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Measuring the impact of biodiversity datasets: data reuse, citations and altmetrics
Khan, Nushrat Thelwall, Mike Kousha, Kayvan
Khan, Nushrat
Thelwall, Mike
Kousha, Kayvan
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2021-02-28
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Abstract
Despite growing evidence of open biodiversity data reuse by scientists, information about how data is reused and cited is rarely openly accessible from research data repositories. This study explores data citation and reuse practices in biodiversity by using openly available metadata for 43,802 datasets indexed in the Global Biodiversity Information Facility (GBIF) and content analyses of articles citing GBIF data. Results from quantitative and content analyses suggest that even though the number of studies making use of openly available biodiversity data has been increasing steadily, best practice for data citation is not yet common. It is encouraging, however, that an increasing number of recent articles (16 out of 23 in 2019) in biodiversity cite datasets in a standard way. A content analysis of a random sample of unique citing articles (n=100) found various types of background (n=18) and foreground (n=81) reuse cases for GBIF data, ranging from combining with other data sources to create species distribution modelling to software testing. This demonstrates some unique research opportunities created by open data. Among the citing articles, 27% mentioned the dataset in references and 13% in data access statements in addition to the methods section. Citation practice was inconsistent especially when a large number of subsets (12~50) were used. Even though many GBIF dataset records had altmetric scores, most posts only mentioned the articles linked to those datasets. Among the altmetric mentions of datasets, blogs can be the most informative, even though rare, and most tweets and Facebook posts were for promotional purposes.
Citation
Khan, N., Thelwall, M. and Kousha, K. (2021) Measuring the impact of biodiversity datasets: data reuse, citations and altmetrics. Scientometrics, 126, pp. 3621–3639. https://doi.org/10.1007/s11192-021-03890-6
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Journal article
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en
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This is an accepted manuscript of an article published by Springer in Scientometrics, available online at: https://doi.org/10.1007/s11192-021-03890-6
The accepted version of the publication may differ from the final published version.
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0138-9130
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Attribution-NonCommercial-NoDerivatives 4.0 International