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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 | 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 |
| 7 | 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 |
| 8 | Editors' Note:Special Issue on Canonical Workflow FrameworksforResearch显示文摘This special issue is on Canonical Workflow Frameworks for Research(CWFR).A workflow refers to a sequence of activities,which may be more or less computer-based,used with regularity in the research process.CWFR aim to identify common patterns in such scientifically motivated workflows and to offer libraries of components based on FAIR Digital Objects as the integrative standard. | Peter Wittenburg Alex Hardisty Amirpasha Mozzafari Limor Peer Nikolay Skvortsov Alessandro Spinuso Zhiming Zhao | 2022 | Data Intelligence2022，4，2： | 0 |
| 9 | Canonical Workflow for Experimental Research显示文摘The overall expectation of introducing Canonical Workflow for Experimental Research and FAIR digital objects(FDOs)can be summarised as reducing the gap between workflow technology and research practices to make experimental work more efficient and improve FAIRness without adding administrative load on the researchers.In this document,we will describe,with the help of an example,how CWFR could work in detail and improve research procedures.We have chosen the example of'experiments with human subjects'which stretches from planning an experiment to storing the collected data in a repository.While we focus on experiments with human subjects,we are convinced that CWFR can be applied to many other data generation processes based on experiments.The main challenge is to identify repeating patterns in existing research practices that can be abstracted to create CWFR.In this document,we will include detailed examples from different disciplines to demonstrate that CWFR can be implemented without violating specific disciplinary or methodological requirements.We do not claim to be comprehensive in all aspects,since these examples are meant to prove the concept of CWFR. | Dirk Betz Claudia Biniossek Christophe Blanchi Felix Henninger Thomas Lauer Philipp Wieder Peter Wittenburg Martin Zunkeler | 2022 | Data Intelligence2022，4，2： | 0 |
| 10 | Canonical Workflow for Machine Learning Tasks显示文摘There is a huge gap between(1)the state of workflow technology on the one hand and the practices in the many labs working with data driven methods on the other and(2)the awareness of the FAIR principles and the lack of changes in practices during the last 5 years.The CWFR concept has been defined which is meant to combine these two intentions,increasing the use of workflow technology and improving FAIR compliance.In the study described in this paper we indicate how this could be applied to machine learning which is now used by almost all research disciplines with the well-known effects of a huge lack of repeatability and reproducibility.Researchers will only change practices if they can work efficiently and are not loaded with additional tasks.A comprehensive CWFR framework would be an umbrella for all steps that need to be carried out to do machine learning on selected data collections and immediately create a comprehensive and FAIR compliant documentation.The researcher is guided by such a framework and information once entered can easily be shared and reused.The many iterations normally required in machine learning can be dealt with efficiently using CWFR methods.Libraries of components that can be easily orchestrated using FAIR Digital Objects as a common entity to document all actions and to exchange information between steps without the researcher needing to understand anything about PIDs and FDO details is probably the way to increase efficiency in repeating research workflows.As the Galaxy project indicates,the availability of supporting tools will be important to let researchers use these methods.Other as the Galaxy framework suggests,however,it would be necessary to include allsteps necessary for doing amachine learning task including those that require human interaction andtodocument all phases with the help of structured FDOs. | Christophe Blanchi Binyam Gebre Peter Wittenburg | 2022 | Data Intelligence2022，4，2： | 0 |
| 11 | Evaluation of Application Possibilities for Packaging Technologies in Canonical Workflows显示文摘InCanonicalWorkflowFramework forResearch(CWFR)'packages'arerelevantin twodifferentdirections.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. | Thomas Jejkal Sabrine Chelbi Andreas Pfeil Peter Wittenburg | 2022 | Data Intelligence2022，4，2： | 0 |
| 12 | Editors’Note显示文摘In 2019 the German Leibniz research organization sponsored a conference on Open Science(OS)with the idea to publish some of the presented papers in the Data Intelligence journal.Becoming engaged as editors,we recognized that the term“Open Science”was coined about 10 years ago with the intention as pointed out by Michael Nielson:“OS is the idea that scientific knowledge of all kinds should be openly shared as early as is practical in the discovery process”. | Peter Wittenburg George Strawn | 2021 | Data Intelligence2021，3，1： | 0 |
| 13 | Comments to Jean-Claude Burgelman’s article Politics and Open Science:How the European Open Science Cloud Became Reality (the Untold Story)显示文摘1.PREFACE Coming from an institute that was devoted to analysing data streams of different sorts from its beginning to understand how the human brain is processing language and how language is supporting cognition,building efficient data infrastructures of different scope was a key to research excellence.While first local infrastructures were sufficient,it became apparent in the 90s that local data would not be sufficient anymore to satisfy all research needs.It was a logical step to first take responsibilities in setting up the specific DOBES(Dokumentation bedrohter Sprachen)infrastructure focussing on languages of the world。 | Peter Wittenburg | 2021 | Data Intelligence2021，3，1： | 0 |
| 14 | Open Science and Data Science显示文摘Data Science(DS)as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections.“Open Science by Design”(OSD),i.e.,making artefacts such as data,metadata,models,and algorithms available and re-usable to peers and beyond as early as possible,is a pre-requisite for a flourishing DS landscape.However,a few major aspects can be identified hampering a fast transition:(1)The classical“Open Science by Publication”(OSP)is not sufficient any longer since it serves different functions,leads to non-acceptable delays and is associated with high curation costs.Changing data lab practices towards OSD requires more fundamental changes than OSP.(2)The classical publication-oriented models for metrics,mainly informed by citations,will not work anymore since the roles of contributors are more difficult to assess and will often change,i.e.,other ways for assigning incentives and recognition need to be found.(3)The huge investments in developing DS skills and capacities by some global companies and strong countries is leading to imbalances and fears by different stakeholders hampering the acceptance of Open Science(OS).(4)Finally,OSD will depend on the availability of a global infrastructure fostering an integrated and interoperable data domain-“one data-domain”as George Strawn calls it-which is still not visible due to differences about the technological key pillars.OS therefore is a need for DS,but it will take much more time to implement it than we may have expected. | Peter Wittenburg | 2021 | Data Intelligence2021，3，1： | 0 |
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