Measuring the impact of biodiversity datasets: data reuse, citations and altmetrics.

Autor/inn/en	Khan, Nushrat; Thelwall, Mike; Kousha, Kayvan
Titel	Measuring the impact of biodiversity datasets: data reuse, citations and altmetrics.
Quelle	In: Scientometrics, (2021) 4, S.3621-3639 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-021-03890-6
Schlagwörter	Open biodiversity data; Altmetrics; Data reuse; Citation practice; Citation analysis
Abstract	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.
Erfasst von	OLC
Update	2023/2/05