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pro vyhledávání: '"Lafia, Sara"'
Data users need relevant context and research expertise to effectively search for and identify relevant datasets. Leading data providers, such as the Inter-university Consortium for Political and Social Research (ICPSR), offer standardized metadata a
Externí odkaz:
http://arxiv.org/abs/2305.18358
Data reuse is a common practice in the social sciences. While published data play an essential role in the production of social science research, they are not consistently cited, which makes it difficult to assess their full scholarly impact and give
Externí odkaz:
http://arxiv.org/abs/2302.08477
Publikováno v:
Journal of Documentation, 2023, Vol. 79, Issue 7, pp. 225-239.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JD-03-2023-0055
A Natural Language Processing Pipeline for Detecting Informal Data References in Academic Literature
Discovering authoritative links between publications and the datasets that they use can be a labor-intensive process. We introduce a natural language processing pipeline that retrieves and reviews publications for informal references to research data
Externí odkaz:
http://arxiv.org/abs/2205.11651
Data archives are an important source of high quality data in many fields, making them ideal sites to study data reuse. By studying data reuse through citation networks, we are able to learn how hidden research communities - those that use the same s
Externí odkaz:
http://arxiv.org/abs/2205.08395
Data citations provide a foundation for studying research data impact. Collecting and managing data citations is a new frontier in archival science and scholarly communication. However, the discovery and curation of research data citations is labor i
Externí odkaz:
http://arxiv.org/abs/2203.05112
Autor:
Thomer, Andrea K., Akmon, Dharma, York, Jeremy, Tyler, Allison R. B., Polasek, Faye, Lafia, Sara, Hemphill, Libby, Yakel, Elizabeth
Data curation is the process of making a dataset fit-for-use and archiveable. It is critical to data-intensive science because it makes complex data pipelines possible, makes studies reproducible, and makes data (re)usable. Yet the complexities of th
Externí odkaz:
http://arxiv.org/abs/2202.04560
This paper describes a machine learning approach for annotating and analyzing data curation work logs at ICPSR, a large social sciences data archive. The systems we studied track curation work and coordinate team decision-making at ICPSR. Repository
Externí odkaz:
http://arxiv.org/abs/2105.00030
Publikováno v:
IASSIST Quarterly. 2024, Vol. 48 Issue 1, p1-17. 17p.
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