Data Citation principles & the attributes that underpin the principles · 2015-12-15 · • The Dataverse Network • National Information Standards Organization (NISO) • Creative

Data Citation principles & the attributes that underpin the principles

Dr Martie van Deventer

Portfolio Manager: CSIR Information Services

CODATA Data Citation Task Team Member

Data citation as a catalyst for good RDM practices Pretoria, South Africa

10 December 2015

Roadmap

• Background to the task team work

• Process followed

• Outcomes

• Principles developed

• What next?

http://www.census.gov/population/cen2000/map02.gif

ncl.ucar.edu

http://onlineqda.hud.ac.uk/Intro_QDA/Examples_of_Qualitative_Data.php

Marie Curie’s notebook aip.org

hudsonalpha.org

NASA Astronomy Picture of the Day

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What are research data?

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Scale: 1:2695194 at Latitude 0°

Source: Baltic Sea Research Institute, Warnemünde.

• Earth quake events => doi:10.1594/GFZ.GEOFON.gfz2009kciu

• Climate models => doi:10.1594/WDCC/dphase_mpeps

• Sea bed photos => doi:10.1594/PANGAEA.757741

• Distributes samples => doi:10.1594/PANGAEA.51749

• Medical case studies => doi:10.1594/eaacinet2007/CR/5-270407

• Computational model => doi:10.4225/02/4E9F69C011BC8

• Audio record => doi:10.1594/PANGAEA.339110

• Videos => doi:10.3207/2959859860

More data ...

Anything that is the foundation

of further research

is regarded as research data.

Data is evidence.

... but why cite the data?

Scholars share data as a matter of course

because it is accepted practice and it is

credited

Data sharing and reuse transactions are high, making

credit, metrics and other assessment

around data effective

Data sharing and data reuse can be measured making it easy to credit

and reward

Recognition and reward: a positive feedback loop

See The Value of Research Data: Metrics for datasets from a cultural and technical point of view http://www.knowledge-exchange.info/datametrics and Knowledge Exchange Workshop: ‘Making Data Count’: http://www.knowledge-exchange.info/Default.aspx?ID=576

Accelerated scientific progress

Transparency – linked also to reproducibility of evidence

Efficiency – quicker to complete research, costing less

Faster to implement findings

ICSTI-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

Bonnie Carroll (U.S. CODATA and CENDI), President, Information International Associates, USA

Members:

Micah Altman, Massachusetts Institute of Technology, USA

Elizabeth Arnaud, Bioversity International, ITALY

# Christine Borgman, University of California, Los Angeles, USA

Todd Carpenter, National Information Standards Organization, USA

Dora Ann Lange Canhos, Environmental Reference Information Center, BRAZIL

Vishwas Chavan, Global Biodiversity Information Facility, DENMARK

Nathan Cunningham, British Antarctic Survey, UNITED KINGDOM

Michael Diepenbroek, World Data Center-MARE / PANGAEA, University Bremen, GERMANY

Puneet Kishor, Creative Commons., USA

John Helly, Scripps Institute for Oceanography and San Diego Supercomputing Center, University of California, USA

Jianhui LI, Chinese Academy of Sciences, CHINA

Brian McMahon, International Union of Crystallography, UNITED KINGDOM Karen Morgenroth, National Research Council Canada, CANADA Yasuhiro Murayama, National Institute of Information and Communications Technology, JAPAN 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, Council on Scientific and Industrial Research, SOUTH AFRICA John Wilbanks, Kaufman Foundation, USA Koji Zettsu, National Institute of Information and Communications Technology, JAPAN

Consultants:

Daniel Cohen, Library of Congress ,USA

Franciel Linares, Information International Associates, USA

Yvonne Socha, MLIS candidate, University of Tennessee, USA

Paul F. Uhlir, U.S. National Committee for CODATA and Board on Research Data and Information, National Academy of Sciences, USA

Membership changed in April 2013 – those changes are not reflected here

Task Group Objectives and Deliverables

• Conduct inventory and analysis of existing literature and existing data citation and attribution initiatives.

• Investigate and analyze how existing data repositories cite and provide attribution to their data sets.

• Identify and obtain input from stakeholders in the library, academic, publishing and research communities.

• Provide an international forum to identify and help reconcile the needs of various stakeholder communities.

• Share information and create greater awareness of these issues internationally.

• Establish a public web presence.

• Conduct meetings and workshops to articulate the state of the art and best practices in this area, and to identify emerging issues.

• Work with the major international, regional, and national standards organizations to develop formal data citation and attribution standards and best practices.

• Promote scientific data attribution by developing models, tools, and practical guidance on how to publish citable and trackable data sets.

Stakeholder consultation • Data centers/ repositories

• Research funders

• Researchers (also though professional societies)

• Publishers and editors

• Funders

Data Citation and Attribution

11 2012

• The importance of citation • Technical issues to consider • Financial issues … • Discipline issues (researcher

perspectives) … • Legal & socio-cultural issues … • Institutional perspectives (data

centres, libraries) • Data citation initiatives

Reviewed literature, conducted interviews & administered questionnaires during Jan-Apr 2012

Analysing what was already in circulation

Organizations that were 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-Associations

• Digital Curation Center, UK

Citation practice: SA 2012

• Very small sample!

• There is no standard practice but the research discipline appears to determine behaviour.

• Usually share their data when personal requests are received. Confidentiality often causes a barrier in sharing.

• Spatial data researchers appear more aware & often share/ use data sets. Others have not given the sharing of data much thought.

• Majority not aware that their data is being cited. They see citation as a sign of courtesy rather than mandatory.

• Majority not aware of existing standards & guidelines.

• Not interested in re-processing the data so that others could make use it.

• Some had used and cited data but the majority had not.

Data Citation and Attribution

15

Data Science Journal, Volume 12, 13 September 2013

2012

• Established that: • Relatively little reuse of

data in most areas • Reuse that was found

was data from curated repositories

• Researchers spend up to 80% of their time cleaning data to make them reusable

• Benefits and challenges of data citation were captured

• Open research questions & gaps identified

‘Article type’ citation is not enough

Task Team Citation Principles

Joint Data Citation Principles

Preamble Sound, reproducible scholarship rests upon a foundation of robust, accessible data. For this to be so in practice as well as theory, data must be accorded due importance in the practice of scholarship and in the enduring scholarly record. In other words, data should be considered legitimate, citable products of research. Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse.

In support of this assertion, and to encourage good practice, we offer a set of guiding principles for data within scholarly literature, another dataset, or any other research object.

The Data Citation Principles cover purpose, function and attributes of citations…

Joint Data Citation Principles

Purpose 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. 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.

Joint Data Citation Principles Attributes

6. Persistence. Unique identifiers, and metadata describing the data and its disposition, should persist -- even beyond the lifespan of the data they describe. 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 [8].

Endorse the Data Citation Principles

https://www.force11.org/datacitation/ endorsements

Generic Data Citation (as it appears in printed reference list)

Note: ● Neither the format nor specific required elements are intended to be defined with this

example. Formats, optional elements, and required elements will vary across publishers and communities. [Principle 8: Interoperability and flexibility].

● As illustrated in the previous examples, intra-work citations may be accompanied with information including the specific portion used. [Principles 7,8].

● As illustrated in the next example, printed citations should be accompanied by metadata that support credit, attribution, specificity, and verification. [Principles 2, 5 and 7].

Author(s), Year, Dataset Title, Data Repository or Archive, Version, Global Persistent Identifier

Principle 2: Credit and Attribution (e.g. authors, repositories or other distributors and contributors)

Principle 4: Unique Identifier (e.g. DOI, Handle.). Principle 5, 6 Access, Persistence: A persistent identifier that provides access and metadata

Principle 7: Specificity and verification (e.g. the specific version used). Versioning or timeslice information should be supplied with any updated or dynamic dataset.

Growing Adoption

https://www.force11.org/datacitation/endorsements

Joint Declaration of Data Citation Principles (Overview)

Citation Examples

Data citation – using different standards

Guides to Data Citation

DCC Guide ‘How to Cite Datasets and Link to Publications’: http://www.dcc.ac.uk/resources/how-guides/cite-datasets

ESRC-UKDA-Data Cite Guide ‘Data Citation: what you need to know’ https://www.ukdataservice.ac.uk/media/104397/data_citation_online.pdf

BL Workshop Series on Data Citation: http://www.bl.uk/aboutus/stratpolprog/digi/datasets/workshoparchive/archive.html

ANDS Series on Data Citation http://www.ands.org.au/training/data-citation.html

RDA Data Citation of Evolving Data: https://rd-alliance.org/system/files/documents/RDA-DC-Recommendations_150924.pdf

Data Citation: From Principles to Practice

CODATA Task Group on Data Citation ‘Data Citation: From Principles to Practice, A Focus on the Research Policy and Funding Community’: http://www.codata.org/task-groups/data-citation-standards-and-practices

Organising an international series of implementation and adoption workshops.

Promote the implementation of data citation principles in the research policy and funding communities throughout the world.

Continue to engage with stakeholders include: government, funders, research performing institutions, research administrators, research librarians, researchers, learned societies, publishers, data archives, journal editors …

What is the policy environment for data citation?

What are current attitudes to data citation?

What infrastructure currently exists to support data citation?

What specific plans for implementation were identified?

We are taking Data Citation workshops on a world tour! China… then Australia, Japan, India and South Africa. Plus: USA, Taipei, Korea, Indonesia, Brazil, EC, France, Israel…

Thank you for your attention!

Slide credits: Hodson, S. 2015. Data Citation: framing the discussion and global context. Referencing data in publications: principles, policy, and practice. Data citation workshop at the Australian Academy of Science, The Shine Dome, Canberra, 28 October 2015. Uhlir, P. & Cohen, D. 2012. Borgman, C. 2015. International Trends of Data Citation and Open Science - Data as the 1st Class Research Product. Data citation workshop at the National Institute of Informatics, in Hitotsubashi, Chiyoda-ward, Tokyo, Japan

Martie van Deventer CODATA Task Team: Data Citation

www.codata.org http://lists.codata.org/mailman/listinfo/codata-international_lists.codata.org

Email: [email protected] Skype: mvandeve1

Tel (Office): +27 12 841 3278| Tel (Cell): +27 82 924 6650

References & readings • 2012. For attribution: Developing data attribution and citation practices and standards. Available:

http://www.nap.edu/catalog/13564/for-attribution-developing-data-attribution-and-citation-practices-and-standards Accessed 4 December 2015

• Out of Cite Out Of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data (2013).

• These reports laid out the landscape of research data attribution and citation issues, practices, and policies. The group was then instrumental in convening an international synthesis group of organizations that cooperated in developing the Joint Data Citation Principles. You may want to have a look at these principles before the workshop.

• The CODATA Task Group activity has integrated with that of the Research Data Alliance. Their current interest is citing evolving/ dynamic data.

• Rodrigo Costas, Ingeborg Meijer, Zohreh Zahedi and Paul Wouters. 2013. The value of research data: Metrics for datasets from a cultural and technical point of view. Center for Science and Technology Studies (CWTS). Leiden University. p.8. Available: http://repository.jisc.ac.uk/6205/1/Value_of_Research_Data.pdf Accessed 09 Dec 2015