Luiz Olavo Bonino da Silva Santos

Universiteit Leiden, Human Genetic, Faculty Member

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Papers by Luiz Olavo Bonino da Silva Santos

Context-Aware Support for Communities of Practice

International Semantic Web Conference, Nov 1, 2004

... utwente.nl Gerd Wagner Eindhoven University of Technology, Eindhoven, The Netherlands G.Wagne... more

A design framework and exemplar metrics for FAIRness

bioRxiv (Cold Spring Harbor Laboratory), Nov 27, 2017

The FAIR Principles 1 (https://doi.org/10.25504/FAIRsharing.WWI10U) provide guidelines for the pu... more The FAIR Principles 1 (https://doi.org/10.25504/FAIRsharing.WWI10U) provide guidelines for the publication of digital resources such as datasets, code, workflows, and research objects, in a manner that makes them Findable, Accessible, Interoperable, and Reusable (FAIR). The Principles have rapidly been adopted by publishers, funders, and pan-disciplinary infrastructure programmes and societies. The Principles are aspirational, in that they do not strictly define how to achieve a state of "FAIRness", but rather they describe a continuum of features, attributes, and behaviors that will move a digital resource closer to that goal. This ambiguity has led to a wide range of interpretations of FAIRness, with some resources even claiming to already "be FAIR"! The increasing number of such statements, the emergence of subjective and self-assessments of FAIRness 2,3 , and the need of data and service providers, journals, funding agencies, and regulatory bodies to qualitatively or quantitatively evaluate such claims, led us to self-assemble and establish a FAIR Metrics group (http://fairmetrics.org) to pursue the goal of defining ways to measure FAIRness. As co-authors of the FAIR Principles and its associated manuscript, founding this small focus group was a natural and timely step for us, and we foresee group membership expanding and broadening according to the needs and enthusiasm of the various stakeholder communities. Nevertheless, in this first phase of group activities we did not work in isolation, but we gathered use cases and requirements from the communities, organizations and projects we are core members of, and where discussions on how to measure FAIRness have also started. Our community network and formal participation encompasses generic and discipline-specific initiatives, including: the Global and Open FAIR (http://go-fair.org), the European Open Science Cloud (EOSC; https://eoscpilot.eu), working groups of the Research Data Alliance (RDA; https://www.rd-alliance.org) and Force11 (https://www.force11.org), the Data Seal of Approval 4 , Nodes of the European ELIXIR infrastructure (https://www.elixir-europe.org), projects under the USA National Institutes of Health (NIH)'s Big Data to Knowledge Initiative (BD2K) and its new Data Commons Pilots (https://commonfund.nih.gov/bd2k/commons). In addition, via the FAIRsharing network and advisory board (https://fairsharing.org), we are also connected to open standards-developing communities and data policy leaders, and also editors and publishers, especially those very active around data matters, such as: Springer Nature's Scientific Data, Nature Genetics and BioMedCentral, PloS Biology, The BMJ, Oxford University Press's GigaScience, F1000Research, Wellcome Open Research, Elsevier, EMBO Press and Ubiquity Press. The converging viewpoints on FAIR metrics and FAIRness, arising from our information-gathering discussions with these various communities and stakeholders groups, can be summarized as it follows: • Metrics should address the multi-dimensionality of the FAIR principles, and encompass all types of digital objects. • Universal metrics may be complemented by additional resource-specific metrics that reflect the expectations of particular communities.

Luiz Olavo Bonino da Silva Santos

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