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Manage Research Data: Cite Data

Library services related to research data management.

Introduction

Citing datasets used in published research is just as important as citing journal articles, books, and other sources that contributed to the research.

By citing your use of a dataset, you are supporting the reproducibility of your research and attributing credit to those who provided the data-including datasets that you have created yourself. Citations also allow for tracking reuse and measuring impact.

Instructions for citation styles do not consistently provide examples for dataset citations. This guide will help you determine the citation elements to include. Refer to your author guidelines or style guide to properly arrange and format these citation elements. Many data providers also recommend their preferred citation or supply an example.

Be sure to provide enough information in your citation so that the reader can identify, retrieve, and access the same unique dataset you have used.

Reommendations from IASSIST

Elements of Data Citation

  • Author: Name(s) of each individual or organizational entity responsible for the creation of the dataset.

  • Date of Publication: Year the dataset was published or disseminated.

  • Title: Complete title of the dataset, including the edition or version number, if applicable.

  • Publisher and/or Distributor: Organizational entity that makes the dataset available by archiving, producing, publishing, and/or distributing the dataset.

  • Electronic Location or Identifier: Web address or unique, persistent, global identifier used to locate the dataset (such as a DOI). Append the date retrieved if the title and locator are not specific to the exact instance of the data you used.

These are the minimum elements required for dataset identification and retrieval. Fewer or additional elements may be requested by author guidelines or style manuals. Be sure to include as many elements as needed to precisely identify the dataset you have used.

Examples

Arrange these elements following the order and punctuation specified by your style guide. If examples for datasets are not provided, the format for books is generally considered a generic format that can be modified for other source types.

APA (6th edition)

  • Smith, T.W., Marsden, P.V., & Hout, M. (2011). General social survey, 1972-2010 cumulative file (ICPSR31521-v1) [data file and codebook]. Chicago, IL: National Opinion Research Center [producer]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. doi: 10.3886/ICPSR31521.v1

MLA (7th edition)

  • Smith, Tom W., Peter V. Marsden, and Michael Hout. General Social Survey, 1972-2010 Cumulative File. ICPSR31521-v1. Chicago, IL: National Opinion Research Center [producer]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2011. Web. 23 Jan 2012. doi:10.3886/ICPSR31521.v1

Chicago (16th edition) (author-date)

  • Smith, Tom W., Peter V. Marsden, and Michael Hout. 2011. General Social Survey, 1972-2010 Cumulative File. ICPSR31521-v1. Chicago, IL: National Opinion Research Center. Distributed by Ann Arbor, MI: Inter-university Consortium for Political and Social Research. doi:10.3886/ICPSR31521.v1

Source: IASSIST Quick Guide to Data Citation

More examples

Recommended Not Recommended
Irino, T; Tada, R (2009): Chemical and mineral compositions of sediments from ODP Site 127-797. PANGAEA. http://dx.doi.org/10.1594/PANGAEA.726855 Irino & Tada (2009). Chemical and mineral compositions of sediments from ODP Site 127-797. Published by PANGAEA [www.pangaea.de]
Elliott, Joshua (2013): Simulated county- and state-level maize yields, 1979-2012. Version 1. Figshare. http://dx.doi.org/10.6084/m9.figshare.501263 501263Elliott’s Maize Yield Data (2013). Data accessed from Figshare [June 15, 2015]
Uniprot Consortium (2014): P0DKE6.Uniprot Knowledgebase. http://www.uniprot.org/uniprot/P0DKE6

Uniprot Database. http://www.uniprot.org

Source: Clarivate Whitepaper on Recommended Practices to Promote Scholarly Data Citation and Tracking

Source

IASSIST is an international organization of professionals working with information technology and data services to support research and teaching in the social sciences. Its 300 members work in a variety of settings, including data archives, statistical agencies, research centers, libraries, academic institutions, government departments, and non-profit organizations. 

Best Practices in Sharing Research Data

Updated Best Practices in Research Data Sharing (November 2023)

  1. When publishing their results, researchers deposit related research data and outputs in a trustworthy data repository that assigns persistent identifiers (DOIs where available). Researchers link to research data using persistent identifiers.
  2. When using research data created by others, researchers provide attribution by citing the datasets in the reference section using persistent identifiers.
  3. Data repositories enable sharing of research outputs in a FAIR way, including support for metadata quality and completeness.
  4. Publishers set appropriate journal data policies, describing the way in which data is to be shared alongside the published article. 
  5. Publishers set instructions for authors to include Data Citations with persistent identifiers in the references section of articles.
  6. Publishers include Data Citations and links to data in Data Availability Statements with persistent identifiers (DOIs where available) in the article metadata registered with Crossref. 
  7. In addition to Data Citations, Data Availability Statements (human- and machine-readable) are included in published articles where appropriate. 
  8. Repositories and publishers connect articles and datasets through persistent identifier connections in the metadata and reference lists.
  9. Funders and research organizations provide researchers with guidance on open science practices, track compliance with open science policies where possible, and promote and incentivize researchers to openly share, cite and link research data.
  10. Funders, policymaking institutions, publishers and research organizations collaborate towards aligning FAIR research data policies and guidelines.
  11. All stakeholders collaborate in the development of tools, processes, and incentives throughout the research cycle to enable sharing of high-quality research data, making all steps in the process clear, easy and efficient for researchers by providing support and guidance.
  12. Stakeholders responsible for research assessment take into account data sharing and data citation in their reward and recognition system structures.

Source:

STM Association, DataCite, & Crossref. (2023). Joint Statement on Research Data. DataCite. https://doi.org/10.5438/CSTD-5T12

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