The acronym hides layers
Posted: Sat Feb 08, 2025 4:31 am
The FAIR acronym contains terms that will be pretty familiar to those working in data creation, curation, access and use over recent decades: Findable, Accessible, Interoperable, Reusable. of operational complexity, but I see it as a catchy opportunity to communicate what we get up to, and as a chance to contribute to making it singapore rcs data better. All 15 of the FAIR principles make good sense but we’re still in the process of defining how they should be applied in the real, heterogeneous world of data management and services.
The UK Data Service partner that provides the trustworthy digital repository function is the UK Data Archive, at the University of Essex. But the distributed partnership of the Service is an exemplar of the complex partnerships that data management, access and preservation depend upon. The service model, and other data infrastructure contributors, from AWS and Azure to Zenodo and Figshare, all depend on interoperability between organisations as well as data. Work like the EOSC, the UK node of the RDA and Enabling FAIR Data/CDF Repository work in the US all depend on communication and cooperation.
The many advances in technical data management are an understandable priority for adoption. But this depends on more than workflows and tools. It’s the domain and disciplinary expertise that continues to drive the advancement of scientific research data management practice. The evolving definitions of FAIR data and the community-driven requirements of the CoreTrustSeal both provide the chance to embrace technical opportunities while making sure that the humans creating, curating and re-using these digital assets remain at the heart of scientific data.
The UK Data Service partner that provides the trustworthy digital repository function is the UK Data Archive, at the University of Essex. But the distributed partnership of the Service is an exemplar of the complex partnerships that data management, access and preservation depend upon. The service model, and other data infrastructure contributors, from AWS and Azure to Zenodo and Figshare, all depend on interoperability between organisations as well as data. Work like the EOSC, the UK node of the RDA and Enabling FAIR Data/CDF Repository work in the US all depend on communication and cooperation.
The many advances in technical data management are an understandable priority for adoption. But this depends on more than workflows and tools. It’s the domain and disciplinary expertise that continues to drive the advancement of scientific research data management practice. The evolving definitions of FAIR data and the community-driven requirements of the CoreTrustSeal both provide the chance to embrace technical opportunities while making sure that the humans creating, curating and re-using these digital assets remain at the heart of scientific data.