Between administration and research: Understanding data management practices in an institutional context is a research paper published in Journal of the Association for Information Science and Technology (2021). On theSindex it has a DataRank of 0.610. It has been cited 17 times, with 11 citing works in its 1-hop citation network.
AbstractResearch 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.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.434
From this paper's citation signal
Citation Network Contribution
0.177
From 7 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 71% comes from its base citations and 29% from the citation network (7 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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