Highlights from the "Advancing FAIR and Go FAIR in the U.S." Workshop

I attended the "Advancing FAIR and Go FAIR in the U.S." workshop in February; the workshop covered topics on how to establish and promote FAIR culture and capabilities within a community. Many of the discussions were synergistic with the CDI activities, so I wanted to share some key points from the workshop with the CDI community. - Sophie Hou

(Logo from the Go FAIR Initiative)

Workshop Info 

Title: Advancing FAIR and Go FAIR in the U.S.  

Date: February 24th to 27th, 2020 

Location: Atlanta, Georgia 

Goals: 

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

Link: https://www.sdsc.edu/services/data_science/research_data_services.html  

Overall Summary: 

• The workshop highlighted that advancing FAIR requires communal effort. 
• In order to "FAIRify," it is important for a community to first determine its scope, goals, and objectives. 

Key Notes: 

• FAIR is an acronym from Findable, Accessible, Interoperable, and Reusable. 
• Typical challenges that a community could face when working on FAIR include:
  • Knowledge gap
  • Institutional inertia
  • Community relationship building
  • Expanding FAIR capacity
  • Best way to adapt and adopt available FAIR resources
• The ultimate goal of enabling FAIR is to allow both humans and machines (especially machines) to use digital resources, so that analytics and re-use can be optimized.
  • According to the Go FAIR Initiative (https://www.go-fair.org), FAIR can also be understood as Fully AI Ready. In other words, machines are able to know what the digital resources mean. Additionally, the digital resources are as distributed/open as possible, but can also be as central/closed as needed.
• Implementation of FAIR can be challenging because many concepts in the principles are multifaceted (including social, resource, and technical considerations).
• In order to advance FAIR, it is important to first establish a good (common) understanding of the FAIR principles.
• FAIR requires technical and disciplinary resources, but it also requires community support.
  • When implement FAIR, we need to review choices and accept challenges; e.g. who is our "community", and determine what is specific to our "community".
  • FAIR is not a “standard”. The local community context is important and necessary.
• The Go FAIR Initiative offers a 7-step "FAIRification" process: https://www.go-fair.org/fair-principles/fairification-process/ 
• Options for conducting a FAIR event/activity with one's community include:
  • Multiple day, experts convening, tutorial/webinar, conference, unconference, hackathon, symposium, sprint, posters, etc.
• Participants of an FAIR event/actiity might have the following expectations:
  • Share best practices/resources/learn new skills
  • Tackle a problem
  • Learn new concepts/skills
  • Use FAIR as a them to track for other topics
  • Collaborate to create a resource to be shared
  • And more!
• Once a community has established its version of FAIR, it is important to connect with other communities. Convergence with different communities is key to grow FAIR.