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| 1 | FAIR Principles:Interpretations and Implementation Considerations显示文摘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. | 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 | 2020 | Data Intelligence2020，2，1： | 26 |
| 2 | Not Ready for Convergence in Data Infrastructures显示文摘Much research is dependent on Information and Communication Technologies(ICT).Researchers in different research domains have set up their own ICT systems(data labs)to support their research,from data collection(observation,experiment,simulation)through analysis(analytics,visualisation)to publication.However,too frequently the Digital Objects(DOs)upon which the research results are based are not curated and thus neither available for reproduction of the research nor utilization for other(e.g.,multidisciplinary)research purposes.The key to curation is rich metadata recording not only a description of the DO and the conditions of its use but also the provenance-the trail of actions performed on the DO along the research workflow.There are increasing real-world requirements for multidisciplinary research.With DOs in domain-specific ICT systems(silos),commonly with inadequate metadata,such research is hindered.Despite wide agreement on principles for achieving FAIR(findable,accessible,interoperable,and reusable)utilization of research data,current practices fall short.FAIR DOs offer a way forward.The paradoxes,barriers and possible solutions are examined.The key is persuading the researcher to adopt best practices which implies decreasing the cost(easy to use autonomic tools)and increasing the benefit(incentives such as acknowledgement and citation)while maintaining researcher independence and flexibility. | Keith Jeffery Peter Wittenburg Larry Lannom George Strawn Claudia Biniossek Dirk Betz Christophe Blanchi | 2021 | Data Intelligence2021，3，1： | 7 |
| 3 | FAIR Convergence Matrix:Optimizing the Reuse of Existing FAIR-Related Resources显示文摘The FAIR principles articulate the behaviors expected from digital artifacts that are Findable,Accessible,Interoperable and Reusable by machines and by people.Although by now widely accepted,the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors.As different communities have their own,often well-established implementation preferences and priorities for data reuse,coordinating a broadly accepted,widely used FAIR implementation approach remains a global challenge.In an effort to accelerate broad community convergence on FAIR implementation options,the GO FAIR community has launched the development of the FAIR Convergence Matrix.The Matrix is a platform that compiles for any community of practice,an inventory of their self-declared FAIR implementation choices and challenges.The Convergence Matrix is itself a FAIR resource,openly available,and encourages voluntary participation by any self-identified community of practice(not only the GO FAIR Implementation Networks).Based on patterns of use and reuse of existing resources,the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services. | Hana Pergl Sustkova Kristina Maria Hettne Peter Wittenburg Annika Jacobsen Tobias Kuhn Robert Pergl Jan Slifka Peter McQuilton Barbara Magagna Susanna-Assunta Sansone Markus Stocker Melanie Imming Larry Lannom Mark Musen Erik Schultes | 2020 | Data Intelligence2020，2，1： | 4 |
| 4 | From Persistent Identifiers to Digital Objects to Make Data Science More Efficient显示文摘Data-intensive science is reality in large scientific organizations such as the Max Planck Society,but due to the inefficiency of our data practices when it comes to integrating data from different sources,many projects cannot be carried out and many researchers are excluded.Since about 80%of the time in data-intensive projects is wasted according to surveys we need to conclude that we are not fit for the challenges that will come with the billions of smart devices producing continuous streams of data-our methods do not scale.Therefore experts worldwide are looking for strategies and methods that have a potential for the future.The first steps have been made since there is now a wide agreement from the Research Data Alliance to the FAIR principles that data should be associated with persistent identifiers(PID)and metadata(MD).In fact after 20 years of experience we can claim that there are trustworthy PID systems already in broad use.It is argued,however,that assigning PIDs is just the first step.If we agree to assign PIDs and also use the PID to store important relationships such as pointing to locations where the bit sequences or different metadata can be accessed,we are close to defining Digital Objects(DOs)which could indeed indicate a solution to solve some of the basic problems in data management and processing.In addition to standardizing the way we assign PIDs,metadata and other state information we could also define a Digital Object Access Protocol as a universal exchange protocol for DOs stored in repositories using different data models and data organizations.We could also associate a type with each DO and a set of operations allowed working on its content which would facilitate the way to automatic processing which has been identified as the major step for scalability in data science and data industry.A globally connected group of experts is now working on establishing testbeds for a DO-based data infrastructure. | Peter Wittenburg | 2019 | Data Intelligence2019，1，1： | 3 |
| 5 | Canonical Workflows to Make Data FAlR显示文摘The FAIR principles have been accepted globally as guidelines for improving data-driven science and data management practices,yet the incentives for researchers to change their practices are presently weak.In addition,data-driven science has been slow to embrace workflow technology despite clear evidence of recurring practices.To overcome these challenges,the Canonical Workflow Frameworks for Research(CWFR)initiative suggests a large-scale introduction of self-documenting workflow scripts to automate recurring processes or fragments thereof.This standardised approach,with FAIR Digital Objects as anchors,will be a significant milestone in the transition to FAIR data without adding additional load onto the researchers who stand to benefit most from it.This paper describes the CWFR approach and the activities of the CWFR initiative over the course of the last year or so,highlights several projects that hold promise for the CWFR approaches,including Galaxy,Jupyter Notebook,and RO Crate,and concludes with an assessment of the state of the field and the challenges ahead. | Peter Wittenburg Alex Hardisty Yann Le Franc Amirpasha Mozaffari Limor Peer Nikolay A.Skvortsov Zhiming Zhao Alessandro Spinuso | 2022 | Data Intelligence2022，4，2： | 2 |
| 6 | Cholesterol gallstone susceptibility loci:A mouse map,candidate gene evaluation, and guide to human LITH genes显示文摘 | Lyons MA Wittenburg H | 2006 | Gastroenterology2006，131，6： | 1 |
| 7 | Scaffold preferences of mesenchymal stromal cells and adipose-derived stem cells from green fluorescent protein transgenic mice influence the tissue engineering of bone显示文摘 | Wittenburg G Flade V Garbe AI | 2014 | BrJ Oral Maxillofac Surg2014，52，5： | 1 |
| 8 | Effects of dietary energy intake during gestation and lactation on milk yield and composition of first,second and fourth parity sows显示文摘 | Beyer M Jentsch W Kuhla S Wittenburg H Kreienbring F Scholze H Rudolph PE Merges CC | | 0，，： | 1 |
| 9 | Percutaneous transgastric irrigation drainage in combination with endoscopic necrosectomy in necrotizing pancreatitis (with videos) <ce:link locator='fx1'/>显示文摘 | Susanne Raczynski Niels Teich Gudrun Borte Henning Wittenburg Joachim M?ssner Karel Caca | 2006 | Gastrointestinal Endoscopy2006，，3： | 1 |
| 10 | State of FAIRness in ESFRI Projects显示文摘Since 2009 initiatives that were selected for the roadmap of the European Strategy Forum on Research Infrastructures started working to build research infrastructures for a wide range of research disciplines.An important result of the strategic discussions was that distributed infrastructure scenarios were now seen as“complex research facilities”in addition to,for example traditional centralised infrastructures such as CERN.In this paper we look at five typical examples of such distributed infrastructures where many researchers working in different centres are contributing data,tools/services and knowledge and where the major task of the research infrastructure initiative is to create a virtually integrated suite of resources allowing researchers to carry out state-of-the-art research.Careful analysis shows that most of these research infrastructures worked on the Findability,Accessibility,Interoperability and Reusability dimensions before the term“FAIR”was actually coined.The definition of the FAIR principles and their wide acceptance can be seen as a confirmation of what these initiatives were doing and it gives new impulse to close still existing gaps.These initiatives also seem to be ready to take up the next steps which will emerge from the definition of FAIR maturity indicators.Experts from these infrastructures should bring in their 10-years’experience in this definition process. | Peter Wittenburg Franciska de Jong Dieter van Uytvanck Massimo Cocco Keith Jeffery Michael Lautenschlager Hannes Thiemann Margareta Hellstrom Ari Asmi Petr Holub | 2020 | Data Intelligence2020，2，1： | 1 |
| 11 | FXR and ABCG5/ABCG8 as determinants of cholesterol gallstone formation from quantitative trait locus mapping in mice显示文摘 | Wittenburg H Lyons MA Li R | 2003 | Gastroenterology2003，125，3： | 1 |
| 12 | Investigating associations between milk metabolite profiles and milk traits of Holstein cows显示文摘 | MELZER N WITTENBURG D HARTWIG S | 2013 | Joumal of Dairy Science2013，96，3： | 1 |
| 13 | Quantitative trait loci that determine lipoprotein cholesterol levels in DBA/2J and CAST/Eiinbred mice 显示文摘 | Lyons MA Wittenburg H Li R | 2003 | J Lipid Res2003，44，5： | 1 |
| 14 | FAIR Practices in Europe显示文摘Institutions driving fundamental research at the cutting edge such as for example from the Max Planck Society(MPS)took steps to optimize data management and stewardship to be able to address new scientific questions.In this paper we selected three institutes from the MPS from the areas of humanities,environmental sciences and natural sciences as examples to indicate the efforts to integrate large amounts of data from collaborators worldwide to create a data space that is ready to be exploited to get new insights based on data intensive science methods.For this integration the typical challenges of fragmentation,bad quality and also social differences had to be overcome.In all three cases,well-managed repositories that are driven by the scientific needs and harmonization principles that have been agreed upon in the community were the core pillars.It is not surprising that these principles are very much aligned with what have now become the FAIR principles.The FAIR principles confirm the correctness of earlier decisions and their clear formulation identified the gaps which the projects need to address. | Peter Wittenburg Michael Lautenschlager Hannes Thiemann Carsten Baldauf Paul Trilsbeek | 2020 | Data Intelligence2020，2，1： | 1 |
| 15 | A Reassessment oftheNew Economics oftheMinimum WageLiteraturewith Monthly Data From the Current Population Survey显示文摘 | Burkhauser R CouchK Wittenburg D | 2000 | Journal ofLabor Economics2000，18，4： | 1 |
| 16 | Biliary cholesterol secretion by the twinned sterol half-transporters ABCG5 and ABCG8显示文摘 | Wittenburg H Carey MC | 2002 | J Clin Invest2002，110，5： | 1 |
| 17 | Lith6: a new QTL for cholesterol gallstones from an intercross of CAST/Ei and DBA/2J inbred mouse strains 显示文摘 | Lyons MA Wittenburg H Li R | 2003 | J Lipid Res2003，44，9： | 1 |
| 18 | Combining data from multiple inbred line crosses improves the power and resolution of quantitative trait loci mapping 显示文摘 | Li R Lyons MA Wittenburg H | 2005 | Geneties2005，169，3： | 1 |
| 19 | Biliary cholesterol secretion by the twinned sterol half-transporters ABCG5 and ABCG8显示文摘 | Wittenburg H Carey MC | 2002 | J Clin Invest2002，110，5： | 1 |
| 20 | FXR and ABCG5/ABCG8 as determinants of cholesterol gallstone formation from quantitative trait locus mapping in mice 显示文摘 | Wittenburg H Lyons MA Li R | 2003 | Gastroenterology2003，125，3： | 1 |
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