Data Management

Benefits of citing data

Proper citation of data sources has both immediate and long term benefits to users and producers of data. “Data citation is the practice of referencing data products used in research. A data citation includes key descriptive information about the data, such as the title, source, and responsible parties” (https://www.usgs.gov/data-management/data-citation). 

Benefits for data producers:

• provides proper attribution and credit
• creates a bibliographic "trail", connecting publications and supporting data, and establishing a timeline of publication and usage
• demonstrates the impact of their work and establishes research data as an important contribution to the scholarly record

Benefits for data users:

• citation makes it easier to find datasets
• supports persistence of datasets
• encourages the reuse of data for new research questions

Benefits for everyone:

• increases transparency and reproducibility

Components of a data citation

Citing data is very similar to citing publications; there are many "correct" formats to use, but we suggest including the following important information:

• creator(s) or contributor(s)
• date of publication
• title of dataset
• publisher
• identifier (e.g. Handle, ARK, DOI) or URL of source
• version, when appropriate
• date accessed, when appropriate

The order of the information is not as important as having sufficient information to find the data set(s) used. Consider the style guidelines of the research domain or lab group, data source, or preferred publisher (see "Related information" below).

A suggested citation format may be specified by some publishers, with specific additional information (e.g. resource type, retrieval data, funder/sponsor). They may also request citation of related publication(s) along with the data. Be sure to review citation style guides carefully. When citation formats are not specified, you can follow your discipline's scholarly citation style. The next section provides examples of common repository styles, as well as APA/MLA/Chicago styles.

Examples of data citation styles

The Digital Curation Centre (DCC) provides additional guidance on how to cite datasets and link to publications

Resources

• The Austin Principles (Linguistics Data Citation)
• Data Archiving and Citation (American Meteorological Society (AMS)) 
• Data Citation (Australian National Data Service (ANDS))
• Data Citation (United States Geological Survey (USGS)) 
• Data Citation Guidelines for Earth Science Data Version 2 (Earth Science Information Partners (ESIP) 2019)
• Data Citation Standards and Practices (CODATA-ICSTI)
• Data Citation Synthesis Group: Joint Declaration of Data Citation Principles (FORCE11) 
• Get Recognition: Data Citation (The DataVerse Network) 
• Provide a citation and document provenance for your dataset (DataONE) 

Joint Declaration of Data Citation Principles

The Data Citation Principles 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.

These citation principles are not comprehensive recommendations for data stewardship. And, as practices vary across communities and technologies will evolve over time, we do not include recommendations for specific implementations, but encourage communities to develop practices and tools that embody these principles.

The principles are grouped so as to facilitate understanding, rather than according to any perceived criteria of importance.

1. Importance

Data should be considered legitimate, citable products of research. Data citations should be accorded the same importance in the scholarly record as citations of other research objects, such as publications.

2. Credit and Attribution

Data citations should facilitate giving scholarly credit and normative and legal attribution to all contributors to the data, recognizing that a single style or mechanism of attribution may not be applicable to all data.

3. Evidence

In scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited.

4. Unique Identification

A data citation should include a persistent method for identification that is machine actionable, globally unique, and widely used by a community.

5. Access

Data citations should facilitate access to the data themselves and to such associated metadata, documentation, code, and other materials, as are necessary for both humans and machines to make informed use of the referenced data.

6. Persistence

Unique identifiers, and metadata describing the data, and its disposition, should persist — even beyond the lifespan of the data they describe.

7. Specificity and Verifiability

Data citations should facilitate identification of, access to, and verification of the specific data that support a claim. Citations or citation metadata should include information about provenance and fixity sufficient to facilitate verifying that the specific timeslice, version and/or granular portion of data retrieved subsequently is the same as was originally cited.

8. Interoperability and Flexibility

Data citation methods should be sufficiently flexible to accommodate the variant practices among communities, but should not differ so much that they compromise interoperability of data citation practices across communities.

For further information, please refer to these examples.

Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11; 2014 https://doi.org/10.25490/a97f-egyk