Managing Scientific Information and Research Data by Baykoucheva Svetla

Author:Baykoucheva, Svetla
Language: eng
Format: epub
Publisher: Elsevier Science
Published: 2015-07-13T04:00:00+00:00

8.5 Citing data

While there are established conventions for citing published papers, there is no uniformly accepted format for citations of digital research data. The currently emerging conventions vary by discipline, but some common elements within these conventions are becoming obvious. An article presents an overview of the citation practices of individual organizations and disciplines and identifies the following set of “first principles” for data citation that can be adapted to different disciplines, organizations, and countries to guide the development and implementation of data citation practices and protocols (CODATA-ICSTI, 2013):

1. Status of data: Data citations should be accorded the same importance in the scholarly record as the citation of other objects.

2. Attribution: Citations should facilitate giving scholarly credit and legal attribution to all parties responsible for those data.

3. Persistence: Citations should be as durable as the cited objects.

4. Access: Citations should facilitate access both to the data themselves and to such associated metadata and documentation as are necessary for both humans and machines to make informed use of the referenced data.

5. Discovery: Citations should support the discovery of data and their documentation.

6. Provenance: Citations should facilitate the establishment of provenance of data.

7. Granularity: Citations should support the finest-grained description necessary to identify the data.

8. Verifiability: Citations should contain information sufficient to identify the data unambiguously.

9. Metadata standards: Citations should employ widely accepted metadata standards.

10. Flexibility: 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 across communities.

Data publication and citation are very important to the scientific community, as they make the scientific process more transparent and allow data creators to receive credit for their work. Just like citation of articles, citation of datasets demonstrates the impact of the research and will benefit authors. Developing mechanisms for peer review of data will ensure the quality of datasets and will allow analysis of data and conclusions made on the basis of these data (Kratz and Strasser, 2014).

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