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General Workflow Idea

To help you getting started with RDM and FAIR, the TAM DataHub is providing applications and templates which are described and linked throughout this User Manual. To get an idea of the general workflow, we explain in this section how you should store and organize your data and research project in order to make the most of the DataHub applications.

Figure: General workflow Figure: How the DataHub supports you in the Research Data Lifecycle.

From Data Storage to Data Sharing

Already in the stage of data collection, organization and storage you should think about how your data will be used by you and/or others and how you want to share your data later on. Usually, there are two motivations behind data sharing:

  • share your data for others to reuse
  • share your data for reproducing your work

When sharing data for others to reuse and for others to run their own analyses on it, the data needs to be in a state that it can be easily accessed and opened by others but at the same time being in a rather "raw" state. For example: If you want to share your eye tracking data for others to reuse, you should not share the eyelinke-edf files but a file format that can be opened by others without having to install eyelink's proprietary software to read the file (e.g., tsv or csv tables). Still, you should share raw data in those tables and not average statistics from your own analyses.

When sharing data for reproducing your own work (e.g., for reviewers or as an online resource) you should also include data and outputs like average statistics, intermediate results or figures.

Those two motivations, however, are not mutually exclusive and can both be served by one data management setup. By applying a research folder structure standard (TONIC) combined with a data structure standard (BIDS) you can keep your source, raw and derivative data in separate folders or in separate repositories or in separate git-branches and have flexibility to include whichever data you need or want for publication and sharing. On the following pages, these standards are explained and incorporated in the DataHub workflow.