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Merge pull request #166 from Remi-Gau/update_readme_template
Update readme template
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manualTests/dummyData/README

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# README
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# README
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(template copied from the BIDS starter kit:
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https://github.com/bids-standard/bids-starter-kit)
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The README is usually the starting point for researchers using your data
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and serves as a guidepost for users of your data. A clear and informative
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README makes your data much more usable.
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In general you can include information in the README that is not captured by some other
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files in the BIDS dataset (dataset_description.json, events.tsv, ...).
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It can also be useful to also include information that might already be
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present in another file of the dataset but might be important for users to be aware of
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before preprocessing or analysing the data.
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If the README gets too long you have the possibility to create a `/doc` folder
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and add it to the `.bidsignore` file to make sure it is ignored by the BIDS validator.
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More info here: https://neurostars.org/t/where-in-a-bids-dataset-should-i-put-notes-about-individual-mri-acqusitions/17315/3
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## Details related to access to the data
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- [ ] Data user agreement
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If the dataset requires a data user agreement, link to the relevant information.
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- [ ] Contact person
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Indicate the name and contact details (email and ORCID) of the person responsible for additional information.
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- [ ] Practical information to access the data
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If there is any special information related to access rights or
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how to download the data make sure to include it.
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For example, if the dataset was curated using datalad,
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make sure to include the relevant section from the datalad handbook:
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http://handbook.datalad.org/en/latest/basics/101-180-FAQ.html#how-can-i-help-others-get-started-with-a-shared-dataset
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## Overview
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- [ ] Project name (if relevant)
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- [ ] Year(s) that the project ran
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If no `scans.tsv` is included, this could at least cover when the data acquisition
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starter and ended. Local time of day is particularly relevant to subject state.
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- [ ] Brief overview of the tasks in the experiment
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A paragraph giving an overview of the experiment. This should include the
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goals or purpose and a discussion about how the experiment tries to achieve
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these goals.
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- [ ] Description of the contents of the dataset
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An easy thing to add is the output of the bids-validator that describes what type of
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data and the number of subject one can expect to find in the dataset.
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- [ ] Independent variables
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A brief discussion of condition variables (sometimes called contrasts
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or independent variables) that were varied across the experiment.
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- [ ] Dependent variables
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A brief discussion of the response variables (sometimes called the
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dependent variables) that were measured and or calculated to assess
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the effects of varying the condition variables. This might also include
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questionnaires administered to assess behavioral aspects of the experiment.
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- [ ] Control variables
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A brief discussion of the control variables --- that is what aspects
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were explicitly controlled in this experiment. The control variables might
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include subject pool, environmental conditions, set up, or other things
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that were explicitly controlled.
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- [ ] Quality assessment of the data
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Provide a short summary of the quality of the data ideally with descriptive statistics if relevant
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and with a link to more comprehensive description (like with MRIQC) if possible.
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## Methods
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### Subjects
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A brief sentence about the subject pool in this experiment.
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Remember that `Control` or `Patient` status should be defined in the `participants.tsv`
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using a group column.
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- [ ] Information about the recruitment procedure
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- [ ] Subject inclusion criteria (if relevant)
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- [ ] Subject exclusion criteria (if relevant)
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### Apparatus
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A summary of the equipment and environment setup for the
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experiment. For example, was the experiment performed in a shielded room
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with the subject seated in a fixed position.
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### Initial setup
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A summary of what setup was performed when a subject arrived.
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### Task organization
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How the tasks were organized for a session.
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This is particularly important because BIDS datasets usually have task data
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separated into different files.)
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- [ ] Was task order counter-balanced?
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- [ ] What other activities were interspersed between tasks?
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- [ ] In what order were the tasks and other activities performed?
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### Task details
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As much detail as possible about the task and the events that were recorded.
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### Additional data acquired
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A brief indication of data other than the
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imaging data that was acquired as part of this experiment. In addition
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to data from other modalities and behavioral data, this might include
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questionnaires and surveys, swabs, and clinical information. Indicate
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the availability of this data.
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This is especially relevant if the data are not included in a `phenotype` folder.
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https://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html#phenotypic-and-assessment-data
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### Experimental location
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This should include any additional information regarding the
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the geographical location and facility that cannot be included
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in the relevant json files.
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### Missing data
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Mention something if some participants are missing some aspects of the data.
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This can take the form of a processing log and/or abnormalities about the dataset.
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Some examples:
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- A brain lesion or defect only present in one participant
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- Some experimental conditions missing on a given run for a participant because
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of some technical issue.
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- Any noticeable feature of the data for certain participants
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- Differences (even slight) in protocol for certain participants.
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### Notes
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Any additional information or pointers to information that
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might be helpful to users of the dataset. Include qualitative information
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related to how the data acquisition went.
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