Lost or found? Discovering data needed for research
arXiv:1909.00464 · doi:10.1162/99608f92.e38165eb
Abstract
Finding data is a necessary precursor to being able to reuse data, although relatively little large-scale empirical evidence exists about how researchers discover, make sense of and (re)use data for research. This study presents evidence from the largest known survey investigating how researchers discover and use data that they do not create themselves. We examine the data needs and discovery strategies of respondents, propose a typology for data reuse and probe the role of social interactions and literature search in data discovery. We consider how data communities can be conceptualized according to data uses and propose practical applications of our findings for designers of data discovery systems and repositories. Specifically, we consider how to design for a diversity of practices, how communities of use can serve as an entry point for design and the role of metadata in supporting both sensemaking and social interactions.
arXiv commentHarvard Data Science Review (2020)
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Cited by in corpus (3)
- Subdivisions and Crossroads: Identifying Hidden Community Structures in a Data Archive's Citation Network
- How and Why do Researchers Reference Data? A Study of Rhetorical Features and Functions of Data References in Academic Articles
- Flexible metadata harvesting for ecology using large language models