LIBER Webinar: A Data Citation Roadmap for Scholarly Data Repositories
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1 LIBER Webinar: A Data Citation Roadmap for Scholarly Data Repositories Martin Fenner (DataCite) Mercè Crosas (Institute for Quantiative Social Science, Harvard University) May 15, 2017
2 2014 Joint Declaration of Data Citation Principles JDDCP endorsed by over 100 scholarly organizations
3 2015 Direct deposition and citation of primary research data
4 2016 Data Citation Implementation Pilot (DCIP) Led by Tim Clark, Maryann Martone, Jeffrey Grethe
5 Participants And you!
6 Data Citation Generic Example! example of a data cita.on as it would appear in a reference list* Principle 2: Credit and A1ribu4on (e.g. authors and data repositories). *Note that the format is not intended to be defined with this example, as formats will vary across publishers and communi.es [Principle 8: Interoperability and flexibility]. Author(s), Publica.on Date, Title, Data Repository or Archive, [Dataset], Persistent Iden.fier, Version Principle 4: Unique Iden4fier (e.g. DOI, Handle). Principle 5, 6 Access, Persistence: A persistent link to a landing page with metadata and access informa4on. Principle 7: Version and granularity (e.g. a version number or 4mestamp) In addi4on, access to versions or subsets should be available from the landing page.
7 Roadmap for! Scientific Publishers! Participants: Elsevier, SpringerNature,! elife, PLOS, Frontiers, Wiley, and others! Elsevier SpringerNature In-person Workshop July 22, 2016 in London, hosted by SpringerNature, continuing work via Telcons.! Roadmap as a set of actions for different steps of research article: pre-submission, submission, production, and publication.! Cousijn, H., Kenall, A., Ganley, E., Harrison, M., Kernohan, D., Murphy, F., Clark, T. (2017). A Data Citation Roadmap for Scientific Publishers, BiorXiv.
8 Publishers Roadmap! Specify how to format Data Citations!! There are several ways data can be linked from ( cited in) scholarly articles:!! reference lists,! data availability statements and! in-text mention of accession numbers.! While a globally unique, machine actionable persistent identifier is needed for all three scenarios, citation metadata (authors, title, publication date, etc.) are specifically recommended for reference lists.!! From:
9 Leads: Martin Fenner (DataCite), Merce Crosas (Dataverse)! Christian Haselgrove Philipe Rocca-Serra Ian Fore Roadmap for Data Repositories! Andy Jenkinson
10 Repositories Roadmap! Approach & Status! Focus on how data repositories can provide metadata required for data citation to manuscript authors and publishers. Data repository landing page plays central role.!! In-person workshop June 22, 2016 in San Diego.!! Eleven recommendations published in December Now collecting feedback and endorsements for implementation.!! Fenner, M., Crosas, M., Grethe, J., Kennedy, D., Hermjakob, H., Rocca-Serra, P., Clark, T. (2016). A Data Citation Roadmap for Scholarly Data Repositories, BiorXiv. doi.org/ /097196!
11 Repositories Roadmap! Building Blocks! Persistent Identifier Landing Page Documentation and Support Metadata schema.org
12 Recommendations: Required! 1. All datasets intended for citation must have a globally unique persistent identifier that can be expressed as unambiguous URL.! 2. Persistent identifiers for datasets must support multiple levels of granularity, where appropriate.! 3. This persistent identifier expressed as URL must resolve to a landing page specific for that dataset.! 4. The persistent identifier must be embedded in the landing page in machine-readable format.! 5. The repository must provide documentation and support for data citation.!
13 Globally Unique! Persistent Identifier! Persistent method for identification: Metadata must persist even beyond the data it describes! Machine actionable: PID resolvable as an HTTP URI! Globally Unique: Must use a prefix if ID only unique within a database! Widely used by a community: For example, in life sciences accession numbers (not DOIs) are widely used.!
14 ! Multiple Levels of Granularity! Support citation of a specific version, as well as citation of unspecified version! In some cases, data is uniquely identified as a collection of many items (example in next slide)!
15
16 Persistent Identifier resolves to! Landing Page! Article Landing Page Data Expectation is that by default the persistent identifier as URL resolves to a human- and machine-readable page with more information. Use content negotiation to resolve the persistent identifier as URL to machine-readable metadata, or to the content itself.
17 Persistent Identifier embedded in the Landing Page! Bibliographic Repository Data Repository Article 1 Landing Page 2 3 Data Finding citation a Data reference PID c b Human readable metadata Machine readable metadata article PID citation metadata data PID d Human Readable Machine Readable <meta name="dc.identifier" content= " />
18 Documentation and Support! The repository must provide documentation about how data should be cited, how metadata can be obtained, and who to contact for more information.! The DCIP FAQ Expert Group has generated example documentation for data repositories, which will be provided on a dedicated website.!
19 Recommendations: Recommended! 6. The landing page should include metadata required for citation and ideally also metadata helping with discovery in human-readable and machine-readable format.! 7. The machine-readable metadata should use schema.org markup in JSON-LD format.! 8. Metadata should also be made available via HTML meta tags to facilitate use by reference managers.! 9. Metadata may be made available for download in Bibtex or other standard bibliographic format.!
20 Citation Metadata!
21 Metadata on Landing Pages:! For Humans!
22 Metadata on Landing Pages: Machine-Readable! The landing page should include metadata required for citation and ideally also metadata helping with discovery. Important for persistent identifiers that don't provide citation metadata in a central searchable index. Less important if metadata also available elsewhere in standardized workflow, e.g. DOIs. Use by reference managers to fetch citation metadata. Used by publishers to validate data citations. Used by indexers for discovery.
23 Metadata on Landing Pages: schema.org! Schema.org is community activity to promote structured data on the internet, started in 2011 by Google, Microsoft, Yahoo, and Yandex. Schema.org can be displayed as microdata or RDFa embedded in HTML, or via JSON-LD. The expert group recommends JSON-LD as the preferred format. Citation metadata are fully supported by schema.org (see earlier citation metadata table), several groups are extending support for more specialized metadata, including in the life sciences. DataCite provides citation metadata in schema.org/json-ld format via DOI content negotiation.
24 Schema.org Example! Available via DOI Content Negotiation curl -LH "Accept: application/vnd.schemaorg.ld+json" c6h02x { "@context": " "@type": "Dataset", "@id": " "additionaltype": "Imaging Data", "name": "Image collection /C6H02X", "url": " "author": [{ "@type": "Person", "givenname": "JL", "familyname": "Breeze" },...
25 Metadata on Landing Pages:! HTML Meta Tags!
26 Recommendations: Optional! 9. Content negotiation for schema.org/json-ld and other content types may be supported so that the persistent identifier expressed as URL resolves directly to machinereadable metadata.! 10. HTTP link headers may be supported to advertise content negotiation options!
27 Content Negotiation for Machine Readable Metadata
28 HTTP Link Headers
29 Metadata in Standard Bibliographic Format
30 Tracking Data Citations The Research Data Alliance (RDA) Scholarly Link Exchange Working Group (Scholix) is working on a framework for standardizing the exchange of article/ data links between scholarly infrastructure providers. The group has broad community participation from repositories, publishers and service providers. If you want to participate please contact one of the co-chairs Adrian Burton (ANDS), Wouter Haak (Elsevier), Paolo Manghi (OpenAIRE), or Martin Fenner (DataCite).
31 Next Steps We are in the process of collecting community feedback regarding the recommendations. We have started to work with data repositories on endorsement and implementation of the recommendations. We will publish an updated recommendations paper incorporating feedback, endorsements and sample implementations.
32 Thanks Martin Fenner & Mercè Crosas DCIP Executive Team Tim Clark, Harvard Medical School & MGH (co-chair), Maryann Martone, Hypothesis & UCSD (co-chair), Carole Goble, University of Manchester & ELIXIR, Jeffrey Grethe, UCSD & biocaddie, Jo McEntyre, EMBL-EBI & ELIXIR, Joan Starr, California Digital Library, Martin Fenner, DataCite, Simon Hodson, CODATA, Chun-Nan Hsu, UCSD Special thanks to Tim Clark for his contributions to these slides
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