Data Citation Principles CODATA TG on Data Citation

Transcription

1 Data Citation Principles CODATA TG on Data Citation Presentation to Dryad membership meeting Oxford, 23 rd May 2013 Co-Chairs Jan Brase, Germany Christine Borgman, US Sarah Callaghan, Presenter) Support: Paul Uhlir, US

2 Overview of the working group Approved at CODATA General Assembly 2010 Co sponsored by ICSTI and supported by the US National Committee for CODATA Objectives: Examine key issues related to data identification, attribution citation and linking Help coordinate activities internationally Promote common practices and standards

3 CODATA Data Citation Task Group Co-Chairs: Jan Brase,(Director, DataCite, and ICSTI representative), Technische Informations Bibliothek (TIB)/German National Library of Science and Technology, GERMANY Sarah Callaghan (U.K. CODATA), The NCAS British Atmospheric Data Centre, STFC Rutherford Appleton Laboratory, UNITED KINGDOM Christine Borgman, University of California, Los Angeles, USA Members: Micah Altman MIT Libraries USA Elizabeth Arnaud Bioversity International ITALY Todd Carpenter National Information Standards Organization USA Vishwas Chavan Global Biodiversity Information Facility DENMARK Paul Groth VU University of Amsterdam THE NETHERLANDS Mark Hahnel FigShare UNITED KINGDOM John Helly Scripps Institution of Oceanography, Climate, Atmospheric Science, and Physical Oceanography USA Puneet Kishor Creative Commons USA Jianhui LI Chinese Academy of Sciences CHINA Franciel Azpurua Linares Information International Associates, USA Karen Morgenroth National Research Council Canada CANADA Yasuhiro Murayama National Institute of Information and Communications Technology JAPAN Fiona Murphy Wiley Europe Ltd UNITED KINGDOM Giri Palanisami Oak Ridge National Laboratory USA Mark Parsons Research Data Alliance/U.S. Center for a Digital Society USA Soren Roug European Environmental Agency BELGIUM Helge Sagen Institute of Marine Research NORWAY Eefke Smit International Association of STM Publishers, THE NETHERLANDS Martie J. van Deventer CSIR South Africa SOUTH AFRICA Michael Witt Purdue University Libraries USA Koji Zettsu National Institute of Information and Communications Technology, JAPANConsultants: William L. Anderson Associate Editor CODATA Data Science Journal USA. Daniel Cohen NRC Board on Research Data and Information, and U.S. Committee for CODATA [on detail from the Library of Congress] USA Yvonne Socha University of Tennessee USA Project Director: Paul Uhlir U.S. National Committee for CODATA, USA CODATA EC Liaison: Bonnie Carroll (U.S. CODATA and CENDI) Information International Associates USA

4 Other Organizations Working on Data Citation International Council for Scientific and Technical Information (ICSTI) DataCite The Dataverse Network National Information Standards Organization (NISO) Creative Commons and Science Commons CENDI U.S. interagency group focused on scientific and technical information issues and coordination of activities. Global Biodiversity Information Facility (GBIF) World Data System (WDS) STM-Association Digital Curation Center, UK Research Data Alliance (RDA) and many more

5 TG Work Products Inventory and analysis of existing literature on data citation and attribution Interviews with a sample of identified stakeholders concerning data citation and attribution practices Data Repositories Publishers Researchers Funding Organizations Public web presence on the CODATA site Symposium and Workshop, Berkeley, CA August 2011: For Attribution: Developing Data Attribution and Citation Practices and Standards 3 Track session at CODATA 2012 on Data Publishing and Data Citation in Cooperation with the WDS Draft report on Current Activities and Best Practices in Data Citation (in external review, expected release Summer 2013)

6 First Principles for Data Citation 1. Status of Data: Data citations should be accorded the same importance in the scholarly record as the citation of other objects. 2. Attribution: A citation to data should facilitate giving scholarly credit and legal attribution to all parties responsible for those data. 3. Persistence: Citations should refer to objects that persist. 4. Access: Citations should facilitate access to data by humans and by machines. 5. Discovery: Citations should support the discovery of data.

7 First Principles for Data Citation 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: A citation should employ existing metadata standards. 10. Flexibility: Citation methods should be sufficiently flexible to accommodate the variant practices among communities.

8 How do Dryad citations match with the principles? 1. Status of Data 2. Attribution 3. Persistence 4. Access 5. Discovery 6. Provenance 7. Granularity 8. Verifiability 9. Metadata Standards 10. Flexibility

9 What do I want Dryad to do? Keep up the good work!! Work with journals ensure that data is cited (where possible) Guidance for authors Train editors and reviewers to look to see if data is cited, and if not to ask why not Keep providing clear information on how the datasets in your archive should be cited In the context of the data citation first principles Keep talking to researchers about citing data We trying to change the culture of research so that citing data is the norm it s not an easy job!

10 And finally: Thanks! Any

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12 Data is the foundation of science without it we can t test our assertions or reproduce our results The Internet allows us to link things to other things quickly and easily Why bother with citations when we can use links? But there are still serious problems to address when it comes to linking data to the scientific record: Data persistence Data and metadata quality Attribution and credit for data producers and many more Data needs to be part of the scientific record (again)

13 No need to reinvent the wheel We already have a working method for linking between publications which is: commonly used understood by the research community used to create metrics to show how much of an impact something has (citation counts) applied to digital objects (digital versions of journal articles) We can extend citation to other things like: data code multimedia And the best bit is, researchers don t need to learn a new method of linking they cite like they normally would!

14 Too big! Not human readable! Needs metadata to make sense of it! Keeps getting updated/changed! Needs to be machine readable! Too complicated! Etc. etc. etc. But data are different from articles! Citation does work, but we need to be clear what it does for data (provides a link to the data version of record) It s not a magic bullet that will solve all our problems "Piled Higher and Deeper" by Jorge Cham

15 Recommended formats for dataset citation Need to tell researchers: Don t get hung up on it! There is no citation police Most of the time the repository should tell you how to cite the dataset on the catalogue page If they don t ask them! DataCite is discipline agnostic concerning matters pertaining to academic style sheet requirements /doc/DataCite-MetadataKernel_v2.2.pdf Applicable for subjects from astronomy to zoology (and more) DUTY CALLS