How to contribute FAIR data?

What is FAIR data about?

It is about scientific data management and stewardship.

What are FAIR principles?

FAIR principles are1:

  • Findable

    • Data and metadata should be easy to find by both humans and computer systems. Basic machine-readable descriptive metadata allows the discovery of relevant data sets and services.

  • Accessible

    • Data and metadata should be stored in long term such that they can be easily accessed and downloaded or locally used by machines and humans using standard communication protocols.

  • Interoperable

    • Data should be ready to be exchanged, interpreted and combined in a (semi)automated way with other data sets – by humans as well as computer systems.

  • Reusable

    • Data and metadata are sufficiently well-described to allow data to be reused in future research, allowing for integration with other compatible data sources. Proper citation must be facilitated, and the conditions under which the data can be used should be clear to machines and humans.

What shall you do to be FAIR?

There are a couple of important things you should follow for your data to be FAIR:

  • Obtain a PID (persistent identifier) for your data set

  • Describe your data with metadata as generous as possible

  • Use mandatory metadata of VI-SEEM which is:

    • data creation date

    • data owner/creator

    • data creation procedure

      • data creation software

      • data preprocessing

    • data how-to documentation URL

  • Describe access restrictions if any

  • Provide your metadata in a machine-readable form (resolvable vocabularies, ontologies, thesauri, standard metadata schemas)

  • Properly cite associated data sets (by providing their PIDs) in your metadata and describe how they relate to your data set

  • For reusability provide complete metadata for each data file including but not limited to:

    • scope

    • particularities/limitations about the data

    • data environment (date of generation, conditions, parameters, software – name and version – used)

    • license information

    • description of data pipeline (preferably in machine readable format) that led to your data

    • use community standards and best practices (describe your reasons if you have to divert from those)

What shall you do to maximize value gained for the VI-SEEM community?

Contribute your data set according to

and be FAIR!