Implementing FAIR data for people and machines: Impacts and implications - results of a research data community workshop

Abstract

The Implementing FAIR Data for People and Machines: Impacts and Implications workshop was organized by the Board on Research Data and Information of the National Academies of Sciences, Engineering, and Medicine (NASEM), the CENDI Federal Information Managers Group, the Research Data Alliance (RDA), and the National Federation of Advanced Information Services (NFAIS), and held at NASEM’s Keck Center in Washington, DC on September 11, 2019. The goals of the Implementing FAIR Data workshop were to discuss the current status of FAIR data implementation, share what is being done to encourage scientists to share data in machine-readable formats, and examine the implications of FAIR data implementation for people and machines. FAIR data policies, tools, and measures of FAIR data compliance were considered from multiple perspectives. Marcia McNutt, President of the National Academy of Sciences (NAS), offered opening remarks, and the keynote address was presented by Barend Mons, Professor of Bioinformatics at Leiden University Medical Center and President of the International Science Council’s Committee on Data (CODATA). Three panel discussions addressed (1) the perspectives of scientists and administrators from U.S. federal agencies, (2) case studies on the implementation of FAIR data practices, and (3) principles and methods of measuring FAIR data compliance. The automation of scientific workflows was discussed by Stuart Feldman, Chief Scientist of Schmidt Futures, a philanthropic organization devoted to investing in research, technology, and science. The workshop closed with highlights and takeaways from each session as summarized by the moderators, followed by general questions.

Scientific modelling can be accessible, interoperable and user friendly: An example for pasture and livestock modelling

“…Because environmental modelling, including pasture and livestock simulation, tends to be driven by the need to address case-specific issues, data and model reuse recommendations are often unclearly defined. Moreover, the collected data are often not made available to other researchers; when they are placed in public repositories data are often findable and accessible but lag in their interoperability and reusability (Borycz & Carroll, 2020). As a result, in the best case substantial manual GIS processing is required before a user can work with previously generated data; in the worst case data may be lost entirely after the modelling results are published.…”

The global environmental agenda urgently needs a semantic web of knowledge

“…Additionally, although our understanding of planetaryscale processes has improved, we are far from being able to accurately track key dynamics and critical thresholds across diverse scales and drivers. Key processes, entities (e.g., nations, watersheds, households, ecological communities) and their interdependencies across scales are far too complicated for individual human brains to disentangle [9]. Simultaneously, today's repositories of human intelligence, such as the scientific publication system, fall short in connecting the pieces of knowledge produced by different fields.…”

“…In the face of widespread use of, and publicity for, "big data-driven" machine learning [9], we believe wider understanding and use of semantics and machine reasoning in scientific modelling is critical to addressing today's sustainability challenges. Approaches such as ARIES have demonstrated how semantics can maximize data and model reusability and interoperability when assessing ecosystem services and, more generally, in modelling complex human-nature interactions and their consequences.…”

Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

“…The FAIR Principles 1 have widely been acknowledged as the way forward for improving the findability, accessibility, interoperability and reusability of data across different sources and disciplines 3 – 5 . Various research communities are currently discussing and testing how to implement these guiding principles 6 – 11 .…”