NISO Discourse Discussion for this sessionhttps://discourse.niso.org/t/fair-data-principles-and-why-they-matter/89FAIREST of them all
Findable, Accessible, Interoperable, Reusable. We all think FAIR data is a “good thing” don’t we. Who could be against something that, once stated, is so blindingly obvious? If it’s not findable, and usable then why are we spending time, money and resources keeping it? And it’s a great acronym as well. FAIR. It “does what it says on the tin"""". If you’ve got a good acronym you’re half way there when it comes to hearts and minds….
But I think FAIR doesn’t go far enough. There are major impact factors that need to be considered alongside FAIR. What about the costs involved in making information FAIR? Where does trust come in? What about the environmental impact?
Time for a new acronym that goes beyond simply “FAIR”.
What should that be? This talk will propose one possible extension to the concept...""Leveraging FAIR Data Principles to Construct the CCC COVID Author Graph
A knowledge graph is an innovative and revealing visual exposition of data. Display of such data is a powerful way to explore connections and query relationships among different entities, but only if the underlying data is of high quality.
The global research community’s effort to fight COVID-19 has led to an explosive increase of manuscripts submitted to peer-reviewed journals. With this influx of submissions, publishers have recognized limitations in the existing methods for identifying appropriate peer reviewers to validate the accuracy, impact, and value of these manuscripts.
To address this challenge, CCC developed the COVID Author Graph, a knowledge graph highlighting peer review data focused on authors who have published in areas with special attention to coronaviruses, SARS, MERS, SARS-CoV-2, and COVID-19. This new approach helps publishers leverage data to aid in the accelerated identification of peer reviewers.
During this session, presenters will illustrate how the FAIR data principles –Findability, Accessibility, Interoperability, and Reuse of digital assets – served as a trusted foundation for building the COVID Author Graph. They will share key learnings with an emphasis on how reliable levels of data quality that FAIR principles make possible enable more sophisticated analysis and help organizations derive actionable business insights.FAIR (meta)data - low-hanging fruit for scholarly publishers (Brian Cody)
Drawing on experience working with journal publishers to collect/enhance/format metadata, this session section overviews FAIR principles and shares concrete steps for beginning your FAIR journey.