分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-27 合作期刊: 《数据智能(英文)》
摘要: 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#2;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.
分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-16 合作期刊: 《数据智能(英文)》
摘要: 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.