Tracing data
A survey investigating disciplinary differences in data citation
Bibliographic Data
| ID | 21256117 |
|---|---|
| Authors | Kathleen Gregory (0000-0001-5475-8632, University of Vienna), Anton Boudreau Ninkov (0000-0002-8276-7656, Université de Montréal), Chantal Ripp (0000-0003-3544-8158, University of Ottawa), Emma Roblin (0000-0002-9179-0620, University of Ottawa), Isabella Peters (0000-0001-5840-0806, ZBW – Leibniz-Informationszentrum Wirtschaft), Stefanie Haustein (0000-0003-0157-1430, University of Ottawa) |
| Year | 2023 |
| Volume | 4 |
| Issue | 3 |
| Pages | 622-649 |
| Publication date | 2023-08-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Quantitative Science Studies (JOURNAL) |
| Journal identifiers | ISSN: 2641-3337 • E-ISSN: 2641-3337 |
| Publisher | MIT Press (PUBLISHER • US) |
| DOI | 10.1162/qss_a_00264 |
| OpenAlex | W4386601204 |
| Language | EN |
| Citations received | 4 |
| References cited | 73 |
Data citations, or citations in reference lists to data, are increasingly seen as an important means to trace data reuse and incentivize data sharing. Although disciplinary differences in data citation practices have been well documented via scientometric approaches, we do not yet know how representative these practices are within disciplines. Nor do we yet have insight into researchers’ motivations for citing—or not citing—data in their academic work. Here, we present the results of the largest known survey (n = 2,492) to explicitly investigate data citation practices, preferences, and motivations, using a representative sample of academic authors by discipline, as represented in the Web of Science (WoS). We present findings about researchers’ current practices and motivations for reusing and citing data and also examine their preferences for how they would like their own data to be cited. We conclude by discussing disciplinary patterns in two broad clusters, focusing on patterns in the social sciences and humanities, and consider the implications of our results for tracing and rewarding data sharing and reuse
Citation · Citation analysis · Data science · Data sharing · Discipline · Reuse · Social science · Sociology · Statistics · Survey data collection · Tracing · World Wide Web · Computer Science · Data Quality and Management · Engineering · Mathematics · Research Data Management Practices · Scientific Computing and Data Management
The sociology of science
The Manufacture of Knowledge
Gravity's Shadow
Big Data, Little Data, No Data
Measuring Research
Data Sharing by Scientists
What do citation counts measure? A review of studies on citing behavior
What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018
Scientists Lift the Lid on Reproducibility
The Fair Guiding Principles for scientific data management and stewardship
Subdivisions and crossroads
Referencing as Persuasion
What Counts as Scientific Data? A Relational Framework
The journal coverage of Web of Science and Scopus
The Matthew Effect in Science, II
Revisiting Qualitative Data Reuse
| Unique citing works | 4 |
|---|---|
| Citations per year | 2 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |