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FAIR Data Point:A FAIR-Oriented Approach for Metadata Publication

Data Intelligence 2023年第1期5卷 163-183页

作者： Luiz Olavo Bonino da Silva Santos Kees Burger rajaram kaliyaperumal Mark D.Wilkinson Services and Cybersecurity group Faculty of Electrical EngineeringMathematics and Computer ScienceUniversity of Twente-Enschede7513GBthe Netherlands Biosemantics group Department of Human GeneticsLeiden University Medical Center-LeidenNL 2333 ZCthe Netherlands Escuela Técnica Superior de Ingenieria Agronomica Alimentaria y de BiosistemasCentro de Biotecnologia y Genomica de Plantas UPM-INIAUniversidad Politecnica de Madrid(UPM)-Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria(INIA)MadridES 28223Spain

Metadata,data about other digital objects,play an important role in FAIR with a direct relation to all FAIR principles.In this paper we present and discuss the FAIR Data Point(FDP),a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR principles.We present the core components and features of the FDP,its approach to metadata provision,the criteria to evaluate whether an application adheres to the FDP specifications and the service to register,index and allow users to search for metadata content of available FDPs.

关键词： FAIR FAIR data point FAIR principles Metadata Interoperability Linked data Semantic interoperability

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The FAIR Data Point:Interfaces and Tooling

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Data Intelligence 2023年第1期5卷 184-201页

作者： Oussama Mohammed Benhamed Kees Burger rajaram kaliyaperumal Luiz Olavo Bonino da Silva Santos Marek Suchánek Jan Slifka Mark D.Wilkinsoni Departamento de Biotecnologia-Biologia Vegetal Escuela Tecnica Superior de Ingenieria AgronomicaAlimentaria y de BiosistemasCentro de Biotecnologia y Genomica de Plantas UPM-INIAUniversidad Politecnica de Madrid(UPM)-Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria(INIA)MadridES 28223Spain Leiden University Medical Center Leiden2333 ZCthe Netherlands University of Twente Enschede7513 GBthe Netherlands Czech Technical University in Prague PragueCZ 15000Czech Republic

While the FAIR Principles do not specify a technical solution for'FAIRness',it was clear from the outset of the FAIR initiative that it would be useful to have commodity software and tooling that would simplify the creation of FAIR-compliant resources.The FAIR Data Point is a metadata repository that follows the DCAT(2)schema,and utilizes the Linked Data Platform to manage the hierarchical metadata layers as LDP Containers.There has been a recent flurry of development activity around the FAIR Data Point that has significantly improved its power and ease-of-use.Here we describe five specific tools—an installer,a loader,two Webbased interfaces,and an indexer-aimed at maximizing the uptake and utility of the FAIR Data Point.

关键词： FAIR Data Linked data Semantic Web Metadata User interfaces Tooling Semantic query

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Making FAIR Easy with FAIR Tools:From Creolization to Convergence

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Data Intelligence 2020年第1期2卷 87-95,305页

作者： Mark Thompson Kees Burger rajaram kaliyaperumal Marco Roos Luiz Olavo Bonino da Silva Santos Leiden University Medical Center Leiden2333 ZAThe Netherlands GO FAIR international Support&Coordination Office(GFISCO) LeidenThe Netherlands

Since their publication in 2016 we have seen a rapid adoption of the FAIR principles in many scientific disciplines where the inherent value of research data and,therefore,the importance of good data management and data stewardship,is recognized.This has led to many communities asking“What is FAIR?”and“How FAIR are we currently?”,questions which were addressed respectively by a publication revisiting the principles and the emergence of FAIR metrics.However,early adopters of the FAIR principles have already run into the next question:“How can we become(more)FAIR?”This question is more difficult to answer,as the principles do not prescribe any specific standard or implementation.Moreover,there does not yet exist a mature ecosystem of tools,platforms and standards to support human and machine agents to manage,produce,publish and consume FAIR data in a user-friendly and efficient(i.e.,“easy”)way.In this paper we will show,however,that there are already many emerging examples of FAIR tools under development.This paper puts forward the position that we are likely already in a creolization phase where FAIR tools and technologies are merging and combining,before converging in a subsequent phase to solutions that make FAIR feasible in daily practice.

关键词： FAIR data FAIR in practice FAIR tools FAIR application support creolization and convergence

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A Generic Workflow for the Data FAIRification Process

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Data Intelligence 2020年第1期2卷 56-65页

作者： Annika Jacobsen rajaram kaliyaperumal Luiz Olavo Bonino da Silva Santos Barend Mons Erik Schultes Marco Roos Mark Thompson Leiden University Medical Center Leiden2333 ZAThe Netherlands GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands

The FAIR guiding principles aim to enhance the Findability,Accessibility,Interoperability and Reusability of digital resources such as data,for both humans and machines.The process of making data FAIR(“FAIRification”)can be described in multiple steps.In this paper,we describe a generic step-by-step FAIRification workflow to be performed in a multidisciplinary team guided by FAIR data stewards.The FAIRification workflow should be applicable to any type of data and has been developed and used for“Bring Your Own Data”(BYOD)workshops,as well as for the FAIRification of e.g.,rare diseases resources.The steps are:1)identify the FAIRification objective,2)analyze data,3)analyze metadata,4)define semantic model for data(4a)and metadata(4b),5)make data(5a)and metadata(5b)linkable,6)host FAIR data,and 7)assess FAIR data.For each step we describe how the data are processed,what expertise is required,which procedures and tools can be used,and which FAIR principles they relate to.

关键词： FAIR data FAIRification workflow FAIR data stewardship Hands-on FAIRification FAIR dissemination

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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 2016.By intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital resources.This has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR solutions.However,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations.Thus,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)-usable.We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR implementations.Any 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 future.Here,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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