Citing DataCrossRef Workshop Day

November 12th 2013

Ed PentzExecutive Director

Joint Statement on

Linkability and Citability of

Research Data:1. Encourage authors of research

papers to deposit researcher validated data in trustworthy and reliable Data Archives.

2. Encourage Data Archives to enable bi-directional linking between datasets and publications using community endorsed unique persistent identifiers such as database accession codes and DOI's.

Joint Statement on

Linkability and Citability of

Research Data:3. DataCite and STM encourage

publishers to make visible or increase visibility of these links from publications to datasets.

4. Support the principle of data reuse and for this purpose actively participate in initiatives for best practice recommendations.

5. Datacite and STM invite other organizations involved in research data management to join and support this statement.

Lots of activityMany groups working on

similar documents, each from a

slightly different perspective

Data Citation Synthesis Group •36 members, ~ 20 organizations

•Weekly meetings for 3 months, led by Force 11

• Comparison of 4 sets of principles:

• Amsterdam Manifesto

• CoData Data Citation Principles

• DataCite Data Citation Principles

• Digital Data Center Data Citation Principles

•Merge them to create a synthesis set of principles - high level and simple

http://www.force11.org/node/4381

The CONSENSUS: 8 principles

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

•Where a specific claim rests upon data, the corresponding data citation should be provided.

4. UNIQUE IDENTIFIERS•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, and other materials, as are necessary for both humans and machines to make informed use of the referenced data.

6. Persistence

•Metadata describing the data, and unique identifiers should persist, even beyond the lifespan of the data they describe.

7. VERSIONING AND GRANULARITY

•Data citations should facilitate identification and access to different versions and/or subsets of data. Citations should include sufficient detail to verifiably link the citing work to the portion and version of data 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.

Kafkas Ş, Kim J-H, McEntyre JR (2013) Database Citation in Full Text Biomedical Articles. PLoS ONE.

Available: http://dx.doi.org/10.1371/journal.pone.0063184

Text-mining can be used to extend structured data citation, and could be a basis for the development of services to help

authors or editors to add structured content at the beginning of the publication process, rather than after the

fact.

Summary•Read STM/DataCite/CrossRef statement on

research data

•Work with appropriate data archives – consider integrating data deposit with manuscript submission – consider mandating data deposit where appropriate

•Instruct authors to cite data in the reference section and provide accession numbers and DataCite DOIs with manuscripts