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Cited by 40scientometrics and bibliometrics researchLibrary Science and InformationWeb visibility and informetrics

Special Issue on Bibliographic Data Sources

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Waltman, Ludo, Larivière, Vincent·2020·Quantitative Science Studies
DOI 10.1162/qss_e_00026
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Abstract

Research in quantitative science studies relies on an increasingly broad range of data sources, providing data on scholarly publications, social media activity, peer review, research funding, the scholarly workforce, scientific prizes, and so on.However, there is one type of data source that remains at the heart of research in quantitative science studies: bibliographic databases.These data sources have increasingly diversified over the last decade.Several organizations now provide large-scale databases of metadata on scholarly publications.For this special issue of Quantitative Science Studies, we invited the providers of major bibliographic data sources to provide insights on how their data can be used to support research in quantitative science studies.This special issue comprises six papers.Three papers cover the most important commercial bibliographic data sources: Web of Science (Clarivate Analytics), Scopus (Elsevier), and Dimensions (Digital Science).Three other papers cover open data sources: Microsoft Academic, Crossref and OpenCitations. 1 There are of course many other bibliographic data sources.However, for this special issue, we have chosen to consider only data sources that cover publications from all fields of science and from all parts of the world.Data sources that focus on specific scientific fields (e.g., PubMed from the US National Library of Medicine) or specific countries (e.g., national databases of scholarly publications) are therefore not included.We hope that this special issue will help authors of submissions to Quantitative Science Studies to choose the most suitable bibliographic data source for their research.In the past, Web of Science and Scopus often were the only data sources between which researchers could choose.Researchers typically used the data source to which their institution happened to have a subscription.In recent years, however, the number of options has increased considerably.This special issue aims to characterize the most important data sources currently available and to show how they differ in various dimensions, for instance in the data they provide, their level of openness, and their support for making research reproducible.As editors of Quantitative Science Studies, we consider openness and reproducibility to be of major importance.Research published in Quantitative Science Studies is expected to be as reproducible as possible, and reproducibility can be promoted by making use of open data.Below we provide a brief overview of some of the key differences between the bibliographic data sources considered in this special issue, focusing on three questions: What does the data source provide?How to get access to the data?And how can the data be used?1 The team of Google Scholar was also invited to contribute to this special issue, but they did not respond to our invitation.

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Authors
Waltman, L., & Larivière, V.
Affiliations
  • Leiden University (NL)
  • Université du Québec à Montréal (CA)
Corresponding author
Waltman, Ludo
ORCID
Venue
Quantitative Science Studies
Publisher
MIT Press
Published
2020-02
Volume
1
Issue
1
Pages
360-362
ISSN
2641-3337 (ISSN-L)
DOI
10.1162/qss_e_00026
OpenAlex
W3007990842
Cited by
40
References
0
Citations by year
2026: 1 · 2025: 7 · 2024: 7 · 2023: 4 · 2022: 6 · 2021: 14 · 2020: 1
Type
journal article
Language
en
Open access
Licence
public-domain
Locations
  • Quantitative Science Studies (publishedVersion, public-domain)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Papyrus : Institutional Repository (Université de Montréal) (submittedVersion, cc-by)
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
  • Physical Sciences › Computer Science › Information Systems › Library Science and Information
  • Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
Keywords
bibliographic databases, Web of Science, Scopus, dimensions, Microsoft Academic, Crossref, OpenCitations, open data, data openness, research reproducibility, Google Scholar

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