for publication (DOI assignmnet)\n\nTherefore you need to find a compromise between putting too few or too\nmany files into a single dataset. \nIngestion of datasets means, that you make data known to the data\ncatalog by providing both metadata about the dataset and the file\nlisting comprising the dataset. For each dataset a persistent\nidentifier (PID) is automatically created.\nIt is important to note that the data catalog is a \"passive\" system in\nthe sense that it has to be told if new data arrives. The data catalog\nhas no direct access to the file systems containing the actual\nfiles. In contrast the datasetIngestor program is run from systems, which\nhave access to the data files.\nThe datasets always belong to an so called ownerGroup. Only members of\nthese groups have access to the data, unless the dataset is being\npublished. At PSI there are two types of ownerGroups,\n\npgroups, starting with letter \"p\". They are used for experimental\ndata linked to a proposal system. They are managed by the digital\nuser office DUO\na-groups, starting with \"a-\" for any other data to be archived\n\nOnce data is contained in the data catalog, this information is\nconsidered to be stored permanently. However after a retention period\nthe connected raw data files may actually be deleted. In this case the\ndataset is marked as deleted in the data catalog, but the data\ncatalog entry persists, in agreement with the FAIR principles.\nWarning: you should not modify the files which make up your dataset\nafter the dataset was ingested to the datacatalog. This means that you\nshould ingest the data only, if you are sure that no further\nmodifications on the files take place. The subsequent archive job will\nonly take care of the files which existed at ingest time and otherwise\nreturn an error message and not archive the data at all.\nGetting started\nYou will need a PSI account and this account needs to be member in so\ncalled p-groups, which are managed by the PSI digital user office\nproposal system and are usually linked to a principal investigator\n(PI). This is required to define the authorization to the data,\ni.e. who is allowed to see which datasets.\nIn addition to these so called user accounts there are a couple of\npre-defined functional accounts which are used for automated\nprocesses. In particular each beamline has one such functional\naccount, e.g. called sls-tomcat, which can be used for automated\ningestion of new data or to query all data generated at a given\nbeamline. These accounts are only defined in the data catalog system\nand are given to the respective beamline managers.\nIf your data can not be linked to this proposal system you can still\nuse the services, but you may need to ask for the creation of a\nso-called a-group and become member in these groups. You can order\nan a-group via ServiceNow under PSI Service Catalog -> IT ->\nIdentity & Access Management -> Order Group / Project drive. Under\nAccount Type choose Archive Group / Project Drive. You will be\nasked about the group members. The group owner is not automatically\nadded to the group members.\nTo use some of the software you may need to install it\nfirst. Installation is described in the appendix Installation of Tools\nIngest\nImportant Update since April 14th 2022\nFor all commandline tools, like the datasetIngestor, datasetRetriever\n etc, using your own user account you have to use the –token\n option with a predefined API token SCICAT-TOKEN. Specifying\n username/password is not possible for normal users (this limitation\n is caused by the switch to a new authentication protocol). The\n easiest to get such an API token is to sign it at\n https://discovery.psi.ch, then follow the \"Login with PSI account\"\n button. This will bring you to the user settings page, from where\n you can copy the token with a click on the corresponding copy\n button. \nFor functional accounts, like beamline accounts you can\nhowever continue to use username/password authentication instead.\nDefinition of input files\nFirst you need to specify the location of the files that you want to\nhave stored as one dataset. A typically example would be all the files\ntaken during a measurement, a scan etc or all output data from an\nanalysis of raw data files. In the simplest case it is sufficient to\ndefine only one location. i.e. the sourceFolder, which should\ncontain all the files (and only those files) that make up the\ndataset. In a more general case you can also specify an explicit list\nof files and/or directories that you want to have assembled to a\ndataset. See the datasetIngestor command options for details. The\nappendix has a Recommended file structure for raw datasets on\ndisk. Please take note of the limitations of a dataset, as\ndefined in the appendix Dataset limitations.\nDefinition of metadata\nThere are two types of metadata which need to be provided:\n\nadministrative metadata: specifies when and where the data is taken,\nwho is the owner etc. There are both mandatory and optional fields\nand the fields depend on the type of the dataset\n(generic/raw/derived), see Section 11.4\nbelow. The most important metadata field for ownership is the value\nof the \"ownerGroup\" field, which defines a group name, whose member\nhave access to the data.\nscientific metadata: this depends on the scientific discipline and\ncan be defined in a flexible way by respective research group. It is\nup to the research groups to define the format(s) of their data that\nthey want to support, ideally on an international level. See also\nthe section About Scientific Values and Units .\n\nTherefore the next step to ingesting your data into the catalog is to\nprepare a file describing what data you have. This is called\nmetadata.json, and can be created with any text editor. It can in\nprinciple be saved anywhere, but keeping it with your archived data is\nrecommended.\nHere is a minimalistic example the file metadata.json for raw data:\n{\n \"creationLocation\": \"/PSI/SLS/TOMCAT\",\n \"sourceFolder\": \"/data/p16/p16623/June2020\",\n \"type\": \"raw\",\n \"ownerGroup\":\"p16623\"\n}\nIn the Appendix Use Case Examples you find many more examples for\nmetadata.json files, both for raw and derived data. Here is a more\nreal life example from Bio department:\n{\n \"principalInvestigator\": \"albrecht.gessler@psi.ch\",\n \"creationLocation\": \"/PSI/EMF/JEOL2200FS\",\n \"dataFormat\": \"TIFF+LZW Image Stack\",\n \"sourceFolder\": \"/gpfs/group/LBR/pXXX/myimages\",\n \"datasetName\": \"myimages\",\n \"owner\": \"Wilhelm Tell\",\n \"ownerEmail\": \"wilhelm.tell@psi.ch\",\n \"type\": \"raw\",\n \"description\": \"EM micrographs of amygdalin\",\n \"ownerGroup\": \"a-12345\",\n \"scientificMetadata\": {\n \"sample\": {\n \"name\": \"Amygdalin beta-glucosidase 1\",\n \"uniprot\": \"P29259\",\n \"species\": \"Apple\"\n },\n \"dataCollection\": {\n \"date\": \"2018-08-01\"\n },\n \"microscopeParameters\": {\n \"pixel size\": {\n \"value\": 0.885,\n \"unit\": \"A\"\n },\n \"voltage\": {\n \"value\": 200,\n \"unit\": \"kV\"\n },\n \"dosePerFrame\": {\n \"value\": 1.277,\n \"unit\": \"e/A2\"\n }\n }\n }\n}\nFor manual creation of this file there are various helper tools\navailable. One option is to use the ScicatEditor\nhttps://bliven_s.gitpages.psi.ch/SciCatEditor/ for creating these\nmetadata files. This is a browser-based tool specifically for\ningesting PSI data. Using the tool avoids syntax errors and provides\ntemplates for common data sets and options. The finished JSON file can\nthen be downloaded or copied into a text editor.\nAnother option for datasets on ra or merlin is to use the SciCat\ngraphical interface from NoMachine. This provides a graphical\ninterface for selecting data to archive. This is particularly useful\nfor data associated with a DUO experiment and p-group. Type SciCat to\nget started after loading the datacatalog module. The GUI also\nreplaces the the command-line ingestion decribed below.\nAfter preparing your metadata.json file, run the following steps to\ningest the data. First, perform a \"dry-run\" that will check the\nmetadata for errors: ( Please note that in the following only the\nLinux type notation is used. For the changes which apply to Windows\nsee the separate section below)\ndatasetIngestor metadata.json\nIt will ask for your PSI credentials and then print some info\nabout the data to be ingested. This command will scan the files, make\nchecks and extract further metadata information from the files and\nfrom the DUO system, unless the corresponding metadata fields are\nalready provided in the metadata.json file. If there are no errors,\nproceed to the real ingestion:\ndatasetIngestor --ingest metadata.json\nFor particularly important datasets, you may also want to use the\nparameter –tapecopies 2 to store redundant copies of the data.\nTo give some numbers, 0.2–0.4% of the tapes get damaged so \nthere is a chance that archiving with only one copy will result in lost \ndata, in very few cases. Keep in mind that archival with redundancy \ndoubles the cost which is billed to the responsible department.\nYou may be asked whether you want to copy the data first to a central\nsystem. This step is needed for all files which are not residing on\none of the central fileservers at PSI. In particular local (windows)\nworkstations/PCs are likely to fall in this category.\nThere are more options for this command, just type\ndatasetIngestor\nto see a list of available options. In particular you can define\nexplicit list of files to be combined into a dataset, which can come\nfrom many different folders by providing a filelisting.txt file\ncontaining this information in addition to the metadata.json file. The\nsection in the Appendix Using the datasetIngestor Tool has more details\nSpecial notes for the decentral use case\nFor Windows\nFor Windows you need execute the corresponding commands inside a\npowershell and use the binary files ending in .exe, e.g.\ndatasetIngestor.exe -token SCICAT-TOKEN -user username:password -copy metadata.json\nFor Windows systems you can only use personal accounts and the data is\nalways handled as decentral case, i.e. the data will first be copied\nfrom the windows machine to a central file server via scp\nfirst. Therefore you need to specify all of the above parameters\n-token, -user and -copy. \nPlease also note the syntax, that has to be used for the definition of\nthe sourceFolder inside the metadata.json file: this has to be in the\nfollowing form:\n\"sourceFolder\": \"/C/Somefolder/etc\",\n, i.e. forward slashes and no colon \":\" after the drive letter like\n\"C:\" in this case.\nFor Linux.\nYou must have a valid kerberos ticket in order to be able to copy the\ndata to the intermediate storage server. You can use the kinit command\nto get this ticket.\nSummary of the different use cases\nThe following table summarizes the different use cases\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOS\nsourceLocation\nAccount-Type\nNeededed parameters\nComment\n\n\n\n\n\nLinux\ncentral\nUser\ntoken\nFetch token via Web GUI discovery.psi.ch\n\n\n\n\nLinux\ncentral\nFunctional\nusername/pw\nThe tool fetches token from API server\n\n\n\n\nLinux\nanywhere/decentral\nUser\ntoken + Kerb ticket\nToken for API, Kerb ticket for copying data\n\n\n\n\nLinux\nanywhere/decentral\nFunctional\nnot supported\nFunctional accounts not existing on ssh server\n\n\n\n\nWindows\ncentral\nUser\n(token)\nNeeds mounting of Windows FS to Arema\n\n\n\n\nWindows\ncentral\nFunctional\n(username/pw)\ndito\n\n\n\n\nWindows\nanywhere/decentral\nUser\ntoken + username/pw\nToken for API, username/pw for copying data\n\n\n\n\nWindows\nanywhere/decentral\nFunctional\nnot supported\nFunctional accounts not existing on ssh server\n\n\n\n\nArchive\nIf there are no errors, your data has been accepted into the data\ncatalog! From now on, no changes should be made to the ingested\ndata. This is important, since the next step is for the system to copy\nall the data to the CSCS Petabyte archive. Writing to tape is slow, so\nthis process may take some time, and it will fail if any\nmodifications are detected.\nTriggering the copy to tape can be done in 3 ways. Either you do it\nautomatically as part of the ingestion\ndatasetIngestor --ingest --autoarchive metadata.json\nIn this case directly after ingestion a job is created to copy the\ndata to tape. Your dataset should now be in the queue. Check the data\ncatalog: https://discovery.psi.ch. Your job should have status\n'WorkInProgress'. You will receive an email when the ingestion is\ncomplete.\nThe second method is to use the discovery.psi.ch to interactively\nstart the archive job: click on the \"Archivable\" button. You should\nsee the newly ingested datasets. Select all the datasets you want to\nhave archived and click 'Archive'. You should see the status change\nfrom 'datasetCreated' to 'scheduleArchiveJob'. This indicates that the\ndata is in the process of being transferred to CSCS. After some time\nthe dataset's status will change to 'datasetOnAchive' indicating the\ndata is stored.\nA third option is to use a command line version datasetArchiver.\ndatasetArchiver [options] (ownerGroup | space separated list of datasetIds) \n\nYou must choose either an ownerGroup, in which case all archivable datasets\nof this ownerGroup not yet archived will be archived.\nOr you choose a (list of) datasetIds, in which case all archivable datasets\nof this list not yet archived will be archived.\n\nList of options:\n\n -devenv\n Use development environment instead or production\n -localenv\n Use local environment (local) instead or production\n -noninteractive\n Defines if no questions will be asked, just do it - make sure you know what you are doing\n -tapecopies int\n Number of tapecopies to be used for archiving (default 1)\n -testenv\n Use test environment (qa) instead or production\n -token string\n Defines optional API token instead of username:password\n -user string\n Defines optional username and password\nRetrieve\nHere we describe the retrieval via the command line tools. A retrieve\nprocess via a desktop GUI application is described in the section SciCatArchiver GUI .\nRetrieving is two-step process: first the data is copied from tape to a\ncentral retrieve server. From there the data needs to be copied to the\nfinal destination system of your choice.\nFirst Step\nFor the first step: login to https://discovery.psi.ch , find the\ndatasets you want to retrieve and selected all \"Retrievable\" datasets\nby clicking the corresponding button. Finally click the retrieve\nbutton. This will create a retrieve job. Once it is finshed you will\nget an email. Depending on the size of your datasets this may take\nminutes (e.g. for 1GB) up to days (e.g for 100TB)\nSecond Step (for Linux)\nStandard commands\nFor the second step you can use the datasetRetriever command, which\nuses the rsync protocol to copy the data to your destination.\nTool to retrieve datasets from the intermediate cache server of the tape archive\nto the destination path on your local system.\nRun script with 1 argument:\n\ndatasetRetriever [options] local-destination-path\n\nPer default all available datasets on the retrieve server will be fetched.\nUse option -dataset or -ownerGroup to restrict the datasets which should be fetched.\n\n -chksum\n Switch on optional chksum verification step (default no checksum tests)\n -dataset string\n Defines single dataset to retrieve (default all available datasets)\n -devenv\n Use development environment (default is to use production system)\n -ownergroup string\n Defines to fetch only datasets of the specified ownerGroup (default is to fetch all available datasets)\n -retrieve\n Defines if this command is meant to actually copy data to the local system (default nothing is done)\n -testenv\n Use test environment (qa) (default is to use production system)\n -token string\n Defines optional API token instead of username:password\n -user string\n Defines optional username and password (default is to prompt for username and password)\nFor the program to check which data is available on the cache server\nand if the catalog knows about these datasets, you can use:\ndatasetRetriever my-local-destination-folder\n\n======Checking for available datasets on archive cache server ebarema4in.psi.ch:\n\nDataset ID Size[MB] Owner SourceFolder\n===================================================================\n0f6fe8b3-d3f1-4cfb-a1af-0464c901a24f 1895 p16371 /sls/MX/Data10/e16371/20171017_E2/cbfs/2017-10-17_22-28-30_Na108_thau7_100degs_dtz60_f_500_Hz_Eth0_6200_eV\n58f2037e-3f9b-4e08-8963-c70c3d29c068 1896 p16371 /sls/MX/Data10/e16371/20171017_E2/cbfs/2017-10-17_21-41-02_cca385a_lyso8_100degs_f_500_Hz_Eth0_6200_eV\ncf8e5b25-9c76-49a7-80d9-fd38a71e0ef8 3782 p16371 /sls/MX/Data10/e16371/20171017_E2/cbfs/2017-10-18_10-15-41_na108_thau6_50degs_lowdose_pos1_f_500_Hz_Eth0_6200_eV\ndf1c7a17-2caa-41ee-af6e-c3cf4452af17 1893 p16371 /sls/MX/Data10/e16371/20171017_E2/cbfs/2017-10-17_20-58-34_cca385a_lyso3_100degs_f_500_Hz_Eth0_6200_eV\nIf you want you can skip the previous step and\ndirectly trigger the file copy by adding the -retrieve flag:\ndatasetRetriever -retrieve \nThis will copy the files into the destinationFolder using the original\nsourceFolder path beneath the destinationFolder. This is especially\nuseful if you want to retrieve many datasets, which you expect to\nappear in the same folder structure as originally. \nS\nOptionally you can also verify the consistency of the copied data by\nusing the -chksum flag\ndatasetRetriever -retrieve -chksum \nIf you just want to retrieve a single dataset do the following:\ndatasetRetriever -retrieve -dataset \nIf you want to retrieve all datasets of a given ownerGroup do the following:\ndatasetRetriever -retrieve -ownergroup \nExpert commands\nIf you prefer to have more control over the file transfer you are free\nto type your own rsync commands, e.g. to simply the folders available\nin the retrieve cache do:\nrsync -e ssh --list-only pb-retrieve.psi.ch:retrieve/\nTo actually copy the data over use:\nrsync -e ssh -av pb-retrieve.psi.ch:retrieve/{shortDatasetId} your-destination-target/\nIn this case the shortDatsetId is the dataseid id without the PSI\nprefix, e.g. for dataset PID\n20.500.11935/08bc2944-e09e-48da-894d-0c5c47977553 the shortDatasetId\nis 08bc2944-e09e-48da-894d-0c5c47977553\nSecond Step (for Windows)\nThe second step for Windows is instead using the sftp\nprotocol. Therefore any sftp client for Windows, like e.g. Filezilla,\ncan then be used to retrieve the data to your local Windows PC. The\nfollowing connection information must be provided, taking the command\nline client access via powershell as an example\n# for the production system\nsftp -P 4222 your-username@pb-retrieve.psi.ch\n# or for the test system\nsftp -P 4222 your-username@pbt-retrieve.psi.ch\nAfter the connection is built up you can copy files recursively,\ne.g. using the \"get -r *\" command. With the filezilla GUI you can\nachieve the same via drag and drop operations\nIngest, Archive and Retrieve with QT desktop application SciCat\nImportant Update since April 14th 2022:\nYou currently first need to get a token before you can use SciCat: the\neasiest to get such an API token is to sign it at\nhttps://discovery.psi.ch, then follow the \"Login with PSI account\"\nbutton. This will bring you to the user settings page, from where you\ncan copy the token with a click on the corresponding copy button.\nGeneral considerations\nSciCat is a GUI based tool designed to make initial\ningests easy. It is especially useful, to ingest data, which can not\nbe ingested automatically. Therefore it is designed in particular to\nassist you when archiving derived datasets. Often, the archival of\nderived data cannot be scheduled in advance, nor does it follow a\nstrict file structure. The SciCat GUI can help you to ingest such\ndatasets more easily. Yet, the ingestion of raw datasets is also\nsupported. Additionally, the tool also allows for the convenient\nretrieval of datasets.\nGetting started\nCurrently, SciCat is supported on PSI-hosted Linux and Windows\nsystems and is accessible on the Ra cluster as part of the datacatalog\nmodule: just type\nmodule load datacatalog\nThen the software can be started with\nSciCat\nOn the SLS beamline consoles the software is also pre-installed in the\n/work/sls/bin folder, which is part of the standard PATH variable. \nIf you are not working on the Ra cluster you can download the\nsoftware on Linux:\n/usr/bin/curl -O https://gitlab.psi.ch/scicat/tools/raw/master/linux/SciCat;chmod +x ./SciCat\nOn Windows the executable can be downloaded from\nhttps://gitlab.psi.ch/scicat/tools/-/blob/master/windows/SciCatGUI_Win10.zip\nTo start the GUI, unzip the directory and execute SciCat.exe\nLogin and permissions\nAfter starting the GUI, you will be asked for a username and password. Please \nenter your PSI credentials. Functional accounts are not supported. \nPgroup selection\nThe first step is always to select the pgroup. If there is no proposal assigned to \nthis account, you will have to specify the information about the PI manually. \n\nArchiving\nAfter selection the files, you will be prompted with a metadata editor, where you can modify \nthe general info, such as dataset name, description etc. Please make \nsure that you select the correct data type (raw or derived). As a general rule of thumb, it is \na derived dataset if you can specify a raw dataset as input. If you want to ingest a derived dataset, \nyou can specify corresponding raw datasets on the \"Input datasets\" tab. \nTo edit scientific metadata, switch to \"Scientific metadata\" tab. \nRetrieval\nRetrieving successfully archived datasets from SciCat is a two-step process. First you will have to \nretrieve to an intermediate server. Once the data is there, you will be notified by email. \nThe final step is to copy the data to the final destination on your machine. \nBoth steps can be steered from within the GUI. \nOn the retrieve page, all datasets of your pgroup are listed. If the data has been archived successfully, \nthe cell in column \"retrievable\" is set to \"true\". To retrieve the data to the intermediate file server,\nselect the datasets that you want to retrieve and click on \"Retrieve.\" After the retrieval, the column\n\"retrieved\" is set to true. You are now able to start copying the data to you local machine by selecting\nthe desired datasets and clicking on \"Save.\" \nSettings\nAdditional settings, such as the default value for certain fields can be modified in settings panel (button\non the lower left corner). \nPublish\nAs part of a publication workflow datasets must become citable via a\ndigital object identifier (DOI). This assignment is done as part of\nthe publication workflow described below. The publication then can\nlink to these published datasets using this DOI. The DOIs can link to\nboth raw and/or derived datasets. The published data and therefore the\nDOI ususally refers to a set of Datasets, thus avoiding the need to\nlist potentially thousands of individual dataset identifiers in a\njournal publication.\nYou publish data in the following way: go to https://discovery.psi.ch ,\nlogin and select all the datasets, that you want to publish under a\nnew DOI. \n\nThen you add these datasest a a \"shopping cart\" by using the \"add to\nCart\" button. You can repeat this often as needed. Once finished with\nthe selection you can \"check out\" the cart (click on the cart in the\ntop bar) and pick the \"Publish\" action.\n\nThis opens a form\nwith prefilled information derived from the connected proposal\ndata. This data can then be edited by the user and finally saved.\n\nThis defines the data as to be published and makes it known to the\ndata catalog, but the corresponding DOI is not yet made globally\navailable. For this last step to happen, someone with access to this\nnewly generated published data definition (e.g. the person defining\nthe published data or e.g. the PI) has to hit the \"register\"\nbutton. This will trigger the global publication of the DOI. The links\non https://doi.org are usually updated within one day, so wait one day\nbefore following these links or searching for the doi via the doi\nreolver.\nAll published data definitions are then openly available via the so\ncalled \"Landing Pages\", which are hosted on https://doi.psi.ch .\nThe file data itself data becomes available via the normal data export\nSystem of the Ra cluster, which requires however a PSI account. If you\nwant to make the file data anonymously available you need to send a\ncorresponding request to stephan.egli@psi.ch for now. This process is\nplanned to be automated in future.\nFor now all publication are triggered by a scientist explicitly,\nwhenever necessary. In future in addition an automated publication\nafter the embargo period (default 3 years after data taking) will be\nimplemented (details to be defined)\nCleanup and Retention\nThis part is not yet defined.\nTroubleshooting\nLocale error message\nIf you get error messages like the following (so far only happened\nfrom Mac Computers)\nperl: warning: Setting locale failed.\nperl: warning: Please check that your locale settings:\n....\nthen you need to prevent that the Mac ssh client sends the\nLCTYPE variable. Just follow the description in:\nhttps://www.cyberciti.biz/faq/os-x-terminal-bash-warning-setlocale-lc_ctype-cannot-change-locale/\nInvalid certificate messages\nThe following message can be safely ignored:\nkey_cert_check_authority: invalid certificate\nCertificate invalid: name is not a listed principal\nIt indicates that no kerberos token was provided for authentication.\nYou can avoid the warning by first running kinit (PSI linux systems).\nLong Running copy commands\nFor decentral ingestion cases, the copy step is indicated by a message\n'Running [/usr/bin/rsync -e ssh -avxz …'. It is expected that this\nstep will take a long time and may appear to have hung. You can check\nwhat files have been successfully transfered using rsync:\nrsync--list-only user_n@pb-archive.psi.ch:archive/UID/PATH/\nwhere UID is the dataset ID (12345678-1234-1234-1234-123456789012) and\nPATH is the absolute path to your data. Note that rsync creates\ndirectories first and that the transfer order is not alphabetical in\nsome cases, but it should be possible to see whether any data has\ntransferred.\nKerberos tickets\nAs a normal user you should have a valid Kerberos ticket. This is\nusually the case on the centrally provided Linux machines\nautomtically. You can verify the existence with the \"klist\"\ncommand. In case no valid ticket is returned you have to get one using\nthe \"kinit\" command. (Note: beamline accounts do not need this)\nklist\n# if no Ticket listed get one by\nkinit\nInstructions to set ACLS in AFS\nIn the AFS file system the user have to permit access to the\nsourceFolder by setting read and lookup ACL permission for the AFS\ngroup “pb-archive”. The easiest way to achieve is to run the following\nscript with the sourceFolder as an argunent\n/afs/psi.ch/service/bin/pb_setacl.sh sourceFolder\nThis script must be run by a person who has the rights to modify the\naccess rights in AFS.\nAppendix\nInstallation of Tools\nAccess to the SciCat GUI\nFor the access to the SciCat web-based user interface no software\nneeds to be installed, simply use your browser to go to\nhttps://discovery.psi.ch. \nLoading datacatalog tools on Clusters\nThe latest datacatalog software is maintained in the PSI module system\non the main clusters (Ra, Merlin). To access it from PSI linux\nsystems, run the following command:\nmodule load datacatalog\n(Non-standard Linux systems) Installing datacatalog tools\nIf you do not have access to PSI modules (for instance, when archiving\nfrom Ubuntu systems), then you can install the datacatalog software\nyourself. These tools require 64-bit linux.\nI suggest storing the SciCat scripts in ~/bin so that they can be\neasily accessed.\nmkdir -p ~/bin\ncd ~/bin\n/usr/bin/curl -O https://gitlab.psi.ch/scicat/tools/raw/master/linux/datasetIngestor\nchmod +x ./datasetIngestor\n/usr/bin/curl -O https://gitlab.psi.ch/scicat/tools/raw/master/linux/datasetRetriever\nchmod +x ./datasetRetriever\n/usr/bin/curl -O https://gitlab.psi.ch/scicat/tools/raw/master/linux/SciCat\nchmod +x ./SciCat\nWhen the scripts are updated you will be prompted to re-run some of\nthe above commands to get the latest version.\nYou can call the ingestion scripts using the full path\n(~/bin/datasetIngestor) or else add ~/bin to your unix PATH. To do so,\nadd the following line to your ~/.bashrc file:\nexport PATH=\"$HOME/bin:$PATH\"\nInstallation on Windows Systems\nOn Windows the executables can be downloaded from the following URL,\njust enter the address in abrowser and download the file\nhttps://gitlab.psi.ch/scicat/tools/-/blob/master/windows/datasetIngestor.exe\nhttps://gitlab.psi.ch/scicat/tools/-/blob/master/windows/SciCatGUI_Win10.zip\nOnline work stations in beamline hutches\nThe command line tools are pre-installed in /work/sls/bin. No further\naction needed\nDataset limitations\nSize limitations\n\na single dataset should currently not have more than 400k files\na single dataset should not be larger than 50 TB\nrecommended size of a single dataset: between 1GB and 1TB\n\nSourceFolder and file names limitations\nThe sourceFolder metadata and the name of the files can contain the following special characters: \n\n\\%\n#\n-\n+\n.\n\\:\n\\=\n\\@\n_\n\nAny other special characters are not guaranteed to work.\nRecommended file structure for raw datasets\nOne recommended way of structuring your data on disk is the following:\ne12345 Metadata Field Definitions\nThe following table defines the mandatory and optional fields for the\nadministrative metadata, which have to be provided (status June\n2021). All fields marked \"m\" are mandatory, the rest is optional. Some\nfields are filled automatically if possible, see comments. For the\nmost recent status see this URL\nhttps://scicatproject.github.io/api-documentation/ and follow the link\ncalled \"Model\" for the respective datamodel (e.g. Dataset), visible\ne.g. inside the GET API call section. Or see the model definitions as\ndefined in the SciCat backend, see the json files in\nhttps://github.com/SciCatProject/catamel/tree/develop/common/models\nAll \"Date\" fields must follow the date/time format defined in RFC\n3339, section 5.6, see https://www.ietf.org/rfc/rfc3339.txt\nMetadata field definitions for datasets of type \"base\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nfield\ntype\nmust\ncomment\n\n\n\n\n\npid\nstring\nm\nfilled by API automatically, do **not** provide this\n\n\n\n\nowner\nstring\nm\nfilled by datasetIngestor if missing\n\n\n\n\nownerEmail\nstring\n \nfilled by datasetIngestor if missing\n\n\n\n\norcidOfOwner\nstring\n \n \n\n\n\n\ncontactEmail\nstring\nm\nfilled by datasetIngestor if missing\n\n\n\n\ndatasetName\nstring\n \nset to \"tail\" of sourceFolder path if missing\n\n\n\n\nsourceFolder\nstring\nm\n \n\n\n\n\nsize\nnumber\n \nautofilled when OrigDataBlock created\n\n\n\n\npackedSize\nnumber\n \nautofilled when DataBlock created\n\n\n\n\ncreationTime\ndate\nm\nfilled by API if missing\n\n\n\n\ntype\nstring\nm\n(raw, derived…)\n\n\n\n\nvalidationStatus\nstring\n \n \n\n\n\n\nkeywords\nArray[string]\n \n \n\n\n\n\ndescription\nstring\n \n \n\n\n\n\nclassification\nstring\n \nfilled by API or datasetIngestor if missing\n\n\n\n\nlicense\nstring\n \nfilled by datasetIngestor if missing (CC By-SA 4.0)\n\n\n\n\nversion\nstring\n \nautofilled by API\n\n\n\n\ndoi\nstring\n \nfilled as part of publication workflow\n\n\n\n\nisPublished\nboolean\n \nfilled by datasetIngestor if missing (false)\n\n\n\n\nownerGroup\nstring\nm\nmust be filled explicitly\n\n\n\n\naccessGroups\nArray[string]\n \nfilled by datasetIngestor to beamline specific group\n\n\n\n\n \n \n \nderived from creationLocation\n\n\n\n\n \n \n \ne.g. /PSI/SLS/TOMCAT -> accessGroups=[\"slstomcat\"]\n\n\n\n\nAdditional fields for type=\"raw\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nfield\ntype\nmust\ncomment\n\n\n\n\n\nprincipalInvestigator\nstring\nm\nfilled in datasetIngestor if missing (proposal must exist)\n\n\n\n\nendTime\ndate\n \nfilled from datasetIngetor if missing\n\n\n\n\ncreationLocation\nstring\nm\nsee known Instrument list below\n\n\n\n\ndataFormat\nstring\n \n \n\n\n\n\nscientificMetadata\nobject\n \n \n\n\n\n\nproposalId\nstring\n \nfilled by API automatically if missing\n\n\n\n\nAdditional fields for type=\"derived\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nfield\ntype\nmust\ncomment\n\n\n\n\n\ninvestigator\nstring\nm\n \n\n\n\n\ninputDatasets\nArray[string]\nm\n \n\n\n\n\nusedSoftware\nstring\nm\n \n\n\n\n\njobParameters\nobject\n \n \n\n\n\n\njobLogData\nstring\n \n \n\n\n\n\nscientificMetadata\nobject\n \n \n\n\n\n\nAbout Scientific Values and Units\nIt is strongly recommended that physical quantities are stored in the\n following format (the field names are just examples, the structure\n with the two fields \"value\" and \"unit\" is important here)\n\"scientificMetadata\": {\n ...\n \"beamlineParameters\": {\n \"Ring current\": {\n \"value\": 402.246,\n \"unit\": \"mA\"\n },\n \"Beam energy\": {\n \"value\": 22595,\n \"unit\": \"eV\"\n }\n }\n ....\n}\nIn future for such quantities the data catalog will automatically add\ntwo additional fields \"valueSI\" and \"unitSI\" with the corresponding\nSI units. The rationale for this is to support value queries in a\nreliable manner across datasets with potentially different units\nchosen for the same quantity:\n\"scientificMetadata\": {\n ...\n \"beamlineParameters\": {\n \"Ring current\": {\n \"value\": 402.246,\n \"unit\": \"mA\",\n \"valueSI\": 0.402246,\n \"unitSI\": \"A\"\n },\n \"Beam energy\": {\n \"value\": 22595,\n \"unit\": \"eV\",\n \"valueSI\": 3.6201179E-15\n \"unitSI\":\"J\"\n }\n }\n ....\n}\nUse Case Examples\nUse Case: Manual ingest using datasetIngestor program\n\nOverview\nData owners may want to define in an adhoc manner the creation of\ndatasets in order to allow a subsequent archiving of the data. The\nmost important use cases are\n\nraw data from a beamline\nderived data created by a scientist\narchiving of historic data\narchiving of data stored on local (decentral) file storage systems\n\nFor this purpose a command line client datasetIngestor is provided\nwhich allows to\n\ningest the meta data and files\noptionally copy the data to a central cache file server\n\nThe necessary steps to use this tool are now described:\n\nPreparation of the meta data\nYou need to create a file metadata.json defining at least the\nadministrative metadata\n\nExample of minimal json file for raw data:\n{\n \"creationLocation\": \"/PSI/SLS/TOMCAT\",\n \"sourceFolder\": \"/scratch/devops\",\n \"type\": \"raw\",\n \"ownerGroup\":\"p16623\"\n}\n\nExample for raw data including scientific metadata\n{\n \"principalInvestigator\": \"egon.meier@psi.ch\",\n \"creationLocation\": \"/PSI/SLS/TOMCAT\",\n \"dataFormat\": \"Tomcat pre HDF5 format 2017\",\n \"sourceFolder\": \"/sls/X02DA/data/e12345/Data10/disk3/817b_B2_\",\n \"owner\": \"Egon Meier\",\n \"ownerEmail\": \"egon.meier@psi.ch\",\n \"type\": \"raw\",\n \"description\": \"Add a short description here for this dataset ...\",\n \"ownerGroup\": \"p12345\",\n \"scientificMetadata\": {\n \"beamlineParameters\": {\n \"Monostripe\": \"Ru/C\",\n \"Ring current\": {\n \"value\": 0.402246,\n \"unit\": \"A\"\n },\n \"Beam energy\": {\n \"value\": 22595,\n \"unit\": \"eV\"\n }\n },\n \"detectorParameters\": {\n \"Objective\": 20,\n \"Scintillator\": \"LAG 20um\",\n \"Exposure time\": {\n \"value\": 0.4,\n \"unit\": \"s\"\n }\n },\n \"scanParameters\": {\n \"Number of projections\": 1801,\n \"Rot Y min position\": {\n \"value\": 0,\n \"unit\": \"deg\"\n },\n \"Inner scan flag\": 0,\n \"File Prefix\": \"817b_B2_\",\n \"Sample In\": {\n \"value\": 0,\n \"unit\": \"m\"\n },\n \"Number of darks\": 10,\n \"Rot Y max position\": {\n \"value\": 180,\n \"unit\": \"deg\"\n },\n \"Angular step\": {\n \"value\": 0.1,\n \"unit\": \"deg\"\n },\n \"Number of flats\": 120,\n \"Sample Out\": {\n \"value\": -0.005,\n \"unit\": \"m\"\n },\n \"Flat frequency\": 0,\n \"Number of inter-flats\": 0\n }\n }\n}\n\nExample of minimal json file for derived data:\n{ \"sourceFolder\" : \"/data/test/myExampleData\",\n \"type\" : \"derived\",\n \"ownerGroup\": \"p12345\",\n \"investigator\":\"federika.marone@psi.ch\",\n \"inputDatasets\": [\"/data/test/input1.dat\",\n \"20.500.11935/000031f3-0675-4d30-b5ca-b9c674bcf027\"],\n \"usedSoftware\": [\"https://gitlab.psi.ch/MyAnalysisRepo/tomcatScripts/commit/60629a1cbef493a26aac626602ba8f1a6c9e14d2\"]\n }\n\nowner and contactEmail will be filled automatically\nimportant: in case you ingest derived datasets with a beamline\naccount , such as slstomcat (instead of a personal account), you have to add the beamline account\nto the accessGroups field like this:\n{ \"sourceFolder\" : \"/data/test/myExampleData\",\n \"type\" : \"derived\",\n \"ownerGroup\": \"p12345\",\n \"accessGroups\":[\"slstomcat\"], \n \"investigator\":\"federika.marone@psi.ch\",\n \"inputDatasets\": [\"/data/test/input1.dat\",\n \"20.500.11935/000031f3-0675-4d30-b5ca-b9c674bcf027\"],\n \"usedSoftware\": [\"https://gitlab.psi.ch/MyAnalysisRepo/tomcatScripts/commit/60629a1cbef493a26aac626602ba8f1a6c9e14d2\"]\n }\n\nExtended derived example\n{\n \"sourceFolder\": \"/some/folder/containg/the/derived/data\",\n \"owner\": \"Thomas Meier\",\n \"ownerEmail\": \"thomas.meier@psi.ch\",\n \"contactEmail\": \"eugen.mueller@psi.ch\",\n \"type\": \"derived\",\n \"ownerGroup\": \"p13268\",\n \"creationTime\": \"2011-09-14T12:08:25.000Z\",\n \"investigator\": \"thomas.meier@psi.ch\",\n \"inputDatasets\": [\n \"20.500.11935/000031f3-0675-4d30-b5ca-b9c674bcf027\",\n \"20.500.11935/000031f3-0675-4d30-b5ca-b9c674bcf028\"\n ],\n \"usedSoftware\": [\"https://gitlab.psi.ch/MyAnalysisRepo/tomcatScripts/commit/60629a1cbef493a26aac626602ba8f1a6c9e14d2\"]\n}\n\n\n\nOptionally: preparation of a file listing file\nPlease note: The following is only needed, if you do not want to\nstore all files in a source Folder, but just a subset. In this case\nyou can specify an explicit list of files and directories. Only the\nfiles specified in this list will be stored as part of the\ndataset. For the directories in this list it is implied that they are\nrecursively descended and all data contained in the directory is taken\nHere is an example for a filelisting.txt file. All entries in this\ntextfiles are path names relativ to the sourceFolder specified in\nthe metadata.json file\nExample of filelisting.txt\ndatafile1\ndatafile2\nspecialStuff/logfile1.log\nallFilesInThisDirectory\n\nOptionally: for multiple datasets to be created\nIf you have many sourceFolders containing data, each to be turned into\na dataset then the easiest method is to define a 'folderlisting.txt'\nfile. (the file must have exactly this name). This is a useful option\nto archive large amounts of \"historic\" data.\nEach line in this file is the absolute path to the sourceFolder In\nthis case it is assumed, that the metadata.json file is valid for all\ndatasets and that all files inside the sourceFolder are part of the\ndataset (i.e. you can not combine the filelisting.txt option with the\nfolderlisting.txt option)\nExample of folderlisting.txt\n/some/folder/containg/the/data/raw/sample1\n/some/folder/containg/the/data/raw/sample2\n/some/folder/containg/the/data/derived\n\nStarting the ingest\nJust run the following command in a terminal as a first test if\neverything is okay. This is a so called \"dry run\" and nothing will\nactually be stored, but the consistency of the data will be checked\nand the folders will be scanned for files\ndatasetIngestor metadata.json [filelisting.txt | 'folderlisting.txt']\nYou will be prompted for your username and password.\nIf everything looks as expected you should now repeat the command with\nthe \"–ingest\" flag to actually store the dataset(s) in the data\ncatalog\ndatasetIngestor --ingest metadata.json [filelisting.txt | 'folderlisting.txt']\nWhen the job is finshed all needed metadata will be ingested into the\ndata catalog (and for decentral data the data will be copied to the\ncentral cache file server).\nIn addition you have the option to directly trigger the archiving of\nthe data to tape by adding the –autoarchive flag. Do this only if you\nsure that this data is worth to be archived\n\n\nUse Case: Automated ingest of raw datasets from beamline or instruments\n\nUsing the datasetIngestor Tool\nThis method usually requires a fully automatic ingestion procedure,\nsince data is produced at regular times and in a predictable way.\nFor each beamline this automation is done together with the experts\nfrom the data catalog group and potentially with the help from the\ncontrols /detector-integration groups. Please contact\nscicatarchivemanager@psi.ch to get in touch.\nThe recommended method is to define preparation scripts, which\nautomatically produce the files metadata.json and optionally\nfilelisting.txt or folderlisting.txt (for multiple datasets) as you\nwould do in the manual case described in the previous section.\nExample of such scripts can be provided by the data catalog team,\nplease contact scicatingestor@psi.ch for further help. The effort to\nimplement such a system depends very much on the availability of the\nmeta data as well as on the effort to convert the existing metadata to\nthe data catalog format inside the converter processes. If the meta\ndata is already available in some form in a file an estimate of the\norder of magnitude of work needed per instrument is 1-2 person-weeks\nof work, including test runs etc. But efforts may also be considerably\nsmaller or larger in some cases.\nThen you run the datasetIngestor program usually under a beamline\nspecic account. In order to run fully automatic all potential\nquestions asked interactively by the program must be pre-answered\nthrough a set of command line options:\ndatasetIngestor [options] metadata-file [filelisting-file|'folderlisting.txt']\n\n -allowexistingsource\n Defines if existing sourceFolders can be reused\n -autoarchive\n Option to create archive job automatically after ingestion\n -copy\n Defines if files should be copied from your local system to a central server before ingest.\n -devenv\n Use development environment instead of production environment (developers only)\n -ingest\n Defines if this command is meant to actually ingest data\n -linkfiles string\n Define what to do with symbolic links: (keep|delete|keepInternalOnly) (default \"keepInternalOnly\")\n -noninteractive\n If set no questions will be asked and the default settings for all undefined flags will be assumed\n -tapecopies int\n Number of tapecopies to be used for archiving (default 1)\n -testenv\n Use test environment (qa) instead of production environment\n -user string\n Defines optional username:password string\n\nhere is a typical example using the MX beamline at SLS as an example\nand ingesting a singel dataset with meta data defined in\nmetadata.json\ndatasetIngestor -ingest \\\n -linkfiles keepInternalOnly \\\n -allowexistingsource \\\n -user slsmx:XXXXXXXX \\\n -noninteractive \\\n metadata.json\n\n\nThis command must be called by the respective data acquisition systems\nat a proper time, i.e. after all the files from the measurement run\nhave been written to disk and all metadata became available (often\nthis meta data is collected by the controls system). \n\nHDF5 Files\nIf the raw data exists in form of HDF5 files, there is a good chance\nthat the meta data can be extracted from the HDF5 files' meta data. In\nsuch a case the meta data extraction must be done as part of the part\nbeamline preparation scripts. Example of such HDF5 extraction scripts\nexist which can the basis of a beamline specific solution, again\nplease contact scicatingestor@psi.ch. These scripts will mostly need\nminimal adjustments for each beamline, mainly specifying the filter\nconditions defining which of the meta data in the HDF5 file are to be\nconsidered meta data for the data catalog.\nVery often the whole dataset will only consist of one HDF5 file, thus\nalso simplifying the filelisting definition.\n\n\nUse Case: Ingest datasets stored on decentral systems\nThese are data that you want to have archived for some reason, but are\nnot available on central file systems. Data from the old PSI archiv\nsystem fall in this category or data from local PCs, Laptops or\ninstruments. If this data is not assigned to a p-group (given via the\nDUO digital user office, usually linked to a proposal) then you must\nassign this data to an a-group. The allocation of an \"a-group\" for\nthis kind of data must be done beforehand by a tool currently in\npreparation at AIT. The \"a-group\" will define the ownership and\ntherefor the access to the data by listing a number of users onside the\ngroup.\nOtherwise just follow the description in the section \"Manual ingest\nusing datasetIngestor program\" and use the option -copy, e.g.\ndatasetIngestor -autoarchive -copy -ingest metadata.json\nThis command will copy the data to a central rsync server, from where\nthe archive system can then copy the files to tape, in this case\n(option -autoarchive) the copy to archive tapes will happen automatically\nOn recent versions of the datasetIngestor program the program detects\nautomatically,if your data lies on central or decentral systems. In\nthe latter case it will, after a confirmation by the user, copy the\ndata automatically to the rsync cache server, even if the copy flag is\nnot provided.\nUse Case: Ingest datasets from simulations/model calculations\nThese can be treated like datasets of type \"base\" or \"raw\". In the\nlatter case specify the field \"creationLocation\" as the name of the\nserver or cluster which produced the simulation files. Otherwise the\nprocedure is identical to the previous use case.\nPolicy settings and email notifications\nThe archiving process can further be configured via policy\nparameters, e.g. if you require a second tape copy for very\nprecious data. Also the details about the notification settings by\nemail for both archive and retrieve jobs can be set here. You reach\nthe menu to set the policy values via the submenu Policies\nin the dropdown menu to the top right of the GUI.\nEmails are automatically sent at the start of every archive and\nretrieve jobs as well as when the job finishes. The email is sent to\nthe person creating the jobs. In addition it is sent the list of\nemails defined in the policy settings. Per default this list is empty\nbut can be extended by you. In the policy one can also switch off the\nemail notification. However emails about error conditions (which can\nbe either user caused or system caused) can not be switched off. Such\nerror messages are always sent to the user as well as the archive\nadministrators.\nFor user caused errors the user has to take action to repair the\nsituation. Typically error cases are, that the user has moved or\nremoved part or all of the files before archiving them. System errors\non the other hand have their reason inside the catalog and archive\nsystem (e.g. a network connection problem or similar) and will be\ntaken care of by the archive managers. In such a case the user\ncreating the job will be informed manually, when the problem is fixed\nagain.\nPolicy parameters can be defined at site level or at ownerGroup\nlevel. For each ownerGroup at least one manager must be defined\n(e.g. a principal investigator (PI) via the linked proposal\ninformation) in the policy model (field \"manager\") . Only the manager\ncan change the policy settings at ownerGroup level, but all group\nmebers can see them.\nChanges to this policy settings only effect future dataset creation\nand archiving\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nParameter\nAllowed Values\nDefault\nLevel\n\n\n\n\n\npolicyPublicationShiftInYears\nsmall positive integer, e.g. 3\n3\nSite (ro)\n\n\n\n\npolicyRetentionShiftInYears\nsmall positive integer, e.g. 10\n10\nSite (ro)\n\n\n\n\n\nautoArchive\ntrue/false\nfalse\nownerGroup\n\n\n\n\ntapeRedundancy\nlow/medium/(high)\nlow\nownerGroup\n\n\n\n\narchiveEmailNotification\ntrue/false\nfalse\nownerGroup\n\n\n\n\narchiveEmailsToBeNotified\nArray of additional emails\n[]\nownerGroup\n\n\n\n\nretrieveEmailNotification\ntrue/false\nfalse\nownerGroup\n\n\n\n\nretrieveEmailsToBeNotified\nArray of additional emails\n[]\nownerGroup\n\n\n\n\n(archiveDelayInDays)\nsmall positive integer, e.g. 7\n0\nownerGroup\n\n\n\n\nThe job Initiator always gets an email unless email notification is disabled.\nAnalyzing Metadata Statistics\nNote: This service is currently (summer 2021) out of order due to the\nmissing JupyterHub environment.\nOverview\nIt is possible to analyze the information about datasets amd jobs etc,\ne.g. for statistical purposes. A Jupyterhub based solution was chosen\nas a tool for allowing to do this analysis in a flexible and\ninteractive manner. This means you can use Jupyter notebooks to query\nthe Data catalog via the API for its data and analyze the results in\nterms of tables and graphs. Example notebooks are provided.\nGetting started\nSimply follow the following link and login with your PSI account:\nhttps://jupyterhub.apps.ocp4a.psi.ch/ . The initial start of the\nJupyter environments takes some time (about 40 seconds), but\nsubsequent starts are much faster. You will then see a \"bootstrap\"\nnotebook which you can execute to populate your Jupyter home directory\nwith the example notebooks. \nThe example notebooks require you to login to the data catalog API\nserver. Here you can again use your personal account, which gives you\naccess to all data, for which you have read access (i.e. for which you\nare member of the associated p-group). Beamline managers can also use\nthe beamline accounts here in order to get the statistics relevant for\nthe whole beamline. You can then look at the example notebooks,\ne.g. datasetAnalyzer.ipynb and run it, look at resulting tables and\ngraphs. Afterwards you can optionally adapt the notebooks to your\nneeds.\nPlease note, that this service is currently only available as a pilot\nwith no guaranteed availability. This also means, that you should\nmake regular backups of your own notebooks which you may develop\nusing this tool. For this you can e.g. simply download the notebook\nand copy it to a place for which backup exists, like your home\ndirectory.\nAccess to the API (for script developers)\nThe data catalog can also be accessed directly via a REST API. There\nexists an API \"Explorer\" which allows to test such API calls\nconveniently. The explorer can be found at\nhttps://dacat-qa.psi.ch/explorer .The explorer works with a test\ndatabase which is separate from the production database and contains\nother data.\nFor most of the API calls you will need an access token first. You\ncreate such an access token by \"login\" to the data catalog via the\nfollowing curl command:\n# for \"functional\" accounts\ncurl -X POST --header 'Content-Type: application/json' -d '{\"username\":\"YOUR-LOGIN\",\"password\":\"YOUR-PASSWORD\"}' 'https://dacat-qa.psi.ch/api/v3/Users/login'\n\n# for normal user accounts\ncurl -X POST --header 'Content-Type: application/json' -d '{\"username\":\"YOUR-LOGIN\",\"password\":\"YOUR-PASSWORD\"}' 'https://dacat-qa.psi.ch/auth/msad'\n\n# reply if succesful:\n{\"id\":\"NQhe3...\",\"ttl\":1209600,\"created\":\"2019-01-22T07:03:21.422Z\",\"userId\":\"5a745bde4d12b30008020843\"}\nThe \"id\" field contains the access token, which you copy in to the corresponding field at the top of the explorer page.\nAfterwards you can test the full API. If you found the right API call\nyou can finally apply the call to the production system by replacing\n\"dacat-qa\" by \"dacat\" and then by retrieving the access token from the\nproduction system.\nUsing datasetIngestor inside wrapper scripts (for developers)\nThe command datasetIngestor returns with a return code equal zero in\ncase the command could be executed succesfully. If the program however\nfails for some reason the return code will be one. Typical examples of\nfailures are that files can not be found or not be accessed. Other\npossibilities are that the catalog system is not available,\ne.g. during scheduled maintenance periods. All outputs describing the\nreason for the failure are written to STDERR. Please have a look at\nthese outputs to understand what the reason for the failure was. If\nyou need help please contact scicatingestor@psi.ch\nPlease note: it is the task of the wrapper scripts to test\nfor the return code and to repeat the command once all conditions for\na succesful execution are fulfilled\nIn case the ingest finishes succesfully the dataset persistent\nidentifiers (PID) of the resulting dataset(s) are written to STDOUT,\none line per dataset.\nIngestion of datasets which should never be published\nFor datasets which should never be published you should add the\nfollowing fields at ingest time to your metadata.json file:\n\"datasetlifecycle\": {\n \"publishable\":false,\n \"dateOfPublishing\":\"2099-12-31T00:00:00.000Z\",\n \"archiveRetentionTime\":\"2099-12-31T00:00:00.000Z\"\n}\n\nthis will move the time of publication to a date in some far future\n(2100 in this case)\n\nRetrieving proposal information\nIn case you need information about the principal investigator you can\nuse the command datasetGetProposal, which returns the proposal\ninformation for a given ownerGroup\n/usr/bin/curl -O https://gitlab.psi.ch/scicat/tools/raw/master/linux/datasetGetProposal;chmod +x ./datasetGetProposal\nLink to Group specific descriptions\n\nBIO department: https://intranet.psi.ch/BIO/ComputingDataCatalog\n\nList of known creationLocation for raw data\nThe following values for the creationLocation should be used for the\nrespective beamlines. They are derived from the identifiers used\ninside the digital user office DUO\nSLS\n\n\n\n\n\n\n\n\n\n\n\n\nBeamline\ncreationLocation\nIngest Account\n\n\n\n\n\nAdress-RIXS\n/PSI/SLS/ADRESS-RIXS\nslsadress-rixs\n\n\n\n\nAdress-SX-ARPES\n/PSI/SLS/ADRESS-SX-ARPES\nslsadress-sx-arpes\n\n\n\n\ncSAXS\n/PSI/SLS/CSAXS\nslscsaxs\n\n\n\n\nMicro-XAS\n/PSI/SLS/MICRO-XAS\nslsmicro-xas\n\n\n\n\nMicro-XAS-Femto\n/PSI/SLS/MICRO-XAS-FEMTO\nslsmicro-xas-femto\n\n\n\n\nMS-Powder\n/PSI/SLS/MS-POWDER\nslsms-powder\n\n\n\n\nMS-Surf-Diffr\n/PSI/SLS/MS-SURF-DIFFR\nslsms-surf-diffr\n\n\n\n\nNano-XAS\n/PSI/SLS/NANOXAS\nslsnanoxas\n\n\n\n\nPearl\n/PSI/SLS/PEARL\nslspearl\n\n\n\n\nPhoenix\n/PSI/SLS/PHOENIX\nslsphoenix\n\n\n\n\nPollux\n/PSI/SLS/POLLUX\nslspollux\n\n\n\n\nMX (PX,PXII,PXIII)\n/PSI/SLS/MX\nslsmx\n\n\n\n\nSIM\n/PSI/SLS/SIM\nslssim\n\n\n\n\nSis-Cophee\n/PSI/SLS/SIS-COPHEE\nslssis-cophee\n\n\n\n\nSis-Hrpes\n/PSI/SLS/SIS-HRPES\nslssis-hrpes\n\n\n\n\nSuper-XAS\n/PSI/SLS/SUPER-XAS\nslssuper-xas\n\n\n\n\nTomcat\n/PSI/SLS/TOMCAT\nslstomcat\n\n\n\n\nVUV\n/PSI/SLS/VUV\nslsvuv\n\n\n\n\nXIL-II\n/PSI/SLS/XIL-II\nslsxil-ii\n\n\n\n\nXtreme\n/PSI/SLS/XTREME\nslsxtreme\n\n\n\n\nThe connected email distribution lists are {ingestAccount}@psi.ch\nSwissfel\n\n\n\n\n\n\n\n\n\n\n\n\nBeamline\ncreationLocation\nIngest Account\n\n\n\n\n\nAlvra\n/PSI/SWISSFEL/ARAMIS-ALVRA\nswissfelaramis-alvra\n\n\n\n\nBernina\n/PSI/SWISSFEL/ARAMIS-BERNINA\nswissfelaramis-bernina\n\n\n\n\nCristallina\n/PSI/SWISSFEL/ARAMIS-CRISTALLINA\nswissfelaramis-cristallina\n\n\n\n\nFurka\n/PSI/SWISSFEL/ATHOS-FURKA\nswissfelathos-furka\n\n\n\n\nMaloja\n/PSI/SWISSFEL/ATHOS-MALOJA\nswissfelathos-maloja\n\n\n\n\nThe connected email distribution lists are {ingestAccount}@psi.ch\nSINQ\n\n\n\n\n\n\n\n\n\n\n\n\nInstrument\ncreationLocation\nIngest Account\n\n\n\n\n\nAMOR\n/PSI/SINQ/AMOR\nsinqamor\n\n\n\n\nDMC\n/PSI/SINQ/DMC\nsinqdmc\n\n\n\n\nEIGER\n/PSI/SINQ/EIGER\nsinqeiger\n\n\n\n\nFOCUS\n/PSI/SINQ/FOCUS\nsinqfocus\n\n\n\n\nHRPT\n/PSI/SINQ/HRPT\nsinqhrpt\n\n\n\n\nICON\n/PSI/SINQ/ICON\nsinqicon\n\n\n\n\nMorpheus\n/PSI/SINQ/MORPHEUS\nsinqmorpheus\n\n\n\n\nNARZISS\n/PSI/SINQ/NARZISS\nsinqnarziss\n\n\n\n\nNEUTRA\n/PSI/SINQ/NEUTRA\nsinqneutra\n\n\n\n\nPOLDI\n/PSI/SINQ/POLDI\nsinqpoldi\n\n\n\n\nRITA-II\n/PSI/SINQ/RITA-II\nsinqrita-ii\n\n\n\n\nSANS-I\n/PSI/SINQ/SANS-I\nsinqsans-i\n\n\n\n\nSANS-II\n/PSI/SINQ/SANS-II\nsinqsans-ii\n\n\n\n\nTASP\n/PSI/SINQ/TASP\nsinqtasp\n\n\n\n\nZEBRA\n/PSI/SINQ/ZEBRA\nsinqzebra\n\n\n\n\n \n \n \n\n\n\n\nThe connected email distribution lists are {ingestAccount}@psi.ch\nSmuS\n\n\n\n\n\n\n\n\n\n\n\n\nInstrument\ncreationLocation\nIngest Account\n\n\n\n\n\nDolly\n/PSI/SMUS/DOLLY\nsmusdolly\n\n\n\n\nGPD\n/PSI/SMUS/GPD\nsmusgpd\n\n\n\n\nGPS\n/PSI/SMUS/GPS\nsmusgps\n\n\n\n\nHAL-9500\n/PSI/SMUS/HAL-9500\nsmushal-9500\n\n\n\n\nLEM\n/PSI/SMUS/LEM\nsmuslem\n\n\n\n\nFLAME\n/PSI/SMUS/FLAME\nsmusflame\n\n\n\n\nThe connected email distribution lists are {ingestAccount}@psi.ch\nUpdate History of Ingest Manual\n\n\n\n\n\n\n\n\n\n\nDate\nUpdates\n\n\n\n\n\n10. September 2018\nInitial Release\n\n\n\n\n6. October 2018\nAdded warning section to not modify data after ingest\n\n\n\n\n10. October 2018\nownerGroup field must be defined explicitly\n\n\n\n\n28. October 2018\nAdded section on datasetRetriever tool\n\n\n\n\n20. November 2018\nRemove ssh key handling description (use Kerberos)\n\n\n\n\n3. December 2018\nRestructure archive stepp, add autoarchive flag\n\n\n\n\n17. January 2019\nUpdate on automatically filled values, more options for datasetIngestor\n\n\n\n\n22. January 2019\nAdded description for API access for script developers, 2 new commands\n\n\n\n\n \ndatasetArchiver and datasetGetProposal\n\n\n\n\n22. February 2019\nAdded known beamlines(instruments (creationLocation) value list\n\n\n\n\n24. February 2019\ndatasetIngestor use cases for automated ingests using beamline accounts\n\n\n\n\n23. April 2019\nAdded AFS infos and available central storage, need for Kerberos tickets\n\n\n\n\n23. April 2019\nAvailability of commands on RA cluster via pmodules\n\n\n\n\n3. May 2019\nAdded size limitation infos\n\n\n\n\n9. May 2019\nAdded hints for accessGroups definition for derived data\n\n\n\n\n \nAdded infos about email notifications\n\n\n\n\n10. May 2019\nAdded ownerGroup filtered retrieve option, decentral case auto detect\n\n\n\n\n7. Juni 2019\nFeedback from Manuel added\n\n\n\n\n21. Oct 2019\nNew version of CLI tools to deal with edge cases (blanks in sourcefolder\n\n\n\n\n \ndangling links, ingest for other person, need for kerberos ticket as user)\n\n\n\n\n14. November 2019\nRestructuring of manual,New CLI tools, auto kinit login\n\n\n\n\n \nProgress indicators, chksum test updated\n\n\n\n\n20. Januar 2020\nAuto fill principalInvestigator if missing\n\n\n\n\n3. March 2020\nAdded Jupyter notebook analysis section\n\n\n\n\n5. March 2020\nAdd hint for datasets not to be published\n\n\n\n\n19. March 2020\nAdded hint that analysis Jupyter tool is in pilot phase only\n\n\n\n\n19. March 2020\nAdded recommendation concerning unit handling for physical quantities\n\n\n\n\n9. July 2020\nAdded GUI tool SciCatArchiver (developer: Klaus Wakonig)\n\n\n\n\n11. July 2020\nInstallation of SciCatArchiver on non-Ra system\n\n\n\n\n14. July 2020\nAdded publication workflow and recommended file structure chapter\n\n\n\n\n16. July 2020\nUpdated SciCat GUI deployment information\n\n\n\n\n31. July 2020\nNew deploy location, + policy parameters, new recommended file structure\n\n\n\n\n27. August 2020\nAdded Windows Support information\n\n\n\n\n10. Sept 2020\nCorrected example JSON syntax in one location\n\n\n\n\n23. November 2020\nCorrected instructions for using the SciCat GUI on Windows 10\n\n\n\n\n19. February 2020\nAdded info about proposalId link\n\n\n\n\n24. Juni 2021\nMajor restructuring of full document for easier readability\n\n\n\n\n9. Dec 2021\nCorrected spelling of value/units convention\n\n\n\n\n23. April 2022\nAdded hint to use -token option for CLI and SciCat GUI as normal user\n\n\n\n\n2. Dec 2022\nExtended ingest use cases description of needed parameters Win+Linux\n\n\n\n\n21. Dec 2023\nInclude redundancy risks and costs and file names limitations\n\n\n\n"},"Ingestor/IngestManual_ESS.html":{"url":"Ingestor/IngestManual_ESS.html","title":"Ingest Instructions ESS","keywords":"","body":"Ingest Instructions ESS\nThis page needs to be updated\n\nAs shown in the picture above the detector and the data collection software is writing into kafka topics. The topics is then being read by a filewriter that in turn writes the dataset to storage and sends back an event when the file is written that contains metadata about the file and the experiment. There is then an ingestion program that will parse the event from the filewriter and gather additional information before triggering REST calls into the backend. \nHow the ingestion program works:\n1. Parse Event\nThe ingestor is subscribing to a topic in Kafka where the filewriter creates an event when a file has been written. This event contains information about location, proposal id and metadata that exist on the file in the form of nexus data. \n2. Login\n Login and get a access token that can be used for interfacing with the backend. We advise to create a special ingestor account to be used when doing automatic ingestion. \n3. Gather more metadata\nThe ingestor contacts the User Office and get's additional information regarding the experiment, the principal investigator and the sample.\n4. Create dataset\n\nUsing the information from the filewriter event with the additional information gathered from the user office a dataset request can be constructed and sent to the backend.\n5. Create OrigDatablocks\n\nAfter a dataset has been created we attach the files that relate, this is done by creating datablocks that attaches to the dataset\n6. Create Sample\n\nIf sample information is available a sample record can be created and attached to the dataset. This is an important step as it makes the data available to a wider audience of people. \n"},"Ingestor/GettingMetadataIntoScicat.html":{"url":"Ingestor/GettingMetadataIntoScicat.html","title":"Manual Ingest the Hard Way","keywords":"","body":"Getting Metadata Into SciCat (The hard way)\nThis page needs to be updated\nManual ingestion\nThe following steps will add a dataset to you system using the API with the linux program curl. \n1.\nLogin to the backend. The default password for the ingestor user is aman. Running the command below in the terminal will yield an access token.\n curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"username\":\"ingestor\", \"password\":\"\"}' 'http://localhost:3000/api/v3/Users/login'\n\nCreate a json file with the contents below and name it metadata.json\n{\n \"creationLocation\": \"/PSI/SLS/TOMCAT\",\n \"sourceFolder\": \"/scratch/devops\",\n \"type\": \"raw\",\n \"ownerGroup\":\"p16623\"\n}\n\ncat the metadata.json file and pipe it to a curl command. Insert your access token in the command below and run it in the terminal:\n\n\ncat metadata.json | curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d @- 'http://localhost:3000/api/v3/Datasets?access_token=YOUR_TOKEN_HERE'\nThere should now be a dataset in your mongoDB instance. \n"},"Development/":{"url":"Development/","title":"Developers Guide","keywords":"","body":"SciCat Data Catalogue Developer Guide\nThis section has been split in two sub sections related to the backend version. \nOn May 2023, the new backend V4.x based on nest.js has been officially released. We quickly realized that we needed two completely different sections to address the specific needs of each individual version.\nDevelopment documentation:\n\nv3.x (based on loopback v3)\nv4.x (based on nest.js)\n\n"},"Development/v3.x/":{"url":"Development/v3.x/","title":"v3.x","keywords":"","body":"SciCat Data Catalogue Developer Guide\nThis guide aims to help developers understand the overall structure of the software and therefore enables people to contribute to the development of the software. See also the Overview in the Operator Manual for a helicopter view of the involved components.\nIf you want to contribute to the software you should also read the Contributing section, which among others links to the git workflow to be used.\nGetting an development environment set up\nThe easiest is to follow this description to get a fully running frontend and backend setup automatically using docker. Otherwise follow the Running section\nArchitecture\nSciCat is built on micro services using the following stack:\nAPI (Backend)\n\nNode 12.x\nLoopback 3\nMongoDB 3.6 or later\n\nThe most important part to understand is how loopback works, because it is the platform which is directly addressed as part of the development. Please check the corresponding Loopback 3 documentation for all details\nWeb Browser Client (Frontend)\n\nAngular 9+ \nAngular Material Widgets and Design \nNGRX for State Management\nLoopback SDK generator for Angular\n\nAgain please of a look at the original documentation to understand how these tools and frameworks work.\nOptional Messaging/Streaming/Processing tools\n\nApache Kafka or RabbitMQ\nNodered Browser-based flow programming\n\n"},"Development/v3.x/StartHere.html":{"url":"Development/v3.x/StartHere.html","title":"Overview of Backend and Model","keywords":"","body":"Overview of Backend and Model\nThis page provides a brief introduction to the overall workings of the SciCat data model, how things connect and links to more details on each component.\nBackend\nFirst off, the backend is the key component here and you will see there is next to hand written code here (aside from some tests). The folder you will be working in most is: common/models and this is NodeJS application based on the Loopback framework. Go away and read the docs for Loopback (3) and come back here when you are fairly comfortable. I'll wait.\nGot it? Good. OK, so, SciCat stores all of its data in a MongoDB but you will almost never need to perform any admin on the database. The connection between loopback and Mongo is handled by the loopback-mongo-connector. The only thing you need to provide is the file in server/datasources.json , this provides the connection string and any connection specific info.\nIf you take a look at a model, you will see that some of them have a datasource of transient and others are specified as MongoDB. The transient models are not stored or handled by the mongo connector. You will see some other sections, so let's go through them:\nModel Definitions:\n\nProperties: These are the values, types and other information. None of these are mongo specific and can all map to a relational database just as easily.\nRelations: These link models, i.e. allowing a DatasetLifecycle to be linked to a Dataset. The mongo connector handles this by embedding the datasetId in the document for you.\nACLs: These are access control limitations and these are very powerful. You can give a user full control of a model, some users can only have read access etc.\nMethods: When you create a model, loopback will automagically generate API endpoints that allows users to PUT, POST, PATCH, GET etc for all the models. However, sometimes this is not enough. For example, you may want to be able to generate a monthly report of all datasets created, so you define the method here, the name of the endpoint and the expected parameters. You will implement the method in the next section we cover now.\n\nModel Implementation:\nAs just mentioned, Loopback handles all the basic RESTful commands for a model for you but there are two ways to extend this:\n\nCustom methods: You can extend your model with methods that allow you to implement logic beyond what loopback provides\nRemote hooks: Before a particular endpoint (or all endpoints) are called, or before a document is saved/edited/accessed, then you can use these hooks to implement some logic. For example, you may want to inject some extra information into a JSON object before it is saved or ensure the user has some access control that is outside of your Loopback system.\n\nFor the best example of these, take a look at the Dataset JS and JSON files, this also shows how relations work between models.\nRunning the backend\nThe backend has some core files missing that are required in order for it to run. Below, we list these files and what they should contain. Ideally, there will be a minimal install of the backend in the source repo that includes minimal config files to remove this barrier.\n\ndatasources.json - This sets up your connection to Mongo and should follow the syntax outlined in loopback . \nconfig.local.js - These are site specific settings for your install, such as the prefix to use for IDs, messages to store in the dataset and the facilities you have\nproviders.json - Contains connection information to LDAP or other authentication sources\ncomponent-config.json - This file connects to RabbitMQ or (in the case of testing) connects to a local rabbitmq, but this file should not be required in the future so the queuing system used is not fixed. \n\nEditing the Configs\ndatasources.json\nMake sure that the host field of the json is configured to your mongo hostname e.g. for a local instance of mongo with hostname \"mongodb\":\n \"mongo\": {\n \"host\": \"mongodb\",\n \"port\": 27017,\n \"url\": \"\",\n \"database\": \"dacat\",\n \"name\": \"mongo\",\n \"connector\": \"mongodb\",\n \"useNewUrlParser\": true,\n \"allowExtendedOperators\":true\n }\nconfig.local.js\nHere you can configure many aspects of scicat for your institute. Here is an example config file, where you delete parts to turn off certain features:\nvar p = require('../package.json');\nvar version = p.version.split('.').shift();\nmodule.exports = {\n restApiRoot: '/api' + (version > 0 ? '/v' + version : ''),\n host: process.env.HOST || '0.0.0.0',\n port: process.env.PORT || 3000,\n pidPrefix: 'PutYourPIDPrefixHere',\n policyPublicationShiftInYears: 3,\n policyRetentionShiftInYears: 10,\n site: 'YOUR-SITE',\n facilities: [\"Facility1\", \"Facility2\"],\n metadataKeysReturnLimit: 100,\n registerMetadataUri : \"https://mds.test.datacite.org/metadata\",\n registerDoiUri : \"https://mds.test.datacite.org/doi\",\n grayLog: {\n enabled: false,\n host: \"my.graylog.host\",\n port: 0000,\n facility: \"facility\",\n owner: \"owner\",\n service: \"service\"\n },\n # Add to this section if you want to use RabbitMQ for the Proposals mechanism\n rabbitmq: {\n enabled: false,\n host: null,\n port: null,\n queue: null\n },\n # Remove this if you want to use the Jobs mechanism without SMTP or configure for your SMTP service.\n smtpSettings: {\n host: 'SMTP.YOUR.DOMAIN',\n port: 25,\n secure: false },\n smtpMessage: {\n from: 'scicatarchivemanager@YOUR.DOMAIN',\n to: undefined,\n subject: '[SciCat '+process.env.NODE_ENV+']',\n text: undefined // can also set html key and this will override this\n },\n # Keep this if you want to set your queue service to rabbitmq, remove if you are not using a messaging service\n queue: 'rabbitmq'\n};\ncomponent-config.json\nIf you want to use RabbitMQ to reach out to a microservice through the Jobs mechanism in Scicat you will need to edit the component-config.json file. You will also need to add the queue: \"rabbitmq\" line in the \nconfig.local.js file for this to work (see above section.) In order to connect to rabbitMQ you will need a user and password already defined in a rabbitMQ service to connect and the service should be up and running.\nTo set this up add the following to the component-config.json and change the username and password to those you have defined in your rabbitMQ service. Also edit the hostname in the uri field to be the hostname of your RabbitMQ service. \n\"../node_modules/loopback-component-mq/lib\":{\n\"options\":{\n\"restPort\":15672,\n\"acls\":[\n{\n\"accessType\":\"*\",\n\"principalType\":\"ROLE\",\n\"principalId\":\"$unauthenticated\",\n\"permission\":\"DENY\"\n}\n]\n},\n\"topology\":{\n\"connection\":{\n\"uri\":\"amqp://:@:5672/\"\n},\n\"exchanges\":[\n{\n\"name\":\"jobs.write\",\n\"type\":\"topic\",\n\"persistent\":true\n}\n],\n\"queues\":[\n{\n\"name\":\"client.jobs.write\",\n\"subscribe\":true\n}\n],\n\"bindings\":[\n{\n\"exchange\":\"jobs.write\",\n\"target\":\"client.jobs.write\",\n\"keys\":[\n\"jobqueue\"\n]\n}\n]\n}\n}\nFor more info on setting up RabbitMQ see the Deploy with Docker Compose section.\nTesting the backend\nAll tests for the backend are in the test folder in the root of the repo and can be run with npm run test. \nPython Client API\nThe API is automatically created from the Swagger/openAPI (http://swagger.io/) specificication created from the data model using the Swagger Codegen code\njava -jar ./swagger-codegen/modules/swagger-codegen-cli/target/swagger-codegen-cli.jar generate -i swagger.json -l python -o dacat-api/client/python\n"},"Development/v3.x/Data_Model.html":{"url":"Development/v3.x/Data_Model.html","title":"An Introduction to the Data Model","keywords":"","body":"An Introduction to the Data Model\nModel\nThere are many models within the catalog and many of them are self explanatory. The API server creates routes for creation, retrieval, modification and deletion. However, it is worth explaining the core models that are used.\nThe following graph shows a simplified class diagramm of the main model classes. It was generated semi-automatically from the swagger.json file using the WAPIml tool\n\nThe Ownable base model\nMost models inherit the ownable base model which regulates access based on the ownerGroup/ accessGroups fields. Relationships are defined by including the ID of the respective model (handled by loopback model definitions).\nDataset\nThis is the base class for all datasets, and is derived from the\nOwnable model. There are two classes that are derived from Dataset:\nRawDataset and DerivedDataset.\nThe data model and its API endpoints are described here.\nRawDataset\nThis is a dataset that has been collected from an experiment. It contains details about who owns it, contact information etc. Most importantly, it contains Scientific Metadata, and this is an extensible object to outline all parameters relevant to the dataset.\nEach raw dataset may contain a link to a single Proposal.\nThe data model and its API endpoints are described here.\nDerivedDataset\nThese are datasets that are created by analyzing the data in raw datasets. The fields allow to link both to the raw data files used in the analysis as well as to a software repository which keeps the source code used for the analysis, thus helping to keep track of the provenance of the data. You can add ScientificMetadata here as well.\nAlthough DerivedDataset does not contain a link to a Proposal, it may\nbe deduced. A corresponding Proposal may be found by identifying all\ninput RawDatasets (either directly or indirectly via other\nDerivedDatasets) from the inputDatasets field. These RawDatasets may\ncontain links to the corresponding Proposal.\nThe data model and its API endpoints are described here.\nDatasetLifecycle\nThere are many temporal operations for a dataset describing what happens to the Dataset during its lifetime. Therefore, this model contains information relating to the status of the dataset but not the metadata itself. The archive location, date that it should become public and information about archiving or retrieval. Technically the DatasetLifecycle is however embedded into the Dataset documents.\nThe data model and its API endpoints are therefore described as part of the Dataset model.\nInstrument\nThis collection is meant to store the available measurement instruments, ideally in a globally unique naming scheme (within the Expands/Panosc EU programs standardization on these topics is pursued). Datasets can link to these instruments (in addition to the creationLocation field), allowing for more detailed information about the measurement devices.\nThe data model and its API endpoints are described here.\nTechnique\nThis collection is meant to store the available measurement techniques, ideally in a globally unique naming scheme (within the Expands/Panosc EU programs standardization on these topics is pursued). Datasets can link to these techniques, allowing for more detailed information about the measurement method applied.\nOrigDatablocks and Datablocks\nSince SciCat contains only metadata, it must include links to the actual storage location of the data. This typically consists of lists of DataFile objects storing the file path, size, ownership, and checksum (but not the data itself). Since the data may be stored on tape archives or otherwise transformed, each file in a dataset is typically recorded in two places:\n\nOrigDatablock stores the path to the file on the original system at the time of ingestion into SciCat.\nDatablock stores the path to the file on the archive system. The archiveId property is used by the storage management system (not part of SciCat) to retrieve the data. For instance, a simple archiveId might give a path on to a tar bundle on the local tape storage. Some facilities omit Datablocks from their databases and use conventions for determining the data location based only on the OrigDatablock paths.\n\nLarge datasets may split files into multiple OrigDatablocks and Datablocks. The details of this are facility specific, and could reflect local organizational preferences or performance considerations. For instance, datasets with many files are often split into multiple OrigDatablock objects to avoid too-large MongoDB objects. Conversely, very large files may be split across several Datablocks during archiving due to limitations of the storage hardware.\nYou should always add the OrigDataBlock information when ingesting, whereas the Datablocks information is optional and would be filled later by the archive system, when it did its work of writing chunks of data to tape. For instance, a Datablockcan correspond to a single tar file which is written to tape and the contents of the Datablock is essentially the file listing of the tar file.\nThe data model and its API endpoints are described here and here.\nJob\nWhen a user wants to archive, retrieve or publish a dataset, SciCat must interact with the facility's storage and archiving system. This is done by creating a Job to track the interaction. The exact actions taken for each Job are often customized for each facility. A common action would be to forward the new Job entry to a Message Broker, e.g. RabbitMQ, from where it can be picked up by any program willing to react to this Job. For example, at PSI the RabbitMQ queue is emptied by a Node-RED process, which reads the Job information from the RabbitMQ queue. Alternatively, you can use other messaging solutions, such as Apache Kafka. In this way the (site specific) logic to handle the Jobs is kept outside the core of the SciCat system, giving a greater degree of flexibility. The external systems should ideally respond with status updates to the Job model, when there job is finished. As the job progresses, the system can make PUT/PATCH API calls back that update the Job Status Message and, when necessary, also the individual status of the involved datasets by updating the DatasetLifecycle information.\nThe data model and its API endpoints are described here.\nProposals\nA proposal is a written intention declaration of what kind of measurement people want to perform when and where. Such information is often available in an existing system outside of the data catalog, like digital user office systems for institutes which offer their instruments to other users.\nThis information can be synchronized into SciCat and facilitates the definition of the ownership of the datasets as well as providing information relevant for publication of data, such as principal investigators, abstracts etc. datasets are normally uniquely associated with one proposal.\nThe proposal information is however optional\nThe data model and its API endpoints are described here.\nPublishedData\nWhen Datasets are published you assign a DOI to a list of datasets. The PublishedData model keeps track of these information, i.e which dataset is being published, authorship, abstract and download links to access the corresponding file contents of the datasets.\nThe data model and its API endpoints are described here.\nUserSettings\nThis collection stores user preferences, such as the list of pre-selected columns in the dataset dashboard view, defaults for the table page size etc.\nThe data model and its API endpoints are described here.\nAttachment\nSome of the above mentioned models can have files (usually images) attached. The size is limited to 16MB for one attachment. Attachments are stored in the database.\nThe data model and its API endpoints are described here.\nExplorer\nWhen you have an instance of the data catalog running, the API endpoints and the related data models can be looked at at the following URL:\n\n/explorer/\nExample instance from ESS\n\nThe API explorer is a feature of Loopback and allows one to test various endpoints. This section outlines the core functionality. The first point to note is that requests require a token. You can use the login route under the User tab to obtain one of these, or use a CURL command.\nMain Screen\nThe top right entry field is where the token should be included. The token will define your access rights, so not all routes will be accessible. Each model has a tab which relates to its endpoint. Expanding the tab shows the routes section below.\n\nRoutes\nEach section outlines the routes for the chosen model. This is configurable but the HTTP verbs are supported here for most routes and conform to the standards. Error codes are also consistent. Expanding the chosen route allows one to test the response with sample data, or their own.\n\nSingle Route\nSelecting a route shows the expected JSON object and provides an editor to allow you to test the route. Response information is shown below.\nClicking the data type box will insert it into your request for easy testing.\n\nModel\nThe model view can be accessed by clicking the link next to Example Value and shows the fields that Loopback is expecting to receive, as well as their type and whether they are required.\n\n"},"Development/v3.x/Running.html":{"url":"Development/v3.x/Running.html","title":"Running the Components","keywords":"","body":"Running locally\ngit clone https://github.com/SciCatProject/backend.git \ncd backend\nnpm install\nnpm start\ngit clone https://github.com/SciCatProject/frontend.git \ncd frontend\nnpm install\nnpx ng serve\nFor convenience, this has been added to the package.json so you can run npm start as an alias.\nAccess here once running.\nRunning with Docker\nThe containers in our repos are based on alpine linux to ensure a minimal size with almost nothing but the required packages. Most standard *nix commands are available.\nThe recommend way is to use scicatlive . This does all for you automatically.\nIf you want to do things manually instead:\ngit clone https://github.com/SciCatProject/frontend.git && cd frontend\ndocker build -t .\ndocker run \nYou should see the container begin and print out the URL, this is retrieved dynamically based on the IP of the container. Changing it to localhost or 0.0.0.0 will break it.\nRunning with Kubernetes\nCreating a Kubernetes environment takes some expertise but the SciCat repo localdeploy contains all the necessary configuration for deploying SciCat to a cluster with a simple readme.\n"},"Development/v3.x/Configuration.html":{"url":"Development/v3.x/Configuration.html","title":"Configuration","keywords":"","body":"Configuration\n\nSciCat Frontend\nSciCat Backend\nDotenv Config\nDOI Config\n\n\n\nSciCat Frontend\nThis is the configuration file for the frontend client. The configuration file allows the systems administrator to configure every aspect of the client including switching on/off almost all non essential features. The configuration file can either be served from the backend, via the /client/config.json endpoint or mounted into the docker container at the following path /usr/share/nginx/html/assets/config.json.\nAn example is shown below\nfrontend/src/assets/config.json:\n\n{\n \"accessTokenPrefix\": \"\", // Set the backend token prefix. Should be empty string for current backend or \"Bearer \" if using scicat-backend-next.\n \"addDatasetEnabled\": true, // Show/hide the \"Create Dataset\" button in the Datasets Dashboard.\n \"archiveWorkflowEnabled\": true, // Enable/disable the archive/retrieve workflow.\n \"datasetReduceEnabled\": true, // Enable/disable the automatic Dataset reduction/analysis workflow.\n \"editDatasetSampleEnabled\": true, // Enable/disable editing of which Sample a Dataset belongs to.\n \"editMetadataEnabled\": true, // Enable/disable editing of Scientific Metadata.\n \"editPublishedData\": true, // Enable/disable editing of Published Data.\n \"editSampleEnabled\": true, // Enable/disable editing of Samples.\n \"externalAuthEndpoint\": \"/auth/msad\", // Endpoint used for third party authentication, e.g, LDAP.\n \"facility\": \"ESS\", // Facility running the SciCat instance.\n \"fileColorEnabled\": true, // Enable/disable file size color representation in the Datasets Dashboard.\n \"fileDownloadEnabled\": true, // Enable/disable download workflow for Dataset datafiles.\n \"gettingStarted\": null, // URL to Getting Started guide for SciCat, displayed on the Help page.\n \"ingestManual\": null, // URL to Ingest Manual for SciCat, displayed on the Help page.\n \"jobsEnabled\": true, // Enable/disable Job workflow.\n \"jsonMetadataEnabled\": true, // Show/hide the \"Show Metadata\" button on the details pages, allowing users to see the JSON represantion of the current document.\n \"jupyterHubUrl\": \"https://jupyterhub.esss.lu.se/\", // URL to Jupyter Hub instance used for data analysis.\n \"landingPage\": \"doi2.psi.ch/detail/\", // URL to the facility's Landing Page for Published Data.\n \"lbBaseURL\": \"http://127.0.0.1:3000\", // URL to the SciCat Backend.\n \"localColumns\": [ // Default columns to be displayed in the table on the Datasets Dashboard.\n {\n \"name\": \"select\",\n \"order\": 0,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"datasetName\",\n \"order\": 1,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"runNumber\",\n \"order\": 2,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"sourceFolder\",\n \"order\": 3,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"size\",\n \"order\": 4,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"creationTime\",\n \"order\": 5,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"type\",\n \"order\": 6,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"image\",\n \"order\": 7,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"metadata\",\n \"order\": 8,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"proposalId\",\n \"order\": 9,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"ownerGroup\",\n \"order\": 10,\n \"type\": \"standard\",\n \"enabled\": false\n },\n {\n \"name\": \"dataStatus\",\n \"order\": 11,\n \"type\": \"standard\",\n \"enabled\": false\n }\n ],\n \"logbookEnabled\": true, // Enable/disable SciChat Logbook integration.\n \"loginFormEnabled\": true, // Enable/disable the local Login form. Should be disabled if using oAuth2 for authentication.\n \"maxDirectDownloadSize\": 5000000000, // Set a maximum allowed file size for downloading datafiles over HTTP.\n \"metadataPreviewEnabled\": true, // Enable/disable Scientific Metadata preview on the Datasets Dashboard.\n \"metadataStructure\": \"tree\", // Allow tree structure for Scientific Metadata. Set to empty string for flat structure.\n \"multipleDownloadAction\": \"http://localhost:3012/zip\", // URL to service handling direct download of datafiles.\n \"multipleDownloadEnabled\": true, // Enable/disable ability to download multiple datafiles.\n \"oAuth2Endpoints\": [], // List of endpoints used for oAuth2 authentication.\n \"policiesEnabled\": true, // Enable/disable Dataset Policies workflow.\n \"retrieveDestinations\": [], // List of destinations for Dataset retrievals.\n \"riotBaseUrl\": \"http://scitest.esss.lu.se/riot\", // URL to SciChat client.\n \"scienceSearchEnabled\": true, // Enable/disable filtering documents on Scientific Metadata.\n \"scienceSearchUnitsEnabled\": true, // Enable/disable filtering documents on Scientific Metadata using units.\n \"searchPublicDataEnabled\": true, // Enable/disable filtering Datasets on public or non-public data.\n \"searchSamples\": true, // Enable/disable searching Samples on Samples Dashboard.\n \"sftpHost\": \"login.esss.dk\", // URL to SFTP service used for downloading files exceeding maximum allowed file size.\n \"shareEnabled\": true, // Enable/disable workflow for sharing Datasets with other users using their email address.\n \"shoppingCartEnabled\": true, // Enable/disable the Dataset cart used for bulk actions.\n \"shoppingCartOnHeader\": true, // Toggle Dataset cart placement, either on header or to the left on the Datasets Dashboard.\n \"tableSciDataEnabled\": true // Enable/disable Scientific Metadata table view on details pages. If disabled, Scientific Metadata is displayed as raw JSON.\n}\nSciCat Backend\nDotenv config\nThis is the configuration file for the backend. This file allows the systems administrator to configure connected services, like authentication services and message queues, and also switching on/off almost all non essential features. The configuration file is a dotenv file and is read by the backend at runtime.\nAn example is shown below\nbackend/.env\nACCESS_GROUPS_STATIC_VALUES=\"group1,group2,group3,...\" // List of groups assigned by default as access groups to all users. Used in the vanilla implementation. Facilities customization might not use this\nACCESS_GROUP_SERVICE_TOKEN=\"90f126864824ede0e22f7b4407aa1a5cd8158e6cabbce39aaf091937589f1750\" // Token needed to access the API specified in ACCESS_GROUP_SERVICE_API_URL\nACCESS_GROUP_SERVICE_API_URL=\"https://my.access.group/service/api/url\" // Url of the service API which is used to provide access groups. At the moment only one value is allowed\nDOI_PREFIX=\"\" // The facility DOI prefix, with trailing slash.\nEXPRESS_SESSION_SECRET=\"\" // *Optional* Secret used to set up express session.\nHTTP_MAX_REDIRECTS=5 // *Optional* Max redirects for http requests. Defaults to 5.\nHTTP_TIMEOUT=5000 // *Optional* Timeout from http requests in ms. Defaults to 5000.\nJWT_SECRET= // The secret for your JWT token, used for authorization.\nJWT_EXPIRES_IN=3600 // *Optional* How long, in seconds, the JWT token is valid. Defaults to `3600`.\nLDAP_URL=\"ldaps://ldap.server.com:636/\" // *Optional* The URL (and port) to your LDAP server.\nLDAP_BIND_DN=\"@server.com\" // *Optional* Bind_DN for your LDAP server.\nLDAP_BIND_CREDENTIALS= // *Optional* Credentials for your LDAP server.\nLDAP_SEARCH_BASE= // *Optional* Search base for your LDAP server.\nLDAP_SEARCH_FILTER=\"(LDAPUsername={{username}})\" // *Optional* Search filter for you LDAP server.\nOIDC_ISSUER=\"https://identity.your.facility/your/realm\" // Full URL of the identity provider\nOIDC_CLIENT_ID= \"scicat\" // Client id used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generated\nOIDC_CLIENT_SECRET=\"90f126864824ede0e22f7b4407aa1a5cd8158e6cabbce39aaf091937589f1750\" // Token used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generated\nOIDC_CALLBACK_URL=\"http://localhost:3000/api/v3/oidc/callback\" // URL of the endpoint that is called when the authentication has been executed with the OIDC service.\nOIDC_SCOPE=\"openid profile email\" // Information returned by the OIDC service together with token\"\nOIDC_SUCCESS_URL=\"http://localhost:3000/explorer\" // URL of the endpoint that is called after a successful authentication. IT is not used in the vanilla implementaation.\nOIDC_ACCESS_GROUPS=\"access_groups\" // field used to retrieve access groups from the OIDC service. It is not used in the vanilla implementation.\nLOGBOOK_ENABLED= // *Optional* Flag to enable/disable the Logbook endpoints. Values \"yes\" or \"no\". Defaults to \"no\".\nLOGBOOK_BASE_URL=\"http://localhost:3030/scichatapi\" // *Optional* The base URL to the SciChat wrapper API. Only required if Logbook is enabled.\nLOGBOOK_USERNAME=\"\" // *Optional* The username used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.\nLOGBOOK_PASSWORD=\"\" // *Optional* The password used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.\nMETADATA_KEYS_RETURN_LIMIT=100 // *Optional* The return limit for the `/Datasets/metadataKeys` endpoint.\nMETADATA_PARENT_INSTANCES_RETURN_LIMIT=100 // *Optional* The return limit of Datasets to extract metadata keys from for the `/Datasets/metadataKeys` endpoint.\nMONGODB_URI=\"mongodb://:@:27017/\" // The URI for your MongoDB instance.\nOAI_PROVIDER_ROUTE=\"\" // *Optional* URI to OAI provider, used for the `/publisheddata/:id/resync` endpoint.\nPID_PREFIX=\"\" // The facility PID prefix, with trailing slash.\nPUBLIC_URL_PREFIX=\"https://doi.ess.eu/detail/\" // The base URL to the facility Landing Page.\nPORT=3000 // *Optional* The port on which you want to access the app. Defaults to `3000`.\nRABBITMQ_ENABLED= *Optional* Flag to enable/disable RabbitMQ consumer. Values \"yes\" or \"no\". Defaults to \"no\".\nRABBITMQ_HOSTNAME=\"localhost\" // *Optional* The hostname of the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nRABBITMQ_USERNAME=\"rabbitmq\" // *Optional* The username used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nRABBITMQ_PASSWORD=\"rabbitmq\" // *Optional* The password used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nREGISTER_DOI_URI=\"https://mds.test.datacite.org/doi\" // URI to the organization that registers the facilities DOIs.\nREGISTER_METADATA_URI=\"https://mds.test.datacite.org/metadata\" // URI to the organization that registers the facilities published data metadata.\nSITE= // The name of your site.\nSMTP_HOST= // Host of SMTP server.\nSMTP_MESSAGE_FROM= // Email address that emails should be sent from.\nSMTP_PORT= // Port of SMTP server.\nSMTP_SECURE= // Secure of SMTP server.\nDOI Config\nThis config file contains the username and password for authenicating against your DOI provider, e.g., DataCite, which is used when registering the DOI and metadata of your published data.\nbackend/src/config/doiconfig.local.json\n\n{\n \"username\": \"\",\n \"password\": \"\"\n}\n"},"Development/v3.x/Theming.html":{"url":"Development/v3.x/Theming.html","title":"Theming","keywords":"","body":"SciCat Frontend Theming file\nThis is the theming file for the frontend client. The theming file allows the systems administrator to configure the color theme of the client. The theming file can either be served from the backend, via the /client/theme.json endpoint or mounted into /usr/share/nginx/html/assets/theme.json.\nAn example is shown below\nfrontend/src/assets/theme.json\n\n{\n \"name\": \"light\", // Name of the theme.\n \"properties\": { // The different theme colors. The colors here are the default ones. If you, in your theme file, choose to leave one of the properties out, the default one will be used instead.\n \"--theme-primary-default\": \"#0099c8\",\n \"--theme-primary-default-contrast\": \"#ffffff\",\n \"--theme-primary-lighter\": \"#99d5e9\",\n \"--theme-primary-lighter-contrast\": \"#000000\",\n \"--theme-primary-darker\": \"#32acd3\",\n \"--theme-primary-darker-contrast\": \"#ffffff\",\n \"--theme-accent-default\": \"#99be00\",\n \"--theme-accent-default-contrast\": \"#ffffff\",\n \"--theme-accent-lighter\": \"#d6e599\",\n \"--theme-accent-lighter-contrast\": \"#000000\",\n \"--theme-accent-darker\": \"#adcb32\",\n \"--theme-accent-darker-contrast\": \"#ffffff\",\n \"--theme-warn-default\": \"#c81919\",\n \"--theme-warn-default-contrast\": \"#ffffff\",\n \"--theme-warn-lighter\": \"#ff6e6e\",\n \"--theme-warn-lighter-contrast\": \"#000000\",\n \"--theme-warn-darker\": \"#820019\",\n \"--theme-warn-darker-contrast\": \"#ffffff\",\n \"--theme-warn-2-default\": \"#ffbe00\",\n \"--theme-warn-2-default-contrast\": \"#ffffff\",\n \"--theme-warn-2-lighter\": \"#e1dc7d\",\n \"--theme-warn-2-lighter-contrast\": \"#000000\",\n \"--theme-warn-2-darker\": \"#d28c14\",\n \"--theme-warn-2-darker-contrast\": \"#ffffff\",\n \"--theme-header-1-default\": \"#003366\",\n \"--theme-header-1-default-contrast\": \"#ffffff\",\n \"--theme-header-1-lighter\": \"#99adc1\",\n \"--theme-header-1-lighter-contrast\": \"#000000\",\n \"--theme-header-1-darker\": \"#325b84\",\n \"--theme-header-1-darker-contrast\": \"#ffffff\",\n \"--theme-header-2-default\": \"#006646\",\n \"--theme-header-2-default-contrast\": \"#ffffff\",\n \"--theme-header-2-lighter\": \"#99c1b5\",\n \"--theme-header-2-lighter-contrast\": \"#000000\",\n \"--theme-header-2-darker\": \"#32846a\",\n \"--theme-header-2-darker-contrast\": \"#ffffff\",\n \"--theme-header-3-default\": \"#ff7d00\",\n \"--theme-header-3-default-contrast\": \"#ffffff\",\n \"--theme-header-3-lighter\": \"#ffcb99\",\n \"--theme-header-3-lighter-contrast\": \"#000000\",\n \"--theme-header-3-darker\": \"#ff9732\",\n \"--theme-header-3-darker-contrast\": \"#ffffff\",\n \"--theme-header-4-default\": \"#821482\",\n \"--theme-header-4-default-contrast\": \"#ffffff\",\n \"--theme-header-4-lighter\": \"#cda1cd\",\n \"--theme-header-4-lighter-contrast\": \"#000000\",\n \"--theme-header-4-darker\": \"#9b429b\",\n \"--theme-header-4-darker-contrast\": \"#ffffff\",\n \"--theme-hover-default\": \"#7f7f7f\",\n \"--theme-hover-default-contrast\": \"#000000\",\n \"--theme-hover-lighter\": \"#e5e5e5\",\n \"--theme-hover-lighter-contrast\": \"#000000\",\n \"--theme-hover-darker\": \"#b2b2b2\",\n \"--theme-hover-darker-contrast\": \"#000000\"\n }\n}\n"},"Development/v3.x/Login.html":{"url":"Development/v3.x/Login.html","title":"Login and Accounts","keywords":"","body":"Login\nTwo login routes are provided - for functional accounts and for AD/LDAP logins.\nLoopback provides a UserIdentity for third party logins, (e.g. AD/LDAP, google, Twitter etc) \nA document is created in the db.UserIdentity collection.\nThe UserIdentity is fetched from MongoDB and stored in the ngrx store, (store.user.userIdentity) where \nit is used to access e.g. email for job initiators.\nThe login method needs to be defined in the client configuartion (environment file) usign the field externalAuthEndpoint. For example externalAuthEndpoint: \"/auth/msad\".\nSee loopback docs\nFor non-third party logins defined, no UserIdentity is created. These are special users for admin purposes including beamline specific users for ingestion etc.\nThese users should be defined in a seperate secrets repo in a file called functionalAccounts.json and are read by createFunctionalAccounts.js in the backend e.g.\n [{\n \"account\": \"admin\",\n \"password\": \"pass\",\n \"email\": \"admin@domain.com\",\n \"role\": \"admin\",\n \"global\": true\n }, {\n \"account\": \"ingestor\",\n \"password\": \"pass\",\n \"email\": \"ingestor@domain.com\",\n \"role\": \"ingestor\",\n \"global\": true\n }]\nOIDC\nFor OIDC integration, see the OIDC for details.\n"},"Development/v3.x/OIDC.html":{"url":"Development/v3.x/OIDC.html","title":"OIDC Integration","keywords":"","body":"OIDC Integration\nSciCat can integrate with one or more OIDC servers to provide Authentication. Integration requires configuration of both backend and frontend in order to setup the redirecting and handshaking that the OAuth2 code flow requires. Additionally, it may involve writing custom code hooks in the backend in order to properly handle profile information obtained by the OIDC Auth Provider.\nBackend Configuration\nConfiguration of the backend for OIDC involves adding a provider to providers.json file. See Start Here for more information on configuring this file.\nHere is an example of a provider that uses Google as an authenticator. Google is used here because it is well-documented and commonly available.\n \"google\": {\n \"provider\": \"oidc\",\n \"authScheme\": \"openid connect\",\n \"module\": \"passport-openidconnect\",\n \"authPath\": \"/auth/google\",\n \"successRedirect\": \"http://localhost/user\",\n \"failureRedirect\": \"http://localhost/login\",\n \"failureFlash\": true,\n \"session\": false,\n \"issuer\": \"https://accounts.google.com\",\n \"authorizationURL\": \"https://accounts.google.com/o/oauth2/v2/auth\",\n \"tokenURL\": \"https://oauth2.googleapis.com/token\",\n \"userInfoURL\": \"https://openidconnect.googleapis.com/v1/userinfo\",\n \"clientID\": \"...\",\n \"clientSecret\": \"...\",\n \"callbackURL\": \"http://localhost:3000/auth/google/callback\",\n \"scope\": [\"email\", \"profile\", \"openid\"],\n \"loginCallback\": \"sampleLoginCallback\"\n }\n\n\nNote that information about Google sign in can be found here, details are beyond the scope of this document.\nNote that OIDC authentication services publish their specific settings in a Discovery document.. To find the settings for, say, google, see https://accounts.google.com/.well-known/openid-configuration.\n\nDetails about the fields in this provider configuration:\n\nprovider is used by the loopback-passport-configurator to build a strategy\nauthScheme is used by the loopback-passport-configurator to build a strategy\nmodule describes the passport module to use. passport-openidconnect is already included in the backend\nauthPath is the path that the backend will create (with the help of loopback-passport-configurator) to begin the authentication process. The backend will redirect users to this path (once it is configured, of course!)\nsuccessRedirect is the URL passed to the OIDC provider instructing it to redirect the user on success. Here we set it to the a development environment's user page.\nfailureRedirect is the URL passed to the OIDC provider instructing it to redirect the user on failed authentication. Here we set it to the a development environment's user page login page.\nfailureFlash is currently unsupported.\nsession is not needed, since the frontend authenticates outside of sessions.\nauthorizationURL is specific to your OIDC provider\ntokenURL is specific to your OIDC provider\nuserInfoURL is specific to your OIDC provider\nclientID is specific to your OIDC provider\nclientSecret is specific to your OIDC provider\nscope is specific to your OIDC provider\nloginCallback optional name of a callback function to run to add profile information to the UserIdentity\n\nSession Secret\nThe nodejs module passport-openidconnect requires the use of the express-session module\nto create a login session for the user.\n This requires a session secret to be configured in config.local.js:\n module.exports = {\n expressSessionSecret: \"thisIsSuperSecretForUserSession\"\nSciCat Frontend Configuration\nThe default login page in the frontend provides a username/password form that is used both for local and LDAP/AD authentication. When using and OIDC authentication provider, SciCat will not ask the user for credential directly. Instead, the user will be redirected to the OIDC provider to authenticate. So, the frontend provides two configuration settings to modify the login page appropriately.\nThese setting are accomplished by modifying the frontend environment document. \n\n loginFormEnabled: false,\n oAuth2Endpoints: [\n {displayText: \"Google\", displayImage: \"../../../assets/images/btn_google_light_normal_ios.svg\", authURL: \"auth/google\"}]\n\n\nloginFormEnabled sets whether to display the username/password form in the login page\noAuth2Endpoints provides information that the frontend uses to display \"Sign in with ...\" buttons. Note that this is an array, multiple buttons for multiple OIDC providers can be configured.\ndisplayText sets the name of the provider to display in the button. In this case, the button will show \"Sign In With Google\"\ndisplayImage defines an image to display in the button. The image will end up being 24px tall. It is recommended that the image be in SVG format.\nauthURL defines the relative path that the user will be redirected to when they click the button. Note that this maps to the authPath setting described above.\n\n\n\nBackend Code Hooks\nConfiguring backend and frontend to authenticate through OIDC is very useful for providing a third party authentication. However, there two issues that come up that can be addressed with backend code hooks:\n\nBy default, the system will authenticate all users who pass come in through the OAuth Provider. \nBy default, the users profile in the application will contain information gathered from the OAuth Provider, and nothing more.\n\nPrevent Authenticating Unkown Users\nIn Backend Configuration, we configured the loginCallback. The value of this file is the name of a function that must be defined and exported. See login-call.js for details.\nCode snippet is presented for as an example, omitting the particular details about communicating with an external system:\n\nmodule.exports.sampleLoginCallback = function(req, done) {\n return function(err, user, identity, token) {\n\n var authInfo = {\n identity: identity,\n };\n if (token) {\n authInfo.accessToken = token;\n }\n\n const requestURL = getUserURL(identity);\n if (!requestURL){\n logger.logError(`unexpected authenticator type: ${identity.provider}`);\n done();\n return;\n }\n request(requestURL, function (error, response, body) {\n // ask external system for the user's profile information\n if (error){\n // and error occurred talking to the external system (possibly from a connection issue)...deny login\n logger.logError(`error talking to external ${error.message}`);\n done(err, null, null);\n return;\n }\n if (response.statusCode == 200) {\n const bodyObj = JSON.parse(body);\n logger.logInfo(\"user service returned\", bodyObj);\n }\n else{\n logger.logError(`external system replied with an error ${response.statusCode} - ${body}.`);\n // external system replied with a negative response...deny login\n done(err, null, null);\n return;\n }\n //accept login\n done(err, user, authInfo);\n });\n };\n};\n\nAdd information to User's Profile\nBy default profile information from the Authentication Provider will be added to the user's profile. This profile is stored in the DaCat database UserIdentity collection. This collection is created by the loopback-component-passport library, and is also used for LDAP authentication. The backend LDAP integrations populate this collection with the user's LDAP profile information automatically with a callback. One of the most important things that is added to the profile is the user's accessGroups field, which is critical to calculating a user's privilege in SciCat.\nOne way to mimic this with an OAuth2 provider is to use a before save Loopback Operation Hook. This hook will get called as the user logs in and allows you to add information to the UserIdentity collection, which will be used in a variety of places, including access controls.\nThe following code snippet is presented for as an example, omitting the particular details about communicating with an external system:\n// Observe saving UserIdentity. This gives us the ability to update the user profile with facility-specific groups\nmodule.exports = function (app) {\n app.models.UserIdentity.observe(\"before save\", function(ctx, next) {\n if (!ctx.data){\n logger.logInfo(\"No context data from UserIdentity\");\n next();\n return;\n }\n request(userURL, function (error, response, _body) {\n // ask external system for user information so we can get group info\n if (error){\n logger.logError(`error talking to external system ${error.message}`);\n next();\n return;\n }\n if (response.statusCode == 200){\n // add groups to profile, saving in the UserIdentity model\n ctx.data.profile.accessGroups = JSON.parse(response.body).groups;\n }\n next();\n });\n });\n};\n\n"},"Development/v3.x/Ngrx.html":{"url":"Development/v3.x/Ngrx.html","title":"State Management in Angular","keywords":"","body":"State Management in Angular\nAlmost all external requests made by the client (SciCat Frontend) are done so via NGRX effects within the state management components.\nLoopback within the backend provies a generated SDK that is used by the frontend to define all requests to the API. The SDK folder should not be edited because it will just overwrite it when a new one is generated.\nThe SDK is updated by running the following in the backend source code root:\n./node_modules/.bin/lb-sdk server/server.js ../frontend/src/app/shared/sdk\nand this is necessary when making any changes to endpoints in the backend.\nNGRX is Redux for Angular and is heavily used in the client to manage state.\n"},"Development/v3.x/Testing_backend.html":{"url":"Development/v3.x/Testing_backend.html","title":"Testing Backend","keywords":"","body":"Testing\nPackages\n\nChai - Assertion\nSupertest - NodeJS testing for HTTP calls\nMocha - Test runner framework\n\nRunning\nnpm run test\nor \nmocha --timeout=5000 --reporter=nyan\nTests\nSome tests to cover the Users API and the RawDataset API have been written but more are needed. There is a LoginUtils file that supports the retrieval of tokens and everything else is documented in the Swagger file\nEnvironment\nIf this is being run independently of an infrastructure then you should provide the environment variable NODE_ENV to be test.\nSet up\nIn the test/config/settings.json file, you should have the following fields:\n{\n \"baseURL\": \"http://localhost:3000/\",\n \"apiPrefix\": \"api/v2/\",\n \"users\": {\n \"admin\": {\n \"username\": \"\",\n \"password\": \"\",\n \"login\": \"api/v2/Users/login\"\n\n },\n \"user\": {\n \"username\": \"\",\n \"password\": \"\",\n \"login\":\"auth/msad\"\n },\n },\n}\nAdding tests\nThis can be done one of two ways:\nSimple Tests\nFor simple tests, you can add it to the test/config/tests.json file. Each entry should have the following syntax:\n{\n \"method\": \"GET\",\n \"route\": \"Datasets?filter=%7B%22limit%22%3A10%7D\",\n \"expect\": 200,\n \"authenticate\": \"admin\"\n}\nAuthenticate can be user or admin, depending on the type of account you have specified.\nIf you include a body property, then that will be sent with the request.\nIf you include a response array, then the string content should be valid JS. Note that this uses the unsafe eval method so be careful!\nWriting Tests\nIf you have a chain of tests, or just tests that might be more complex, then you can write them in the test folder. See any js file in there for examples.\n"},"Development/v3.x/Testing.html":{"url":"Development/v3.x/Testing.html","title":"Testing Frontend","keywords":"","body":"Testing\nAll components of SciCat include unit test coverage and the frontend has additional end to end user interface testing. Tests are run by CI before any change request is approved.\nE2E (Protractor)\nInitially Protractor was used for end to end testing but this was abandoned in favour of the far more robust and flexible Cypress.\nCypress E2E\nCypress is not based on Selenium. \nnpx cypress run runs all tests headless (without displaying the browser).\nnpx cypress open opens the Cypress GUI in the browser.\nnpx cypress run --spec ./cypress/integration/policy-delegate.spec.js will run the single test file policy-delegate.spec.js.\nUnit (Jasmine, run by Karma)\nng test runs all unit tests.\nMocking Services\nCreate a mock service and implement only what you need\nclass MockUserApi {\n\n getCurrentId() {\n return 123;\n }\n}\nNow, set your testbed to override the service you are mocking.\nTestBed.overrideComponent(AppComponent, {\n set: {\n providers: [\n { provide: UserApi, useClass: MockUserApi }\n ]\n }\nThe practice I have followed is to create a MockStubs file that contains only the necessary mocked methods for services or pipes or components. This file can be added to to allow almost complete coverage of the app.\nAnatomy of a Unit Test\nBeforeEach\nThese are called to set up the test and involve 2 important methods.\nconfigureTestingModule\nThis should include imports for all the modules used in the component. If the component also includes other components, then they should be included. This is also true for any pipes that may be used in the template. I am not sure if there is a fix for this but that does mean that, if a subcomponent uses a pipe then it should be included in all the specs.\noverrideComponent\nTest cases for a component should test as few things outside of the component as possible (services etc should have their own tests), but this causes problems for the services implemented in each component. This is especially true for the loopback services, but also HTTP and Router. In the overrideComponent method, you can specify the service names and provide an alternative class to use.\nFor every service your component uses, there should be a Mock equivalent that provides hardcoded data in response to their calls. First, check the service exists in MockStubs.ts in the root of the app folder. If it is not in there, please implement it only in that file. You do not need to implement every method, others can add to these when they are needed.\nThe format in the spec file should be as follows:\n{ provide: RawDatasetApi, useClass: MockDatasetApi }\nThe default test merely tests the creation of a component. This is merely opinion but a component should not be considered complete on a branch until the initial tests pass. A release should not go live until the coverage of the tests covers more than just basic functionality, i.e. 80% coverage.\nState management/NGRX testing\nTests of ngrx state management should test e.g. if a new state object is returned.\nSample ngrx reducer test:\n describe('SelectDatasetAction', () => {\n it('should return the state', () => {\n const data:DatasetInterface = {owner: '', contactEmail: '', sourceFolder: '', creationTime: new Date(), type: '', ownerGroup: ''};\n const payload = new Dataset({pid: 'pid 1', ...data});\n const action = new fromActions.SelectDatasetAction(payload);\n const state = fromDatasets.datasetsReducer(initialDatasetState, action);\n expect(state.selectedSets2).toEqual([payload]);\n expect(state.selectedSets2.constructor).toBe(Array);\n });\n });\n\n"},"Development/v3.x/Contributing.html":{"url":"Development/v3.x/Contributing.html","title":"Contributing","keywords":"","body":"Contributing\nThere are 3 core ways to get involved with the Data Catalogue project:\n\nDocumentation - Fixing errors, covering weaker areas, adding examples\nGithub Issues - This should be the first area to focus on for those that want to amend the code. To see how the project is developed, see the Git Workflow page.\nTesting\n\nFor information about the development method we follow, please check Development Methodology.\nIssues\nIssues are handled within the Github Issue tracker and should follow the template:\n## Issue Name\n\n### Summary\n\n### Steps to Reproduce\n\n### Current Behaviour\n\n### Expected Behaviour\n\n### Extra Details\n\nHere you should include details about the system (if it is unique) and possible information about a fix (feel free to link to code where relevant). Screenshots/GIFs are also fine here.\n\nMerge Requests\nThere should be no pushing directly to the master or develop branches. To implement a fix, one should open a branch with the naming: hotfix/ISSUE-NAME from the develop branch and complete all work there. When it is complete, a Merge Request should be opened that follows this template:\n## Description\n\n## Motivation \n\nLink to any open issues here\n\n## Fixes:\n\n* \n* \n\n## Changes:\n\n* \n* \n\n## Tests included/Docs Updated?\n\n- [ ] Included for each change/fix?\n- [ ] Passing? (Merge will not be approved unless this is checked)\n- [ ] Docs updated?\n\n## Extra Information/Screenshots\n\nWhen the request is created, it should be assigned to any other appropriate team member with develop as the target branch. Tests must be written for all features/changes made and any major changes should be updated in the docs repo.\n"},"Development/v3.x/Development_Methods.html":{"url":"Development/v3.x/Development_Methods.html","title":"Git Flow Workflow","keywords":"","body":"Git Flow Workflow\n\nCurrently, this project is being developed by following a variation of the git flow model.\nIn summary, the following branches should exist and be in the following states: \n\nmaster - always ready to deploy (manual/time delayed integrations from the develop branch) \ndevelop - merge hotfixes and features into this (this should map to a corresponding development environment on your system) \nhotfix/ - forks from master to fix a known issue (this issue should be documented in the repository and a branch can be created from within Github \nfeature/ - forks from master to a feature branch, explained here\n\nReleases\nA release branch should be created if:\n\nA fixed release cycle has been set\nA number of features or hotfixes have been incorporated and a release is needed.\n\nReleases should be merged into the master branch and tagged with the version number of that release. It goes without saying that a release branch should not be created unless the develop branch is passing all tests.\nSample Workflow - Creating a feature\ngit pull origin develop\ngit checkout -b feature/feature_name\n# Do some work\ngit add \ngit commit -m 'COMMIT MESSAGE'\n\ngit push origin feature/feature_name\n# Click the link shown in the output to create a merge request\n\n# TO RELEASE (ONCE TESTED ON DEVELOP)\ngit checkout -b release/0.1.1\ngit merge --no-ff develop\ngit checkout master\ngit merge --no-ff release/0.1.1\ngit branch -D release/0.1.1\nCollaborators and Code Review\nAll those involved with development on a project should follow this process exactly. This would mean no committing directly to the master or develop branches and (unless absolutely necessary) pull requests should be opened in Github and assigned to anyone that is not yourself.\nWhen your branch has been pushed up to the origin (https://github.com, then please open a pull request with your branch name as the source and the develop as the target. You can then assign the pull request to somone else within your development team. If you do not know who that is then please assign it to someone working at PSI.\nNOTE When a new branch is opened it must be opened from the develop branch. Merge requests opened from other branches will not be accepted.\nOnce Merged\nWhen your code has been merged by the assignee, you will receive an email and you can then do the following commands\ngit pull origin master #always make sure you are up to date\ngit branch -d #remove local branch\nWorking locally\nThe general git advice for working locally is to commit often. Not every commit needs to be pushed immediately but it is worth ensuring that the remote is never too far behind your local. Always be sure to pull before you push and test all merge conflicts once you have fixed them. NOTE: The angular cli (and webpack) will provide feedback on unhandled merges so it is worth using! For detailed info on best practices this is a great guide.\nNo-FF (TL;DR - THIS SHOULD ALWAYS BE USED)\nNo-ff is a vital flag because it forces a merge commit to be inserted into the history of the main branch, without it, merge will try and fast forward the HEAD of the branch to the latest commit. In essence, without this flag, there will not be a visible merge commit in the history,\nINFO: Rebase vs. Merge\nA lot of information can be found on the web but the basics of it are:\n\nRebase rewrites histories (and allows for interactive editing of commits)\nMerge will maintain history of commits (although the squash flag can reduce this and bring your branch tip back in line with the master.\n\nGit Pull\nSome sites have mentioned that simply using a git pull (which is essentially a git fetch followed by a git merge will likely cause issues in large teams due to the changing nature of the branch meaning that you will experience merge commits. It is my personal opinion that this should not be a problem if work is never done directly on the master and the regular practice of pulling down before any major action is followed.\nHowever, it should be noted that git fetch origin and git rebase --preserve-merges origin/ will ensure that a history is maintained and merge conflicts are avoided where possible.\nOnce a feature is complete (and tested locally), it should be merged into the latest version of develop (following the release cycle outlined above) and tested on the whole. If the tests pass, the develop can then be merged with master and any new merges to master should cause it to redeploy.\nCurrent status:\nCI is in the process of being implemented and should be prioritised at each site.\nUseful git commands\n\ngit branch -r - List all branches\ngit fetch -p - Update branches from remote and remove deleted branches locally\ngit merge --no-ff - Merge branch into current branch\ngit branch -d - Remove branch once merged \n\nGit Flow Plugin\nSupport for the git flow commands as an extension to git can be found here:\nhttps://github.com/nvie/gitflow\nThis does mean that the master and develop branches cannot be protected as they need to be pushed to from the local repository.\nQuickstart\ngit flow init (accept all prompts)\n\n#feature\ngit flow feature start NAME\n# commits etc\ngit push origin feature/NAME # open merge request on Github here\n\n#release\ngit flow release start vVERSION_NUMBER\n# commits and bump version number\ngit flow release finish vVERSION_NUMBER\ngit push origin --all # OR git push origin master\n"},"Development/v4.x/overview.html":{"url":"Development/v4.x/overview.html","title":"Overview","keywords":"","body":"Overview of SciCat\nThis page needs to be updated\nThis page provides a brief introduction to the overall workings of the SciCat data model, how things connect and links to more details on each component.\nBackend\nThe SciCat backend provides the REST API of the all system. It uses MongoDB to store all persistent information and leverages individual dtos to validate the information provided in input.\nModel Definitions:\n\nProperties: These are the values, types and other information. None of these are mongo specific and can all map to a relational database just as easily.\nRelations: These link models, i.e. allowing a DatasetLifecycle to be linked to a Dataset. The mongo connector handles this by embedding the datasetId in the document for you.\nACLs: These are access control limitations and these are very powerful. You can give a user full control of a model, some users can only have read access etc.\nMethods: When you create a model, loopback will automagically generate API endpoints that allows users to PUT, POST, PATCH, GET etc for all the models. However, sometimes this is not enough. For example, you may want to be able to generate a monthly report of all datasets created, so you define the method here, the name of the endpoint and the expected parameters. You will implement the method in the next section we cover now.\n\nModel Implementation:\nSciCat backend leverages nest.js and each endpoint is implemented by a specific function in the controller component of the associated model.\nThe models are defined in the model folder of each endpoint/model.\nIt leverages nest.js interface to Mongoose.\nMode details are found in each individual page\nRunning the backend\nThe backend can be run as it is, provided that a number of environmental variables are defined.\nFor the list of the environment variables available please see the Backend Configuration page.\nTo start the backend is as simple as running the following command:\nnpm start\nTesting the backend\nThere are multiple type of tests that can be run on the backend:\n\nunit tests\nThey can be run using the command:npm run tests\n\napi tests\nThese tests are defined in the test folder and tests the endpoint. \nTo run them use the command:npm run test:api:mocha\n\n\n"},"Development/v4.x/Data_Model.html":{"url":"Development/v4.x/Data_Model.html","title":"An Introduction to the Data Model","keywords":"","body":"An Introduction to the Data Model\nModel\nThere are many models within the catalog and many of them are self explanatory. The API server creates routes for creation, retrieval, modification and deletion. However, it is worth explaining the core models that are used.\nThe following graph shows a simplified class diagramm of the main model classes. It was generated semi-automatically from the swagger.json file using the WAPIml tool\n\nThe Ownable base model\nMost models inherit the ownable base model which regulates access based on the ownerGroup/ accessGroups fields. Relationships are defined by including the ID of the respective model (handled by loopback model definitions).\nDataset\nThis is the base class for all datasets, and is derived from the\nOwnable model. There are two classes that are derived from Dataset:\nRawDataset and DerivedDataset.\nThe data model and its API endpoints are described here.\nRawDataset\nThis is a dataset that has been collected from an experiment. It contains details about who owns it, contact information etc. Most importantly, it contains Scientific Metadata, and this is an extensible object to outline all parameters relevant to the dataset.\nEach raw dataset may contain a link to a single Proposal.\nThe data model and its API endpoints are described here.\nDerivedDataset\nThese are datasets that are created by analyzing the data in raw datasets. The fields allow to link both to the raw data files used in the analysis as well as to a software repository which keeps the source code used for the analysis, thus helping to keep track of the provenance of the data. You can add ScientificMetadata here as well.\nAlthough DerivedDataset does not contain a link to a Proposal, it may\nbe deduced. A corresponding Proposal may be found by identifying all\ninput RawDatasets (either directly or indirectly via other\nDerivedDatasets) from the inputDatasets field. These RawDatasets may\ncontain links to the corresponding Proposal.\nThe data model and its API endpoints are described here.\nDatasetLifecycle\nThere are many temporal operations for a dataset describing what happens to the Dataset during its lifetime. Therefore, this model contains information relating to the status of the dataset but not the metadata itself. The archive location, date that it should become public and information about archiving or retrieval. Technically the DatasetLifecycle is however embedded into the Dataset documents.\nThe data model and its API endpoints are therefore described as part of the Dataset model.\nInstrument\nThis collection is meant to store the available measurement instruments, ideally in a globally unique naming scheme (within the Expands/Panosc EU programs standardization on these topics is pursued). Datasets can link to these instruments (in addition to the creationLocation field), allowing for more detailed information about the measurement devices.\nThe data model and its API endpoints are described here.\nTechnique\nThis collection is meant to store the available measurement techniques, ideally in a globally unique naming scheme (within the Expands/Panosc EU programs standardization on these topics is pursued). Datasets can link to these techniques, allowing for more detailed information about the measurement method applied.\nOrigDatablocks and Datablocks\nSince SciCat contains only metadata, it must include links to the actual storage location of the data. This typically consists of lists of DataFile objects storing the file path, size, ownership, and checksum (but not the data itself). Since the data may be stored on tape archives or otherwise transformed, each file in a dataset is typically recorded in two places:\n\nOrigDatablock stores the path to the file on the original system at the time of ingestion into SciCat.\nDatablock stores the path to the file on the archive system. The archiveId property is used by the storage management system (not part of SciCat) to retrieve the data. For instance, a simple archiveId might give a path on to a tar bundle on the local tape storage. Some facilities omit Datablocks from their databases and use conventions for determining the data location based only on the OrigDatablock paths.\n\nLarge datasets may split files into multiple OrigDatablocks and Datablocks. The details of this are facility specific, and could reflect local organizational preferences or performance considerations. For instance, datasets with many files are often split into multiple OrigDatablock objects to avoid too-large MongoDB objects. Conversely, very large files may be split across several Datablocks during archiving due to limitations of the storage hardware.\nYou should always add the OrigDataBlock information when ingesting, whereas the Datablocks information is optional and would be filled later by the archive system, when it did its work of writing chunks of data to tape. For instance, a Datablockcan correspond to a single tar file which is written to tape and the contents of the Datablock is essentially the file listing of the tar file.\nThe data model and its API endpoints are described here and here.\nJob\nWhen a user wants to archive, retrieve or publish a dataset, SciCat must interact with the facility's storage and archiving system. This is done by creating a Job to track the interaction. The exact actions taken for each Job are often customized for each facility. A common action would be to forward the new Job entry to a Message Broker, e.g. RabbitMQ, from where it can be picked up by any program willing to react to this Job. For example, at PSI the RabbitMQ queue is emptied by a Node-RED process, which reads the Job information from the RabbitMQ queue. Alternatively, you can use other messaging solutions, such as Apache Kafka. In this way the (site specific) logic to handle the Jobs is kept outside the core of the SciCat system, giving a greater degree of flexibility. The external systems should ideally respond with status updates to the Job model, when there job is finished. As the job progresses, the system can make PUT/PATCH API calls back that update the Job Status Message and, when necessary, also the individual status of the involved datasets by updating the DatasetLifecycle information.\nThe data model and its API endpoints are described here.\nProposals\nA proposal is a written intention declaration of what kind of measurement people want to perform when and where. Such information is often available in an existing system outside of the data catalog, like digital user office systems for institutes which offer their instruments to other users.\nThis information can be synchronized into SciCat and facilitates the definition of the ownership of the datasets as well as providing information relevant for publication of data, such as principal investigators, abstracts etc. datasets are normally uniquely associated with one proposal.\nThe proposal information is however optional\nThe data model and its API endpoints are described here.\nPublishedData\nWhen Datasets are published you assign a DOI to a list of datasets. The PublishedData model keeps track of these information, i.e which dataset is being published, authorship, abstract and download links to access the corresponding file contents of the datasets.\nThe data model and its API endpoints are described here.\nUserSettings\nThis collection stores user preferences, such as the list of pre-selected columns in the dataset dashboard view, defaults for the table page size etc.\nThe data model and its API endpoints are described here.\nAttachment\nSome of the above mentioned models can have files (usually images) attached. The size is limited to 16MB for one attachment. Attachments are stored in the database.\nThe data model and its API endpoints are described here.\nExplorer\nWhen you have an instance of the data catalog running, the API endpoints and the related data models can be looked at at the following URL:\n\n/explorer/\nExample instance from ESS\n\nThe API explorer is a feature of Loopback and allows one to test various endpoints. This section outlines the core functionality. The first point to note is that requests require a token. You can use the login route under the User tab to obtain one of these, or use a CURL command.\nMain Screen\nThe top right entry field is where the token should be included. The token will define your access rights, so not all routes will be accessible. Each model has a tab which relates to its endpoint. Expanding the tab shows the routes section below.\n\nRoutes\nEach section outlines the routes for the chosen model. This is configurable but the HTTP verbs are supported here for most routes and conform to the standards. Error codes are also consistent. Expanding the chosen route allows one to test the response with sample data, or their own.\n\nSingle Route\nSelecting a route shows the expected JSON object and provides an editor to allow you to test the route. Response information is shown below.\nClicking the data type box will insert it into your request for easy testing.\n\nModel\nThe model view can be accessed by clicking the link next to Example Value and shows the fields that Loopback is expecting to receive, as well as their type and whether they are required.\n\n"},"Development/v4.x/running.html":{"url":"Development/v4.x/running.html","title":"Running the Components","keywords":"","body":"Run SciCat\nThere are multiple ways to run SciCat, which are listed and described below.\nRun locally\nBackend\nTo run the backend successfully, you need to have a mongodb instance accessible.\nFirst we need to get the latest code available from the repo\ngit clone https://github.com/SciCatProject/backend.git \ncd backend\nnpm install\nBefore you csan run the backend successfully, you need to configure it.\nPlease refer to the configuration page for more details.\nOnce the configuration is set, you can start the backend with the command:\nnpm start\nThe backend api will be accessible at the addess http://localhost:3000\nFrontend\nSame as the backend, we need to get the code from the repo:\ngit clone https://github.com/SciCatProject/frontend.git \ncd frontend\nnpm install\nMake sure that you have the proper configuration and than you are ready to start the UI with the command:\nnpx ng serve\nFor convenience, this command has been added to the package.json so you can run npm start as an alias.\nThe frontend application can be accessed at the URL http://localhost:4200.\nRunning with Docker\nThe recommend way is to use scicatlive, althought it is not completely up-to-date.SciCatLive provides an all-in-one bundle containing all the necessary pieces to run an instance of SciCat successfully.\nIf you would like to run the latest versions, or a specific version of backend and frontend, you can find the images ready at the following URLs:\n\nBackend: https://github.com/SciCatProject/frontend/pkgs/container/frontend\nFrontend: https://github.com/SciCatProject/scicat-backend-next/pkgs/container/backend-next\n\nRunning with Kubernetes\nCreating a Kubernetes environment takes some expertise. There is an effort in the community to provide examples and use cases on how to run SciCat on Kubernetes. Stay tuned and check back soon.\n"},"Development/v4.x/configuration.html":{"url":"Development/v4.x/configuration.html","title":"Configuration","keywords":"","body":"Configuration\n\nSciCat Frontend\nSciCat Backend\nDotenv Config\nDOI Config\n\n\n\nSciCat Frontend\nThis is the configuration file for the frontend client. The configuration file allows the systems administrator to configure every aspect of the client including switching on/off almost all non essential features. \nThe configuration file can be served from the backend, via the admin/client/config.json endpoint. The frontend would expect the backend to be served on port 3000 in a routing configuration for this to work. The configuration file can also be mounted to the volume /home/node/app/dist/config/frontend.config.json for it to be served through this endpoint. \nAn example is shown below\nfrontend.config.json:\n\n{\n \"accessTokenPrefix\": \"\", // Set the backend token prefix. Should be empty string for current backend or \"Bearer \" if using scicat-backend-next.\n \"addDatasetEnabled\": true, // Show/hide the \"Create Dataset\" button in the Datasets Dashboard.\n \"archiveWorkflowEnabled\": true, // Enable/disable the archive/retrieve workflow.\n \"datasetReduceEnabled\": true, // Enable/disable the automatic Dataset reduction/analysis workflow.\n \"editDatasetSampleEnabled\": true, // Enable/disable editing of which Sample a Dataset belongs to.\n \"editMetadataEnabled\": true, // Enable/disable editing of Scientific Metadata.\n \"editPublishedData\": true, // Enable/disable editing of Published Data.\n \"editSampleEnabled\": true, // Enable/disable editing of Samples.\n \"externalAuthEndpoint\": \"/auth/msad\", // Endpoint used for third party authentication, e.g, LDAP.\n \"facility\": \"ESS\", // Facility running the SciCat instance.\n \"fileColorEnabled\": true, // Enable/disable file size color representation in the Datasets Dashboard.\n \"fileDownloadEnabled\": true, // Enable/disable download workflow for Dataset datafiles.\n \"gettingStarted\": null, // URL to Getting Started guide for SciCat, displayed on the Help page.\n \"ingestManual\": null, // URL to Ingest Manual for SciCat, displayed on the Help page.\n \"jobsEnabled\": true, // Enable/disable Job workflow.\n \"jsonMetadataEnabled\": true, // Show/hide the \"Show Metadata\" button on the details pages, allowing users to see the JSON represantion of the current document.\n \"jupyterHubUrl\": \"https://jupyterhub.esss.lu.se/\", // URL to Jupyter Hub instance used for data analysis.\n \"landingPage\": \"doi2.psi.ch/detail/\", // URL to the facility's Landing Page for Published Data.\n \"lbBaseURL\": \"http://127.0.0.1:3000\", // URL to the SciCat Backend.\n \"localColumns\": [ // Default columns to be displayed in the table on the Datasets Dashboard.\n {\n \"name\": \"select\",\n \"order\": 0,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"datasetName\",\n \"order\": 1,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"runNumber\",\n \"order\": 2,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"sourceFolder\",\n \"order\": 3,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"size\",\n \"order\": 4,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"creationTime\",\n \"order\": 5,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"type\",\n \"order\": 6,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"image\",\n \"order\": 7,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"metadata\",\n \"order\": 8,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"proposalId\",\n \"order\": 9,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"ownerGroup\",\n \"order\": 10,\n \"type\": \"standard\",\n \"enabled\": false\n },\n {\n \"name\": \"dataStatus\",\n \"order\": 11,\n \"type\": \"standard\",\n \"enabled\": false\n }\n ],\n \"logbookEnabled\": true, // Enable/disable SciChat Logbook integration.\n \"loginFormEnabled\": true, // Enable/disable the local Login form. Should be disabled if using oAuth2 for authentication.\n \"maxDirectDownloadSize\": 5000000000, // Set a maximum allowed file size for downloading datafiles over HTTP.\n \"metadataPreviewEnabled\": true, // Enable/disable Scientific Metadata preview on the Datasets Dashboard.\n \"metadataStructure\": \"tree\", // Allow tree structure for Scientific Metadata. Set to empty string for flat structure.\n \"multipleDownloadAction\": \"http://localhost:3012/zip\", // URL to service handling direct download of datafiles.\n \"multipleDownloadEnabled\": true, // Enable/disable ability to download multiple datafiles.\n \"oAuth2Endpoints\": [], // List of endpoints used for oAuth2 authentication.\n \"policiesEnabled\": true, // Enable/disable Dataset Policies workflow.\n \"retrieveDestinations\": [], // List of destinations for Dataset retrievals.\n \"riotBaseUrl\": \"http://scitest.esss.lu.se/riot\", // URL to SciChat client.\n \"scienceSearchEnabled\": true, // Enable/disable filtering documents on Scientific Metadata.\n \"scienceSearchUnitsEnabled\": true, // Enable/disable filtering documents on Scientific Metadata using units.\n \"searchPublicDataEnabled\": true, // Enable/disable filtering Datasets on public or non-public data.\n \"searchSamples\": true, // Enable/disable searching Samples on Samples Dashboard.\n \"sftpHost\": \"login.esss.dk\", // URL to SFTP service used for downloading files exceeding maximum allowed file size.\n \"shareEnabled\": true, // Enable/disable workflow for sharing Datasets with other users using their email address.\n \"shoppingCartEnabled\": true, // Enable/disable the Dataset cart used for bulk actions.\n \"shoppingCartOnHeader\": true, // Toggle Dataset cart placement, either on header or to the left on the Datasets Dashboard.\n \"tableSciDataEnabled\": true // Enable/disable Scientific Metadata table view on details pages. If disabled, Scientific Metadata is displayed as raw JSON.\n}\nSciCat Backend\nDotenv config\nThis is the configuration file for the backend. This file allows the systems administrator to configure connected services, like authentication services and message queues, and also switching on/off almost all non essential features. The configuration file is a dotenv file and is read by the backend at runtime.\nThere are currently many configurable additions to SciCat which makes it very flexible these are:\n\nOIDC for authenticatoin\nLDAP for authentication\nElastic Search \nSMTP for sending emails to notify users of SciCat jobs\nAMQP to provide a message queue for the jobs \n\nAn minimal example is shown below followed by a full example:\nMinimal Backend Config Example\n ADMIN_GROUPS [string] Optional Comma separated list of admin groups with admin permission assigned to the listed users. Example: \"admin, ingestor\". For more details check: Scicat Documentation\n\n EXPRESS_SESSION_SECRET [string] Optional Secret used to set up express session.\n\n HTTP_MAX_REDIRECTS [number] Optional Max redirects for http requests. Defaults to 5.\n\n HTTP_TIMEOUT [number] Optional Timeout from http requests in ms. Defaults to 5000.\n\n JWT_SECRET [string] The secret for your JWT token, used for authorization.\n\n JWT_EXPIRES_IN [number] Optional How long, in seconds, the JWT token is valid. Defaults to 3600.\n\n MONGODB_URI [string] The URI for your MongoDB instance.\nFull Backend Config Example\nbackend/.env\n\nValid environment variables for the .env file. See .env.example for examples value formats.\n\n## Data Management\n\n ADMIN_GROUPS [string] Optional Comma separated list of admin groups with admin permission assigned to the listed users. Example: \"admin, ingestor\". For more details check: Scicat Documentation\n\n CREATE_DATASET_GROUPS [string] Optional Comma seperated list of create dataset groups. Users belong to the listed groups can create dataset with/without PID. Example: \"group1, group2\". For more details check: Scicat Documentation\n\n CREATE_DATASET_WITH_PID_GROUPS [string] Optional Comma seperated list of create dataset with pid groups. Users belong to the listed groups can create dataset with PID. Example: \"group1, group2\". For more details check: Scicat Documentation\n\n DELETE_GROUPS [string] Optional Comma seperated list of delete groups. Users belong to the listed groups can delete any dataset, origDatablocks, datablocks etc. For more details check: Scicat Documentation\n\n DATASET_CREATION_VALIDATION_ENABLED [boolean] Flag to enable/disable dataset validation to validate if requested new dataset is valid with given regular expression. Preconfigure DATASET_CREATION_VALIDATION_REGEX variable is required. Default value: false\n\n DATASET_CREATION_VALIDATION_REGEX [string] Regular expression validation for new dataset request. Default value: \"\"\n\n PROPOSAL_GROUPS [string] Optional Comma separated list of proposal groups with permission to create any proposals. Example: \"proposaladmin, proposalingestor\". For more details check: Scicat Documentation\n\n SAMPLE_GROUPS [string] Optional Comma separated list of sample groups with permission to create any samples. Example: \"sampleadmin, sampleingestor\". For more details check: Scicat Documentation\n\n## Access groups from external administration systems\n\n ACCESS_GROUPS_STATIC_VALUES [string] Optional Comma separated list of access groups automatically assigned to all users. Example: \"scicat, user\"\n\n ACCESS_GROUPS_SERVICE_TOKEN [string] Optional Authentication token used if access groups are obtained from a third party service. This value is not used by the vanilla installation, but only if the instance is customized to use an external service to provide user groups, like the ESS example\n\n ACCESS_GROUP_SERVICE_API_URL [string] Optional URL of the service providing the users' access groups. This value is not used by the vanilla installation, but only if the instance is customized to use an external service to provide user groups, like the ESS example\n\n## Secrets, tokens and http settings\n\n EXPRESS_SESSION_SECRET [string] Optional Secret used to set up express session.\n\n HTTP_MAX_REDIRECTS [number] Optional Max redirects for http requests. Defaults to 5.\n\n HTTP_TIMEOUT [number] Optional Timeout from http requests in ms. Defaults to 5000.\n\n JWT_SECRET [string] The secret for your JWT token, used for authorization.\n\n JWT_EXPIRES_IN [number] Optional How long, in seconds, the JWT token is valid. Defaults to 3600.\n\n## LDAP Settings - only required for authentication using LDAP\n\n LDAP_URL [string] Optional The URL to your LDAP server.\n\n LDAP_BIND_DN [string] Optional Bind_DN for your LDAP server.\n\n LDAP_BIND_CREDENTIALS [string] Optional Credentials for your LDAP server.\n\n LDAP_SEARCH_BASE [string] Optional Search base for your LDAP server.\n\n LDAP_SEARCH_FILTER [string] Optional Search filter for you LDAP server.\n\n## OIDC Settings - only required for OIDC authentication\n\n OIDC_ISSUER [string] Optional URL of the oidc server providing the authentication service. Example: https://identity.esss.dk/realm/ess.\n\n OIDC_CLIENT_ID [string] Optional Identity of the client that we want to use to obtain the user token. Example: scicat\n\n OIDC_CLIENT_SECRET [string] Optional Secret to provide to the oidc service to obtain the user token. Example: Aa1JIw3kv3mQlGFWhRrE3gOdkH6xreAwro\n\n OIDC_CALLBACK_URL [string] Optional SciCat callback URL that we want th eoidc service to redirect to, in case of successful login. Example: http://myscicat/api/v3/oidc/callback\n\n OIDC_SCOPE [string] Optional Space separated list of the info returned by the oidc service. Example: \"openid profile email\"\n\n OIDC_SUCCESS_URL [string] Optional SciCat Frontend auth-callback URL. Required in order to pass user credentials to SciCat Frontend after OIDC login. Example: https://myscicatfrontend/auth-callback\n\n OIDC_ACCESS_GROUPS [string] Optional Functionality is still unclear.\n\n## Scicat metadata settings\n\n PID_PREFIX [string] The facility PID prefix, with trailing slash.\n\n LOGBOOK_ENABLED [string] Optional Flag to enable/disable the Logbook endpoints. Values \"yes\" or \"no\". Defaults to \"no\".\n\n LOGBOOK_BASE_URL [string] Optional The base URL to the Logbook API. Only required if Logbook is enabled.\n\n METADATA_KEYS_RETURN_LIMIT [number] Optional The return limit for the /Datasets/metadataKeys endpoint.\n\n METADATA_PARENT_INSTANCES_RETURN_LIMIT Optional The return limit of Datasets to extract metadata keys from for the /Datasets/metadataKeys endpoint.\n\n MONGODB_URI [string] The URI for your MongoDB instance.\n\n SITE [string] The name of your site.\n\n POLICY_PUBLICATION_SHIFT [integer] Optional Embargo period expressed in years. Default value: 3 years\n\n POLICY_RETENTION_SHIFT [integer] Optional Retention period (aka how long the facility will hold on to data) expressed in years. Default value: -1 (data will be hold indefinitely)\n\n## Settings for publishing data sets\n\n OAI_PROVIDER_ROUTE [string] Optional URI to OAI provider, used for the /publisheddata/:id/resync endpoint.\n\n DOI_PREFIX [string] The facility DOI prefix, with trailing slash.\n\n PUBLIC_URL_PREFIX [string] The base URL to the facility Landing Page.\n\n PORT [number] Optional The port on which you want to access the app. Defaults to 3000.\n REGISTER_DOI_URI [string] URI to the organization that registers the facilities DOI's.\n\n REGISTER_METADATA_URI [string] URI to the organization that registers the facilities published data metadata.\n\n## Message queue settings - only required when using the jobs settings. This currently does not work in release 4.4 but will be released soon \n\n\n## SMTP Settings - need this option if you require Scicat to send emails. \n\n SMTP_HOST [string] Optional Host of SMTP server.\n\n SMTP_MESSAGE_FROM [string] Optional Email address that emails should be sent from.\n\n SMTP_PORT [string] Optional Port of SMTP server.\n\n SMTP_SECURE [string] Optional Secure of SMTP server.\n\n\n## Elastic Search settings - use these to enable elastic search \n\n ELASTICSEARCH_ENABLED [string] Flag to enable/disable the Elasticsearch endpoints. Values \"yes\" or \"no\". Defaults to \"no\"\n\n ES_HOST [string] Host of Elasticsearch server instance\n\n ES_USERNAME [string] Optional Elasticsearch username that can be customized when loads Elasticsearch server. Default value: 'elastic'\n\n ES_PASSWORD [string] Elasticsearch password that can be customized when loads Elasticsearch server.\n\n MONGODB_COLLECTION [string] Collection name to be mapped into specified Elasticsearch index. Used for data synchronization between mongoDB and Elasticsearch index.\n\n ES_MAX_RESULT [number] Maximum records can be indexed into Elasticsearch. Default value: 10000\n\n ES_FIELDS_LIMIT [number] The total number of fields in an index. Default value: 1000\n\n ES_REFRESH [string] If set to wait_for Elasticsearch will wait till data is insereted to specificied index then return response. Unless you have a good reason to wait for the change to become visible, always use false (the default setting).\nDOI Config\nThis config file contains the username and password for authenicating against your DOI provider, e.g., DataCite, which is used when registering the DOI and metadata of your published data.\nbackend/src/config/doiconfig.local.json\n\n{\n \"username\": \"\",\n \"password\": \"\"\n}\n"},"Development/v4.x/backend/configuration.html":{"url":"Development/v4.x/backend/configuration.html","title":"Backend","keywords":"","body":"Configuration\nWhen using the official release image, Backend configuration can be achieved by setting the environmental variables listed below through an orchestration/containerization system, the .env file or with a suitable method compatible with your environment.The current source code contains an example .env file, named .env.sample listing all the environment variable available to configure the backend.\nIf you are compiling the application from source, you can edit the file serc/config/configuration.ts with the correct values for your infrastructure. This option is still undocumented, although it is our intention to provide a detailed how-to guide as soon as we can.\nEnvironment Variables\nThis is complete the list of environment variable that can be used to configure SciCat backend.\nThe list is compiled according to the configuration class defined in src/config/configuration.ts\n\nADMIN_GROUPS:list of groups that have admin priviligesdefault: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nDELETE_GROUPS:list of groups that are allowed to delete contentdefault: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nCREATE_DATASET_GROUPS:list of non admin groups that are allowed to create datasets without pid. The pid is assigned by the system. If set to \"all\", all users can create a dataset belonging to any of the groups they belong to.default: \"#all\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nCREATE_DATASET_WITH_PID_GROUPS:list of non admin groups that are allowed to create datasets with explicit pid. If set to \"#all\", all users can create a dataset belonging to any of the groups they belong to and with esplicit pid.If the pid verification is enabled, pid will be validated agains the specification passed.default: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nCREATE_DATASET_PRIVILEGED_GROUPS:list of non admin groups that are allowed to create datasets for groups they do not belong to. If set to \"#all\", all users can create a dataset belonging to any group with explicit pid.If the pid verification is enabled, pid will be validated agains the specification passed.default: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nPROPOSAL_GROUPS:list of non admin groups that are allowed to create and update proposals for groups they do not belong to. If set to \"#all\", all users can create a dataset belonging to any group with explicit pid.default: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nSAMPLE_GROUPS:list of non admin groups that are allowed to create and update samples for the groups they belong to. If set to \"#all\", all users can create a dataset belonging to their group.default: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nSAMPLE_PRIVILEGED_GROUPS:list of non admin groups that are allowed to create samples for any groups, but can only update samples belonging to groups they belong to.\ndefault: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\n\n\nACCESS_GROUPS_STATIC_VALUES:List of groups assigned by default to all users. Used in the vanilla implementation for easy configuration.If you do not want or need to assign any default group, it should be set to empty string \"\".Default value: \"\"format: Comman separated list of strings. Leading and trailing spaces are trimmedexample: \"group1,group2,group3,...\" \n\nACCESS_GROUP_SERVICE_TOKEN:\nAccess token needed to access the API specified in ACCESS_GROUP_SERVICE_API_URL, used to retrieve access groups from a third party system._format*: string \n\nACCESS_GROUP_SERVICE_API_URL:Well formed url of the service API used to provide access groups. Only one value is allowed.format: stringexample: \"https://my.access.group/service/api/url\"\n\nDOIPREFIX:The facility DOI prefix, with trailing slash._default: \"\"format: string \n\nEXPRESS_SESSION_SECRET:Secret used to set up express session.default: \"\"format: string \n\nLOGOUT_URL:URL specified upon successful logout. It is returned in the json object for the frontend, or third party UI, to be used locally.default: \"\"format: string \n\nHTTP_MAX_REDIRECTS:Max number of redirects for http requests.default: 5format: integer \n\nHTTP_TIMEOUT:Timeout from http requests in ms.default: 5000format: integer\n\nJWTSECRET:The secret used to create any JWT token, used for authorization._default: \"\"format: string \n\nJWT_EXPIRES_IN:Expiration time of any JWT token in seconds.default: 3600 (s)format: integer \n\nJWT_NEVER_EXPIRES:Length of time that the never expiring jwt token will last.default: 100yformat: string as in number of years \n\nLDAP_URL:Full URI (including port) of your local LDAP server, if this is your selected authentication method.default: No defaultexample: ldaps://ldap.server.com:636/format: string \n\nLDAP_BIND_DN:Bind DN to access information on your LDAP server.default: No defaultformat: string \n\nLDAP_BIND_CREDENTIALS:Credentials associated with your bind DN to acccess your LDAP server.default: No defaultformat: string \n\nLDAP_SEARCH_BASE:Search base for your LDAP server.default: No defaultformat: string \n\nLDAP_SEARCH_FILTER:Search filter for you LDAP server.default: No defaultformat: stringexample: \"(LDAPUsername=)\" \n\nLDAP_MODE:type of ldap server we are communicating withNEEDS TO BE UPDATED. Not sure which other values are accepteddefault: adformat: stringacceptable values: ad \n\nLDAP_EXTERNAL_ID:LDAP matching field that provides the external iddefault: sAMAccountNameformat: string \n\nLDAP_USERNAME:LDAP field providing the usernamedefault: displayNameformat: string \n\nOIDC_ISSUER:Full URL of your OIDC identity providerdefault: No defaultformat: stringexample: \"https://identity.your.facility/your/realm\" \n\nOIDC_CLIENT_ID:Client id used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generateddefault: No defaultformat: stringexample: \"scicat\" \n\nOIDC_CLIENT_SECRET:Token used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generatedexample: \"90f1268...\" \n\nOIDC_CALLBACK_URL:URL of the endpoint that is called when the authentication has been executed with the OIDC service.default: No defaultformat: stringexample: \"http://localhost:3000/api/v3/oidc/callback\" \n\nOIDC_SCOPE:Information returned by the OIDC service together with tokendefault: No defaultformat: stringexample: \"openid profile email\" \n\nOIDC_SUCCESS_URL:Frontend URL that the user is directed to after a successful authentication. It must be a valid frontend URL.default: No defaultformat: stringexample: \"http://localhost:3000/Datasets\" \n\nOIDC_ACCESS_GROUPS:field used to retrieve access groups from the OIDC service. It is not used in the vanilla implementation.default: No defaultformat: stringexample: \"access_groups\" \n\nOIDC_ACCESS_GROUPS_PROPERTY:name of the OIDC property used to retrieve the users groups from OIDC.default: noneformat: string \n\nOIDC_AUTO_LOGOUT:if enabled, when login out from SciCat, we logout from OIDC also.default: falseformat: boolean \n\nOIDC_RETURN_URL:URL the user is redirected after a successful logoutdefault: noneformat: string \n\nLOGBOOK_ENABLED:Flag to enable/disable the Logbook endpoints.accept values: \"yes\", \"no\"default: noformat: string \n\nLOGBOOK_BASE_URL:The base URL to the SciChat wrapper API. Only required if Logbook is enabled.default: \"http://localhost:3030/scichatapi\"format: string \n\nLOGBOOK_USERNAME:The username used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.default: No defaultformat: string \n\nLOGBOOK_PASSWORD:The password used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.default: No defaultformat: string \n\nMETADATA_KEYS_RETURN_LIMIT:The maximum number of keys returned by the /Datasets/metadataKeys endpoint.default: No defaultformat: integer \n\nMETADATA_PARENT_INSTANCES_RETURN_LIMIT:The maximum number of Datasets used to extract metadata keys in the /Datasets/metadataKeys endpoint.default: No defaultformat: integer \n\nMONGODB_URI:The URI for your MongoDB instance.default: No defaultformat: string \"mongodb://:@:27017/\" \n\nOAI_PROVIDER_ROUTE:URI to OAI provider, which is used in the /publisheddata/:id/resync endpoint.default: no defaultformat: string \n\nPID_PREFIX:The facility PID prefix, with trailing slash.default: no defaultformat: string \n\nPUBLIC_URL_PREFIX:The base URL to the facility Landing Page.default: No defaultformat: stringexample: \"https://doi.ess.eu/detail/\" \n\nPORT:The port on which the backend listen on.default: 3000format: integer \n\nRABBITMQ_ENABLED:Flag to enable/disable RabbitMQ consumer.accepted values: \"yes\", \"no\"deprecated. Will be removed in future releases.default: noformat: string \n\nRABBITMQ_HOSTNAME:The hostname of the RabbitMQ message broker. Only required if RabbitMQ is enabled.deprecated. Will be removed in future releases.default: no defaultdefault: string \n\nRABBITMQ_USERNAME:The username used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is enabled.deprecated. Will be removed in future releases.default: no defaultformat: string \n\nRABBITMQ_PASSWORD:The password used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is\nenabled.deprecated. Will be removed in future releases.default: no defaultformat: string \n\nREGISTER_DOI_URI:URI to the organization that registers the facilities DOIs.default: no defaultformat: stringexample: \"https://mds.test.datacite.org/doi\" \n\nREGISTER_METADATA_URI:URI to the organization that registers the facilities published data metadata.default: no defaultformat: stringexample: =\"https://mds.test.datacite.org/metadata\" \n\nDOI_USERNAME:\nUsername used to authenticate on the DOI sitedefault: no defaultformat: string \n\nDOI_PASSWORD:Password used to authenticate on the DOI sitedefault: no defaultformat: string \n\nSITE:The name of your site.default: no defaultformat: string \n\nSMTP_HOST:Host of SMTP server.deprecated. Will be removed in future releases.default: no defaultformat: string \n\nSMTP_MESSAGE_FROM:Email address that emails should be sent from.deprecated. Will be removed in future releases.default: no defaultformat: string, email \n\nSMTP_PORT:Port of SMTP server.deprecated. Will be removed in future releases.default: no defaultformat: string \n\nSMTP_SECURE:Secure of SMTP server.deprecated. Will be removed in future releases.default: no defaultformat: string \n\nPOLICY_PUBLICATION_SHIFT:Number of years that needs to elapse before the dataset is made publicly acceessibledefault: 3format: integer \n\nPOLICY_RETENTION_SHIFT:Number of years that the datasets are kept online before are archived or deleted. A negative value means that they are never archived/deleteddefault: -1format: integer \n\nELASTICSEARCH_ENABLED:Flag to enable/disable the ElasticSearch serviceaccept values: \"yes\", \"no\"default: no defaultformat: string \n\nES_HOST:The base URL to the Elasticsearch cluster. Use http if xpack.security is disableddefault: no defaultformat: stringexample: \"https://localhost:9200\" or \"http://localhost:9200\" \n\nMONGODB_COLLECTION:Collection name to be mapped into specified Elasticsearch indexdefault: no defaultformat: string \n\nES_MAX_RESULT:Maximum records can be indexed into Elasticsearch.default: 10000format: number \n\nES_FIELDS_LIMIT:The total number of fields in an index.default: 1000format: number \n\nES_INDEX:The total number of fields in an index.default: no defaultformat: string \n\nES_REFRESH:The total number of fields in an index.accept values: true, false, \"waitfor\"_default: falseformat: boolean or string \n\nES_USERNAME:Elasticsearch cluster username.default: no default, optional.format: string \n\nELASTIC_PASSWORD:Elasticsearch cluster password.default: no default.format: string \n\n\n"},"Development/v4.x/backend/authorization.html":{"url":"Development/v4.x/backend/authorization.html","title":"Authorization","keywords":"","body":"Authorization\naka permissions\naka who can do what\nSciCat backend v4.x rely on CASL to manage permissions.The default vanilla installation of the backend is configured with the permissions described and linked below.To avoid confusion and clarify the terminology used below, the term User indicates a normal authenticated user with no elevated permissions, while Admin indicates any user who belongs to a group that it is listed in the environmental variable ADMIN_GROUPS.By default ADMIN_GROUPS is set to groups: admin, ingestor, archivemanager.\nSpecial case is for deleting items in SciCat. Users with groups listed in DELETE_GROUPS, are allowed to perform delete. Default value is archivemanager.\nIMPORTANT In V3.x, permissions were managed through roles. In V4.x, roles are not used, and they are converted to user group.\nIn the vanilla installation, the default functional accounts are assigned to groups as follow:\n\nuser: admingroup: admin \n\nuser: ingestorgroup: ingestor \n\nuser: archiveManagergroup: archivemanager\n\n\nThis allow for the flexibility required by many installations in different facilities with different needs. \nGroup Lists available in Vanilla Configuration\nThe permissions in the vanilla installation provides a set of user groups which acquires specific set of permissions. In order to assign a set of permissions to a specific group of user, add such group to the correct list indicated below.\n\n\n\nConfiguration Group List\nDescription\nCASL ability actions\n\n\n\n\nauthenticated users\nAuthenticated users can view/access all datasets that belong to one of the groups they belong to\nDatasetReadOwn\n\n\n\nUsers can view attachments for datasets belonging to one of their group\nDatasetAttachmentReadOwn\n\n\n\nUsers are allowed to view origdatablocks for datasets belonging to one of their group\nDatasetOrigdatablockReadOwn\n\n\n\nUsers are allowed to view datablocks for datasets belonging to one of their group\nDatasetDatablockReadOwn\n\n\n\nUsers can view the logbook of the datasets that belong to one of their group\nDatasetLogbookReadOwn\n\n\n\n\n\n\nCREATE_DATASET_GROUPS\nUsers of the listed groups can create and modify datasets for any of the groups they belong to. At creation time, the system assignes a pid to the new datasets. If the user assigns one, the system will ignore it.\nDatasetCreateOwn , DatasetReadOwn , DatasetUpdateOwn\n\n\n\nUsers are allowed to perform all operations on attachments for datasets belonging to one of their group\nDatasetAttachmentCreateOwn , DatasetAttachmentReadOwn , DatasetAtatchementUpdateOwn , DatasetAttachmentDeleteOwn\n\n\n\nUsers are allowed to create and update origdatablocks for datasets belonging to one of their group\nDatasetOrigdatablockCreateOwn , DatasetOrigdatablockReadOwn , DatasetOrigdatablockUpdateOwn\n\n\n\nUsers are allowed to create and update datablocks for datasets belonging to one of their group\nDatasetDatablockCreateOwn , DatasetDatablockReadOwn , DatasetDatablockUpdateOwn\n\n\n\nUsers can view the logbook of the datasets that belong to one of their group\nDatasetLogbookReadOwn\n\n\n\n\n\n\nCREATE_DATASET_WITH_PID_GROUPS\nUsers of the listed groups can create and modify datasets for any of the groups they belong to. They are allowed to specify the dataset pid. If they decided not to specify a pid, the system will assign one.\nDatasetCreateOwn , DatasetReadOwn , DatasetUpdateOwn\n\n\n\nUsers are allowed to perform all operations on attachments for datasets belonging to one of their group\nDatasetAttachmentCreateOwn , DatasetAttachmentReadOwn , DatasetAtatchementUpdateOwn , DatasetAttachmentDeleteOwn\n\n\n\nUsers are allowed to create and update origdatablocks for datasets belonging to one of their group\nDatasetOrigdatablockCreateOwn , DatasetOrigdatablockReadOwn , DatasetOrigdatablockUpdateOwn\n\n\n\nUsers are allowed to create and update datablocks for datasets belonging to one of their group\nDatasetDatablockCreateOwn , DatasetDatablockReadOwn , DatasetDatablockUpdateOwn\n\n\n\nUsers can view the logbook of the datasets that belong to one of their group\nDatasetLogbookReadOwn\n\n\n\n\n\n\nCREATE_DATASET_PRIVELEGED_GROUPS\nUsers of the listed groups can create datasets for any group, but can only modify datasets belong to one of the group they belong to. They are allowed to specify pids for new datasets. This settings are suggessted for ingestion functional accounts\nDatasetCreateAll , DatasetReadOwn , DatasetUpdateOwn\n\n\n\nUsers are allowed to perform all operations on attachments for datasets belonging to one of their group\nDatasetAttachmentCreateOwn , DatasetAttachmentReadOwn , DatasetAtatchementUpdateOwn , DatasetAttachmentDeleteOwn\n\n\n\nUsers are allowed to create origdatablocks for any datasets, but can only update them for datasets belonging to one of their group\nDatasetOrigdatablockCreateAny , DatasetOrigdatablockReadOwn , DatasetOrigdatablockUpdateOwn\n\n\n\nUsers are allowed to create and update datablocks for datasets belonging to one of their group\nDatasetDatablockCreateOwn , DatasetDatablockReadOwn , DatasetDatablockUpdateOwn\n\n\n\nUsers can view the logbook of the datasets that belong to one of their group\nDatasetLogbookReadOwn\n\n\n\n\n\n\nADMIN_GROUPS\nUsers of the listed groups can create and modify datasets belonging to any group. They are allowed to specify the dataset's pid at creation time\nDatasetCreateAny , DatasetReadAny , DatasetUpdateAny\n\n\n\nUsers are allowed to perform all operations on attachments for any datasets\nDatasetAttachmentCreateAny , DatasetAttachmentReadAny , DatasetAtatchementUpdateAny , DatasetAttachmentDeleteAny\n\n\n\nUsers are allowed to perform all operations on origdatablocks for any datasets, except delete\nDatasetOrigdatablockCreateAny , DatasetOrigdatablockReadAny , DatasetOrigdatablockUpdateAny\n\n\n\nUsers are allowed to perform all operations on datablocks for any datasets, except delete\nDatasetDatablockCreateAny , DatasetDatablockReadAny , DatasetDatablockUpdateAny\n\n\n\nUsers can view logbook for any datasets\nDatasetLogbookReadAny\n\n\n\n\n\n\nDELETE_GROUPS\nUsers whos group is listed here are allowed to delete datasets, origdatablock or datablock\nDatasetDeleteAny , DatasetOrigdatablockDeleteAny , DatasetDatablockDeleteAny\n\n\n\nSubsystems\n\nDatasets\nOrigDatablocks\nJobs\nUsers\n\nN.B.: we know that many subsystems are still missing. We are working on reviewing the authorization model for each one of them and producing the relative documentation. We welcome any contribution.\n"},"Development/v4.x/backend/authorization/authorization_datasets.html":{"url":"Development/v4.x/backend/authorization/authorization_datasets.html","title":"Datasets","keywords":"","body":"Datasets Authoorization\nCASL ability actions\nThis is the list of the permissions methods available for datasets and all their endpoints\nEndpoint authorization\n\nDatasetCreate\nDatasetRead\nDatasetUpdate\nDatasetDelete\nDatasetAttachmentCreate\nDatasetAttachmentRead\nDatasetAttachmentUpdate\nDatasetAttachmentDelete\nDatasetOrigdatablockCreate\nDatasetOrigdatablockRead\nDatasetOrigdatablockUpdate\nDatasetOrigdatablockDelete\nDatasetDatablockCreate\nDatasetDatablockRead\nDatasetDatablockUpdate\nDatasetDatablockDelete\nDatasetLogbookReadInstance authorization\n\nDatasetCreateOwnerNoPid\nDatasetCreateOwnerWithPid\nDatasetCreateAny\nDatasetReadManyPublic\nDatasetReadManyAccess\nDatasetReadManyOwner\nDatasetReadOnePublic\nDatasetReadOneAccess\nDatasetReadOneOwner\nDatasetReadAny\nDatasetUpdateOwner\nDatasetUpdateAny\nDetasetDeleteOwner\nDatasetDeleteAny\nDatasetAttachmentCreateOwner\nDatasetAttachmentCreateAny\nDatasetAttachmentReadPublic\nDatasetAttachmentReadAccess\nDatasetAttachmentReadOwner\nDatasetAttachmentReadAny\nDatasetAtatchementUpdateOwner\nDatasetAtatchementUpdateAny\nDatasetAttachmentDeleteOwner\nDatasetAttachmentDeleteAny\nDatasetOrigdatablockCreateOwner\nDatasetOrigdatablockCreateAny\nDatasetOrigdatablockReadPublic\nDatasetOrigdatablockReadAccess\nDatasetOrigdatablockReadOwner\nDatasetOrigdatablockReadAny\nDatasetOrigdatablockUpdateOwner\nDatasetOrigdatablockUpdateAny\nDatasetOrigdatablockDeleteAny\nDatasetDatablockCreateOwner\nDatasetDatablockCreateAny\nDatasetDatablockReadPublic\nDatasetDatablockReadAccess\nDatasetDatablockReadOwner\nDatasetDatablockReadAny\nDatasetDatablockUpdateOwner\nDatasetDatablockUpdateAny\nDatasetDatablockDeleteOwner\nDatasetDatablockDeleteAny\nDatasetLogbookReadOwner\nDatasetLogbookReadAny\n\nImplementation\nHow the different level of authorization translates in data condition applied byt he backend.\n\n* Public\nisPublished = true\n\n\n* Access (condition ar applied in logical or)\nisPublished = true\nownerGroup is one of the groups that the user belongs\naccessGroups are one of the groups that the user belongs\nsharedWith contains the user's email\n\n\n* Owner \nownerGroup is one of the groups that the user belongs\n\n\n* Any \nUser can perform the action to any dataset\n\n\n\nPriority\n\n%%{init: {'theme' : 'base', 'themeVariables': { 'fontSize': '11px', 'fontFamily' : 'monospace'}}}%%\ngraph LR;\n DatasetCreate-->DatasetCreateOwnerNoPid;\n DatasetCreateOwnerNoPid-->DatasetCreateOwnerWithPid;\n DatasetCreateOwnerWithPid-->DatasetCreateAny;\n DatasetRead-->DatasetReadManyPublic;\n DatasetReadManyPublic-->DatasetReadManyAccess;\n DatasetReadManyAccess-->DatasetReadManyOwner;\n DatasetReadManyOwner-->DatasetReadAny;\n DatasetRead-->DatasetReadOnePublic;\n DatasetReadOnePublic-->DatasetReadOneAccess;\n DatasetReadOneAccess-->DatasetReadOneOwner;\n DatasetReadOneOwner-->DatasetReadAny;\n DatasetUpdate-->DatasetUpdateOwner;\n DatasetUpdateOwner-->DatasetUpdateAny;\n DatasetDelete-->DatasetDeleteOwner;\n DatasetDeleteOwner-->DatasetDeleteAny;\n\n\nAuthorization table\n\n\n\nHTTP method\nEndpoint\nEndpoint Authorization\nAnonymous\nAuthenticated User\nCreate Dataset Groups\nCreate Dataset with Pid Groups\nCreate Dataset Privileged Groups\nAdmin Groups\nDelete Groups\nNotes\n\n\n\n\nPOST\nDatasets\nDatasetCreate\nno\nno\nOwner, w/o PIDDatasetCreateOwnerNoPid\nOwner, w/ PIDDatasetCreateOwnerWithPid\nAnyDatasetCreateAny\nAnyDatasetCreateAny\nno\n\n\nPOST\nDatasets/isValid\nDatasetCreate\nno\nno\nOwner, w/o PIDDatasetCreateOwnerNoPid\nOwner, W/ PIDDatasetCreateOwnerWithPid\nAnyDatasetCreateAny\nAnyDatasetCreateAny\nno\n\n\nGET\nDatasets\nDatasetRead\nPublicDatasetReadPublic\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nAnyDatasetReadyAny\nno\n\n\nGET\nDatasets/fullquery\nDatasetRead\nPublicDatasetReadManyPublic\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nAnyDatasetReadAny\nno\n\n\nGET\nDatasets/fullfacet\nDatasetRead\nPublicDatasetReadManyPublic\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nAnyDatasetReadAny\nno\n\n\nGET\nDatasets/metadataKeys\nDatasetRead\nPublicDatasetReadManyPublic\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nAnyDatasetReadAny\nno\n\n\nGET\nDatasets/count\nDatasetRead\nPublicDatasetReadManyPublic\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nAnyDatasetReadAny\nno\n\n\nGET\nDatasets/findOne\nDatasetRead\nPublicDatasetReadOnePublic\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nAnyDatasetReadAny\nno\n\n\nGET\nDatasets/pid\nDatasetRead\nPublicDatasetReadOnePublic\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nAnyDatasetReadAny\nno\n\n\nPATCH\nDatasets/pid\nDatasetUpdate\nno\nno\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nAnyDatasetUpdateAny\nno\n\n\nPUT\nDatasets/pid\nDatasetUpdate\nno\nno\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nAnyDatasetUpdateAny\nno\n\n\nPOST\nDatasets/pid/appendToArrayField\nDatasetUpdate\nno\nno\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nAnyDatasetUpdateAny\nno\n\n\n\n\n\n\n\n\n\n\n\n\n\nDELETE\nDatasets/pid\nDatasetDelete\nno\nno\nno\nno\nno\nno\nAnyDatasetDeleteAny\n\n\n\n\n\n\n\n\n\n\n\n\n\nGET\nDatasets/pid/thumbnail\nDatasetRead\nPublicDatasetReadPublic\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nAnyDatasetReadAny\nno\n\n\n\n\n\n\n\n\n\n\n\n\n\nPOST\nDatasets/pid/attachments\nDatasetAttachmentCreate\nno\nno\nOwnerDatasetAttachmentCreateOwner\nOwnerDatasetAttachmentCreateOwner\nAnyDatasetAttachmentCreateAny\nAnyDatasetAttachmentCreateAny\nno\n\n\nGET\nDatasets/pid/attachments\nDatasetAttachmemntRead\nPublicDatasetAttachmentReadPublic\nHas AccessDatasetAttachmentReadAccess\nHas AccessDatasetAttachmentReadAccess\nHas AccessDatasetAttachmentReadAccess\nHas AccessDatasetAttachmentReadAccess\nAnyDatasetAttachmentReadAny\nno\n\n\nPUT\nDatasets/pid/attachments/aid\nDatasetAttachmemntUpdate\nno\nno\nOwnerDatasetAttachmentUpdateOwner\nOwnerDatasetAttachmentUpdateOwner\nOwnerDatasetAttachmentUpdateOwner\nAnyDatasetAttachmentCreateAny\nno\n\n\nDELETE\nDatasets/pid/attachments/aid\nDatasetAttachmemntDelete\nno\nno\nOwnerDatasetAttachmentDeleteOwner\nOwnerDatasetAttachmentDeleteOwner\nOwnerDatasetAttachmentDeleteOwner\nAnyDatasetAttachmentDeleteAny\nno\n\n\n\n\n\n\n\n\n\n\n\n\n\nPOST\nDatasets/pid/origdatablocks\nDatasetOrigdatablocksCreate\nno\nno\nOwnerDatasetOrigdatablockCreateOwner\nOwnerDatasetOrigdatablockCreateOwner\nAnyDatasetOrigdatablockCreateAny\nAnyDatasetOrigdatablockCreateAny\nno\n\n\nPOST\nDatasets/pid/origdatablocks/isValid\nDatasetOrigdatablocksCreate\nno\nno\nOwnerDatasetOrigdatablockCreateOwner\nOwnerDatasetOrigdatablockCreateOwner\nAnyDatasetOrigdatablockCreateAny\nAnyDatasetOrigdatablockCreateAny\nno\n\n\nGET\nDatasets/pid/origdatablocks\nDatasetOrigdatablocksRead\nPublicDatasetOrigdatablockReadPublic\nHas AccessDatasetOrigdatablockReadOAccess\nHas AccessDatasetOrigdatablockReadAccess\nHas AccessDatasetOrigdatablockReadAccess\nHas AccessDatasetOrigdatablockReadAccess\nAnyDatasetOrigdatablockReadAny\nno\n\n\nPATCH\nDatasets/pid/origdatablocks/oid\nDatasetOrigdatablocksUpdate\nno\nno\nOwnerDatasetOrigdatablockUpdateOwner\nOwnerDatasetOrigdatablockUpdateOwner\nOwnerDatasetOrigdatablockUpdateOwner\nAnyDatasetOrigdatablockCreateAny\nno\n\n\n\nDELETE\nDatasets/pid/origdatablocks/oid\nDatasetOrigdatablocksDelete\nno\nno\nno\nno\nno\nno\nAnyDatasetOrigdatablockDeleteAny\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPOST\nDatasets/pid/datablocks\nDatasetDatablocksCreate\nno\nno\nOwnerDatasetDatablockCreateOwner\nOwnerDatasetDatablockCreateOwner\nOwnerDatasetDatablockCreateOwner\nAnyDatasetDatablockCreateAny\nno\n\n\n\nGET\nDatasets/pid/datablocks\nDatasetOrigdatablocksRead\nPublicDatasetDatablockReadPublic\nHas AccessDatasetDatablockReadAccess\nHas AccessDatasetDatablockReadAccess\nHas AccessDatasetDatablockReadAccess\nHas AccessDatasetDatablockReadAccess\nAnyDatasetDatablockReadAny\nno\n\n\n\nPATCH\nDatasets/pid/datablocks/oid\nDatasetDatablocksUpdate\nno\nno\nOwnerDatasetDatablockUpdateOwner\nOwnerDatasetDatablockUpdateOwner\nOwnerDatasetDatablockUpdateOwner\nAnyDatasetDatablockCreateAny\nno\n\n\n\nDELETE\nDatasets/pid/datablocks/oid\nDatasetDatablocksDelete\nno\nno\nno\nno\nno\nno\nAnyDatasetDatablockDeleteAny\n\n\n\n\n\n\n\n\n\n\n\n\n\nGET\nDatasets/pid/logbook\nDatasetLogbookRead\nno\nOwnerDatasetLogbookReadOwner\nOwnerDatasetLogbookReadOwner\nOwnerDatasetLogbookReadOwner\nOwnerDatasetLogbookReadOwner\nAnyDatasetLogbookReadAny\nno\n\n\n\n"},"Development/v4.x/backend/authorization/authorization_origdatablocks.html":{"url":"Development/v4.x/backend/authorization/authorization_origdatablocks.html","title":"OrigDatablocks","keywords":"","body":"OrigDatablock Authorization\nCASL ability actions\nThis is the list of the permissions methods available for origdatablock and all their endpoints\nEndpoint Authorization\n\nOrigdatablockCreate\nOrigdatablockRead\nOrigdatablockUpdate\nOrigdatablockDelete\n\n(Data) Instance Authorization\n\nOrigdatablockCreateOwner\nOrigdatablockCreateAny\nOrigdatablockReadManyPublic\nOrigdatablockReadManyAccess\nOrigdatablockReadManyOwner\nOrigdatablockReadOnePublic\nOrigdatablockReadOneAccess\nOrigdatablockReadOneOwner\nOrigdatablockReadAny\nOrigdatablockUpdateOwner\nOrigdatablockUpdateAny\nOrigdatablockDeleteAny\n\nPriority\n\n%%{init: {'theme' : 'base', 'themeVariables': { 'fontSize': '11px', 'fontFamily' : 'monospace'}}}%%\ngraph LR;\n DatasetOrigdatablockCreate-->DatasetOrigdatablockCreateOwner;\n DatasetOrigdatablockCreateOwner-->DatasetOrigdatablockCreateAny;\n DatasetOrigdatablockRead-->DatasetOrigdatablockReadManyPublic;\n DatasetOrigdatablockReadManyPublic-->DatasetOrigdatablockReadManyAccess;\n DatasetOrigdatablockReadManyAccess-->DatasetOrigdatablockReadAny;\n DatasetOrigdatablockRead-->DatasetOrigdatablockReadOnePublic;\n DatasetOrigdatablockReadOnePublic-->DatasetOrigdatablockReadOneAccess;\n DatasetOrigdatablockReadOneAccess-->DatasetOrigdatablockReadAny;\n DatasetOrigdatablockUpdate-->DatasetOrigdatablockUpdateOwner;\n DatasetOrigdatablockUpdateOwner-->DatasetOrigdatablockUpdateAny;\n DatasetOrigdatablockDelete-->DatasetOrigdatablockDeleteOwner;\n DatasetOrigdatablockDeleteOwner-->DatasetOrigdatablockDelteAny;\n\n\nAuthorization table\n\n\n\nHTTP method\nEndpoint\nEndpoint Authentication\nAnonymous\nAuthenticated User\nCreate Dataset Groups\nCreate Dataset with Pid Groups\nCreate Dataset Privileged Groups\nAdmin Groups\nDelete Groups\nNotes\n\n\n\n\nPOST\norigdatablocks\nOrigdatablockCreate\nno\nno\nOwnerOrigdatablockCreateOwn\nOwnerOrigidatablockCreateOwn\nAnyOrigdatablockCreateAny\nAny OrigdatablockCreateAny\nno\n\n\nPOST\norigdatablocks/isValid\nOrigdatablockCreate\nno\nno\nOwnerOrigdatablockCreateOwn\nOwnerOrigdatablockCreateOwn\nAnyOrigdatablockCreateAny\nAnyOrigdatablockCreateAny\nno\n\n\nGET\norigdatablocks\nOrigdatablockRead\nPublicOrigdatablockReadManyPublic\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nAnyOrigdatablockReadAny\nno\n\n\nGET\norigdatablocks/fullquery\nOrigdatablockRead\nPublicOrigdatablockReadManyPublic\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nAnyOrigdatablockReadAny\nno\n\n\nGET\norigdatablocks/fullquery/files\nOrigdatablockRead\nPublicOrigdatablockReadManyPublic\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nAnyOrigdatablockReadAny\nno\n\n\nGET\norigdatablocks/fullfacet\nOrigdatablockRead\nPublicOrigdatablockReadManyPublic\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nAnyOrigdatablockReadAny\nno\n\n\nGET\norigdatablocks/oid\nOrigdatablockRead\nPublicOrigdatablockReadOnePublic\nHas AccessOrigdatablockReadOneAccess\nHas AccessOrigdatablockReadOneAccess\nHas AccessOrigdatablockReadOneAccess\nHas AccessOrigdatablockReadOneAccess\nAnyOrigdatablockReadAny\nno\n\n\nPATCH\norigdatablocks/oid\nOrigdatablockUpdate\nno\nno\nOwnerOrigdatablockUpdateOwner\nOwnerOrigdatablockUpdateOwner\nOwnerOrigdatablockUpdateOwner\nAnyOrigdatablockUpdateAny\nno\n\n\nDELETE\norigdatablocks/oid\nOrigdatablockDelete\nno\nno\nno\nno\nno\nno\nAnyOrigdatablockDeleteAny\n\n\n\n"},"Development/v4.x/backend/authorization/authorization_proposals.html":{"url":"Development/v4.x/backend/authorization/authorization_proposals.html","title":"Proposals","keywords":"","body":"Proposals Authorization\nCASL ability actions\nThis is the list of the permissions methods available for Proposals and all their endpoints\nEndpoint Authorization\n\nProposalCreate\nProposalRead\nProposalUpdate\nProposalDelete\nProposalAttachmentCreate\nProposalAttachmentRead\nProposalAttachmentUpdate\nProposalAttachmentDelete\nProposalDatasetRead\n\n(Data) Instance Authorization\n\nProposalCreateOwner\nProposalCreateAny\nProposalReadManyPublic\nProposalReadManyAccess\nProposalReadManyOwner\nProposalReadOnePublic\nProposalReadOneAccess\nProposalReadOneOwner\nProposalReadAny\nProposalUpdateOwner\nProposalUpdateAny\nProposalDeleteOwner\nProposalDeleteAny\nProposalAttachmentCreateOnwer\nProposalAttachmentCreateAny\nProposalAttachmentReadManyPublic\nProposalAttachmentReadManyAccess\nProposalAttachmentReadManyOwner\nProposalAttachmentReadManyAny\nProposalAttachmentUpdateOwner\nProposalAttachmentUpdateAny\nProposalAttachmentDeleteOwner\nProposalAttachmentDeleteAny\nProposalDatasetReadPublic\nProposalDatasetReadAccess\nProposalDatasetReadOwner\nProposalDatasetReadAny\n\nPriority\n\ngraph LR;\n ProposalCreate-->ProposalsCreateOwner;\n ProposalCreateOwner-->ProposalCreateAny;\n ProposalRead-->ProposalReadManyPublic;\n ProposalReadManyPublic-->ProposalReadManyAccess;\n ProposalReadManyAccess-->ProposalReadManyOwner;\n ProposalReadManyOwner-->ProposalReadAny;\n ProposalRead-->ProposalReadOnePublic;\n ProposalReadOnePublic-->ProposalReadOneAccess;\n ProposalReadOneAccess-->ProposalReadOneOwner;\n ProposalReadOneOwner-->ProposalReadAny;\n ProposalUpdate-->ProposalUpdateOwner;\n ProposalUpdateOwner-->ProposalUpdateAny;\n ProposalDelete-->ProposalDeleteOwner;\n ProposalDeleteOwner-->ProposalDeleteAny;\n ProposalAttachmentCreate-->ProposalAttachmentCreateOnwer;\n ProposalAttachmentCreateOnwer-->ProposalAttachmentCreateAny;\n ProposalAttachmentRead-->ProposalAttachmentReadManyPublic;\n ProposalAttachmentReadManyPublic-->ProposalAttachmentReadManyAccess;\n ProposalAttachmentReadManyAccess-->ProposalAttachmentReadManyOwner;\n ProposalAttachmentReadManyOwner-->ProposalAttachmentReadManyAny;\n ProposalAttachmentUpdate-->ProposalAttachmentUpdateOwner;\n ProposalAttachmentUpdateOwner-->ProposalAttachmentUpdateAny;\n ProposalAttachmentDelete-->ProposalAttachmentDeleteOwner;\n ProposalAttachmentDeleteOwner-->ProposalAttachmentDeleteAny;\n ProposalDatasetRead-->ProposalDatasetReadPublic;\n ProposalDatasetReadPublic-->ProposalDatasetReadAccess;\n ProposalDatasetReadAccess-->ProposalDatasetReadOwner;\n ProposalDatasetReadOwner-->ProposalDatasetReadAny;\n\n\nAuthorization table\n\n\n\nHTTP method\nEndpoint\nEndpoint Authentication\nAnonymous\nAuthenticated User\nProposals Groups\nAdmin Groups\nDelete Groups\nNotes\n\n\n\n\nPOST\nProposals\nProposalCreate\nno\nno\nAnyProposalCreateAny\nAnyProposalCreateAny\nno\n\n\n\nGET\nProposals\nProposalRead\nPublicProposalReadManyPublic\nHas AccessProposalReadManyAccess\nHas AccessProposalReadManyAccess\nAnyProposalReadAny\nno\n\n\n\nGET\nProposals/fullquery\nProposalRead\nPublicProposalReadManyPublic\nHas AccessProposalReadManyAccess\nHas AccessProposalReadManyAccess\nAnyProposalReadAny\nno\n\n\n\nGET\nProposals/fullfacet\nProposalRead\nPublicProposalReadManyPublic\nHas AccessProposalReadManyAccess\nHas AccessProposalReadManyAccess\nAnyProposalReadAny\nno\n\n\n\nGET\nProposals/pid\nProposalRead\nPublicProposalReadOnePublic\nHas AccessProposalReadOneAccess\nHas AccessProposalReadOneAccess\nAnyProposalReadAny\nno\n\n\n\nGET\nProposals/fullquery\nProposalRead\nPublicProposalReadOnePublic\nHas AccessProposalReadOneAccess\nHas AccessProposalReadOneAccess\nAnyProposalReadAny\nno\n\n\n\nPATCH\nProposals/pid\nProposalUpdate\nno\nno\nOwnerProposalUpdateOwn\nAnyProposalUpdateAny\nno\n\n\n\nDELETE\nProposals/pid\nProposalDelete\nno\nno\nno\nno\nAnyProposalDeleteAny\n\n\n\n\n\n\n\n\n\nPOST\nProposals/pid/attachements\nProposalAttachementCreate\nno\nno\nAnyProposalAttachmentCreateAny\nAnyProposalAttachmentCreateAny\nno\n\n\n\nGET\nProposals/pid/attachements\nProposalAttachmentRead\nPublicProposalAttachmentReadManyPublic\nHas AccessProposalAttachmentReadManyAccess\nHas AccessProposalAttachmentReadManyAccess\nAnyProposalAttachmentReadManyAny\nno\n\n\n\nPATCH\nProposals/pid/attachments/aid\nProposalAttachmentUpdate\nno\nno\nOwnerProposalAttachmentUpdateOwner\nAnyProposalAttachmentUpdateAny\nno\n\n\n\nDELETE\nProposals/pid/attachment/aid\nProposalAttachmentDelete\nno\nno\nOnwerProposalAttachmentDeleteOwner\nAnyProposalAttachmentDeleteAny\nno\n\n\n\n\n\n\n\n\n\nGET\nProposals/pid/datasets\nProposalDatasetRead\nPublicProposalDatasetReadOnePublic\nHas AccessProposalDatasetReadOneAccess\nHas AccessProposalDatasetReadOneAccess\nAnyProposalDatasetReadOneAny\nno\n\n\n\n"},"Development/v4.x/backend/authorization/authorization_jobs.html":{"url":"Development/v4.x/backend/authorization/authorization_jobs.html","title":"Jobs","keywords":"","body":"Jobs Authorization\nCASL ability actions\nThis is the list of the permissions methods available for Jobs and all their endpoints.The authorization for jobs is consistently different from all the other endpoints \nEndpoint Authorization\n\nJobCreate\nJobRead\nJobStatusUpdate\nJobDelete\n\n(Data) Instance Authorization\n\njob_create_configuration: the job create section of the configuration dictates if the user can create the job \nJobCreateAny: users with this privileges can create jobs for any of the users that are defined in the create section of the job configuration \nJobReadAccess\nJobReadAny\njob_satatus_update_configuration: the job update section in configuration dictates if the user can update the status of the job\nJobStatusUpdateAny: users with this privileges can update the status of any job.\nJobDeleteAny\n\nPriority\n\ngraph LR;\n JobCreate-->job_create_configuration;\n job_create_configuration-->JobCreateAny;\n JobRead-->JobReadAccess;\n JobReadAccess-->JobReadAny;\n JobStatusUpdate-->job_status_update_configuration;\n job_status_update_configuration-->JobStatusUpdateAny;\n JobDelete-->JobDeleteAny;\n\n\nAuthorization table\n\n\n\nHTTP method\nEndpoint\nEndpoint Authentication\nAnonymous\nAuthenticated\nCreate Jobs Groups\nUpdate Jobs Groups\nAdmin Groups\nDelete Groups\nNotes\n\n\n\n\nPOST\nJobs\nplease see job createauthorization table\nplease see job createauthorization table\nplease see job createauthorization table\nAnyJobsCreateOwner\nno\nAnyJobsCreateAny\nno\n\n\n\nGET\nJobs\nJobRead\nno\nHas AccessJobReadAccess\nHas AccessJobReadAccess\nno\nAnyJobReadAny\nno\n\n\n\nGET\nJobs/jid\nJobRead\nno\nHas AccessJobReadAccess\nHas AccessJobReadAccess\nno\nAnyJobReadAny\nno\n\n\n\nPOST\nJobs/statusUpdate\nplease see job createauthorization table\nno\nplease see job updateauthorization table\nno\nOwnerJobStatusUpdateOwner\nAnyJobStatusUpdateAny\nno\n\n\n\nDELETE\nJobs/jid\nJobDelete\nno\nno\nno\nno\nno\nJobDeleteAny\n\n\n\nJob Create Authorization Table\nThese authorization permissions are configured directly in the create section of the job configuration.They apply to the jobs endpoint POST:Jobs\n| Job Create Authorization | Endpoint Authentication | Endpoint Authentication Description | Instance Authentication | Instance Authentication Description |\n| --- | --- | --- | --- | --- |\n| #all | #all | any user can access this endpoint, both anonymous and authenticated | #all | Any user can create this instance of the job |\n| #admin | ADMINGROUPS | Only users belonging to any group listed in ADMIN_GROUPS will be able to access the endpoint | ADMIN_GROUPS | Only users belonging to any group listed in ADMIN_GROUPS will be able to create this instance of the job |\n| #authenticated | #user | any valid users can access the endpoint, independently from their groups | #user | any valid users can cretae this instance of the job |\n| #datasetPublic | #all | any user can access this endpoint, both anonymous and and authenticated | #datasetPublic | the job instance will be created only if all the datasets listed are public |\n| #datasetAccess | #user | any valid user can access this endpoint, independently from their groups | #datasetAccess | the job instance will be created only if the user has access to all the datasets listed |\n| #datasetOwner | #user | any valid user can access this endpoint, independently from their groups | #datasetOwner | the job instance will be created only if the user is part of all the datasets owner group |\n| GROUP | GROUP | only users that belongs to the specified group can access the endpoint | GROUP | the job instance will be created only if all the datasets listed belong to the group specified |\n| USER | USER | only the specified user can access the endpoint | #datasetOwner_ | the job instance will be created only if all the datasets listed are owned by any of the user's groups |\nIMPORTANT: use option #all carefully, as it allows anybody to create a new job. It is mostly use for debuging and testing\nJob Status Update Authorization Table\nThese authorization permissions are configured directly in the update section of the job configuration.They apply to the jobs endpoint POST:Jobs/statusUpdate| Job Status Update Authorization | Endpoint Authentication | Endpoint Authentication Description | Instance Authentication | Instance Authentication Description |\n| --- | --- | --- | --- | --- |\n| #all | #all | any user can access this endpoint, both anonymous and authenticated | #all | Any user can update the status of this job instance |\n| #owner | #user | valid user can access the endpoint | #jobOwner | a user that belongs to the group listed as job owner can perform the update |\n| #admin | ADMINGROUPS | Only users belonging to any group listed in ADMINGROUPS will be able to access the endpoint | ADMINGROUPS | Only users belonging to any group listed in ADMIN_GROUPS are able to update the job status |\n| \\ | \\ | only users that belongs to the specified group can access the endpoint | \\ | the job instance will be created only if all the datasets listed belong to the group specified |\n| \\ | \\__ | only the specified user can access the endpoint | #datasetOwner_ | the job instance will be created only if all the datasets listed are owned by any of the user's groups |\nIMPORTANT: use option #all carefully, as it allows anybody to update the status of the job. It is mostly use for debuging and testing\n"},"Development/v4.x/backend/authorization/authorization_users.html":{"url":"Development/v4.x/backend/authorization/authorization_users.html","title":"Users","keywords":"","body":"Users Authorization\nCASL ability actions\nThis is the list of the permissions methods available for datasets and all their endpoints\nEndpoint authorization\n\nUserLogin\nUserRead\nUserCreate\nUserUpdate\nUserPassword\nUserDelete\n\nInstance authorization\n\nUserReadOwn\nUserReadAny\nUserCreateOwn\nUserCreateAny\nUserUpdateOwn\nUserUpdateAny\nUserPasswordOwn\nUserPasswordAny\nUserDeleteAny\n\nPriority\n\ngraph TD;\n UserLogin(E)\n UserCreate(E)-->UserCreateOwn(I)-->UserCreateAny(I);\n UserRead(E)-->UserReadOwn(I)-->UserReadAny(I);\n UserUpdate(E)-->UserUpdateOwner(I)-->UserUpdateAny(I);\n UserPassword(E)-->UserPasswordOwner(I)-->UserPasswordAny(I);\n UserDelete(E)-->UserDeleteOwn(I)-->UserDeleteAny(I);\n\n\n\n\nAuthorization table:\n\n\n\nHTTP method\nEndpoint\nEndpoint Authorization\nAnonymous\nAuthenticated User\nUser Privileged Groups\nAdmin Groups\nUser Delete Groups\n\n\n\n\nPOST\nUsers/jwt\nUserRead\nno\nOwnUserReadOwn\nno\nno\nno\n\n\nPOST\nUsers/login\nUserLogin\nno\nno\nno\nno\nno\n\n\nGET\nUsers/id\nUserRead\nno\nOwnUserReadOwn\nAnyUserReadAny\nAnyUserReadAny\nno\n\n\nGET\nUsers/id/userIdentity\nUserRead\nno\nOwnUserReadOwn\nAnyUserReadAny\nAnyUserReadAny\nno\n\n\nPOST\nUsers/id/settings\nUserCreate\nno\nOwnUserCreateOwn\nAnyUserCreateAny\nAnyUserCreateAny\nno\n\n\nGET\nUsers/id/settings\nUserUpdate\nno\nOwnUserReadOwn\nAnyUserReadAny\nAny UserReadAny\nno\n\n\nPUT\nUsers/id/settings\nUserUpdate\nno\nOwnUserUpdateOwn\nAnyUserUpdateAny\nAnyUserUpdateAny\nno\n\n\nPATCH\nUsers/id/settings\nUserUpdate\nno\nOwnUserUpdateOwn\nAnyUserUpdateAny\nAnyUserUpdateAny\nno\n\n\nPATCH\nUsers/id/password\nUserPassword\nno\nOwnUserPasswordOwn\nAnyUserPasswordAny\nAnyUserPasswordAny\nno\n\n\nDELETE\nUsers/id\nUserDelete\nno\nno\nno\nno\nAnyUserDeleteAny\n\n\nDELETE\nUsers/id/settings\nUserDelete\nno\nno\nno\nno\nAnyUserDeleteAny\n\n\nGET\nUsers/id/authorization/dataset/create\nUserRead\nno\nOwnUserReadOwn\nOwnUserReadOwn\nAnyUserReadAny\nno\n\n\nGET\nUsers/logout\nUserLogout\nno\nOwnUserLogoutOwn\nno\nno\nno\n\n\nGET\nuseridentities/findOne\nUserRead\nno\nOwnUserReadOwn\nAnyUserReadAny\nAnyUserReadAny\nno\n\n\n\n"},"Development/v4.x/backend/authorization/authorization_samples.html":{"url":"Development/v4.x/backend/authorization/authorization_samples.html","title":"Samples","keywords":"","body":"Samples Authorization\nCASL ability actions\nThis is the list of the permissions methods available for Samples and all their endpoints\nEndpoint Authorization\n\nSampleCreate\nSampleRead\nSampleUpdate\nSampleDelete\nSampleAttachmentCreate\nSampleAttachmentRead\nSampleAttachmentUpdate\nSampleAttachmentDelete\nSampleDatasetRead\n\n(Data) Instance Authorization\n\nSampleCreateOwner\nSampleCreateAny\nSampleReadManyPublic\nSampleReadManyAccess\nSampleReadManyOwner\nSampleReadOnePublic\nSampleReadOneAccess\nSampleReadOneOwner\nSampleReadAny\nSampleUpdateOwner\nSampleUpdateAny\nSampleDeleteOwner\nSampleDeleteAny\nSampleAttachmentCreateOwner\nSampleAttachmentCreateAny\nSampleAttachmentReadManyPublic\nSampleAttachmentReadManyAccess\nSampleAttachmentReadManyOwner\nSampleAttachmentReadManyAny\nSampleAttachmentUpdateOwner\nSampleAttachmentUpdateAny\nSampleAttachmentDeleteOwner\nSampleAttachmentDeleteAny\nSampleDatasetReadPublic\nSampleDatasetReadAccess\nSampleDatasetReadOwner\nSampleDatasetReadAny\n\nPriority\n\ngraph LR;\n SampleCreate-->SampleCreateOwner;\n SampleCreateOwner-->SampleCreateAny;\n SampleRead-->SampleReadManyPublic;\n SampleReadManyPublic-->SampleReadManyAccess;\n SampleReadManyAccess-->SampleReadManyOwner;\n SampleReadManyOwner-->SampleReadAny;\n SampleRead-->SampleReadOnePublic;\n SampleReadOnePublic-->SampleReadOneAccess;\n SampleReadOneAccess-->SampleReadOneOwner;\n SampleReadOneOwner-->SampleReadAny;\n SampleUpdate-->SampleUpdateOwner;\n SampleUpdateOwner-->SampleUpdateAny;\n SampleDelete-->SampleDeleteOwner;\n SampleDeleteOwner-->SampleDeleteAny;\n SampleAttachmentCreate-->SampleAttachmentCreateOwner;\n SampleAttachmentCreateOwner-->SampleAttachmentCreateAny;\n SampleAttachmentRead-->SampleAttachmentReadManyPublic;\n SampleAttachmentReadManyPublic-->SampleAttachmentReadManyAccess;\n SampleAttachmentReadManyAccess-->SampleAttachmentReadManyOwner;\n SampleAttachmentReadManyOwner-->SampleAttachmentReadManyAny;\n SampleAttachmentUpdate-->SampleAttachmentUpdateOwner;\n SampleAttachmentUpdateOwner-->SampleAttachmentUpdateAny;\n SampleAttachmentDelete-->SampleAttachmentDeleteOwner;\n SampleAttachmentDeleteOwner-->SampleAttachmentDeleteAny;\n SampleDatasetRead-->SampleDatasetReadPublic;\n SampleDatasetReadPublic-->SampleDatasetReadAccess;\n SampleDatasetReadAccess-->SampleDatasetReadOwner;\n SampleDatasetReadOwner-->SampleDatasetReadAny;\n\n\nAuthorization table\n\n\n\nHTTP method\nEndpoint\nEndpoint Authentication\nAnonymous\nAuthenticated User\nSample Groups\nSample Privileged Groups\nAdmin Groups\nDelete Groups\nNotes\n\n\n\n\nPOST\nSamples\nSampleCreate\nno\nno\nOwnerSampleCreateOwner\nAnySampleCreateAny\nAnySampleCreateAny\nno\n\n\n\nGET\nSamples\nSampleRead\nPublicSampleReadManyPublic\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nAnySampleReadAny\nno\n\n\n\nGET\nSamples/fullquery\nSampleRead\nPublicSampleReadManyPublic\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nAnySampleReadAny\nno\n\n\n\nGET\nSamples/fullfacet\nSampleRead\nPublicSampleReadManyPublic\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nAnySampleReadAny\nno\n\n\n\nGET\nSamples/pid\nSampleRead\nPublicSampleReadOnePublic\nHas AccessSampleReadOneAccess\nHas AccessSampleReadOneAccess\nHas AccessSampleReadOneAccess\nAnySampleReadAny\nno\n\n\n\nGET\nSamples/fullquery\nSampleRead\nPublicSampleReadOnePublic\nHas AccessSampleReadOneAccess\nHas AccessSampleReadOneAccess\nHas AccessSampleReadOneAccess\nAnySampleReadAny\nno\n\n\n\nPATCH\nSamples/pid\nSampleUpdate\nno\nno\nOwnerSampleUpdateOwn\nOwnerSampleUpdateOwn\nAnySampleUpdateAny\nno\n\n\n\nDELETE\nSamples/pid\nSampleDelete\nno\nno\nno\nno\nno\nAnySampleDeleteAny\n\n\n\n\n\n\n\n\n\nPOST\nSamples/pid/Attachments\nSampleAttachmentCreate\nno\nno\nOwnerSampleAttachmentCreateOwner\nAnySampleAttachmentCreateAny\nAnySampleAttachmentCreateAny\nno\n\n\n\nGET\nSamples/pid/Attachments\nSampleAttachmentRead\nPublicSampleAttachmentReadManyPublic\nHas AccessSampleAttachmentReadManyAccess\nHas AccessSampleAttachmentReadManyAccess\nHas AccessSampleAttachmentReadManyAccess\nAnySampleAttachmentReadManyAny\nno\n\n\n\nDELETE\nSamples/pid/attachment/aid\nSampleAttachmentDelete\nno\nno\nOwnerSampleAttachmentDeleteOwner\nOwnerSampleAttachmentDeleteOwner\nAnySampleAttachmentDeleteAny\nAnySampleAttachmentDeleteAny\n\n\n\n\n\n\n\n\n\nGET\nSamples/pid/datasets\nSampleDatasetRead\nPublicSampleDatasetReadOnePublic\nHas AccessSampleDatasetReadOneAccess\nHas AccessSampleDatasetReadOneAccess\nHas AccessSampleDatasetReadOneAccess\nAnySampleDatasetReadOneAny\nno\n\n\n\n"},"Development/v4.x/elasticsearch/elasticsearch.html":{"url":"Development/v4.x/elasticsearch/elasticsearch.html","title":"Elastic Search","keywords":"","body":"Getting started\nTo be able to start application with Elasticsearch you will need to:\n\nStart with npm prepare:local to executes docker-compose file which pull and run Elasticsearch cluster as a docker container.\nStart the application with npm run start\n\nCheck the .env file\nFollowing environment variables are required to run Elasticsearch with Scicat Backend:\n\n\n\nVariables\nValues\nDescription\n\n\n\n\nELASTICSEARCH_ENABLED\nyes or no\nFlag to enable/disable the ElasticSearch service\n\n\nES_HOST\nhttps://localhost:9200\nUse http if xpack.security.enabled set to false\n\n\nES_USERNAME (optional)\nelastic\nElasticsearch cluster username. Can be customized with docker image environment values with ELASTIC_USERNAME\n\n\nES_PASSWORD\npassword\nElasticsearch cluster password. Can be customized with docker image environment values ELASTIC_PASSWORD\n\n\nMONGODB_COLLECTION\nDataset\nCollection name to be mapped into specified Elasticsearch index\n\n\nES_MAX_RESULT\n210000\nMaximum records can be indexed into Elasticsearch. If not set, default is 10000\n\n\nES_FIELDS_LIMIT\n400000\nThe total number of fields in an index. If not set, default is 1000\n\n\nES_INDEX\ndataset\nSetting default index for the application\n\n\nES_REFRESH\nwait_for\nWait for the change to become visible - CRUD actions\n\n\n\n\n\n\n** NOTE **\nES_REFRESH=\"wait_for\" is used for better testing flow. For production it should be set to false. Detailed docs about this setting can be found: docs-refresh\nSteps to synchronize data with Elasitcsearch\nTo enable search queries in Elasticsearch, you should adhere to the following steps:\n\nOpen Swagger page http://localhost:3000/explorer, Authorize with admin token\nOpen elastic-search endpoints and execute create-index.\nNOTE: This step can be skipped if you want to use default index dataset\nexecute sync-database to sync data into the given index.\n\nIf you see the following response, it means Elasticsearch is ready for use.\n{\n \"total\": 21670,\n \"failed\": 0,\n \"retry\": 0,\n \"successful\": 21670,\n \"noop\": 0,\n \"time\": 4777,\n \"bytes\": 279159021,\n \"aborted\": false\n}\nIndex, analysis and mapping settings\nIndex settings in Elasticsearch are used to define various configurations and behaviors for an index. Index settings are vital for optimizing Elasticsearch's performance, relevance of search results, and resource management, ensuring that the index operates efficiently and effectively according to the specific needs of the application using it.\nPre-defined index settings & dynamic mapping for nested objects can be found /src/elastic-search/configuration/indexSetting.ts\nFields to be mapped into Elasticsearch index can be found /src/elastic-search/configuration/datasetFieldMapping.ts\nIndex settings:\nexport const defaultElasticSettings = {\n index: {\n max_result_window: process.env.ES_MAX_RESULT || 2000000,\n number_of_replicas: 0,\n mapping: {\n total_fields: {\n limit: process.env.ES_FIELDS_LIMIT || 2000000,\n },\n nested_fields: {\n limit: 1000,\n },\n },\n },\n analysis: {\n analyzer: {\n autocomplete: {\n tokenizer: \"autocomplete\",\n filter: [\"lowercase\"],\n },\n autocomplete_search: {\n tokenizer: \"lowercase\",\n },\n },\n tokenizer: {\n autocomplete: autocomplete_tokenizer,\n },\n },\n} as IndicesIndexSettingsAnalysis;\n\n\n\nObject\nDescription\n\n\n\n\nindex\nIt contains settings that determine how an Elasticsearch index works, like how many results you can get from a search and how many copies of the data it should store. More details in index modules\n\n\nindex.max_result_window\nDetermines the maximum number of documents returned in a query. Default is 10000\n\n\nindex.number_of_replicas\nSets the number of replica shards. Default is 1\n\n\nindex.mapping.total_fields.limit\nSets the maximum number of fields an index can have. Default is 1000\n\n\nindex.mapping.nested_fields.limit\nSets the maximum number of nested fields. Default is 50\n\n\nanalysis\nIt defines how text data is processed before it is indexed, including custom analyzers and tokenizers which control the conversion of text into tokens or terms. More details in analyzer and analysis\n\n\n\n\nDynamic mapping:\nDynamic mapping is currently only used for scientific field. More details in Dynamic template\nexport const dynamic_template: Record[] = [\n {\n string_as_keyword: {\n path_match: \"scientificMetadata.*.*\",\n match_mapping_type: \"string\",\n mapping: {\n type: \"keyword\",\n ignore_above: 256,\n },\n },\n },\n {\n long_as_double: {\n path_match: \"scientificMetadata.*.*\",\n match_mapping_type: \"long\",\n mapping: {\n type: \"double\",\n coerce: true,\n ignore_malformed: true,\n },\n },\n },\n {\n date_as_keyword: {\n path_match: \"scientificMetadata.*.*\",\n match_mapping_type: \"date\",\n mapping: {\n type: \"keyword\",\n ignore_above: 256,\n },\n },\n },\n];\nAPIs\nNOTE: All requests require admin permissions.\nFor search query integration it is suggested to use client search service directly, which can be found /src/elastic-search/elastic-search.service.ts\nFollowing table is existing controller endpoints:\n\n\n\nendpoint\nparameters\nrequest\ndescription\n\n\n\n\n/api/v3/elastic-search/create-index?index=\nstring\nquery\nCreate an index with this name\n\n\n/api/v3/elastic-search/sync-database?index=\nstring\nquery\nSync datasets into elastic search with this index\n\n\n/api/v3/elastic-search/search\n{ \"text\": \"\", \"ownerGroup\": [], \"creationLocation\": [], \"type\": [], \"keywords\": [], \"isPublished\": false, \"scientific\": [{\"lhs\": \"\", \"relation\": \"\", \"rhs\": 0, \"unit\": \"\"}]}\nbody\nSearch query\n\n\n/api/v3/elastic-search/delete-index\nstring\nquery\nDelete an index with this name\n\n\n/api/v3/elastic-search/get-index\nstring\nquery\nGet an index settings with this name\n\n\n/api/v3/elastic-search/update-index\nto be updated\nto be updated\nto be updated\n\n\n\nSetting up a local Elasticsearch cluster\nDocker Configuration\nFor local development, you can spin up an Elasticsearch cluster using Docker with the following configuration:\nElasticsearch image configuration can be found in ./CI/E2E/docker-compose-local.yaml\nes01:\n image: docker.elastic.co/elasticsearch/elasticsearch:8.8.2\n ports:\n - 9200:9200\n environment:\n - xpack.security.enabled=false\n - node.name=es01\n - ES_JAVA_OPTS=-Xms2g -Xmx2g\n - cluster.name=es-cluster\n - cluster.initial_master_nodes=es01\n - ELASTIC_PASSWORD=password\n - bootstrap.memory_lock=true\n mem_limit: 4g\n ulimits:\n memlock:\n soft: -1\n hard: -1\n\n\n\nSettings\nDescription\n\n\n\n\nImage Version\nDocker image for Elasticsearch: docker.elastic.co/elasticsearch/elasticsearch:8.8.2.\n\n\nPort\nContainer-to-host port mapping: 9200:9200.\n\n\nmem_limit\nMaximum container memory, influenced by ES_JAVA_OPTS.\n\n\nulimits.memlock (soft, hard)\nMemory locking limits: -1 for unlimited.\n\n\nVariables\nDescription\n\n\nxpack.security.enabled\nToggle for Elasticsearch security; false disables authentication for development.\n\n\nnode.name\nUnique Elasticsearch node name; essential for cluster node identification.\n\n\nES_JAVA_OPTS\nJVM heap memory settings for Elasticsearch performance (e.g., -Xms2g -Xmx2g).\n\n\ncluster.name\nName of the Elasticsearch cluster for node grouping.\n\n\nELASTIC_PASSWORD\nPassword for the 'elastic' user; not needed if security is disabled.\n\n\nbootstrap.memory_lock\nPrevents swapping Elasticsearch memory to disk when enabled.\n\n\n\nUsing Helm for Kubernetes\nFor those looking to deploy on Kubernetes, refer to the official Helm chart documentation for Elasticsearch. Helm charts provide a more production-ready setup and can manage complex configurations.\nMore configuration information:\n\nMore complicated examples with docker-compose configuration\nMore service configures for production\n\n"},"Development/v4.x/frontend/configuration.html":{"url":"Development/v4.x/frontend/configuration.html","title":"Frontend","keywords":"","body":"Configuration\n\nSciCat Frontend\nSciCat Backend\nDotenv Config\nDOI Config\n\n\n\nSciCat Frontend\nThis is the configuration file for the frontend client. The configuration file allows the systems administrator to configure every aspect of the client including switching on/off almost all non essential features. The configuration file can either be served from the backend, via the /client/config.json endpoint or mounted into the docker container at the following path /usr/share/nginx/html/assets/config.json.\nAn example is shown below\nfrontend/src/assets/config.json:\n\n{\n \"accessTokenPrefix\": \"\", // Set the backend token prefix. Should be empty string for current backend or \"Bearer \" if using scicat-backend-next.\n \"addDatasetEnabled\": true, // Show/hide the \"Create Dataset\" button in the Datasets Dashboard.\n \"archiveWorkflowEnabled\": true, // Enable/disable the archive/retrieve workflow.\n \"datasetReduceEnabled\": true, // Enable/disable the automatic Dataset reduction/analysis workflow.\n \"editDatasetSampleEnabled\": true, // Enable/disable editing of which Sample a Dataset belongs to.\n \"editMetadataEnabled\": true, // Enable/disable editing of Scientific Metadata.\n \"editPublishedData\": true, // Enable/disable editing of Published Data.\n \"editSampleEnabled\": true, // Enable/disable editing of Samples.\n \"externalAuthEndpoint\": \"/auth/msad\", // Endpoint used for third party authentication, e.g, LDAP.\n \"facility\": \"ESS\", // Facility running the SciCat instance.\n \"fileColorEnabled\": true, // Enable/disable file size color representation in the Datasets Dashboard.\n \"fileDownloadEnabled\": true, // Enable/disable download workflow for Dataset datafiles.\n \"gettingStarted\": null, // URL to Getting Started guide for SciCat, displayed on the Help page.\n \"ingestManual\": null, // URL to Ingest Manual for SciCat, displayed on the Help page.\n \"jobsEnabled\": true, // Enable/disable Job workflow.\n \"jsonMetadataEnabled\": true, // Show/hide the \"Show Metadata\" button on the details pages, allowing users to see the JSON represantion of the current document.\n \"jupyterHubUrl\": \"https://jupyterhub.esss.lu.se/\", // URL to Jupyter Hub instance used for data analysis.\n \"landingPage\": \"doi2.psi.ch/detail/\", // URL to the facility's Landing Page for Published Data.\n \"lbBaseURL\": \"http://127.0.0.1:3000\", // URL to the SciCat Backend.\n \"localColumns\": [ // Default columns to be displayed in the table on the Datasets Dashboard.\n {\n \"name\": \"select\",\n \"order\": 0,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"datasetName\",\n \"order\": 1,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"runNumber\",\n \"order\": 2,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"sourceFolder\",\n \"order\": 3,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"size\",\n \"order\": 4,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"creationTime\",\n \"order\": 5,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"type\",\n \"order\": 6,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"image\",\n \"order\": 7,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"metadata\",\n \"order\": 8,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"proposalId\",\n \"order\": 9,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"ownerGroup\",\n \"order\": 10,\n \"type\": \"standard\",\n \"enabled\": false\n },\n {\n \"name\": \"dataStatus\",\n \"order\": 11,\n \"type\": \"standard\",\n \"enabled\": false\n }\n ],\n \"logbookEnabled\": true, // Enable/disable SciChat Logbook integration.\n \"loginFormEnabled\": true, // Enable/disable the local Login form. Should be disabled if using oAuth2 for authentication.\n \"maxDirectDownloadSize\": 5000000000, // Set a maximum allowed file size for downloading datafiles over HTTP.\n \"metadataPreviewEnabled\": true, // Enable/disable Scientific Metadata preview on the Datasets Dashboard.\n \"metadataStructure\": \"tree\", // Allow tree structure for Scientific Metadata. Set to empty string for flat structure.\n \"multipleDownloadAction\": \"http://localhost:3012/zip\", // URL to service handling direct download of datafiles.\n \"multipleDownloadEnabled\": true, // Enable/disable ability to download multiple datafiles.\n \"oAuth2Endpoints\": [], // List of endpoints used for oAuth2 authentication.\n \"policiesEnabled\": true, // Enable/disable Dataset Policies workflow.\n \"retrieveDestinations\": [], // List of destinations for Dataset retrievals.\n \"riotBaseUrl\": \"http://scitest.esss.lu.se/riot\", // URL to SciChat client.\n \"scienceSearchEnabled\": true, // Enable/disable filtering documents on Scientific Metadata.\n \"scienceSearchUnitsEnabled\": true, // Enable/disable filtering documents on Scientific Metadata using units.\n \"searchPublicDataEnabled\": true, // Enable/disable filtering Datasets on public or non-public data.\n \"searchSamples\": true, // Enable/disable searching Samples on Samples Dashboard.\n \"sftpHost\": \"login.esss.dk\", // URL to SFTP service used for downloading files exceeding maximum allowed file size.\n \"shareEnabled\": true, // Enable/disable workflow for sharing Datasets with other users using their email address.\n \"shoppingCartEnabled\": true, // Enable/disable the Dataset cart used for bulk actions.\n \"shoppingCartOnHeader\": true, // Toggle Dataset cart placement, either on header or to the left on the Datasets Dashboard.\n \"tableSciDataEnabled\": true // Enable/disable Scientific Metadata table view on details pages. If disabled, Scientific Metadata is displayed as raw JSON.\n}\nSciCat Backend\nDotenv config\nThis is the configuration file for the backend. This file allows the systems administrator to configure connected services, like authentication services and message queues, and also switching on/off almost all non essential features. The configuration file is a dotenv file and is read by the backend at runtime.\nAn example is shown below\nbackend/.env\nACCESS_GROUPS_STATIC_VALUES=\"group1,group2,group3,...\" // List of groups assigned by default as access groups to all users. Used in the vanilla implementation. Facilities customization might not use this\nACCESS_GROUP_SERVICE_TOKEN=\"90f126864824ede0e22f7b4407aa1a5cd8158e6cabbce39aaf091937589f1750\" // Token needed to access the API specified in ACCESS_GROUP_SERVICE_API_URL\nACCESS_GROUP_SERVICE_API_URL=\"https://my.access.group/service/api/url\" // Url of the service API which is used to provide access groups. At the moment only one value is allowed\nDOI_PREFIX=\"\" // The facility DOI prefix, with trailing slash.\nEXPRESS_SESSION_SECRET=\"\" // *Optional* Secret used to set up express session.\nHTTP_MAX_REDIRECTS=5 // *Optional* Max redirects for http requests. Defaults to 5.\nHTTP_TIMEOUT=5000 // *Optional* Timeout from http requests in ms. Defaults to 5000.\nJWT_SECRET= // The secret for your JWT token, used for authorization.\nJWT_EXPIRES_IN=3600 // *Optional* How long, in seconds, the JWT token is valid. Defaults to `3600`.\nLDAP_URL=\"ldaps://ldap.server.com:636/\" // *Optional* The URL (and port) to your LDAP server.\nLDAP_BIND_DN=\"@server.com\" // *Optional* Bind_DN for your LDAP server.\nLDAP_BIND_CREDENTIALS= // *Optional* Credentials for your LDAP server.\nLDAP_SEARCH_BASE= // *Optional* Search base for your LDAP server.\nLDAP_SEARCH_FILTER=\"(LDAPUsername={{username}})\" // *Optional* Search filter for you LDAP server.\nOIDC_ISSUER=\"https://identity.your.facility/your/realm\" // Full URL of the identity provider\nOIDC_CLIENT_ID= \"scicat\" // Client id used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generated\nOIDC_CLIENT_SECRET=\"90f126864824ede0e22f7b4407aa1a5cd8158e6cabbce39aaf091937589f1750\" // Token used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generated\nOIDC_CALLBACK_URL=\"http://localhost:3000/api/v3/oidc/callback\" // URL of the endpoint that is called when the authentication has been executed with the OIDC service.\nOIDC_SCOPE=\"openid profile email\" // Information returned by the OIDC service together with token\"\nOIDC_SUCCESS_URL=\"http://localhost:3000/explorer\" // URL of the endpoint that is called after a successful authentication. IT is not used in the vanilla implementaation.\nOIDC_ACCESS_GROUPS=\"access_groups\" // field used to retrieve access groups from the OIDC service. It is not used in the vanilla implementation.\nLOGBOOK_ENABLED= // *Optional* Flag to enable/disable the Logbook endpoints. Values \"yes\" or \"no\". Defaults to \"no\".\nLOGBOOK_BASE_URL=\"http://localhost:3030/scichatapi\" // *Optional* The base URL to the SciChat wrapper API. Only required if Logbook is enabled.\nLOGBOOK_USERNAME=\"\" // *Optional* The username used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.\nLOGBOOK_PASSWORD=\"\" // *Optional* The password used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.\nMETADATA_KEYS_RETURN_LIMIT=100 // *Optional* The return limit for the `/Datasets/metadataKeys` endpoint.\nMETADATA_PARENT_INSTANCES_RETURN_LIMIT=100 // *Optional* The return limit of Datasets to extract metadata keys from for the `/Datasets/metadataKeys` endpoint.\nMONGODB_URI=\"mongodb://:@:27017/\" // The URI for your MongoDB instance.\nOAI_PROVIDER_ROUTE=\"\" // *Optional* URI to OAI provider, used for the `/publisheddata/:id/resync` endpoint.\nPID_PREFIX=\"\" // The facility PID prefix, with trailing slash.\nPUBLIC_URL_PREFIX=\"https://doi.ess.eu/detail/\" // The base URL to the facility Landing Page.\nPORT=3000 // *Optional* The port on which you want to access the app. Defaults to `3000`.\nRABBITMQ_ENABLED= *Optional* Flag to enable/disable RabbitMQ consumer. Values \"yes\" or \"no\". Defaults to \"no\".\nRABBITMQ_HOSTNAME=\"localhost\" // *Optional* The hostname of the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nRABBITMQ_USERNAME=\"rabbitmq\" // *Optional* The username used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nRABBITMQ_PASSWORD=\"rabbitmq\" // *Optional* The password used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nREGISTER_DOI_URI=\"https://mds.test.datacite.org/doi\" // URI to the organization that registers the facilities DOIs.\nREGISTER_METADATA_URI=\"https://mds.test.datacite.org/metadata\" // URI to the organization that registers the facilities published data metadata.\nSITE= // The name of your site.\nSMTP_HOST= // Host of SMTP server.\nSMTP_MESSAGE_FROM= // Email address that emails should be sent from.\nSMTP_PORT= // Port of SMTP server.\nSMTP_SECURE= // Secure of SMTP server.\nDOI Config\nThis config file contains the username and password for authenicating against your DOI provider, e.g., DataCite, which is used when registering the DOI and metadata of your published data.\nbackend/src/config/doiconfig.local.json\n\n{\n \"username\": \"\",\n \"password\": \"\"\n}\n"},"Development/v4.x/Login.html":{"url":"Development/v4.x/Login.html","title":"Login and Accounts","keywords":"","body":"Login\nTwo login routes are provided - for functional accounts and for AD/LDAP logins.\nLoopback provides a UserIdentity for third party logins, (e.g. AD/LDAP, google, Twitter etc) \nA document is created in the db.UserIdentity collection.\nThe UserIdentity is fetched from MongoDB and stored in the ngrx store, (store.user.userIdentity) where \nit is used to access e.g. email for job initiators.\nThe login method needs to be defined in the client configuartion (environment file) usign the field externalAuthEndpoint. For example externalAuthEndpoint: \"/auth/msad\".\nSee loopback docs\nFor non-third party logins defined, no UserIdentity is created. These are special users for admin purposes including beamline specific users for ingestion etc.\nThese users should be defined in a seperate secrets repo in a file called functionalAccounts.json and are read by createFunctionalAccounts.js in the backend e.g.\n [{\n \"account\": \"admin\",\n \"password\": \"pass\",\n \"email\": \"admin@domain.com\",\n \"role\": \"admin\",\n \"global\": true\n }, {\n \"account\": \"ingestor\",\n \"password\": \"pass\",\n \"email\": \"ingestor@domain.com\",\n \"role\": \"ingestor\",\n \"global\": true\n }]\nOIDC\nFor OIDC integration, see the OIDC for details.\n"},"Development/v4.x/backend/testing_backend.html":{"url":"Development/v4.x/backend/testing_backend.html","title":"Backend","keywords":"","body":"Testing\nPackages\nFollowing is the list of hte main packages used to build, run and manage testing for the project\n\nChai - Assertion\nSupertest - NodeJS testing for HTTP calls\nMocha - Test runner framework\n\nAPI tests\nSciCat backend v4.x comes with many API tests. They are locate under the /test folder in the backend repository\nIn order to run the tests you need to have the backend running with an accessible mongodb instance.\nThe backend can be run from terminl with the command:\nnpm run dev\nIn a different terminal, you can run all the following tests:\n\nunit tests:npm run test\nlinting tests:npm run lint\napi tests based on mocha\nnpm run test:api:mocha\n\nIf you would like to run the mocha tests directly, you can use the following command: \nmocha --config ./test/config/.mocharc.json --timeout=5000 --reporter=nyan\nIf you are interested in a specific subset of API tests, you can add the --grep option to the mocha command.For example if you are interested in running only the test named Authorization functionalities, you can use the following command:mocha --config ./test/config/.mocharc.json --grep \\\"Authorization functionalities\\ --timeout=5000 --reporter=nyan\nCoverage\nThe following is the list of the current tests groups. We are trying to cover all the different components and endpoints of SciCat backend v4.x and we keep adding new tests every release cycle.\nThe coverage might not be complete. If you find any use case that is not covered by our tests, feel free to open an issue or, even better, to write a new set of tests and submit a PR.\nList of tests: \n\n\n\ntest file\nmain code\ntitle\ndescription\n\n\n\n\nAuth.js\n0100\nAuthorization functionalities\nTest login and logout for functional accounts\n\n\nCheckDifferentDatasetTypes.js\n0200\nCheck Different Dataset Types\nCheck different dataset types and their inheritance\n\n\nDatasetAuthorization.js\n0300\nTest access to dataset\nTest different use cases with different user and restricted access\n\n\nDatasetFilter.js\n0400\nTest retrieving datasets using filtering capabilities\nLeverage fullquery endpoint to retrieve different group of datasets\n\n\nDatasetLifecycle.js\n0500\nTest facet and filter queries\n\n\n\nDatasetTypes.js\n0550\n\n\n\n\nDerivedDatasetDatablock.js\n0600\n\n\n\n\nDerivedDataset.js\n0700\nDerived Datasets\ntest derived datasets functionalities\n\n\nDerivedDatasetOrigDatablock.js\n0800\nDerivedDatasetOrigDatablock\nTest OrigDatablocks and their relation to derived Datasets\n\n\nInstrument.js\n0900\nInstrument\ninstrument management, creation, update, deletion and search\n\n\nInstrumentsFilter.js\n1000\nTest retrieving instruments using filtering capabilities\n\n\n\nJobs.js\n1100\nTest New Job Model\n\n\n\nOrigDatablockForRawDataset\n1200\nTest OrigDatablocks and their relation to raw Datasets using origdatablocks endpoint\n\n\n\nPolicy.js\n1300\nSimple Policy tests\n\n\n\nProposalAuthorization.js\n1400\nTest access to proposal\n\n\n\nProposal.js\n1500\nSimple Proposal\n\n\n\nPublishedData.js\n1600\nTest of access to published data\n\n\n\nRandomizedDatasetPermissions.js\n1700\npermission test with bigger amount of data\n\n\n\nRawDatasetDatablock.js\n1800\nTest Datablocks and their relation to raw Datasets\n\n\n\nRawDataset.js\n1900\nRaw Datasets\n\n\n\nRawDatasetOrigDatablock.js\n2000\nTest OrigDatablocks and their relation to raw Datasets\n\n\n\nResetDataset.js\n2100\nCreate Dataset and its Datablocks, then reset Datablocks and embedded Datasetlifecycle status\n\n\n\nSample.js\n2200\nSimple Sample\nSamples functionality tests, like create, update and delete\n\n\nSampleAuthorization.js\n2250\nSample Authorization\nTest authorization for all samples operations\n\n\nUserAuthorization.js\n2300\nUser Authorization\ntest that user authorization are correct\n\n\nUsers.js\n2400\nLogin with functional accounts\n\n\n\nTest data\nIt is our intention to save all test data in the file TestData.js.In some cases, we need to code variation of the data directly in the test file.\nFunctional and Test accounts\nAll tests are performed assuming the following user accounts, groups and configuration are present.\nUsers and groups can be defined in the configuration structure defined in src/config/configuration.ts, although the preferred method for configuring functional/tests accounts is through the functional_accounts.json file present in the following folder ``\nThese settings are meant only for testing and to demonstrate the capabilities of backend V4.x. IT is highly recommended to remove accounts that are not needed and change passwords and group affiliations as needed.\nDeafault Accounts\nThe following table lists the accounts that are provided by default in a vanilla installation.\nPlease make sure to change them, update their password or remove them entirely in your production environment.\n\n\n\nAccount\nGroups\nAdmin Group\nDelete Group\nCreate Dataset w/o pid\nCreate Dataset w/ pid\nCreate Dataset Privileged\nCreate Job\nUpdate Job\n\n\n\n\nadmin\nadmin, global\nyes\nno\nAny\n\n\ningestor\ningestor\nyes\nno\nAny\n\n\narchiveManager\narchivemanager\nyes\nyes\nno\n\n\nproposalIngestor\nproposalingestor\nno\nno\nno\n\n\nuser 1\ngroup1\nno\nno\nOwn\n\n\nuser 2\ngroup2\nno\nno\nOwn\n\n\nuser 3\ngroup3\nno\nno\nOwn\n\n\nuser 4\ngroup4\nno\nno\nno\n\n\n\nGroup permissions\n\n\n\nEnvironmental variable\nList of groups\n\n\n\n\nADMIN_GROUPS\nadmin, ingestor, archivemanager\n\n\nDELETE_GROUPS\narchivemanager\n\n\nCREATE_DATASET_GROUPS\ngroup1,group2,group3\n\n\n\nTest Details\nThis section provides details on how all the tests files listed above are organized. Each subsection, provides a list of the test included in each file and the details of each one of them.\n\nDatasets \n0900: Instrument\n2300: User Authorization\n\n"},"Development/v4.x/backend/testing/instrument.html":{"url":"Development/v4.x/backend/testing/instrument.html","title":"Instrument","keywords":"","body":"Instrument\nInstrument test that the functionalities for creating, updating, deleting and retrieving instruments are working correctly\n\n\n\nTest Number\nHTTP Method\nEndpoint\nAuthenticated User\nExpected Request Status\nInput\nResults\n\n\n\n\n0010\nPOST\nInstruments\ningestor\n201\nTestData.InstrumentCorrect1\nTestData.InstrumentCorrect1\n\n\n0020\nPOST\nInstruments\ningestor\n201\nTestData.InstrumentCorrect2\nTestData.InstrumentCorrect2\n\n\n0030\nPOST\nInstruments\ningestor\n201\nTestData.InstrumentCorrect3\nTestData.InstrumentCorrect3\n\n\n0040\nPOST\nInstruments\ningestor\n400\nTestData.InstrumentCorrect2\nError: duplicate entry\n\n\n0050\nPOST\nInstruments\ningestor\n400\nTestData.InstrumentWrong1\nValidation Error\n\n\n0060\nPOST\nInstruments\nuser1\n400\nTestData.InstrumentCorrect2\nUnauthorized\n\n\n0070\nGET\nInstruments/instrumentId1\ningestor\n200\nn/a\nTestData.InstrumentCorrect1\n\n\n0080\nGET\nInstruments/instrumentId2\ningestor\n200\nn/a\nTestData.InstrumentCorrect2\n\n\n0090\nGET\nInstruments\ningestor\n200\nn/a\nTestData.InstrumentCorrect 1,2,3\n\n\n0100\nGET\nInstruments\ningestor\n200\n{where: {customMetadata: { main_user: \"ESS\"}}}\nTestData.InstrumentCorrect 1,2\n\n\n0110\nGET\nInstruments/findOne\ningestor\n200\nn/a\nTestData.InstrumentCorrect1\n\n\n0120\nGET\nInstruments/findOne\ningestor\n200\n{where: {customMetadata: { main_user: \"ESS\"}}}\nTestData.InstrumentCorrect 1\n\n\n0130\nGET\nInstruments/findOne\ningestor\n200\n{where: {customMetadata: { main_user: { like : \"somebody\"}}}}\nTestData.InstrumentCorrect 3\n\n\n0140\nGET\nInstruments\nuser1\n200\nn/a\nTestData.InstrumentCorrect 1,2,3\n\n\n0150\nPATCH\nInstruments/instrumentId2\ningestor\n200\n{ name: newName }\nTestData.InstrumentCorrect2 with name as newName\n\n\n0160\nGET\nInstruments/instrumentId2\ningestor\n200\nn/a\nTestData.InstrumentCorrect2 with name as newName\n\n\n0170\nGET\nInstruments/\ningestor\n200\n{where: {name: \"newName\"}}\nTestData.InstrumentCorrect2 with name as newName\n\n\n0180\nDELETE\nInstruments/instrumentId1\ningestor\n400\nn/a\nn/a\n\n\n0190\nDELETE\nInstruments/instrumentId1\narchiveManager\n200\nn/a\nn/a\n\n\n0200\nDELETE\nInstruments/instrumentId2\narchiveManager\n200\nn/a\nn/a\n\n\n0210\nDELETE\nInstruments/instrumentId3\narchiveManager\n200\nn/a\nn/a\n\n\n\n"},"Development/v4.x/backend/testing/user_authorization.html":{"url":"Development/v4.x/backend/testing/user_authorization.html","title":"User Authorization","keywords":"","body":"User Authorization\nUser authorization tests access to the Users endpoints according to the default configuration provided in the vanilla installation and illustrated at the beginning of this document.\n\n\n\nTest Number\nHTTP Method\nEndpoint\nAuthenticated User\nUser to be checked\nRequest Status\nResults\n\n\n\n\n0010\nGET\nUsers/uid/authorization/dataset/create\ningestor\nherself\n200\n{authorization: true}\n\n\n0020\nGET\nUsers/uid/authorization/dataset/create\nadmin\nherself\n200\n{authorization: true}\n\n\n0030\nGET\nUsers/uid/authorization/dataset/create\nadmin\nuser1\n200\n{authorization: true}\n\n\n0040\nGET\nUsers/uid/authorization/dataset/create\nadmin\nuser2\n200\n{authorization: true}\n\n\n0050\nGET\nUsers/uid/authorization/dataset/create\nadmin\nuser3\n200\n{authorization: true}\n\n\n0060\nGET\nUsers/uid/authorization/dataset/create\nadmin\nuser4\n200\n{authorization: false}\n\n\n0070\nGET\nUsers/uid/authorization/dataset/create\nuser1\nherself\n200\n{authorization: true}\n\n\n0080\nGET\nUsers/uid/authorization/dataset/create\nuser1\nadmin\n403\nn/a\n\n\n0090\nGET\nUsers/uid/authorization/dataset/create\nuser1\nuser2\n403\nn/a\n\n\n0100\nGET\nUsers/uid/authorization/dataset/create\nuser1\nuser3\n403\nn/a\n\n\n0110\nGET\nUsers/uid/authorization/dataset/create\nuser1\nuser4\n403\nn/a\n\n\n0120\nGET\nUsers/uid/authorization/dataset/create\nuser2\nherself\n200\n{authorization: true}\n\n\n0130\nGET\nUsers/uid/authorization/dataset/create\nuser3\nherself\n200\n{authorization: true}\n\n\n0140\nGET\nUsers/uid/authorization/dataset/create\nuser4\nherself\n200\n{authorization: false}\n\n\n0144\nGET\nUsers/uid/authorization/dataset/create\nanonymous\nadmin\n401\nn/a\n\n\n0146\nGET\nUsers/uid/authorization/dataset/create\nanonymous\nuser1\n401\nn/a\n\n\n0150\nPOST\nUsers/jwt\nanonymous\nn/a\n201\n{jwt: }\n\n\n0160\nPOST\nUsers/jwt\nadmin\nn/a\n201\n{jwt: }\n\n\n0170\nPOST\nUsers/jwt\nuser1\nn/a\n201\n{jwt: }\n\n\n0180\nGET\nUsers/uid\nadmin\nherself\n200\nAdmin user model without password\n\n\n0190\nGET\nUsers/uid\nadmin\nuser1\n200\nUser1 user model without password\n\n\n0200\nGET\nUsers/uid\nuser1\nherself\n200\nUser1 user model without password\n\n\n0210\nGET\nUsers/uid\nuser1\nadmin\n403\nn/a\n\n\n0220\nGET\nUsers/uid\nuser1\nuser2\n403\nn/a\n\n\n0230\nGET\nUsers/uid\nanonymous\nadmin\n401\nn/a\n\n\n0240\nGET\nUsers/uid\nanonymous\nuser1\n401\nn/a\n\n\n0250\nGET\nUsers/uid/userIdentity\nadmin\nherself\n200\nAdmin user identity model\n\n\n0260\nGET\nUsers/uid/userIdentity\nadmin\nuser1\n200\nUser1 user identity model\n\n\n0270\nGET\nUsers/uid/userIdentity\nuser1\nherself\n200\nUser1 user identity model\n\n\n0280\nGET\nUsers/uid/userIdentity\nuser1\nadmin\n403\nn/a\n\n\n0290\nGET\nUsers/uid/userIdentity\nuser1\nuser2\n403\nn/a\n\n\n0300\nGET\nUsers/uid/userIdentity\nanonymous\nadmin\n401\nn/a\n\n\n0310\nGET\nUsers/uid/userIdentity\nanonymous\nuser1\n401\nn/a\n\n\n0320\nGET\nUsers/uid/settings\nadmin\nherself\n200\nAdmin user settings model\n\n\n0330\nGET\nUsers/uid/settings\nadmin\nuser1\n200\nUser1 user settings model\n\n\n0340\nGET\nUsers/uid/settings\nuser1\nherself\n200\nUser1 user settings model\n\n\n0350\nGET\nUsers/uid/settings\nuser1\nadmin\n403\nn/a\n\n\n0360\nGET\nUsers/uid/settings\nuser1\nuser2\n403\nn/a\n\n\n0370\nGET\nUsers/uid/settings\nanonymous\nadmin\n401\nn/a\n\n\n0380\nGET\nUsers/uid/settings\nanonymous\nuser1\n401\nn/a\n\n\n0390\nGET\nUsers/userIdentities/findOne\nadmin\nherself\n200\nAdmin user identity model\n\n\n0400\nGET\nUsers/userIdentities/findOne\nadmin\nuser1\n200\nUser1 user identity 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current SciCat Backend accepts id fields in the database named pid, doi, or similar, this implementation requires there to be an id field of the form _id on every document. It is therefore necessary to run a database migration script towards MongoDB instance from a place where you have access to it and can install migrate-mongo package.\nAll database migration scripts are located in the migrations folder.\n\nTo run the migrations locally you just need to install all dependencies and run npm run start which will run the migrations first and start the application. If you want to run the migrations manually you can install migrate-mongo package globally and run migrate-mongo up or if it is already installed locally because is part of the dependencies it is possible to just run: node ./node_modules/migrate-mongo/bin/migrate-mongo.js up.\nTo run the migrations on the server you will need to copy the migration scripts together with migrate-mongo-config.js file to a location were you can access your MongoDB instance and have migrate-mongo package installed either globally(which is preferred) or locally if global installation is not possible. 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Overview","keywords":"","body":"\nSciCat Metadata Catalogue\n\nWhy SciCat ?\nSee the SciCat Home Webpage for an overview of how SciCat can help to manage scientific data.\nStructure of Documentation\nThe documentaion is split into the following chapters:\n\nUser Guide - Users of the system can come here to see screen captures, FAQs and find resources on how to better understand SciCat.\nOperator Guide - System admins read this part to set up SciCat for their location\nIngestor Guide - Instrument responsibles read this to understand how data can ge ingested into SciCat either manually or in an automated fashion\nDeveloper Guide - Developers who want to contribute to the project should read this chapter.\n\nTalks\nSciCat Project: Data Catalog System (2017)\nSciCat talk from Luke Gorman on Joint ExPaNDS and PaNOSC Meeting Feb 2020\n"},"Users/":{"url":"Users/","title":"User Guide","keywords":"","body":"User Manual\nThe User Manual summarizes the main functionality of the SciCat Web application by showing screenshot examples for all the parts of the application.\nIn the following the concept of a dataset is of central importance. SciCat stores information about datasets, i.e. a collection of files, rather than single files. The persons responsible for adding new data to the data catalog do this, by defining these sets of files and jointly ingesting them to the system.\nPlease note that the data catalog does not store the file contents itself. It just stores the metadata , i.e. semantic information which is useful to understand the contents of the files contained in the dataset and the list of the filenames, their sizes etc.\nThe datasets can contain raw data , e.g. data coming from a measurement device or instrument and also derived data, which is data derived (calculated) from the raw data. \n"},"Users/Anonymous.html":{"url":"Users/Anonymous.html","title":"Anonymous View","keywords":"","body":"Anonymous View\nAs soon as you visit the starting page of the GUI application, and you are not yet logged in, you will see a list of datasets, which have been published, and can therefore be looked at anonymously:\nYou can filter the datasets by choosing the \"facets\" on the left navigation section (in the screenshot for the location field a value of \"V20\" was selected).\n\nFrom this list you can choose a particular dataset to see the details of this dataset\n\n"},"Users/Login.html":{"url":"Users/Login.html","title":"Login","keywords":"","body":"Login\nTo get access to all the data, for which you have read access, you first have to login. To login hit the \"Sign in\" Icon at the top right corner.\n\nThere are two types of account associated with the DataCatalog: Functional and User. A functional account will primarily be used by software and system administrators to deal with backups and other tasks.\nUser accounts are tied into the login system that is used by your institution, for example: Active Directory. You are able to log in to the system using the same credentials you use on that account. This process is called authenitication in IT tech terminology\nWhen you login as a user your user management system will assign groups to the logged in user. Each dataset is also assigned to one such group (via the so called ownerGroup field), and you can view the datasets only, if you are member of the corresponding group. The logic that defines, what parts of the data you can see, is called \"authorization\" in IT terminology\nAnother example login page from PSI is here\n\n"},"Users/Dashboard.html":{"url":"Users/Dashboard.html","title":"Dashboard","keywords":"","body":"Dashboard\nThe dashboard is the first page that you see after being logged in. It contains an overview of all datasets that you have access to.\n\nMenu access to different information pages\nYou can always navigate to other parts of the application, simply by clicking on the user icon on the top right corner \n\nFiltering Datasets\nYou can currently filter across 5 different fields:\n\nLocation (= field creationLocation)\nGroups (= field ownerGroup)\nType (=field type - e.g. raw data or derived data )\nKeywords =field keywords, the tags added to the datasets)\nStart - End Date ( = field createdAt, show datasets captured between the dates that you have set)\n\nThe text fields provide an auto completion, which becomes visible as you type. \nOne click on the date calendar selects the start date and a second selects the end date. Make sure you select 2 dates.\nIn the following screenshot the datasets are filterd by the condition ownerGroup=\"p17301\"\n\nSearching\nThe text field at the top of the navigation bar allows you to search the metadata for any word contained in the metadata (but not arbitrary substrings). The search starts automatically when to start to type in this textfield, so better type fast ;-) \nConfigure table columns\nThe cog wheel symbol on the top right allows to define the columns, that you want to see in the table\nView Details\nTo view a dataset simply click on it in the table and a more detailed view will load (this is covered in the next section)\n"},"Users/Dataset_Details.html":{"url":"Users/Dataset_Details.html","title":"Dataset Details","keywords":"","body":"Dataset Details View\nTo view a dataset simply click on it in the table in the dashboard and a more detailed view will load\n\nFrom here you can navigate to the Datafiles File Listing View as well to a view, where you can add image attachments\nYou can also navigate to a Jupyterhub instance, by clicking the orange button at the top of the page. This leads you (if configured) to a Jupyterhub site, where you can login in order to analyze the data in the meta data catalog. This is mainly meant as a way to pursue statistical analysis on the meta data.\nE.g. at PSI you will be given a set of predefined Jupyter notebooks, which retrieve the data contained in the data catalog by calling the data catalog interfaces (API server) and then provide pivot tables and graphs of the data, such as accumulated data over time etc. \n"},"Users/Dataset_Details_Files.html":{"url":"Users/Dataset_Details_Files.html","title":"Dataset Filelisting","keywords":"","body":"Dataset File Listing View\nTo view the files contained in a dataset go to the \"Datafiles tab\" in the dataset details view. This will then show the files\n\n"},"Users/Dataset_Attachments.html":{"url":"Users/Dataset_Attachments.html","title":"Dataset Attachments","keywords":"","body":"Dataset Attachments\nOn the dataset details page, you can click on the Attachments tab\n\nSimply follow the instructions to upload an image. The size is restricted to be below 16 MB however\nThis short video demonstrates how you can add an attachment to your dataset.\n"},"Users/View_Raw_Metadata.html":{"url":"Users/View_Raw_Metadata.html","title":"View Raw JSON data","keywords":"","body":"View Raw Metadata\nTo see all details of the stored metadata as a raw JSON format, just click the \"SHow Metadata\" button on the bottom of the dataset details page\n\n"},"Users/Edit_Scientific_Metadata.html":{"url":"Users/Edit_Scientific_Metadata.html","title":"Edit Scientific Metadata","keywords":"","body":"Edit Scientific Metadata\nIf enabled, fields in the scientific metadata can be modified and edited by the owner of the data by hitting the \"Edit\" Icon. The user can add,remove or change metadata fields, every change will create a new record in the databse with it's history.\n\nThis short video demonstrates how you can edit the metadata.\n"},"Users/Publishing.html":{"url":"Users/Publishing.html","title":"Dataset Publishing","keywords":"","body":"Publishing the data\nData that is known to the data catalog can be published. The publication workflow does the following:\n\nIt defines a set of datasets to be published\nIt assigns metadata relevant to the publication of the datasets, such as author, abstract etc\nIt assigns a digital object identifier DOI to the published data, which can e.g. be used to link from a journal article to the data\nIt makes the data publicly available by providing a landing page that describes the data.\nIt publishes the DOI to the worldwide DOI system , e.g. from Datacite\n\nSo the first step is to select the datasets, that should be published\n\nThen by hitting the \"Add to Cart\" button these datasets are available in the Cart. This step can be repeated to add further datasets. Once the selection is finished you open the Cart ( click on the Cart symbol and choose actions ) to see the selected datasets:\n\nHere you can still change your selection, remove datasets etc. Once this is finished simply hit the publish button. This leads you to the following screen:\n\nSome of these fields will be pre-filled with information derived from the proposal data, such as the abstract. Independent of the pre-filling you can change the contents as you like until you are satisfied. Then hit the publish button, which leads you to the resulting display page:\n\nInitially the status field is in state \"pending\". This means, the published data information has been stored, but not yet made public to the worldwide DOI system, and no landing page has been created yet. This gives the possibility, that (potentially another person) can have a look at the data and do further editing by hitting the Edit button:\n\nOnce this is finished one can hit the \"register\" button (not shown in previous screenshot, because already in state registered)to register the DOI and thus making the data public. The resulting public landing page for this data then looks somethink like this\n\nFinally you can have a look at all the published data by going to the Published Data menu item (again by clicking the user icon at the top right corner and choosing \"Published Data\"):\n\nThis short video demonstrates how you can add an attachment to your dataset and publish the data.\n"},"Users/Jobs.html":{"url":"Users/Jobs.html","title":"Archival and Retrieval Jobs","keywords":"","body":"Archival and Retrieval Jobs\nIf enabled by your SciCat operator you can submit Jobs to both archive data to permanent (tape) storage or retrieve it from tere\nThis short video demonstrates how you can archive your data to tape.\nArchiving\nTo start archive jobs you best select datasets, which are in an archivable state by using the appropriate radio button on top of the dataset table. The state archivable means, that there exist files for this dataset and the dataset has not yet been archived already.\n\nThen you select the datasets you want to have archived and hit the Archive button on the top right\n\nAfter a confirmation dialog a archive job will be submitted, which will take care e.g. of copying the data from disk to tape (status at PSI). It is up to the SciCat operator to define the detail action which will be triggerd by such a Job, see the Operator manual for details\nAs an alternative way of selecting the files you can first fill the Cart with the selected datasets, then go to the Cart to \"checkout\", and choose the proper action from the list of available actions, which could be archive, retrieve, publish etc\n\nRetrieval\nIn the case of dataset retrieval you do the same steps, just select Datasets in retrievable state and make your selection, then hit the Retrieve button. This will copy the data from tape to an intermediate central storage , from where you can copy the data to the final destination of your choice, using either a command line tool or a GUI or simply an rsync client (this describes the status at PSI, your Site may have decided to do something else)\nJob results\nYou can check the status of the Jobs submitted in the Jobs menu item , which you can select via the menu dropdown (click on the users icon on the top right, see dashboard info ). YOu can see both your own Jobs and other peoples jobs\n\nPolicy Configuration for Archiving and Retrieving\nYou can define for each proposal a default behaviour concerning the treatment of the data in case of archiving and retrieval. The following tables shows the settings for each proposal/pgroup\n\nMultiple of these settings can be edited simultaneously by first selecting the rows\n\nAnd then editing them:\n\n"},"Users/SampleData.html":{"url":"Users/SampleData.html","title":"Sample Data Entry","keywords":"","body":"Sample Data Entry\nThe sample data interface allows samples to be added by users. \n\n"},"Operator/":{"url":"Operator/","title":"Operator Guide","keywords":"","body":"Overview\nGetting SciCat up and running at your site should be rather straight forward for a test deployment. However turning into a production ready system may involve a bit more work, because different existing systems will need to be interfaced to SciCat\nUnderstanding the Components\nFor the subsequent sections it will be useful to have a \"helicopter\" overview of the various components that need to play together seamlessly. The following diagram shows these components and also shows potentially existing components at your site, that you would likely want to interface to SciCat. The specific diagram reflects essentially the situation at PSI as of Sept. 2020. Of course your situation may look different. The diagram should therefore be seen as an example , which you need to adapt to your situation.\n\nBackend\nAt the heart of the SciCat architecture there is the REST API server. This is a NodeJS application that uses the Loopback 3 framework to generate RESTful APIs from JSON files that define models (such as: Users, Datasets, Instruments etc). Following the Swagger/OpenAPI format SDKs can be generated in almost any language.\nThe persistence layer behind this API server is a MongoDB instance, i.e an open source, NoSQL, document-based database solution. The API server handles alll the bi-directional communication from the REST interface to the Database.\nThese two components together comprise the \"backend\" of the architecture.\nFrontend\nTo the REST server an arbitrary number of \"clients\" (frontends) can be connected. One of the most important clients is the web based GUI frontend. This allows to communicate with the data catalog in a user friendly way. It is based on the Angular (9+) technology and uses ngrx to communicate with the SciCat API and provide a searchable interface for datasets, as well as the option to carry out actions (i.e. archiving).\nIn addition to the GUI other clients exist, such as command line (CLI) clients (example exist written in GO and Python) or desktop based GUI applications based on Qt. The CLI tools are especially useful for automated workflows, e.g. to get the data into the data catalog. This process is termed \"ingestion\" of the data. But they can also be used to add the data manually, especially for derived data, since this part of the workflow is often not possible to automate, in particular in truly experimental setups.\nMessaging infrastructure\nAs a way to loosely couple external systems the API server can be connected to messaging system. In particular RabbitMQ (used at PSI) and Apache Kafka are in use. Such systems can e.g. be used to interface to an tape archive system. To add the specific business logic you can e.g. add your own scripting layer. At PSI however a Node-RED based solution proved to be a stable and flexible platform for this purpose. Node-RED is a A NodeJS based visual programming tool to handle flows of data from one source to another. The following shows the Nod-RED flow used for communicating job requests to the PSI archive system\n\nPublishing Server\nIn order to publish data you need to run a landing page server and you need to assign DOIs to your published data. Since the API server may be operated in an intranet, with no access to the internet the following architecture was chosen at PSI:\nAn OAI-PMH server is running in a DMZ connected to a local Mongo instance. At publication time the data from SciCat is pushed to the external OAI-PMH server.\nFrom this server the landing page server can fetch the information about the published data. Also external DOI systems connect to this OAI-PMH server to synchronize the data with the world wide DOI system.\nIf a user wants to download the full datasets of the published data, the data is copied from the internal file server to a https file server (acting as a cache file server) , which subsequently allows anonymous download of the data\nChat Server as electronic logbook\nTODO ESS\nExternal systems\nThe most important external system that you want to connect to is likely your local identity management (IDM) system, e.g. an LDAP or AD based solution. This is usually the source of both the authentication and the authorization information, the latter being realized by simple group membership of the users.\nAnother important external system that you may want to connect, is your local proposal handling system (such as DUO at PSI). This allows population of proposal metadata, such as proposal id, title, and additional access permissions. If you do not have such a system you can fill the Proposal Information also by hand, or just leave it empty. If you have such a system you need to write scripts which synchronize the data in these systems with the corresponding tables in the SciCat Database, i.e. the Proposal collection.\nFinally, external storage systems, in particular those dealing with storage on tape, are good candidates for external systems.\nThe SciCat architecture does not require any connection to the file contents, since it deals with metadata only. However, the GUI can link to storage systems, which offer to download data using the https protocol.\nUnderlying Infrastructure\nYou may or may not run the infrastructure as part of a Kubernetes cluster. E.g. at PSI the API server, the GUI application, RabbitMQ and the Node-RED instances are all deployed to a Kubernetes cluster, whereas the Mongo DB ist kept outside Kubernetes.\nKubernetes is not necessary to have, but can simplify operations. Also, the separation into internet and intranet zones can be defined as required. You can, of course, operate the whole infrastructure directly in internet accessible servers, if security policies permit.\n"},"Operator/Step_by_Step_Setup.html":{"url":"Operator/Step_by_Step_Setup.html","title":"Step by Step instruction","keywords":"","body":"Step by Step manual to Setup SciCat without Containers\nSetup Backend\nMongo\nThe first part is to setup a Mongo DB, if not yet existing. You will need at least version 3.4 . In the simplest case a simple\nyum install mongodb-server\n(on RedHat systems) or similar will suffice.\nA production ready setup however may require to setup a replicated DB server. Follow the Mongo DB Manuals in this case. E.g. at PSI we operate a 3 fold relicated Mongo DB server.\nRemark for Windows\nIt is possible to run a SciCat development and test instance on Windows.\nYou can use the Chocolatey package manager: https://chocolatey.org/\nchoco install mongodb –pre\nChocolatey starts the mongodb service automaticly. The default port is 21017.\nInstall MondoDB Shell\nchoco install mongodb-shell\nHint to add full text indexing\nMongo DB needs to work with Indices to speed up the queries. All of these index definitions are created automatically, with one exception. This exception is described here: the following command must be executed after the database has been created in MongoDB, i.e. after starting the backend once.\nLog into mongodb at the console, e.g. run \nIf not using Kubernetes\nmongo dacat\nin Windows cmd or powershell\nmongosh dacat\nor, if using Kubernetes\nkubectl exec -it mongo dacat\nand enter\ndb.Dataset.createIndex( { \"$**\": \"text\" } )\n\nNote: If the index is not created as above, frontend e2e test fails.\nAPI Server\nPrerequisites\nFirst you need to have node/npm installed\nnpm version 6 or higher\nNode version 10 or higher\nThe needed database will be created automatically when the API server starts. \nGet code\ngit clone https://github.com/SciCatProject/backend.git\ncd backend\ngit checkout master # (or develop)\nnpm install\nPlease note: the master branch is often quite behind the development, therefore to get all new features you may want to use the develop branch instead.\nSetup configuration\nThere are 5 configuration files that need to be adjusted to your situation. There are example configuration files for each of the 4 configuration files inside the server directory\n\nfunctionalAccounts.json : defines the functional accounts, such as admin, ingestor etc\ndatasources.json : defines the connection to the Mongo DB -sample datasources.json\nproviders.json : defines the authentication source for user accounts, e.g. how to link to your local LDAP/AD server\nconfig.local.js : defines the API server details, API root address, your local site name and policy settings for , the PID prefix for your datasets, the type of message broker to use, the connection to an email send server etc\ncomponent-config.json: defines the Express framework components, in particular if the explorer is enabled and optionally definition of the RabbitMQ topology, if used.\n\nAdjusting these settings to your infrastructure should be straight forward. In case you do not understand a setting just leave it at its default.\nNote for Windows\nCreate an .env file based on .env.example and use 127.0.0.1 instead of localhost\nMONGODB_URI=\"mongodb://127.0.0.1:27017/dacat\" \nEmail Notifications\nWhen jobs have been submitted successfully, the node-mailer package can send an email to the user that initiated.\nThe details are loaded into server/config.local.js and the following block provides an example:\nsmtpSettings: {\n host: 'HOSTPATH',\n port: 587,\n secure: false,\n auth: {user: DOMAIN\\\\USER', pass: 'PWD'}\n },\n smtpMessage: {\n from: 'gac-dacats@psi.ch',\n to: undefined,\n subject: '[SciCat]',\n text: undefined // can also set html key and this will override this\n }\nStart metadata server\nnpm run start\nTest functionality\nnpm run test\nYou can in addition test the API server using the \"explorer\", e.g if you run the instance locally point your browser to https://localhost:3000/explorer (note the explorer might be disabled for production environments, check the component-config.json file)\n\nYou can use this web interface to test all the available API end points. If you test endpoints, which are protected by authentication, you need to set an accessToken field on top of the explorer web page. You can login in frontend web UI and find the token under \"username->settings\".\nSetup GUI frontend\nThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.\nPrerequisites\nWhat things you need to install the software and how to install them\nAngular v8 or higher\nnpm version 6 or higher\nnode version 10 or higher\nInstalling\nUse git to clone the repository\ngit clone https://github.com/SciCatProject/frontend\ncd frontend\nFirst install the required modules\nnpm install\nThen build with the Angular CLI\nnpx ng build\nYou can deploy a test server with the following command:\nnpx ng serve\nRunning the unit tests\nTo run the unit tests, type:\nnpm test\nEnd to end tests\nTo run the end to end tests, type:\nnpm run e2e\nConfiguration\nYou can fine tune the features that you want to use in the GUI by setting the flags inside the src/environments folder. In this folder you find several example configuraion files used by the Sites. Please adjust to your needs. The angular.json file contains a section \"configurations\" which defines a set of prepared configurations, that you can choose from. This defines which files will be picked up at build time. The build system defaults to the environment.ts, but if you e.g. do ng build --env=development then environment.development.ts will be used instead. Here is an example configuration file\nThe most important configuration setting is the connection to the API server\nlbBaseURL: \"https://your-loopbak-api-server\",\nMany of the other settings are boolean flags, which allow you to switch on/off features of the GUI\nAgain, if in doubt, just leave the setting as defined in the available example environment files or leave them empty.\nOptional Extras\nConnecting to RabbitMQ\nSome extra configuration needs to be done in order to get Scicat talking to RabbitMQ. The rabbitmq mechanism is used to trigger asynchronous jobs\nthrough the archive and retrieve methods. \nSetting up RabbitMQ\nWhen using RabbitMQ with Scicat it needs to be configured initially, before the rest of the Scicat services are run. \nIt must be set up with a user before the backend is started, otherwise the backend cannot connect. \nIt is easy to set up a local rabbitmq server as a separate service before starting Scicat through docker compose.\nA basic docker compose for rabbitmq looks like:\nversion: \"3.8\"\nservices:\n local-rabbitmq:\n hostname: local-rabbitmq\n image: 'bitnami/rabbitmq:latest'\n labels:\n kompose.service.type: nodeport\n ports:\n - '4369:4369'\n - '5672:5672'\n - '25672:25672'\n - '15672:15672'\n volumes:\n - 'rabbitmq_data:/bitnami'\nvolumes:\n rabbitmq_data:\n driver: local\nThis set up gives you both a rabbitmq service and a Management Portal which can be useful for debugging.\nBefore connecting to the backend or the Management Portal a user with administration privileges needs to be configured manually which can be done via CLI. \nTo understand more about setting up rabbitmq please see the documentation at https://www.rabbitmq.com/cli.html.\nIf you have followed the set up with docker, the following commands can be executed in the docker container to set up initial users and the management portal.\n# To add the management plugin\nrabbitmq-plugins enable rabbitmq_management\n\n#to add a user\nrabbitmqctl add_user \nrabbitmqctl set_user_tags administrator\nrabbitmqctl set_permissions -p / '.*' '.*' '.*'\nOnce this is done you can log into the Management Portal hosted at http://localhost:15672. Any other users, exchanges , queues etc. can be configured manually.\nConnecting Scicat to RabbitMQ\nThe backend handles the connection to rabbitMQ through the component-config.local.json file. The backend will set up all the relative queues and exchanges in a running service, but the service must contain a user with administrative privileges\nbefore running the backend. A basic rabbitMQ set up in the component-config.local.json file looks like the following:\n\"topology\":{\n\"connection\":{\n\"uri\":\"amqp://my-admin-user:newpassword@local-rabbitmq\" # the uri of your service with your administrative user\n},\n\"exchanges\":[\n{\n\"name\":\"jobs.write\",\n\"type\":\"topic\",\n\"persistent\":true\n}\n],\n\"queues\":[\n{\n\"name\":\"client.jobs.write\",\n\"subscribe\":true\n}\n],\n\"bindings\":[\n{\n\"exchange\":\"jobs.write\",\n\"target\":\"client.jobs.write\",\n\"keys\":[\n\"jobqueue\"\n]\n}\n]\n}\n}\nThe exchange and the binding key must be named jobs.write and jobqueue respectively, the queue name can take any value. \nThe backend expects and exchange called jobs.write to publish jobs messages to bound with that key.\nUse in Practice\nIf you wish to have two Scicat services connecting to the same RabbitMQ server (e.g. scicat-dev and scicat-prod) you \ncould generate two queues bound to the jobs.write exchange. However these would be bound with the same key that\ncould cause cross talk. RabbitMQ allows you to set up any number of virtual hosts within the same server therefore it is\nbest to set up two vhosts (e.g. dev and prod) with the exchange, queues and bindings set as in the component-config.local.json file above.\n"},"Operator/Step_by_Step_Setup_Docker.html":{"url":"Operator/Step_by_Step_Setup_Docker.html","title":"Deploy with Docker Compose","keywords":"","body":"Setup SciCat with Docker Compose\nThe scicatlive repo defines a quick and convenient method for getting started. It sets up the Mongo DB , backend and frontend, all using docker containers, starts the containers and connects them , all with a single command. This is also a convenient method to contribute to the development of this project easily.\n"},"Operator/Step_by_Step_Setup_Kubernetes.html":{"url":"Operator/Step_by_Step_Setup_Kubernetes.html","title":"Deploy with Kubernetes","keywords":"","body":"Setup SciCat with Kubernetes\nThe procedure is described in the localdeploy repository\nIt uses a minikube based Kubernetes system as the main deployment platform. It should run both on Linux and Mac Systems and is a convenient way to get a full \"production like\" system running on your laptop.\n"},"Operator/Step_by_Step_Setup_Zip_Service.html":{"url":"Operator/Step_by_Step_Setup_Zip_Service.html","title":"Deploy Zip Service","keywords":"","body":"Step by Step manual to Setup the Zip Service\nSetup Zip Service\nThe zip service repository can be found here.\nTo install it, you first have to add a configuration file (local.config.json) to the source directory. A sample for this file can be found in CI/ESS or in the description of the repository.\nThen build the image and run it.\nJWT\nIn order for SciCat to authenticate against the zip service, the jwt secret has to be configured the same in both services. The jwt secret for the zip service can be configured in the file local.config.json. The configuration file containing the jwt secret for the SciCat backend can be found under the backend filepath:\n/home/node/app/server/config.json\nVolumes\nThe data volumes have to be mounted to the zip service in order for it to access the files, zip them and offer them for download. The filepath, stored in the OriginalDatablock and Datafile objects, have to match a valid path on the zip service.\n"},"Ingestor/":{"url":"Ingestor/","title":"Ingestor Guide","keywords":"","body":"Ingesting Data into SciCat\nThis manual collects various approaches for ingesting data\n\nIngest Manual from PSI : contains partly tools, which are not yet open sourced, but soon will\nIngest Instructions from ESS\nMinimalistic example via CURL\n\n"},"Ingestor/ingestManual.html":{"url":"Ingestor/ingestManual.html","title":"Ingest Manual PSI","keywords":"","body":"\nTable of Contents\n\n\n1. Overview and Concepts\n2. The Concept of Datasets\n3. Getting started\n4. Ingest\n\n4.1. Important Update since April 14th 2022\n4.2. Definition of input files\n4.3. Definition of metadata\n4.4. Special notes for the decentral use case\n\n4.4.1. For Windows\n4.4.2. For Linux.\n\n\n4.5. Summary of the different use cases\n\n\n5. Archive\n6. Retrieve\n\n6.1. First Step\n6.2. Second Step (for Linux)\n\n6.2.1. Standard commands\n6.2.2. Expert commands\n\n\n6.3. Second Step (for Windows)\n\n\n7. Ingest, Archive and Retrieve with QT desktop application SciCat\n\n7.1. Important Update since April 14th 2022:\n7.2. General considerations\n7.3. Getting started\n7.4. Login and permissions\n7.5. Pgroup selection\n7.6. Archiving\n7.7. Retrieval\n7.8. Settings\n\n\n8. Publish\n9. Cleanup and Retention\n10. Troubleshooting\n\n10.1. Locale error message\n10.2. Invalid certificate messages\n10.3. Long Running copy commands\n10.4. Kerberos tickets\n10.5. Instructions to set ACLS in AFS\n\n\n11. Appendix\n\n11.1. Installation of Tools\n\n11.1.1. Access to the SciCat GUI\n11.1.2. Loading datacatalog tools on Clusters\n11.1.3. (Non-standard Linux systems) Installing datacatalog tools\n11.1.4. Installation on Windows Systems\n11.1.5. Online work stations in beamline hutches\n\n\n11.2. Dataset limitations\n11.2.1. Size limitations\n11.2.2. SourceFolder and file names limitations\n11.3. Recommended file structure for raw datasets\n11.4. Metadata Field Definitions\n\n11.4.1. Metadata field definitions for datasets of type \"base\"\n11.4.2. Additional fields for type=\"raw\"\n11.4.3. Additional fields for type=\"derived\"\n\n\n11.5. About Scientific Values and Units\n11.6. Use Case Examples\n\n11.6.1. Use Case: Manual ingest using datasetIngestor program\n11.6.2. Use Case: Automated ingest of raw datasets from beamline or instruments\n11.6.3. Use Case: Ingest datasets stored on decentral systems\n11.6.4. Use Case: Ingest datasets from simulations/model calculations\n\n\n11.7. Policy settings and email notifications\n11.8. Analyzing Metadata Statistics\n\n11.8.1. Overview\n11.8.2. Getting started\n\n\n11.9. Access to the API (for script developers)\n11.10. Using datasetIngestor inside wrapper scripts (for developers)\n11.11. Ingestion of datasets which should never be published\n11.12. Retrieving proposal information\n11.13. Link to Group specific descriptions\n11.14. List of known creationLocation for raw data\n\n11.14.1. SLS\n11.14.2. Swissfel\n11.14.3. SINQ\n11.14.4. SmuS\n\n\n\n\n12. Update History of Ingest Manual\n\n\n\n\n\nOverview and Concepts\nPSI offers a Data Catalog Service for annotated long-term data storage\n, retrieval and publishing. The annotation information , i.e. metadata\nis stored in a central database to allow for fast query for the\ndata. The raw data itself is stored on the PetaByte Archive at the\nSwiss National Supercomputing Centre (CSCS). The Data Catalog and\nArchive is designed to be suitable for:\n\nRaw data generated by PSI instruments or simulations\nDerived data produced by processing the raw input data\nData required to reproduce PSI research and publications, e.g FAIR data\n\nAll data which are added to the data catalog must either not be\nclassified or have a classification level of \"normal\".\nYou are not allowed to add any personal or private data. You are not\nallowed to use the data catalog as a backup system. Data must come\nfrom scientific activities pursued at PSI. If data from external\npartner institutes should be stored, then this needs a dedicated\ncontract signed by the management.\nThe service is based on the catalog system SciCat, documented at\nhttps://scicatproject.github.io/ and\nhttps://github.com/SciCatProject/, which is an open source system that\nallows to ingest and retrieve datasets in different ways, matching the\nrequirements of the respective use cases. The use cases differ in the\nlevel of automation provided.\nData is always stored in terms of datasets, which you can think of as a\ncollection of files combined with administrativ and scientific metadata.\nThis manual describes how you can use this services by following the\nmain steps in the lifecycle of the data management:\n\nDefinition and ingestion of metadata\nArchiving of the datasets\nRetrieving of datasets\nPublishing of datasets\nRetention of datasets\n\nNote: as of today (June 2021) the services can be only be used from\nwithin the PSI intranet with the exception of the published data,\nwhich is by definition publicly available. Although the service itself\ncan be used from any operating system, the command line and\nGUI tools currently offered are available only for Linux and Windows\nplatforms.\nThe Concept of Datasets\nFor the following it is useful to have a better understanding of the\nconcept of a dataset. A dataset is a logical grouping of potentially\nmany files. It is up to the scientist to define datasets from the\nfiles. When defining datasets take the following conditions into\naccount\n\na dataset is the smallest unit for adding meta data\na dataset is the smallest unit for data handling (archiving and retrieval)\na dataset is the smallest unit for publication (DOI assignmnet)\n\nTherefore you need to find a compromise between putting too few or too\nmany files into a single dataset. \nIngestion of datasets means, that you make data known to the data\ncatalog by providing both metadata about the dataset and the file\nlisting comprising the dataset. For each dataset a persistent\nidentifier (PID) is automatically created.\nIt is important to note that the data catalog is a \"passive\" system in\nthe sense that it has to be told if new data arrives. The data catalog\nhas no direct access to the file systems containing the actual\nfiles. In contrast the datasetIngestor program is run from systems, which\nhave access to the data files.\nThe datasets always belong to an so called ownerGroup. Only members of\nthese groups have access to the data, unless the dataset is being\npublished. At PSI there are two types of ownerGroups,\n\npgroups, starting with letter \"p\". They are used for experimental\ndata linked to a proposal system. They are managed by the digital\nuser office DUO\na-groups, starting with \"a-\" for any other data to be archived\n\nOnce data is contained in the data catalog, this information is\nconsidered to be stored permanently. However after a retention period\nthe connected raw data files may actually be deleted. In this case the\ndataset is marked as deleted in the data catalog, but the data\ncatalog entry persists, in agreement with the FAIR principles.\nWarning: you should not modify the files which make up your dataset\nafter the dataset was ingested to the datacatalog. This means that you\nshould ingest the data only, if you are sure that no further\nmodifications on the files take place. The subsequent archive job will\nonly take care of the files which existed at ingest time and otherwise\nreturn an error message and not archive the data at all.\nGetting started\nYou will need a PSI account and this account needs to be member in so\ncalled p-groups, which are managed by the PSI digital user office\nproposal system and are usually linked to a principal investigator\n(PI). This is required to define the authorization to the data,\ni.e. who is allowed to see which datasets.\nIn addition to these so called user accounts there are a couple of\npre-defined functional accounts which are used for automated\nprocesses. In particular each beamline has one such functional\naccount, e.g. called sls-tomcat, which can be used for automated\ningestion of new data or to query all data generated at a given\nbeamline. These accounts are only defined in the data catalog system\nand are given to the respective beamline managers.\nIf your data can not be linked to this proposal system you can still\nuse the services, but you may need to ask for the creation of a\nso-called a-group and become member in these groups. You can order\nan a-group via ServiceNow under PSI Service Catalog -> IT ->\nIdentity & Access Management -> Order Group / Project drive. Under\nAccount Type choose Archive Group / Project Drive. You will be\nasked about the group members. The group owner is not automatically\nadded to the group members.\nTo use some of the software you may need to install it\nfirst. Installation is described in the appendix Installation of Tools\nIngest\nImportant Update since April 14th 2022\nFor all commandline tools, like the datasetIngestor, datasetRetriever\n etc, using your own user account you have to use the –token\n option with a predefined API token SCICAT-TOKEN. Specifying\n username/password is not possible for normal users (this limitation\n is caused by the switch to a new authentication protocol). The\n easiest to get such an API token is to sign it at\n https://discovery.psi.ch, then follow the \"Login with PSI account\"\n button. This will bring you to the user settings page, from where\n you can copy the token with a click on the corresponding copy\n button. \nFor functional accounts, like beamline accounts you can\nhowever continue to use username/password authentication instead.\nDefinition of input files\nFirst you need to specify the location of the files that you want to\nhave stored as one dataset. A typically example would be all the files\ntaken during a measurement, a scan etc or all output data from an\nanalysis of raw data files. In the simplest case it is sufficient to\ndefine only one location. i.e. the sourceFolder, which should\ncontain all the files (and only those files) that make up the\ndataset. In a more general case you can also specify an explicit list\nof files and/or directories that you want to have assembled to a\ndataset. See the datasetIngestor command options for details. The\nappendix has a Recommended file structure for raw datasets on\ndisk. Please take note of the limitations of a dataset, as\ndefined in the appendix Dataset limitations.\nDefinition of metadata\nThere are two types of metadata which need to be provided:\n\nadministrative metadata: specifies when and where the data is taken,\nwho is the owner etc. There are both mandatory and optional fields\nand the fields depend on the type of the dataset\n(generic/raw/derived), see Section 11.4\nbelow. The most important metadata field for ownership is the value\nof the \"ownerGroup\" field, which defines a group name, whose member\nhave access to the data.\nscientific metadata: this depends on the scientific discipline and\ncan be defined in a flexible way by respective research group. It is\nup to the research groups to define the format(s) of their data that\nthey want to support, ideally on an international level. See also\nthe section About Scientific Values and Units .\n\nTherefore the next step to ingesting your data into the catalog is to\nprepare a file describing what data you have. This is called\nmetadata.json, and can be created with any text editor. It can in\nprinciple be saved anywhere, but keeping it with your archived data is\nrecommended.\nHere is a minimalistic example the file metadata.json for raw data:\n{\n \"creationLocation\": \"/PSI/SLS/TOMCAT\",\n \"sourceFolder\": \"/data/p16/p16623/June2020\",\n \"type\": \"raw\",\n \"ownerGroup\":\"p16623\"\n}\nIn the Appendix Use Case Examples you find many more examples for\nmetadata.json files, both for raw and derived data. Here is a more\nreal life example from Bio department:\n{\n \"principalInvestigator\": \"albrecht.gessler@psi.ch\",\n \"creationLocation\": \"/PSI/EMF/JEOL2200FS\",\n \"dataFormat\": \"TIFF+LZW Image Stack\",\n \"sourceFolder\": \"/gpfs/group/LBR/pXXX/myimages\",\n \"datasetName\": \"myimages\",\n \"owner\": \"Wilhelm Tell\",\n \"ownerEmail\": \"wilhelm.tell@psi.ch\",\n \"type\": \"raw\",\n \"description\": \"EM micrographs of amygdalin\",\n \"ownerGroup\": \"a-12345\",\n \"scientificMetadata\": {\n \"sample\": {\n \"name\": \"Amygdalin beta-glucosidase 1\",\n \"uniprot\": \"P29259\",\n \"species\": \"Apple\"\n },\n \"dataCollection\": {\n \"date\": \"2018-08-01\"\n },\n \"microscopeParameters\": {\n \"pixel size\": {\n \"value\": 0.885,\n \"unit\": \"A\"\n },\n \"voltage\": {\n \"value\": 200,\n \"unit\": \"kV\"\n },\n \"dosePerFrame\": {\n \"value\": 1.277,\n \"unit\": \"e/A2\"\n }\n }\n }\n}\nFor manual creation of this file there are various helper tools\navailable. One option is to use the ScicatEditor\nhttps://bliven_s.gitpages.psi.ch/SciCatEditor/ for creating these\nmetadata files. This is a browser-based tool specifically for\ningesting PSI data. Using the tool avoids syntax errors and provides\ntemplates for common data sets and options. The finished JSON file can\nthen be downloaded or copied into a text editor.\nAnother option for datasets on ra or merlin is to use the SciCat\ngraphical interface from NoMachine. This provides a graphical\ninterface for selecting data to archive. This is particularly useful\nfor data associated with a DUO experiment and p-group. Type SciCat to\nget started after loading the datacatalog module. The GUI also\nreplaces the the command-line ingestion decribed below.\nAfter preparing your metadata.json file, run the following steps to\ningest the data. First, perform a \"dry-run\" that will check the\nmetadata for errors: ( Please note that in the following only the\nLinux type notation is used. For the changes which apply to Windows\nsee the separate section below)\ndatasetIngestor metadata.json\nIt will ask for your PSI credentials and then print some info\nabout the data to be ingested. This command will scan the files, make\nchecks and extract further metadata information from the files and\nfrom the DUO system, unless the corresponding metadata fields are\nalready provided in the metadata.json file. If there are no errors,\nproceed to the real ingestion:\ndatasetIngestor --ingest metadata.json\nFor particularly important datasets, you may also want to use the\nparameter –tapecopies 2 to store redundant copies of the data.\nTo give some numbers, 0.2–0.4% of the tapes get damaged so \nthere is a chance that archiving with only one copy will result in lost \ndata, in very few cases. Keep in mind that archival with redundancy \ndoubles the cost which is billed to the responsible department.\nYou may be asked whether you want to copy the data first to a central\nsystem. This step is needed for all files which are not residing on\none of the central fileservers at PSI. In particular local (windows)\nworkstations/PCs are likely to fall in this category.\nThere are more options for this command, just type\ndatasetIngestor\nto see a list of available options. In particular you can define\nexplicit list of files to be combined into a dataset, which can come\nfrom many different folders by providing a filelisting.txt file\ncontaining this information in addition to the metadata.json file. The\nsection in the Appendix Using the datasetIngestor Tool has more details\nSpecial notes for the decentral use case\nFor Windows\nFor Windows you need execute the corresponding commands inside a\npowershell and use the binary files ending in .exe, e.g.\ndatasetIngestor.exe -token SCICAT-TOKEN -user username:password -copy metadata.json\nFor Windows systems you can only use personal accounts and the data is\nalways handled as decentral case, i.e. the data will first be copied\nfrom the windows machine to a central file server via scp\nfirst. Therefore you need to specify all of the above parameters\n-token, -user and -copy. \nPlease also note the syntax, that has to be used for the definition of\nthe sourceFolder inside the metadata.json file: this has to be in the\nfollowing form:\n\"sourceFolder\": \"/C/Somefolder/etc\",\n, i.e. forward slashes and no colon \":\" after the drive letter like\n\"C:\" in this case.\nFor Linux.\nYou must have a valid kerberos ticket in order to be able to copy the\ndata to the intermediate storage server. You can use the kinit command\nto get this ticket.\nSummary of the different use cases\nThe following table summarizes the different use cases\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOS\nsourceLocation\nAccount-Type\nNeededed parameters\nComment\n\n\n\n\n\nLinux\ncentral\nUser\ntoken\nFetch token via Web GUI discovery.psi.ch\n\n\n\n\nLinux\ncentral\nFunctional\nusername/pw\nThe tool fetches token from API server\n\n\n\n\nLinux\nanywhere/decentral\nUser\ntoken + Kerb ticket\nToken for API, Kerb ticket for copying data\n\n\n\n\nLinux\nanywhere/decentral\nFunctional\nnot supported\nFunctional accounts not existing on ssh server\n\n\n\n\nWindows\ncentral\nUser\n(token)\nNeeds mounting of Windows FS to Arema\n\n\n\n\nWindows\ncentral\nFunctional\n(username/pw)\ndito\n\n\n\n\nWindows\nanywhere/decentral\nUser\ntoken + username/pw\nToken for API, username/pw for copying data\n\n\n\n\nWindows\nanywhere/decentral\nFunctional\nnot supported\nFunctional accounts not existing on ssh server\n\n\n\n\nArchive\nIf there are no errors, your data has been accepted into the data\ncatalog! From now on, no changes should be made to the ingested\ndata. This is important, since the next step is for the system to copy\nall the data to the CSCS Petabyte archive. Writing to tape is slow, so\nthis process may take some time, and it will fail if any\nmodifications are detected.\nTriggering the copy to tape can be done in 3 ways. Either you do it\nautomatically as part of the ingestion\ndatasetIngestor --ingest --autoarchive metadata.json\nIn this case directly after ingestion a job is created to copy the\ndata to tape. Your dataset should now be in the queue. Check the data\ncatalog: https://discovery.psi.ch. Your job should have status\n'WorkInProgress'. You will receive an email when the ingestion is\ncomplete.\nThe second method is to use the discovery.psi.ch to interactively\nstart the archive job: click on the \"Archivable\" button. You should\nsee the newly ingested datasets. Select all the datasets you want to\nhave archived and click 'Archive'. You should see the status change\nfrom 'datasetCreated' to 'scheduleArchiveJob'. This indicates that the\ndata is in the process of being transferred to CSCS. After some time\nthe dataset's status will change to 'datasetOnAchive' indicating the\ndata is stored.\nA third option is to use a command line version datasetArchiver.\ndatasetArchiver [options] (ownerGroup | space separated list of datasetIds) \n\nYou must choose either an ownerGroup, in which case all archivable datasets\nof this ownerGroup not yet archived will be archived.\nOr you choose a (list of) datasetIds, in which case all archivable datasets\nof this list not yet archived will be archived.\n\nList of options:\n\n -devenv\n Use development environment instead or production\n -localenv\n Use local environment (local) instead or production\n -noninteractive\n Defines if no questions will be asked, just do it - make sure you know what you are doing\n -tapecopies int\n Number of tapecopies to be used for archiving (default 1)\n -testenv\n Use test environment (qa) instead or production\n -token string\n Defines optional API token instead of username:password\n -user string\n Defines optional username and password\nRetrieve\nHere we describe the retrieval via the command line tools. A retrieve\nprocess via a desktop GUI application is described in the section SciCatArchiver GUI .\nRetrieving is two-step process: first the data is copied from tape to a\ncentral retrieve server. From there the data needs to be copied to the\nfinal destination system of your choice.\nFirst Step\nFor the first step: login to https://discovery.psi.ch , find the\ndatasets you want to retrieve and selected all \"Retrievable\" datasets\nby clicking the corresponding button. Finally click the retrieve\nbutton. This will create a retrieve job. Once it is finshed you will\nget an email. Depending on the size of your datasets this may take\nminutes (e.g. for 1GB) up to days (e.g for 100TB)\nSecond Step (for Linux)\nStandard commands\nFor the second step you can use the datasetRetriever command, which\nuses the rsync protocol to copy the data to your destination.\nTool to retrieve datasets from the intermediate cache server of the tape archive\nto the destination path on your local system.\nRun script with 1 argument:\n\ndatasetRetriever [options] local-destination-path\n\nPer default all available datasets on the retrieve server will be fetched.\nUse option -dataset or -ownerGroup to restrict the datasets which should be fetched.\n\n -chksum\n Switch on optional chksum verification step (default no checksum tests)\n -dataset string\n Defines single dataset to retrieve (default all available datasets)\n -devenv\n Use development environment (default is to use production system)\n -ownergroup string\n Defines to fetch only datasets of the specified ownerGroup (default is to fetch all available datasets)\n -retrieve\n Defines if this command is meant to actually copy data to the local system (default nothing is done)\n -testenv\n Use test environment (qa) (default is to use production system)\n -token string\n Defines optional API token instead of username:password\n -user string\n Defines optional username and password (default is to prompt for username and password)\nFor the program to check which data is available on the cache server\nand if the catalog knows about these datasets, you can use:\ndatasetRetriever my-local-destination-folder\n\n======Checking for available datasets on archive cache server ebarema4in.psi.ch:\n\nDataset ID Size[MB] Owner SourceFolder\n===================================================================\n0f6fe8b3-d3f1-4cfb-a1af-0464c901a24f 1895 p16371 /sls/MX/Data10/e16371/20171017_E2/cbfs/2017-10-17_22-28-30_Na108_thau7_100degs_dtz60_f_500_Hz_Eth0_6200_eV\n58f2037e-3f9b-4e08-8963-c70c3d29c068 1896 p16371 /sls/MX/Data10/e16371/20171017_E2/cbfs/2017-10-17_21-41-02_cca385a_lyso8_100degs_f_500_Hz_Eth0_6200_eV\ncf8e5b25-9c76-49a7-80d9-fd38a71e0ef8 3782 p16371 /sls/MX/Data10/e16371/20171017_E2/cbfs/2017-10-18_10-15-41_na108_thau6_50degs_lowdose_pos1_f_500_Hz_Eth0_6200_eV\ndf1c7a17-2caa-41ee-af6e-c3cf4452af17 1893 p16371 /sls/MX/Data10/e16371/20171017_E2/cbfs/2017-10-17_20-58-34_cca385a_lyso3_100degs_f_500_Hz_Eth0_6200_eV\nIf you want you can skip the previous step and\ndirectly trigger the file copy by adding the -retrieve flag:\ndatasetRetriever -retrieve \nThis will copy the files into the destinationFolder using the original\nsourceFolder path beneath the destinationFolder. This is especially\nuseful if you want to retrieve many datasets, which you expect to\nappear in the same folder structure as originally. \nS\nOptionally you can also verify the consistency of the copied data by\nusing the -chksum flag\ndatasetRetriever -retrieve -chksum \nIf you just want to retrieve a single dataset do the following:\ndatasetRetriever -retrieve -dataset \nIf you want to retrieve all datasets of a given ownerGroup do the following:\ndatasetRetriever -retrieve -ownergroup \nExpert commands\nIf you prefer to have more control over the file transfer you are free\nto type your own rsync commands, e.g. to simply the folders available\nin the retrieve cache do:\nrsync -e ssh --list-only pb-retrieve.psi.ch:retrieve/\nTo actually copy the data over use:\nrsync -e ssh -av pb-retrieve.psi.ch:retrieve/{shortDatasetId} your-destination-target/\nIn this case the shortDatsetId is the dataseid id without the PSI\nprefix, e.g. for dataset PID\n20.500.11935/08bc2944-e09e-48da-894d-0c5c47977553 the shortDatasetId\nis 08bc2944-e09e-48da-894d-0c5c47977553\nSecond Step (for Windows)\nThe second step for Windows is instead using the sftp\nprotocol. Therefore any sftp client for Windows, like e.g. Filezilla,\ncan then be used to retrieve the data to your local Windows PC. The\nfollowing connection information must be provided, taking the command\nline client access via powershell as an example\n# for the production system\nsftp -P 4222 your-username@pb-retrieve.psi.ch\n# or for the test system\nsftp -P 4222 your-username@pbt-retrieve.psi.ch\nAfter the connection is built up you can copy files recursively,\ne.g. using the \"get -r *\" command. With the filezilla GUI you can\nachieve the same via drag and drop operations\nIngest, Archive and Retrieve with QT desktop application SciCat\nImportant Update since April 14th 2022:\nYou currently first need to get a token before you can use SciCat: the\neasiest to get such an API token is to sign it at\nhttps://discovery.psi.ch, then follow the \"Login with PSI account\"\nbutton. This will bring you to the user settings page, from where you\ncan copy the token with a click on the corresponding copy button.\nGeneral considerations\nSciCat is a GUI based tool designed to make initial\ningests easy. It is especially useful, to ingest data, which can not\nbe ingested automatically. Therefore it is designed in particular to\nassist you when archiving derived datasets. Often, the archival of\nderived data cannot be scheduled in advance, nor does it follow a\nstrict file structure. The SciCat GUI can help you to ingest such\ndatasets more easily. Yet, the ingestion of raw datasets is also\nsupported. Additionally, the tool also allows for the convenient\nretrieval of datasets.\nGetting started\nCurrently, SciCat is supported on PSI-hosted Linux and Windows\nsystems and is accessible on the Ra cluster as part of the datacatalog\nmodule: just type\nmodule load datacatalog\nThen the software can be started with\nSciCat\nOn the SLS beamline consoles the software is also pre-installed in the\n/work/sls/bin folder, which is part of the standard PATH variable. \nIf you are not working on the Ra cluster you can download the\nsoftware on Linux:\n/usr/bin/curl -O https://gitlab.psi.ch/scicat/tools/raw/master/linux/SciCat;chmod +x ./SciCat\nOn Windows the executable can be downloaded from\nhttps://gitlab.psi.ch/scicat/tools/-/blob/master/windows/SciCatGUI_Win10.zip\nTo start the GUI, unzip the directory and execute SciCat.exe\nLogin and permissions\nAfter starting the GUI, you will be asked for a username and password. Please \nenter your PSI credentials. Functional accounts are not supported. \nPgroup selection\nThe first step is always to select the pgroup. If there is no proposal assigned to \nthis account, you will have to specify the information about the PI manually. \n\nArchiving\nAfter selection the files, you will be prompted with a metadata editor, where you can modify \nthe general info, such as dataset name, description etc. Please make \nsure that you select the correct data type (raw or derived). As a general rule of thumb, it is \na derived dataset if you can specify a raw dataset as input. If you want to ingest a derived dataset, \nyou can specify corresponding raw datasets on the \"Input datasets\" tab. \nTo edit scientific metadata, switch to \"Scientific metadata\" tab. \nRetrieval\nRetrieving successfully archived datasets from SciCat is a two-step process. First you will have to \nretrieve to an intermediate server. Once the data is there, you will be notified by email. \nThe final step is to copy the data to the final destination on your machine. \nBoth steps can be steered from within the GUI. \nOn the retrieve page, all datasets of your pgroup are listed. If the data has been archived successfully, \nthe cell in column \"retrievable\" is set to \"true\". To retrieve the data to the intermediate file server,\nselect the datasets that you want to retrieve and click on \"Retrieve.\" After the retrieval, the column\n\"retrieved\" is set to true. You are now able to start copying the data to you local machine by selecting\nthe desired datasets and clicking on \"Save.\" \nSettings\nAdditional settings, such as the default value for certain fields can be modified in settings panel (button\non the lower left corner). \nPublish\nAs part of a publication workflow datasets must become citable via a\ndigital object identifier (DOI). This assignment is done as part of\nthe publication workflow described below. The publication then can\nlink to these published datasets using this DOI. The DOIs can link to\nboth raw and/or derived datasets. The published data and therefore the\nDOI ususally refers to a set of Datasets, thus avoiding the need to\nlist potentially thousands of individual dataset identifiers in a\njournal publication.\nYou publish data in the following way: go to https://discovery.psi.ch ,\nlogin and select all the datasets, that you want to publish under a\nnew DOI. \n\nThen you add these datasest a a \"shopping cart\" by using the \"add to\nCart\" button. You can repeat this often as needed. Once finished with\nthe selection you can \"check out\" the cart (click on the cart in the\ntop bar) and pick the \"Publish\" action.\n\nThis opens a form\nwith prefilled information derived from the connected proposal\ndata. This data can then be edited by the user and finally saved.\n\nThis defines the data as to be published and makes it known to the\ndata catalog, but the corresponding DOI is not yet made globally\navailable. For this last step to happen, someone with access to this\nnewly generated published data definition (e.g. the person defining\nthe published data or e.g. the PI) has to hit the \"register\"\nbutton. This will trigger the global publication of the DOI. The links\non https://doi.org are usually updated within one day, so wait one day\nbefore following these links or searching for the doi via the doi\nreolver.\nAll published data definitions are then openly available via the so\ncalled \"Landing Pages\", which are hosted on https://doi.psi.ch .\nThe file data itself data becomes available via the normal data export\nSystem of the Ra cluster, which requires however a PSI account. If you\nwant to make the file data anonymously available you need to send a\ncorresponding request to stephan.egli@psi.ch for now. This process is\nplanned to be automated in future.\nFor now all publication are triggered by a scientist explicitly,\nwhenever necessary. In future in addition an automated publication\nafter the embargo period (default 3 years after data taking) will be\nimplemented (details to be defined)\nCleanup and Retention\nThis part is not yet defined.\nTroubleshooting\nLocale error message\nIf you get error messages like the following (so far only happened\nfrom Mac Computers)\nperl: warning: Setting locale failed.\nperl: warning: Please check that your locale settings:\n....\nthen you need to prevent that the Mac ssh client sends the\nLCTYPE variable. Just follow the description in:\nhttps://www.cyberciti.biz/faq/os-x-terminal-bash-warning-setlocale-lc_ctype-cannot-change-locale/\nInvalid certificate messages\nThe following message can be safely ignored:\nkey_cert_check_authority: invalid certificate\nCertificate invalid: name is not a listed principal\nIt indicates that no kerberos token was provided for authentication.\nYou can avoid the warning by first running kinit (PSI linux systems).\nLong Running copy commands\nFor decentral ingestion cases, the copy step is indicated by a message\n'Running [/usr/bin/rsync -e ssh -avxz …'. It is expected that this\nstep will take a long time and may appear to have hung. You can check\nwhat files have been successfully transfered using rsync:\nrsync--list-only user_n@pb-archive.psi.ch:archive/UID/PATH/\nwhere UID is the dataset ID (12345678-1234-1234-1234-123456789012) and\nPATH is the absolute path to your data. Note that rsync creates\ndirectories first and that the transfer order is not alphabetical in\nsome cases, but it should be possible to see whether any data has\ntransferred.\nKerberos tickets\nAs a normal user you should have a valid Kerberos ticket. This is\nusually the case on the centrally provided Linux machines\nautomtically. You can verify the existence with the \"klist\"\ncommand. In case no valid ticket is returned you have to get one using\nthe \"kinit\" command. (Note: beamline accounts do not need this)\nklist\n# if no Ticket listed get one by\nkinit\nInstructions to set ACLS in AFS\nIn the AFS file system the user have to permit access to the\nsourceFolder by setting read and lookup ACL permission for the AFS\ngroup “pb-archive”. The easiest way to achieve is to run the following\nscript with the sourceFolder as an argunent\n/afs/psi.ch/service/bin/pb_setacl.sh sourceFolder\nThis script must be run by a person who has the rights to modify the\naccess rights in AFS.\nAppendix\nInstallation of Tools\nAccess to the SciCat GUI\nFor the access to the SciCat web-based user interface no software\nneeds to be installed, simply use your browser to go to\nhttps://discovery.psi.ch. \nLoading datacatalog tools on Clusters\nThe latest datacatalog software is maintained in the PSI module system\non the main clusters (Ra, Merlin). To access it from PSI linux\nsystems, run the following command:\nmodule load datacatalog\n(Non-standard Linux systems) Installing datacatalog tools\nIf you do not have access to PSI modules (for instance, when archiving\nfrom Ubuntu systems), then you can install the datacatalog software\nyourself. These tools require 64-bit linux.\nI suggest storing the SciCat scripts in ~/bin so that they can be\neasily accessed.\nmkdir -p ~/bin\ncd ~/bin\n/usr/bin/curl -O https://gitlab.psi.ch/scicat/tools/raw/master/linux/datasetIngestor\nchmod +x ./datasetIngestor\n/usr/bin/curl -O https://gitlab.psi.ch/scicat/tools/raw/master/linux/datasetRetriever\nchmod +x ./datasetRetriever\n/usr/bin/curl -O https://gitlab.psi.ch/scicat/tools/raw/master/linux/SciCat\nchmod +x ./SciCat\nWhen the scripts are updated you will be prompted to re-run some of\nthe above commands to get the latest version.\nYou can call the ingestion scripts using the full path\n(~/bin/datasetIngestor) or else add ~/bin to your unix PATH. To do so,\nadd the following line to your ~/.bashrc file:\nexport PATH=\"$HOME/bin:$PATH\"\nInstallation on Windows Systems\nOn Windows the executables can be downloaded from the following URL,\njust enter the address in abrowser and download the file\nhttps://gitlab.psi.ch/scicat/tools/-/blob/master/windows/datasetIngestor.exe\nhttps://gitlab.psi.ch/scicat/tools/-/blob/master/windows/SciCatGUI_Win10.zip\nOnline work stations in beamline hutches\nThe command line tools are pre-installed in /work/sls/bin. No further\naction needed\nDataset limitations\nSize limitations\n\na single dataset should currently not have more than 400k files\na single dataset should not be larger than 50 TB\nrecommended size of a single dataset: between 1GB and 1TB\n\nSourceFolder and file names limitations\nThe sourceFolder metadata and the name of the files can contain the following special characters: \n\n\\%\n#\n-\n+\n.\n\\:\n\\=\n\\@\n_\n\nAny other special characters are not guaranteed to work.\nRecommended file structure for raw datasets\nOne recommended way of structuring your data on disk is the following:\ne12345 Metadata Field Definitions\nThe following table defines the mandatory and optional fields for the\nadministrative metadata, which have to be provided (status June\n2021). All fields marked \"m\" are mandatory, the rest is optional. Some\nfields are filled automatically if possible, see comments. For the\nmost recent status see this URL\nhttps://scicatproject.github.io/api-documentation/ and follow the link\ncalled \"Model\" for the respective datamodel (e.g. Dataset), visible\ne.g. inside the GET API call section. Or see the model definitions as\ndefined in the SciCat backend, see the json files in\nhttps://github.com/SciCatProject/catamel/tree/develop/common/models\nAll \"Date\" fields must follow the date/time format defined in RFC\n3339, section 5.6, see https://www.ietf.org/rfc/rfc3339.txt\nMetadata field definitions for datasets of type \"base\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nfield\ntype\nmust\ncomment\n\n\n\n\n\npid\nstring\nm\nfilled by API automatically, do **not** provide this\n\n\n\n\nowner\nstring\nm\nfilled by datasetIngestor if missing\n\n\n\n\nownerEmail\nstring\n \nfilled by datasetIngestor if missing\n\n\n\n\norcidOfOwner\nstring\n \n \n\n\n\n\ncontactEmail\nstring\nm\nfilled by datasetIngestor if missing\n\n\n\n\ndatasetName\nstring\n \nset to \"tail\" of sourceFolder path if missing\n\n\n\n\nsourceFolder\nstring\nm\n \n\n\n\n\nsize\nnumber\n \nautofilled when OrigDataBlock created\n\n\n\n\npackedSize\nnumber\n \nautofilled when DataBlock created\n\n\n\n\ncreationTime\ndate\nm\nfilled by API if missing\n\n\n\n\ntype\nstring\nm\n(raw, derived…)\n\n\n\n\nvalidationStatus\nstring\n \n \n\n\n\n\nkeywords\nArray[string]\n \n \n\n\n\n\ndescription\nstring\n \n \n\n\n\n\nclassification\nstring\n \nfilled by API or datasetIngestor if missing\n\n\n\n\nlicense\nstring\n \nfilled by datasetIngestor if missing (CC By-SA 4.0)\n\n\n\n\nversion\nstring\n \nautofilled by API\n\n\n\n\ndoi\nstring\n \nfilled as part of publication workflow\n\n\n\n\nisPublished\nboolean\n \nfilled by datasetIngestor if missing (false)\n\n\n\n\nownerGroup\nstring\nm\nmust be filled explicitly\n\n\n\n\naccessGroups\nArray[string]\n \nfilled by datasetIngestor to beamline specific group\n\n\n\n\n \n \n \nderived from creationLocation\n\n\n\n\n \n \n \ne.g. /PSI/SLS/TOMCAT -> accessGroups=[\"slstomcat\"]\n\n\n\n\nAdditional fields for type=\"raw\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nfield\ntype\nmust\ncomment\n\n\n\n\n\nprincipalInvestigator\nstring\nm\nfilled in datasetIngestor if missing (proposal must exist)\n\n\n\n\nendTime\ndate\n \nfilled from datasetIngetor if missing\n\n\n\n\ncreationLocation\nstring\nm\nsee known Instrument list below\n\n\n\n\ndataFormat\nstring\n \n \n\n\n\n\nscientificMetadata\nobject\n \n \n\n\n\n\nproposalId\nstring\n \nfilled by API automatically if missing\n\n\n\n\nAdditional fields for type=\"derived\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nfield\ntype\nmust\ncomment\n\n\n\n\n\ninvestigator\nstring\nm\n \n\n\n\n\ninputDatasets\nArray[string]\nm\n \n\n\n\n\nusedSoftware\nstring\nm\n \n\n\n\n\njobParameters\nobject\n \n \n\n\n\n\njobLogData\nstring\n \n \n\n\n\n\nscientificMetadata\nobject\n \n \n\n\n\n\nAbout Scientific Values and Units\nIt is strongly recommended that physical quantities are stored in the\n following format (the field names are just examples, the structure\n with the two fields \"value\" and \"unit\" is important here)\n\"scientificMetadata\": {\n ...\n \"beamlineParameters\": {\n \"Ring current\": {\n \"value\": 402.246,\n \"unit\": \"mA\"\n },\n \"Beam energy\": {\n \"value\": 22595,\n \"unit\": \"eV\"\n }\n }\n ....\n}\nIn future for such quantities the data catalog will automatically add\ntwo additional fields \"valueSI\" and \"unitSI\" with the corresponding\nSI units. The rationale for this is to support value queries in a\nreliable manner across datasets with potentially different units\nchosen for the same quantity:\n\"scientificMetadata\": {\n ...\n \"beamlineParameters\": {\n \"Ring current\": {\n \"value\": 402.246,\n \"unit\": \"mA\",\n \"valueSI\": 0.402246,\n \"unitSI\": \"A\"\n },\n \"Beam energy\": {\n \"value\": 22595,\n \"unit\": \"eV\",\n \"valueSI\": 3.6201179E-15\n \"unitSI\":\"J\"\n }\n }\n ....\n}\nUse Case Examples\nUse Case: Manual ingest using datasetIngestor program\n\nOverview\nData owners may want to define in an adhoc manner the creation of\ndatasets in order to allow a subsequent archiving of the data. The\nmost important use cases are\n\nraw data from a beamline\nderived data created by a scientist\narchiving of historic data\narchiving of data stored on local (decentral) file storage systems\n\nFor this purpose a command line client datasetIngestor is provided\nwhich allows to\n\ningest the meta data and files\noptionally copy the data to a central cache file server\n\nThe necessary steps to use this tool are now described:\n\nPreparation of the meta data\nYou need to create a file metadata.json defining at least the\nadministrative metadata\n\nExample of minimal json file for raw data:\n{\n \"creationLocation\": \"/PSI/SLS/TOMCAT\",\n \"sourceFolder\": \"/scratch/devops\",\n \"type\": \"raw\",\n \"ownerGroup\":\"p16623\"\n}\n\nExample for raw data including scientific metadata\n{\n \"principalInvestigator\": \"egon.meier@psi.ch\",\n \"creationLocation\": \"/PSI/SLS/TOMCAT\",\n \"dataFormat\": \"Tomcat pre HDF5 format 2017\",\n \"sourceFolder\": \"/sls/X02DA/data/e12345/Data10/disk3/817b_B2_\",\n \"owner\": \"Egon Meier\",\n \"ownerEmail\": \"egon.meier@psi.ch\",\n \"type\": \"raw\",\n \"description\": \"Add a short description here for this dataset ...\",\n \"ownerGroup\": \"p12345\",\n \"scientificMetadata\": {\n \"beamlineParameters\": {\n \"Monostripe\": \"Ru/C\",\n \"Ring current\": {\n \"value\": 0.402246,\n \"unit\": \"A\"\n },\n \"Beam energy\": {\n \"value\": 22595,\n \"unit\": \"eV\"\n }\n },\n \"detectorParameters\": {\n \"Objective\": 20,\n \"Scintillator\": \"LAG 20um\",\n \"Exposure time\": {\n \"value\": 0.4,\n \"unit\": \"s\"\n }\n },\n \"scanParameters\": {\n \"Number of projections\": 1801,\n \"Rot Y min position\": {\n \"value\": 0,\n \"unit\": \"deg\"\n },\n \"Inner scan flag\": 0,\n \"File Prefix\": \"817b_B2_\",\n \"Sample In\": {\n \"value\": 0,\n \"unit\": \"m\"\n },\n \"Number of darks\": 10,\n \"Rot Y max position\": {\n \"value\": 180,\n \"unit\": \"deg\"\n },\n \"Angular step\": {\n \"value\": 0.1,\n \"unit\": \"deg\"\n },\n \"Number of flats\": 120,\n \"Sample Out\": {\n \"value\": -0.005,\n \"unit\": \"m\"\n },\n \"Flat frequency\": 0,\n \"Number of inter-flats\": 0\n }\n }\n}\n\nExample of minimal json file for derived data:\n{ \"sourceFolder\" : \"/data/test/myExampleData\",\n \"type\" : \"derived\",\n \"ownerGroup\": \"p12345\",\n \"investigator\":\"federika.marone@psi.ch\",\n \"inputDatasets\": [\"/data/test/input1.dat\",\n \"20.500.11935/000031f3-0675-4d30-b5ca-b9c674bcf027\"],\n \"usedSoftware\": [\"https://gitlab.psi.ch/MyAnalysisRepo/tomcatScripts/commit/60629a1cbef493a26aac626602ba8f1a6c9e14d2\"]\n }\n\nowner and contactEmail will be filled automatically\nimportant: in case you ingest derived datasets with a beamline\naccount , such as slstomcat (instead of a personal account), you have to add the beamline account\nto the accessGroups field like this:\n{ \"sourceFolder\" : \"/data/test/myExampleData\",\n \"type\" : \"derived\",\n \"ownerGroup\": \"p12345\",\n \"accessGroups\":[\"slstomcat\"], \n \"investigator\":\"federika.marone@psi.ch\",\n \"inputDatasets\": [\"/data/test/input1.dat\",\n \"20.500.11935/000031f3-0675-4d30-b5ca-b9c674bcf027\"],\n \"usedSoftware\": [\"https://gitlab.psi.ch/MyAnalysisRepo/tomcatScripts/commit/60629a1cbef493a26aac626602ba8f1a6c9e14d2\"]\n }\n\nExtended derived example\n{\n \"sourceFolder\": \"/some/folder/containg/the/derived/data\",\n \"owner\": \"Thomas Meier\",\n \"ownerEmail\": \"thomas.meier@psi.ch\",\n \"contactEmail\": \"eugen.mueller@psi.ch\",\n \"type\": \"derived\",\n \"ownerGroup\": \"p13268\",\n \"creationTime\": \"2011-09-14T12:08:25.000Z\",\n \"investigator\": \"thomas.meier@psi.ch\",\n \"inputDatasets\": [\n \"20.500.11935/000031f3-0675-4d30-b5ca-b9c674bcf027\",\n \"20.500.11935/000031f3-0675-4d30-b5ca-b9c674bcf028\"\n ],\n \"usedSoftware\": [\"https://gitlab.psi.ch/MyAnalysisRepo/tomcatScripts/commit/60629a1cbef493a26aac626602ba8f1a6c9e14d2\"]\n}\n\n\n\nOptionally: preparation of a file listing file\nPlease note: The following is only needed, if you do not want to\nstore all files in a source Folder, but just a subset. In this case\nyou can specify an explicit list of files and directories. Only the\nfiles specified in this list will be stored as part of the\ndataset. For the directories in this list it is implied that they are\nrecursively descended and all data contained in the directory is taken\nHere is an example for a filelisting.txt file. All entries in this\ntextfiles are path names relativ to the sourceFolder specified in\nthe metadata.json file\nExample of filelisting.txt\ndatafile1\ndatafile2\nspecialStuff/logfile1.log\nallFilesInThisDirectory\n\nOptionally: for multiple datasets to be created\nIf you have many sourceFolders containing data, each to be turned into\na dataset then the easiest method is to define a 'folderlisting.txt'\nfile. (the file must have exactly this name). This is a useful option\nto archive large amounts of \"historic\" data.\nEach line in this file is the absolute path to the sourceFolder In\nthis case it is assumed, that the metadata.json file is valid for all\ndatasets and that all files inside the sourceFolder are part of the\ndataset (i.e. you can not combine the filelisting.txt option with the\nfolderlisting.txt option)\nExample of folderlisting.txt\n/some/folder/containg/the/data/raw/sample1\n/some/folder/containg/the/data/raw/sample2\n/some/folder/containg/the/data/derived\n\nStarting the ingest\nJust run the following command in a terminal as a first test if\neverything is okay. This is a so called \"dry run\" and nothing will\nactually be stored, but the consistency of the data will be checked\nand the folders will be scanned for files\ndatasetIngestor metadata.json [filelisting.txt | 'folderlisting.txt']\nYou will be prompted for your username and password.\nIf everything looks as expected you should now repeat the command with\nthe \"–ingest\" flag to actually store the dataset(s) in the data\ncatalog\ndatasetIngestor --ingest metadata.json [filelisting.txt | 'folderlisting.txt']\nWhen the job is finshed all needed metadata will be ingested into the\ndata catalog (and for decentral data the data will be copied to the\ncentral cache file server).\nIn addition you have the option to directly trigger the archiving of\nthe data to tape by adding the –autoarchive flag. Do this only if you\nsure that this data is worth to be archived\n\n\nUse Case: Automated ingest of raw datasets from beamline or instruments\n\nUsing the datasetIngestor Tool\nThis method usually requires a fully automatic ingestion procedure,\nsince data is produced at regular times and in a predictable way.\nFor each beamline this automation is done together with the experts\nfrom the data catalog group and potentially with the help from the\ncontrols /detector-integration groups. Please contact\nscicatarchivemanager@psi.ch to get in touch.\nThe recommended method is to define preparation scripts, which\nautomatically produce the files metadata.json and optionally\nfilelisting.txt or folderlisting.txt (for multiple datasets) as you\nwould do in the manual case described in the previous section.\nExample of such scripts can be provided by the data catalog team,\nplease contact scicatingestor@psi.ch for further help. The effort to\nimplement such a system depends very much on the availability of the\nmeta data as well as on the effort to convert the existing metadata to\nthe data catalog format inside the converter processes. If the meta\ndata is already available in some form in a file an estimate of the\norder of magnitude of work needed per instrument is 1-2 person-weeks\nof work, including test runs etc. But efforts may also be considerably\nsmaller or larger in some cases.\nThen you run the datasetIngestor program usually under a beamline\nspecic account. In order to run fully automatic all potential\nquestions asked interactively by the program must be pre-answered\nthrough a set of command line options:\ndatasetIngestor [options] metadata-file [filelisting-file|'folderlisting.txt']\n\n -allowexistingsource\n Defines if existing sourceFolders can be reused\n -autoarchive\n Option to create archive job automatically after ingestion\n -copy\n Defines if files should be copied from your local system to a central server before ingest.\n -devenv\n Use development environment instead of production environment (developers only)\n -ingest\n Defines if this command is meant to actually ingest data\n -linkfiles string\n Define what to do with symbolic links: (keep|delete|keepInternalOnly) (default \"keepInternalOnly\")\n -noninteractive\n If set no questions will be asked and the default settings for all undefined flags will be assumed\n -tapecopies int\n Number of tapecopies to be used for archiving (default 1)\n -testenv\n Use test environment (qa) instead of production environment\n -user string\n Defines optional username:password string\n\nhere is a typical example using the MX beamline at SLS as an example\nand ingesting a singel dataset with meta data defined in\nmetadata.json\ndatasetIngestor -ingest \\\n -linkfiles keepInternalOnly \\\n -allowexistingsource \\\n -user slsmx:XXXXXXXX \\\n -noninteractive \\\n metadata.json\n\n\nThis command must be called by the respective data acquisition systems\nat a proper time, i.e. after all the files from the measurement run\nhave been written to disk and all metadata became available (often\nthis meta data is collected by the controls system). \n\nHDF5 Files\nIf the raw data exists in form of HDF5 files, there is a good chance\nthat the meta data can be extracted from the HDF5 files' meta data. In\nsuch a case the meta data extraction must be done as part of the part\nbeamline preparation scripts. Example of such HDF5 extraction scripts\nexist which can the basis of a beamline specific solution, again\nplease contact scicatingestor@psi.ch. These scripts will mostly need\nminimal adjustments for each beamline, mainly specifying the filter\nconditions defining which of the meta data in the HDF5 file are to be\nconsidered meta data for the data catalog.\nVery often the whole dataset will only consist of one HDF5 file, thus\nalso simplifying the filelisting definition.\n\n\nUse Case: Ingest datasets stored on decentral systems\nThese are data that you want to have archived for some reason, but are\nnot available on central file systems. Data from the old PSI archiv\nsystem fall in this category or data from local PCs, Laptops or\ninstruments. If this data is not assigned to a p-group (given via the\nDUO digital user office, usually linked to a proposal) then you must\nassign this data to an a-group. The allocation of an \"a-group\" for\nthis kind of data must be done beforehand by a tool currently in\npreparation at AIT. The \"a-group\" will define the ownership and\ntherefor the access to the data by listing a number of users onside the\ngroup.\nOtherwise just follow the description in the section \"Manual ingest\nusing datasetIngestor program\" and use the option -copy, e.g.\ndatasetIngestor -autoarchive -copy -ingest metadata.json\nThis command will copy the data to a central rsync server, from where\nthe archive system can then copy the files to tape, in this case\n(option -autoarchive) the copy to archive tapes will happen automatically\nOn recent versions of the datasetIngestor program the program detects\nautomatically,if your data lies on central or decentral systems. In\nthe latter case it will, after a confirmation by the user, copy the\ndata automatically to the rsync cache server, even if the copy flag is\nnot provided.\nUse Case: Ingest datasets from simulations/model calculations\nThese can be treated like datasets of type \"base\" or \"raw\". In the\nlatter case specify the field \"creationLocation\" as the name of the\nserver or cluster which produced the simulation files. Otherwise the\nprocedure is identical to the previous use case.\nPolicy settings and email notifications\nThe archiving process can further be configured via policy\nparameters, e.g. if you require a second tape copy for very\nprecious data. Also the details about the notification settings by\nemail for both archive and retrieve jobs can be set here. You reach\nthe menu to set the policy values via the submenu Policies\nin the dropdown menu to the top right of the GUI.\nEmails are automatically sent at the start of every archive and\nretrieve jobs as well as when the job finishes. The email is sent to\nthe person creating the jobs. In addition it is sent the list of\nemails defined in the policy settings. Per default this list is empty\nbut can be extended by you. In the policy one can also switch off the\nemail notification. However emails about error conditions (which can\nbe either user caused or system caused) can not be switched off. Such\nerror messages are always sent to the user as well as the archive\nadministrators.\nFor user caused errors the user has to take action to repair the\nsituation. Typically error cases are, that the user has moved or\nremoved part or all of the files before archiving them. System errors\non the other hand have their reason inside the catalog and archive\nsystem (e.g. a network connection problem or similar) and will be\ntaken care of by the archive managers. In such a case the user\ncreating the job will be informed manually, when the problem is fixed\nagain.\nPolicy parameters can be defined at site level or at ownerGroup\nlevel. For each ownerGroup at least one manager must be defined\n(e.g. a principal investigator (PI) via the linked proposal\ninformation) in the policy model (field \"manager\") . Only the manager\ncan change the policy settings at ownerGroup level, but all group\nmebers can see them.\nChanges to this policy settings only effect future dataset creation\nand archiving\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nParameter\nAllowed Values\nDefault\nLevel\n\n\n\n\n\npolicyPublicationShiftInYears\nsmall positive integer, e.g. 3\n3\nSite (ro)\n\n\n\n\npolicyRetentionShiftInYears\nsmall positive integer, e.g. 10\n10\nSite (ro)\n\n\n\n\n\nautoArchive\ntrue/false\nfalse\nownerGroup\n\n\n\n\ntapeRedundancy\nlow/medium/(high)\nlow\nownerGroup\n\n\n\n\narchiveEmailNotification\ntrue/false\nfalse\nownerGroup\n\n\n\n\narchiveEmailsToBeNotified\nArray of additional emails\n[]\nownerGroup\n\n\n\n\nretrieveEmailNotification\ntrue/false\nfalse\nownerGroup\n\n\n\n\nretrieveEmailsToBeNotified\nArray of additional emails\n[]\nownerGroup\n\n\n\n\n(archiveDelayInDays)\nsmall positive integer, e.g. 7\n0\nownerGroup\n\n\n\n\nThe job Initiator always gets an email unless email notification is disabled.\nAnalyzing Metadata Statistics\nNote: This service is currently (summer 2021) out of order due to the\nmissing JupyterHub environment.\nOverview\nIt is possible to analyze the information about datasets amd jobs etc,\ne.g. for statistical purposes. A Jupyterhub based solution was chosen\nas a tool for allowing to do this analysis in a flexible and\ninteractive manner. This means you can use Jupyter notebooks to query\nthe Data catalog via the API for its data and analyze the results in\nterms of tables and graphs. Example notebooks are provided.\nGetting started\nSimply follow the following link and login with your PSI account:\nhttps://jupyterhub.apps.ocp4a.psi.ch/ . The initial start of the\nJupyter environments takes some time (about 40 seconds), but\nsubsequent starts are much faster. You will then see a \"bootstrap\"\nnotebook which you can execute to populate your Jupyter home directory\nwith the example notebooks. \nThe example notebooks require you to login to the data catalog API\nserver. Here you can again use your personal account, which gives you\naccess to all data, for which you have read access (i.e. for which you\nare member of the associated p-group). Beamline managers can also use\nthe beamline accounts here in order to get the statistics relevant for\nthe whole beamline. You can then look at the example notebooks,\ne.g. datasetAnalyzer.ipynb and run it, look at resulting tables and\ngraphs. Afterwards you can optionally adapt the notebooks to your\nneeds.\nPlease note, that this service is currently only available as a pilot\nwith no guaranteed availability. This also means, that you should\nmake regular backups of your own notebooks which you may develop\nusing this tool. For this you can e.g. simply download the notebook\nand copy it to a place for which backup exists, like your home\ndirectory.\nAccess to the API (for script developers)\nThe data catalog can also be accessed directly via a REST API. There\nexists an API \"Explorer\" which allows to test such API calls\nconveniently. The explorer can be found at\nhttps://dacat-qa.psi.ch/explorer .The explorer works with a test\ndatabase which is separate from the production database and contains\nother data.\nFor most of the API calls you will need an access token first. You\ncreate such an access token by \"login\" to the data catalog via the\nfollowing curl command:\n# for \"functional\" accounts\ncurl -X POST --header 'Content-Type: application/json' -d '{\"username\":\"YOUR-LOGIN\",\"password\":\"YOUR-PASSWORD\"}' 'https://dacat-qa.psi.ch/api/v3/Users/login'\n\n# for normal user accounts\ncurl -X POST --header 'Content-Type: application/json' -d '{\"username\":\"YOUR-LOGIN\",\"password\":\"YOUR-PASSWORD\"}' 'https://dacat-qa.psi.ch/auth/msad'\n\n# reply if succesful:\n{\"id\":\"NQhe3...\",\"ttl\":1209600,\"created\":\"2019-01-22T07:03:21.422Z\",\"userId\":\"5a745bde4d12b30008020843\"}\nThe \"id\" field contains the access token, which you copy in to the corresponding field at the top of the explorer page.\nAfterwards you can test the full API. If you found the right API call\nyou can finally apply the call to the production system by replacing\n\"dacat-qa\" by \"dacat\" and then by retrieving the access token from the\nproduction system.\nUsing datasetIngestor inside wrapper scripts (for developers)\nThe command datasetIngestor returns with a return code equal zero in\ncase the command could be executed succesfully. If the program however\nfails for some reason the return code will be one. Typical examples of\nfailures are that files can not be found or not be accessed. Other\npossibilities are that the catalog system is not available,\ne.g. during scheduled maintenance periods. All outputs describing the\nreason for the failure are written to STDERR. Please have a look at\nthese outputs to understand what the reason for the failure was. If\nyou need help please contact scicatingestor@psi.ch\nPlease note: it is the task of the wrapper scripts to test\nfor the return code and to repeat the command once all conditions for\na succesful execution are fulfilled\nIn case the ingest finishes succesfully the dataset persistent\nidentifiers (PID) of the resulting dataset(s) are written to STDOUT,\none line per dataset.\nIngestion of datasets which should never be published\nFor datasets which should never be published you should add the\nfollowing fields at ingest time to your metadata.json file:\n\"datasetlifecycle\": {\n \"publishable\":false,\n \"dateOfPublishing\":\"2099-12-31T00:00:00.000Z\",\n \"archiveRetentionTime\":\"2099-12-31T00:00:00.000Z\"\n}\n\nthis will move the time of publication to a date in some far future\n(2100 in this case)\n\nRetrieving proposal information\nIn case you need information about the principal investigator you can\nuse the command datasetGetProposal, which returns the proposal\ninformation for a given ownerGroup\n/usr/bin/curl -O https://gitlab.psi.ch/scicat/tools/raw/master/linux/datasetGetProposal;chmod +x ./datasetGetProposal\nLink to Group specific descriptions\n\nBIO department: https://intranet.psi.ch/BIO/ComputingDataCatalog\n\nList of known creationLocation for raw data\nThe following values for the creationLocation should be used for the\nrespective beamlines. They are derived from the identifiers used\ninside the digital user office DUO\nSLS\n\n\n\n\n\n\n\n\n\n\n\n\nBeamline\ncreationLocation\nIngest Account\n\n\n\n\n\nAdress-RIXS\n/PSI/SLS/ADRESS-RIXS\nslsadress-rixs\n\n\n\n\nAdress-SX-ARPES\n/PSI/SLS/ADRESS-SX-ARPES\nslsadress-sx-arpes\n\n\n\n\ncSAXS\n/PSI/SLS/CSAXS\nslscsaxs\n\n\n\n\nMicro-XAS\n/PSI/SLS/MICRO-XAS\nslsmicro-xas\n\n\n\n\nMicro-XAS-Femto\n/PSI/SLS/MICRO-XAS-FEMTO\nslsmicro-xas-femto\n\n\n\n\nMS-Powder\n/PSI/SLS/MS-POWDER\nslsms-powder\n\n\n\n\nMS-Surf-Diffr\n/PSI/SLS/MS-SURF-DIFFR\nslsms-surf-diffr\n\n\n\n\nNano-XAS\n/PSI/SLS/NANOXAS\nslsnanoxas\n\n\n\n\nPearl\n/PSI/SLS/PEARL\nslspearl\n\n\n\n\nPhoenix\n/PSI/SLS/PHOENIX\nslsphoenix\n\n\n\n\nPollux\n/PSI/SLS/POLLUX\nslspollux\n\n\n\n\nMX (PX,PXII,PXIII)\n/PSI/SLS/MX\nslsmx\n\n\n\n\nSIM\n/PSI/SLS/SIM\nslssim\n\n\n\n\nSis-Cophee\n/PSI/SLS/SIS-COPHEE\nslssis-cophee\n\n\n\n\nSis-Hrpes\n/PSI/SLS/SIS-HRPES\nslssis-hrpes\n\n\n\n\nSuper-XAS\n/PSI/SLS/SUPER-XAS\nslssuper-xas\n\n\n\n\nTomcat\n/PSI/SLS/TOMCAT\nslstomcat\n\n\n\n\nVUV\n/PSI/SLS/VUV\nslsvuv\n\n\n\n\nXIL-II\n/PSI/SLS/XIL-II\nslsxil-ii\n\n\n\n\nXtreme\n/PSI/SLS/XTREME\nslsxtreme\n\n\n\n\nThe connected email distribution lists are {ingestAccount}@psi.ch\nSwissfel\n\n\n\n\n\n\n\n\n\n\n\n\nBeamline\ncreationLocation\nIngest Account\n\n\n\n\n\nAlvra\n/PSI/SWISSFEL/ARAMIS-ALVRA\nswissfelaramis-alvra\n\n\n\n\nBernina\n/PSI/SWISSFEL/ARAMIS-BERNINA\nswissfelaramis-bernina\n\n\n\n\nCristallina\n/PSI/SWISSFEL/ARAMIS-CRISTALLINA\nswissfelaramis-cristallina\n\n\n\n\nFurka\n/PSI/SWISSFEL/ATHOS-FURKA\nswissfelathos-furka\n\n\n\n\nMaloja\n/PSI/SWISSFEL/ATHOS-MALOJA\nswissfelathos-maloja\n\n\n\n\nThe connected email distribution lists are {ingestAccount}@psi.ch\nSINQ\n\n\n\n\n\n\n\n\n\n\n\n\nInstrument\ncreationLocation\nIngest Account\n\n\n\n\n\nAMOR\n/PSI/SINQ/AMOR\nsinqamor\n\n\n\n\nDMC\n/PSI/SINQ/DMC\nsinqdmc\n\n\n\n\nEIGER\n/PSI/SINQ/EIGER\nsinqeiger\n\n\n\n\nFOCUS\n/PSI/SINQ/FOCUS\nsinqfocus\n\n\n\n\nHRPT\n/PSI/SINQ/HRPT\nsinqhrpt\n\n\n\n\nICON\n/PSI/SINQ/ICON\nsinqicon\n\n\n\n\nMorpheus\n/PSI/SINQ/MORPHEUS\nsinqmorpheus\n\n\n\n\nNARZISS\n/PSI/SINQ/NARZISS\nsinqnarziss\n\n\n\n\nNEUTRA\n/PSI/SINQ/NEUTRA\nsinqneutra\n\n\n\n\nPOLDI\n/PSI/SINQ/POLDI\nsinqpoldi\n\n\n\n\nRITA-II\n/PSI/SINQ/RITA-II\nsinqrita-ii\n\n\n\n\nSANS-I\n/PSI/SINQ/SANS-I\nsinqsans-i\n\n\n\n\nSANS-II\n/PSI/SINQ/SANS-II\nsinqsans-ii\n\n\n\n\nTASP\n/PSI/SINQ/TASP\nsinqtasp\n\n\n\n\nZEBRA\n/PSI/SINQ/ZEBRA\nsinqzebra\n\n\n\n\n \n \n \n\n\n\n\nThe connected email distribution lists are {ingestAccount}@psi.ch\nSmuS\n\n\n\n\n\n\n\n\n\n\n\n\nInstrument\ncreationLocation\nIngest Account\n\n\n\n\n\nDolly\n/PSI/SMUS/DOLLY\nsmusdolly\n\n\n\n\nGPD\n/PSI/SMUS/GPD\nsmusgpd\n\n\n\n\nGPS\n/PSI/SMUS/GPS\nsmusgps\n\n\n\n\nHAL-9500\n/PSI/SMUS/HAL-9500\nsmushal-9500\n\n\n\n\nLEM\n/PSI/SMUS/LEM\nsmuslem\n\n\n\n\nFLAME\n/PSI/SMUS/FLAME\nsmusflame\n\n\n\n\nThe connected email distribution lists are {ingestAccount}@psi.ch\nUpdate History of Ingest Manual\n\n\n\n\n\n\n\n\n\n\nDate\nUpdates\n\n\n\n\n\n10. September 2018\nInitial Release\n\n\n\n\n6. October 2018\nAdded warning section to not modify data after ingest\n\n\n\n\n10. October 2018\nownerGroup field must be defined explicitly\n\n\n\n\n28. October 2018\nAdded section on datasetRetriever tool\n\n\n\n\n20. November 2018\nRemove ssh key handling description (use Kerberos)\n\n\n\n\n3. December 2018\nRestructure archive stepp, add autoarchive flag\n\n\n\n\n17. January 2019\nUpdate on automatically filled values, more options for datasetIngestor\n\n\n\n\n22. January 2019\nAdded description for API access for script developers, 2 new commands\n\n\n\n\n \ndatasetArchiver and datasetGetProposal\n\n\n\n\n22. February 2019\nAdded known beamlines(instruments (creationLocation) value list\n\n\n\n\n24. February 2019\ndatasetIngestor use cases for automated ingests using beamline accounts\n\n\n\n\n23. April 2019\nAdded AFS infos and available central storage, need for Kerberos tickets\n\n\n\n\n23. April 2019\nAvailability of commands on RA cluster via pmodules\n\n\n\n\n3. May 2019\nAdded size limitation infos\n\n\n\n\n9. May 2019\nAdded hints for accessGroups definition for derived data\n\n\n\n\n \nAdded infos about email notifications\n\n\n\n\n10. May 2019\nAdded ownerGroup filtered retrieve option, decentral case auto detect\n\n\n\n\n7. Juni 2019\nFeedback from Manuel added\n\n\n\n\n21. Oct 2019\nNew version of CLI tools to deal with edge cases (blanks in sourcefolder\n\n\n\n\n \ndangling links, ingest for other person, need for kerberos ticket as user)\n\n\n\n\n14. November 2019\nRestructuring of manual,New CLI tools, auto kinit login\n\n\n\n\n \nProgress indicators, chksum test updated\n\n\n\n\n20. Januar 2020\nAuto fill principalInvestigator if missing\n\n\n\n\n3. March 2020\nAdded Jupyter notebook analysis section\n\n\n\n\n5. March 2020\nAdd hint for datasets not to be published\n\n\n\n\n19. March 2020\nAdded hint that analysis Jupyter tool is in pilot phase only\n\n\n\n\n19. March 2020\nAdded recommendation concerning unit handling for physical quantities\n\n\n\n\n9. July 2020\nAdded GUI tool SciCatArchiver (developer: Klaus Wakonig)\n\n\n\n\n11. July 2020\nInstallation of SciCatArchiver on non-Ra system\n\n\n\n\n14. July 2020\nAdded publication workflow and recommended file structure chapter\n\n\n\n\n16. July 2020\nUpdated SciCat GUI deployment information\n\n\n\n\n31. July 2020\nNew deploy location, + policy parameters, new recommended file structure\n\n\n\n\n27. August 2020\nAdded Windows Support information\n\n\n\n\n10. Sept 2020\nCorrected example JSON syntax in one location\n\n\n\n\n23. November 2020\nCorrected instructions for using the SciCat GUI on Windows 10\n\n\n\n\n19. February 2020\nAdded info about proposalId link\n\n\n\n\n24. Juni 2021\nMajor restructuring of full document for easier readability\n\n\n\n\n9. Dec 2021\nCorrected spelling of value/units convention\n\n\n\n\n23. April 2022\nAdded hint to use -token option for CLI and SciCat GUI as normal user\n\n\n\n\n2. Dec 2022\nExtended ingest use cases description of needed parameters Win+Linux\n\n\n\n\n21. Dec 2023\nInclude redundancy risks and costs and file names limitations\n\n\n\n"},"Ingestor/IngestManual_ESS.html":{"url":"Ingestor/IngestManual_ESS.html","title":"Ingest Instructions ESS","keywords":"","body":"Ingest Instructions ESS\nThis page needs to be updated\n\nAs shown in the picture above the detector and the data collection software is writing into kafka topics. The topics is then being read by a filewriter that in turn writes the dataset to storage and sends back an event when the file is written that contains metadata about the file and the experiment. There is then an ingestion program that will parse the event from the filewriter and gather additional information before triggering REST calls into the backend. \nHow the ingestion program works:\n1. Parse Event\nThe ingestor is subscribing to a topic in Kafka where the filewriter creates an event when a file has been written. This event contains information about location, proposal id and metadata that exist on the file in the form of nexus data. \n2. Login\n Login and get a access token that can be used for interfacing with the backend. We advise to create a special ingestor account to be used when doing automatic ingestion. \n3. Gather more metadata\nThe ingestor contacts the User Office and get's additional information regarding the experiment, the principal investigator and the sample.\n4. Create dataset\n\nUsing the information from the filewriter event with the additional information gathered from the user office a dataset request can be constructed and sent to the backend.\n5. Create OrigDatablocks\n\nAfter a dataset has been created we attach the files that relate, this is done by creating datablocks that attaches to the dataset\n6. Create Sample\n\nIf sample information is available a sample record can be created and attached to the dataset. This is an important step as it makes the data available to a wider audience of people. \n"},"Ingestor/GettingMetadataIntoScicat.html":{"url":"Ingestor/GettingMetadataIntoScicat.html","title":"Manual Ingest the Hard Way","keywords":"","body":"Getting Metadata Into SciCat (The hard way)\nThis page needs to be updated\nManual ingestion\nThe following steps will add a dataset to you system using the API with the linux program curl. \n1.\nLogin to the backend. The default password for the ingestor user is aman. Running the command below in the terminal will yield an access token.\n curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{\"username\":\"ingestor\", \"password\":\"\"}' 'http://localhost:3000/api/v3/Users/login'\n\nCreate a json file with the contents below and name it metadata.json\n{\n \"creationLocation\": \"/PSI/SLS/TOMCAT\",\n \"sourceFolder\": \"/scratch/devops\",\n \"type\": \"raw\",\n \"ownerGroup\":\"p16623\"\n}\n\ncat the metadata.json file and pipe it to a curl command. Insert your access token in the command below and run it in the terminal:\n\n\ncat metadata.json | curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d @- 'http://localhost:3000/api/v3/Datasets?access_token=YOUR_TOKEN_HERE'\nThere should now be a dataset in your mongoDB instance. \n"},"Development/":{"url":"Development/","title":"Developers Guide","keywords":"","body":"SciCat Data Catalogue Developer Guide\nThis section has been split in two sub sections related to the backend version. \nOn May 2023, the new backend V4.x based on nest.js has been officially released. We quickly realized that we needed two completely different sections to address the specific needs of each individual version.\nDevelopment documentation:\n\nv3.x (based on loopback v3)\nv4.x (based on nest.js)\n\n"},"Development/v3.x/":{"url":"Development/v3.x/","title":"v3.x","keywords":"","body":"SciCat Data Catalogue Developer Guide\nThis guide aims to help developers understand the overall structure of the software and therefore enables people to contribute to the development of the software. See also the Overview in the Operator Manual for a helicopter view of the involved components.\nIf you want to contribute to the software you should also read the Contributing section, which among others links to the git workflow to be used.\nGetting an development environment set up\nThe easiest is to follow this description to get a fully running frontend and backend setup automatically using docker. Otherwise follow the Running section\nArchitecture\nSciCat is built on micro services using the following stack:\nAPI (Backend)\n\nNode 12.x\nLoopback 3\nMongoDB 3.6 or later\n\nThe most important part to understand is how loopback works, because it is the platform which is directly addressed as part of the development. Please check the corresponding Loopback 3 documentation for all details\nWeb Browser Client (Frontend)\n\nAngular 9+ \nAngular Material Widgets and Design \nNGRX for State Management\nLoopback SDK generator for Angular\n\nAgain please of a look at the original documentation to understand how these tools and frameworks work.\nOptional Messaging/Streaming/Processing tools\n\nApache Kafka or RabbitMQ\nNodered Browser-based flow programming\n\n"},"Development/v3.x/StartHere.html":{"url":"Development/v3.x/StartHere.html","title":"Overview of Backend and Model","keywords":"","body":"Overview of Backend and Model\nThis page provides a brief introduction to the overall workings of the SciCat data model, how things connect and links to more details on each component.\nBackend\nFirst off, the backend is the key component here and you will see there is next to hand written code here (aside from some tests). The folder you will be working in most is: common/models and this is NodeJS application based on the Loopback framework. Go away and read the docs for Loopback (3) and come back here when you are fairly comfortable. I'll wait.\nGot it? Good. OK, so, SciCat stores all of its data in a MongoDB but you will almost never need to perform any admin on the database. The connection between loopback and Mongo is handled by the loopback-mongo-connector. The only thing you need to provide is the file in server/datasources.json , this provides the connection string and any connection specific info.\nIf you take a look at a model, you will see that some of them have a datasource of transient and others are specified as MongoDB. The transient models are not stored or handled by the mongo connector. You will see some other sections, so let's go through them:\nModel Definitions:\n\nProperties: These are the values, types and other information. None of these are mongo specific and can all map to a relational database just as easily.\nRelations: These link models, i.e. allowing a DatasetLifecycle to be linked to a Dataset. The mongo connector handles this by embedding the datasetId in the document for you.\nACLs: These are access control limitations and these are very powerful. You can give a user full control of a model, some users can only have read access etc.\nMethods: When you create a model, loopback will automagically generate API endpoints that allows users to PUT, POST, PATCH, GET etc for all the models. However, sometimes this is not enough. For example, you may want to be able to generate a monthly report of all datasets created, so you define the method here, the name of the endpoint and the expected parameters. You will implement the method in the next section we cover now.\n\nModel Implementation:\nAs just mentioned, Loopback handles all the basic RESTful commands for a model for you but there are two ways to extend this:\n\nCustom methods: You can extend your model with methods that allow you to implement logic beyond what loopback provides\nRemote hooks: Before a particular endpoint (or all endpoints) are called, or before a document is saved/edited/accessed, then you can use these hooks to implement some logic. For example, you may want to inject some extra information into a JSON object before it is saved or ensure the user has some access control that is outside of your Loopback system.\n\nFor the best example of these, take a look at the Dataset JS and JSON files, this also shows how relations work between models.\nRunning the backend\nThe backend has some core files missing that are required in order for it to run. Below, we list these files and what they should contain. Ideally, there will be a minimal install of the backend in the source repo that includes minimal config files to remove this barrier.\n\ndatasources.json - This sets up your connection to Mongo and should follow the syntax outlined in loopback . \nconfig.local.js - These are site specific settings for your install, such as the prefix to use for IDs, messages to store in the dataset and the facilities you have\nproviders.json - Contains connection information to LDAP or other authentication sources\ncomponent-config.json - This file connects to RabbitMQ or (in the case of testing) connects to a local rabbitmq, but this file should not be required in the future so the queuing system used is not fixed. \n\nEditing the Configs\ndatasources.json\nMake sure that the host field of the json is configured to your mongo hostname e.g. for a local instance of mongo with hostname \"mongodb\":\n \"mongo\": {\n \"host\": \"mongodb\",\n \"port\": 27017,\n \"url\": \"\",\n \"database\": \"dacat\",\n \"name\": \"mongo\",\n \"connector\": \"mongodb\",\n \"useNewUrlParser\": true,\n \"allowExtendedOperators\":true\n }\nconfig.local.js\nHere you can configure many aspects of scicat for your institute. Here is an example config file, where you delete parts to turn off certain features:\nvar p = require('../package.json');\nvar version = p.version.split('.').shift();\nmodule.exports = {\n restApiRoot: '/api' + (version > 0 ? '/v' + version : ''),\n host: process.env.HOST || '0.0.0.0',\n port: process.env.PORT || 3000,\n pidPrefix: 'PutYourPIDPrefixHere',\n policyPublicationShiftInYears: 3,\n policyRetentionShiftInYears: 10,\n site: 'YOUR-SITE',\n facilities: [\"Facility1\", \"Facility2\"],\n metadataKeysReturnLimit: 100,\n registerMetadataUri : \"https://mds.test.datacite.org/metadata\",\n registerDoiUri : \"https://mds.test.datacite.org/doi\",\n grayLog: {\n enabled: false,\n host: \"my.graylog.host\",\n port: 0000,\n facility: \"facility\",\n owner: \"owner\",\n service: \"service\"\n },\n # Add to this section if you want to use RabbitMQ for the Proposals mechanism\n rabbitmq: {\n enabled: false,\n host: null,\n port: null,\n queue: null\n },\n # Remove this if you want to use the Jobs mechanism without SMTP or configure for your SMTP service.\n smtpSettings: {\n host: 'SMTP.YOUR.DOMAIN',\n port: 25,\n secure: false },\n smtpMessage: {\n from: 'scicatarchivemanager@YOUR.DOMAIN',\n to: undefined,\n subject: '[SciCat '+process.env.NODE_ENV+']',\n text: undefined // can also set html key and this will override this\n },\n # Keep this if you want to set your queue service to rabbitmq, remove if you are not using a messaging service\n queue: 'rabbitmq'\n};\ncomponent-config.json\nIf you want to use RabbitMQ to reach out to a microservice through the Jobs mechanism in Scicat you will need to edit the component-config.json file. You will also need to add the queue: \"rabbitmq\" line in the \nconfig.local.js file for this to work (see above section.) In order to connect to rabbitMQ you will need a user and password already defined in a rabbitMQ service to connect and the service should be up and running.\nTo set this up add the following to the component-config.json and change the username and password to those you have defined in your rabbitMQ service. Also edit the hostname in the uri field to be the hostname of your RabbitMQ service. \n\"../node_modules/loopback-component-mq/lib\":{\n\"options\":{\n\"restPort\":15672,\n\"acls\":[\n{\n\"accessType\":\"*\",\n\"principalType\":\"ROLE\",\n\"principalId\":\"$unauthenticated\",\n\"permission\":\"DENY\"\n}\n]\n},\n\"topology\":{\n\"connection\":{\n\"uri\":\"amqp://:@:5672/\"\n},\n\"exchanges\":[\n{\n\"name\":\"jobs.write\",\n\"type\":\"topic\",\n\"persistent\":true\n}\n],\n\"queues\":[\n{\n\"name\":\"client.jobs.write\",\n\"subscribe\":true\n}\n],\n\"bindings\":[\n{\n\"exchange\":\"jobs.write\",\n\"target\":\"client.jobs.write\",\n\"keys\":[\n\"jobqueue\"\n]\n}\n]\n}\n}\nFor more info on setting up RabbitMQ see the Deploy with Docker Compose section.\nTesting the backend\nAll tests for the backend are in the test folder in the root of the repo and can be run with npm run test. \nPython Client API\nThe API is automatically created from the Swagger/openAPI (http://swagger.io/) specificication created from the data model using the Swagger Codegen code\njava -jar ./swagger-codegen/modules/swagger-codegen-cli/target/swagger-codegen-cli.jar generate -i swagger.json -l python -o dacat-api/client/python\n"},"Development/v3.x/Data_Model.html":{"url":"Development/v3.x/Data_Model.html","title":"An Introduction to the Data Model","keywords":"","body":"An Introduction to the Data Model\nModel\nThere are many models within the catalog and many of them are self explanatory. The API server creates routes for creation, retrieval, modification and deletion. However, it is worth explaining the core models that are used.\nThe following graph shows a simplified class diagramm of the main model classes. It was generated semi-automatically from the swagger.json file using the WAPIml tool\n\nThe Ownable base model\nMost models inherit the ownable base model which regulates access based on the ownerGroup/ accessGroups fields. Relationships are defined by including the ID of the respective model (handled by loopback model definitions).\nDataset\nThis is the base class for all datasets, and is derived from the\nOwnable model. There are two classes that are derived from Dataset:\nRawDataset and DerivedDataset.\nThe data model and its API endpoints are described here.\nRawDataset\nThis is a dataset that has been collected from an experiment. It contains details about who owns it, contact information etc. Most importantly, it contains Scientific Metadata, and this is an extensible object to outline all parameters relevant to the dataset.\nEach raw dataset may contain a link to a single Proposal.\nThe data model and its API endpoints are described here.\nDerivedDataset\nThese are datasets that are created by analyzing the data in raw datasets. The fields allow to link both to the raw data files used in the analysis as well as to a software repository which keeps the source code used for the analysis, thus helping to keep track of the provenance of the data. You can add ScientificMetadata here as well.\nAlthough DerivedDataset does not contain a link to a Proposal, it may\nbe deduced. A corresponding Proposal may be found by identifying all\ninput RawDatasets (either directly or indirectly via other\nDerivedDatasets) from the inputDatasets field. These RawDatasets may\ncontain links to the corresponding Proposal.\nThe data model and its API endpoints are described here.\nDatasetLifecycle\nThere are many temporal operations for a dataset describing what happens to the Dataset during its lifetime. Therefore, this model contains information relating to the status of the dataset but not the metadata itself. The archive location, date that it should become public and information about archiving or retrieval. Technically the DatasetLifecycle is however embedded into the Dataset documents.\nThe data model and its API endpoints are therefore described as part of the Dataset model.\nInstrument\nThis collection is meant to store the available measurement instruments, ideally in a globally unique naming scheme (within the Expands/Panosc EU programs standardization on these topics is pursued). Datasets can link to these instruments (in addition to the creationLocation field), allowing for more detailed information about the measurement devices.\nThe data model and its API endpoints are described here.\nTechnique\nThis collection is meant to store the available measurement techniques, ideally in a globally unique naming scheme (within the Expands/Panosc EU programs standardization on these topics is pursued). Datasets can link to these techniques, allowing for more detailed information about the measurement method applied.\nOrigDatablocks and Datablocks\nSince SciCat contains only metadata, it must include links to the actual storage location of the data. This typically consists of lists of DataFile objects storing the file path, size, ownership, and checksum (but not the data itself). Since the data may be stored on tape archives or otherwise transformed, each file in a dataset is typically recorded in two places:\n\nOrigDatablock stores the path to the file on the original system at the time of ingestion into SciCat.\nDatablock stores the path to the file on the archive system. The archiveId property is used by the storage management system (not part of SciCat) to retrieve the data. For instance, a simple archiveId might give a path on to a tar bundle on the local tape storage. Some facilities omit Datablocks from their databases and use conventions for determining the data location based only on the OrigDatablock paths.\n\nLarge datasets may split files into multiple OrigDatablocks and Datablocks. The details of this are facility specific, and could reflect local organizational preferences or performance considerations. For instance, datasets with many files are often split into multiple OrigDatablock objects to avoid too-large MongoDB objects. Conversely, very large files may be split across several Datablocks during archiving due to limitations of the storage hardware.\nYou should always add the OrigDataBlock information when ingesting, whereas the Datablocks information is optional and would be filled later by the archive system, when it did its work of writing chunks of data to tape. For instance, a Datablockcan correspond to a single tar file which is written to tape and the contents of the Datablock is essentially the file listing of the tar file.\nThe data model and its API endpoints are described here and here.\nJob\nWhen a user wants to archive, retrieve or publish a dataset, SciCat must interact with the facility's storage and archiving system. This is done by creating a Job to track the interaction. The exact actions taken for each Job are often customized for each facility. A common action would be to forward the new Job entry to a Message Broker, e.g. RabbitMQ, from where it can be picked up by any program willing to react to this Job. For example, at PSI the RabbitMQ queue is emptied by a Node-RED process, which reads the Job information from the RabbitMQ queue. Alternatively, you can use other messaging solutions, such as Apache Kafka. In this way the (site specific) logic to handle the Jobs is kept outside the core of the SciCat system, giving a greater degree of flexibility. The external systems should ideally respond with status updates to the Job model, when there job is finished. As the job progresses, the system can make PUT/PATCH API calls back that update the Job Status Message and, when necessary, also the individual status of the involved datasets by updating the DatasetLifecycle information.\nThe data model and its API endpoints are described here.\nProposals\nA proposal is a written intention declaration of what kind of measurement people want to perform when and where. Such information is often available in an existing system outside of the data catalog, like digital user office systems for institutes which offer their instruments to other users.\nThis information can be synchronized into SciCat and facilitates the definition of the ownership of the datasets as well as providing information relevant for publication of data, such as principal investigators, abstracts etc. datasets are normally uniquely associated with one proposal.\nThe proposal information is however optional\nThe data model and its API endpoints are described here.\nPublishedData\nWhen Datasets are published you assign a DOI to a list of datasets. The PublishedData model keeps track of these information, i.e which dataset is being published, authorship, abstract and download links to access the corresponding file contents of the datasets.\nThe data model and its API endpoints are described here.\nUserSettings\nThis collection stores user preferences, such as the list of pre-selected columns in the dataset dashboard view, defaults for the table page size etc.\nThe data model and its API endpoints are described here.\nAttachment\nSome of the above mentioned models can have files (usually images) attached. The size is limited to 16MB for one attachment. Attachments are stored in the database.\nThe data model and its API endpoints are described here.\nExplorer\nWhen you have an instance of the data catalog running, the API endpoints and the related data models can be looked at at the following URL:\n\n/explorer/\nExample instance from ESS\n\nThe API explorer is a feature of Loopback and allows one to test various endpoints. This section outlines the core functionality. The first point to note is that requests require a token. You can use the login route under the User tab to obtain one of these, or use a CURL command.\nMain Screen\nThe top right entry field is where the token should be included. The token will define your access rights, so not all routes will be accessible. Each model has a tab which relates to its endpoint. Expanding the tab shows the routes section below.\n\nRoutes\nEach section outlines the routes for the chosen model. This is configurable but the HTTP verbs are supported here for most routes and conform to the standards. Error codes are also consistent. Expanding the chosen route allows one to test the response with sample data, or their own.\n\nSingle Route\nSelecting a route shows the expected JSON object and provides an editor to allow you to test the route. Response information is shown below.\nClicking the data type box will insert it into your request for easy testing.\n\nModel\nThe model view can be accessed by clicking the link next to Example Value and shows the fields that Loopback is expecting to receive, as well as their type and whether they are required.\n\n"},"Development/v3.x/Running.html":{"url":"Development/v3.x/Running.html","title":"Running the Components","keywords":"","body":"Running locally\ngit clone https://github.com/SciCatProject/backend.git \ncd backend\nnpm install\nnpm start\ngit clone https://github.com/SciCatProject/frontend.git \ncd frontend\nnpm install\nnpx ng serve\nFor convenience, this has been added to the package.json so you can run npm start as an alias.\nAccess here once running.\nRunning with Docker\nThe containers in our repos are based on alpine linux to ensure a minimal size with almost nothing but the required packages. Most standard *nix commands are available.\nThe recommend way is to use scicatlive . This does all for you automatically.\nIf you want to do things manually instead:\ngit clone https://github.com/SciCatProject/frontend.git && cd frontend\ndocker build -t .\ndocker run \nYou should see the container begin and print out the URL, this is retrieved dynamically based on the IP of the container. Changing it to localhost or 0.0.0.0 will break it.\nRunning with Kubernetes\nCreating a Kubernetes environment takes some expertise but the SciCat repo localdeploy contains all the necessary configuration for deploying SciCat to a cluster with a simple readme.\n"},"Development/v3.x/Configuration.html":{"url":"Development/v3.x/Configuration.html","title":"Configuration","keywords":"","body":"Configuration\n\nSciCat Frontend\nSciCat Backend\nDotenv Config\nDOI Config\n\n\n\nSciCat Frontend\nThis is the configuration file for the frontend client. The configuration file allows the systems administrator to configure every aspect of the client including switching on/off almost all non essential features. The configuration file can either be served from the backend, via the /client/config.json endpoint or mounted into the docker container at the following path /usr/share/nginx/html/assets/config.json.\nAn example is shown below\nfrontend/src/assets/config.json:\n\n{\n \"accessTokenPrefix\": \"\", // Set the backend token prefix. Should be empty string for current backend or \"Bearer \" if using scicat-backend-next.\n \"addDatasetEnabled\": true, // Show/hide the \"Create Dataset\" button in the Datasets Dashboard.\n \"archiveWorkflowEnabled\": true, // Enable/disable the archive/retrieve workflow.\n \"datasetReduceEnabled\": true, // Enable/disable the automatic Dataset reduction/analysis workflow.\n \"editDatasetSampleEnabled\": true, // Enable/disable editing of which Sample a Dataset belongs to.\n \"editMetadataEnabled\": true, // Enable/disable editing of Scientific Metadata.\n \"editPublishedData\": true, // Enable/disable editing of Published Data.\n \"editSampleEnabled\": true, // Enable/disable editing of Samples.\n \"externalAuthEndpoint\": \"/auth/msad\", // Endpoint used for third party authentication, e.g, LDAP.\n \"facility\": \"ESS\", // Facility running the SciCat instance.\n \"fileColorEnabled\": true, // Enable/disable file size color representation in the Datasets Dashboard.\n \"fileDownloadEnabled\": true, // Enable/disable download workflow for Dataset datafiles.\n \"gettingStarted\": null, // URL to Getting Started guide for SciCat, displayed on the Help page.\n \"ingestManual\": null, // URL to Ingest Manual for SciCat, displayed on the Help page.\n \"jobsEnabled\": true, // Enable/disable Job workflow.\n \"jsonMetadataEnabled\": true, // Show/hide the \"Show Metadata\" button on the details pages, allowing users to see the JSON represantion of the current document.\n \"jupyterHubUrl\": \"https://jupyterhub.esss.lu.se/\", // URL to Jupyter Hub instance used for data analysis.\n \"landingPage\": \"doi2.psi.ch/detail/\", // URL to the facility's Landing Page for Published Data.\n \"lbBaseURL\": \"http://127.0.0.1:3000\", // URL to the SciCat Backend.\n \"localColumns\": [ // Default columns to be displayed in the table on the Datasets Dashboard.\n {\n \"name\": \"select\",\n \"order\": 0,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"datasetName\",\n \"order\": 1,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"runNumber\",\n \"order\": 2,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"sourceFolder\",\n \"order\": 3,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"size\",\n \"order\": 4,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"creationTime\",\n \"order\": 5,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"type\",\n \"order\": 6,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"image\",\n \"order\": 7,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"metadata\",\n \"order\": 8,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"proposalId\",\n \"order\": 9,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"ownerGroup\",\n \"order\": 10,\n \"type\": \"standard\",\n \"enabled\": false\n },\n {\n \"name\": \"dataStatus\",\n \"order\": 11,\n \"type\": \"standard\",\n \"enabled\": false\n }\n ],\n \"logbookEnabled\": true, // Enable/disable SciChat Logbook integration.\n \"loginFormEnabled\": true, // Enable/disable the local Login form. Should be disabled if using oAuth2 for authentication.\n \"maxDirectDownloadSize\": 5000000000, // Set a maximum allowed file size for downloading datafiles over HTTP.\n \"metadataPreviewEnabled\": true, // Enable/disable Scientific Metadata preview on the Datasets Dashboard.\n \"metadataStructure\": \"tree\", // Allow tree structure for Scientific Metadata. Set to empty string for flat structure.\n \"multipleDownloadAction\": \"http://localhost:3012/zip\", // URL to service handling direct download of datafiles.\n \"multipleDownloadEnabled\": true, // Enable/disable ability to download multiple datafiles.\n \"oAuth2Endpoints\": [], // List of endpoints used for oAuth2 authentication.\n \"policiesEnabled\": true, // Enable/disable Dataset Policies workflow.\n \"retrieveDestinations\": [], // List of destinations for Dataset retrievals.\n \"riotBaseUrl\": \"http://scitest.esss.lu.se/riot\", // URL to SciChat client.\n \"scienceSearchEnabled\": true, // Enable/disable filtering documents on Scientific Metadata.\n \"scienceSearchUnitsEnabled\": true, // Enable/disable filtering documents on Scientific Metadata using units.\n \"searchPublicDataEnabled\": true, // Enable/disable filtering Datasets on public or non-public data.\n \"searchSamples\": true, // Enable/disable searching Samples on Samples Dashboard.\n \"sftpHost\": \"login.esss.dk\", // URL to SFTP service used for downloading files exceeding maximum allowed file size.\n \"shareEnabled\": true, // Enable/disable workflow for sharing Datasets with other users using their email address.\n \"shoppingCartEnabled\": true, // Enable/disable the Dataset cart used for bulk actions.\n \"shoppingCartOnHeader\": true, // Toggle Dataset cart placement, either on header or to the left on the Datasets Dashboard.\n \"tableSciDataEnabled\": true // Enable/disable Scientific Metadata table view on details pages. If disabled, Scientific Metadata is displayed as raw JSON.\n}\nSciCat Backend\nDotenv config\nThis is the configuration file for the backend. This file allows the systems administrator to configure connected services, like authentication services and message queues, and also switching on/off almost all non essential features. The configuration file is a dotenv file and is read by the backend at runtime.\nAn example is shown below\nbackend/.env\nACCESS_GROUPS_STATIC_VALUES=\"group1,group2,group3,...\" // List of groups assigned by default as access groups to all users. Used in the vanilla implementation. Facilities customization might not use this\nACCESS_GROUP_SERVICE_TOKEN=\"90f126864824ede0e22f7b4407aa1a5cd8158e6cabbce39aaf091937589f1750\" // Token needed to access the API specified in ACCESS_GROUP_SERVICE_API_URL\nACCESS_GROUP_SERVICE_API_URL=\"https://my.access.group/service/api/url\" // Url of the service API which is used to provide access groups. At the moment only one value is allowed\nDOI_PREFIX=\"\" // The facility DOI prefix, with trailing slash.\nEXPRESS_SESSION_SECRET=\"\" // *Optional* Secret used to set up express session.\nHTTP_MAX_REDIRECTS=5 // *Optional* Max redirects for http requests. Defaults to 5.\nHTTP_TIMEOUT=5000 // *Optional* Timeout from http requests in ms. Defaults to 5000.\nJWT_SECRET= // The secret for your JWT token, used for authorization.\nJWT_EXPIRES_IN=3600 // *Optional* How long, in seconds, the JWT token is valid. Defaults to `3600`.\nLDAP_URL=\"ldaps://ldap.server.com:636/\" // *Optional* The URL (and port) to your LDAP server.\nLDAP_BIND_DN=\"@server.com\" // *Optional* Bind_DN for your LDAP server.\nLDAP_BIND_CREDENTIALS= // *Optional* Credentials for your LDAP server.\nLDAP_SEARCH_BASE= // *Optional* Search base for your LDAP server.\nLDAP_SEARCH_FILTER=\"(LDAPUsername={{username}})\" // *Optional* Search filter for you LDAP server.\nOIDC_ISSUER=\"https://identity.your.facility/your/realm\" // Full URL of the identity provider\nOIDC_CLIENT_ID= \"scicat\" // Client id used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generated\nOIDC_CLIENT_SECRET=\"90f126864824ede0e22f7b4407aa1a5cd8158e6cabbce39aaf091937589f1750\" // Token used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generated\nOIDC_CALLBACK_URL=\"http://localhost:3000/api/v3/oidc/callback\" // URL of the endpoint that is called when the authentication has been executed with the OIDC service.\nOIDC_SCOPE=\"openid profile email\" // Information returned by the OIDC service together with token\"\nOIDC_SUCCESS_URL=\"http://localhost:3000/explorer\" // URL of the endpoint that is called after a successful authentication. IT is not used in the vanilla implementaation.\nOIDC_ACCESS_GROUPS=\"access_groups\" // field used to retrieve access groups from the OIDC service. It is not used in the vanilla implementation.\nLOGBOOK_ENABLED= // *Optional* Flag to enable/disable the Logbook endpoints. Values \"yes\" or \"no\". Defaults to \"no\".\nLOGBOOK_BASE_URL=\"http://localhost:3030/scichatapi\" // *Optional* The base URL to the SciChat wrapper API. Only required if Logbook is enabled.\nLOGBOOK_USERNAME=\"\" // *Optional* The username used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.\nLOGBOOK_PASSWORD=\"\" // *Optional* The password used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.\nMETADATA_KEYS_RETURN_LIMIT=100 // *Optional* The return limit for the `/Datasets/metadataKeys` endpoint.\nMETADATA_PARENT_INSTANCES_RETURN_LIMIT=100 // *Optional* The return limit of Datasets to extract metadata keys from for the `/Datasets/metadataKeys` endpoint.\nMONGODB_URI=\"mongodb://:@:27017/\" // The URI for your MongoDB instance.\nOAI_PROVIDER_ROUTE=\"\" // *Optional* URI to OAI provider, used for the `/publisheddata/:id/resync` endpoint.\nPID_PREFIX=\"\" // The facility PID prefix, with trailing slash.\nPUBLIC_URL_PREFIX=\"https://doi.ess.eu/detail/\" // The base URL to the facility Landing Page.\nPORT=3000 // *Optional* The port on which you want to access the app. Defaults to `3000`.\nRABBITMQ_ENABLED= *Optional* Flag to enable/disable RabbitMQ consumer. Values \"yes\" or \"no\". Defaults to \"no\".\nRABBITMQ_HOSTNAME=\"localhost\" // *Optional* The hostname of the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nRABBITMQ_USERNAME=\"rabbitmq\" // *Optional* The username used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nRABBITMQ_PASSWORD=\"rabbitmq\" // *Optional* The password used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nREGISTER_DOI_URI=\"https://mds.test.datacite.org/doi\" // URI to the organization that registers the facilities DOIs.\nREGISTER_METADATA_URI=\"https://mds.test.datacite.org/metadata\" // URI to the organization that registers the facilities published data metadata.\nSITE= // The name of your site.\nSMTP_HOST= // Host of SMTP server.\nSMTP_MESSAGE_FROM= // Email address that emails should be sent from.\nSMTP_PORT= // Port of SMTP server.\nSMTP_SECURE= // Secure of SMTP server.\nDOI Config\nThis config file contains the username and password for authenicating against your DOI provider, e.g., DataCite, which is used when registering the DOI and metadata of your published data.\nbackend/src/config/doiconfig.local.json\n\n{\n \"username\": \"\",\n \"password\": \"\"\n}\n"},"Development/v3.x/Theming.html":{"url":"Development/v3.x/Theming.html","title":"Theming","keywords":"","body":"SciCat Frontend Theming file\nThis is the theming file for the frontend client. The theming file allows the systems administrator to configure the color theme of the client. The theming file can either be served from the backend, via the /client/theme.json endpoint or mounted into /usr/share/nginx/html/assets/theme.json.\nAn example is shown below\nfrontend/src/assets/theme.json\n\n{\n \"name\": \"light\", // Name of the theme.\n \"properties\": { // The different theme colors. The colors here are the default ones. If you, in your theme file, choose to leave one of the properties out, the default one will be used instead.\n \"--theme-primary-default\": \"#0099c8\",\n \"--theme-primary-default-contrast\": \"#ffffff\",\n \"--theme-primary-lighter\": \"#99d5e9\",\n \"--theme-primary-lighter-contrast\": \"#000000\",\n \"--theme-primary-darker\": \"#32acd3\",\n \"--theme-primary-darker-contrast\": \"#ffffff\",\n \"--theme-accent-default\": \"#99be00\",\n \"--theme-accent-default-contrast\": \"#ffffff\",\n \"--theme-accent-lighter\": \"#d6e599\",\n \"--theme-accent-lighter-contrast\": \"#000000\",\n \"--theme-accent-darker\": \"#adcb32\",\n \"--theme-accent-darker-contrast\": \"#ffffff\",\n \"--theme-warn-default\": \"#c81919\",\n \"--theme-warn-default-contrast\": \"#ffffff\",\n \"--theme-warn-lighter\": \"#ff6e6e\",\n \"--theme-warn-lighter-contrast\": \"#000000\",\n \"--theme-warn-darker\": \"#820019\",\n \"--theme-warn-darker-contrast\": \"#ffffff\",\n \"--theme-warn-2-default\": \"#ffbe00\",\n \"--theme-warn-2-default-contrast\": \"#ffffff\",\n \"--theme-warn-2-lighter\": \"#e1dc7d\",\n \"--theme-warn-2-lighter-contrast\": \"#000000\",\n \"--theme-warn-2-darker\": \"#d28c14\",\n \"--theme-warn-2-darker-contrast\": \"#ffffff\",\n \"--theme-header-1-default\": \"#003366\",\n \"--theme-header-1-default-contrast\": \"#ffffff\",\n \"--theme-header-1-lighter\": \"#99adc1\",\n \"--theme-header-1-lighter-contrast\": \"#000000\",\n \"--theme-header-1-darker\": \"#325b84\",\n \"--theme-header-1-darker-contrast\": \"#ffffff\",\n \"--theme-header-2-default\": \"#006646\",\n \"--theme-header-2-default-contrast\": \"#ffffff\",\n \"--theme-header-2-lighter\": \"#99c1b5\",\n \"--theme-header-2-lighter-contrast\": \"#000000\",\n \"--theme-header-2-darker\": \"#32846a\",\n \"--theme-header-2-darker-contrast\": \"#ffffff\",\n \"--theme-header-3-default\": \"#ff7d00\",\n \"--theme-header-3-default-contrast\": \"#ffffff\",\n \"--theme-header-3-lighter\": \"#ffcb99\",\n \"--theme-header-3-lighter-contrast\": \"#000000\",\n \"--theme-header-3-darker\": \"#ff9732\",\n \"--theme-header-3-darker-contrast\": \"#ffffff\",\n \"--theme-header-4-default\": \"#821482\",\n \"--theme-header-4-default-contrast\": \"#ffffff\",\n \"--theme-header-4-lighter\": \"#cda1cd\",\n \"--theme-header-4-lighter-contrast\": \"#000000\",\n \"--theme-header-4-darker\": \"#9b429b\",\n \"--theme-header-4-darker-contrast\": \"#ffffff\",\n \"--theme-hover-default\": \"#7f7f7f\",\n \"--theme-hover-default-contrast\": \"#000000\",\n \"--theme-hover-lighter\": \"#e5e5e5\",\n \"--theme-hover-lighter-contrast\": \"#000000\",\n \"--theme-hover-darker\": \"#b2b2b2\",\n \"--theme-hover-darker-contrast\": \"#000000\"\n }\n}\n"},"Development/v3.x/Login.html":{"url":"Development/v3.x/Login.html","title":"Login and Accounts","keywords":"","body":"Login\nTwo login routes are provided - for functional accounts and for AD/LDAP logins.\nLoopback provides a UserIdentity for third party logins, (e.g. AD/LDAP, google, Twitter etc) \nA document is created in the db.UserIdentity collection.\nThe UserIdentity is fetched from MongoDB and stored in the ngrx store, (store.user.userIdentity) where \nit is used to access e.g. email for job initiators.\nThe login method needs to be defined in the client configuartion (environment file) usign the field externalAuthEndpoint. For example externalAuthEndpoint: \"/auth/msad\".\nSee loopback docs\nFor non-third party logins defined, no UserIdentity is created. These are special users for admin purposes including beamline specific users for ingestion etc.\nThese users should be defined in a seperate secrets repo in a file called functionalAccounts.json and are read by createFunctionalAccounts.js in the backend e.g.\n [{\n \"account\": \"admin\",\n \"password\": \"pass\",\n \"email\": \"admin@domain.com\",\n \"role\": \"admin\",\n \"global\": true\n }, {\n \"account\": \"ingestor\",\n \"password\": \"pass\",\n \"email\": \"ingestor@domain.com\",\n \"role\": \"ingestor\",\n \"global\": true\n }]\nOIDC\nFor OIDC integration, see the OIDC for details.\n"},"Development/v3.x/OIDC.html":{"url":"Development/v3.x/OIDC.html","title":"OIDC Integration","keywords":"","body":"OIDC Integration\nSciCat can integrate with one or more OIDC servers to provide Authentication. Integration requires configuration of both backend and frontend in order to setup the redirecting and handshaking that the OAuth2 code flow requires. Additionally, it may involve writing custom code hooks in the backend in order to properly handle profile information obtained by the OIDC Auth Provider.\nBackend Configuration\nConfiguration of the backend for OIDC involves adding a provider to providers.json file. See Start Here for more information on configuring this file.\nHere is an example of a provider that uses Google as an authenticator. Google is used here because it is well-documented and commonly available.\n \"google\": {\n \"provider\": \"oidc\",\n \"authScheme\": \"openid connect\",\n \"module\": \"passport-openidconnect\",\n \"authPath\": \"/auth/google\",\n \"successRedirect\": \"http://localhost/user\",\n \"failureRedirect\": \"http://localhost/login\",\n \"failureFlash\": true,\n \"session\": false,\n \"issuer\": \"https://accounts.google.com\",\n \"authorizationURL\": \"https://accounts.google.com/o/oauth2/v2/auth\",\n \"tokenURL\": \"https://oauth2.googleapis.com/token\",\n \"userInfoURL\": \"https://openidconnect.googleapis.com/v1/userinfo\",\n \"clientID\": \"...\",\n \"clientSecret\": \"...\",\n \"callbackURL\": \"http://localhost:3000/auth/google/callback\",\n \"scope\": [\"email\", \"profile\", \"openid\"],\n \"loginCallback\": \"sampleLoginCallback\"\n }\n\n\nNote that information about Google sign in can be found here, details are beyond the scope of this document.\nNote that OIDC authentication services publish their specific settings in a Discovery document.. To find the settings for, say, google, see https://accounts.google.com/.well-known/openid-configuration.\n\nDetails about the fields in this provider configuration:\n\nprovider is used by the loopback-passport-configurator to build a strategy\nauthScheme is used by the loopback-passport-configurator to build a strategy\nmodule describes the passport module to use. passport-openidconnect is already included in the backend\nauthPath is the path that the backend will create (with the help of loopback-passport-configurator) to begin the authentication process. The backend will redirect users to this path (once it is configured, of course!)\nsuccessRedirect is the URL passed to the OIDC provider instructing it to redirect the user on success. Here we set it to the a development environment's user page.\nfailureRedirect is the URL passed to the OIDC provider instructing it to redirect the user on failed authentication. Here we set it to the a development environment's user page login page.\nfailureFlash is currently unsupported.\nsession is not needed, since the frontend authenticates outside of sessions.\nauthorizationURL is specific to your OIDC provider\ntokenURL is specific to your OIDC provider\nuserInfoURL is specific to your OIDC provider\nclientID is specific to your OIDC provider\nclientSecret is specific to your OIDC provider\nscope is specific to your OIDC provider\nloginCallback optional name of a callback function to run to add profile information to the UserIdentity\n\nSession Secret\nThe nodejs module passport-openidconnect requires the use of the express-session module\nto create a login session for the user.\n This requires a session secret to be configured in config.local.js:\n module.exports = {\n expressSessionSecret: \"thisIsSuperSecretForUserSession\"\nSciCat Frontend Configuration\nThe default login page in the frontend provides a username/password form that is used both for local and LDAP/AD authentication. When using and OIDC authentication provider, SciCat will not ask the user for credential directly. Instead, the user will be redirected to the OIDC provider to authenticate. So, the frontend provides two configuration settings to modify the login page appropriately.\nThese setting are accomplished by modifying the frontend environment document. \n\n loginFormEnabled: false,\n oAuth2Endpoints: [\n {displayText: \"Google\", displayImage: \"../../../assets/images/btn_google_light_normal_ios.svg\", authURL: \"auth/google\"}]\n\n\nloginFormEnabled sets whether to display the username/password form in the login page\noAuth2Endpoints provides information that the frontend uses to display \"Sign in with ...\" buttons. Note that this is an array, multiple buttons for multiple OIDC providers can be configured.\ndisplayText sets the name of the provider to display in the button. In this case, the button will show \"Sign In With Google\"\ndisplayImage defines an image to display in the button. The image will end up being 24px tall. It is recommended that the image be in SVG format.\nauthURL defines the relative path that the user will be redirected to when they click the button. Note that this maps to the authPath setting described above.\n\n\n\nBackend Code Hooks\nConfiguring backend and frontend to authenticate through OIDC is very useful for providing a third party authentication. However, there two issues that come up that can be addressed with backend code hooks:\n\nBy default, the system will authenticate all users who pass come in through the OAuth Provider. \nBy default, the users profile in the application will contain information gathered from the OAuth Provider, and nothing more.\n\nPrevent Authenticating Unkown Users\nIn Backend Configuration, we configured the loginCallback. The value of this file is the name of a function that must be defined and exported. See login-call.js for details.\nCode snippet is presented for as an example, omitting the particular details about communicating with an external system:\n\nmodule.exports.sampleLoginCallback = function(req, done) {\n return function(err, user, identity, token) {\n\n var authInfo = {\n identity: identity,\n };\n if (token) {\n authInfo.accessToken = token;\n }\n\n const requestURL = getUserURL(identity);\n if (!requestURL){\n logger.logError(`unexpected authenticator type: ${identity.provider}`);\n done();\n return;\n }\n request(requestURL, function (error, response, body) {\n // ask external system for the user's profile information\n if (error){\n // and error occurred talking to the external system (possibly from a connection issue)...deny login\n logger.logError(`error talking to external ${error.message}`);\n done(err, null, null);\n return;\n }\n if (response.statusCode == 200) {\n const bodyObj = JSON.parse(body);\n logger.logInfo(\"user service returned\", bodyObj);\n }\n else{\n logger.logError(`external system replied with an error ${response.statusCode} - ${body}.`);\n // external system replied with a negative response...deny login\n done(err, null, null);\n return;\n }\n //accept login\n done(err, user, authInfo);\n });\n };\n};\n\nAdd information to User's Profile\nBy default profile information from the Authentication Provider will be added to the user's profile. This profile is stored in the DaCat database UserIdentity collection. This collection is created by the loopback-component-passport library, and is also used for LDAP authentication. The backend LDAP integrations populate this collection with the user's LDAP profile information automatically with a callback. One of the most important things that is added to the profile is the user's accessGroups field, which is critical to calculating a user's privilege in SciCat.\nOne way to mimic this with an OAuth2 provider is to use a before save Loopback Operation Hook. This hook will get called as the user logs in and allows you to add information to the UserIdentity collection, which will be used in a variety of places, including access controls.\nThe following code snippet is presented for as an example, omitting the particular details about communicating with an external system:\n// Observe saving UserIdentity. This gives us the ability to update the user profile with facility-specific groups\nmodule.exports = function (app) {\n app.models.UserIdentity.observe(\"before save\", function(ctx, next) {\n if (!ctx.data){\n logger.logInfo(\"No context data from UserIdentity\");\n next();\n return;\n }\n request(userURL, function (error, response, _body) {\n // ask external system for user information so we can get group info\n if (error){\n logger.logError(`error talking to external system ${error.message}`);\n next();\n return;\n }\n if (response.statusCode == 200){\n // add groups to profile, saving in the UserIdentity model\n ctx.data.profile.accessGroups = JSON.parse(response.body).groups;\n }\n next();\n });\n });\n};\n\n"},"Development/v3.x/Ngrx.html":{"url":"Development/v3.x/Ngrx.html","title":"State Management in Angular","keywords":"","body":"State Management in Angular\nAlmost all external requests made by the client (SciCat Frontend) are done so via NGRX effects within the state management components.\nLoopback within the backend provies a generated SDK that is used by the frontend to define all requests to the API. The SDK folder should not be edited because it will just overwrite it when a new one is generated.\nThe SDK is updated by running the following in the backend source code root:\n./node_modules/.bin/lb-sdk server/server.js ../frontend/src/app/shared/sdk\nand this is necessary when making any changes to endpoints in the backend.\nNGRX is Redux for Angular and is heavily used in the client to manage state.\n"},"Development/v3.x/Testing_backend.html":{"url":"Development/v3.x/Testing_backend.html","title":"Testing Backend","keywords":"","body":"Testing\nPackages\n\nChai - Assertion\nSupertest - NodeJS testing for HTTP calls\nMocha - Test runner framework\n\nRunning\nnpm run test\nor \nmocha --timeout=5000 --reporter=nyan\nTests\nSome tests to cover the Users API and the RawDataset API have been written but more are needed. There is a LoginUtils file that supports the retrieval of tokens and everything else is documented in the Swagger file\nEnvironment\nIf this is being run independently of an infrastructure then you should provide the environment variable NODE_ENV to be test.\nSet up\nIn the test/config/settings.json file, you should have the following fields:\n{\n \"baseURL\": \"http://localhost:3000/\",\n \"apiPrefix\": \"api/v2/\",\n \"users\": {\n \"admin\": {\n \"username\": \"\",\n \"password\": \"\",\n \"login\": \"api/v2/Users/login\"\n\n },\n \"user\": {\n \"username\": \"\",\n \"password\": \"\",\n \"login\":\"auth/msad\"\n },\n },\n}\nAdding tests\nThis can be done one of two ways:\nSimple Tests\nFor simple tests, you can add it to the test/config/tests.json file. Each entry should have the following syntax:\n{\n \"method\": \"GET\",\n \"route\": \"Datasets?filter=%7B%22limit%22%3A10%7D\",\n \"expect\": 200,\n \"authenticate\": \"admin\"\n}\nAuthenticate can be user or admin, depending on the type of account you have specified.\nIf you include a body property, then that will be sent with the request.\nIf you include a response array, then the string content should be valid JS. Note that this uses the unsafe eval method so be careful!\nWriting Tests\nIf you have a chain of tests, or just tests that might be more complex, then you can write them in the test folder. See any js file in there for examples.\n"},"Development/v3.x/Testing.html":{"url":"Development/v3.x/Testing.html","title":"Testing Frontend","keywords":"","body":"Testing\nAll components of SciCat include unit test coverage and the frontend has additional end to end user interface testing. Tests are run by CI before any change request is approved.\nE2E (Protractor)\nInitially Protractor was used for end to end testing but this was abandoned in favour of the far more robust and flexible Cypress.\nCypress E2E\nCypress is not based on Selenium. \nnpx cypress run runs all tests headless (without displaying the browser).\nnpx cypress open opens the Cypress GUI in the browser.\nnpx cypress run --spec ./cypress/integration/policy-delegate.spec.js will run the single test file policy-delegate.spec.js.\nUnit (Jasmine, run by Karma)\nng test runs all unit tests.\nMocking Services\nCreate a mock service and implement only what you need\nclass MockUserApi {\n\n getCurrentId() {\n return 123;\n }\n}\nNow, set your testbed to override the service you are mocking.\nTestBed.overrideComponent(AppComponent, {\n set: {\n providers: [\n { provide: UserApi, useClass: MockUserApi }\n ]\n }\nThe practice I have followed is to create a MockStubs file that contains only the necessary mocked methods for services or pipes or components. This file can be added to to allow almost complete coverage of the app.\nAnatomy of a Unit Test\nBeforeEach\nThese are called to set up the test and involve 2 important methods.\nconfigureTestingModule\nThis should include imports for all the modules used in the component. If the component also includes other components, then they should be included. This is also true for any pipes that may be used in the template. I am not sure if there is a fix for this but that does mean that, if a subcomponent uses a pipe then it should be included in all the specs.\noverrideComponent\nTest cases for a component should test as few things outside of the component as possible (services etc should have their own tests), but this causes problems for the services implemented in each component. This is especially true for the loopback services, but also HTTP and Router. In the overrideComponent method, you can specify the service names and provide an alternative class to use.\nFor every service your component uses, there should be a Mock equivalent that provides hardcoded data in response to their calls. First, check the service exists in MockStubs.ts in the root of the app folder. If it is not in there, please implement it only in that file. You do not need to implement every method, others can add to these when they are needed.\nThe format in the spec file should be as follows:\n{ provide: RawDatasetApi, useClass: MockDatasetApi }\nThe default test merely tests the creation of a component. This is merely opinion but a component should not be considered complete on a branch until the initial tests pass. A release should not go live until the coverage of the tests covers more than just basic functionality, i.e. 80% coverage.\nState management/NGRX testing\nTests of ngrx state management should test e.g. if a new state object is returned.\nSample ngrx reducer test:\n describe('SelectDatasetAction', () => {\n it('should return the state', () => {\n const data:DatasetInterface = {owner: '', contactEmail: '', sourceFolder: '', creationTime: new Date(), type: '', ownerGroup: ''};\n const payload = new Dataset({pid: 'pid 1', ...data});\n const action = new fromActions.SelectDatasetAction(payload);\n const state = fromDatasets.datasetsReducer(initialDatasetState, action);\n expect(state.selectedSets2).toEqual([payload]);\n expect(state.selectedSets2.constructor).toBe(Array);\n });\n });\n\n"},"Development/v3.x/Contributing.html":{"url":"Development/v3.x/Contributing.html","title":"Contributing","keywords":"","body":"Contributing\nThere are 3 core ways to get involved with the Data Catalogue project:\n\nDocumentation - Fixing errors, covering weaker areas, adding examples\nGithub Issues - This should be the first area to focus on for those that want to amend the code. To see how the project is developed, see the Git Workflow page.\nTesting\n\nFor information about the development method we follow, please check Development Methodology.\nIssues\nIssues are handled within the Github Issue tracker and should follow the template:\n## Issue Name\n\n### Summary\n\n### Steps to Reproduce\n\n### Current Behaviour\n\n### Expected Behaviour\n\n### Extra Details\n\nHere you should include details about the system (if it is unique) and possible information about a fix (feel free to link to code where relevant). Screenshots/GIFs are also fine here.\n\nMerge Requests\nThere should be no pushing directly to the master or develop branches. To implement a fix, one should open a branch with the naming: hotfix/ISSUE-NAME from the develop branch and complete all work there. When it is complete, a Merge Request should be opened that follows this template:\n## Description\n\n## Motivation \n\nLink to any open issues here\n\n## Fixes:\n\n* \n* \n\n## Changes:\n\n* \n* \n\n## Tests included/Docs Updated?\n\n- [ ] Included for each change/fix?\n- [ ] Passing? (Merge will not be approved unless this is checked)\n- [ ] Docs updated?\n\n## Extra Information/Screenshots\n\nWhen the request is created, it should be assigned to any other appropriate team member with develop as the target branch. Tests must be written for all features/changes made and any major changes should be updated in the docs repo.\n"},"Development/v3.x/Development_Methods.html":{"url":"Development/v3.x/Development_Methods.html","title":"Git Flow Workflow","keywords":"","body":"Git Flow Workflow\n\nCurrently, this project is being developed by following a variation of the git flow model.\nIn summary, the following branches should exist and be in the following states: \n\nmaster - always ready to deploy (manual/time delayed integrations from the develop branch) \ndevelop - merge hotfixes and features into this (this should map to a corresponding development environment on your system) \nhotfix/ - forks from master to fix a known issue (this issue should be documented in the repository and a branch can be created from within Github \nfeature/ - forks from master to a feature branch, explained here\n\nReleases\nA release branch should be created if:\n\nA fixed release cycle has been set\nA number of features or hotfixes have been incorporated and a release is needed.\n\nReleases should be merged into the master branch and tagged with the version number of that release. It goes without saying that a release branch should not be created unless the develop branch is passing all tests.\nSample Workflow - Creating a feature\ngit pull origin develop\ngit checkout -b feature/feature_name\n# Do some work\ngit add \ngit commit -m 'COMMIT MESSAGE'\n\ngit push origin feature/feature_name\n# Click the link shown in the output to create a merge request\n\n# TO RELEASE (ONCE TESTED ON DEVELOP)\ngit checkout -b release/0.1.1\ngit merge --no-ff develop\ngit checkout master\ngit merge --no-ff release/0.1.1\ngit branch -D release/0.1.1\nCollaborators and Code Review\nAll those involved with development on a project should follow this process exactly. This would mean no committing directly to the master or develop branches and (unless absolutely necessary) pull requests should be opened in Github and assigned to anyone that is not yourself.\nWhen your branch has been pushed up to the origin (https://github.com, then please open a pull request with your branch name as the source and the develop as the target. You can then assign the pull request to somone else within your development team. If you do not know who that is then please assign it to someone working at PSI.\nNOTE When a new branch is opened it must be opened from the develop branch. Merge requests opened from other branches will not be accepted.\nOnce Merged\nWhen your code has been merged by the assignee, you will receive an email and you can then do the following commands\ngit pull origin master #always make sure you are up to date\ngit branch -d #remove local branch\nWorking locally\nThe general git advice for working locally is to commit often. Not every commit needs to be pushed immediately but it is worth ensuring that the remote is never too far behind your local. Always be sure to pull before you push and test all merge conflicts once you have fixed them. NOTE: The angular cli (and webpack) will provide feedback on unhandled merges so it is worth using! For detailed info on best practices this is a great guide.\nNo-FF (TL;DR - THIS SHOULD ALWAYS BE USED)\nNo-ff is a vital flag because it forces a merge commit to be inserted into the history of the main branch, without it, merge will try and fast forward the HEAD of the branch to the latest commit. In essence, without this flag, there will not be a visible merge commit in the history,\nINFO: Rebase vs. Merge\nA lot of information can be found on the web but the basics of it are:\n\nRebase rewrites histories (and allows for interactive editing of commits)\nMerge will maintain history of commits (although the squash flag can reduce this and bring your branch tip back in line with the master.\n\nGit Pull\nSome sites have mentioned that simply using a git pull (which is essentially a git fetch followed by a git merge will likely cause issues in large teams due to the changing nature of the branch meaning that you will experience merge commits. It is my personal opinion that this should not be a problem if work is never done directly on the master and the regular practice of pulling down before any major action is followed.\nHowever, it should be noted that git fetch origin and git rebase --preserve-merges origin/ will ensure that a history is maintained and merge conflicts are avoided where possible.\nOnce a feature is complete (and tested locally), it should be merged into the latest version of develop (following the release cycle outlined above) and tested on the whole. If the tests pass, the develop can then be merged with master and any new merges to master should cause it to redeploy.\nCurrent status:\nCI is in the process of being implemented and should be prioritised at each site.\nUseful git commands\n\ngit branch -r - List all branches\ngit fetch -p - Update branches from remote and remove deleted branches locally\ngit merge --no-ff - Merge branch into current branch\ngit branch -d - Remove branch once merged \n\nGit Flow Plugin\nSupport for the git flow commands as an extension to git can be found here:\nhttps://github.com/nvie/gitflow\nThis does mean that the master and develop branches cannot be protected as they need to be pushed to from the local repository.\nQuickstart\ngit flow init (accept all prompts)\n\n#feature\ngit flow feature start NAME\n# commits etc\ngit push origin feature/NAME # open merge request on Github here\n\n#release\ngit flow release start vVERSION_NUMBER\n# commits and bump version number\ngit flow release finish vVERSION_NUMBER\ngit push origin --all # OR git push origin master\n"},"Development/v4.x/overview.html":{"url":"Development/v4.x/overview.html","title":"Overview","keywords":"","body":"Overview of SciCat\nThis page needs to be updated\nThis page provides a brief introduction to the overall workings of the SciCat data model, how things connect and links to more details on each component.\nBackend\nThe SciCat backend provides the REST API of the all system. It uses MongoDB to store all persistent information and leverages individual dtos to validate the information provided in input.\nModel Definitions:\n\nProperties: These are the values, types and other information. None of these are mongo specific and can all map to a relational database just as easily.\nRelations: These link models, i.e. allowing a DatasetLifecycle to be linked to a Dataset. The mongo connector handles this by embedding the datasetId in the document for you.\nACLs: These are access control limitations and these are very powerful. You can give a user full control of a model, some users can only have read access etc.\nMethods: When you create a model, loopback will automagically generate API endpoints that allows users to PUT, POST, PATCH, GET etc for all the models. However, sometimes this is not enough. For example, you may want to be able to generate a monthly report of all datasets created, so you define the method here, the name of the endpoint and the expected parameters. You will implement the method in the next section we cover now.\n\nModel Implementation:\nSciCat backend leverages nest.js and each endpoint is implemented by a specific function in the controller component of the associated model.\nThe models are defined in the model folder of each endpoint/model.\nIt leverages nest.js interface to Mongoose.\nMode details are found in each individual page\nRunning the backend\nThe backend can be run as it is, provided that a number of environmental variables are defined.\nFor the list of the environment variables available please see the Backend Configuration page.\nTo start the backend is as simple as running the following command:\nnpm start\nTesting the backend\nThere are multiple type of tests that can be run on the backend:\n\nunit tests\nThey can be run using the command:npm run tests\n\napi tests\nThese tests are defined in the test folder and tests the endpoint. \nTo run them use the command:npm run test:api:mocha\n\n\n"},"Development/v4.x/Data_Model.html":{"url":"Development/v4.x/Data_Model.html","title":"An Introduction to the Data Model","keywords":"","body":"An Introduction to the Data Model\nModel\nThere are many models within the catalog and many of them are self explanatory. The API server creates routes for creation, retrieval, modification and deletion. However, it is worth explaining the core models that are used.\nThe following graph shows a simplified class diagramm of the main model classes. It was generated semi-automatically from the swagger.json file using the WAPIml tool\n\nThe Ownable base model\nMost models inherit the ownable base model which regulates access based on the ownerGroup/ accessGroups fields. Relationships are defined by including the ID of the respective model (handled by loopback model definitions).\nDataset\nThis is the base class for all datasets, and is derived from the\nOwnable model. There are two classes that are derived from Dataset:\nRawDataset and DerivedDataset.\nThe data model and its API endpoints are described here.\nRawDataset\nThis is a dataset that has been collected from an experiment. It contains details about who owns it, contact information etc. Most importantly, it contains Scientific Metadata, and this is an extensible object to outline all parameters relevant to the dataset.\nEach raw dataset may contain a link to a single Proposal.\nThe data model and its API endpoints are described here.\nDerivedDataset\nThese are datasets that are created by analyzing the data in raw datasets. The fields allow to link both to the raw data files used in the analysis as well as to a software repository which keeps the source code used for the analysis, thus helping to keep track of the provenance of the data. You can add ScientificMetadata here as well.\nAlthough DerivedDataset does not contain a link to a Proposal, it may\nbe deduced. A corresponding Proposal may be found by identifying all\ninput RawDatasets (either directly or indirectly via other\nDerivedDatasets) from the inputDatasets field. These RawDatasets may\ncontain links to the corresponding Proposal.\nThe data model and its API endpoints are described here.\nDatasetLifecycle\nThere are many temporal operations for a dataset describing what happens to the Dataset during its lifetime. Therefore, this model contains information relating to the status of the dataset but not the metadata itself. The archive location, date that it should become public and information about archiving or retrieval. Technically the DatasetLifecycle is however embedded into the Dataset documents.\nThe data model and its API endpoints are therefore described as part of the Dataset model.\nInstrument\nThis collection is meant to store the available measurement instruments, ideally in a globally unique naming scheme (within the Expands/Panosc EU programs standardization on these topics is pursued). Datasets can link to these instruments (in addition to the creationLocation field), allowing for more detailed information about the measurement devices.\nThe data model and its API endpoints are described here.\nTechnique\nThis collection is meant to store the available measurement techniques, ideally in a globally unique naming scheme (within the Expands/Panosc EU programs standardization on these topics is pursued). Datasets can link to these techniques, allowing for more detailed information about the measurement method applied.\nOrigDatablocks and Datablocks\nSince SciCat contains only metadata, it must include links to the actual storage location of the data. This typically consists of lists of DataFile objects storing the file path, size, ownership, and checksum (but not the data itself). Since the data may be stored on tape archives or otherwise transformed, each file in a dataset is typically recorded in two places:\n\nOrigDatablock stores the path to the file on the original system at the time of ingestion into SciCat.\nDatablock stores the path to the file on the archive system. The archiveId property is used by the storage management system (not part of SciCat) to retrieve the data. For instance, a simple archiveId might give a path on to a tar bundle on the local tape storage. Some facilities omit Datablocks from their databases and use conventions for determining the data location based only on the OrigDatablock paths.\n\nLarge datasets may split files into multiple OrigDatablocks and Datablocks. The details of this are facility specific, and could reflect local organizational preferences or performance considerations. For instance, datasets with many files are often split into multiple OrigDatablock objects to avoid too-large MongoDB objects. Conversely, very large files may be split across several Datablocks during archiving due to limitations of the storage hardware.\nYou should always add the OrigDataBlock information when ingesting, whereas the Datablocks information is optional and would be filled later by the archive system, when it did its work of writing chunks of data to tape. For instance, a Datablockcan correspond to a single tar file which is written to tape and the contents of the Datablock is essentially the file listing of the tar file.\nThe data model and its API endpoints are described here and here.\nJob\nWhen a user wants to archive, retrieve or publish a dataset, SciCat must interact with the facility's storage and archiving system. This is done by creating a Job to track the interaction. The exact actions taken for each Job are often customized for each facility. A common action would be to forward the new Job entry to a Message Broker, e.g. RabbitMQ, from where it can be picked up by any program willing to react to this Job. For example, at PSI the RabbitMQ queue is emptied by a Node-RED process, which reads the Job information from the RabbitMQ queue. Alternatively, you can use other messaging solutions, such as Apache Kafka. In this way the (site specific) logic to handle the Jobs is kept outside the core of the SciCat system, giving a greater degree of flexibility. The external systems should ideally respond with status updates to the Job model, when there job is finished. As the job progresses, the system can make PUT/PATCH API calls back that update the Job Status Message and, when necessary, also the individual status of the involved datasets by updating the DatasetLifecycle information.\nThe data model and its API endpoints are described here.\nProposals\nA proposal is a written intention declaration of what kind of measurement people want to perform when and where. Such information is often available in an existing system outside of the data catalog, like digital user office systems for institutes which offer their instruments to other users.\nThis information can be synchronized into SciCat and facilitates the definition of the ownership of the datasets as well as providing information relevant for publication of data, such as principal investigators, abstracts etc. datasets are normally uniquely associated with one proposal.\nThe proposal information is however optional\nThe data model and its API endpoints are described here.\nPublishedData\nWhen Datasets are published you assign a DOI to a list of datasets. The PublishedData model keeps track of these information, i.e which dataset is being published, authorship, abstract and download links to access the corresponding file contents of the datasets.\nThe data model and its API endpoints are described here.\nUserSettings\nThis collection stores user preferences, such as the list of pre-selected columns in the dataset dashboard view, defaults for the table page size etc.\nThe data model and its API endpoints are described here.\nAttachment\nSome of the above mentioned models can have files (usually images) attached. The size is limited to 16MB for one attachment. Attachments are stored in the database.\nThe data model and its API endpoints are described here.\nExplorer\nWhen you have an instance of the data catalog running, the API endpoints and the related data models can be looked at at the following URL:\n\n/explorer/\nExample instance from ESS\n\nThe API explorer is a feature of Loopback and allows one to test various endpoints. This section outlines the core functionality. The first point to note is that requests require a token. You can use the login route under the User tab to obtain one of these, or use a CURL command.\nMain Screen\nThe top right entry field is where the token should be included. The token will define your access rights, so not all routes will be accessible. Each model has a tab which relates to its endpoint. Expanding the tab shows the routes section below.\n\nRoutes\nEach section outlines the routes for the chosen model. This is configurable but the HTTP verbs are supported here for most routes and conform to the standards. Error codes are also consistent. Expanding the chosen route allows one to test the response with sample data, or their own.\n\nSingle Route\nSelecting a route shows the expected JSON object and provides an editor to allow you to test the route. Response information is shown below.\nClicking the data type box will insert it into your request for easy testing.\n\nModel\nThe model view can be accessed by clicking the link next to Example Value and shows the fields that Loopback is expecting to receive, as well as their type and whether they are required.\n\n"},"Development/v4.x/running.html":{"url":"Development/v4.x/running.html","title":"Running the Components","keywords":"","body":"Run SciCat\nThere are multiple ways to run SciCat, which are listed and described below.\nRun locally\nBackend\nTo run the backend successfully, you need to have a mongodb instance accessible.\nFirst we need to get the latest code available from the repo\ngit clone https://github.com/SciCatProject/backend.git \ncd backend\nnpm install\nBefore you csan run the backend successfully, you need to configure it.\nPlease refer to the configuration page for more details.\nOnce the configuration is set, you can start the backend with the command:\nnpm start\nThe backend api will be accessible at the addess http://localhost:3000\nFrontend\nSame as the backend, we need to get the code from the repo:\ngit clone https://github.com/SciCatProject/frontend.git \ncd frontend\nnpm install\nMake sure that you have the proper configuration and than you are ready to start the UI with the command:\nnpx ng serve\nFor convenience, this command has been added to the package.json so you can run npm start as an alias.\nThe frontend application can be accessed at the URL http://localhost:4200.\nRunning with Docker\nThe recommend way is to use scicatlive, althought it is not completely up-to-date.SciCatLive provides an all-in-one bundle containing all the necessary pieces to run an instance of SciCat successfully.\nIf you would like to run the latest versions, or a specific version of backend and frontend, you can find the images ready at the following URLs:\n\nBackend: https://github.com/SciCatProject/frontend/pkgs/container/frontend\nFrontend: https://github.com/SciCatProject/scicat-backend-next/pkgs/container/backend-next\n\nRunning with Kubernetes\nCreating a Kubernetes environment takes some expertise. There is an effort in the community to provide examples and use cases on how to run SciCat on Kubernetes. Stay tuned and check back soon.\n"},"Development/v4.x/configuration.html":{"url":"Development/v4.x/configuration.html","title":"Configuration","keywords":"","body":"Configuration\n\nSciCat Frontend\nSciCat Backend\nDotenv Config\nDOI Config\n\n\n\nSciCat Frontend\nThis is the configuration file for the frontend client. The configuration file allows the systems administrator to configure every aspect of the client including switching on/off almost all non essential features. \nThe configuration file can be served from the backend, via the admin/client/config.json endpoint. The frontend would expect the backend to be served on port 3000 in a routing configuration for this to work. The configuration file can also be mounted to the volume /home/node/app/dist/config/frontend.config.json for it to be served through this endpoint. \nAn example is shown below\nfrontend.config.json:\n\n{\n \"accessTokenPrefix\": \"\", // Set the backend token prefix. Should be empty string for current backend or \"Bearer \" if using scicat-backend-next.\n \"addDatasetEnabled\": true, // Show/hide the \"Create Dataset\" button in the Datasets Dashboard.\n \"archiveWorkflowEnabled\": true, // Enable/disable the archive/retrieve workflow.\n \"datasetReduceEnabled\": true, // Enable/disable the automatic Dataset reduction/analysis workflow.\n \"editDatasetSampleEnabled\": true, // Enable/disable editing of which Sample a Dataset belongs to.\n \"editMetadataEnabled\": true, // Enable/disable editing of Scientific Metadata.\n \"editPublishedData\": true, // Enable/disable editing of Published Data.\n \"editSampleEnabled\": true, // Enable/disable editing of Samples.\n \"externalAuthEndpoint\": \"/auth/msad\", // Endpoint used for third party authentication, e.g, LDAP.\n \"facility\": \"ESS\", // Facility running the SciCat instance.\n \"fileColorEnabled\": true, // Enable/disable file size color representation in the Datasets Dashboard.\n \"fileDownloadEnabled\": true, // Enable/disable download workflow for Dataset datafiles.\n \"gettingStarted\": null, // URL to Getting Started guide for SciCat, displayed on the Help page.\n \"ingestManual\": null, // URL to Ingest Manual for SciCat, displayed on the Help page.\n \"jobsEnabled\": true, // Enable/disable Job workflow.\n \"jsonMetadataEnabled\": true, // Show/hide the \"Show Metadata\" button on the details pages, allowing users to see the JSON represantion of the current document.\n \"jupyterHubUrl\": \"https://jupyterhub.esss.lu.se/\", // URL to Jupyter Hub instance used for data analysis.\n \"landingPage\": \"doi2.psi.ch/detail/\", // URL to the facility's Landing Page for Published Data.\n \"lbBaseURL\": \"http://127.0.0.1:3000\", // URL to the SciCat Backend.\n \"localColumns\": [ // Default columns to be displayed in the table on the Datasets Dashboard.\n {\n \"name\": \"select\",\n \"order\": 0,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"datasetName\",\n \"order\": 1,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"runNumber\",\n \"order\": 2,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"sourceFolder\",\n \"order\": 3,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"size\",\n \"order\": 4,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"creationTime\",\n \"order\": 5,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"type\",\n \"order\": 6,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"image\",\n \"order\": 7,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"metadata\",\n \"order\": 8,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"proposalId\",\n \"order\": 9,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"ownerGroup\",\n \"order\": 10,\n \"type\": \"standard\",\n \"enabled\": false\n },\n {\n \"name\": \"dataStatus\",\n \"order\": 11,\n \"type\": \"standard\",\n \"enabled\": false\n }\n ],\n \"logbookEnabled\": true, // Enable/disable SciChat Logbook integration.\n \"loginFormEnabled\": true, // Enable/disable the local Login form. Should be disabled if using oAuth2 for authentication.\n \"maxDirectDownloadSize\": 5000000000, // Set a maximum allowed file size for downloading datafiles over HTTP.\n \"metadataPreviewEnabled\": true, // Enable/disable Scientific Metadata preview on the Datasets Dashboard.\n \"metadataStructure\": \"tree\", // Allow tree structure for Scientific Metadata. Set to empty string for flat structure.\n \"multipleDownloadAction\": \"http://localhost:3012/zip\", // URL to service handling direct download of datafiles.\n \"multipleDownloadEnabled\": true, // Enable/disable ability to download multiple datafiles.\n \"oAuth2Endpoints\": [], // List of endpoints used for oAuth2 authentication.\n \"policiesEnabled\": true, // Enable/disable Dataset Policies workflow.\n \"retrieveDestinations\": [], // List of destinations for Dataset retrievals.\n \"riotBaseUrl\": \"http://scitest.esss.lu.se/riot\", // URL to SciChat client.\n \"scienceSearchEnabled\": true, // Enable/disable filtering documents on Scientific Metadata.\n \"scienceSearchUnitsEnabled\": true, // Enable/disable filtering documents on Scientific Metadata using units.\n \"searchPublicDataEnabled\": true, // Enable/disable filtering Datasets on public or non-public data.\n \"searchSamples\": true, // Enable/disable searching Samples on Samples Dashboard.\n \"sftpHost\": \"login.esss.dk\", // URL to SFTP service used for downloading files exceeding maximum allowed file size.\n \"shareEnabled\": true, // Enable/disable workflow for sharing Datasets with other users using their email address.\n \"shoppingCartEnabled\": true, // Enable/disable the Dataset cart used for bulk actions.\n \"shoppingCartOnHeader\": true, // Toggle Dataset cart placement, either on header or to the left on the Datasets Dashboard.\n \"tableSciDataEnabled\": true // Enable/disable Scientific Metadata table view on details pages. If disabled, Scientific Metadata is displayed as raw JSON.\n}\nSciCat Backend\nDotenv config\nThis is the configuration file for the backend. This file allows the systems administrator to configure connected services, like authentication services and message queues, and also switching on/off almost all non essential features. The configuration file is a dotenv file and is read by the backend at runtime.\nThere are currently many configurable additions to SciCat which makes it very flexible these are:\n\nOIDC for authenticatoin\nLDAP for authentication\nElastic Search \nSMTP for sending emails to notify users of SciCat jobs\nAMQP to provide a message queue for the jobs \n\nAn minimal example is shown below followed by a full example:\nMinimal Backend Config Example\n ADMIN_GROUPS [string] Optional Comma separated list of admin groups with admin permission assigned to the listed users. Example: \"admin, ingestor\". For more details check: Scicat Documentation\n\n EXPRESS_SESSION_SECRET [string] Optional Secret used to set up express session.\n\n HTTP_MAX_REDIRECTS [number] Optional Max redirects for http requests. Defaults to 5.\n\n HTTP_TIMEOUT [number] Optional Timeout from http requests in ms. Defaults to 5000.\n\n JWT_SECRET [string] The secret for your JWT token, used for authorization.\n\n JWT_EXPIRES_IN [number] Optional How long, in seconds, the JWT token is valid. Defaults to 3600.\n\n MONGODB_URI [string] The URI for your MongoDB instance.\nFull Backend Config Example\nbackend/.env\n\nValid environment variables for the .env file. See .env.example for examples value formats.\n\n## Data Management\n\n ADMIN_GROUPS [string] Optional Comma separated list of admin groups with admin permission assigned to the listed users. Example: \"admin, ingestor\". For more details check: Scicat Documentation\n\n CREATE_DATASET_GROUPS [string] Optional Comma seperated list of create dataset groups. Users belong to the listed groups can create dataset with/without PID. Example: \"group1, group2\". For more details check: Scicat Documentation\n\n CREATE_DATASET_WITH_PID_GROUPS [string] Optional Comma seperated list of create dataset with pid groups. Users belong to the listed groups can create dataset with PID. Example: \"group1, group2\". For more details check: Scicat Documentation\n\n DELETE_GROUPS [string] Optional Comma seperated list of delete groups. Users belong to the listed groups can delete any dataset, origDatablocks, datablocks etc. For more details check: Scicat Documentation\n\n DATASET_CREATION_VALIDATION_ENABLED [boolean] Flag to enable/disable dataset validation to validate if requested new dataset is valid with given regular expression. Preconfigure DATASET_CREATION_VALIDATION_REGEX variable is required. Default value: false\n\n DATASET_CREATION_VALIDATION_REGEX [string] Regular expression validation for new dataset request. Default value: \"\"\n\n PROPOSAL_GROUPS [string] Optional Comma separated list of proposal groups with permission to create any proposals. Example: \"proposaladmin, proposalingestor\". For more details check: Scicat Documentation\n\n SAMPLE_GROUPS [string] Optional Comma separated list of sample groups with permission to create any samples. Example: \"sampleadmin, sampleingestor\". For more details check: Scicat Documentation\n\n## Access groups from external administration systems\n\n ACCESS_GROUPS_STATIC_VALUES [string] Optional Comma separated list of access groups automatically assigned to all users. Example: \"scicat, user\"\n\n ACCESS_GROUPS_SERVICE_TOKEN [string] Optional Authentication token used if access groups are obtained from a third party service. This value is not used by the vanilla installation, but only if the instance is customized to use an external service to provide user groups, like the ESS example\n\n ACCESS_GROUP_SERVICE_API_URL [string] Optional URL of the service providing the users' access groups. This value is not used by the vanilla installation, but only if the instance is customized to use an external service to provide user groups, like the ESS example\n\n## Secrets, tokens and http settings\n\n EXPRESS_SESSION_SECRET [string] Optional Secret used to set up express session.\n\n HTTP_MAX_REDIRECTS [number] Optional Max redirects for http requests. Defaults to 5.\n\n HTTP_TIMEOUT [number] Optional Timeout from http requests in ms. Defaults to 5000.\n\n JWT_SECRET [string] The secret for your JWT token, used for authorization.\n\n JWT_EXPIRES_IN [number] Optional How long, in seconds, the JWT token is valid. Defaults to 3600.\n\n## LDAP Settings - only required for authentication using LDAP\n\n LDAP_URL [string] Optional The URL to your LDAP server.\n\n LDAP_BIND_DN [string] Optional Bind_DN for your LDAP server.\n\n LDAP_BIND_CREDENTIALS [string] Optional Credentials for your LDAP server.\n\n LDAP_SEARCH_BASE [string] Optional Search base for your LDAP server.\n\n LDAP_SEARCH_FILTER [string] Optional Search filter for you LDAP server.\n\n## OIDC Settings - only required for OIDC authentication\n\n OIDC_ISSUER [string] Optional URL of the oidc server providing the authentication service. Example: https://identity.esss.dk/realm/ess.\n\n OIDC_CLIENT_ID [string] Optional Identity of the client that we want to use to obtain the user token. Example: scicat\n\n OIDC_CLIENT_SECRET [string] Optional Secret to provide to the oidc service to obtain the user token. Example: Aa1JIw3kv3mQlGFWhRrE3gOdkH6xreAwro\n\n OIDC_CALLBACK_URL [string] Optional SciCat callback URL that we want th eoidc service to redirect to, in case of successful login. Example: http://myscicat/api/v3/oidc/callback\n\n OIDC_SCOPE [string] Optional Space separated list of the info returned by the oidc service. Example: \"openid profile email\"\n\n OIDC_SUCCESS_URL [string] Optional SciCat Frontend auth-callback URL. Required in order to pass user credentials to SciCat Frontend after OIDC login. Example: https://myscicatfrontend/auth-callback\n\n OIDC_ACCESS_GROUPS [string] Optional Functionality is still unclear.\n\n## Scicat metadata settings\n\n PID_PREFIX [string] The facility PID prefix, with trailing slash.\n\n LOGBOOK_ENABLED [string] Optional Flag to enable/disable the Logbook endpoints. Values \"yes\" or \"no\". Defaults to \"no\".\n\n LOGBOOK_BASE_URL [string] Optional The base URL to the Logbook API. Only required if Logbook is enabled.\n\n METADATA_KEYS_RETURN_LIMIT [number] Optional The return limit for the /Datasets/metadataKeys endpoint.\n\n METADATA_PARENT_INSTANCES_RETURN_LIMIT Optional The return limit of Datasets to extract metadata keys from for the /Datasets/metadataKeys endpoint.\n\n MONGODB_URI [string] The URI for your MongoDB instance.\n\n SITE [string] The name of your site.\n\n POLICY_PUBLICATION_SHIFT [integer] Optional Embargo period expressed in years. Default value: 3 years\n\n POLICY_RETENTION_SHIFT [integer] Optional Retention period (aka how long the facility will hold on to data) expressed in years. Default value: -1 (data will be hold indefinitely)\n\n## Settings for publishing data sets\n\n OAI_PROVIDER_ROUTE [string] Optional URI to OAI provider, used for the /publisheddata/:id/resync endpoint.\n\n DOI_PREFIX [string] The facility DOI prefix, with trailing slash.\n\n PUBLIC_URL_PREFIX [string] The base URL to the facility Landing Page.\n\n PORT [number] Optional The port on which you want to access the app. Defaults to 3000.\n REGISTER_DOI_URI [string] URI to the organization that registers the facilities DOI's.\n\n REGISTER_METADATA_URI [string] URI to the organization that registers the facilities published data metadata.\n\n## Message queue settings - only required when using the jobs settings. This currently does not work in release 4.4 but will be released soon \n\n\n## SMTP Settings - need this option if you require Scicat to send emails. \n\n SMTP_HOST [string] Optional Host of SMTP server.\n\n SMTP_MESSAGE_FROM [string] Optional Email address that emails should be sent from.\n\n SMTP_PORT [string] Optional Port of SMTP server.\n\n SMTP_SECURE [string] Optional Secure of SMTP server.\n\n\n## Elastic Search settings - use these to enable elastic search \n\n ELASTICSEARCH_ENABLED [string] Flag to enable/disable the Elasticsearch endpoints. Values \"yes\" or \"no\". Defaults to \"no\"\n\n ES_HOST [string] Host of Elasticsearch server instance\n\n ES_USERNAME [string] Optional Elasticsearch username that can be customized when loads Elasticsearch server. Default value: 'elastic'\n\n ES_PASSWORD [string] Elasticsearch password that can be customized when loads Elasticsearch server.\n\n MONGODB_COLLECTION [string] Collection name to be mapped into specified Elasticsearch index. Used for data synchronization between mongoDB and Elasticsearch index.\n\n ES_MAX_RESULT [number] Maximum records can be indexed into Elasticsearch. Default value: 10000\n\n ES_FIELDS_LIMIT [number] The total number of fields in an index. Default value: 1000\n\n ES_REFRESH [string] If set to wait_for Elasticsearch will wait till data is insereted to specificied index then return response. Unless you have a good reason to wait for the change to become visible, always use false (the default setting).\nDOI Config\nThis config file contains the username and password for authenicating against your DOI provider, e.g., DataCite, which is used when registering the DOI and metadata of your published data.\nbackend/src/config/doiconfig.local.json\n\n{\n \"username\": \"\",\n \"password\": \"\"\n}\n"},"Development/v4.x/backend/configuration.html":{"url":"Development/v4.x/backend/configuration.html","title":"Backend","keywords":"","body":"Configuration\nWhen using the official release image, Backend configuration can be achieved by setting the environmental variables listed below through an orchestration/containerization system, the .env file or with a suitable method compatible with your environment.The current source code contains an example .env file, named .env.sample listing all the environment variable available to configure the backend.\nIf you are compiling the application from source, you can edit the file serc/config/configuration.ts with the correct values for your infrastructure. This option is still undocumented, although it is our intention to provide a detailed how-to guide as soon as we can.\nEnvironment Variables\nThis is complete the list of environment variable that can be used to configure SciCat backend.\nThe list is compiled according to the configuration class defined in src/config/configuration.ts\n\nADMIN_GROUPS:list of groups that have admin priviligesdefault: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nDELETE_GROUPS:list of groups that are allowed to delete contentdefault: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nCREATE_DATASET_GROUPS:list of non admin groups that are allowed to create datasets without pid. The pid is assigned by the system. If set to \"all\", all users can create a dataset belonging to any of the groups they belong to.default: \"#all\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nCREATE_DATASET_WITH_PID_GROUPS:list of non admin groups that are allowed to create datasets with explicit pid. If set to \"#all\", all users can create a dataset belonging to any of the groups they belong to and with esplicit pid.If the pid verification is enabled, pid will be validated agains the specification passed.default: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nCREATE_DATASET_PRIVILEGED_GROUPS:list of non admin groups that are allowed to create datasets for groups they do not belong to. If set to \"#all\", all users can create a dataset belonging to any group with explicit pid.If the pid verification is enabled, pid will be validated agains the specification passed.default: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nPROPOSAL_GROUPS:list of non admin groups that are allowed to create and update proposals for groups they do not belong to. If set to \"#all\", all users can create a dataset belonging to any group with explicit pid.default: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nSAMPLE_GROUPS:list of non admin groups that are allowed to create and update samples for the groups they belong to. If set to \"#all\", all users can create a dataset belonging to their group.default: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\nSAMPLE_PRIVILEGED_GROUPS:list of non admin groups that are allowed to create samples for any groups, but can only update samples belonging to groups they belong to.\ndefault: \"\"format: comma separated list of strings. Leading and trailing spaces are trimmed \n\n\n\nACCESS_GROUPS_STATIC_VALUES:List of groups assigned by default to all users. Used in the vanilla implementation for easy configuration.If you do not want or need to assign any default group, it should be set to empty string \"\".Default value: \"\"format: Comman separated list of strings. Leading and trailing spaces are trimmedexample: \"group1,group2,group3,...\" \n\nACCESS_GROUP_SERVICE_TOKEN:\nAccess token needed to access the API specified in ACCESS_GROUP_SERVICE_API_URL, used to retrieve access groups from a third party system._format*: string \n\nACCESS_GROUP_SERVICE_API_URL:Well formed url of the service API used to provide access groups. Only one value is allowed.format: stringexample: \"https://my.access.group/service/api/url\"\n\nDOIPREFIX:The facility DOI prefix, with trailing slash._default: \"\"format: string \n\nEXPRESS_SESSION_SECRET:Secret used to set up express session.default: \"\"format: string \n\nLOGOUT_URL:URL specified upon successful logout. It is returned in the json object for the frontend, or third party UI, to be used locally.default: \"\"format: string \n\nHTTP_MAX_REDIRECTS:Max number of redirects for http requests.default: 5format: integer \n\nHTTP_TIMEOUT:Timeout from http requests in ms.default: 5000format: integer\n\nJWTSECRET:The secret used to create any JWT token, used for authorization._default: \"\"format: string \n\nJWT_EXPIRES_IN:Expiration time of any JWT token in seconds.default: 3600 (s)format: integer \n\nJWT_NEVER_EXPIRES:Length of time that the never expiring jwt token will last.default: 100yformat: string as in number of years \n\nLDAP_URL:Full URI (including port) of your local LDAP server, if this is your selected authentication method.default: No defaultexample: ldaps://ldap.server.com:636/format: string \n\nLDAP_BIND_DN:Bind DN to access information on your LDAP server.default: No defaultformat: string \n\nLDAP_BIND_CREDENTIALS:Credentials associated with your bind DN to acccess your LDAP server.default: No defaultformat: string \n\nLDAP_SEARCH_BASE:Search base for your LDAP server.default: No defaultformat: string \n\nLDAP_SEARCH_FILTER:Search filter for you LDAP server.default: No defaultformat: stringexample: \"(LDAPUsername=)\" \n\nLDAP_MODE:type of ldap server we are communicating withNEEDS TO BE UPDATED. Not sure which other values are accepteddefault: adformat: stringacceptable values: ad \n\nLDAP_EXTERNAL_ID:LDAP matching field that provides the external iddefault: sAMAccountNameformat: string \n\nLDAP_USERNAME:LDAP field providing the usernamedefault: displayNameformat: string \n\nOIDC_ISSUER:Full URL of your OIDC identity providerdefault: No defaultformat: stringexample: \"https://identity.your.facility/your/realm\" \n\nOIDC_CLIENT_ID:Client id used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generateddefault: No defaultformat: stringexample: \"scicat\" \n\nOIDC_CLIENT_SECRET:Token used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generatedexample: \"90f1268...\" \n\nOIDC_CALLBACK_URL:URL of the endpoint that is called when the authentication has been executed with the OIDC service.default: No defaultformat: stringexample: \"http://localhost:3000/api/v3/oidc/callback\" \n\nOIDC_SCOPE:Information returned by the OIDC service together with tokendefault: No defaultformat: stringexample: \"openid profile email\" \n\nOIDC_SUCCESS_URL:Frontend URL that the user is directed to after a successful authentication. It must be a valid frontend URL.default: No defaultformat: stringexample: \"http://localhost:3000/Datasets\" \n\nOIDC_ACCESS_GROUPS:field used to retrieve access groups from the OIDC service. It is not used in the vanilla implementation.default: No defaultformat: stringexample: \"access_groups\" \n\nOIDC_ACCESS_GROUPS_PROPERTY:name of the OIDC property used to retrieve the users groups from OIDC.default: noneformat: string \n\nOIDC_AUTO_LOGOUT:if enabled, when login out from SciCat, we logout from OIDC also.default: falseformat: boolean \n\nOIDC_RETURN_URL:URL the user is redirected after a successful logoutdefault: noneformat: string \n\nLOGBOOK_ENABLED:Flag to enable/disable the Logbook endpoints.accept values: \"yes\", \"no\"default: noformat: string \n\nLOGBOOK_BASE_URL:The base URL to the SciChat wrapper API. Only required if Logbook is enabled.default: \"http://localhost:3030/scichatapi\"format: string \n\nLOGBOOK_USERNAME:The username used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.default: No defaultformat: string \n\nLOGBOOK_PASSWORD:The password used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.default: No defaultformat: string \n\nMETADATA_KEYS_RETURN_LIMIT:The maximum number of keys returned by the /Datasets/metadataKeys endpoint.default: No defaultformat: integer \n\nMETADATA_PARENT_INSTANCES_RETURN_LIMIT:The maximum number of Datasets used to extract metadata keys in the /Datasets/metadataKeys endpoint.default: No defaultformat: integer \n\nMONGODB_URI:The URI for your MongoDB instance.default: No defaultformat: string \"mongodb://:@:27017/\" \n\nOAI_PROVIDER_ROUTE:URI to OAI provider, which is used in the /publisheddata/:id/resync endpoint.default: no defaultformat: string \n\nPID_PREFIX:The facility PID prefix, with trailing slash.default: no defaultformat: string \n\nPUBLIC_URL_PREFIX:The base URL to the facility Landing Page.default: No defaultformat: stringexample: \"https://doi.ess.eu/detail/\" \n\nPORT:The port on which the backend listen on.default: 3000format: integer \n\nRABBITMQ_ENABLED:Flag to enable/disable RabbitMQ consumer.accepted values: \"yes\", \"no\"deprecated. Will be removed in future releases.default: noformat: string \n\nRABBITMQ_HOSTNAME:The hostname of the RabbitMQ message broker. Only required if RabbitMQ is enabled.deprecated. Will be removed in future releases.default: no defaultdefault: string \n\nRABBITMQ_USERNAME:The username used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is enabled.deprecated. Will be removed in future releases.default: no defaultformat: string \n\nRABBITMQ_PASSWORD:The password used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is\nenabled.deprecated. Will be removed in future releases.default: no defaultformat: string \n\nREGISTER_DOI_URI:URI to the organization that registers the facilities DOIs.default: no defaultformat: stringexample: \"https://mds.test.datacite.org/doi\" \n\nREGISTER_METADATA_URI:URI to the organization that registers the facilities published data metadata.default: no defaultformat: stringexample: =\"https://mds.test.datacite.org/metadata\" \n\nDOI_USERNAME:\nUsername used to authenticate on the DOI sitedefault: no defaultformat: string \n\nDOI_PASSWORD:Password used to authenticate on the DOI sitedefault: no defaultformat: string \n\nSITE:The name of your site.default: no defaultformat: string \n\nSMTP_HOST:Host of SMTP server.deprecated. Will be removed in future releases.default: no defaultformat: string \n\nSMTP_MESSAGE_FROM:Email address that emails should be sent from.deprecated. Will be removed in future releases.default: no defaultformat: string, email \n\nSMTP_PORT:Port of SMTP server.deprecated. Will be removed in future releases.default: no defaultformat: string \n\nSMTP_SECURE:Secure of SMTP server.deprecated. Will be removed in future releases.default: no defaultformat: string \n\nPOLICY_PUBLICATION_SHIFT:Number of years that needs to elapse before the dataset is made publicly acceessibledefault: 3format: integer \n\nPOLICY_RETENTION_SHIFT:Number of years that the datasets are kept online before are archived or deleted. A negative value means that they are never archived/deleteddefault: -1format: integer \n\nELASTICSEARCH_ENABLED:Flag to enable/disable the ElasticSearch serviceaccept values: \"yes\", \"no\"default: no defaultformat: string \n\nES_HOST:The base URL to the Elasticsearch cluster. Use http if xpack.security is disableddefault: no defaultformat: stringexample: \"https://localhost:9200\" or \"http://localhost:9200\" \n\nMONGODB_COLLECTION:Collection name to be mapped into specified Elasticsearch indexdefault: no defaultformat: string \n\nES_MAX_RESULT:Maximum records can be indexed into Elasticsearch.default: 10000format: number \n\nES_FIELDS_LIMIT:The total number of fields in an index.default: 1000format: number \n\nES_INDEX:The total number of fields in an index.default: no defaultformat: string \n\nES_REFRESH:The total number of fields in an index.accept values: true, false, \"waitfor\"_default: falseformat: boolean or string \n\nES_USERNAME:Elasticsearch cluster username.default: no default, optional.format: string \n\nELASTIC_PASSWORD:Elasticsearch cluster password.default: no default.format: string \n\n\n"},"Development/v4.x/backend/authorization.html":{"url":"Development/v4.x/backend/authorization.html","title":"Authorization","keywords":"","body":"Authorization\naka permissions\naka who can do what\nSciCat backend v4.x rely on CASL to manage permissions.The default vanilla installation of the backend is configured with the permissions described and linked below.To avoid confusion and clarify the terminology used below, the term User indicates a normal authenticated user with no elevated permissions, while Admin indicates any user who belongs to a group that it is listed in the environmental variable ADMIN_GROUPS.By default ADMIN_GROUPS is set to groups: admin, ingestor, archivemanager.\nSpecial case is for deleting items in SciCat. Users with groups listed in DELETE_GROUPS, are allowed to perform delete. Default value is archivemanager.\nIMPORTANT In V3.x, permissions were managed through roles. In V4.x, roles are not used, and they are converted to user group.\nIn the vanilla installation, the default functional accounts are assigned to groups as follow:\n\nuser: admingroup: admin \n\nuser: ingestorgroup: ingestor \n\nuser: archiveManagergroup: archivemanager\n\n\nThis allow for the flexibility required by many installations in different facilities with different needs. \nGroup Lists available in Vanilla Configuration\nThe permissions in the vanilla installation provides a set of user groups which acquires specific set of permissions. In order to assign a set of permissions to a specific group of user, add such group to the correct list indicated below.\n\n\n\nConfiguration Group List\nDescription\nCASL ability actions\n\n\n\n\nauthenticated users\nAuthenticated users can view/access all datasets that belong to one of the groups they belong to\nDatasetReadOwn\n\n\n\nUsers can view attachments for datasets belonging to one of their group\nDatasetAttachmentReadOwn\n\n\n\nUsers are allowed to view origdatablocks for datasets belonging to one of their group\nDatasetOrigdatablockReadOwn\n\n\n\nUsers are allowed to view datablocks for datasets belonging to one of their group\nDatasetDatablockReadOwn\n\n\n\nUsers can view the logbook of the datasets that belong to one of their group\nDatasetLogbookReadOwn\n\n\n\n\n\n\nCREATE_DATASET_GROUPS\nUsers of the listed groups can create and modify datasets for any of the groups they belong to. At creation time, the system assignes a pid to the new datasets. If the user assigns one, the system will ignore it.\nDatasetCreateOwn , DatasetReadOwn , DatasetUpdateOwn\n\n\n\nUsers are allowed to perform all operations on attachments for datasets belonging to one of their group\nDatasetAttachmentCreateOwn , DatasetAttachmentReadOwn , DatasetAtatchementUpdateOwn , DatasetAttachmentDeleteOwn\n\n\n\nUsers are allowed to create and update origdatablocks for datasets belonging to one of their group\nDatasetOrigdatablockCreateOwn , DatasetOrigdatablockReadOwn , DatasetOrigdatablockUpdateOwn\n\n\n\nUsers are allowed to create and update datablocks for datasets belonging to one of their group\nDatasetDatablockCreateOwn , DatasetDatablockReadOwn , DatasetDatablockUpdateOwn\n\n\n\nUsers can view the logbook of the datasets that belong to one of their group\nDatasetLogbookReadOwn\n\n\n\n\n\n\nCREATE_DATASET_WITH_PID_GROUPS\nUsers of the listed groups can create and modify datasets for any of the groups they belong to. They are allowed to specify the dataset pid. If they decided not to specify a pid, the system will assign one.\nDatasetCreateOwn , DatasetReadOwn , DatasetUpdateOwn\n\n\n\nUsers are allowed to perform all operations on attachments for datasets belonging to one of their group\nDatasetAttachmentCreateOwn , DatasetAttachmentReadOwn , DatasetAtatchementUpdateOwn , DatasetAttachmentDeleteOwn\n\n\n\nUsers are allowed to create and update origdatablocks for datasets belonging to one of their group\nDatasetOrigdatablockCreateOwn , DatasetOrigdatablockReadOwn , DatasetOrigdatablockUpdateOwn\n\n\n\nUsers are allowed to create and update datablocks for datasets belonging to one of their group\nDatasetDatablockCreateOwn , DatasetDatablockReadOwn , DatasetDatablockUpdateOwn\n\n\n\nUsers can view the logbook of the datasets that belong to one of their group\nDatasetLogbookReadOwn\n\n\n\n\n\n\nCREATE_DATASET_PRIVELEGED_GROUPS\nUsers of the listed groups can create datasets for any group, but can only modify datasets belong to one of the group they belong to. They are allowed to specify pids for new datasets. This settings are suggessted for ingestion functional accounts\nDatasetCreateAll , DatasetReadOwn , DatasetUpdateOwn\n\n\n\nUsers are allowed to perform all operations on attachments for datasets belonging to one of their group\nDatasetAttachmentCreateOwn , DatasetAttachmentReadOwn , DatasetAtatchementUpdateOwn , DatasetAttachmentDeleteOwn\n\n\n\nUsers are allowed to create origdatablocks for any datasets, but can only update them for datasets belonging to one of their group\nDatasetOrigdatablockCreateAny , DatasetOrigdatablockReadOwn , DatasetOrigdatablockUpdateOwn\n\n\n\nUsers are allowed to create and update datablocks for datasets belonging to one of their group\nDatasetDatablockCreateOwn , DatasetDatablockReadOwn , DatasetDatablockUpdateOwn\n\n\n\nUsers can view the logbook of the datasets that belong to one of their group\nDatasetLogbookReadOwn\n\n\n\n\n\n\nADMIN_GROUPS\nUsers of the listed groups can create and modify datasets belonging to any group. They are allowed to specify the dataset's pid at creation time\nDatasetCreateAny , DatasetReadAny , DatasetUpdateAny\n\n\n\nUsers are allowed to perform all operations on attachments for any datasets\nDatasetAttachmentCreateAny , DatasetAttachmentReadAny , DatasetAtatchementUpdateAny , DatasetAttachmentDeleteAny\n\n\n\nUsers are allowed to perform all operations on origdatablocks for any datasets, except delete\nDatasetOrigdatablockCreateAny , DatasetOrigdatablockReadAny , DatasetOrigdatablockUpdateAny\n\n\n\nUsers are allowed to perform all operations on datablocks for any datasets, except delete\nDatasetDatablockCreateAny , DatasetDatablockReadAny , DatasetDatablockUpdateAny\n\n\n\nUsers can view logbook for any datasets\nDatasetLogbookReadAny\n\n\n\n\n\n\nDELETE_GROUPS\nUsers whos group is listed here are allowed to delete datasets, origdatablock or datablock\nDatasetDeleteAny , DatasetOrigdatablockDeleteAny , DatasetDatablockDeleteAny\n\n\n\nSubsystems\n\nDatasets\nOrigDatablocks\nJobs\nUsers\n\nN.B.: we know that many subsystems are still missing. We are working on reviewing the authorization model for each one of them and producing the relative documentation. We welcome any contribution.\n"},"Development/v4.x/backend/authorization/authorization_datasets.html":{"url":"Development/v4.x/backend/authorization/authorization_datasets.html","title":"Datasets","keywords":"","body":"Datasets Authoorization\nCASL ability actions\nThis is the list of the permissions methods available for datasets and all their endpoints\nEndpoint authorization\n\nDatasetCreate\nDatasetRead\nDatasetUpdate\nDatasetDelete\nDatasetAttachmentCreate\nDatasetAttachmentRead\nDatasetAttachmentUpdate\nDatasetAttachmentDelete\nDatasetOrigdatablockCreate\nDatasetOrigdatablockRead\nDatasetOrigdatablockUpdate\nDatasetOrigdatablockDelete\nDatasetDatablockCreate\nDatasetDatablockRead\nDatasetDatablockUpdate\nDatasetDatablockDelete\nDatasetLogbookReadInstance authorization\n\nDatasetCreateOwnerNoPid\nDatasetCreateOwnerWithPid\nDatasetCreateAny\nDatasetReadManyPublic\nDatasetReadManyAccess\nDatasetReadManyOwner\nDatasetReadOnePublic\nDatasetReadOneAccess\nDatasetReadOneOwner\nDatasetReadAny\nDatasetUpdateOwner\nDatasetUpdateAny\nDetasetDeleteOwner\nDatasetDeleteAny\nDatasetAttachmentCreateOwner\nDatasetAttachmentCreateAny\nDatasetAttachmentReadPublic\nDatasetAttachmentReadAccess\nDatasetAttachmentReadOwner\nDatasetAttachmentReadAny\nDatasetAtatchementUpdateOwner\nDatasetAtatchementUpdateAny\nDatasetAttachmentDeleteOwner\nDatasetAttachmentDeleteAny\nDatasetOrigdatablockCreateOwner\nDatasetOrigdatablockCreateAny\nDatasetOrigdatablockReadPublic\nDatasetOrigdatablockReadAccess\nDatasetOrigdatablockReadOwner\nDatasetOrigdatablockReadAny\nDatasetOrigdatablockUpdateOwner\nDatasetOrigdatablockUpdateAny\nDatasetOrigdatablockDeleteAny\nDatasetDatablockCreateOwner\nDatasetDatablockCreateAny\nDatasetDatablockReadPublic\nDatasetDatablockReadAccess\nDatasetDatablockReadOwner\nDatasetDatablockReadAny\nDatasetDatablockUpdateOwner\nDatasetDatablockUpdateAny\nDatasetDatablockDeleteOwner\nDatasetDatablockDeleteAny\nDatasetLogbookReadOwner\nDatasetLogbookReadAny\n\nImplementation\nHow the different level of authorization translates in data condition applied byt he backend.\n\n* Public\nisPublished = true\n\n\n* Access (condition ar applied in logical or)\nisPublished = true\nownerGroup is one of the groups that the user belongs\naccessGroups are one of the groups that the user belongs\nsharedWith contains the user's email\n\n\n* Owner \nownerGroup is one of the groups that the user belongs\n\n\n* Any \nUser can perform the action to any dataset\n\n\n\nPriority\n\n%%{init: {'theme' : 'base', 'themeVariables': { 'fontSize': '11px', 'fontFamily' : 'monospace'}}}%%\ngraph LR;\n DatasetCreate-->DatasetCreateOwnerNoPid;\n DatasetCreateOwnerNoPid-->DatasetCreateOwnerWithPid;\n DatasetCreateOwnerWithPid-->DatasetCreateAny;\n DatasetRead-->DatasetReadManyPublic;\n DatasetReadManyPublic-->DatasetReadManyAccess;\n DatasetReadManyAccess-->DatasetReadManyOwner;\n DatasetReadManyOwner-->DatasetReadAny;\n DatasetRead-->DatasetReadOnePublic;\n DatasetReadOnePublic-->DatasetReadOneAccess;\n DatasetReadOneAccess-->DatasetReadOneOwner;\n DatasetReadOneOwner-->DatasetReadAny;\n DatasetUpdate-->DatasetUpdateOwner;\n DatasetUpdateOwner-->DatasetUpdateAny;\n DatasetDelete-->DatasetDeleteOwner;\n DatasetDeleteOwner-->DatasetDeleteAny;\n\n\nAuthorization table\n\n\n\nHTTP method\nEndpoint\nEndpoint Authorization\nAnonymous\nAuthenticated User\nCreate Dataset Groups\nCreate Dataset with Pid Groups\nCreate Dataset Privileged Groups\nAdmin Groups\nDelete Groups\nNotes\n\n\n\n\nPOST\nDatasets\nDatasetCreate\nno\nno\nOwner, w/o PIDDatasetCreateOwnerNoPid\nOwner, w/ PIDDatasetCreateOwnerWithPid\nAnyDatasetCreateAny\nAnyDatasetCreateAny\nno\n\n\nPOST\nDatasets/isValid\nDatasetCreate\nno\nno\nOwner, w/o PIDDatasetCreateOwnerNoPid\nOwner, W/ PIDDatasetCreateOwnerWithPid\nAnyDatasetCreateAny\nAnyDatasetCreateAny\nno\n\n\nGET\nDatasets\nDatasetRead\nPublicDatasetReadPublic\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nAnyDatasetReadyAny\nno\n\n\nGET\nDatasets/fullquery\nDatasetRead\nPublicDatasetReadManyPublic\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nAnyDatasetReadAny\nno\n\n\nGET\nDatasets/fullfacet\nDatasetRead\nPublicDatasetReadManyPublic\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nAnyDatasetReadAny\nno\n\n\nGET\nDatasets/metadataKeys\nDatasetRead\nPublicDatasetReadManyPublic\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nAnyDatasetReadAny\nno\n\n\nGET\nDatasets/count\nDatasetRead\nPublicDatasetReadManyPublic\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nHas AccessDatasetReadManyAccess\nAnyDatasetReadAny\nno\n\n\nGET\nDatasets/findOne\nDatasetRead\nPublicDatasetReadOnePublic\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nAnyDatasetReadAny\nno\n\n\nGET\nDatasets/pid\nDatasetRead\nPublicDatasetReadOnePublic\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nHas AccessDatasetReadOneAccess\nAnyDatasetReadAny\nno\n\n\nPATCH\nDatasets/pid\nDatasetUpdate\nno\nno\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nAnyDatasetUpdateAny\nno\n\n\nPUT\nDatasets/pid\nDatasetUpdate\nno\nno\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nAnyDatasetUpdateAny\nno\n\n\nPOST\nDatasets/pid/appendToArrayField\nDatasetUpdate\nno\nno\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nOwnerDatasetUpdateOwner\nAnyDatasetUpdateAny\nno\n\n\n\n\n\n\n\n\n\n\n\n\n\nDELETE\nDatasets/pid\nDatasetDelete\nno\nno\nno\nno\nno\nno\nAnyDatasetDeleteAny\n\n\n\n\n\n\n\n\n\n\n\n\n\nGET\nDatasets/pid/thumbnail\nDatasetRead\nPublicDatasetReadPublic\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nHas AccessDatasetReadAccess\nAnyDatasetReadAny\nno\n\n\n\n\n\n\n\n\n\n\n\n\n\nPOST\nDatasets/pid/attachments\nDatasetAttachmentCreate\nno\nno\nOwnerDatasetAttachmentCreateOwner\nOwnerDatasetAttachmentCreateOwner\nAnyDatasetAttachmentCreateAny\nAnyDatasetAttachmentCreateAny\nno\n\n\nGET\nDatasets/pid/attachments\nDatasetAttachmemntRead\nPublicDatasetAttachmentReadPublic\nHas AccessDatasetAttachmentReadAccess\nHas AccessDatasetAttachmentReadAccess\nHas AccessDatasetAttachmentReadAccess\nHas AccessDatasetAttachmentReadAccess\nAnyDatasetAttachmentReadAny\nno\n\n\nPUT\nDatasets/pid/attachments/aid\nDatasetAttachmemntUpdate\nno\nno\nOwnerDatasetAttachmentUpdateOwner\nOwnerDatasetAttachmentUpdateOwner\nOwnerDatasetAttachmentUpdateOwner\nAnyDatasetAttachmentCreateAny\nno\n\n\nDELETE\nDatasets/pid/attachments/aid\nDatasetAttachmemntDelete\nno\nno\nOwnerDatasetAttachmentDeleteOwner\nOwnerDatasetAttachmentDeleteOwner\nOwnerDatasetAttachmentDeleteOwner\nAnyDatasetAttachmentDeleteAny\nno\n\n\n\n\n\n\n\n\n\n\n\n\n\nPOST\nDatasets/pid/origdatablocks\nDatasetOrigdatablocksCreate\nno\nno\nOwnerDatasetOrigdatablockCreateOwner\nOwnerDatasetOrigdatablockCreateOwner\nAnyDatasetOrigdatablockCreateAny\nAnyDatasetOrigdatablockCreateAny\nno\n\n\nPOST\nDatasets/pid/origdatablocks/isValid\nDatasetOrigdatablocksCreate\nno\nno\nOwnerDatasetOrigdatablockCreateOwner\nOwnerDatasetOrigdatablockCreateOwner\nAnyDatasetOrigdatablockCreateAny\nAnyDatasetOrigdatablockCreateAny\nno\n\n\nGET\nDatasets/pid/origdatablocks\nDatasetOrigdatablocksRead\nPublicDatasetOrigdatablockReadPublic\nHas AccessDatasetOrigdatablockReadOAccess\nHas AccessDatasetOrigdatablockReadAccess\nHas AccessDatasetOrigdatablockReadAccess\nHas AccessDatasetOrigdatablockReadAccess\nAnyDatasetOrigdatablockReadAny\nno\n\n\nPATCH\nDatasets/pid/origdatablocks/oid\nDatasetOrigdatablocksUpdate\nno\nno\nOwnerDatasetOrigdatablockUpdateOwner\nOwnerDatasetOrigdatablockUpdateOwner\nOwnerDatasetOrigdatablockUpdateOwner\nAnyDatasetOrigdatablockCreateAny\nno\n\n\n\nDELETE\nDatasets/pid/origdatablocks/oid\nDatasetOrigdatablocksDelete\nno\nno\nno\nno\nno\nno\nAnyDatasetOrigdatablockDeleteAny\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPOST\nDatasets/pid/datablocks\nDatasetDatablocksCreate\nno\nno\nOwnerDatasetDatablockCreateOwner\nOwnerDatasetDatablockCreateOwner\nOwnerDatasetDatablockCreateOwner\nAnyDatasetDatablockCreateAny\nno\n\n\n\nGET\nDatasets/pid/datablocks\nDatasetOrigdatablocksRead\nPublicDatasetDatablockReadPublic\nHas AccessDatasetDatablockReadAccess\nHas AccessDatasetDatablockReadAccess\nHas AccessDatasetDatablockReadAccess\nHas AccessDatasetDatablockReadAccess\nAnyDatasetDatablockReadAny\nno\n\n\n\nPATCH\nDatasets/pid/datablocks/oid\nDatasetDatablocksUpdate\nno\nno\nOwnerDatasetDatablockUpdateOwner\nOwnerDatasetDatablockUpdateOwner\nOwnerDatasetDatablockUpdateOwner\nAnyDatasetDatablockCreateAny\nno\n\n\n\nDELETE\nDatasets/pid/datablocks/oid\nDatasetDatablocksDelete\nno\nno\nno\nno\nno\nno\nAnyDatasetDatablockDeleteAny\n\n\n\n\n\n\n\n\n\n\n\n\n\nGET\nDatasets/pid/logbook\nDatasetLogbookRead\nno\nOwnerDatasetLogbookReadOwner\nOwnerDatasetLogbookReadOwner\nOwnerDatasetLogbookReadOwner\nOwnerDatasetLogbookReadOwner\nAnyDatasetLogbookReadAny\nno\n\n\n\n"},"Development/v4.x/backend/authorization/authorization_origdatablocks.html":{"url":"Development/v4.x/backend/authorization/authorization_origdatablocks.html","title":"OrigDatablocks","keywords":"","body":"OrigDatablock Authorization\nCASL ability actions\nThis is the list of the permissions methods available for origdatablock and all their endpoints\nEndpoint Authorization\n\nOrigdatablockCreate\nOrigdatablockRead\nOrigdatablockUpdate\nOrigdatablockDelete\n\n(Data) Instance Authorization\n\nOrigdatablockCreateOwner\nOrigdatablockCreateAny\nOrigdatablockReadManyPublic\nOrigdatablockReadManyAccess\nOrigdatablockReadManyOwner\nOrigdatablockReadOnePublic\nOrigdatablockReadOneAccess\nOrigdatablockReadOneOwner\nOrigdatablockReadAny\nOrigdatablockUpdateOwner\nOrigdatablockUpdateAny\nOrigdatablockDeleteAny\n\nPriority\n\n%%{init: {'theme' : 'base', 'themeVariables': { 'fontSize': '11px', 'fontFamily' : 'monospace'}}}%%\ngraph LR;\n DatasetOrigdatablockCreate-->DatasetOrigdatablockCreateOwner;\n DatasetOrigdatablockCreateOwner-->DatasetOrigdatablockCreateAny;\n DatasetOrigdatablockRead-->DatasetOrigdatablockReadManyPublic;\n DatasetOrigdatablockReadManyPublic-->DatasetOrigdatablockReadManyAccess;\n DatasetOrigdatablockReadManyAccess-->DatasetOrigdatablockReadAny;\n DatasetOrigdatablockRead-->DatasetOrigdatablockReadOnePublic;\n DatasetOrigdatablockReadOnePublic-->DatasetOrigdatablockReadOneAccess;\n DatasetOrigdatablockReadOneAccess-->DatasetOrigdatablockReadAny;\n DatasetOrigdatablockUpdate-->DatasetOrigdatablockUpdateOwner;\n DatasetOrigdatablockUpdateOwner-->DatasetOrigdatablockUpdateAny;\n DatasetOrigdatablockDelete-->DatasetOrigdatablockDeleteOwner;\n DatasetOrigdatablockDeleteOwner-->DatasetOrigdatablockDelteAny;\n\n\nAuthorization table\n\n\n\nHTTP method\nEndpoint\nEndpoint Authentication\nAnonymous\nAuthenticated User\nCreate Dataset Groups\nCreate Dataset with Pid Groups\nCreate Dataset Privileged Groups\nAdmin Groups\nDelete Groups\nNotes\n\n\n\n\nPOST\norigdatablocks\nOrigdatablockCreate\nno\nno\nOwnerOrigdatablockCreateOwn\nOwnerOrigidatablockCreateOwn\nAnyOrigdatablockCreateAny\nAny OrigdatablockCreateAny\nno\n\n\nPOST\norigdatablocks/isValid\nOrigdatablockCreate\nno\nno\nOwnerOrigdatablockCreateOwn\nOwnerOrigdatablockCreateOwn\nAnyOrigdatablockCreateAny\nAnyOrigdatablockCreateAny\nno\n\n\nGET\norigdatablocks\nOrigdatablockRead\nPublicOrigdatablockReadManyPublic\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nAnyOrigdatablockReadAny\nno\n\n\nGET\norigdatablocks/fullquery\nOrigdatablockRead\nPublicOrigdatablockReadManyPublic\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nAnyOrigdatablockReadAny\nno\n\n\nGET\norigdatablocks/fullquery/files\nOrigdatablockRead\nPublicOrigdatablockReadManyPublic\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nAnyOrigdatablockReadAny\nno\n\n\nGET\norigdatablocks/fullfacet\nOrigdatablockRead\nPublicOrigdatablockReadManyPublic\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nHas AccessOrigdatablockReadManyAccess\nAnyOrigdatablockReadAny\nno\n\n\nGET\norigdatablocks/oid\nOrigdatablockRead\nPublicOrigdatablockReadOnePublic\nHas AccessOrigdatablockReadOneAccess\nHas AccessOrigdatablockReadOneAccess\nHas AccessOrigdatablockReadOneAccess\nHas AccessOrigdatablockReadOneAccess\nAnyOrigdatablockReadAny\nno\n\n\nPATCH\norigdatablocks/oid\nOrigdatablockUpdate\nno\nno\nOwnerOrigdatablockUpdateOwner\nOwnerOrigdatablockUpdateOwner\nOwnerOrigdatablockUpdateOwner\nAnyOrigdatablockUpdateAny\nno\n\n\nDELETE\norigdatablocks/oid\nOrigdatablockDelete\nno\nno\nno\nno\nno\nno\nAnyOrigdatablockDeleteAny\n\n\n\n"},"Development/v4.x/backend/authorization/authorization_proposals.html":{"url":"Development/v4.x/backend/authorization/authorization_proposals.html","title":"Proposals","keywords":"","body":"Proposals Authorization\nCASL ability actions\nThis is the list of the permissions methods available for Proposals and all their endpoints\nEndpoint Authorization\n\nProposalCreate\nProposalRead\nProposalUpdate\nProposalDelete\nProposalAttachmentCreate\nProposalAttachmentRead\nProposalAttachmentUpdate\nProposalAttachmentDelete\nProposalDatasetRead\n\n(Data) Instance Authorization\n\nProposalCreateOwner\nProposalCreateAny\nProposalReadManyPublic\nProposalReadManyAccess\nProposalReadManyOwner\nProposalReadOnePublic\nProposalReadOneAccess\nProposalReadOneOwner\nProposalReadAny\nProposalUpdateOwner\nProposalUpdateAny\nProposalDeleteOwner\nProposalDeleteAny\nProposalAttachmentCreateOnwer\nProposalAttachmentCreateAny\nProposalAttachmentReadManyPublic\nProposalAttachmentReadManyAccess\nProposalAttachmentReadManyOwner\nProposalAttachmentReadManyAny\nProposalAttachmentUpdateOwner\nProposalAttachmentUpdateAny\nProposalAttachmentDeleteOwner\nProposalAttachmentDeleteAny\nProposalDatasetReadPublic\nProposalDatasetReadAccess\nProposalDatasetReadOwner\nProposalDatasetReadAny\n\nPriority\n\ngraph LR;\n ProposalCreate-->ProposalsCreateOwner;\n ProposalCreateOwner-->ProposalCreateAny;\n ProposalRead-->ProposalReadManyPublic;\n ProposalReadManyPublic-->ProposalReadManyAccess;\n ProposalReadManyAccess-->ProposalReadManyOwner;\n ProposalReadManyOwner-->ProposalReadAny;\n ProposalRead-->ProposalReadOnePublic;\n ProposalReadOnePublic-->ProposalReadOneAccess;\n ProposalReadOneAccess-->ProposalReadOneOwner;\n ProposalReadOneOwner-->ProposalReadAny;\n ProposalUpdate-->ProposalUpdateOwner;\n ProposalUpdateOwner-->ProposalUpdateAny;\n ProposalDelete-->ProposalDeleteOwner;\n ProposalDeleteOwner-->ProposalDeleteAny;\n ProposalAttachmentCreate-->ProposalAttachmentCreateOnwer;\n ProposalAttachmentCreateOnwer-->ProposalAttachmentCreateAny;\n ProposalAttachmentRead-->ProposalAttachmentReadManyPublic;\n ProposalAttachmentReadManyPublic-->ProposalAttachmentReadManyAccess;\n ProposalAttachmentReadManyAccess-->ProposalAttachmentReadManyOwner;\n ProposalAttachmentReadManyOwner-->ProposalAttachmentReadManyAny;\n ProposalAttachmentUpdate-->ProposalAttachmentUpdateOwner;\n ProposalAttachmentUpdateOwner-->ProposalAttachmentUpdateAny;\n ProposalAttachmentDelete-->ProposalAttachmentDeleteOwner;\n ProposalAttachmentDeleteOwner-->ProposalAttachmentDeleteAny;\n ProposalDatasetRead-->ProposalDatasetReadPublic;\n ProposalDatasetReadPublic-->ProposalDatasetReadAccess;\n ProposalDatasetReadAccess-->ProposalDatasetReadOwner;\n ProposalDatasetReadOwner-->ProposalDatasetReadAny;\n\n\nAuthorization table\n\n\n\nHTTP method\nEndpoint\nEndpoint Authentication\nAnonymous\nAuthenticated User\nProposals Groups\nAdmin Groups\nDelete Groups\nNotes\n\n\n\n\nPOST\nProposals\nProposalCreate\nno\nno\nAnyProposalCreateAny\nAnyProposalCreateAny\nno\n\n\n\nGET\nProposals\nProposalRead\nPublicProposalReadManyPublic\nHas AccessProposalReadManyAccess\nHas AccessProposalReadManyAccess\nAnyProposalReadAny\nno\n\n\n\nGET\nProposals/fullquery\nProposalRead\nPublicProposalReadManyPublic\nHas AccessProposalReadManyAccess\nHas AccessProposalReadManyAccess\nAnyProposalReadAny\nno\n\n\n\nGET\nProposals/fullfacet\nProposalRead\nPublicProposalReadManyPublic\nHas AccessProposalReadManyAccess\nHas AccessProposalReadManyAccess\nAnyProposalReadAny\nno\n\n\n\nGET\nProposals/pid\nProposalRead\nPublicProposalReadOnePublic\nHas AccessProposalReadOneAccess\nHas AccessProposalReadOneAccess\nAnyProposalReadAny\nno\n\n\n\nGET\nProposals/fullquery\nProposalRead\nPublicProposalReadOnePublic\nHas AccessProposalReadOneAccess\nHas AccessProposalReadOneAccess\nAnyProposalReadAny\nno\n\n\n\nPATCH\nProposals/pid\nProposalUpdate\nno\nno\nOwnerProposalUpdateOwn\nAnyProposalUpdateAny\nno\n\n\n\nDELETE\nProposals/pid\nProposalDelete\nno\nno\nno\nno\nAnyProposalDeleteAny\n\n\n\n\n\n\n\n\n\nPOST\nProposals/pid/attachements\nProposalAttachementCreate\nno\nno\nAnyProposalAttachmentCreateAny\nAnyProposalAttachmentCreateAny\nno\n\n\n\nGET\nProposals/pid/attachements\nProposalAttachmentRead\nPublicProposalAttachmentReadManyPublic\nHas AccessProposalAttachmentReadManyAccess\nHas AccessProposalAttachmentReadManyAccess\nAnyProposalAttachmentReadManyAny\nno\n\n\n\nPATCH\nProposals/pid/attachments/aid\nProposalAttachmentUpdate\nno\nno\nOwnerProposalAttachmentUpdateOwner\nAnyProposalAttachmentUpdateAny\nno\n\n\n\nDELETE\nProposals/pid/attachment/aid\nProposalAttachmentDelete\nno\nno\nOnwerProposalAttachmentDeleteOwner\nAnyProposalAttachmentDeleteAny\nno\n\n\n\n\n\n\n\n\n\nGET\nProposals/pid/datasets\nProposalDatasetRead\nPublicProposalDatasetReadOnePublic\nHas AccessProposalDatasetReadOneAccess\nHas AccessProposalDatasetReadOneAccess\nAnyProposalDatasetReadOneAny\nno\n\n\n\n"},"Development/v4.x/backend/authorization/authorization_jobs.html":{"url":"Development/v4.x/backend/authorization/authorization_jobs.html","title":"Jobs","keywords":"","body":"Jobs Authorization\nCASL ability actions\nThis is the list of the permissions methods available for Jobs and all their endpoints.The authorization for jobs is consistently different from all the other endpoints \nEndpoint Authorization\n\nJobCreate\nJobRead\nJobStatusUpdate\nJobDelete\n\n(Data) Instance Authorization\n\njob_create_configuration: the job create section of the configuration dictates if the user can create the job \nJobCreateAny: users with this privileges can create jobs for any of the users that are defined in the create section of the job configuration \nJobReadAccess\nJobReadAny\njob_satatus_update_configuration: the job update section in configuration dictates if the user can update the status of the job\nJobStatusUpdateAny: users with this privileges can update the status of any job.\nJobDeleteAny\n\nPriority\n\ngraph LR;\n JobCreate-->job_create_configuration;\n job_create_configuration-->JobCreateAny;\n JobRead-->JobReadAccess;\n JobReadAccess-->JobReadAny;\n JobStatusUpdate-->job_status_update_configuration;\n job_status_update_configuration-->JobStatusUpdateAny;\n JobDelete-->JobDeleteAny;\n\n\nAuthorization table\n\n\n\nHTTP method\nEndpoint\nEndpoint Authentication\nAnonymous\nAuthenticated\nCreate Jobs Groups\nUpdate Jobs Groups\nAdmin Groups\nDelete Groups\nNotes\n\n\n\n\nPOST\nJobs\nplease see job createauthorization table\nplease see job createauthorization table\nplease see job createauthorization table\nAnyJobsCreateOwner\nno\nAnyJobsCreateAny\nno\n\n\n\nGET\nJobs\nJobRead\nno\nHas AccessJobReadAccess\nHas AccessJobReadAccess\nno\nAnyJobReadAny\nno\n\n\n\nGET\nJobs/jid\nJobRead\nno\nHas AccessJobReadAccess\nHas AccessJobReadAccess\nno\nAnyJobReadAny\nno\n\n\n\nPOST\nJobs/statusUpdate\nplease see job createauthorization table\nno\nplease see job updateauthorization table\nno\nOwnerJobStatusUpdateOwner\nAnyJobStatusUpdateAny\nno\n\n\n\nDELETE\nJobs/jid\nJobDelete\nno\nno\nno\nno\nno\nJobDeleteAny\n\n\n\nJob Create Authorization Table\nThese authorization permissions are configured directly in the create section of the job configuration.They apply to the jobs endpoint POST:Jobs \n\n\n\nJob Create Authorization\nEndpoint Authentication\nEndpoint Authentication Description\nInstance Authentication\nInstance Authentication Description\n\n\n\n\n#all\n#all\nany user can access this endpoint, both anonymous and authenticated\n#all\nAny user can create this instance of the job\n\n\n#admin\nADMIN_GROUPS\nOnly users belonging to any group listed in ADMIN_GROUPS will be able to access the endpoint\nADMIN_GROUPS\nOnly users belonging to any group listed in ADMIN_GROUPS will be able to create this instance of the job\n\n\n#authenticated\n#user\nany valid users can access the endpoint, independently from their groups\n#user\nany valid users can cretae this instance of the job\n\n\n#datasetPublic\n#all\nany user can access this endpoint, both anonymous and and authenticated\n#datasetPublic\nthe job instance will be created only if all the datasets listed are public\n\n\n#datasetAccess\n#user\nany valid user can access this endpoint, independently from their groups\n#datasetAccess\nthe job instance will be created only if the user has access to all the datasets listed\n\n\n#datasetOwner\n#user\nany valid user can access this endpoint, independently from their groups\n#datasetOwner\nthe job instance will be created only if the user is part of all the datasets owner group\n\n\nGROUP\nGROUP\nonly users that belongs to the specified group can access the endpoint\nGROUP\nthe job instance will be created only if all the datasets listed belong to the group specified\n\n\nUSER\nUSER\nonly the specified user can access the endpoint\n#datasetOwner\nthe job instance will be created only if all the datasets listed are owned by any of the user's groups\n\n\n\nIMPORTANT: use option #all carefully, as it allows anybody to create a new job. It is mostly use for debuging and testing\nJob Status Update Authorization Table\nThese authorization permissions are configured directly in the update section of the job configuration.They apply to the jobs endpoint POST:Jobs/statusUpdate \n\n\n\nJob Status Update Authorization\nEndpoint Authentication\nEndpoint Authentication Description\nInstance Authentication\nInstance Authentication Description\n\n\n\n\n#all\n#all\nany user can access this endpoint, both anonymous and authenticated\n#all\nAny user can update the status of this job instance\n\n\n#owner\n#user\nvalid user can access the endpoint\n#jobOwner\na user that belongs to the group listed as job owner can perform the update\n\n\n#admin\nADMIN_GROUPS\nOnly users belonging to any group listed in ADMIN_GROUPS will be able to access the endpoint\nADMIN_GROUPS\nOnly users belonging to any group listed in ADMIN_GROUPS are able to update the job status\n\n\nGROUP\nGROUP\nonly users that belongs to the specified group can access the endpoint\nGROUP\nthe job instance will be created only if all the datasets listed belong to the group specified\n\n\nUSER\nUSER\nonly the specified user can access the endpoint\n#datasetOwner\nthe job instance will be created only if all the datasets listed are owned by any of the user's groups\n\n\n\nIMPORTANT: use option #all carefully, as it allows anybody to update the status of the job. It is mostly use for debuging and testing\nJob Authorization priority\nThe endpoint authorization is the most permissive authorization across all the jobs defined.\nThe priority between job create and status update authorization is as follow:\n\ngraph LR;\n all-->user;\n user-->GROUP;\n GROUP-->USER;\n USER-->ADMIN_GROUPS;\n"},"Development/v4.x/backend/authorization/authorization_users.html":{"url":"Development/v4.x/backend/authorization/authorization_users.html","title":"Users","keywords":"","body":"Users Authorization\nCASL ability actions\nThis is the list of the permissions methods available for datasets and all their endpoints\nEndpoint authorization\n\nUserLogin\nUserRead\nUserCreate\nUserUpdate\nUserPassword\nUserDelete\n\nInstance authorization\n\nUserReadOwn\nUserReadAny\nUserCreateOwn\nUserCreateAny\nUserUpdateOwn\nUserUpdateAny\nUserPasswordOwn\nUserPasswordAny\nUserDeleteAny\n\nPriority\n\ngraph TD;\n UserLogin(E)\n UserCreate(E)-->UserCreateOwn(I)-->UserCreateAny(I);\n UserRead(E)-->UserReadOwn(I)-->UserReadAny(I);\n UserUpdate(E)-->UserUpdateOwner(I)-->UserUpdateAny(I);\n UserPassword(E)-->UserPasswordOwner(I)-->UserPasswordAny(I);\n UserDelete(E)-->UserDeleteOwn(I)-->UserDeleteAny(I);\n\n\n\n\nAuthorization table:\n\n\n\nHTTP method\nEndpoint\nEndpoint Authorization\nAnonymous\nAuthenticated User\nUser Privileged Groups\nAdmin Groups\nUser Delete Groups\n\n\n\n\nPOST\nUsers/jwt\nUserRead\nno\nOwnUserReadOwn\nno\nno\nno\n\n\nPOST\nUsers/login\nUserLogin\nno\nno\nno\nno\nno\n\n\nGET\nUsers/id\nUserRead\nno\nOwnUserReadOwn\nAnyUserReadAny\nAnyUserReadAny\nno\n\n\nGET\nUsers/id/userIdentity\nUserRead\nno\nOwnUserReadOwn\nAnyUserReadAny\nAnyUserReadAny\nno\n\n\nPOST\nUsers/id/settings\nUserCreate\nno\nOwnUserCreateOwn\nAnyUserCreateAny\nAnyUserCreateAny\nno\n\n\nGET\nUsers/id/settings\nUserUpdate\nno\nOwnUserReadOwn\nAnyUserReadAny\nAny UserReadAny\nno\n\n\nPUT\nUsers/id/settings\nUserUpdate\nno\nOwnUserUpdateOwn\nAnyUserUpdateAny\nAnyUserUpdateAny\nno\n\n\nPATCH\nUsers/id/settings\nUserUpdate\nno\nOwnUserUpdateOwn\nAnyUserUpdateAny\nAnyUserUpdateAny\nno\n\n\nPATCH\nUsers/id/password\nUserPassword\nno\nOwnUserPasswordOwn\nAnyUserPasswordAny\nAnyUserPasswordAny\nno\n\n\nDELETE\nUsers/id\nUserDelete\nno\nno\nno\nno\nAnyUserDeleteAny\n\n\nDELETE\nUsers/id/settings\nUserDelete\nno\nno\nno\nno\nAnyUserDeleteAny\n\n\nGET\nUsers/id/authorization/dataset/create\nUserRead\nno\nOwnUserReadOwn\nOwnUserReadOwn\nAnyUserReadAny\nno\n\n\nGET\nUsers/logout\nUserLogout\nno\nOwnUserLogoutOwn\nno\nno\nno\n\n\nGET\nuseridentities/findOne\nUserRead\nno\nOwnUserReadOwn\nAnyUserReadAny\nAnyUserReadAny\nno\n\n\n\n"},"Development/v4.x/backend/authorization/authorization_samples.html":{"url":"Development/v4.x/backend/authorization/authorization_samples.html","title":"Samples","keywords":"","body":"Samples Authorization\nCASL ability actions\nThis is the list of the permissions methods available for Samples and all their endpoints\nEndpoint Authorization\n\nSampleCreate\nSampleRead\nSampleUpdate\nSampleDelete\nSampleAttachmentCreate\nSampleAttachmentRead\nSampleAttachmentUpdate\nSampleAttachmentDelete\nSampleDatasetRead\n\n(Data) Instance Authorization\n\nSampleCreateOwner\nSampleCreateAny\nSampleReadManyPublic\nSampleReadManyAccess\nSampleReadManyOwner\nSampleReadOnePublic\nSampleReadOneAccess\nSampleReadOneOwner\nSampleReadAny\nSampleUpdateOwner\nSampleUpdateAny\nSampleDeleteOwner\nSampleDeleteAny\nSampleAttachmentCreateOwner\nSampleAttachmentCreateAny\nSampleAttachmentReadManyPublic\nSampleAttachmentReadManyAccess\nSampleAttachmentReadManyOwner\nSampleAttachmentReadManyAny\nSampleAttachmentUpdateOwner\nSampleAttachmentUpdateAny\nSampleAttachmentDeleteOwner\nSampleAttachmentDeleteAny\nSampleDatasetReadPublic\nSampleDatasetReadAccess\nSampleDatasetReadOwner\nSampleDatasetReadAny\n\nPriority\n\ngraph LR;\n SampleCreate-->SampleCreateOwner;\n SampleCreateOwner-->SampleCreateAny;\n SampleRead-->SampleReadManyPublic;\n SampleReadManyPublic-->SampleReadManyAccess;\n SampleReadManyAccess-->SampleReadManyOwner;\n SampleReadManyOwner-->SampleReadAny;\n SampleRead-->SampleReadOnePublic;\n SampleReadOnePublic-->SampleReadOneAccess;\n SampleReadOneAccess-->SampleReadOneOwner;\n SampleReadOneOwner-->SampleReadAny;\n SampleUpdate-->SampleUpdateOwner;\n SampleUpdateOwner-->SampleUpdateAny;\n SampleDelete-->SampleDeleteOwner;\n SampleDeleteOwner-->SampleDeleteAny;\n SampleAttachmentCreate-->SampleAttachmentCreateOwner;\n SampleAttachmentCreateOwner-->SampleAttachmentCreateAny;\n SampleAttachmentRead-->SampleAttachmentReadManyPublic;\n SampleAttachmentReadManyPublic-->SampleAttachmentReadManyAccess;\n SampleAttachmentReadManyAccess-->SampleAttachmentReadManyOwner;\n SampleAttachmentReadManyOwner-->SampleAttachmentReadManyAny;\n SampleAttachmentUpdate-->SampleAttachmentUpdateOwner;\n SampleAttachmentUpdateOwner-->SampleAttachmentUpdateAny;\n SampleAttachmentDelete-->SampleAttachmentDeleteOwner;\n SampleAttachmentDeleteOwner-->SampleAttachmentDeleteAny;\n SampleDatasetRead-->SampleDatasetReadPublic;\n SampleDatasetReadPublic-->SampleDatasetReadAccess;\n SampleDatasetReadAccess-->SampleDatasetReadOwner;\n SampleDatasetReadOwner-->SampleDatasetReadAny;\n\n\nAuthorization table\n\n\n\nHTTP method\nEndpoint\nEndpoint Authentication\nAnonymous\nAuthenticated User\nSample Groups\nSample Privileged Groups\nAdmin Groups\nDelete Groups\nNotes\n\n\n\n\nPOST\nSamples\nSampleCreate\nno\nno\nOwnerSampleCreateOwner\nAnySampleCreateAny\nAnySampleCreateAny\nno\n\n\n\nGET\nSamples\nSampleRead\nPublicSampleReadManyPublic\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nAnySampleReadAny\nno\n\n\n\nGET\nSamples/fullquery\nSampleRead\nPublicSampleReadManyPublic\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nAnySampleReadAny\nno\n\n\n\nGET\nSamples/fullfacet\nSampleRead\nPublicSampleReadManyPublic\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nHas AccessSampleReadManyAccess\nAnySampleReadAny\nno\n\n\n\nGET\nSamples/pid\nSampleRead\nPublicSampleReadOnePublic\nHas AccessSampleReadOneAccess\nHas AccessSampleReadOneAccess\nHas AccessSampleReadOneAccess\nAnySampleReadAny\nno\n\n\n\nGET\nSamples/fullquery\nSampleRead\nPublicSampleReadOnePublic\nHas AccessSampleReadOneAccess\nHas AccessSampleReadOneAccess\nHas AccessSampleReadOneAccess\nAnySampleReadAny\nno\n\n\n\nPATCH\nSamples/pid\nSampleUpdate\nno\nno\nOwnerSampleUpdateOwn\nOwnerSampleUpdateOwn\nAnySampleUpdateAny\nno\n\n\n\nDELETE\nSamples/pid\nSampleDelete\nno\nno\nno\nno\nno\nAnySampleDeleteAny\n\n\n\n\n\n\n\n\n\nPOST\nSamples/pid/Attachments\nSampleAttachmentCreate\nno\nno\nOwnerSampleAttachmentCreateOwner\nAnySampleAttachmentCreateAny\nAnySampleAttachmentCreateAny\nno\n\n\n\nGET\nSamples/pid/Attachments\nSampleAttachmentRead\nPublicSampleAttachmentReadManyPublic\nHas AccessSampleAttachmentReadManyAccess\nHas AccessSampleAttachmentReadManyAccess\nHas AccessSampleAttachmentReadManyAccess\nAnySampleAttachmentReadManyAny\nno\n\n\n\nDELETE\nSamples/pid/attachment/aid\nSampleAttachmentDelete\nno\nno\nOwnerSampleAttachmentDeleteOwner\nOwnerSampleAttachmentDeleteOwner\nAnySampleAttachmentDeleteAny\nAnySampleAttachmentDeleteAny\n\n\n\n\n\n\n\n\n\nGET\nSamples/pid/datasets\nSampleDatasetRead\nPublicSampleDatasetReadOnePublic\nHas AccessSampleDatasetReadOneAccess\nHas AccessSampleDatasetReadOneAccess\nHas AccessSampleDatasetReadOneAccess\nAnySampleDatasetReadOneAny\nno\n\n\n\n"},"Development/v4.x/elasticsearch/elasticsearch.html":{"url":"Development/v4.x/elasticsearch/elasticsearch.html","title":"Elastic Search","keywords":"","body":"Getting started\nTo be able to start application with Elasticsearch you will need to:\n\nStart with npm prepare:local to executes docker-compose file which pull and run Elasticsearch cluster as a docker container.\nStart the application with npm run start\n\nCheck the .env file\nFollowing environment variables are required to run Elasticsearch with Scicat Backend:\n\n\n\nVariables\nValues\nDescription\n\n\n\n\nELASTICSEARCH_ENABLED\nyes or no\nFlag to enable/disable the ElasticSearch service\n\n\nES_HOST\nhttps://localhost:9200\nUse http if xpack.security.enabled set to false\n\n\nES_USERNAME (optional)\nelastic\nElasticsearch cluster username. Can be customized with docker image environment values with ELASTIC_USERNAME\n\n\nES_PASSWORD\npassword\nElasticsearch cluster password. Can be customized with docker image environment values ELASTIC_PASSWORD\n\n\nMONGODB_COLLECTION\nDataset\nCollection name to be mapped into specified Elasticsearch index\n\n\nES_MAX_RESULT\n210000\nMaximum records can be indexed into Elasticsearch. If not set, default is 10000\n\n\nES_FIELDS_LIMIT\n400000\nThe total number of fields in an index. If not set, default is 1000\n\n\nES_INDEX\ndataset\nSetting default index for the application\n\n\nES_REFRESH\nwait_for\nWait for the change to become visible - CRUD actions\n\n\n\n\n\n\n** NOTE **\nES_REFRESH=\"wait_for\" is used for better testing flow. For production it should be set to false. Detailed docs about this setting can be found: docs-refresh\nSteps to synchronize data with Elasitcsearch\nTo enable search queries in Elasticsearch, you should adhere to the following steps:\n\nOpen Swagger page http://localhost:3000/explorer, Authorize with admin token\nOpen elastic-search endpoints and execute create-index.\nNOTE: This step can be skipped if you want to use default index dataset\nexecute sync-database to sync data into the given index.\n\nIf you see the following response, it means Elasticsearch is ready for use.\n{\n \"total\": 21670,\n \"failed\": 0,\n \"retry\": 0,\n \"successful\": 21670,\n \"noop\": 0,\n \"time\": 4777,\n \"bytes\": 279159021,\n \"aborted\": false\n}\nIndex, analysis and mapping settings\nIndex settings in Elasticsearch are used to define various configurations and behaviors for an index. Index settings are vital for optimizing Elasticsearch's performance, relevance of search results, and resource management, ensuring that the index operates efficiently and effectively according to the specific needs of the application using it.\nPre-defined index settings & dynamic mapping for nested objects can be found /src/elastic-search/configuration/indexSetting.ts\nFields to be mapped into Elasticsearch index can be found /src/elastic-search/configuration/datasetFieldMapping.ts\nIndex settings:\nexport const defaultElasticSettings = {\n index: {\n max_result_window: process.env.ES_MAX_RESULT || 2000000,\n number_of_replicas: 0,\n mapping: {\n total_fields: {\n limit: process.env.ES_FIELDS_LIMIT || 2000000,\n },\n nested_fields: {\n limit: 1000,\n },\n },\n },\n analysis: {\n analyzer: {\n autocomplete: {\n tokenizer: \"autocomplete\",\n filter: [\"lowercase\"],\n },\n autocomplete_search: {\n tokenizer: \"lowercase\",\n },\n },\n tokenizer: {\n autocomplete: autocomplete_tokenizer,\n },\n },\n} as IndicesIndexSettingsAnalysis;\n\n\n\nObject\nDescription\n\n\n\n\nindex\nIt contains settings that determine how an Elasticsearch index works, like how many results you can get from a search and how many copies of the data it should store. More details in index modules\n\n\nindex.max_result_window\nDetermines the maximum number of documents returned in a query. Default is 10000\n\n\nindex.number_of_replicas\nSets the number of replica shards. Default is 1\n\n\nindex.mapping.total_fields.limit\nSets the maximum number of fields an index can have. Default is 1000\n\n\nindex.mapping.nested_fields.limit\nSets the maximum number of nested fields. Default is 50\n\n\nanalysis\nIt defines how text data is processed before it is indexed, including custom analyzers and tokenizers which control the conversion of text into tokens or terms. More details in analyzer and analysis\n\n\n\n\nDynamic mapping:\nDynamic mapping is currently only used for scientific field. More details in Dynamic template\nexport const dynamic_template: Record[] = [\n {\n string_as_keyword: {\n path_match: \"scientificMetadata.*.*\",\n match_mapping_type: \"string\",\n mapping: {\n type: \"keyword\",\n ignore_above: 256,\n },\n },\n },\n {\n long_as_double: {\n path_match: \"scientificMetadata.*.*\",\n match_mapping_type: \"long\",\n mapping: {\n type: \"double\",\n coerce: true,\n ignore_malformed: true,\n },\n },\n },\n {\n date_as_keyword: {\n path_match: \"scientificMetadata.*.*\",\n match_mapping_type: \"date\",\n mapping: {\n type: \"keyword\",\n ignore_above: 256,\n },\n },\n },\n];\nAPIs\nNOTE: All requests require admin permissions.\nFor search query integration it is suggested to use client search service directly, which can be found /src/elastic-search/elastic-search.service.ts\nFollowing table is existing controller endpoints:\n\n\n\nendpoint\nparameters\nrequest\ndescription\n\n\n\n\n/api/v3/elastic-search/create-index?index=\nstring\nquery\nCreate an index with this name\n\n\n/api/v3/elastic-search/sync-database?index=\nstring\nquery\nSync datasets into elastic search with this index\n\n\n/api/v3/elastic-search/search\n{ \"text\": \"\", \"ownerGroup\": [], \"creationLocation\": [], \"type\": [], \"keywords\": [], \"isPublished\": false, \"scientific\": [{\"lhs\": \"\", \"relation\": \"\", \"rhs\": 0, \"unit\": \"\"}]}\nbody\nSearch query\n\n\n/api/v3/elastic-search/delete-index\nstring\nquery\nDelete an index with this name\n\n\n/api/v3/elastic-search/get-index\nstring\nquery\nGet an index settings with this name\n\n\n/api/v3/elastic-search/update-index\nto be updated\nto be updated\nto be updated\n\n\n\nSetting up a local Elasticsearch cluster\nDocker Configuration\nFor local development, you can spin up an Elasticsearch cluster using Docker with the following configuration:\nElasticsearch image configuration can be found in ./CI/E2E/docker-compose-local.yaml\nes01:\n image: docker.elastic.co/elasticsearch/elasticsearch:8.8.2\n ports:\n - 9200:9200\n environment:\n - xpack.security.enabled=false\n - node.name=es01\n - ES_JAVA_OPTS=-Xms2g -Xmx2g\n - cluster.name=es-cluster\n - cluster.initial_master_nodes=es01\n - ELASTIC_PASSWORD=password\n - bootstrap.memory_lock=true\n mem_limit: 4g\n ulimits:\n memlock:\n soft: -1\n hard: -1\n\n\n\nSettings\nDescription\n\n\n\n\nImage Version\nDocker image for Elasticsearch: docker.elastic.co/elasticsearch/elasticsearch:8.8.2.\n\n\nPort\nContainer-to-host port mapping: 9200:9200.\n\n\nmem_limit\nMaximum container memory, influenced by ES_JAVA_OPTS.\n\n\nulimits.memlock (soft, hard)\nMemory locking limits: -1 for unlimited.\n\n\nVariables\nDescription\n\n\nxpack.security.enabled\nToggle for Elasticsearch security; false disables authentication for development.\n\n\nnode.name\nUnique Elasticsearch node name; essential for cluster node identification.\n\n\nES_JAVA_OPTS\nJVM heap memory settings for Elasticsearch performance (e.g., -Xms2g -Xmx2g).\n\n\ncluster.name\nName of the Elasticsearch cluster for node grouping.\n\n\nELASTIC_PASSWORD\nPassword for the 'elastic' user; not needed if security is disabled.\n\n\nbootstrap.memory_lock\nPrevents swapping Elasticsearch memory to disk when enabled.\n\n\n\nUsing Helm for Kubernetes\nFor those looking to deploy on Kubernetes, refer to the official Helm chart documentation for Elasticsearch. Helm charts provide a more production-ready setup and can manage complex configurations.\nMore configuration information:\n\nMore complicated examples with docker-compose configuration\nMore service configures for production\n\n"},"Development/v4.x/frontend/configuration.html":{"url":"Development/v4.x/frontend/configuration.html","title":"Frontend","keywords":"","body":"Configuration\n\nSciCat Frontend\nSciCat Backend\nDotenv Config\nDOI Config\n\n\n\nSciCat Frontend\nThis is the configuration file for the frontend client. The configuration file allows the systems administrator to configure every aspect of the client including switching on/off almost all non essential features. The configuration file can either be served from the backend, via the /client/config.json endpoint or mounted into the docker container at the following path /usr/share/nginx/html/assets/config.json.\nAn example is shown below\nfrontend/src/assets/config.json:\n\n{\n \"accessTokenPrefix\": \"\", // Set the backend token prefix. Should be empty string for current backend or \"Bearer \" if using scicat-backend-next.\n \"addDatasetEnabled\": true, // Show/hide the \"Create Dataset\" button in the Datasets Dashboard.\n \"archiveWorkflowEnabled\": true, // Enable/disable the archive/retrieve workflow.\n \"datasetReduceEnabled\": true, // Enable/disable the automatic Dataset reduction/analysis workflow.\n \"editDatasetSampleEnabled\": true, // Enable/disable editing of which Sample a Dataset belongs to.\n \"editMetadataEnabled\": true, // Enable/disable editing of Scientific Metadata.\n \"editPublishedData\": true, // Enable/disable editing of Published Data.\n \"editSampleEnabled\": true, // Enable/disable editing of Samples.\n \"externalAuthEndpoint\": \"/auth/msad\", // Endpoint used for third party authentication, e.g, LDAP.\n \"facility\": \"ESS\", // Facility running the SciCat instance.\n \"fileColorEnabled\": true, // Enable/disable file size color representation in the Datasets Dashboard.\n \"fileDownloadEnabled\": true, // Enable/disable download workflow for Dataset datafiles.\n \"gettingStarted\": null, // URL to Getting Started guide for SciCat, displayed on the Help page.\n \"ingestManual\": null, // URL to Ingest Manual for SciCat, displayed on the Help page.\n \"jobsEnabled\": true, // Enable/disable Job workflow.\n \"jsonMetadataEnabled\": true, // Show/hide the \"Show Metadata\" button on the details pages, allowing users to see the JSON represantion of the current document.\n \"jupyterHubUrl\": \"https://jupyterhub.esss.lu.se/\", // URL to Jupyter Hub instance used for data analysis.\n \"landingPage\": \"doi2.psi.ch/detail/\", // URL to the facility's Landing Page for Published Data.\n \"lbBaseURL\": \"http://127.0.0.1:3000\", // URL to the SciCat Backend.\n \"localColumns\": [ // Default columns to be displayed in the table on the Datasets Dashboard.\n {\n \"name\": \"select\",\n \"order\": 0,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"datasetName\",\n \"order\": 1,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"runNumber\",\n \"order\": 2,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"sourceFolder\",\n \"order\": 3,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"size\",\n \"order\": 4,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"creationTime\",\n \"order\": 5,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"type\",\n \"order\": 6,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"image\",\n \"order\": 7,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"metadata\",\n \"order\": 8,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"proposalId\",\n \"order\": 9,\n \"type\": \"standard\",\n \"enabled\": true\n },\n {\n \"name\": \"ownerGroup\",\n \"order\": 10,\n \"type\": \"standard\",\n \"enabled\": false\n },\n {\n \"name\": \"dataStatus\",\n \"order\": 11,\n \"type\": \"standard\",\n \"enabled\": false\n }\n ],\n \"logbookEnabled\": true, // Enable/disable SciChat Logbook integration.\n \"loginFormEnabled\": true, // Enable/disable the local Login form. Should be disabled if using oAuth2 for authentication.\n \"maxDirectDownloadSize\": 5000000000, // Set a maximum allowed file size for downloading datafiles over HTTP.\n \"metadataPreviewEnabled\": true, // Enable/disable Scientific Metadata preview on the Datasets Dashboard.\n \"metadataStructure\": \"tree\", // Allow tree structure for Scientific Metadata. Set to empty string for flat structure.\n \"multipleDownloadAction\": \"http://localhost:3012/zip\", // URL to service handling direct download of datafiles.\n \"multipleDownloadEnabled\": true, // Enable/disable ability to download multiple datafiles.\n \"oAuth2Endpoints\": [], // List of endpoints used for oAuth2 authentication.\n \"policiesEnabled\": true, // Enable/disable Dataset Policies workflow.\n \"retrieveDestinations\": [], // List of destinations for Dataset retrievals.\n \"riotBaseUrl\": \"http://scitest.esss.lu.se/riot\", // URL to SciChat client.\n \"scienceSearchEnabled\": true, // Enable/disable filtering documents on Scientific Metadata.\n \"scienceSearchUnitsEnabled\": true, // Enable/disable filtering documents on Scientific Metadata using units.\n \"searchPublicDataEnabled\": true, // Enable/disable filtering Datasets on public or non-public data.\n \"searchSamples\": true, // Enable/disable searching Samples on Samples Dashboard.\n \"sftpHost\": \"login.esss.dk\", // URL to SFTP service used for downloading files exceeding maximum allowed file size.\n \"shareEnabled\": true, // Enable/disable workflow for sharing Datasets with other users using their email address.\n \"shoppingCartEnabled\": true, // Enable/disable the Dataset cart used for bulk actions.\n \"shoppingCartOnHeader\": true, // Toggle Dataset cart placement, either on header or to the left on the Datasets Dashboard.\n \"tableSciDataEnabled\": true // Enable/disable Scientific Metadata table view on details pages. If disabled, Scientific Metadata is displayed as raw JSON.\n}\nSciCat Backend\nDotenv config\nThis is the configuration file for the backend. This file allows the systems administrator to configure connected services, like authentication services and message queues, and also switching on/off almost all non essential features. The configuration file is a dotenv file and is read by the backend at runtime.\nAn example is shown below\nbackend/.env\nACCESS_GROUPS_STATIC_VALUES=\"group1,group2,group3,...\" // List of groups assigned by default as access groups to all users. Used in the vanilla implementation. Facilities customization might not use this\nACCESS_GROUP_SERVICE_TOKEN=\"90f126864824ede0e22f7b4407aa1a5cd8158e6cabbce39aaf091937589f1750\" // Token needed to access the API specified in ACCESS_GROUP_SERVICE_API_URL\nACCESS_GROUP_SERVICE_API_URL=\"https://my.access.group/service/api/url\" // Url of the service API which is used to provide access groups. At the moment only one value is allowed\nDOI_PREFIX=\"\" // The facility DOI prefix, with trailing slash.\nEXPRESS_SESSION_SECRET=\"\" // *Optional* Secret used to set up express session.\nHTTP_MAX_REDIRECTS=5 // *Optional* Max redirects for http requests. Defaults to 5.\nHTTP_TIMEOUT=5000 // *Optional* Timeout from http requests in ms. Defaults to 5000.\nJWT_SECRET= // The secret for your JWT token, used for authorization.\nJWT_EXPIRES_IN=3600 // *Optional* How long, in seconds, the JWT token is valid. Defaults to `3600`.\nLDAP_URL=\"ldaps://ldap.server.com:636/\" // *Optional* The URL (and port) to your LDAP server.\nLDAP_BIND_DN=\"@server.com\" // *Optional* Bind_DN for your LDAP server.\nLDAP_BIND_CREDENTIALS= // *Optional* Credentials for your LDAP server.\nLDAP_SEARCH_BASE= // *Optional* Search base for your LDAP server.\nLDAP_SEARCH_FILTER=\"(LDAPUsername={{username}})\" // *Optional* Search filter for you LDAP server.\nOIDC_ISSUER=\"https://identity.your.facility/your/realm\" // Full URL of the identity provider\nOIDC_CLIENT_ID= \"scicat\" // Client id used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generated\nOIDC_CLIENT_SECRET=\"90f126864824ede0e22f7b4407aa1a5cd8158e6cabbce39aaf091937589f1750\" // Token used to convert OIDC code to OIDC token. This is assigned in the OIDC service when the token is generated\nOIDC_CALLBACK_URL=\"http://localhost:3000/api/v3/oidc/callback\" // URL of the endpoint that is called when the authentication has been executed with the OIDC service.\nOIDC_SCOPE=\"openid profile email\" // Information returned by the OIDC service together with token\"\nOIDC_SUCCESS_URL=\"http://localhost:3000/explorer\" // URL of the endpoint that is called after a successful authentication. IT is not used in the vanilla implementaation.\nOIDC_ACCESS_GROUPS=\"access_groups\" // field used to retrieve access groups from the OIDC service. It is not used in the vanilla implementation.\nLOGBOOK_ENABLED= // *Optional* Flag to enable/disable the Logbook endpoints. Values \"yes\" or \"no\". Defaults to \"no\".\nLOGBOOK_BASE_URL=\"http://localhost:3030/scichatapi\" // *Optional* The base URL to the SciChat wrapper API. Only required if Logbook is enabled.\nLOGBOOK_USERNAME=\"\" // *Optional* The username used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.\nLOGBOOK_PASSWORD=\"\" // *Optional* The password used to authenticate to the SciChat wrapper API. Only required if Logbook is enabled.\nMETADATA_KEYS_RETURN_LIMIT=100 // *Optional* The return limit for the `/Datasets/metadataKeys` endpoint.\nMETADATA_PARENT_INSTANCES_RETURN_LIMIT=100 // *Optional* The return limit of Datasets to extract metadata keys from for the `/Datasets/metadataKeys` endpoint.\nMONGODB_URI=\"mongodb://:@:27017/\" // The URI for your MongoDB instance.\nOAI_PROVIDER_ROUTE=\"\" // *Optional* URI to OAI provider, used for the `/publisheddata/:id/resync` endpoint.\nPID_PREFIX=\"\" // The facility PID prefix, with trailing slash.\nPUBLIC_URL_PREFIX=\"https://doi.ess.eu/detail/\" // The base URL to the facility Landing Page.\nPORT=3000 // *Optional* The port on which you want to access the app. Defaults to `3000`.\nRABBITMQ_ENABLED= *Optional* Flag to enable/disable RabbitMQ consumer. Values \"yes\" or \"no\". Defaults to \"no\".\nRABBITMQ_HOSTNAME=\"localhost\" // *Optional* The hostname of the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nRABBITMQ_USERNAME=\"rabbitmq\" // *Optional* The username used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nRABBITMQ_PASSWORD=\"rabbitmq\" // *Optional* The password used to authenticate to the RabbitMQ message broker. Only required if RabbitMQ is enabled.\nREGISTER_DOI_URI=\"https://mds.test.datacite.org/doi\" // URI to the organization that registers the facilities DOIs.\nREGISTER_METADATA_URI=\"https://mds.test.datacite.org/metadata\" // URI to the organization that registers the facilities published data metadata.\nSITE= // The name of your site.\nSMTP_HOST= // Host of SMTP server.\nSMTP_MESSAGE_FROM= // Email address that emails should be sent from.\nSMTP_PORT= // Port of SMTP server.\nSMTP_SECURE= // Secure of SMTP server.\nDOI Config\nThis config file contains the username and password for authenicating against your DOI provider, e.g., DataCite, which is used when registering the DOI and metadata of your published data.\nbackend/src/config/doiconfig.local.json\n\n{\n \"username\": \"\",\n \"password\": \"\"\n}\n"},"Development/v4.x/Login.html":{"url":"Development/v4.x/Login.html","title":"Login and Accounts","keywords":"","body":"Login\nTwo login routes are provided - for functional accounts and for AD/LDAP logins.\nLoopback provides a UserIdentity for third party logins, (e.g. AD/LDAP, google, Twitter etc) \nA document is created in the db.UserIdentity collection.\nThe UserIdentity is fetched from MongoDB and stored in the ngrx store, (store.user.userIdentity) where \nit is used to access e.g. email for job initiators.\nThe login method needs to be defined in the client configuartion (environment file) usign the field externalAuthEndpoint. For example externalAuthEndpoint: \"/auth/msad\".\nSee loopback docs\nFor non-third party logins defined, no UserIdentity is created. These are special users for admin purposes including beamline specific users for ingestion etc.\nThese users should be defined in a seperate secrets repo in a file called functionalAccounts.json and are read by createFunctionalAccounts.js in the backend e.g.\n [{\n \"account\": \"admin\",\n \"password\": \"pass\",\n \"email\": \"admin@domain.com\",\n \"role\": \"admin\",\n \"global\": true\n }, {\n \"account\": \"ingestor\",\n \"password\": \"pass\",\n \"email\": \"ingestor@domain.com\",\n \"role\": \"ingestor\",\n \"global\": true\n }]\nOIDC\nFor OIDC integration, see the OIDC for details.\n"},"Development/v4.x/backend/testing_backend.html":{"url":"Development/v4.x/backend/testing_backend.html","title":"Backend","keywords":"","body":"Testing\nPackages\nFollowing is the list of hte main packages used to build, run and manage testing for the project\n\nChai - Assertion\nSupertest - NodeJS testing for HTTP calls\nMocha - Test runner framework\n\nAPI tests\nSciCat backend v4.x comes with many API tests. They are locate under the /test folder in the backend repository\nIn order to run the tests you need to have the backend running with an accessible mongodb instance.\nThe backend can be run from terminl with the command:\nnpm run dev\nIn a different terminal, you can run all the following tests:\n\nunit tests:npm run test\nlinting tests:npm run lint\napi tests based on mocha\nnpm run test:api:mocha\n\nIf you would like to run the mocha tests directly, you can use the following command: \nmocha --config ./test/config/.mocharc.json --timeout=5000 --reporter=nyan\nIf you are interested in a specific subset of API tests, you can add the --grep option to the mocha command.For example if you are interested in running only the test named Authorization functionalities, you can use the following command:mocha --config ./test/config/.mocharc.json --grep \\\"Authorization functionalities\\ --timeout=5000 --reporter=nyan\nCoverage\nThe following is the list of the current tests groups. We are trying to cover all the different components and endpoints of SciCat backend v4.x and we keep adding new tests every release cycle.\nThe coverage might not be complete. If you find any use case that is not covered by our tests, feel free to open an issue or, even better, to write a new set of tests and submit a PR.\nList of tests: \n\n\n\ntest file\nmain code\ntitle\ndescription\n\n\n\n\nAuth.js\n0100\nAuthorization functionalities\nTest login and logout for functional accounts\n\n\nCheckDifferentDatasetTypes.js\n0200\nCheck Different Dataset Types\nCheck different dataset types and their inheritance\n\n\nDatasetAuthorization.js\n0300\nTest access to dataset\nTest different use cases with different user and restricted access\n\n\nDatasetFilter.js\n0400\nTest retrieving datasets using filtering capabilities\nLeverage fullquery endpoint to retrieve different group of datasets\n\n\nDatasetLifecycle.js\n0500\nTest facet and filter queries\n\n\n\nDatasetTypes.js\n0550\n\n\n\n\nDerivedDatasetDatablock.js\n0600\n\n\n\n\nDerivedDataset.js\n0700\nDerived Datasets\ntest derived datasets functionalities\n\n\nDerivedDatasetOrigDatablock.js\n0800\nDerivedDatasetOrigDatablock\nTest OrigDatablocks and their relation to derived Datasets\n\n\nInstrument.js\n0900\nInstrument\ninstrument management, creation, update, deletion and search\n\n\nInstrumentsFilter.js\n1000\nTest retrieving instruments using filtering capabilities\n\n\n\nJobs.js\n1100\nTest New Job Model\n\n\n\nOrigDatablockForRawDataset\n1200\nTest OrigDatablocks and their relation to raw Datasets using origdatablocks endpoint\n\n\n\nPolicy.js\n1300\nSimple Policy tests\n\n\n\nProposalAuthorization.js\n1400\nTest access to proposal\n\n\n\nProposal.js\n1500\nSimple Proposal\n\n\n\nPublishedData.js\n1600\nTest of access to published data\n\n\n\nRandomizedDatasetPermissions.js\n1700\npermission test with bigger amount of data\n\n\n\nRawDatasetDatablock.js\n1800\nTest Datablocks and their relation to raw Datasets\n\n\n\nRawDataset.js\n1900\nRaw Datasets\n\n\n\nRawDatasetOrigDatablock.js\n2000\nTest OrigDatablocks and their relation to raw Datasets\n\n\n\nResetDataset.js\n2100\nCreate Dataset and its Datablocks, then reset Datablocks and embedded Datasetlifecycle status\n\n\n\nSample.js\n2200\nSimple Sample\nSamples functionality tests, like create, update and delete\n\n\nSampleAuthorization.js\n2250\nSample Authorization\nTest authorization for all samples operations\n\n\nUserAuthorization.js\n2300\nUser Authorization\ntest that user authorization are correct\n\n\nUsers.js\n2400\nLogin with functional accounts\n\n\n\nTest data\nIt is our intention to save all test data in the file TestData.js.In some cases, we need to code variation of the data directly in the test file.\nFunctional and Test accounts\nAll tests are performed assuming the following user accounts, groups and configuration are present.\nUsers and groups can be defined in the configuration structure defined in src/config/configuration.ts, although the preferred method for configuring functional/tests accounts is through the functional_accounts.json file present in the following folder ``\nThese settings are meant only for testing and to demonstrate the capabilities of backend V4.x. IT is highly recommended to remove accounts that are not needed and change passwords and group affiliations as needed.\nDeafault Accounts\nThe following table lists the accounts that are provided by default in a vanilla installation.\nPlease make sure to change them, update their password or remove them entirely in your production environment.\n\n\n\nAccount\nGroups\nAdmin Group\nDelete Group\nCreate Dataset w/o pid\nCreate Dataset w/ pid\nCreate Dataset Privileged\nCreate Job\nUpdate Job\n\n\n\n\nadmin\nadmin, global\nyes\nno\nAny\n\n\ningestor\ningestor\nyes\nno\nAny\n\n\narchiveManager\narchivemanager\nyes\nyes\nno\n\n\nproposalIngestor\nproposalingestor\nno\nno\nno\n\n\nuser 1\ngroup1\nno\nno\nOwn\n\n\nuser 2\ngroup2\nno\nno\nOwn\n\n\nuser 3\ngroup3\nno\nno\nOwn\n\n\nuser 4\ngroup4\nno\nno\nno\n\n\n\nGroup permissions\n\n\n\nEnvironmental variable\nList of groups\n\n\n\n\nADMIN_GROUPS\nadmin, ingestor, archivemanager\n\n\nDELETE_GROUPS\narchivemanager\n\n\nCREATE_DATASET_GROUPS\ngroup1,group2,group3\n\n\n\nTest Details\nThis section provides details on how all the tests files listed above are organized. Each subsection, provides a list of the test included in each file and the details of each one of them.\n\nDatasets \n0900: Instrument\n2300: User Authorization\n\n"},"Development/v4.x/backend/testing/instrument.html":{"url":"Development/v4.x/backend/testing/instrument.html","title":"Instrument","keywords":"","body":"Instrument\nInstrument test that the functionalities for creating, updating, deleting and retrieving instruments are working correctly\n\n\n\nTest Number\nHTTP Method\nEndpoint\nAuthenticated User\nExpected Request Status\nInput\nResults\n\n\n\n\n0010\nPOST\nInstruments\ningestor\n201\nTestData.InstrumentCorrect1\nTestData.InstrumentCorrect1\n\n\n0020\nPOST\nInstruments\ningestor\n201\nTestData.InstrumentCorrect2\nTestData.InstrumentCorrect2\n\n\n0030\nPOST\nInstruments\ningestor\n201\nTestData.InstrumentCorrect3\nTestData.InstrumentCorrect3\n\n\n0040\nPOST\nInstruments\ningestor\n400\nTestData.InstrumentCorrect2\nError: duplicate entry\n\n\n0050\nPOST\nInstruments\ningestor\n400\nTestData.InstrumentWrong1\nValidation Error\n\n\n0060\nPOST\nInstruments\nuser1\n400\nTestData.InstrumentCorrect2\nUnauthorized\n\n\n0070\nGET\nInstruments/instrumentId1\ningestor\n200\nn/a\nTestData.InstrumentCorrect1\n\n\n0080\nGET\nInstruments/instrumentId2\ningestor\n200\nn/a\nTestData.InstrumentCorrect2\n\n\n0090\nGET\nInstruments\ningestor\n200\nn/a\nTestData.InstrumentCorrect 1,2,3\n\n\n0100\nGET\nInstruments\ningestor\n200\n{where: {customMetadata: { main_user: \"ESS\"}}}\nTestData.InstrumentCorrect 1,2\n\n\n0110\nGET\nInstruments/findOne\ningestor\n200\nn/a\nTestData.InstrumentCorrect1\n\n\n0120\nGET\nInstruments/findOne\ningestor\n200\n{where: {customMetadata: { main_user: \"ESS\"}}}\nTestData.InstrumentCorrect 1\n\n\n0130\nGET\nInstruments/findOne\ningestor\n200\n{where: {customMetadata: { main_user: { like : \"somebody\"}}}}\nTestData.InstrumentCorrect 3\n\n\n0140\nGET\nInstruments\nuser1\n200\nn/a\nTestData.InstrumentCorrect 1,2,3\n\n\n0150\nPATCH\nInstruments/instrumentId2\ningestor\n200\n{ name: newName }\nTestData.InstrumentCorrect2 with name as newName\n\n\n0160\nGET\nInstruments/instrumentId2\ningestor\n200\nn/a\nTestData.InstrumentCorrect2 with name as newName\n\n\n0170\nGET\nInstruments/\ningestor\n200\n{where: {name: \"newName\"}}\nTestData.InstrumentCorrect2 with name as newName\n\n\n0180\nDELETE\nInstruments/instrumentId1\ningestor\n400\nn/a\nn/a\n\n\n0190\nDELETE\nInstruments/instrumentId1\narchiveManager\n200\nn/a\nn/a\n\n\n0200\nDELETE\nInstruments/instrumentId2\narchiveManager\n200\nn/a\nn/a\n\n\n0210\nDELETE\nInstruments/instrumentId3\narchiveManager\n200\nn/a\nn/a\n\n\n\n"},"Development/v4.x/backend/testing/user_authorization.html":{"url":"Development/v4.x/backend/testing/user_authorization.html","title":"User Authorization","keywords":"","body":"User Authorization\nUser authorization tests access to the Users endpoints according to the default configuration provided in the vanilla installation and illustrated at the beginning of this document.\n\n\n\nTest Number\nHTTP Method\nEndpoint\nAuthenticated User\nUser to be checked\nRequest Status\nResults\n\n\n\n\n0010\nGET\nUsers/uid/authorization/dataset/create\ningestor\nherself\n200\n{authorization: true}\n\n\n0020\nGET\nUsers/uid/authorization/dataset/create\nadmin\nherself\n200\n{authorization: true}\n\n\n0030\nGET\nUsers/uid/authorization/dataset/create\nadmin\nuser1\n200\n{authorization: true}\n\n\n0040\nGET\nUsers/uid/authorization/dataset/create\nadmin\nuser2\n200\n{authorization: true}\n\n\n0050\nGET\nUsers/uid/authorization/dataset/create\nadmin\nuser3\n200\n{authorization: true}\n\n\n0060\nGET\nUsers/uid/authorization/dataset/create\nadmin\nuser4\n200\n{authorization: false}\n\n\n0070\nGET\nUsers/uid/authorization/dataset/create\nuser1\nherself\n200\n{authorization: true}\n\n\n0080\nGET\nUsers/uid/authorization/dataset/create\nuser1\nadmin\n403\nn/a\n\n\n0090\nGET\nUsers/uid/authorization/dataset/create\nuser1\nuser2\n403\nn/a\n\n\n0100\nGET\nUsers/uid/authorization/dataset/create\nuser1\nuser3\n403\nn/a\n\n\n0110\nGET\nUsers/uid/authorization/dataset/create\nuser1\nuser4\n403\nn/a\n\n\n0120\nGET\nUsers/uid/authorization/dataset/create\nuser2\nherself\n200\n{authorization: true}\n\n\n0130\nGET\nUsers/uid/authorization/dataset/create\nuser3\nherself\n200\n{authorization: true}\n\n\n0140\nGET\nUsers/uid/authorization/dataset/create\nuser4\nherself\n200\n{authorization: false}\n\n\n0144\nGET\nUsers/uid/authorization/dataset/create\nanonymous\nadmin\n401\nn/a\n\n\n0146\nGET\nUsers/uid/authorization/dataset/create\nanonymous\nuser1\n401\nn/a\n\n\n0150\nPOST\nUsers/jwt\nanonymous\nn/a\n201\n{jwt: }\n\n\n0160\nPOST\nUsers/jwt\nadmin\nn/a\n201\n{jwt: }\n\n\n0170\nPOST\nUsers/jwt\nuser1\nn/a\n201\n{jwt: }\n\n\n0180\nGET\nUsers/uid\nadmin\nherself\n200\nAdmin user model without password\n\n\n0190\nGET\nUsers/uid\nadmin\nuser1\n200\nUser1 user model without password\n\n\n0200\nGET\nUsers/uid\nuser1\nherself\n200\nUser1 user model without password\n\n\n0210\nGET\nUsers/uid\nuser1\nadmin\n403\nn/a\n\n\n0220\nGET\nUsers/uid\nuser1\nuser2\n403\nn/a\n\n\n0230\nGET\nUsers/uid\nanonymous\nadmin\n401\nn/a\n\n\n0240\nGET\nUsers/uid\nanonymous\nuser1\n401\nn/a\n\n\n0250\nGET\nUsers/uid/userIdentity\nadmin\nherself\n200\nAdmin user identity model\n\n\n0260\nGET\nUsers/uid/userIdentity\nadmin\nuser1\n200\nUser1 user identity model\n\n\n0270\nGET\nUsers/uid/userIdentity\nuser1\nherself\n200\nUser1 user identity model\n\n\n0280\nGET\nUsers/uid/userIdentity\nuser1\nadmin\n403\nn/a\n\n\n0290\nGET\nUsers/uid/userIdentity\nuser1\nuser2\n403\nn/a\n\n\n0300\nGET\nUsers/uid/userIdentity\nanonymous\nadmin\n401\nn/a\n\n\n0310\nGET\nUsers/uid/userIdentity\nanonymous\nuser1\n401\nn/a\n\n\n0320\nGET\nUsers/uid/settings\nadmin\nherself\n200\nAdmin user settings model\n\n\n0330\nGET\nUsers/uid/settings\nadmin\nuser1\n200\nUser1 user settings model\n\n\n0340\nGET\nUsers/uid/settings\nuser1\nherself\n200\nUser1 user settings model\n\n\n0350\nGET\nUsers/uid/settings\nuser1\nadmin\n403\nn/a\n\n\n0360\nGET\nUsers/uid/settings\nuser1\nuser2\n403\nn/a\n\n\n0370\nGET\nUsers/uid/settings\nanonymous\nadmin\n401\nn/a\n\n\n0380\nGET\nUsers/uid/settings\nanonymous\nuser1\n401\nn/a\n\n\n0390\nGET\nUsers/userIdentities/findOne\nadmin\nherself\n200\nAdmin user identity model\n\n\n0400\nGET\nUsers/userIdentities/findOne\nadmin\nuser1\n200\nUser1 user identity model\n\n\n0410\nGET\nUsers/userIdentities/findOne\nuser1\nherself\n200\nUser1 user identity model\n\n\n0420\nGET\nUsers/userIdentities/findOne\nuser1\nadmin\n403\nn/a\n\n\n0430\nGET\nUsers/userIdentities/findOne\nuser1\nuser2\n403\nn/a\n\n\n0440\nGET\nUsers/userIdentities/findOne\nanonymous\nadmin\n401\nn/a\n\n\n0450\nGET\nUsers/userIdentities/findOne\nanonymous\nuser1\n401\nn/a\n\n\n0460\nPOST\nUsers/uid/jwt\nadmin\nherself\n201\n{ \"jwt\" : \"\"}\n\n\n0470\nPOST\nUsers/uid/jwt\nadmin\nuser1\n201\n{ \"jwt\" : \"\"}\n\n\n0480\nPOST\nUsers/uid/jwt\nuser1\nherself\n403\nn/a\n\n\n0490\nPOST\nUsers/uid/jwt\nuser1\nadmin\n403\nn/a\n\n\n0500\nPOST\nUsers/uid/jwt\nuser1\nuser2\n403\nn/a\n\n\n0510\nPOST\nUsers/uid/jwt\nanonymous\nadmin\n401\nn/a\n\n\n0520\nPOST\nUsers/uid/jwt\nanonymous\nuser1\n401\nn/a\n\n\n\n"},"Development/v4.x/Migration.html":{"url":"Development/v4.x/Migration.html","title":"Migration v3.x to v4.x","keywords":"","body":"Migrating from the old SciCat Backend\nWhere the current SciCat Backend accepts id fields in the database named pid, doi, or similar, this implementation requires there to be an id field of the form _id on every document. It is therefore necessary to run a database migration script towards MongoDB instance from a place where you have access to it and can install migrate-mongo package.\nAll database migration scripts are located in the migrations folder.\n\nTo run the migrations locally you just need to install all dependencies and run npm run start which will run the migrations first and start the application. If you want to run the migrations manually you can install migrate-mongo package globally and run migrate-mongo up or if it is already installed locally because is part of the dependencies it is possible to just run: node ./node_modules/migrate-mongo/bin/migrate-mongo.js up.\nTo run the migrations on the server you will need to copy the migration scripts together with migrate-mongo-config.js file to a location were you can access your MongoDB instance and have migrate-mongo package installed either globally(which is preferred) or locally if global installation is not possible. Then modify the config file to contain your MongoDB instance details like database name and url. Once modified, start the migration by running\n\nmigrate-mongo up\n\nif migrate-mongo is installed globally at the location where you want to run it from or if it is locally installed just run\n./node_modules/migrate-mongo/bin/migrate-mongo.js up\n\nMigration documentation and NestJs resources\nFollowing are the post that I found useful working on the migration:\n\nSchema and DTOs: https://betterprogramming.pub/how-to-use-data-transfer-objects-dto-for-validation-in-nest-js-7ff95309f650\nValidation:\nOfficial documentation\nCustom validation with datasbase in NestJs\nValidating nested objects with class-validator in NestJs \nValidating numeric query parameters in NestJS\nInjecting request object to a custom validation class in NestJS\n\n\nSwagger and OpenAPI:\nhttps://docs.nestjs.com/openapi/introduction\nhttps://docs.nestjs.com/openapi/types-and-parameters\nhttps://docs.nestjs.com/openapi/decorators\n\n\n\n\nFor the full documentation please go to the SciCat home page and follow the documentation link\n"},"Development/Documentation.html":{"url":"Development/Documentation.html","title":"Documentation","keywords":"","body":"Documentation\nOverview\nThe documentation consists of three main parts.\n\nThe \"Home Webgpage\" of the project , describing the purpose and use cases for the software. The source is located at https://github.com/SciCatProject/scicatproject.github.io \nThe documentation proper, split into User,Operator,Ingestor and Developer manual. The source is located at https://github.com/SciCatProject/documentation . It covers all components of the software, i.e frontend and backend. The documentation tool honkit (successor of gitbook) is used.\nThe documentation of the REST API of the backend. This is generated from the swagger.json file, which itself is generated from the model descriptions in loopback\n\nAll parts are hosted on the GitHub Pages platform. The documentation part is \"injected\" into the GitHub pages automatically by a travis job, which runs after each commit to the documentation repository\nThe live web site is then visible on the following URLs respectively\n\nhttps://scicatproject.github.io/\nhttps://scicatproject.github.io/documentation\nhttps://scicatproject.github.io/api\n\nChanges and Deployment of Home Webpage\ngit clone https://github.com/SciCatProject/scicatproject.github.io`\ncd scicatproject.github.io\n\n# make your changes on index.html directly, then git add and git commit as usual\n\ngit push origin master\nAfter pushing the changes they will immediately become visible at the URL https://scicatproject.github.io/\nChanges and Deployment of Manuals\ngit clone https://github.com/SciCatProject/documentation.git`\ncd documentation\n\n# make your edit/add/commit cycle\n\nnpm install honkit --save\nnpx honkit build\nServing the Documentation locally\nnpx honkit serve --port 4040\nPublishing the documentation\ngit push origin master\nThe push to the origin repo will trigger a travis job, which will publish the resulting documentation to https://scicatproject.github.io/documentation\nChanges and Deployment of API Documentation\nUse the following command to regenerate the API with an up-to-date swagger.json file\n # to install redoc-cli:\n # sudo npm install -g redoc-cli\n\n # to get an updated swagger.json file\n # wget https://YOUR-API-SERVER/explorer/swagger.json\n\n redoc-cli bundle -o index.html swagger.json\n\n # the make commit which replaces the index.html file at \n # https://github.com/SciCatProject/scicatproject.github.io/tree/master/api\n"}}}
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