data citation metrics : best practice to enable new metrics for research data

DATA CITATION METRICS BEST PRACTICE TO ENABLE NEW METRICS FOR RESEARCH DATA NIGEL ROBINSON GFII - DECEMBER 2015

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OVERVIEW

1 Why metrics for data?

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Challenges & best practice

Delivering metrics with the Data Citation IndexSM

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DEFINITIONS

Data • Facts collected for reference or analysis. • Non traditional scholarly output of scientific

research often analysed in traditional research publications. May include numerical, textual, image, video or software information

Data repository • An online resource where data are deposited

and stored for preservation and access

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CITATION IS STILL THE METRIC OF CHOICE

• Surveys – John Kratz & Carly Strasser - California Digital Library

Making Data Count. Scientific Data http://dx.doi.org/10.1038/sdata.2015.39

Future • Citations • Downloads • Altmetrics/”Anything and everything” • Peer review/community feedback • Use outside scholarly literature (e.g.

patents) • Reuse/”actual use”

Current

Sarah Callaghan – RDA Bibliometrics working group

http://dx.doi.org/10.1038/sdata.2015.39

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1400 repositories identified:

• Editorial Content - ensuring that material is desirable to the research community.

• Persistence and stability of the repository, with a steady flow of new information.

• Thoroughness and detail of descriptive information.

• Links from data to research literature.

REPOSITORY SELECTION & EVALUATION

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TYPES OF DATA BY DISCIPLINE

ART & HUMANITIES

CULTURAL HERITAGE

LANGUAGE CORPUS

IMAGE COLLECTIONS

RECORDINGS

SOCIAL SCIENCES

POLL DATA

ECONOMIC STATISTICS

LONGITUDINAL DATA

NATIONAL CENSUS

PUBLIC OPINION SURVEYS

SCIENCE & TECHNOLOGY MAPS ALGORITHMS GENOMICS SKY SURVEYS ASTROPHYSICS REMOTE SENSING MUSEUM SPECIMENS SOFTWARE

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Commentaires de présentation

Types of data included range across the broad scientific disciplines which may be familiar form the Web of Science Core Collection and other TR literature databases

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DISCOVERY & CITATION • Seed data

– 5M data records from over 300 repositories – Around 3M citations

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DCI has now gained ~5m data records with citations gathered from the repository depositions and curation. The focus is now to gain data citations directly from the literature. We are capturing formally cited data objects from the bibliographies and will link those with the data objects in the Data Citation Index; but will also harvest informal data citations from the full text of the article.

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DATA CITATION CHALLENGES Current citation style (in full text of article as informal citations)

Desired/future citation style (as formally cited references)

U.S. Dept. of Justice, Bureau of Justice Statistics (1996): MURDER CASES IN 33 LARGE URBAN COUNTIES IN THE UNITED STATES, 1988. Version 1. Inter-university Consortium for Political and Social Research. http://dx.doi.org/10.3886/ICPSR09907.v1

Lee, Seung-Jae; Lee, He-Jin; Cho, Ji-Hoon; Rho, Sangchul; Hwang, Daehee (2008): GSE11574: The responses of astrocytes stimulated by extracellular a-synuclein. Gene Expression Omnibus. http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11574

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Currently, most data citations are informal mentions in the full text of the article. This is different to how literature is cited with a structured bibliography. While a number of standards and recommendations are proposed by bodies such as the Research Data Alliance, Force 11 group and others, the uptake of formal data citation is in its infancy so we need to develop a means to gather data citations from the text until such time as a more formal citation is commonplace. [FYI Likely 5 years – according to some experts]

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BEST PRACTICES FOR PARTICIPATION AND CITATION • Authors and Researchers

• Repositories, Data publishers, Data providers

• Literature publishers and Funders

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BEST PRACTICE FOR RESEARCHER/DATA AUTHOR

• Treat data equally with other citable research output

• Deposit data in an established data repository committed to long term preservation

• Practice detailed, formal data citation in data and publications

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BEST PRACTICE FOR RESEARCHER/ DATA AUTHOR

• Unique ID in

repository • Date provided • Author • Repository • URL/DOI • Title • Year Published

• Allows creation of a data citation using DataCite guidelines

• Compliance with DataCite Metadata schema

• Allows matching of data citations encountered to known data records

• Provide metadata to enable citation and discovery

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BEST PRACTICE FOR REPOSITORY / DATA PUBLISHER / DATA PROVIDER • Curate and validate

metadata for completeness, accuracy, attribution and consistency

• Issue permanent IDs for data objects

• Provide unique landing pages for data objects

• Establish update and versioning policies

REUTERS/Kim Hong-Ji

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LITERATURE PUBLISHER & FUNDING ORGANIZATION

• Establish clear data management plan policies and guidelines for data deposition

• Develop formal data citation policies and clear guidance for authors on citation formats

• Enforce requirements for data deposition and citation

• Establish metadata criteria for persistent and unique identification of cited data

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Présentateur


As there are no well defined standards from data citation which are commonly in use, DCI provides a data citation for every data object, following the DataCite recommendations (http://www.datacite.org/whycitedata)

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Associate articles with the data they use to provide more complete discovery of scientific output. With discovery comes reuse and citation and recognition

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Link out directly to the original item, in this case

a Data Study.

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Each data object is linked to the repository location of the data to allow the user to see the repository record and download the data

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Présentateur


Data Citation is an important feature of DCI. We have a base level citation count and have built the links needed to provide data citations as we gather than from the literature. This should be very familiar in use to those who use citations in WOS

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BENEFITS AND VALUE

Data Repository

Data Object metadata

Web of Science DCI

Publisher

Publication

Researcher

Discovery Metrics Value

Citation count API Metrics Evaluation

Citation count API Metrics Evaluation

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Through powerful Web of Science platform discovery capabilities the Data Citation Index will expose important research data and drive attribution , access and reuse. Discovery of data most important to scholarly research Data linked to published research literature Measures of data citation, use and reuse with attribution assisted by identifiers New metrics for digital scholarship

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THANK YOU

White paper on best practice & further information http://wokinfo.com/products_tools/multidisciplinary/dci/

Nigel Robinson [email protected]

http://wokinfo.com/products_tools/multidisciplinary/dci/

data citation metrics : best practice to enable new metrics for research data

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