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@@ -8,8 +8,8 @@ Developers: [Robert Huber](mailto:[email protected]), [Anusuriya Devaraju](mailto: | |
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| ## Overview | ||
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| F-UJI is a web service to programatically assess FAIRness of research data objects based on [metrics](https://doi.org/10.5281/zenodo.3775793) developed by the [FAIRsFAIR](https://www.fairsfair.eu/) project. | ||
| The service will be applied to demostrate the evaluation of objects in repositories selected for in-depth collaboration with the project. | ||
| F-UJI is a web service to programmatically assess FAIRness of research data objects based on [metrics](https://doi.org/10.5281/zenodo.3775793) developed by the [FAIRsFAIR](https://www.fairsfair.eu/) project. | ||
| The service will be applied to demonstrate the evaluation of objects in repositories selected for in-depth collaboration with the project. | ||
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| The '__F__' stands for FAIR (of course) and '__UJI__' means 'Test' in Malay. So __F-UJI__ is a FAIR testing tool. | ||
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| ### Clients and User Interface | ||
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| A web demo using F-UJI is available at https://www.f-uji.net | ||
| A web demo using F-UJI is available at <https://www.f-uji.net>. | ||
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| An R client package that was generated from the F-UJI OpenAPI definition is available from https://github.com/NFDI4Chem/rfuji. | ||
| An R client package that was generated from the F-UJI OpenAPI definition is available from <https://github.com/NFDI4Chem/rfuji>. | ||
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| An open source web client for F-UJI is available at https://github.com/MaastrichtU-IDS/fairificator. | ||
| An open source web client for F-UJI is available at <https://github.com/MaastrichtU-IDS/fairificator>. | ||
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| ## Assessment Scope, Constraint and Limitation | ||
| The service is **in development** and its assessment depends on several factors. | ||
| - In the FAIR ecosystem, FAIR assessment must go beyond the object itself. FAIR enabling services and repositories are vital to ensure that research data objects remain FAIR over time. Importantly, machine-readable services (e.g., registries) and documents (e.g., policies) are required to enable automated tests. | ||
| - In addition to repository and services requirements, automated testing depends on clear machine assessable criteria. Some aspects (rich, plurality, accurate, relevant) specified in FAIR principles still require human mediation and interpretation. | ||
| - The tests must focus on generally applicable data/metadata characteristics until domain/community-driven criteria have been agreed (e.g., appropriate schemas and required elements for usage/access control, etc.). For example, for some of the metrics (i.e., on I and R principles), the automated tests we proposed only inspect the ‘surface’ of criteria to be evaluated. Therefore, tests are designed in consideration of generic cross-domain metadata standards such as dublin core, dcat, datacite, schema.org, etc. | ||
| - FAIR assessment is performed based on aggregated metadata; this includes metadata embedded in the data (landing) page, metadata retrieved from a PID provider (e.g., Datacite content negotiation) and other services (e.g., re3data). | ||
| - The tests must focus on generally applicable data/metadata characteristics until domain/community-driven criteria have been agreed (e.g., appropriate schemas and required elements for usage/access control, etc.). For example, for some metrics (i.e., on I and R principles), the automated tests we proposed only inspect the ‘surface’ of criteria to be evaluated. Therefore, tests are designed in consideration of generic cross-domain metadata standards such as Dublin Core, DCAT, DataCite, schema.org, etc. | ||
| - FAIR assessment is performed based on aggregated metadata; this includes metadata embedded in the data (landing) page, metadata retrieved from a PID provider (e.g., DataCite content negotiation) and other services (e.g., re3data). | ||
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| ## Requirements | ||
| [Python](https://www.python.org/downloads/) `3.11+` | ||
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| ### Google Dataset Search | ||
| * Download the latest Dataset Search corpus file from: https://www.kaggle.com/googleai/dataset-search-metadata-for-datasets | ||
| * Open file fuji_server/helper/create_google_cache_db.py and set variable 'google_file_location' according to the file location of the corpus file | ||
| * Run create_google_cache_db.py which creates a SQLite database in the data directory. From root directory run `python3 -m fuji_server.helper.create_google_cache_db`. | ||
| * Download the latest Dataset Search corpus file from: <https://www.kaggle.com/googleai/dataset-search-metadata-for-datasets> | ||
| * Open file `fuji_server/helper/create_google_cache_db.py` and set variable 'google_file_location' according to the file location of the corpus file | ||
| * Run `create_google_cache_db.py` which creates a SQLite database in the data directory. From root directory run `python3 -m fuji_server.helper.create_google_cache_db`. | ||
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| The service was generated by the [swagger-codegen](https://github.com/swagger-api/swagger-codegen) project. By using the | ||
| [OpenAPI-Spec](https://github.com/swagger-api/swagger-core/wiki) from a remote server, you can easily generate a server stub. | ||
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| ## Usage | ||
| Before running the service, please set user details in the configuration file, see config/users.py. | ||
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| To install F-UJI, you may execute the following python-based or docker-based installation commands from the root directory: | ||
| To install F-UJI, you may execute the following Python-based or docker-based installation commands from the root directory: | ||
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| ### Python module-based installation: | ||
| ### Python module-based installation | ||
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| From the fuji source folder run | ||
| From the fuji source folder run: | ||
| ```bash | ||
| pip install . | ||
| ``` | ||
| The F-UJI server can now be started with. | ||
| python -m pip install . | ||
| ``` | ||
| The F-UJI server can now be started with: | ||
| ```bash | ||
| python -m fuji_server -c fuji_server/config/server.ini | ||
| ``` | ||
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| ### Docker-based installation: | ||
| ### Docker-based installation | ||
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| ```bash | ||
| docker run -d -p 1071:1071 ghcr.io/pangaea-data-publisher/fuji | ||
| ``` | ||
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| To access the OpenAPI user interface, open the url below on the browser: | ||
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| ``` | ||
| http://localhost:1071/fuji/api/v1/ui/ | ||
| ``` | ||
| To access the OpenAPI user interface, open the URL below in the browser: | ||
| <http://localhost:1071/fuji/api/v1/ui/> | ||
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| Your OpenAPI definition lives here: | ||
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| ``` | ||
| http://localhost:1071/fuji/api/v1/openapi.json | ||
| ``` | ||
| <http://localhost:1071/fuji/api/v1/swagger.json> | ||
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| You can provide a different server config file this way: | ||
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| ### Notes | ||
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| To avoid tika startup warning message, set environment variable TIKA_LOG_PATH. For more information, see [https://github.com/chrismattmann/tika-python](https://github.com/chrismattmann/tika-python) | ||
| To avoid Tika startup warning message, set environment variable `TIKA_LOG_PATH`. For more information, see [https://github.com/chrismattmann/tika-python](https://github.com/chrismattmann/tika-python) | ||
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| If you receive the exception 'urllib2.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] on MacOS, run the install command shipped with Python : | ||
| ./Install\ Certificates.command | ||
| If you receive the exception `urllib2.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED]` on macOS, run the install command shipped with Python: | ||
| `./Install\ Certificates.command`. | ||
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| F-UJI is using [basic authentication](https://en.wikipedia.org/wiki/Basic_access_authentication), so username and password have to be provided for each REST call which can be configured in fuji_server/config/users.py | ||
| F-UJI is using [basic authentication](https://en.wikipedia.org/wiki/Basic_access_authentication), so username and password have to be provided for each REST call which can be configured in `fuji_server/config/users.py`. | ||
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| ## License | ||
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