This working group seeks to support the reuse of characterization and evaluation data (both phenotypic data and data from the ~omics domain) along a concise and measurable set of principles that we refer to as the FAIR Data Principles.

Using the FAIR Data approach, data should be:

  1. Findable – Easy to find by both humans and computer systems and based on mandatory description of the metadata that allow the discovery of interesting datasets;
  2. Accessible – Stored for long term such that they can be easily accessed and/or downloaded with well-defined license and access conditions (Open Access when possible), whether at the level of metadata, or at the level of the actual data content;
  3. Interoperable – Ready to be combined with other datasets by humans as well as computer systems;
  4. Reusable – Ready to be used for future research and to be processed further using computational methods.

An important step in the FAIR Data approach is to publish existing and new datasets in a semantically interoperable format that can be understood by computer systems. By semantically annotating data items and metadata, we can use computer systems to (semi) automatically combine different data sources, resulting in richer knowledge discovery activities.

Expected outputs include:

·      Demonstrations of FAIR Data point infrastructure of Multi Crop Passport descriptor data.

·      An overview description of the FAIR data ecosystem and tools developed to consume data from FAIR data points.

·      Identification of venues and individuals to host hackathons or bring your own data (BYOD) events in which data providers learn to FAIRify their data.