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A first sketch of the "FAIR Molecule" Digital Object representation of molecular digital twins, based on the ongoing discussions around the FAIR Digital Object Framework (https://osf.io/pd3zn/).

User Scenario for the FAIR Funder Implementation Study, co-authored by the FFIS Collaboration: GO FAIR International Support and Coordination Office, Leiden, Netherlands E. A. Schultes 0000-0001-8888-635X H. Pergl Sustkova 0000-0002-4462-6465 Health Research Board, Dublin, Ireland A. Montesanti 0000-0003-0413-2003 ZonMw, The Hague, Netherlands S. M. Bloemers 0000-0003-3710-3188 Stanford University, Stanford, CA, USA M. A. Musen 0000-0003-3325-793X J. B. Graybeal 0000-0001-6875-5360 Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK S-A Sansone 0000-0001-5306-5690 P. McQuilton 0000-0003-2687-1982 Czech Technical University in Prague, Faculty of Information Technology, Czech Republic R. Pergl 0000-0003-2980-4400 Castor EDC, Amsterdam, Netherlands S. L. Knijnenburg 0000-0002-2475-6254 Purple Polar Bear, Utrecht, Netherlands A.C. van Arkel 0000-0003-4668-2580 GO FAIR Foundation, Leiden, Netherlands B. Meerman 0000-0002-0071-2660

A place to keep resources related to the FAIR Funder Implementation Study (formerly the FAIR Funder Pilot https://arxiv.org/abs/1902.11162 )

Convergence on FAIR Data Trains in Hamburg

Here we have made the latest version of the 'FAIR principles explain, FAIR implementation choices and challenges' available. Please note that this is work in progress and that we are currently working these into a manuscript. If you would like to cite this work please see details under 'Citation'. Feel free to contact us if you have any questions (erik.schultes@go-fair.org)

This paper gives a summary of implementation activities in the realm of FAIR Digital Objects (FDO). It gives an idea which software components are robust and used for many years, which components are comparatively new and are being tested out in pilot projects and what the challenges are that need to be urgently addressed by the FDO community. After basically only one year of advancing the FDO specifications by the FDO Forum we can recognise an increasing momentum to test and integrate essential FDO components. However, many developments still occur as soloistic engagements that offer a scattered picture. It is widely agreed that it is now time to combine these different pilots to comprehensive testbeds, to identify still existing gaps and to turn some services into components of a convincing and stable infrastructure. This step is urgently needed to convince even more institutions to invest in FDO technology and therefore to increase FAIRness of the evolving global data space.

In Canonical Workflow Framework for Research (CWFR) “packages” are relevant in two different directions. In data science, workflows are in general being executed on a set of files which have been aggregated for specific purposes, such as for training a model in deep learning. We call this type of “package” a data collection and its aggregation and metadata description is motivated by research interests. The other type of “packages” relevant for CWFR are supposed to represent workflows in a self-describing and self-contained way for later execution. In this paper, we will review different packaging technologies and investigate their usability in the context of CWFR. For this purpose, we draw on an exemplary use case and show how packaging technologies can support its realization. We conclude that packaging technologies of different flavors help on providing inputs and outputs for workflow steps in a machine-readable way, as well as on representing a workflow and all its artifacts in a self-describing and self-contained way.

FAIR Digital Objects (FDOs) are especially needed when it comes to linking together heterogeneous data from different communities in order to advance cross-disciplinary research. However, it is still neither clear what the minimum set of attributes making a FDO is nor do we have a model for representing these attributes in a semantically unambiguous and machine-actionable way. The session will discuss requirements to be taken into account for future discussions on a semantic model for FDOs., GO FAIR Implementation Network, Go Inter, FDO Forum

This report presents a study for aflexible framework to create, document and publish semanticmappings and cross-walks linking different semantic artefacts within a particular scientificcommunity and across scientific domains. These mappings and cross-walks should be FAIR, as proposed in the FAIR Semantics recommendations.The study draws on the broad expertise of the authors and 25 interviews conducted with community experts. A description fora proposed follow-up implementation project is part of the report.

With this paper we want to describe the motivation and basic ideas behind CWFR. Two working meetings were held to discuss the CWFR concept and to relate it with other work around “workflows” that has already been done. We intend to further develop this paper dependent on the growing insights based on the discussions and interactions we are planning to organise.

This policy was authored by representatives of the EOSC FAIR Working Group and EOSC Architecture Working group. See Appendix 3 for details. This second draft of the policy was released in May 2020for community feedback and comment. We welcome responses to and comments on this second version. Please share them with the wider community on https://pidforum.org or email them to us pid-policy@eoscsecretariat.eu. We understand that some areas may require discussion and encourage you to have that discussion on https://pidforum.org. That is where we will also provide details of opportunities for face-to-face feedback and discussion. We will developa final policy that will be delivered to the EOSC Governance Board in October 2020.

Die FAIR-Prinzipien werden seit ihrer Veröffentlichung im Jahr 2016 von einem breiten Spektrum an Stakeholdern zitiert, übernommen und angewendet. Ziel der 15 FAIR-Leitprinzipien ist es nicht, konkrete technische Implementierungen vorzuschreiben, sondern Leitlinien zur Verbesserung der Auffindbarkeit, Zugänglichkeit, Interoperabilität und Nachnutzbarkeit digitaler Ressourcen an die Hand zu geben. Dies hat vermutlich zur breiten Anwendung der FAIR-Prinzipien beigetragen, da einzelne Stakeholder-Communitys ihre eigenen Lösungen umsetzen können. Allerdings gingen damit auch uneinheitliche Interpretationen einher, die das Risiko inkompatibler Umsetzungen zur Folge haben. Wenngleich die FAIR-Prinzipien auf einer übergeordneten Ebene formuliert sind und sich in vielfältiger Weise interpretieren und umsetzen lassen, bedarf es für eine wirkliche Interoperabilität somit einer stärkeren Konvergenz weithin zugänglicher und (wieder)verwendbarer Implementierungsentscheidungen. Mit den Überlegungen zur Umsetzung der FAIR-Prinzipien unterstützen wir eine schnellere globale Mitwirkung und Konvergenz hin zu zugänglichen, soliden, auf breiter Ebene angewandten und einheitlichen FAIR-Implementierungen. Jede selbst identifizierte Stakeholder-Community kann sich entweder dafür entscheiden, Lösungen aus vorhandenen Implementierungen wiederzuverwenden, oder bei Bedarf die Herausforderung annehmen, die benötigte Lösung, die in Zukunft idealerweise wiederum von anderen Communitys angewandt werden kann, selbst zu entwickeln. Nachfolgend wollen wir für jedes der FAIR-Prinzipien Interpretationen und Überlegungen zu seiner Implementierung (Entscheidungen und Herausforderungen) erörtern. Die vorliegende deutsche Übersetzung des Dokuments entstand im Rahmen des Verbundprojektes EcoDM mit der Förderung des Bundesministeriums für Bildung und Forschung (BMBF) unter dem Förderkennzeichen 16DWWQP.

The open science movement is gaining strength and momentum worldwide, signalling a fundamental shift in how scientific research is made accessible and reusable. In order to fulfill the promises of open science, reliable and sustainable research data infrastructures must be developed. While the FAIR data principles provide a promising conceptual basis for developing such data infrastructures, they do not provide technological guidance on how to do so. Computer science is uniquely situated to fill this gap by researching and developing tools and technical specifications which can help to realize the creation of FAIR data infrastructures. To this end, this Dagstuhl Perspectives Workshop brought together computer scientists and digital infrastructure experts from across disciplinary domains to discuss key challenges and technical solutions to implementing and promoting the establishment of FAIR-compliant infrastructures for research data. This manifesto reports the findings from the workshop and provides recommendations along two lines: (1) how computer science can contribute to implementing FAIR data infrastructures and (2) how to make computer science research itself more FAIR., Dagstuhl Manifestos, Volume 8, Issue 1, pages 1-34

This policy was authored by representatives of the EOSC FAIR Working Group and EOSC Architecture Working group. See Appendix 3 for details. This initial policy was released in December 2019 for community feedback and comment. We welcome responses to and comments on this first version. Please share them with the wider community on https://pidforum.org or email them to us pid-policy@eoscsecretariat.eu. We understand that some areas may require discussion and encourage you to have that discussion on https://pidforum.org. That is where we will also provide details of opportunities for face-to-face feedback and discussion. We will develop a second version for March 2020 and a final policy will be delivered to the EOSC Governance Board in October 2020.