2026-06-08T11:04:13Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/957082024-10-14T23:54:31Zcom_20.500.14352_14col_20.500.14352_15

00925njm 22002777a 4500 dc Álvarez Bornstein, Belén author Morillo, Fernanda author Bordons, María author 2017 The study of acknowledgments as a source of data on research funding is gaining ground in science, as exemplified by the present inclusion of this information in bibliographic databases such as Web of Science (WoS). The objective of this paper is to explore the completeness and accuracy of WoS in extracting and processing funding acknowledgment data. With this purpose, a random sample of articles published in eight thematic areas and selected from the scientific output of Spain in 2014 are analyzed. Funding information that appears in original articles is recorded by WoS in the “funding text” field, but is also extracted to the “funding agency” and “grant number” subfields. In the extraction process, some funding information was lost in 12% of the articles and the distribution of agencies and grant numbers by subfield was not always consistent. In addition, funding support is often incompletely reported by authors: in about half of the articles we studied, the country of origin of the funder or the grant number(s) were not mentioned. We propose and discuss the need to develop more detailed guidelines on how to acknowledge funding. More accurate documentation of funding sources in published articles would benefit researchers, funders and journals, and enhance the reliability and usefulness of studies on funding acknowledgments. Álvarez-Bornstein, B., Morillo, F. & Bordons, M. Funding acknowledgments in the Web of Science: completeness and accuracy of collected data. Scientometrics 112, 1793–1812 (2017). https://doi.org/10.1007/s11192-017-2453-4 0138-9130 10.1007/s11192-017-2453-4 https://hdl.handle.net/20.500.14352/95708 1588-2861 https://doi.org/10.1007/s11192-017-2453-4 Funding Acknowledgments in the Web of Science: Completeness and Accuracy of Collected Data