Research Data Management

Background

The consistent management of research data is especially crucial for the success of any long-term and large-scale collaborative research. RDM is also “the basis for efficiency, continuity, and quality of the research, as well as for maximum impact and outreach, including the long-term publication of data and their accessibility” (Finkel et al., 2020, p. 1).

As emphasized by several researchers, as well as by funding agencies and publishers, organizing and sharing research data is a fundamental part of the research process, thus comprehensive RDM is indispensable not only for ensuring reproducible and open scientific research, but increases citation rates for publications, and fosters research reproducibility (Borycz, 2021).

The main drivers of RDM are Open Science and Open Data. These initiatives gained momentum with the adoption of the Fair Access to Science and Technology Research Act in 2013 (US Congress, 2013), but this culture change is strongly supported also by the European Commission through the European Open Science Cloud (EOSC, n.d.).

RDM is often viewed as a set of mechanical, managerial, and technical handling processes (Ojanen et al., 2020). However, by encouraging the collaboration between researchers, and fostering better science, it can lead to better decision-making (USGS, n.d.).

RDM falls mainly into the domain and responsibility of researchers and data professionals. The latter group includes (data) librarians, data curators, and data stewards. It is unimaginable without services, offered by research offices, academic libraries, and computing (information technology) service units. Beside of these professionals, data scientists must be knowledgeable of RDM because it is one of the main services that provide them with the data that they are working with. Contrarily, RDM is also a data science issue, which should not be restricted to machine learning or statistics, thus data scientists need to face the challenges of organizing and storing data. As declared by Davenport and Patil (2012, p. 73), data scientist’s job is “bringing structure to large quantities of formless data and making analysis possible.” In the light of this, we need to acknowledge that their job goes beyond formal data analysis, among others by including RDM.

Strongly related to the broad and varied aspects of RDM and data science are the activities of the Research Data Alliance, which is a “community-driven organization dedicated to the development and use of technical, social, and community infrastructure promoting data sharing and data-driven exploration. This organization is particularly important for “the global academic community where research infrastructure is often ad hoc, may have a short shelf-life, and hard to fund (Berman & Crosas, 2020).

Data stewardship also deserves attention because these professionals take care of data assets that do not belong to the stewards themselves. Data stewards’ aim is ensuring that data-related work is performed in accordance with policies and practices. Together with preservation of data, their activities also fall into the domain of data science.