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| 1 | 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 |
| 2 | Gastrointestinal Cancer and the Long-term Use of Pravastatin in the Elderly显示文摘 | Dirk Devroey Frank Buntinx Willem Betz Jan Vandevoorde Jan Kartounian | 2003 | Acta Oncologica2003，，4： | 1 |
| 3 | 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 |
| 4 | Reproducible Research Publication Workflow:A Canonical Workflow Framework and FAIR Digital Object Approach to Quality Research Output显示文摘In this paper we present the Reproducible Research Publication Workflow(RRPW)as an example of how generic canonical workflows can be applied to a specific context.The RRPW includes essential steps between submission and final publication of the manuscript and the research artefacts(i.e.,data,code,etc.)that underlie the scholarly claims in the manuscript.A key aspect of the RRPW is the inclusion of artefact review and metadata creation as part of the publication workflow.The paper discusses a formalized technical structure around a set of canonical steps which helps codify and standardize the process for researchers,curators,and publishers.The proposed application of canonical workflows can help achieve the goals of improved transparency and reproducibility,increase FAIR compliance of all research artefacts at all steps,and facilitate better exchange of annotated and machine-readable metadata. | Limor Peer Claudia Biniossek Dirk Betz Thu-Mai Christian | 2022 | Data Intelligence2022，4，2： | 0 |
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