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CONTRIBUTING

+

If you contributed to the COBIDAS Checklist and your name is not listed, please +add it by adding your details in +this spreadsheet +and let us know about it: we'll take care of the rest.

+

Make sure that you are also listed as a contributor on our +OSF project.

+

Requirements

+

You will need to have installed.

+ +

If you are running Windows 10, you will want to work directly in a Linux Windows +sub-system.

+

An simple way to do this is to install +Ubuntu from the Windows App store.

+

Python 3.8 or above

+

An easy way to install Python is to rely on +Conda

+

If you are on Linux this should be enough:

+
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+bash Miniconda3-latest-Linux-x86_64.sh
+
+ + +

node.js

+

A good way to To install javascript node.js, is to +install node version manager (NVM) to help you +deal with different version of node.js.

+

In a terminal or in Windows WSL2, you can install NVM by typing:

+
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.33.11/install.sh | bash
+
+

Then close your terminal and reopen it then run the following to install the +version 12 of node.js (using the latest version of node.js should be fine, +so this is more there as an example).

+
nvm install node
+nvm install 12
+
+

Set up

+

Fork and clone the repo. Preferably set up a python virtual environment.

+

Then run make install.

+
git clone https://github.com/YOUR_GITHUB_USERNAME/eCobidas.git
+cd eCobidas
+virtualenv -p python3.8 env
+source env/bin/activate
+make install
+
+

Downloading the spreadsheet and converting them

+

A lot of recipe are already in tox.ini to facilitate daily work.

+

For example:

+
    +
  • make neurovault
    • +
  • make artemis
    • +
  • make pet
    • +
+

Here is a description of the Neurovault recipe make neurovault.

+
# remove previous runs:
+rm -rf inputs/neurovault
+rm -rf schemas/neurovault/
+
+# use a bash script to download the spreadsheet
+bash download_tsv.sh neurovault
+
+# use the local ecobidas python package to convert the spreadsheet in jsonld
+ecobidas_convert --schema neurovault
+
+# make sure the jsonld are valid with some node.js and reproschema validation
+reproschema -l DEBUG validate schemas/neurovault
+
+

Serving the local protocols and activities to test them with the reproschema-ui

+
python local_server.py
+
+

Editing the spreadsheets

+

DO NOT EDIT THE DIRECTLY IN THE REPOSITORY!!!

+

Instead edit them on the google drive and use download_tsv.sh to download them +locally. Or type make download_all to download update all the spreadsheets.

+

Visual studio code settings

+

Load the ecobidas.code-workspace file in the .vscode folder.

+

Requirements and set up

+

You will need to have python installed.

+

There are few dependencies (for now). One of them is the +reproschema-py package that +helps to validate that the schema you create is valid.

+

Preferably use a virtual environment:

+
virtualenv -p python3.8 env
+source env/bin/activate
+pip install -r requirements.txt
+
+

Install the "dev" version of reproschema

+
# clone Rémi's fork of Reproschema on your machine, right outside of this repo
+git clone https://github.com/Remi-Gau/reproschema-py.git  ../../
+# checkout the "dev" branch
+cd ../../reproschema-py
+git checkout remi_schema_creator
+# pip install it
+pip install -e .
+
+

Install the package

+

To be able to use the command line tool, do the following from within the repo

+
pip install -e .
+
+

Generate the schemas

+

If you have installed the package the following command line call should run the +conversion.

+
ecobidas_convert    #<-- to create a whole protocol + activities
+
+

You only need to specify:

+
    +
  • schema the name of the protocol you want to convert (it can be several of protocols at once),
    • +
  • out_dir where the schemas is meant to be created on your computer,
    • +
  • repo the URL of the repository where the schema will be hosted
    • +
  • branch of the repository where the schema will be hosted
    • +
+

Implementation

+

TODO ecobidas convert calls: the highest level function that you will use to create the schema is ...

+ + +

Additional "entry points" should be set up in cli.py and adapted in setup.py.

+

This code also relies on several classes from the "dev" branch of reproschema-py:

+
base.py               # --> Base()
+  ├── protocol.py     # --> Protocol()
+  ├── activity.py     # --> Activity()
+  └── item.py         # --> Item(), ResponseOptions()
+
+

Class inheritance

+
Base()
+  ├── Protocol()
+  ├── Activity()
+  ├── Item()
+  └── ResponseOptions()
+
+

Validate the output

+
reproschema -l DEBUG validate path_to_the_file_to_validate
+
+

View the results

+

After pushing to github:

+
https://www.repronim.org/reproschema-ui/#/?url=url-to-protocol-schema
+
+https://www.repronim.org/reproschema-ui/#/activities/0?url=url-to-activity-schema
+
+

Source spreadsheets

+

Content used to programmatically generate the different schemas used for the different eCOBIDAS apps.

+

TODO The google spreadsheet can be downloaded using ...

+

TODO The csv files can be turned into schemas using ...

+

The content of the different columns is described in data_dictionary.json and +the json files associated with each file.

+ + + + + + +
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i=document.createElement("iframe");i.width=i.height=i.frameBorder="0",document.body.appendChild(this.iframe=i),this.w.document.open(),this.w.document.write(` + + + + + + + + + + + + + + + + + + + + + + +
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eCOBIDAS - Abstract OHBM 2020

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Title

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eCOBIDAS: a webapp checklist to improve neuroimaging methods and results +reporting

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Authors

+

If you have participated directly or indirectly to this project and you would +like to be added as author, please create a profile on the OHBM 2020 abstract +submission site (https://ww4.aievolution.com/hbm2001/), and add your name below:

+
    +
  • Rémi Gau
    • +
  • Tom Nichols
    • +
  • Cassandra Gould van Praag
    • +
  • Satrajit Ghosh
    • +
  • Sanu Ann Abraham
    • +
  • Kristina Wiebels
    • +
  • Zsuzsika Sjoerds
    • +
  • David Moreau
    • +
+

Intro

+

In any scientific study, a complete and precise method section is necessary to +understand and evaluate the results, plan replications, seed new research and +compare outcomes across studies. However, a large number of neuroimaging studies +fail to report important details necessary for independent investigators to +achieve these goals [1]. In order to improve this situation, the Committee on +Best Practices in Data Analysis and Sharing (COBIDAS) of OHBM released a report +to establish a set of best practices for methods and results reporting in f/MRI +research [4]. This has been recently followed by a similar initiative for EEG +and MEG [5]. The COBIDAS reports are accompanied by checklist tables meant to +help authors ensure that their methods and results description complies with the +Committee’s recommendations. These checklists are a valuable resource, but the +static PDF tables do not lend themselves to an actionable format, and users may +be required to scroll through pages of items that do not concern their specific +use-case. Moreover, while these checklists help generate a human readable method +section, they do not provide ways to create a machine-readable equivalent that +would encapsulate a large part of the metadata in a study. The goal of the +eCOBIDAS webapp is to provide a user-friendly solution for researchers to fill +out the COBIDAS checklists, while generating a machine-readable summary of a +method section. This summary can then be used to automate method section writing +for authors, or to facilitate the assessment of a study quality during +peer-review.

+

Methods

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The tables of the COBIDAS reports were reduced to600 individual items. A subset +of items was selected and converted into a hierarchical schema representation in +accordance with the repronim schema (https://github.com/ReproNim/reproschema). +The use of this schema representation offers several advantages, as it allows: +Efficient linking between the metadata about each item and the data provided by +the user; Enforcing of output format consistency and of input validation +directly at the data acquisition stage (i.e., while the user is filling out the +checklist); Specifying user interface details for certain use-cases (e.g., to +let users be presented only with items or sections that concern them). We then +adapted the JavaScript-based webapp used for the visualization of the repronim +schema to allow users to browse and complete each section of the checklist.

+

Results

+

The development of the webapp started at the OHBM Hackathon 2019 and aims to be +a project developed by the community for the community. All information is +centralized on an open science framework project [2]. The current prototype of +the webapp can be accessed athttps://ohbm.github.io/eCOBIDAS/#/. It is +based on the 88 metadata items available to characterise a collection of +neuroimaging results uploaded on the neurovault website [3]. This only +constitutes a subset of the full f/MRI COBIDAS checklist and will be expanded in +the near future. Users can fill out the different sections of the checklist +(study design, participants, preprocessing, etc.) and are only presented with +items that concern their use-case. Finally, the output can be exported in a set +of machine-readable JSON files.

+

Conclusion

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Future developments of the webapp will involve:

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  • Expansion of the list of items covered to include at first all the items + from Carp 2012, and then the entirety of the COBIDAS f/MRI checklist;
    • +
  • Establish a list of recommended MEEG items to create a version for + electrophysiology studies
    • +
  • Automatically generate a PDF summary and a method section after the + checklist has been filled
    • +
  • Automatically fill the checklist by allowing the application to query the + content of:
      +
    • a BIDS dataset
      • +
    • an SPM or FSL analysis pipeline (matlabbatch.mat or design.fsf).
      • +
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    • +
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eCOBIDAS is part of a broader trend towards open and reproducible science, to +facilitate valid and robust inferences in neuroscience. We encourage any member +of the community to contribute to this project.

+

Categories

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  • Informatics others
    • +
  • Workflow
    • +
  • Other methods
    • +
  • Keywords
    • +
  • Checklist
    • +
  • Workflows
    • +
  • Statistical methods
    • +
  • Webapp
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  • Machine-reading
    • +
  • Standardization
    • +
  • COBIDAS
    • +
  • Transparency
    • +
  • Replicability
    • +
  • Reproducibility
    • +
+

Reference

+

[1] Carp J. (2018), ‘The secret lives of experiments: Methods reporting in the +fMRI literature’, NeuroImage. vol. 63, no. 1, pp. 289-300, doi: +10.1016/j.neuroimage.2012.07.004

+

[2] Gau, Remi et al (2019), ‘COBIDAS Checklist’, OSF, doi: 10.17605/OSF.IO/ANVQY

+

[3] Gorgolewski K. J. (2015) et al. ‘NeuroVault.org: a web-based repository for +collecting and sharing unthresholded statistical maps of the brain’, Frontiers +in Neuroinformatics. vol. 9, no. 8 doi: 10.3389/fninf.2015.00008

+

[4] Nichols T. E. et al. (2017), ‘Best Practices in Data Analysis and Sharing in +Neuroimaging using MRI’, Nature Neuroscience, vol. 20, no 3, pp. 299-303, doi: +10.1038/nn.4500.

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[5] Pernet, C. R. et al. (2018), Best Practices in Data Analysis and Sharing in +Neuroimaging Using MEEG, OSF Preprints, August 9. doi:10.31219/osf.io/a8dhx

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Improving the COBIDAS checklist for better neuroimaging methods and results reporting

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Remi Gau (ORCID)

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Project Description

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In 2012, in his review of the methods and results reporting of more than 200 +fMRI papers, Joshua Carp wrote: +"Although many journals urge authors to describe their methods to a level of +detail such that independent investigators can fully reproduce their efforts, +the results described here suggest that few studies meet this criterion."

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A few years ago, in order to improve the situation with respect to +reproducibility +in f/MRI research, the Committee on Best Practices in Data Analysis and Sharing +(COBIDAS) +of OHBM released a report to +promote best practices for methods and results reporting. This was recently +followed by a similar initiative for EEG and MEG.

+

So far these guidelines do not seem to have been widely adopted and anecdotal +evidence +(see that twitter poll and thread) +suggests that even among people who know about the report few of them use it to +write or review papers. One likely reason for this might be the unwieldy nature +of the report. Anyone who has used this checklist tends to agree that it is a +great resource but that it is a bit cumbersome to interpret and apply.

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So the short term goal of this project is to facilitate the use of this +checklist. But, if done right, this could also in the long term enhance the +adoption of emerging neuroimaging standards +(the Brain imaging data structure, +fMRIprep, NIDM...), facilitate data sharing and pre-registration, help with +peer-review...

+

Short term goal

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The short term goal of this project is to make the COBIDAS report easier to +use: we want to create a website with a clickable checklist that generates a +json file at the end.

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By turning the checklist into a website users could more rapidly click through +it and provide more of the information requested by the COBIDAS report. This +would generate a small text file (a json file) that summarizes what option was +chosen for each item of the checklist. This machine readable file could then be +used to automatically generate part of the methods section of an article.

+

Other potential goals (e.g. interaction with BIDS and NIDM, further integration +with main neuroimaging softwares...) and potential applications (improving +data-sharing and peer-review) of this project are described in this +repository.

+

Skills required to participate

+

One or more of those:

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  • To be enthusiastic about reproducibility
    • +
  • Familiarity with the COBIDAS report for f/MRI, MEEG,
    • +
  • To know something about web design,
    • +
  • Familiarity with one or more of the main neuroimaging software for + fMRI + (SPM, FSL...) or for + M/EEG + (Fieldtrip, EEGlab...)
    • +
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Milestones

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  • +

    Discuss conceptual and structural details of the COBIDAS-json file.

    +
    • +
  • +

    Create a template of the COBIDAS-json file

    +
    • +
  • +

    Create a proof of concept website that can:

    +
      +
    • given a template COBIDAS-json file generates a checklist to clicked + through by users,
      • +
    • outputs a populated COBIDAS-json file once the user is done,
      • +
    • generate a method section using a populated COBIDAS-json file.
      • +
    +
    • +
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Preparation material

+

Jeanette Mumford has a +30 min video on her +youtube channel explaining the background behind the COBIDAS report and giving a +run through of the checklist.

+

The COBIDAS report:

+ +

A spreadsheet version of the COBIDAS checklist (thanks +to Cass!!!)

+

The secret lives of experiments: methods reporting in the fMRI literature

+

A manifesto for reproducible science

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GitHub repo

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The github repository of this project can be found here

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    General organization

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    The general workflow of this project is the following:

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      turning the recommendation guidelines into spreadsheets

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      turning the spreadsheets into a "schema" representation

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      using a "front-end" user-interface that will read those schema and serve a + web-app to the user.

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    To execute that work, this project is organized around several "repositories":

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      the eCOBIDAS repository centralises + most of the information and workflow to convert the guidelines into a + checklist webapp,

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      the Reproschema user interface + contains the "front-end" code of the user interface to render the checklist + webapp,

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      the ReproSchema repository + contains the formal "definition" of the terms used to describe the content + of the checklist as a schema,

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      the repositories containing a) an instance of the user interface and b) a + schema to serve a specific checklist :

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      the + google drive + where we work synchronously on the + spreadsheets,

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      an associated + zotero library to + keep track of references related to this project,

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      a project on the open-science framework that allows + to "connect" all those elements together in one place.

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    the eCOBIDAS repository

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    This repository hosts the workflow that will turn the reports published by the +Committee on Best Practices in Data Analysis and Sharing (COBIDAS) of the +organization for human brain mapping (OHBM) into a checklists for improving +methods and results reporting in (f)MRI, (i)EEG, MEG.

    +

    By extension, this workflow can also be used on other types of guidelines (like +the ones for PET imaging and eyetracking).

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+├── .github           <-- continuous integration "scripts"
+├── activities        <-- deprecated: ignore this
+├── communication     <-- abstracts and presentations about the project
+├── docs              <-- content of the documentation
+├── inputs            <-- checklists spreadsheets as CSV files, boilerplate for method section generation
+├── schema            <-- deprecated: ignore this
+├── schemas           <-- where the schemas are kept locally
+├── python            <-- python package to convert the CSV spreadsheets into schemas
+└── tests             <-- python script to test that the schema files are valid JSON-LD
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    In the schemas folder you might find different folders some of them with the +following names:

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    ├── protocols         <-- schema for the checklists putting together several "sections" together
+├── activities        <-- schema of the different "sections"of the checklistss with their items
+└── response_options  <-- contains the pre-set list of response options to some checklist items
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    Spreadsheet content and organization

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    The first step of the workflow involves taking the recommendation guidelines and +converting that into a spreadsheet that contains all the items of the future +checklist.

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    This step is by far the most labor intensive and has its dedicated page in the +documentation

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    Converting the spreadsheet into a schema

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    Most of that is covered in the section on +how the checklist is rendered and in the +README in the python folder.

    +

    How is the Reproschema organized

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    The first step of the workflow involves taking a spreadsheet that contains all +the items of the checklist and turning that into a representation that can +efficiently link the metadata about each item to the data imputed by the user. +We are using the ReproSchema +initiative from ReproNim to do this. Basically, it +means turning your 'dumb' spreadsheet into an equivalent but 'smarter' +representation of it: a bunch of hierarchically organized json files that link +to each other.

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    On top of the inherent +advantages +of this schema representation:

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      its use simplifies the rendering of the checklist by using the + reproschema-ui made for it,

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      this representation allows specification of user interface option that can + simplify the user experience: it allows us to specify the conditions that + will make certain items visible or not and thus will prevent users to be + presented with items that are not relevant to them (e.g answer PET related + when they have only run an fMRI study).

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    The reproschema is organized in a hierarchical manner with several levels, the +main ones being

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      The lowest level is the item level where there is one question for each + item with an expected format for the user interface: is this yes / no + question (boolean), a multiple choice, a float or an integer...

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      The second level is the activity level that contains a set of items. In + the original repronim project this would constitute usually a questionnaire: + like all the items of the Edinburgh handedness inventory would constitute + one activity. In our case, we are using it to define to break a checklist + into sub-sections of a method section like preprocessing, design, + participants...

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      The highest level is the protocol level that contains a set of activities. + At the moment this level is under-used in our checklist but could be used to + define activity sets for different use case: fMRI, MEEG, pre-registration...

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    If you want to know more about Reproschema, we suggest you have look at the +documentation

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    + + + + + + + + + \ No newline at end of file diff --git a/goals/index.html b/goals/index.html new file mode 100644 index 00000000..7e03f97c --- /dev/null +++ b/goals/index.html @@ -0,0 +1,671 @@ + + + + + + + + + + + + + + + + + + + + + + Goals - eCOBIDAS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Goals

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    By turning recommendations and guidelines into a series of checklists hosted on +a website, users could more rapidly click through it and provide more of the +recommended information. This would generate a small text file that summarizes +what option was chosen for each item of the checklist. This machine readable +file could then be used to automatically generate part of the methods section of +an article.

    +

    But, if done right, this could also in the long term enhance the adoption of +emerging neuroimaging standards (BIDS, fMRIprep, NIDM...), facilitate data +sharing and pre-registration, help with peer-review...

    +

    Vision

    +

    We envision that using checklists to report methods and results can:

    +
      +
    1. +

      provide comprehensive human and machine readable descriptions of the data + collection and analysis pipelines used in published papers: in other words + we want an app to help document pipelines to improve the reproducibility + of our work and to reduce inefficiencies and frictions when trying to build + on each other's work.

      +
      2. +
    2. +

      facilitate the creation and preparation of pre-registration and registered + reports by reminding of future analysis steps that we might otherwise + overlook or forget about: in other words we want an app to help us think + about and create pipelines before we start collecting data.

      +
      3. +
    3. +

      help make peer-review more objective: we want an app to help us check + pipelines.

      +
      4. +
    4. +

      facilitate systematic literature reviews and meta-analyses (use the app to + read pipelines)

      +
      5. +
    5. +

      facilitate data sharing (use the app to standardize the report of + information on a pipeline)

      +
      6. +
    +

    Constraints

    +

    The implementation of this project should remain flexible enough to:

    +
      +
    • +

      accommodate the inclusion of new items in the checklist as new methods + mature (e.g. new multivariate analysis, high-resolution MRI...),

      +
      • +
    • +

      easily fork the project and convert it to create a checklist-website for a + different field.

      +
      • +
    +

    Milestones

    +

    We are trying to divide our goals and milestones in 3 main categories:

    +
      +
    1. short term
      2. +
    2. intermediate
      3. +
    3. long term
      4. +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/how-to-contribute/index.html b/how-to-contribute/index.html new file mode 100644 index 00000000..7818ceda --- /dev/null +++ b/how-to-contribute/index.html @@ -0,0 +1,826 @@ + + + + + + + + + + + + + + + + + + + + + + How to contribute - eCOBIDAS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
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    + + + +
    +
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    + + + +
    +
    + + + + + + + + +

    How to contribute

    +
    +

    Join our working group at the +International Neuroinformatics Coordinating Facility

    +
    +

    There are many ways in which you can contribute to this project.

    +

    We have a list of milestones +for the different features we would like to include in this project.

    +

    Those milestones have +several opened issues related to +them: have a look through those issues to see if there is any of them where you +think you can help.

    +

    We are also track the progress to our different goals using some of the +integrated kanban boards that +github offers.

    +
    +

    Kanban boards:

    +

    They are a great way to keep track of who is doing what, how the different +tasks of your project are moving along...

    +
    +

    Suggestions

    +

    If you are unsure where to start, maybe have a read through some of those +sections about the project. It might give you ideas.

    + +

    Otherwise here are some suggestions of the different tasks that you can +contribute to. If however you feel more interested to start hacking at some of +the issues related to long other goals, feel free to do so.

    +

    Spreadsheets

    +

    A lot of the work requires to interact with the checklists in their spreadsheet +format.

    +

    The spreadsheets are hosted on this +google drive folder +and we try to keep a back-up in the +csv folder.

    +

    The MRI part is the more advanced at this moment but we are looking for people +to help with the M/EEG part.

    +

    The content of the spreadsheets and the work involved there is described in more +details here.

    +

    Boilerplate method sections

    +

    We want to create +boilerplate method sections +corresponding to a single item or a set of items of the checklist to automate +methods writing once the checklist is completed. There is a lot of work to do +there in terms of writing the boilerplate text as well as automating the methods +section generation.

    +

    Boilerplate related issues listed +here.

    +

    Documentation

    +

    If you don't understand something about the project, its goals, its +implementation or how to use, then it's most likely that we did not do a good +enough job at explaining and describing it. +Get in touch +and we can work together to improve our documentation.

    +

    There are also +several aspects of the documentation +that need to be expanded.

    +

    Conversion scripts

    +

    We use some +python code to +convert the spreadsheets into the set of jsonld files that the user interface +needs as inputs to display the checklists. This needs further improvement so if +python is your jam, feel free to dive in.

    +

    We are also using the +Reproschema python package to +validate the schemas created. This package should also in the long-term help +automating the creation of schemas, so there are definitely some features +waiting to be written there.

    +

    There is more information on how to set things up in the +CONTRIBUTING.md.

    +

    User interface

    +

    The user interface is Javascript based +and uses the Vue framework and this also needs a lot of +tweaking, so if Javascript is cup of tea: +get in touch!

    +

    Here are +some of the issues +related to the UI.

    +

    Style guide

    +

    Python

    +

    We rely on black to automate the python code +formatting.

    +

    We have additional checks on Github for code style and quality.

    +
      +
    • +

      pep8 speaks for code style

      +
      • +
    • +

      the awesome sourcery for code + quality and refactoring suggestions.

      +
      • +
    +

    Markdown

    +
      +
    • +

      For markdown styling we rely on + remark.

      +
      • +
    • +

      We check for dead links using the + markdown-link-check + github action.

      +
      • +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/how-to-render-the-checklist/index.html b/how-to-render-the-checklist/index.html new file mode 100644 index 00000000..2f472220 --- /dev/null +++ b/how-to-render-the-checklist/index.html @@ -0,0 +1,733 @@ + + + + + + + + + + + + + + + + + + + + + + Viewing the checklist - eCOBIDAS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    How the checklist is rendered

    +

    This part gets a bit more "techy" but we will do our best to guide you through +it.

    +

    First make sure you are familiar with the structure of this project by reading +the general organization documentation.

    +

    Turn the spreadsheet into the schema

    +

    This part is done by a bit of python code and you can find more information in +the README in the python folder.

    +

    Making the new schema available to the UI

    +

    If the previous step went smoothly you now need to make the newly created files +available on your remote repository on github so that the user-interface can +'see' them. This you can do by committing the files newly created on your local +repository and pushing them the remote.

    +

    For example if the schema is hosted on the neurovault branch of your origin +remote repository, the following should do the trick (assuming that you are +already on the neurovault branch of your local repo):

    +
    git add --all
+git commit -m 'your commit message'
+git push origin neurovault
+
    +

    Rendering the checklist

    +

    There are 2 ways to visualize the checklist you have created.

    +

    Using the online user interface

    +

    You can point the already existing user-interface to the schema of the protocol +or the activity you have just created.

    +

    If you want to visualize an activity on its own, you can use the +reproschema-ui. To do that you can +point the UI to the raw content of this activity.

    +

    To get access to the raw content of an activity you must click on the Raw +button on github once you have opened its page, +see for example the PHQ-9 acvitiy here. +This will open this URL: +https://raw.githubusercontent.com/ReproNim/reproschema-library/master/activities/PHQ-9/PHQ9_schema.

    +

    You can then pass the the URL of raw content to the UI using the following +template URL:

    +
    https://www.repronim.org/reproschema-ui/#/activities/0?url=url-to-activity-schema
+
    +

    To view a protocol, you can also use the reproschema-ui with the following +template URL:

    +
    https://www.repronim.org/reproschema-ui/#/?url=url-to-protocol-schema
+
    +

    Serving the app locally

    +

    If you want to use serve the app locally on your computer, you will need to +install node.js (the "backend" version of Javascript). +A good way to do this is to use node version manager: some installation +instructions are available on the +user-interface repository.

    + + +

    Make sure that that you have set the UI correctly so it will read the schema +from the right repository. This can be be set by modifying its +reproschema-ui/src/config.js file (e.g see +here).

    +

    Modify the githubSrc so that it points to the URL of the schema of your +protocol:

    +
    module.exports = {
+    /* eslint-disable */
+    githubSrc:
+        "https://raw.githubusercontent.com/your-gtihub-account/your-repository/branch-name/folder/protcol_schema_filename",
+    banner: "This is a test",
+    assetsPublicPath: "/your-repository/",
+    backendServer: null,
+    consent: true,
+};
+
    +

    Once you have done that you can launch the app with:

    +
    npm install # Install the Javascript dependencies
+npm run serve # Run the development server locally
+
    +

    Open your browser and go to localhost:8080

    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
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Each T1w (T1-weighted) volume was corrected for INU (intensity non-uniformity) using N4BiasFieldCorrection v2.1.0 [5] and skull-stripped using antsBrainExtraction.sh v2.1.0 (using the OASIS template). Brain surfaces were reconstructed using recon-all from FreeSurfer v6.0.1 [6, RRID:SCR_001847], and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of Mindboggle [21, RRID:SCR_002438]. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c [7, RRID:SCR_008796] was performed through nonlinear registration with the antsRegistration tool of ANTs v2.1.0 [8, RRID:SCR_004757], using brain-extracted versions of both T1w volume and template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast [17] (FSL v5.0.9, RRID:SCR_002823).Functional data was slice time corrected using 3dTshift from AFNI v16.2.07 [11, RRID:SCR_005927] and motion corrected using mcflirt (FSL v5.0.9 [9]). This was followed by co-registration to the corresponding T1w using boundary-based registration [16] with six degrees of freedom, using bbregister (FreeSurfer v6.0.1). Motion correcting transformations, BOLD-to-T1w transformation and T1w-to-template (MNI) warp were concatenated and applied in a single step using antsApplyTransforms (ANTs v2.1.0) using Lanczos interpolation.Physiological noise regressors were extracted applying CompCor [18]. Principal components were estimated for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor). A mask to exclude signal with cortical origin was obtained by eroding the brain mask, ensuring it only contained subcortical structures. Six tCompCor components were then calculated including only the top 5% variable voxels within that subcortical mask. For aCompCor, six components were calculated within the intersection of the subcortical mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run. Frame-wise displacement [19] was calculated for each functional run using the implementation of Nipype. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + COBIDAS report + + + + PDF + + + + + + + + + + + + + + JSON-LD + + + { "@context": "https://raw.githubusercontent.com/ReproNim/reproschema/1.0.0-rc1/contexts/generic", "@type": "reproschema:Activity", "@id": "neurovault_MRI_acquisition_schema", "prefLabel": "MRI acquisition", "description": "neurovault MRI acquisition", "schemaVersion": "1.0.0-rc1", "version": "0.0.1", "ui": { "shuffle": false, "order": [ "items/scanner_make", "items/scanner_model", "items/field_strength" ], "addProperties": [ { "variableName": "scanner_make", "isAbout": "items/scanner_make", "isVis": true, "valueRequired": false } ] }} + pybids reproschema methods section + + + + + diff --git a/img/show_hidden_columns.png b/img/show_hidden_columns.png new file mode 100644 index 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--git a/index.html b/index.html new file mode 100644 index 00000000..df82ab03 --- /dev/null +++ b/index.html @@ -0,0 +1,690 @@ + + + + + + + + + + + + + + + + + + + + eCOBIDAS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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    Welcome to the eCOBIDAS documentation

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    Motivations

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    Poor methods and results description hinders the reproducibility and the +replicability of research. It also makes it hard to compare new and old results +and generally increases inefficiency in the research process. This project is +built on the hope that improving methods and results reporting could improve our +research.

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    See here for more background information.

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    Goals

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    The short term goal of this project is to make the COBIDAS report (and other +best practices for methods reporting) easier to use: we want to create a website +with a clickable checklist that, at the end, automatically generates most of the +method section of a (f)MRI / (i)EEG / MEG / PET paper.

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    See here for more information on our vision and general goals. +You can also go directly to one of the following section to see a breakdown of +those goals and see some extensions we have in mind for the project.

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    1. short term goals
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    2. intermediate goals
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    3. long term goals
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    If you are interested by any of those, +get in touch. +Contributing to this project does not necessarily require super-advanced +technical skills (except maybe a certain love for working with spreadsheet and +wanting them to be super organized) :wink:.

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    Project structure

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    See here for more information about how this +whole project is organized.

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    Spreadsheets

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    A lot of the work for this projects starts with some (usually pretty big) +spreadsheets.

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    See here for a description of their content and what the +work involves.

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    How to run the checklist

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    See here for more information on how to +work on the checklist on your own computer.

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    References

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    See here.

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    "pie in the sky" goals

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    On the long term, we want those web-app to integrate better with other data +standards and softwares to further reduce how much manual entry is required from +the user.

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    Similarly we want to broaden the use-cases for the app.

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    Improve user-friendliness

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    Improving default answers

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    If some data is gathered about the content of the method section of a sample of articles in the literature +(see +Carp, 2012 or the +eyetracking guidelines), it should be possible to +create or better organize list of response choices. +It will take less time for users to tick a box rather than type something. +Similarly we could decide to make the most common choices or the "better" option more prominent in a list of response options.

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    Adding a help section for each item

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    The content of some item might be quite obscure for some users, so embedding +some help about each item would be a desirable goal.

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    Integration with the main neuroimaging software

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    Plugins

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    A side branch of this project includes developing plugins for the main +neuroimaging software toolboxes (SPM, FSL, AFNI). Such a plugin would receive +processing batch files as input (e.g matlabbatch.mat for SPM or design.fsf +for FSL) and would create output files (e.g. in json format) readable by the +checklist website, which in turn would be able to display the neuroimaging +processing pipeline and/or the methods section for an article with the +appropriate references (akin to what fMRIprep already does).

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    In a similar way, FSL already outputs methods description for every +preprocessing or statistical analysis that can be used to write the methods +section of a paper as explained by Jeanette Mumford in this +video.

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    Integration with BIDS and NIDM

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    The process of filling in an online checklist can be simplified if the required +information can be directly accessed from the metadata in some of the existing +standards for data and results like:

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      the brain imaging data structure (BIDS) used + for that study,

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      the NIDM results of + any mass-univariate analysis performed for this study.

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    BIDS is an emerging standard to organize neuroimaging data (MRI, fMRI, +EEG...) and its associated metadata regarding acquisition (like repetition time, +echo time, slice order...), experimental design (number of subjects, conditions, +stimulus onsets...). If a data set is organized following the BIDS standard, it +is possible to query its content using +pybids or +bids-matlab and even write part +of the method section relative to the acquisition using the reports module of +those packages (see for example +pybids reports).

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    NIDM results is a way to package mass-univariate fMRI results in a software +independent way. This way it can for example facilitate reading FSL results +through SPM or facilitate data sharing (like NIDM results is supported by the +data sharing platform Neurovault and this greatly +facilitate uploading your results there). NIDM contains many metadata with a +correspondence in the COBIDAS checklist (see +here).

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    For BIDS and NIDM results, machine reading through a dataset organized as BIDS +and NIDM results could automatically fill part of the COBIDAS checklist.

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    Conversely it is possible to help users create some files that make up a BIDS +dataset by having them fill in part of the checklist.

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    An implementation of this, will most likely rely on the respective schema of +those resources:

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    Integration with existing ontologies

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    To automate the creation certain aspects we could try to rely on some existing +ontologies that create list of term definitions to generate or enrich the list +of response choices to some items.

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    Examples of this could rely on +scicrunch and the +NIDM-terms or the +Research resource IDentifiers.

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    Though more challenging, it could be imagined that pipeline-creating-tools like +(nipype, +PORCUPINE, +GIRAFFE) +could also generate json files to be used for method section generation.

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    Facilitate data sharing

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    If the output created by the checklist app is accepted by neuroimaging databases +(e.g. Neurovault) as metadata input, this could +reduces friction for data sharing. Users wouldn't have to fill several times the +same information: when pre-registering, when writing their methods, when +submitting to a journal (if it has a checklist of its own), when sharing data. +For example the metadata fields from +neurovault +are already present in the COBIDAS checklist.

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    As some journals start having submission-checklists (e.g eLife, +nature research journals...), +filling in the a checklist only once could reduce 'the submission paperwork' by +writing part of the method section of the article AND generating the appropriate +submission-checklist for a given journal.

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    Reviewers could also use the website to systematically cross-check that all the +required methods and results information are present for a given paper. A tool +to compare different outputs from the web app could help editors visualize +agreement across reviewers' evaluation of a paper.

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    If the process is made sufficiently seamless, this could in return potentially +incentivize more journals to adopt submission-checklists.

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    • https://github.com/guiomar/Armitage_BIDS_NLG
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    • or try to reuse some of pybids-reports
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    + + + + + + + + + \ No newline at end of file diff --git a/mid-term/index.html b/mid-term/index.html new file mode 100644 index 00000000..1d83c260 --- /dev/null +++ b/mid-term/index.html @@ -0,0 +1,688 @@ + + + + + + + + + + + + + + + + + + + + + + Mid term goals - eCOBIDAS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Intermediate goals

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    Once we have a prototype for a checklist we want to optimize them to make that +they are comprehensive and cover all the items of the guidelines and to minimize +the amount of time the user has to interact with the checklist.

    +

    Improve user-friendliness

    +

    In line with the idea of trying to minimize how much time the users have to +spend using the app, we want to pre-select response choices to some items +depending on the software users report having used in their analysis.

    +

    For example, if SPM was used to do slice timing correction, there is no reason +to ask users what type of interpolation was used as SPM does not give users the +possibility to choose that parameter. Finding out what are the choice options +for each item for the neuroimaging software used could help users navigate the +app faster.

    +

    Another potential way to improve users experience is to only expose users to +items that are relevant to them. For example, we want to make sure that the +items related "arterial spin labelling" are displayed only if the user mentioned +they used this technique in a previous section of the checklist.

    +

    Improving the wording

    +

    The questions of the checklist must be as unambiguous as possible. This should +be improved through early user feedback.

    +

    Extended checklists

    +

    Right now, several of the +prototypes +contains only a subset of all the questions from the reports they came from. For +example, the MRI checklist only contain the items corresponding to the metadata +of a collection of results uploaded on Neurovault.

    +

    In the near future, we want to be able to extend those checklists so they +include all the items listed in their guidelines.

    + +

    Data processed with some standardized pipelines (like fMRIprep) could facilitate +filling in the checklist: ticking the box corresponding to that pipeline would +automatically populate all the relevant fields in the COBIDAS-json file.

    +

    Standardize terminology

    +

    Ensure that the across apps the same terminology is used by trying to rely on +the BIDS terminology with reference to other lexicons or ontology.

    + + + + + + +
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    + + + + + + + + + \ No newline at end of file diff --git a/motivations/index.html b/motivations/index.html new file mode 100644 index 00000000..2c4ed40a --- /dev/null +++ b/motivations/index.html @@ -0,0 +1,672 @@ + + + + + + + + + + + + + + + + + + + + + + Motivations - eCOBIDAS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Project motivations

    +

    Improve transparency and reproducibility

    +

    In 2012, in a +review of the methods of more +than 200 fMRI papers, the author found that "Although many journals urge +authors to describe their methods to a level of detail such that independent +investigators can fully reproduce their efforts, the results described here +suggest that few studies meet this criterion."

    +

    A few years ago, in order to improve reproducibility in f/MRI research, the +Committee on Best Practices in Data Analysis and Sharing +(COBIDAS) +of OHBM released a report to +promote best practices for methods and results reporting. This was recently +followed by a similar initiative for EEG and MEG.

    +

    Contrary to what the most optimistic people might have thought, these guidelines +do not seem to have been widely adopted and anecdotal evidence +(see that twitter poll and thread) +suggests that, even among people who know about the report, few of them use it +to write or review papers.

    +

    A survey from ISMRM seems +to suggest that the lack of method details is big problem in MRI research in +general but also that there seems to be little knowledge of the existence of the +COBIDAS report outside of some circles.

    +

    One possible reason for this might be the unwieldy nature of the report. +Anticipating this issue, the authors of the guidelines included a checklist that +still ended up taking almost 30 of the 70 pages of the whole document. Anyone +who has used this checklist tends to agree that it is a great resource but that +it is a bit cumbersome to interpret and apply.

    +

    So the goal of this project is to facilitate the use of this checklist and of +similar guidelines.

    +

    Reduce inefficiencies

    +

    Currently, the research workflow around methods reporting has us do the same +thing several times (very often manually) which leads to inefficiencies and +multiplies the chances for human errors like forgetting to report some +parameters or mis-reporting it.

    +

    For example, a researcher would have to write or rewrite some aspects of the +methods used when

    +
      +
    1. +

      when preparing a pre-registration describing the planned study

      +
      2. +
    2. +

      during the data curation process that usually involves adding metadata + elements that relate to the details of the data acquisition,

      +
      3. +
    3. +

      when actually working the methods and results section where we have to back + and forth to the code we used to run the experiment and to the dataset to + make sure important details are accurately reported,

      +
      4. +
    4. +

      when sharing raw or derived data which also usually involves adding a + minimum of methods-related metadata if the shared data is to be meaningfully + reusable.

      +
      5. +
    +

    Another source of inefficiency is the time lost trying to figure out:

    +
      +
    • +

      what the authors of a paper actually did

      +
        +
      • when we would like to compare our results to theirs
        • +
      • when reviewing papers
        • +
      +
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    • +

      what we actually did 6 months ago but forgot to make a note of it.

      +
      • +
    +

    So a potential side effect of using a checklist to systematically capture how +data was acquired and analyzed would therefore be to make us more efficient. +Having filled in a checklist once to generate a small set of files that capture +the vast majority of our acquisition and analysis pipeline, we could easily +share those files with our data, when submitting a paper or to generate a method +section automatically.

    + + + + + + +
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    + + + + + + + + + \ No newline at end of file diff --git a/preset-responses/index.html b/preset-responses/index.html new file mode 100644 index 00000000..2ae0599e --- /dev/null +++ b/preset-responses/index.html @@ -0,0 +1,634 @@ + + + + + + + + + + + + + + + + + + + + + + Presets responses - eCOBIDAS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    response listgoogle spreadsheetjsonld
    mri_softwaresspreadsheetjsonld
    stimulus_presentation_softwaresspreadsheetjsonld
    multiple_comparisonsspreadsheetjsonld
    interpolationsspreadsheetjsonld
    cost_functionsspreadsheetjsonld
    meeg_reference_electrodesspreadsheetjsonld
    meeg_analysis_softwaresspreadsheetjsonld
    meeg_amplifier_brandsspreadsheetjsonld
    meeg_acquisition_softwaresspreadsheetjsonld
    eeg_cap_typesspreadsheetjsonld
    booleanspreadsheetjsonld
    spm_versionspreadsheetjsonld
    linux_versionspreadsheetjsonld
    type_osspreadsheetjsonld
    windows_versionspreadsheetjsonld
    macos_versionspreadsheetjsonld
    eyetracker_preproc_softwaresspreadsheetjsonld
    eyetracker_model_namespreadsheetjsonld
    eyetracker_producerspreadsheetjsonld
    + + + + + + +
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    + + + + + + + + + \ No newline at end of file diff --git a/references/index.html b/references/index.html new file mode 100644 index 00000000..8d6b9be3 --- /dev/null +++ b/references/index.html @@ -0,0 +1,680 @@ + + + + + + + + + + + + + + + + + + + + References - eCOBIDAS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    References

    +

    There is zotero group +where we try to keep track the relevant literature related to this project. Let +us know if we missed something.

    +

    About the checklist

    + +

    The COBIDAS reports

    + +

    Jeanette Mumford has a +30 min video explaining +the background behind the COBIDAS report and giving a run through of the +checklist.

    +

    Presentation slides made about this project can be found in the +presentations folder.

    +

    The original spreadsheet version of the COBIDAS +checklist (thanks to Cass!!!)

    + + + + + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/search/search_index.json b/search/search_index.json new file mode 100644 index 00000000..38447fd4 --- /dev/null +++ b/search/search_index.json @@ -0,0 +1 @@ +{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Welcome to the eCOBIDAS documentation","text":""},{"location":"#motivations","title":"Motivations","text":"

    Poor methods and results description hinders the reproducibility and the replicability of research. It also makes it hard to compare new and old results and generally increases inefficiency in the research process. This project is built on the hope that improving methods and results reporting could improve our research.

    See here for more background information.

    "},{"location":"#goals","title":"Goals","text":"

    The short term goal of this project is to make the COBIDAS report (and other best practices for methods reporting) easier to use: we want to create a website with a clickable checklist that, at the end, automatically generates most of the method section of a (f)MRI / (i)EEG / MEG / PET paper.

    See here for more information on our vision and general goals. You can also go directly to one of the following section to see a breakdown of those goals and see some extensions we have in mind for the project.

    1. short term goals
    2. intermediate goals
    3. long term goals

    If you are interested by any of those, get in touch. Contributing to this project does not necessarily require super-advanced technical skills (except maybe a certain love for working with spreadsheet and wanting them to be super organized) :wink:.

    "},{"location":"#project-structure","title":"Project structure","text":"

    See here for more information about how this whole project is organized.

    "},{"location":"#spreadsheets","title":"Spreadsheets","text":"

    A lot of the work for this projects starts with some (usually pretty big) spreadsheets.

    See here for a description of their content and what the work involves.

    "},{"location":"#how-to-run-the-checklist","title":"How to run the checklist","text":"

    See here for more information on how to work on the checklist on your own computer.

    "},{"location":"#references","title":"References","text":"

    See here.

    "},{"location":"CONTRIBUTING/","title":"CONTRIBUTING","text":"

    If you contributed to the COBIDAS Checklist and your name is not listed, please add it by adding your details in this spreadsheet and let us know about it: we'll take care of the rest.

    Make sure that you are also listed as a contributor on our OSF project.

    "},{"location":"CONTRIBUTING/#requirements","title":"Requirements","text":"

    You will need to have installed.

    • Python
    • node.js
    • Git

    If you are running Windows 10, you will want to work directly in a Linux Windows sub-system.

    An simple way to do this is to install Ubuntu from the Windows App store.

    "},{"location":"CONTRIBUTING/#python-38-or-above","title":"Python 3.8 or above","text":"

    An easy way to install Python is to rely on Conda

    If you are on Linux this should be enough:

    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n
    "},{"location":"CONTRIBUTING/#nodejs","title":"node.js","text":"

    A good way to To install javascript node.js, is to install node version manager (NVM) to help you deal with different version of node.js.

    In a terminal or in Windows WSL2, you can install NVM by typing:

    curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.33.11/install.sh | bash\n

    Then close your terminal and reopen it then run the following to install the version 12 of node.js (using the latest version of node.js should be fine, so this is more there as an example).

    nvm install node\nnvm install 12\n
    "},{"location":"CONTRIBUTING/#set-up","title":"Set up","text":"

    Fork and clone the repo. Preferably set up a python virtual environment.

    Then run make install.

    git clone https://github.com/YOUR_GITHUB_USERNAME/eCobidas.git\ncd eCobidas\nvirtualenv -p python3.8 env\nsource env/bin/activate\nmake install\n
    "},{"location":"CONTRIBUTING/#downloading-the-spreadsheet-and-converting-them","title":"Downloading the spreadsheet and converting them","text":"

    A lot of recipe are already in tox.ini to facilitate daily work.

    For example:

    • make neurovault
    • make artemis
    • make pet

    Here is a description of the Neurovault recipe make neurovault.

    # remove previous runs:\nrm -rf inputs/neurovault\nrm -rf schemas/neurovault/\n\n# use a bash script to download the spreadsheet\nbash download_tsv.sh neurovault\n\n# use the local ecobidas python package to convert the spreadsheet in jsonld\necobidas_convert --schema neurovault\n\n# make sure the jsonld are valid with some node.js and reproschema validation\nreproschema -l DEBUG validate schemas/neurovault\n
    "},{"location":"CONTRIBUTING/#serving-the-local-protocols-and-activities-to-test-them-with-the-reproschema-ui","title":"Serving the local protocols and activities to test them with the reproschema-ui","text":"
    python local_server.py\n
    "},{"location":"CONTRIBUTING/#editing-the-spreadsheets","title":"Editing the spreadsheets","text":"

    DO NOT EDIT THE DIRECTLY IN THE REPOSITORY!!!

    Instead edit them on the google drive and use download_tsv.sh to download them locally. Or type make download_all to download update all the spreadsheets.

    "},{"location":"CONTRIBUTING/#visual-studio-code-settings","title":"Visual studio code settings","text":"

    Load the ecobidas.code-workspace file in the .vscode folder.

    "},{"location":"CONTRIBUTING/#requirements-and-set-up","title":"Requirements and set up","text":"

    You will need to have python installed.

    There are few dependencies (for now). One of them is the reproschema-py package that helps to validate that the schema you create is valid.

    Preferably use a virtual environment:

    virtualenv -p python3.8 env\nsource env/bin/activate\npip install -r requirements.txt\n
    "},{"location":"CONTRIBUTING/#install-the-dev-version-of-reproschema","title":"Install the \"dev\" version of reproschema","text":"
    # clone R\u00e9mi's fork of Reproschema on your machine, right outside of this repo\ngit clone https://github.com/Remi-Gau/reproschema-py.git  ../../\n# checkout the \"dev\" branch\ncd ../../reproschema-py\ngit checkout remi_schema_creator\n# pip install it\npip install -e .\n
    "},{"location":"CONTRIBUTING/#install-the-package","title":"Install the package","text":"

    To be able to use the command line tool, do the following from within the repo

    pip install -e .\n
    "},{"location":"CONTRIBUTING/#generate-the-schemas","title":"Generate the schemas","text":"

    If you have installed the package the following command line call should run the conversion.

    ecobidas_convert    #<-- to create a whole protocol + activities\n

    You only need to specify:

    • schema the name of the protocol you want to convert (it can be several of protocols at once),
    • out_dir where the schemas is meant to be created on your computer,
    • repo the URL of the repository where the schema will be hosted
    • branch of the repository where the schema will be hosted
    "},{"location":"CONTRIBUTING/#implementation","title":"Implementation","text":"

    TODO ecobidas convert calls: the highest level function that you will use to create the schema is ...

    Additional \"entry points\" should be set up in cli.py and adapted in setup.py.

    This code also relies on several classes from the \"dev\" branch of reproschema-py:

    base.py               # --> Base()\n  \u251c\u2500\u2500 protocol.py     # --> Protocol()\n  \u251c\u2500\u2500 activity.py     # --> Activity()\n  \u2514\u2500\u2500 item.py         # --> Item(), ResponseOptions()\n

    Class inheritance

    Base()\n  \u251c\u2500\u2500 Protocol()\n  \u251c\u2500\u2500 Activity()\n  \u251c\u2500\u2500 Item()\n  \u2514\u2500\u2500 ResponseOptions()\n
    "},{"location":"CONTRIBUTING/#validate-the-output","title":"Validate the output","text":"
    reproschema -l DEBUG validate path_to_the_file_to_validate\n
    "},{"location":"CONTRIBUTING/#view-the-results","title":"View the results","text":"

    After pushing to github:

    https://www.repronim.org/reproschema-ui/#/?url=url-to-protocol-schema\n\nhttps://www.repronim.org/reproschema-ui/#/activities/0?url=url-to-activity-schema\n
    "},{"location":"CONTRIBUTING/#source-spreadsheets","title":"Source spreadsheets","text":"

    Content used to programmatically generate the different schemas used for the different eCOBIDAS apps.

    TODO The google spreadsheet can be downloaded using ...

    TODO The csv files can be turned into schemas using ...

    The content of the different columns is described in data_dictionary.json and the json files associated with each file.

    "},{"location":"apps-table/","title":"Apps","text":"web app reference spreadsheet Github repository core participants link spreadsheet core behavior link spreadsheet mri all_sequences link spreadsheet neurovault link spreadsheet github eyetracking link spreadsheet github pet link spreadsheet github reexecution link spreadsheet github artemis link spreadsheet github"},{"location":"general-organization/","title":"General organization","text":"

    The general workflow of this project is the following:

    • turning the recommendation guidelines into spreadsheets

    • turning the spreadsheets into a \"schema\" representation

    • using a \"front-end\" user-interface that will read those schema and serve a web-app to the user.

    To execute that work, this project is organized around several \"repositories\":

    • the eCOBIDAS repository centralises most of the information and workflow to convert the guidelines into a checklist webapp,

    • the Reproschema user interface contains the \"front-end\" code of the user interface to render the checklist webapp,

    • the ReproSchema repository contains the formal \"definition\" of the terms used to describe the content of the checklist as a schema,

    • the repositories containing a) an instance of the user interface and b) a schema to serve a specific checklist :

      • the one for the MRI version based of the Neurovault metadata \"checklist\" hosted on the OHBM Github organization and that serves this checklist,

      • the one for the PET imaging version that serves this checklist,

    • the google drive where we work synchronously on the spreadsheets,

    • an associated zotero library to keep track of references related to this project,

    • a project on the open-science framework that allows to \"connect\" all those elements together in one place.

    "},{"location":"general-organization/#the-ecobidas-repository","title":"the eCOBIDAS repository","text":"

    This repository hosts the workflow that will turn the reports published by the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) of the organization for human brain mapping (OHBM) into a checklists for improving methods and results reporting in (f)MRI, (i)EEG, MEG.

    By extension, this workflow can also be used on other types of guidelines (like the ones for PET imaging and eyetracking).

    .\n\u251c\u2500\u2500 .github           <-- continuous integration \"scripts\"\n\u251c\u2500\u2500 activities        <-- deprecated: ignore this\n\u251c\u2500\u2500 communication     <-- abstracts and presentations about the project\n\u251c\u2500\u2500 docs              <-- content of the documentation\n\u251c\u2500\u2500 inputs            <-- checklists spreadsheets as CSV files, boilerplate for method section generation\n\u251c\u2500\u2500 schema            <-- deprecated: ignore this\n\u251c\u2500\u2500 schemas           <-- where the schemas are kept locally\n\u251c\u2500\u2500 python            <-- python package to convert the CSV spreadsheets into schemas\n\u2514\u2500\u2500 tests             <-- python script to test that the schema files are valid JSON-LD\n

    In the schemas folder you might find different folders some of them with the following names:

    \u251c\u2500\u2500 protocols         <-- schema for the checklists putting together several \"sections\" together\n\u251c\u2500\u2500 activities        <-- schema of the different \"sections\"of the checklistss with their items\n\u2514\u2500\u2500 response_options  <-- contains the pre-set list of response options to some checklist items\n
    "},{"location":"general-organization/#spreadsheet-content-and-organization","title":"Spreadsheet content and organization","text":"

    The first step of the workflow involves taking the recommendation guidelines and converting that into a spreadsheet that contains all the items of the future checklist.

    This step is by far the most labor intensive and has its dedicated page in the documentation

    "},{"location":"general-organization/#converting-the-spreadsheet-into-a-schema","title":"Converting the spreadsheet into a schema","text":"

    Most of that is covered in the section on how the checklist is rendered and in the README in the python folder.

    "},{"location":"general-organization/#how-is-the-reproschema-organized","title":"How is the Reproschema organized","text":"

    The first step of the workflow involves taking a spreadsheet that contains all the items of the checklist and turning that into a representation that can efficiently link the metadata about each item to the data imputed by the user. We are using the ReproSchema initiative from ReproNim to do this. Basically, it means turning your 'dumb' spreadsheet into an equivalent but 'smarter' representation of it: a bunch of hierarchically organized json files that link to each other.

    On top of the inherent advantages of this schema representation:

    • its use simplifies the rendering of the checklist by using the reproschema-ui made for it,

    • this representation allows specification of user interface option that can simplify the user experience: it allows us to specify the conditions that will make certain items visible or not and thus will prevent users to be presented with items that are not relevant to them (e.g answer PET related when they have only run an fMRI study).

    The reproschema is organized in a hierarchical manner with several levels, the main ones being

    1. The lowest level is the item level where there is one question for each item with an expected format for the user interface: is this yes / no question (boolean), a multiple choice, a float or an integer...

    2. The second level is the activity level that contains a set of items. In the original repronim project this would constitute usually a questionnaire: like all the items of the Edinburgh handedness inventory would constitute one activity. In our case, we are using it to define to break a checklist into sub-sections of a method section like preprocessing, design, participants...

    3. The highest level is the protocol level that contains a set of activities. At the moment this level is under-used in our checklist but could be used to define activity sets for different use case: fMRI, MEEG, pre-registration...

    If you want to know more about Reproschema, we suggest you have look at the documentation

    • main documentation
    • FAQ
    "},{"location":"goals/","title":"Goals","text":"

    By turning recommendations and guidelines into a series of checklists hosted on a website, users could more rapidly click through it and provide more of the recommended information. This would generate a small text file that summarizes what option was chosen for each item of the checklist. This machine readable file could then be used to automatically generate part of the methods section of an article.

    But, if done right, this could also in the long term enhance the adoption of emerging neuroimaging standards (BIDS, fMRIprep, NIDM...), facilitate data sharing and pre-registration, help with peer-review...

    "},{"location":"goals/#vision","title":"Vision","text":"

    We envision that using checklists to report methods and results can:

    1. provide comprehensive human and machine readable descriptions of the data collection and analysis pipelines used in published papers: in other words we want an app to help document pipelines to improve the reproducibility of our work and to reduce inefficiencies and frictions when trying to build on each other's work.

    2. facilitate the creation and preparation of pre-registration and registered reports by reminding of future analysis steps that we might otherwise overlook or forget about: in other words we want an app to help us think about and create pipelines before we start collecting data.

    3. help make peer-review more objective: we want an app to help us check pipelines.

    4. facilitate systematic literature reviews and meta-analyses (use the app to read pipelines)

    5. facilitate data sharing (use the app to standardize the report of information on a pipeline)

    "},{"location":"goals/#constraints","title":"Constraints","text":"

    The implementation of this project should remain flexible enough to:

    • accommodate the inclusion of new items in the checklist as new methods mature (e.g. new multivariate analysis, high-resolution MRI...),

    • easily fork the project and convert it to create a checklist-website for a different field.

    "},{"location":"goals/#milestones","title":"Milestones","text":"

    We are trying to divide our goals and milestones in 3 main categories:

    1. short term
    2. intermediate
    3. long term
    "},{"location":"how-to-contribute/","title":"How to contribute","text":"

    Join our working group at the International Neuroinformatics Coordinating Facility

    There are many ways in which you can contribute to this project.

    We have a list of milestones for the different features we would like to include in this project.

    Those milestones have several opened issues related to them: have a look through those issues to see if there is any of them where you think you can help.

    We are also track the progress to our different goals using some of the integrated kanban boards that github offers.

    Kanban boards:

    They are a great way to keep track of who is doing what, how the different tasks of your project are moving along...

    "},{"location":"how-to-contribute/#suggestions","title":"Suggestions","text":"

    If you are unsure where to start, maybe have a read through some of those sections about the project. It might give you ideas.

    • the motivations of this project
    • the different short and long term goals
    • the general organization of the project

    Otherwise here are some suggestions of the different tasks that you can contribute to. If however you feel more interested to start hacking at some of the issues related to long other goals, feel free to do so.

    "},{"location":"how-to-contribute/#spreadsheets","title":"Spreadsheets","text":"

    A lot of the work requires to interact with the checklists in their spreadsheet format.

    The spreadsheets are hosted on this google drive folder and we try to keep a back-up in the csv folder.

    The MRI part is the more advanced at this moment but we are looking for people to help with the M/EEG part.

    The content of the spreadsheets and the work involved there is described in more details here.

    "},{"location":"how-to-contribute/#boilerplate-method-sections","title":"Boilerplate method sections","text":"

    We want to create boilerplate method sections corresponding to a single item or a set of items of the checklist to automate methods writing once the checklist is completed. There is a lot of work to do there in terms of writing the boilerplate text as well as automating the methods section generation.

    Boilerplate related issues listed here.

    "},{"location":"how-to-contribute/#documentation","title":"Documentation","text":"

    If you don't understand something about the project, its goals, its implementation or how to use, then it's most likely that we did not do a good enough job at explaining and describing it. Get in touch and we can work together to improve our documentation.

    There are also several aspects of the documentation that need to be expanded.

    "},{"location":"how-to-contribute/#conversion-scripts","title":"Conversion scripts","text":"

    We use some python code to convert the spreadsheets into the set of jsonld files that the user interface needs as inputs to display the checklists. This needs further improvement so if python is your jam, feel free to dive in.

    We are also using the Reproschema python package to validate the schemas created. This package should also in the long-term help automating the creation of schemas, so there are definitely some features waiting to be written there.

    There is more information on how to set things up in the CONTRIBUTING.md.

    "},{"location":"how-to-contribute/#user-interface","title":"User interface","text":"

    The user interface is Javascript based and uses the Vue framework and this also needs a lot of tweaking, so if Javascript is cup of tea: get in touch!

    Here are some of the issues related to the UI.

    "},{"location":"how-to-contribute/#style-guide","title":"Style guide","text":""},{"location":"how-to-contribute/#python","title":"Python","text":"

    We rely on black to automate the python code formatting.

    We have additional checks on Github for code style and quality.

    • pep8 speaks for code style

    • the awesome sourcery for code quality and refactoring suggestions.

    "},{"location":"how-to-contribute/#markdown","title":"Markdown","text":"

    • For markdown styling we rely on remark.

    • We check for dead links using the markdown-link-check github action.

    "},{"location":"how-to-render-the-checklist/","title":"How the checklist is rendered","text":"

    This part gets a bit more \"techy\" but we will do our best to guide you through it.

    First make sure you are familiar with the structure of this project by reading the general organization documentation.

    "},{"location":"how-to-render-the-checklist/#turn-the-spreadsheet-into-the-schema","title":"Turn the spreadsheet into the schema","text":"

    This part is done by a bit of python code and you can find more information in the README in the python folder.

    "},{"location":"how-to-render-the-checklist/#making-the-new-schema-available-to-the-ui","title":"Making the new schema available to the UI","text":"

    If the previous step went smoothly you now need to make the newly created files available on your remote repository on github so that the user-interface can 'see' them. This you can do by committing the files newly created on your local repository and pushing them the remote.

    For example if the schema is hosted on the neurovault branch of your origin remote repository, the following should do the trick (assuming that you are already on the neurovault branch of your local repo):

    git add --all\ngit commit -m 'your commit message'\ngit push origin neurovault\n
    "},{"location":"how-to-render-the-checklist/#rendering-the-checklist","title":"Rendering the checklist","text":"

    There are 2 ways to visualize the checklist you have created.

    "},{"location":"how-to-render-the-checklist/#using-the-online-user-interface","title":"Using the online user interface","text":"

    You can point the already existing user-interface to the schema of the protocol or the activity you have just created.

    If you want to visualize an activity on its own, you can use the reproschema-ui. To do that you can point the UI to the raw content of this activity.

    To get access to the raw content of an activity you must click on the Raw button on github once you have opened its page, see for example the PHQ-9 acvitiy here. This will open this URL: https://raw.githubusercontent.com/ReproNim/reproschema-library/master/activities/PHQ-9/PHQ9_schema.

    You can then pass the the URL of raw content to the UI using the following template URL:

    https://www.repronim.org/reproschema-ui/#/activities/0?url=url-to-activity-schema\n

    To view a protocol, you can also use the reproschema-ui with the following template URL:

    https://www.repronim.org/reproschema-ui/#/?url=url-to-protocol-schema\n
    "},{"location":"how-to-render-the-checklist/#serving-the-app-locally","title":"Serving the app locally","text":"

    If you want to use serve the app locally on your computer, you will need to install node.js (the \"backend\" version of Javascript). A good way to do this is to use node version manager: some installation instructions are available on the user-interface repository.

    Make sure that that you have set the UI correctly so it will read the schema from the right repository. This can be be set by modifying its reproschema-ui/src/config.js file (e.g see here).

    Modify the githubSrc so that it points to the URL of the schema of your protocol:

    module.exports = {\n    /* eslint-disable */\n    githubSrc:\n        \"https://raw.githubusercontent.com/your-gtihub-account/your-repository/branch-name/folder/protcol_schema_filename\",\n    banner: \"This is a test\",\n    assetsPublicPath: \"/your-repository/\",\n    backendServer: null,\n    consent: true,\n};\n

    Once you have done that you can launch the app with:

    npm install # Install the Javascript dependencies\nnpm run serve # Run the development server locally\n

    Open your browser and go to localhost:8080

    "},{"location":"long-term/","title":"\"pie in the sky\" goals","text":"

    On the long term, we want those web-app to integrate better with other data standards and softwares to further reduce how much manual entry is required from the user.

    Similarly we want to broaden the use-cases for the app.

    "},{"location":"long-term/#improve-user-friendliness","title":"Improve user-friendliness","text":""},{"location":"long-term/#improving-default-answers","title":"Improving default answers","text":"

    If some data is gathered about the content of the method section of a sample of articles in the literature (see Carp, 2012 or the eyetracking guidelines), it should be possible to create or better organize list of response choices. It will take less time for users to tick a box rather than type something. Similarly we could decide to make the most common choices or the \"better\" option more prominent in a list of response options.

    "},{"location":"long-term/#adding-a-help-section-for-each-item","title":"Adding a help section for each item","text":"

    The content of some item might be quite obscure for some users, so embedding some help about each item would be a desirable goal.

    "},{"location":"long-term/#integration-with-the-main-neuroimaging-software","title":"Integration with the main neuroimaging software","text":""},{"location":"long-term/#plugins","title":"Plugins","text":"

    A side branch of this project includes developing plugins for the main neuroimaging software toolboxes (SPM, FSL, AFNI). Such a plugin would receive processing batch files as input (e.g matlabbatch.mat for SPM or design.fsf for FSL) and would create output files (e.g. in json format) readable by the checklist website, which in turn would be able to display the neuroimaging processing pipeline and/or the methods section for an article with the appropriate references (akin to what fMRIprep already does).

    In a similar way, FSL already outputs methods description for every preprocessing or statistical analysis that can be used to write the methods section of a paper as explained by Jeanette Mumford in this video.

    "},{"location":"long-term/#integration-with-bids-and-nidm","title":"Integration with BIDS and NIDM","text":"

    The process of filling in an online checklist can be simplified if the required information can be directly accessed from the metadata in some of the existing standards for data and results like:

    1. the brain imaging data structure (BIDS) used for that study,

    2. the NIDM results of any mass-univariate analysis performed for this study.

    BIDS is an emerging standard to organize neuroimaging data (MRI, fMRI, EEG...) and its associated metadata regarding acquisition (like repetition time, echo time, slice order...), experimental design (number of subjects, conditions, stimulus onsets...). If a data set is organized following the BIDS standard, it is possible to query its content using pybids or bids-matlab and even write part of the method section relative to the acquisition using the reports module of those packages (see for example pybids reports).

    NIDM results is a way to package mass-univariate fMRI results in a software independent way. This way it can for example facilitate reading FSL results through SPM or facilitate data sharing (like NIDM results is supported by the data sharing platform Neurovault and this greatly facilitate uploading your results there). NIDM contains many metadata with a correspondence in the COBIDAS checklist (see here).

    For BIDS and NIDM results, machine reading through a dataset organized as BIDS and NIDM results could automatically fill part of the COBIDAS checklist.

    Conversely it is possible to help users create some files that make up a BIDS dataset by having them fill in part of the checklist.

    An implementation of this, will most likely rely on the respective schema of those resources:

    • BIDS schema.
    • NIDM schema: link missing
    "},{"location":"long-term/#integration-with-existing-ontologies","title":"Integration with existing ontologies","text":"

    To automate the creation certain aspects we could try to rely on some existing ontologies that create list of term definitions to generate or enrich the list of response choices to some items.

    Examples of this could rely on scicrunch and the NIDM-terms or the Research resource IDentifiers.

    "},{"location":"long-term/#link-to-pipeline-creating-tools","title":"Link to pipeline-creating tools","text":"

    Though more challenging, it could be imagined that pipeline-creating-tools like (nipype, PORCUPINE, GIRAFFE) could also generate json files to be used for method section generation.

    "},{"location":"long-term/#facilitate-data-sharing","title":"Facilitate data sharing","text":"

    If the output created by the checklist app is accepted by neuroimaging databases (e.g. Neurovault) as metadata input, this could reduces friction for data sharing. Users wouldn't have to fill several times the same information: when pre-registering, when writing their methods, when submitting to a journal (if it has a checklist of its own), when sharing data. For example the metadata fields from neurovault are already present in the COBIDAS checklist.

    "},{"location":"long-term/#link-to-journal-specific-checklists","title":"Link to journal specific checklists","text":"

    As some journals start having submission-checklists (e.g eLife, nature research journals...), filling in the a checklist only once could reduce 'the submission paperwork' by writing part of the method section of the article AND generating the appropriate submission-checklist for a given journal.

    Reviewers could also use the website to systematically cross-check that all the required methods and results information are present for a given paper. A tool to compare different outputs from the web app could help editors visualize agreement across reviewers' evaluation of a paper.

    If the process is made sufficiently seamless, this could in return potentially incentivize more journals to adopt submission-checklists.

    "},{"location":"mid-term/","title":"Intermediate goals","text":"

    Once we have a prototype for a checklist we want to optimize them to make that they are comprehensive and cover all the items of the guidelines and to minimize the amount of time the user has to interact with the checklist.

    "},{"location":"mid-term/#improve-user-friendliness","title":"Improve user-friendliness","text":"

    In line with the idea of trying to minimize how much time the users have to spend using the app, we want to pre-select response choices to some items depending on the software users report having used in their analysis.

    For example, if SPM was used to do slice timing correction, there is no reason to ask users what type of interpolation was used as SPM does not give users the possibility to choose that parameter. Finding out what are the choice options for each item for the neuroimaging software used could help users navigate the app faster.

    Another potential way to improve users experience is to only expose users to items that are relevant to them. For example, we want to make sure that the items related \"arterial spin labelling\" are displayed only if the user mentioned they used this technique in a previous section of the checklist.

    "},{"location":"mid-term/#improving-the-wording","title":"Improving the wording","text":"

    The questions of the checklist must be as unambiguous as possible. This should be improved through early user feedback.

    "},{"location":"mid-term/#extended-checklists","title":"Extended checklists","text":"

    Right now, several of the prototypes contains only a subset of all the questions from the reports they came from. For example, the MRI checklist only contain the items corresponding to the metadata of a collection of results uploaded on Neurovault.

    In the near future, we want to be able to extend those checklists so they include all the items listed in their guidelines.

    "},{"location":"mid-term/#links-to-standardized-pipelines","title":"Links to standardized pipelines","text":"

    Data processed with some standardized pipelines (like fMRIprep) could facilitate filling in the checklist: ticking the box corresponding to that pipeline would automatically populate all the relevant fields in the COBIDAS-json file.

    "},{"location":"mid-term/#standardize-terminology","title":"Standardize terminology","text":"

    Ensure that the across apps the same terminology is used by trying to rely on the BIDS terminology with reference to other lexicons or ontology.

    "},{"location":"motivations/","title":"Project motivations","text":""},{"location":"motivations/#improve-transparency-and-reproducibility","title":"Improve transparency and reproducibility","text":"

    In 2012, in a review of the methods of more than 200 fMRI papers, the author found that \"Although many journals urge authors to describe their methods to a level of detail such that independent investigators can fully reproduce their efforts, the results described here suggest that few studies meet this criterion.\"

    A few years ago, in order to improve reproducibility in f/MRI research, the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) of OHBM released a report to promote best practices for methods and results reporting. This was recently followed by a similar initiative for EEG and MEG.

    Contrary to what the most optimistic people might have thought, these guidelines do not seem to have been widely adopted and anecdotal evidence (see that twitter poll and thread) suggests that, even among people who know about the report, few of them use it to write or review papers.

    A survey from ISMRM seems to suggest that the lack of method details is big problem in MRI research in general but also that there seems to be little knowledge of the existence of the COBIDAS report outside of some circles.

    One possible reason for this might be the unwieldy nature of the report. Anticipating this issue, the authors of the guidelines included a checklist that still ended up taking almost 30 of the 70 pages of the whole document. Anyone who has used this checklist tends to agree that it is a great resource but that it is a bit cumbersome to interpret and apply.

    So the goal of this project is to facilitate the use of this checklist and of similar guidelines.

    "},{"location":"motivations/#reduce-inefficiencies","title":"Reduce inefficiencies","text":"

    Currently, the research workflow around methods reporting has us do the same thing several times (very often manually) which leads to inefficiencies and multiplies the chances for human errors like forgetting to report some parameters or mis-reporting it.

    For example, a researcher would have to write or rewrite some aspects of the methods used when

    1. when preparing a pre-registration describing the planned study

    2. during the data curation process that usually involves adding metadata elements that relate to the details of the data acquisition,

    3. when actually working the methods and results section where we have to back and forth to the code we used to run the experiment and to the dataset to make sure important details are accurately reported,

    4. when sharing raw or derived data which also usually involves adding a minimum of methods-related metadata if the shared data is to be meaningfully reusable.

    Another source of inefficiency is the time lost trying to figure out:

    • what the authors of a paper actually did

      • when we would like to compare our results to theirs
      • when reviewing papers

    • what we actually did 6 months ago but forgot to make a note of it.

    So a potential side effect of using a checklist to systematically capture how data was acquired and analyzed would therefore be to make us more efficient. Having filled in a checklist once to generate a small set of files that capture the vast majority of our acquisition and analysis pipeline, we could easily share those files with our data, when submitting a paper or to generate a method section automatically.

    "},{"location":"preset-responses/","title":"Presets responses","text":"response list google spreadsheet jsonld mri_softwares spreadsheet jsonld stimulus_presentation_softwares spreadsheet jsonld multiple_comparisons spreadsheet jsonld interpolations spreadsheet jsonld cost_functions spreadsheet jsonld meeg_reference_electrodes spreadsheet jsonld meeg_analysis_softwares spreadsheet jsonld meeg_amplifier_brands spreadsheet jsonld meeg_acquisition_softwares spreadsheet jsonld eeg_cap_types spreadsheet jsonld boolean spreadsheet jsonld spm_version spreadsheet jsonld linux_version spreadsheet jsonld type_os spreadsheet jsonld windows_version spreadsheet jsonld macos_version spreadsheet jsonld eyetracker_preproc_softwares spreadsheet jsonld eyetracker_model_name spreadsheet jsonld eyetracker_producer spreadsheet jsonld"},{"location":"references/","title":"References","text":"

    There is zotero group where we try to keep track the relevant literature related to this project. Let us know if we missed something.

    "},{"location":"references/#about-the-checklist","title":"About the checklist","text":"

    • website

    • our project on the Open-Science Framework with the DOI:10.17605/OSF.IO/ANVQY is a where we try to centralize all the information across repos, google drive...

    • a twitter thread

    "},{"location":"references/#the-cobidas-reports","title":"The COBIDAS reports","text":"

    • for MRI and fMRI

    • for EEG and MEG

    • MEEG report presentation at OHBM 2019

    Jeanette Mumford has a 30 min video explaining the background behind the COBIDAS report and giving a run through of the checklist.

    Presentation slides made about this project can be found in the presentations folder.

    The original spreadsheet version of the COBIDAS checklist (thanks to Cass!!!)

    "},{"location":"references/#related-reporting-guidelines","title":"Related reporting guidelines","text":"
    • Guideline for Reporting Standards of Eye-tracking Research in Decision Sciences
    • Guidelines for the content and format of PET brain data in publications and archives: A consensus paper
    "},{"location":"references/#related-checklists","title":"Related checklists","text":"

    • A consensus-based transparency checklist and its shinyapp

    • Checklist for Artificial Intelligence in Medical Imaging

    • Consensus on the reporting and experimental design of clinical and cognitive-beharioural neurofeedback studies

    "},{"location":"short-term/","title":"Short term goals","text":"

    The short term goal of this project is to make the COBIDAS report (and similar guidelines) easier to use. We want to create a prototype website with a clickable checklist that, at the end, automatically generates most of the method section of a (f)MRI or (i)EEG / MEG or PET paper.

    So far the common short goals of all the versions of the app (for MRI, PET...) are:

    • Create a set of tools and a proof of concept web-app that can:

      • [x] convert a set of spreadsheet of items into a schema that represents all

      • [x] from this schema generate a checklist to be clicked through by users,

      • [x] outputs a set of JSON-LD files once the user is done,

      • [ ] generate a method section using these JSON-LD files and some boilerplate template of a method section where the content of the JSON-LD files could be reinjected.

    For the spreadsheets that represent the recommendation guidelines, the initial curation process must:

    • identify high-priority items for each checklist,

    • ensure that those high priority items has been properly atomized (meaning that it is only made of a single question) and curated (define an item name, a question, the type of response expected and an eventual list of response choices).

    "},{"location":"short-term/#mri","title":"MRI","text":"

    The current version of MRI prototype is inspired from Neurovault, so we would have to expand from there.

    The goal for the MRI app would to be able to describe a typical fMRI study with:

    • a single functional task
    • one anatomical scan
    • using mass uni-variate analysis
    "},{"location":"short-term/#meeg","title":"M/EEG","text":"

    The MRI version is currently ahead and the work done there can pave the way for the MEEG version.

    The MEEG COBIDAS guidelines have recently been published (see the preprint and webpage).

    The main short term goals for the MEEG version are:

    • [x] Identify overlaps between the MEEG and the MRI spreadsheets and harmonize both versions by extracting the common parts into standalone spreadsheets: for example there could be one common spreadsheet for participant sample description.

    • [ ] Consolidate the other items of the spreadsheet, as it is still missing a lot of information

    • [ ] Identify high-priority items in the checklist (similar to Carp 2012 for fMRI, e.g. Luck & Gaspelin 2015)

    "},{"location":"short-term/#positron-emission-tomography-and-eyetracking","title":"Positron emission tomography and Eyetracking","text":"

    Those 2 projects are more quite ahead already as they both started from fairly standardized spreadsheets.

    Both could benefit from a better definition of the response types and response options.

    "},{"location":"spreadsheets/","title":"Spreadsheets","text":"

    The spreadsheets that allow us to generate the different checklists are hosted on this google drive folder and we try to keep a back-up in the folder.

    The

    Will

    inputs folder.

    web app reference spreadsheet Github repository COBIDAS MRI link neurovault link spreadsheet pet link spreadsheet github eyetracking link spreadsheet github artemis link spreadsheet github reexecution link spreadsheet github"},{"location":"spreadsheets/#to-do","title":"To do","text":"

    Here is a short list of the different things to keep in mind when working on one of the spreadsheet.

    Each line must correspond to one checklist item that must have only one unambiguous item with an associated question. Any item that opens the possibility of a response of the form:

    If A was used, then list the parameters B, C, D\n

    Then it must be broken down into several questions:

    1. Was A used?\n2. If so, what parameter was used for B?\n3. What parameter was used for C?\n...\n

    For each item:

    • make sure it has a name, preferred label, description: for some of those a formula in the spreadsheet should automatically take care of that

    • make sure that there is a clear specific and unambiguous question associated to this item

    • identify the response type expected

    • create a response choice list where needed

    • mark the item as high-priority to be in the next release of the app.

    • assess whether there is way to not expose users to that item (or restrict list of the response choices for that item) if it is not relevant to their use-case

    "},{"location":"spreadsheets/#working-with-the-spreadsheets","title":"Working with the spreadsheets","text":"

    This describes some rules and tips when working with the spreadsheets.

    "},{"location":"spreadsheets/#style-guide","title":"Style guide","text":"

    Where relevant we try to use snake_case and stick to lower case.

    "},{"location":"spreadsheets/#hidden-columns","title":"Hidden columns","text":"

    If some columns do not appear, it is possible that they have been hidden by someone else. You will simply have to click on the double black arrow at the limit between columns to display them back.

    "},{"location":"spreadsheets/#formatting","title":"Formatting","text":"

    The MRI spreadsheet has some conditional formatting implemented so some cells will appear red when there is an error to be fixed (or if the cell is empty and should be filled). Some items that require more work might be manually highlighted in orange.

    "},{"location":"spreadsheets/#filtering","title":"Filtering","text":"

    If you want to only see certain rows, it is better to filter them rather than hide them.

    The column headers can be used to filter which item to display: for example, the activity_name column can be filtered using the arrow in the top cell in order to see only the items corresponding to one or more main sections (e.g., \"Acquisition\", \"Experimental design\", \"Preprocessing\", etc.).

    "},{"location":"spreadsheets/#formulas-and-automation","title":"Formulas and automation","text":"

    There is a certain level of automation built into those spreadsheets to fill in certain columns. For example, the preferred label of an item is generated from the item name by removing any underscore.

    Given that some of the guidelines used as source material often had several sub and sub-sub-sections organized in a hierarchical fashion, this was in some cases used to generate the item names.

    Below is an example how the item name for the MRI spreadsheet is determined by the content of the right-most non-empty column on the lef of the item column.

    item_name Imaging type imaging_type Essential imaging parameters All acquisitions voxel dimension voxel_dimension Essential imaging parameters Slice timing slice_timing"},{"location":"spreadsheets/#spreadsheet-content","title":"Spreadsheet content","text":"

    Here follows a description of the columns' content.

    "},{"location":"spreadsheets/#content-common-to-all-spreadsheets","title":"Content common to all spreadsheets","text":"

    The description of the columns common to all spreadsheet is described in the data dictionary in the inputs folder.

    Each column is described by an element in the JSON data dictionarry.

    \"column_name\": {\n    \"LongName\": \"\",\n    \"VariableName\": \"name of the corresponding variable, if relevant, in the conversion scripts\",\n    \"Description\": \"\",\n    \"Levels\": \"describes the different possibilities in this column\"\n}\n
    "},{"location":"spreadsheets/#preset-responses","title":"preset responses","text":"response list google spreadsheet jsonld mri_softwares spreadsheet jsonld stimulus_presentation_softwares spreadsheet jsonld multiple_comparisons spreadsheet jsonld interpolations spreadsheet jsonld cost_functions spreadsheet jsonld meeg_reference_electrodes spreadsheet jsonld meeg_analysis_softwares spreadsheet jsonld meeg_amplifier_brands spreadsheet jsonld meeg_acquisition_softwares spreadsheet jsonld eeg_cap_types spreadsheet jsonld boolean spreadsheet jsonld spm_version spreadsheet jsonld linux_version spreadsheet jsonld type_os spreadsheet jsonld windows_version spreadsheet jsonld macos_version spreadsheet jsonld eyetracker_preproc_softwares spreadsheet jsonld eyetracker_model_name spreadsheet jsonld eyetracker_producer spreadsheet jsonld"},{"location":"spreadsheets/#extra-columns","title":"Extra columns","text":"

    • Software defaults columns (spm_default, fsl_default) refer to the default value used for this item by a given software.

    • Integration with other sources:

      • Brain Imaging Data Structure dataset: the bids_* columns denote if information about this item can be found in a BIDS dataset and if so where.

      • nidm_results: mention where information this item can be found in an NIDM results package

      • neurovault collection: refers to the name of this item in a Neurovault collection

    • Meta-analysis: These columns denote whether or not each item could be important to evaluate studies for a meta-analysis.

      • use_case_meta-analysis

        • yes, include for meta-analyses
        • no, exclude for meta-analyses
        • maybe - meta-analysis_comment
    "},{"location":"spreadsheets/#percent-of-studies-reporting-the-item","title":"Percent of studies reporting the item","text":"

    Comparison to Carp 2012

    Some columns list the percentage of studies that reported each item.

    For the MRI spreadsheet, this is taken from Carp, 2012.

    • in_Carp2012: name of the item in that paper

    • percent_of_studies: percent of studies reporting the item. If the number is in bold, it was approximately extracted from one of the figures of the paper. (because it was not reported in the text of the article).

    • percent_of_studies_anat: same as above but for the anatomical data

    The Eyetracking spreadsheet percent_reported column also has numbers on the frequency at which items are present in the literature (see the preprint)

    "},{"location":"communication/","title":"Abstracts, posters, and talks about the eCobidas project","text":""},{"location":"communication/#links-to-other-presentations","title":"Links to other presentations","text":"
    • Hackathon results OHBM 2019 - presented at the open science room by Cass: https://slides.com/cassgvp/deck.
    "},{"location":"communication/abstracts/OHBM-2020-abstract/","title":"eCOBIDAS - Abstract OHBM 2020","text":""},{"location":"communication/abstracts/OHBM-2020-abstract/#title","title":"Title","text":"

    eCOBIDAS: a webapp checklist to improve neuroimaging methods and results reporting

    "},{"location":"communication/abstracts/OHBM-2020-abstract/#authors","title":"Authors","text":"

    If you have participated directly or indirectly to this project and you would like to be added as author, please create a profile on the OHBM 2020 abstract submission site (https://ww4.aievolution.com/hbm2001/), and add your name below:

    • R\u00e9mi Gau
    • Tom Nichols
    • Cassandra Gould van Praag
    • Satrajit Ghosh
    • Sanu Ann Abraham
    • Kristina Wiebels
    • Zsuzsika Sjoerds
    • David Moreau
    "},{"location":"communication/abstracts/OHBM-2020-abstract/#intro","title":"Intro","text":"

    In any scientific study, a complete and precise method section is necessary to understand and evaluate the results, plan replications, seed new research and compare outcomes across studies. However, a large number of neuroimaging studies fail to report important details necessary for independent investigators to achieve these goals [1]. In order to improve this situation, the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) of OHBM released a report to establish a set of best practices for methods and results reporting in f/MRI research [4]. This has been recently followed by a similar initiative for EEG and MEG [5]. The COBIDAS reports are accompanied by checklist tables meant to help authors ensure that their methods and results description complies with the Committee\u2019s recommendations. These checklists are a valuable resource, but the static PDF tables do not lend themselves to an actionable format, and users may be required to scroll through pages of items that do not concern their specific use-case. Moreover, while these checklists help generate a human readable method section, they do not provide ways to create a machine-readable equivalent that would encapsulate a large part of the metadata in a study. The goal of the eCOBIDAS webapp is to provide a user-friendly solution for researchers to fill out the COBIDAS checklists, while generating a machine-readable summary of a method section. This summary can then be used to automate method section writing for authors, or to facilitate the assessment of a study quality during peer-review.

    "},{"location":"communication/abstracts/OHBM-2020-abstract/#methods","title":"Methods","text":"

    The tables of the COBIDAS reports were reduced to600 individual items. A subset of items was selected and converted into a hierarchical schema representation in accordance with the repronim schema (https://github.com/ReproNim/reproschema). The use of this schema representation offers several advantages, as it allows: Efficient linking between the metadata about each item and the data provided by the user; Enforcing of output format consistency and of input validation directly at the data acquisition stage (i.e., while the user is filling out the checklist); Specifying user interface details for certain use-cases (e.g., to let users be presented only with items or sections that concern them). We then adapted the JavaScript-based webapp used for the visualization of the repronim schema to allow users to browse and complete each section of the checklist.

    "},{"location":"communication/abstracts/OHBM-2020-abstract/#results","title":"Results","text":"

    The development of the webapp started at the OHBM Hackathon 2019 and aims to be a project developed by the community for the community. All information is centralized on an open science framework project [2]. The current prototype of the webapp can be accessed athttps://ohbm.github.io/eCOBIDAS/#/. It is based on the 88 metadata items available to characterise a collection of neuroimaging results uploaded on the neurovault website [3]. This only constitutes a subset of the full f/MRI COBIDAS checklist and will be expanded in the near future. Users can fill out the different sections of the checklist (study design, participants, preprocessing, etc.) and are only presented with items that concern their use-case. Finally, the output can be exported in a set of machine-readable JSON files.

    "},{"location":"communication/abstracts/OHBM-2020-abstract/#conclusion","title":"Conclusion","text":"

    Future developments of the webapp will involve:

    • Expansion of the list of items covered to include at first all the items from Carp 2012, and then the entirety of the COBIDAS f/MRI checklist;
    • Establish a list of recommended MEEG items to create a version for electrophysiology studies
    • Automatically generate a PDF summary and a method section after the checklist has been filled
    • Automatically fill the checklist by allowing the application to query the content of:
      • a BIDS dataset
      • an SPM or FSL analysis pipeline (matlabbatch.mat or design.fsf).

    eCOBIDAS is part of a broader trend towards open and reproducible science, to facilitate valid and robust inferences in neuroscience. We encourage any member of the community to contribute to this project.

    "},{"location":"communication/abstracts/OHBM-2020-abstract/#categories","title":"Categories","text":"
    • Informatics others
    • Workflow
    • Other methods
    • Keywords
    • Checklist
    • Workflows
    • Statistical methods
    • Webapp
    • Machine-reading
    • Standardization
    • COBIDAS
    • Transparency
    • Replicability
    • Reproducibility
    "},{"location":"communication/abstracts/OHBM-2020-abstract/#reference","title":"Reference","text":"

    [1] Carp J. (2018), \u2018The secret lives of experiments: Methods reporting in the fMRI literature\u2019, NeuroImage. vol. 63, no. 1, pp. 289-300, doi: 10.1016/j.neuroimage.2012.07.004

    [2] Gau, Remi et al (2019), \u2018COBIDAS Checklist\u2019, OSF, doi: 10.17605/OSF.IO/ANVQY

    [3] Gorgolewski K. J. (2015) et al. \u2018NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the brain\u2019, Frontiers in Neuroinformatics. vol. 9, no. 8 doi: 10.3389/fninf.2015.00008

    [4] Nichols T. E. et al. (2017), \u2018Best Practices in Data Analysis and Sharing in Neuroimaging using MRI\u2019, Nature Neuroscience, vol. 20, no 3, pp. 299-303, doi: 10.1038/nn.4500.

    [5] Pernet, C. R. et al. (2018), Best Practices in Data Analysis and Sharing in Neuroimaging Using MEEG, OSF Preprints, August 9. doi:10.31219/osf.io/a8dhx

    "},{"location":"communication/abstracts/OHBM-hackathon-2019-project-description/","title":"Improving the COBIDAS checklist for better neuroimaging methods and results reporting","text":"

    Remi Gau (ORCID)

    "},{"location":"communication/abstracts/OHBM-hackathon-2019-project-description/#project-description","title":"Project Description","text":"

    In 2012, in his review of the methods and results reporting of more than 200 fMRI papers, Joshua Carp wrote: \"Although many journals urge authors to describe their methods to a level of detail such that independent investigators can fully reproduce their efforts, the results described here suggest that few studies meet this criterion.\"

    A few years ago, in order to improve the situation with respect to reproducibility in f/MRI research, the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) of OHBM released a report to promote best practices for methods and results reporting. This was recently followed by a similar initiative for EEG and MEG.

    So far these guidelines do not seem to have been widely adopted and anecdotal evidence (see that twitter poll and thread) suggests that even among people who know about the report few of them use it to write or review papers. One likely reason for this might be the unwieldy nature of the report. Anyone who has used this checklist tends to agree that it is a great resource but that it is a bit cumbersome to interpret and apply.

    So the short term goal of this project is to facilitate the use of this checklist. But, if done right, this could also in the long term enhance the adoption of emerging neuroimaging standards (the Brain imaging data structure, fMRIprep, NIDM...), facilitate data sharing and pre-registration, help with peer-review...

    "},{"location":"communication/abstracts/OHBM-hackathon-2019-project-description/#short-term-goal","title":"Short term goal","text":"

    The short term goal of this project is to make the COBIDAS report easier to use: we want to create a website with a clickable checklist that generates a json file at the end.

    By turning the checklist into a website users could more rapidly click through it and provide more of the information requested by the COBIDAS report. This would generate a small text file (a json file) that summarizes what option was chosen for each item of the checklist. This machine readable file could then be used to automatically generate part of the methods section of an article.

    Other potential goals (e.g. interaction with BIDS and NIDM, further integration with main neuroimaging softwares...) and potential applications (improving data-sharing and peer-review) of this project are described in this repository.

    "},{"location":"communication/abstracts/OHBM-hackathon-2019-project-description/#skills-required-to-participate","title":"Skills required to participate","text":"

    One or more of those:

    • To be enthusiastic about reproducibility
    • Familiarity with the COBIDAS report for f/MRI, MEEG,
    • To know something about web design,
    • Familiarity with one or more of the main neuroimaging software for fMRI (SPM, FSL...) or for M/EEG (Fieldtrip, EEGlab...)
    "},{"location":"communication/abstracts/OHBM-hackathon-2019-project-description/#milestones","title":"Milestones","text":"

    • Discuss conceptual and structural details of the COBIDAS-json file.

    • Create a template of the COBIDAS-json file

    • Create a proof of concept website that can:

      • given a template COBIDAS-json file generates a checklist to clicked through by users,
      • outputs a populated COBIDAS-json file once the user is done,
      • generate a method section using a populated COBIDAS-json file.
    "},{"location":"communication/abstracts/OHBM-hackathon-2019-project-description/#preparation-material","title":"Preparation material","text":"

    Jeanette Mumford has a 30 min video on her youtube channel explaining the background behind the COBIDAS report and giving a run through of the checklist.

    The COBIDAS report:

    • for MRI and fMRI
    • for EEG and MEG

    A spreadsheet version of the COBIDAS checklist (thanks to Cass!!!)

    The secret lives of experiments: methods reporting in the fMRI literature

    A manifesto for reproducible science

    "},{"location":"communication/abstracts/OHBM-hackathon-2019-project-description/#github-repo","title":"GitHub repo","text":"

    The github repository of this project can be found here

    "},{"location":"methods_writing/","title":"Index","text":"
    • https://github.com/guiomar/Armitage_BIDS_NLG
    • or try to reuse some of pybids-reports
    "}]} \ No newline at end of file diff --git a/short-term/index.html b/short-term/index.html new file mode 100644 index 00000000..f5172444 --- /dev/null +++ b/short-term/index.html @@ -0,0 +1,699 @@ + + + + + + + + + + + + + + + + + + + + + + Short term goals - eCOBIDAS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + + + + + + +

    Short term goals

    +

    The short term goal of this project is to make the COBIDAS report (and similar +guidelines) easier to use. We want to create a prototype website with a +clickable checklist that, at the end, automatically generates most of the method +section of a (f)MRI or (i)EEG / MEG or PET paper.

    +
    + +

    So far the common short goals of all the versions of the app (for MRI, PET...) +are:

    +
      +
    • +

      Create a set of tools and a proof of concept web-app that can:

      +
        +
      • +

        [x] convert a set of spreadsheet of items into a schema that represents all

        +
        • +
      • +

        [x] from this schema generate a checklist to be clicked through by users,

        +
        • +
      • +

        [x] outputs a set of JSON-LD files once the user is done,

        +
        • +
      • +

        [ ] generate a method section using these JSON-LD files and some boilerplate + template of a method section where the content of the JSON-LD files + could be reinjected.

        +
        • +
      +
      • +
    +
    + +

    For the spreadsheets that represent the recommendation guidelines, the initial +curation process must:

    +
      +
    • +

      identify high-priority items for each checklist,

      +
      • +
    • +

      ensure that those high priority items has been properly atomized (meaning + that it is only made of a single question) and curated (define an item name, + a question, the type of response expected and an eventual list of response + choices).

      +
      • +
    + + +
    + +

    MRI

    +

    The current version of MRI prototype is inspired from +Neurovault, so we would have to expand from there.

    +

    The goal for the MRI app would to be able to describe a typical fMRI study with:

    +
      +
    • a single functional task
      • +
    • one anatomical scan
      • +
    • using mass uni-variate analysis
      • +
    +

    M/EEG

    +

    The MRI version is currently ahead and the work done there can pave the way for +the MEEG version.

    +

    The MEEG COBIDAS guidelines have recently been published (see the +preprint and +webpage).

    +

    The main short term goals for the MEEG version are:

    +
      +
    • +

      [x] Identify overlaps between the MEEG and the MRI spreadsheets and harmonize + both versions by extracting the common parts into standalone spreadsheets: + for example there could be one common spreadsheet for participant sample + description.

      +
      • +
    • +

      [ ] Consolidate the other items of the spreadsheet, as it is still missing a lot + of information

      +
      • +
    • +

      [ ] Identify high-priority items in the checklist (similar to Carp 2012 for + fMRI, e.g. + Luck & Gaspelin 2015)

      +
      • +
    +

    Positron emission tomography and Eyetracking

    +

    Those 2 projects are more quite ahead already as they both started from fairly +standardized spreadsheets.

    +

    Both could benefit from a better definition of the response types and response options.

    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
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    + + + + + + + + + \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 00000000..209794d2 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,93 @@ + + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + + None + 2024-05-18 + daily + + \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz new file mode 100644 index 0000000000000000000000000000000000000000..1a920d61c3e0fd124d744bb0b9c710a2a2134907 GIT binary patch literal 209 zcmV;?051O@iwFqnH%Vp!|8r?{Wo=<_E_iKh0PWVj4#FT12k_meAnXO&Xi^QOo1>FH z0HN5DP^f@b-(LC=)7hC@IR0Gjw?gZ8ufa + + + + + + + + + + + + + + + + + + + + Working with the spreadsheets - eCOBIDAS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
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    + + + +
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    + + + + + + + + +

    Spreadsheets

    +

    The spreadsheets that allow us to generate the different checklists are hosted +on this +google drive folder +and we try to keep a back-up in the +folder.

    +

    The

    +
    +Will +
    +

    inputs folder.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    web appreferencespreadsheetGithub repository
    COBIDAS MRIlink
    neurovaultlinkspreadsheet
    petlinkspreadsheetgithub
    eyetrackinglinkspreadsheetgithub
    artemislinkspreadsheetgithub
    reexecutionlinkspreadsheetgithub
    +

    To do

    +

    Here is a short list of the different things to keep in mind when working on one +of the spreadsheet.

    +

    Each line must correspond to one checklist item that must have only one +unambiguous item with an associated question. Any item that opens the +possibility of a response of the form:

    +
    If A was used, then list the parameters B, C, D
+
    +

    Then it must be broken down into several questions:

    +
    1. Was A used?
+2. If so, what parameter was used for B?
+3. What parameter was used for C?
+...
+
    +

    For each item:

    +
      +
    • +

      make sure it has a name, preferred label, description: for some of those a + formula in the spreadsheet should automatically take care of that

      +
      • +
    • +

      make sure that there is a clear specific and unambiguous question associated + to this item

      +
      • +
    • +

      identify the response type expected

      +
      • +
    • +

      create a response choice list where needed

      +
      • +
    • +

      mark the item as high-priority to be in the next release of the app.

      +
      • +
    • +

      assess whether there is way to not expose users to that item (or + restrict list of the response choices for that item) if it is not relevant + to their use-case

      +
      • +
    + + +

    Working with the spreadsheets

    +

    This describes some rules and tips when working with the spreadsheets.

    +

    Style guide

    +

    Where relevant we try to use snake_case and stick to lower case.

    +

    Hidden columns

    +

    If some columns do not appear, it is possible that they have been hidden by +someone else. You will simply have to click on the double black arrow at the +limit between columns to display them back.

    +

    +

    Formatting

    +

    The MRI spreadsheet has some conditional formatting implemented so some cells +will appear red when there is an error to be fixed (or if the cell is empty and +should be filled). Some items that require more work might be manually +highlighted in orange.

    +

    Filtering

    +

    If you want to only see certain rows, it is better to filter them rather than +hide them.

    +

    The column headers can be used to filter which item to display: for example, the +activity_name column can be filtered using the arrow in the top cell in order +to see only the items corresponding to one or more main sections (e.g., +"Acquisition", "Experimental design", "Preprocessing", etc.).

    +

    Formulas and automation

    +

    There is a certain level of automation built into those spreadsheets to fill in +certain columns. For example, the preferred label of an item is generated from +the item name by removing any underscore.

    +

    Given that some of the guidelines used as source material often had several sub +and sub-sub-sections organized in a hierarchical fashion, this was in some cases +used to generate the item names.

    +

    Below is an example how the item name for the MRI spreadsheet is determined by +the content of the right-most non-empty column on the lef of the item column.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    item_name
    Imaging typeimaging_type
    Essential imaging parametersAll acquisitionsvoxel dimensionvoxel_dimension
    Essential imaging parametersSlice timingslice_timing
    +

    Spreadsheet content

    +

    Here follows a description of the columns' content.

    +

    Content common to all spreadsheets

    +

    The description of the columns common to all spreadsheet is described in the +data dictionary +in the +inputs folder.

    +

    Each column is described by an element in the JSON data dictionarry.

    +
    "column_name": {
+    "LongName": "",
+    "VariableName": "name of the corresponding variable, if relevant, in the conversion scripts",
+    "Description": "",
+    "Levels": "describes the different possibilities in this column"
+}
+
    + + +

    preset responses

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    response listgoogle spreadsheetjsonld
    mri_softwaresspreadsheetjsonld
    stimulus_presentation_softwaresspreadsheetjsonld
    multiple_comparisonsspreadsheetjsonld
    interpolationsspreadsheetjsonld
    cost_functionsspreadsheetjsonld
    meeg_reference_electrodesspreadsheetjsonld
    meeg_analysis_softwaresspreadsheetjsonld
    meeg_amplifier_brandsspreadsheetjsonld
    meeg_acquisition_softwaresspreadsheetjsonld
    eeg_cap_typesspreadsheetjsonld
    booleanspreadsheetjsonld
    spm_versionspreadsheetjsonld
    linux_versionspreadsheetjsonld
    type_osspreadsheetjsonld
    windows_versionspreadsheetjsonld
    macos_versionspreadsheetjsonld
    eyetracker_preproc_softwaresspreadsheetjsonld
    eyetracker_model_namespreadsheetjsonld
    eyetracker_producerspreadsheetjsonld
    +

    Extra columns

    +
      +
    • +

      Software defaults columns (spm_default, fsl_default) refer to the + default value used for this item by a given software.

      +
      • +
    • +

      Integration with other sources:

      +
        +
      • +

        Brain Imaging Data Structure dataset: the bids_* columns denote if + information about this item can be found in a + BIDS dataset and if so where.

        +
        • +
      • +

        nidm_results: mention where information this item can be found in an + NIDM results package

        +
        • +
      • +

        neurovault collection: refers to the name of this item in a Neurovault + collection

        +
        • +
      +
      • +
    • +

      Meta-analysis: These columns denote whether or not each item could be + important to evaluate studies for a meta-analysis.

      +
        +
      • +

        use_case_meta-analysis

        +
          +
        • yes, include for meta-analyses
          • +
        • no, exclude for meta-analyses
          • +
        • maybe - meta-analysis_comment
          • +
        +
        • +
      +
      • +
    +

    Percent of studies reporting the item

    +

    Comparison to Carp 2012

    +

    Some columns list the percentage of studies that reported each item.

    +

    For the MRI spreadsheet, this is taken from +Carp, 2012.

    +
      +
    • +

      in_Carp2012: name of the item in that paper

      +
      • +
    • +

      percent_of_studies: percent of studies reporting the item. If the number + is in bold, it was approximately extracted from one of the figures of + the paper. (because it was not reported in the text of the article).

      +
      • +
    • +

      percent_of_studies_anat: same as above but for the anatomical data

      +
      • +
    +

    The Eyetracking spreadsheet percent_reported column also has numbers on the +frequency at which items are present in the literature (see the +preprint)

    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + \ No newline at end of file