# README ## Contact person: - Rémi Gau - email: remi.gau@gmail.com - ORCID: 0000-0002-1535-9767 ## Access to the data See the datalad section below. ## Overview - [ ] Project name (if relevant) - [x] Year(s) that the project ran - from 2007 to 2010 - [ ] Brief overview of the tasks in the experiment A paragraph giving an overview of the experiment. This should include the goals or purpose and a discussion about how the experiment tries to achieve these goals. - [ ] Description of the contents of the dataset An easy thing to add is the output of the bids-validator that describes what type of data and the number of subject one can expect to find in the dataset. - [ ] Independent variables A brief discussion of condition variables (sometimes called contrasts or independent variables) that were varied across the experiment. - [ ] Dependent variables A brief discussion of the response variables (sometimes called the dependent variables) that were measured and or calculated to assess the effects of varying the condition variables. This might also include questionnaires administered to assess behavioral aspects of the experiment. - [ ] Control variables A brief discussion of the control variables --- that is what aspects were explicitly controlled in this experiment. The control variables might include subject pool, environmental conditions, set up, or other things that were explicitly controlled. - [ ] Quality assessment of the data Provide a short summary of the quality of the data ideally with descriptive statistics if relevant and with a link to more comprehensive description (like with MRIQC) if possible. ## Methods ### Apparatus A summary of the equipment and environment setup for the experiment. For example, was the experiment performed in a shielded room with the subject seated in a fixed position. ### Initial setup A summary of what setup was performed when a subject arrived. ### Task organization How the tasks were organized for a session. This is particularly important because BIDS datasets usually have task data separated into different files.) - [ ] Was task order counter-balanced? - [ ] What other activities were interspersed between tasks? - [ ] In what order were the tasks and other activities performed? ### Task details As much detail as possible about the task and the events that were recorded. ### Missing data Mention something if some participants are missing some aspects of the data. This can take the form of a processing log and/or abnormalities about the dataset. Some examples: - A brain lesion or defect only present in one participant - Some experimental conditions missing on a given run for a participant because of some technical issue. - Any noticeable feature of the data for certain participants - Differences (even slight) in protocol for certain participants. --- [![made-with-datalad](https://www.datalad.org/badges/made_with.svg)](https://datalad.org) ## DataLad datasets and how to use them This repository is a [DataLad](https://www.datalad.org/) dataset. It provides fine-grained data access down to the level of individual files, and allows for tracking future updates. In order to use this repository for data retrieval, [DataLad](https://www.datalad.org/) is required. It is a free and open source command line tool, available for all major operating systems, and builds up on Git and [git-annex](https://git-annex.branchable.com/) to allow sharing, synchronizing, and version controlling collections of large files. You can find information on how to install DataLad at [handbook.datalad.org/en/latest/intro/installation.html](http://handbook.datalad.org/en/latest/intro/installation.html). ### Get the dataset A DataLad dataset can be `cloned` by running ``` datalad clone ``` Once a dataset is cloned, it is a light-weight directory on your local machine. At this point, it contains only small metadata and information on the identity of the files in the dataset, but not actual _content_ of the (sometimes large) data files. ### Retrieve dataset content After cloning a dataset, you can retrieve file contents by running ``` datalad get ` ``` This command will trigger a download of the files, directories, or subdatasets you have specified. DataLad datasets can contain other datasets, so called _subdatasets_. If you clone the top-level dataset, subdatasets do not yet contain metadata and information on the identity of files, but appear to be empty directories. In order to retrieve file availability metadata in subdatasets, run ``` datalad get -n ``` Afterwards, you can browse the retrieved metadata to find out about subdataset contents, and retrieve individual files with `datalad get`. If you use `datalad get `, all contents of the subdataset will be downloaded at once. ### Stay up-to-date DataLad datasets can be updated. The command `datalad update` will _fetch_ updates and store them on a different branch (by default `remotes/origin/master`). Running ``` datalad update --merge ``` will _pull_ available updates and integrate them in one go. ### Find out what has been done DataLad datasets contain their history in the `git log`. By running `git log` (or a tool that displays Git history) in the dataset or on specific files, you can find out what has been done to the dataset or to individual files by whom, and when. ### More information More information on DataLad and how to use it can be found in the DataLad Handbook at [handbook.datalad.org](http://handbook.datalad.org/en/latest/index.html). The chapter "DataLad datasets" can help you to familiarize yourself with the concept of a dataset.