Reasons to Cite Data
Citing data encourages the replication of scientific results, improves research standards, and gives proper credit to data producers. Data citation helps to: (adapted from DataCite: Cite Your Data)
Enable easy reuse and verification of data: provide the means to share data with future researchers to enable reproducibility of findings
Allow the impact of data to be tracked: support collaboration, and proper attribution and credit
Create a scholarly structure that recognizes and rewards data producers: foster faster and more efficient research progress
How to Cite Data
- Joint Declaration of Data Citation Principles (2014) – Cover purpose, function and attributes of citations. These principles recognize the dual necessity of creating citation practices that are both human understandable and machine-actionable. From FORCE11
- DataCite – An international association that aims to support researchers by enabling them to locate, identify, and cite research datasets with confidence. DataCite promotes the use of persistent identifiers for datasets.
- How to Cite Datasets and Link to Publications Ball, A. & Duke, M. (2012) – This guide will help you create links between your academic publications and the underlying datasets, so that anyone viewing the publication will be able to locate the dataset and vice versa. It provides a working knowledge of the issues and challenges involved, and of how current approaches seek to address them. This guide should interest researchers and principal investigators working on data-led research, as well as the data repositories with which they work.
- Developing Data Attribution and Citation Practices and Standards: Summary of an International Workshop (2012) – Report based on Board of Research Data and Information workshop on data attribution and citation. Free pdf available once user registers at National Academies.
Discussion for Data Citation
- The Anatomy of a Data Citation: Discovery, Reuse and Credit (2012) – Study examining frequency and quality of data citations in journal articles. Findings show majority of journal articles fail include adequate citations to data used in secondary analysis studies. Encourages librarians to advocate data citation in scholarly communications.
- Why are the attribution and citation of scientific data important? (2012) – Christine Borgman discusses how the following features of the scholarly infrastructure for digital objects impact data citation and attribution: social practice, usability, identity, persistence, discoverability, provenance, relationships, intellectual property, and policy.
- How to Cite Datasets and Link to Publications (2012) – Guide to creating links between publications and their underlying data.
- Interagency Data Stewardship/Citations/provider guidelines (2011) – Guidelines for citing data with examples. This wikipage has been developed by the Federation of Earth Science Information Providers but content is relevant to other science disciplines.
- Making your code citable – Information provided by GitHub for why and how to make your GitHub repositories of code citable.
- Data Citation Awareness - ANDS Data Citation Guide – Guide developed by the Australian National Data Service. Contains suggestions about citing data, in addition to discussing issues surrounding data citation.
- Guide to Data Citation – Guide developed by Cornell University's Research Data Management Services Group on why and how to cite data
- Cite Your Data – Background information on data citation from DataCite.
- Why and How to Cite Data – Information provided by the Inter-University Consortium for Political and Social Research (ICPSR).
- Quick Guide to Data Citation – Provided by the International Association for Social Science Information Services & Technology (iassist).