The FAIR principles
In the data management world, making data "FAIR" is the ideal situation. Making your data FAIR facilitates knowledge discovery by assisting humans and machines in their discovery of and access to the data.
See all FAIR principles and their explanation on the GO FAIR website. See also this FAIR data checklist to see if you have met all FAIR requirements.
Findable by both humans and machines
Include metadata that allow the discovery of interesting datasets: the dataset should be findable with a google datasets search.
- Select a data repository early on.
- Make sure the repository provides a persistent identifier for your data.
- Check the repository's data format and metadata requirements: do they provide descriptive information about the context, quality and condition, or characteristics of the data?
Accessible: stored for the long term with well-defined access conditions
Think about the security, legal conditions, sustainability and access conditions of the data.
- guarantee longevity of the data, e.g., by submitting it to a repository that has a certification like the Data Seal of Approval or an ISO certification
- check and describe the legal conditions under which the data can be made available
- establish an embargo period if necessary
- make sure your ICT infrastructure will keep the (meta)data available even in case of equipment failure or human error
Interoperable: ready to be combined with other datasets
Think about the software, documentation standards (e.g., the same labels for the same variables) and formats. This differs for different disciplines.
- select commonly used data formats (such as BIDS for neuroimaging data)
- select commonly used vocabularies (controlled vocabularies if applicable) for data items
- if your (meta)data relates to other datasets, indicate how
Reusable
Think about the licensing and provenance (can you trust this data?) of the data.
- make sure you keep proper provenance information: details about how and where the data was generated, including machine settings, and details about all processing steps, such as the software tools with their versions and parameters
- select the right minimal metadata standard and collect the necessary metadata, see link
- select a license for the (meta)data and the associated software tools
- make sure the important conclusions of your study will not only be available in a paper in narrated form, but also in a digital file (e.g., a nanopublication)