Between administration and research: Understanding data management practices in an institutional context

Abstract

Research Data Management (RDM) promises to make research outputs more transparent, findable, and reproducible. Strategies to streamline data management across disciplines are of key importance. This paper presents results of an institutional survey (N = 258) at a medium-sized Austrian university with a STEM focus, supplemented with interviews (N = 18), to give an overview of the state-of-play of RDM practices across faculties and disciplinary contexts.RDM services are on the rise but remain somewhat behind leading countries like the Netherlands and UK, showing only the beginnings of a culture attuned to RDM. There is considerable variation between faculties and institutes with respect to data amounts, complexity of data sets, data collection and analysis, and data archiving. Data sharing practices within fields tend to be inconsistent.RDM is predominantly regarded as an administrative task, to the detriment of considerations of good research practice. Problems with RDM fall in two categories: Generic problems transcend specific research interests, infrastructures, and departments while discipline-specific problems need a more targeted approach. The paper extends the state-of-the-art on RDM practices by combining in-depth qualitative material with quantified, detailed data about RDM practices and needs. The findings should be of interest to any comparable research institution with a similar agenda.Recent years have seen rapid growth in the adoption of Research Data Management (RDM) policies across research institutions. Accompanying these developments, there is now a quickly growing literature studying data

Data sharing for global, unspecified reuse

“…When it comes to assessing these claims, the sampling strategies employed by previous studies are instructive, as they offer a glimpse on how they have interpreted data (sharing) practices relative to modes of scientific organisation. These strategies range from (single) case studies relying heavily on ethnographic methods (interviews, participant observation) (Hsu et al 2015;Leonelli 2016;Myneni et al 2016;Wallis et al 2013); qualitative small-N analyses (Kurata et al 2017); quantitative studies of disciplinary data practices (Anderson et al 2007;Borghi and Van Gulick 2018;Chen and Wu 2017); comparative studies within institutions (Akers and Doty 2013;Cox and Williamson 2015;Mancilla et al 2019;Reichmann et al 2021;Schöpfel et al 2018); to large-scale national or international surveys (Aydinoglu et al 2017;Chigwada, Chiparausha, and Kasiroori 2017;Elsayed and Saleh 2018;Koopman and Jager 2016;Liu and Ding 2016;Tenopir et al 2011). The sampling strategies associated with these approaches correspond to (broadly) administrative versus (broadly) epistemic concerns, depending on the theoretical framework (research areas, specialties, disciplines, universities and departments) used.…”

“…Data practices and Open Science: A priori conceptions of data sharing (Reichmann et al 2021) document the large variability of data practices at a technical university. In retrospect, the discussions surrounding research data management are predicated upon a priori conceptions of data sharing as part of good scientific practice.…”

Data Stewardship – Austrian National Strategy and Alignment

“…FAIR Data Austria not only dealt with the role of a Data Steward or their required competencies, but also recognized that their establishment at an institution always depends on the existing organizational culture, available resources and expectations (REICHMANN et al, 2021;HASANI-MAVRIQI et al, 2022).…”