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Considerations for the Conduction and Interpretation of FAIRness Evaluations

Data Intelligence 2020年第1期2卷 285-292页

作者： Ricardo de Miranda Azevedo michel dumontier Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands

The FAIR principles were received with broad acceptance in several scientific ***,there is still some degree of uncertainty on how they should be *** self-report questionnaires have been proposed to assess the implementation of the FAIR ***,the FAIRmetrics group released 14,general-purpose maturity for representing ***,these metrics were conducted as open-answer ***,these metrics have been implemented into a software that can automatically harvest metadata from metadata providers and generate a principle-specific FAIRness *** so many different approaches for FAIRness evaluations,we believe that further clarification on their limitations and advantages,as well as on their interpretation and interplay should be considered.

关键词： FAIR metrics FAIR principles FAIR data FAIRness Implementation Maturity indicators Scientific community

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LUCE:A blockchain-based data sharing platform for monitoring data License accoUntability and CompliancE

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Blockchain(Research and Applications) 2022年第4期3卷 123-132页

作者： Visara Urovi Vikas Jaiman Arno Angerer michel dumontier Institute of Data Science(IDS) Maastricht University6200 MD Maastrichtthe Netherlands School of Business and Economics Maastricht University6200 MD Maastrichtthe Netherlands

Easy access to data is one of the main avenues to accelerate scientific *** a key element of scientific innovations,data sharing allows the reproduction of results and helps prevent data fabrication,falsification,and *** the research benefits from data reuse are widely acknowledged,the data collections existing today are still kept in ***,monitoring what happens to data once they have been handed to a third party is currently not feasible within the current data sharing *** propose a blockchain-based system to trace data collections and potentially create a more trustworthy data sharing *** this paper,we present the LUCE(License accoUntability and CompliancE)architecture as a decentralized blockchain-based platform supporting data sharing and *** is designed to provide full transparency on what happens to the data after they are shared with third *** contributions of this work consist of i)the design of a decentralized data sharing solution with accountability and compliance by design and ii)the inclusion of a dynamic consent model for personalized data sharing preferences and for enabling legal compliance *** test the scalability of the platform in a real-time environment where a growing number of users access and reuse different *** to existing data sharing solutions,LUCE provides transparency over data sharing practices,enables data reuse,and supports regulatory *** experimentation shows that the platform can be scaled for a large number of users.

关键词： Blockchain Distributed ledgers Data sharing Data reuse Dataset tracking GDPR

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Distributed Analytics on Sensitive Medical Data:The Personal Health Train

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Data Intelligence 2020年第1期2卷 96-107,305,306,307页

作者： Oya Beyan Ananya Choudhury Johan van Soest Oliver Kohlbacher Lukas Zimmermann Holger Stenzhorn Md.Rezaul Karim michel dumontier Stefan Decker Luiz Olavo Bonino da Silva Santos Andre Dekker Fraunhofer Institute for Applied Information Technology(FIT) 53754 Sankt AugustinGermany RWTH Aachen University 52056 AachenGermany Department of Radiation Oncology(MAASTRO) GROW School for Oncology and Developmental BiologyMaastricht University Medical Center6200 MD MaastrichtThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science University of TübingenTubingenBaden-Württemberg 72076Germany Quantitative Biology Center University of TübingenTubingenBaden-Württemberg 72076Germany Institute for Translational Bioinformatics University of TubingenTubingenBaden-Württemberg 72076Germany Center for Bioinformatics University of TübingenGermany GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands

In recent years,as newer technologies have evolved around the healthcare ecosystem,more and more data have been *** analytics could power the data collected from numerous sources,both from healthcare institutions,or generated by individuals themselves via apps and devices,and lead to innovations in treatment and diagnosis of diseases;improve the care given to the patient;and empower citizens to participate in the decision-making process regarding their own health and ***,the sensitive nature of the health data prohibits healthcare organizations from sharing the *** Personal Health Train(PHT)is a novel approach,aiming to establish a distributed data analytics infrastructure enabling the(re)use of distributed healthcare data,while data owners stay in control of their own *** main principle of the PHT is that data remain in their original location,and analytical tasks visit data sources and execute the *** PHT provides a distributed,flexible approach to use data in a network of participants,incorporating the FAIR *** facilitates the responsible use of sensitive and/or personal data by adopting international principles and *** paper presents the concepts and main components of the PHT and demonstrates how it complies with FAIR principles.

关键词： Distributed analytics Data reuse FAIR Health data Ethics and privacy

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FAIR Principles:Interpretations and Implementation Considerations

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Data Intelligence 2020年第1期2卷 10-29,293-302,322页

作者： Annika Jacobsen Ricardo de Miranda Azevedo Nick Juty Dominique Batista Simon Coles Ronald Cornet Melanie Courtot Merce Crosas michel dumontier Chris T.Evelo Carole Goble Giancarlo Guizzardi Karsten Kryger Hansen Ali Hasnain Kristina Hettne Jaap Heringa Rob W.W.Hooft Melanie Imming Keith G.Jeffery Rajaram Kaliyaperumal Martijn GKersloot Christine R.Kirkpatrick Tobias Kuhn Ignasi Labastida Barbara Magagna PeterMcQuilton Natalie Meyers Annalisa Montesanti Mirjam van Reisen Philippe Rocca-Serra Robert Pergl Susanna-Assunta Sansone Luiz Olavo Bonino da Silva Santos Juliane Schneider George Strawn Mark Thompson Andra Waagmeester Tobias Weigel Mark D.Wilkinson Egon L.Willighagen Peter Wittenburg Marco Roos Barend Mons Erik Schultes Leiden University Medical Center Leiden2333 ZAThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK School of Chemistry Faculty of Engineering and Physical SciencesUniversity of SouthamptonSO171BJUK Amsterdam UMC University of AmsterdamAmsterdam 1000 GGThe Netherlands European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK Harvard University CambridgeMassachusetts 02138USA Department of Bioinformatics–BiGCaT NUTRIMMaastricht UniversityMaastricht 6229 ERThe Netherlands Conceptual and Cognitive Modeling Research Group(CORE) Free University of Bozen-BolzanoBolzano 39100Italy Aalborg University Aalborg DK-9220Denmark Insight Centre for Data Analytics National University of Ireland GalwayH91 TK33Ireland Centre for Digital Scholarship Leiden University LibrariesLeiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Dutch Techcentre for Life Sciences(DTL) UtrechtThe Netherlands SURF Utrecht 3511 EPThe Netherlands Keith G Jeffery Consultants FaringdonUK Castor EDC Paasheuvelweg 25Wing 5D1105 BPAmsterdamThe Netherlands San Diego Supercomputer Center University of California San DiegoLa JollaCalifornia 92093USA Learning and Research Resources Centre(CRAI) Universitat de Barcelona08007 BarcelonaSpain Environment Agency Austria A-1090 ViennaAustria University of Notre Dame 75004 ParisFrance Health Research Board(HRB) Dublin 2DO2 H638Ireland Liacs Institute of Advanced Computer Science Leiden University2311 GJ LeidenThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands Harvard Catalys

The FAIR principles have been widely cited,endorsed and adopted by a broad range of stakeholders since their publication in *** intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital *** has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR ***,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible ***,while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways,for true interoperability we need to support convergence in implementation choices that are widely accessible and(re)-*** introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR *** self-identified stakeholder community may either choose to reuse solutions from existing implementations,or when they spot a gap,accept the challenge to create the needed solution,which,ideally,can be used again by other communities in the ***,we provide interpretations and implementation considerations(choices and challenges)for each FAIR principle.

关键词： FAIR guiding principles FAIR implementation FAIR convergence FAIR communities choices and challenges

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