Lyubomir Penev, Vishwas Chavan, Gregor Hagedorn, Daniel Mietchen, Teodor Georgiev, David Roberts, Vincent Smith. 2011. Data publishing from the viewpoint of a biodiversity publisher. TDWG 2011 Annual Conference, Data Citation Workshop at the Astor Crown Plaza Hotel, New Orleans, Louisiana, USA. 16 - 21st October 2011.

1. Data citation from the
perspective of a scholarly
publisher
Lyubomir Penev
TDWG Data Citation Workshop, New Orleans, Oct 2011
ViBRANT

3. Why cite data and who is
interested in that?
 Data authors, data managers and their
institutions: get credit for their work
 Science managers: measure impact of
published data through citation metrics
 Publishers: drive visits and citations of data
to their journals and/or data repositories;
 End users of data: facilitate data discovery,
use and re-use
 Society in general: benefits from public
investments in data collecting and
maintenance

4. How to cite data and track
citations?
 By in-text citation combined with a
reference in the reference list (just as with
articles)
 By using DataCite (or other) DOIs either
within the text, or in the references (or both)
 By using other permanent identifiers at
dataset or single record level (= data
usage)
 A community agreed norm of a citation
format is needed!

5. The data citation policy of Dryad
(www.datadryad.org)
 Data citation in Dryad separated from the citation of the
scholarly publication
 Data authors can differ from the authors of the paper
the same data support
 Data usage statement recommended (e.g., Data
Resources section after the Material and Methods
section)
 Two components of data citation within a paper:
 An in-text citation statement containing an in-text reference pointer
that directs the reader to a formal data reference in the paper’s
reference list
 A formal data reference within the article‘s reference list.
 DataCite DOIs assigned to data files within a data
package for easy location and tracking

6. Three main types of data citation
 Citation of data firstly published within an
article they underpin
OR
 Citation of data previously published in
association with another article
OR
 Citation of data previously deposited in a data
repository but never published before in
association with an article

7. Citation of data firstly published within an article

8. Citation of data previously published in
association with an article

9. Citation of data previously deposited in a data
repository but never published before in association
with an article

10. Conclusions
 The culture of data publishing and data citing is
still to be developed and accepted by the
community
 Data Citation metrics lacking; Thomson-
Reuters approach to this still unclear!
 Data Citation and Data Usage are two sides of
the same process
 Hence, all interested parties (authors,
publishers, data repositories, institutions, end
users) should work together towards
establishing a stable data publishing and
citation landscape!