In the research community, we hear more and more about the FAIR Data Principles but what do they mean?

  • They are not rules.
  • They are not a standard.
  • They are not a requirement.

They are a way of reaching for best data practices, coming to a convergence on what those are, and how to get there. A group of roughly 40 community representatives came together on February 25-27, 2020 to discuss how to support FAIR in the United States with the following goals in mind:

Facilitate development of a community of practice for FAIR awareness and capacity-building in the U.S.; Improve understanding of FAIR technologies, and how to teach this to others; and, Preparation for teaching or supporting FAIR data management and policies for researchers, local institutions, professional organizations, and others.

See Workshop Materials and Presentations and Notes from Maria Praetzellis

Workshop Overview

GO FAIR Europe, represented by Albert Mons (Phortos Consulting), and Luiz Bonino and Erik Schultes (GO FAIR International Support and Coordination Office (GFISCO)), gave an overview of how their organization works to support and foster the growth of data practices and training that adhere to the FAIR Data Principles.

Building on the European work, the group, consisting of librarians, data scientists and scientific data curators, discussed how fostering FAIR data practices would best be encouraged, and what kind of structure could be built in institutions to make this happen.

Workshop participants presented on different aspects of FAIR including:

  • FAIR analysis of existing repositories
  • Tools to assist in FAIR data practices
  • Approaches to FAIR training and events.

Don’t let your research data be lonely! Using the FAIR data principles in research data management (RDM) creates an excellent basis for connection. The research community will be able to discover and possibly access and reuse your FAIR data with or without permission through machine communication channels.

FAIR Engagement

How can we help individuals and institutions translate what FAIR means to their own work?

Christine Kirkpatrick of SDSC presented on how to drive cultural change, managing blind spots, and how to organize around collective impact models. And since the FAIR principles can only be effective within a community, how do we do together what we cannot do alone? How do we use the power of community to drive engagement and articulate why FAIR is important, and how it rewards those who understand and use it?

FAIR Implementation

How do you use FAIR? It’s a set of principles, not an instruction guide. Several presentations were given on how FAIR was being translated into action.

EOSC-Nordic FAIR Maturity evaluation of data repositories

Andreas O Jaunsen (NeIC, WP4 lead)

This presentation featured the development of FAIR maturity indicators for repositories. The maturity evaluator service provides efficiency, scalability and reproducibility through comparing datasets to the indicators and measures of FAIRness through machine-actionability.

FAIR Digital Object Framework

Luiz Bonino (University of Twente)

Currently under development, the FAIR Digital Object Framework is a model to represent objects in the digital space based on the FAIR principles with the aim of supporting machine actionability.

Ontologies

Several presentations stressed the importance of ontology use in FAIR-izing datasets. The use of ontologies when creating metadata for datasets enhance machine-actionability, consistency, interoperability and, when used in an RDF format, allow for persistency.

FAIR Training

Some practical advice was given on creating FAIR training and workshops. Chris Erdmann shared his experiences from coordinating and running FAIR Training programs, and Natalie Meyers presented a primer on holding a FAIR event/workshop. Christine Kirkpatrick gave an excellent and highly amusing presentation on pedagogy, and Nancy J. Hoebelheinrich presented on where we can find resources for finding FAIR training materials.

Reflections

Workshop participants spent a lot of time looking at what exists from GO FAIR training resources to engagement opportunities in our communities. The group also relfected on our current assessment of the data landscape, especially in the areas of:

  • Capacity building
  • Making progress
  • Change, build, and train

One of the concrete outcomes from the workshop is the GO FAIR US website and communication channels. Visit the website for further information on how to become active in the GO FAIR US community!

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