Good data management is a necessary precursor for FAIR and open, and enables data to be created which is fit for sharing and reuse | Higman, 2019
Research data management (RDM), data stewardship, open data, open science and FAIR. They are overlapping but different concepts, each of which emphasises different aspects of managing and sharing research data. In this section we explain the distinction in more detail and zoom in on ways to start the conversation with researchers about these topics.
A difference in definition
Open data, open science, FAIR data, data stewardship, RDM. These are terms that will not have escaped your attention as a future data supporter. But ... What do they mean and, more importantly, how do you find the right entrance to support researchers between all of the data jargon out there? In the presentation below, we will first show you a number of definitions for the concepts mentioned. You will see that open science, data management and data stewardship describe a process: a way in which you go through the phases of the research cycle. Open data and/or FAIR data can be the concrete and tangible research output that results from these processes and that can be built upon.
From open to 'shades of FAIR'
The concept of open data (Open Knowledge Foundation, n.d.) followed almost automatically from the concept of open access (Openaccess.nl, n.d.), but also brought resistance (Higman, 2019). In particular, there is the misunderstanding that all data should necessarily be available in open access - in the public domain. The adage 'open if possible, closed if necessary' tries to find a way out of this misunderstanding (European Commission, 2017). The concept of FAIR also provides a starting point for softening black-and-white discussions about open access to research data.
Data resulting from publicly funded research must be made FAIR and citable, and be as open as possible, as closed as necessary | European Commission, 2017
The authors of the folder 'How open is it' (SPARC & PLOS, 2014) showed the 'Shades of Open' for open access to scientific publications. In a similar way, research data can be divided into 'Shades of Open' (LCRDM, 2019) and shades of FAIR (Mons, et al., 2017). There is no one-size-fits-all approach, and this awareness alone provides room for discussion with researchers. Especially after the introduction of the General Data Protection Regulation in 2018, which has made researchers more concerned about the risks of sharing research data, FAIR is a good starting point for discussions (Higman, 2019).
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