Illustration inspired by the work of Chien Shiung Wu

データ引用の現状とこれまでの進展
The current trends and progress indata citation
JOSS, 27 May 2019
大場郁子

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現状と今後のトレンドについて

JOSS May 20192

データ引用とは何か?
• A reference made to data in the same way as researchers routinely provide abibliographic reference to journal articles and books• When public datasets have Digital Object Identifiers (DOIs), or equivalent identifiers,
it is the same as citing a journal article• For researchers (authors) data citation should be simple and straightforward• Include the minimum information recommended by DataCite and follow journal stylee.g. for Nature: authors, title, publisher (repository name), identifier, year
Creator (author)

Title

Publisher (repository name)Publication year

Identifier (DOI)

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研究者はデータを共有することによりクレジットが得られることを求めており、データ引用によりこれが可能となる

58% Think researchers do not get sufficient credit for sharing data
46% Would be motivated to share by “getting proper credit”
value a data citation as much as an article citation (decline on 201755% Wouldand 2016)

79% Would be willing to reuse open data in the futurethat re-use that resulted in credit as a co-author would motivate them
60% Note“quite a lot” or “a lot”
https://figshare.com/articles/Five_Essential_Factors_for_Da
ta_Sharing/7807949

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助成金団体は研究データとデータ引用を重要視している
UK concordat on open research data, endorsed by funding agencies including
Wellcome Trust and the UK research councils: “The obligation to recognisethrough citation and acknowledgement the original creators of the data must
be respected”
https://www.ukri.org/files/legacy/documents/concordatonopenresearchdatapdf
National Science Foundation (US) “For all new grant applications from 14
January [2013], the US National Science Foundation (NSF) asks a principal
investigator to list his or her research “products” rather than “publications” inthe biographical sketch section. This means that, according to the NSF, ascientist’s worth is not dependent solely on publications. Data sets, softwareand other non-traditional research products will count too.”
http://www.nature.com/nature/journal/v493/n7431/full/493159a.html

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ジャーナルのデータポリシーによりデータ引用を推奨• Nearly 1,700 (~70%) Springer Nature journals now have a policy• Four types of policy, which increase in their stringency frombut all promote data citation in instructions to authors• All policies and journals promote data citation in Information forauthors• Similar data policy initiatives since introduced at Elsevier2,Wiley3, Taylor & Francis4 and BMJ group
1. Standardising and harmonising research data policy in scholarly publishing
Iain Hrynaszkiewicz, Aliaksandr Birukou, Mathias Astell, Sowmya Swaminathan, Amye Kenall, Varsha Khodiyar
International Journal of Digital Curation; doi: https://doi.org/10.2218/ijdc.v12i1.531
2. https://www.elsevier.com/authors/author-services/research-data/data-guidelines
3. https://authorservices.wiley.com/author-resources/Journal-Authors/licensing-open-access/open-access/data-sharing.html
4. https://authorservices.taylorandfrancis.com/understanding-our-data-sharing-policies/6

ジャーナルポリシー事例：ネイチャー・リサーチ

https://www.nature.com/documents/nr-data-availability-statements-data-citations.pdf

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論文内のデータ引用事例
Paper published in
Nature (https://doi.org/10.1038/nature23654)

Dataset published in the Springer
Nature figshare repository(https://doi.org/10.6084/m9.figshare.c.3814360)

Dataset cited in Dataavailability statement(Reference #43)

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ジャーナルのポリシーによりデータ引用は増えているのか？
•According to datafrom DataCite andCrossRef, yes•

But progress has
been slow and onlya small proportionof papers include
data citations in thereference lists•

More commonly
and traditionally,
data are referenced
in the main text
rather thanreference list

Image credit: Kristian Garza and Martin Fenner https://doi.org/10.5438/h16y-3d72 available under CC BY

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データ引用の認知度を向上するための取り組み

JOSS May 2019

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業界・コミュニティ内でジャーナルのデータ・引用ポリシーを標準化するための取り組み
Co-chairs:

Natasha Simons (ANDS), Simon Goudie (Wiley), Azhar
Hussain (Jisc), Iain Hrynaszkiewicz (Springer Nature)• A group in the Research Data Alliancehas defined a common framework forresearch data policy for all journals andpublishers• Includes support for data citation as amandatory feature in all policies
https://www.rd-alliance.org/groups/data-policy-standardisation-and-implementation

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大規模な機械可読データ引用を実現するための出版社間コラボレーション• Working group of publishers defined a
Publishers' Data Citation Roadmap to
aid in implementation of data citation inarticles and books• Document covers policy, author
guidelines and content production andtechnical issues• Many publishers collaborating withinfrastructure providers (repositories,DataCite, CrossRef) and policy makers
to take a common approach• An aim is to keep the process simple forresearchers (authors) while delivering
benefits of better measurement ofcredit and reuse of data by improving
content (XML) structure
A Data Citation Roadmap for Scientific Publishers. Helena Cousijn, Amye Kenall, Emma Ganley, Melissa Harrison, David Kernohan, Fiona
Murphy, Patrick Polischuk, Maryann Martone, Timothy Clark
Scientific Data volume5, Article number: 180259 (2018)https://www.force11.org/group/dcip/eg3publisherearlyadopters12

出版社によるデータ引用導入のタイムライン

Meanwhile, researchers are encouraged to cite in their reference lists
any publicly available datasets they generate or analyse in theirstudies

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データ引用の最先端事例： Scientific Data
Part of the Nature ResearchGroup, Scientific Data is an openaccess journal for descriptions ofscientifically valuable datasets.
• Broad scope covering physical,
life and quantitative socialsciences• Data-focused peer-review
process• Supports community datarepositories• Integrated submission of datato general repositories• Data deposition and data
citation are mandatory forsubmission14

データ引用のベストプラクティス事例：Scientific Data
• Data citations are both human readable (pictured) andmachine readable• Publisher XML includes a specific reference item
type/tag to indicate which references are for datasets,
rather than papers/articles/chapters/websites• Enables downstream delivery and consumption ofdata citations and links, and enhancement to thereader experience with data links, for example• Helps users, such as institutions, infrastructure
providers, researchers understand and measure datareuse

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データー論文間リンクを向上するために、出版社と基盤提供者が協力• Implementaiton of the Scholix
guidelines enables connections
between articles and datasets acrosspublishers and repositories.
• Article-data links should beexchanged in a dynamic andstandardized way, enhancing
discoverability of all contributors’content.
• Information about these links isstored in open hubs (e.g. Crossref,DataCite) so other systems can
extract relevant connections.
• Can be used to enhance functionalityof literature databases e.g. EU
PubMed Central• http://www.scholix.org/guidelines

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ご清聴ありがとうございました。
大場郁子
ikuko.oba@nature.com
シュプリンガー・ネイチャーにおける研究データサポートやその他の取り組みについては、以下の窓口までお問い合わせください。

The story behind the image
Chien Shiung Wu (1912–1997)
Chien Shiung Wu was a Chinese American experimental
physicist best known for conducting The Wu experimentthat bears her name. This experiment showed that theconservation of parity was violated by a weak interaction
and it was possible to distinguish between a mirrored
variation of the world and the mirror image of the current
world. This discovery earned Wu the Wolf Prize in Physics
in 1978.

Email: researchdata@springernature.com
Website: http://go.nature.com/ResearchDataServices
Slide acknowledgements:
Rebecca Grant, Varsha Khodiyar, Iain Hrynaszkiewicz
This presentation is licensed as CC-BY-ND