FAIR Digital Objects (FAIR DOs) describe a concept of virtual data objects that has been developed and used by RDA in various Working and Interest Groups. FAIR DOs may represent data, software, or other research resources. They are uniquely identified by a Persistent Identifier (PID) and metadata rich enough to enable them to be reliably found, used and cited.
FAIR DOs are currently discussed world-wide, in many RDA groups, in the European Open Science Cloud, the FDO Forum https://fairdo.org, as well as in other initiatives with the need to design and develop large integrative research data infrastructures to facilitate findability, accessibility, interoperability and reusability (FAIR).
Although there exists a list of demonstrators and testbeds, there is still a need for collecting convincing ideas for potential success stories and “killer applications”.
In this session we would like to collect and to discuss FAIR Digital Object applications, success stories, and use cases. The session is open for potential contributors to give a short presentation (5 minutes) of their cases and ideas.
Collaborative session notes: https://docs.google.com/document/d/1QwOeRHJsZ-GTq1KvPAS8yQn3iM6a7J76MPCErlwqR1U/edit?usp=sharing
- Short introduction IG Data Fabric (5m) [Rainer Stotzka]
- High Level Views on FDO (10m) [Christine Kilpatrick]
- Up to 7 teasers (3-5m each) and discussions
- FDO Use Case from Earth System Science [Ivonne Anders]
- Computational Workflows as FAIR Digital Objects using RO-Crate [Stian Soiland-Reyes]
- FAIR Digital Objects in Materials Science and Engineering [Rossella Aversa, Zachary Trautt]
- CAS Data Infrastructure [Xin Chen]
- RPID [Rob Quick]
- A Concept towards a FAIR Photovoltaic System [Jan Schweikert]
- Applying the FAIR DO Concept to a Humanities Use Case [Andreas Pfeil]
- Collection of “other amazing super-duper application ideas”
- Summarizing results, defining next steps (10m)
Community data experts, FAIR DO experts, application users, everybody who is interested in FAIR Digital Objects
The Data Fabric IG (DFIG) identified that working with data in the many scientific labs and most probably also in other areas such as industry and governance is highly inefficient and too costly. Excellent scientists working on data-intensive science tasks are forced to spend about 75% of their time to manage, find, combine and curate data. What a waste of time and capacity. The DFIG is therefore looking at the data creation and consumption cycle to identify opportunities to optimize the work with data, to place current RDA activities in the overall landscape, to look at what other communities are doing in this area, and to foster testing and adoption of RDA outputs. The goal of DFIG finally is to identify common components and define their characteristics and services that can be used across boundaries in such a way that they can be combined to solve a variety of data scenarios such as replicating data in federations, developing virtual research environments, and automating regular data management tasks. Much important work is being done on data publishing and citation, but DFIG believes that we need to start at early moments in the "Data Fabrics" in the labs to organize, document, and manage data professionally if we want to meet the requirements of the coming decades.
The group is meeting monthly to exchange information about FAIR Digital objects, see: https://www.rd-alliance.org/group/data-fabric-ig/wiki/monthly-meetings