Open research data repositories: Practices, norms, and metadata for sharing images is a research paper published in Journal of the Association for Information Science and Technology (2021). On theSindex it has a DataRank of 0.500. It has been cited 27 times.
AbstractOpen research data repositories are promoted as one of the cornerstones in the open research paradigm, promoting collaboration, interoperability, and large‐scale sharing and reuse. There is, however, a lack of research investigating what these sharing platforms actually share and a more critical interface analysis of the norms and practices embedded in this datafication of academic practice is needed. This article takes image data sharing in the humanities as a case study for investigating the possibilities and constraints in 5 open research data repositories. By analyzing the visual and textual content of the interface along with the technical means for metadata, the study shows how the platforms are differentiated in terms of signifiers of research paradigms, but that beneath the rhetoric of the interface, they are designed in a similar way, which does not correspond well with the image researchers' need for detailed metadata. Combined with the problem of copyright limitations, these data‐sharing tools are simply not sophisticated enough when it comes to sharing and reusing images. The result also corresponds with previous research showing that these tools are used not so much for sharing research data, but more for promoting researcher personas.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.500
From this paper's citation signal
Citation Network Contribution
0
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Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
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.