Joint declaration of data citation principles
Creators: name: Callaghan, Sarah
Publisher: name: Zenodo
Year: 2014
Subjects: subject: data and information
subject: computer science
subject: https://id.loc.gov/authorities/subjects/sh85040468.html
subject: https://id.loc.gov/authorities/subjects/sh85084334.html

description: A poster highlighting joint declaration of data citation principles. Purpose 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. 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. Evidence. In scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited. Function 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. Attributes 6. Persistence. Unique identifiers, and metadata describing the data and its disposition, should persist -- even beyond the lifespan of the data they describe [6]. 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.
description: Previously curated at: http://cedadocs.ceda.ac.uk/1171/ The publish date on this item was its original published date. This item was previously associated with content (as an official url) at: https://www.force11.org/datacitation. This work was funded by: https://www.force11.org/datacitationsynthesisgroup. Main files in this record: JointPrinciplesPoster.ppt JointPrinciplesPoster.pdf Item originally deposited with Centre for Environmental Data Analysis (CEDA) document repository by Miss Hayley Gray. Transferred to CEDA document repository community on Zenodo on 2022-11-24
description: {"references": ["References", "M. Altman &amp; G. King, 2007. A Proposed Standard for the Scholarly Citation of Quantitative Data, D-Lib", "Ball, A., Duke, M. (2012). 'Data Citation and Linking'. DCC Briefing Papers. Edinburgh: Digital Curation Centre.", "CODATA-ICSTI Task Group on Data Citation, 2013; Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data. Data Science Journal", "P. Uhlir (ed.),2011. For Attribution -- Developing Data Attribution and Citation Practices and Standards. National Academies of Sciences"]}