five

FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources

收藏
科学数据银行2020-10-17 更新2026-04-23 收录
下载链接:
https://www.scidb.cn/en/detail?dataSetId=767141754330677248
下载链接
链接失效反馈
官方服务:
资源简介:
Figure 1 shows convergence Matrix Process Overview. The questionnaire is composed and maintained by FAIR Experts, an effort was made to ensure broad coverage of technologies and other Resources and how they relate to each of the FAIR principles. The questionnaire is encoded in a machine-readable Wizard Knowledge Model, which then exposes the questions in a user-friendly interface (screenshot). The community spokesperson registers in the Wizard, completes a few questions profiling the community, then begins to answer the 61 questions in the questionnaire. Default answers, drop-downs and autocomplete make the completion of the form easier and help achieve the machine readability. At some point in the future, Communities and trusted third-parties (e.g., funding agencies, publishers, data stewards, etc.) could publish customized Knowledge Models that will offer recommendations on, or even require the use of, certain Resources. This function could be a powerful driver of convergence. The drop-down and autocorrect is provided by FAIRsharing. The data input by the Community Spokesperson is captured as stand-alone nanopublications (capturing an assertion about the “Implementation Choice Made” and documenting the decision with a collection provenance metadata). The nanopublications will be made available on the distributed nanopublication server network, and will be available to any other organizations for hosting and serving. The resulting open knowledge graph is generated from the stored data and can be viewed as a public good, advising a myriad of decisions needed to launch and sustain the Internet of FAIR Data and Services.
提供机构:
Leiden University Medical Center; Corporation for National Research Initiatives; Leiden University Libraries; Stanford Center for Biomedical Informatics Research; Vrije Universiteit Amsterdam; TIB Leibniz Information Centre for Science and Technology; Environment Agency Austria; Max Planck Computing and Data Facility; Susanna-Assunta Sansone; Czech Technical University in Prague; University of Oxford; SURF; GO FAIR International Support and Coordination Office
创建时间:
2020-10-17
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作