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Standardising research data policies, research data network

Research data network, September 2016, David Kernohan, Jisc.

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Standardising research data policies, research data network

  1. 1. Standardising Research Data Policies David Kernohan Image: dkernohan (cc-by)
  2. 2. The story so far… » Based on a previous investigation we wanted to look at the viability of a registry service for journal data policies… »… in future this may complement the information available via our SHERPA services. »We convened an expert group with international representation to support the project. 14/09/2016 Jisc Journal Research Data Policy Registry
  3. 3. The story so far… » ”The prototype that could be built would not be an authoritative source of information for researchers or support staff as it would not contain the information required at the level of data type […]To answer the question set using the sources available, a high degree of subjectivity and interpretation had to be applied as there were very few standard terms or definitions. Interpretation of policy was often best undertaken at the domain level, which further compounded the problems of building a scalable, generic database to codify the information.” (Naughton and Kernohan, 2016) Article in “UKSG Insights”: http://doi.org/10.1629/uksg.28
  4. 4. The story so far… Initial Aim To develop templates and guidance for journal publishers around research data policies Long-term aim To revisit the idea of a policy registry, including an investigation of the potential for machine-readable licence based solutions
  5. 5. Collaboration »Linked to ongoing NIH-supported BioCaddie project, hosted by the Force11 Collaboration »Aimed at developing an implementation pathway for the JDDCP principles… The Data Citation Implementation Pilot (DCIP) 14/09/2016 Jisc Journal Research Data Policy Registry
  6. 6. The Joint Declaration of Data Citation Principles (JDDCP) 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 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 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 andVerifiability 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 verfiying 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 https://www.force11.org/group/joint-declaration-data-citation- principles-final (Martone (ed), 2014) 14/09/2016 Jisc Journal Research Data Policy Registry
  7. 7. The work of the DCIP » Five “expert groups” › EG1: FAQs/Documentation – developing documentation/guidance aimed at publishers › EG2: Identifiers › EG3: Publisher Early Adopters– mapping the publication process as it pertains to data. › EG4: Repository Early Adopters – focus on machine readable landing pages and metadata sharing › EG5: JATS (Journal ArticleTag Suite) 14/09/2016 Jisc Journal Research Data Policy Registry
  8. 8. Publisher early adopters group 14/09/2016 Jisc Journal Research Data Policy Registry Suite of standardised policy summaries? Display data availability statement? Data capture improvements? Correct XML/JATS tagging? Consistent [data] tag? Author guidance & clear policiesAuthor submission Review Production Publication Editorial guidance Reviewer guidance Standardised mark-up and formatting Clear, consistent display in pdf and html versions Data availability to reviewers?
  9. 9. Towards policy standardisation Springer Nature Data PolicyTypes 14/09/2016 Jisc Journal Research Data Policy Registry PolicyType Policy Summary Example Journal Type 1 Data sharing and data citation is encouraged. Photosynthesis Research (Instructions for Authors) Type 2 Data sharing and evidence of data sharing encouraged. Plant and Soil (Instructions for Authors) Type 3 Data sharing encouraged and statements of data availability required. PalgraveCommunications (Editorial policies) Type 4 Data sharing, evidence of data sharing and peer review of data required. Scientific Data (Data policies) From: http://www.springernature.com/gp/group/data-policy/policy-types
  10. 10. The Springer Approach (1) » Goal is to eventually see all 3,000+ Springer Nature journals conform to one of the four data policy types. » Currently 350 journals, aiming at 1,000 by the end of 2016. » Slow, journal-specific process in collaboration with editorial teams. Started with “easy” journals where there is already interest in data. (source: informal discussion with Iain H at Springer) 14/09/2016 Jisc Journal Research Data Policy Registry
  11. 11. The springer approach (2) 14/09/2016 Jisc Journal Research Data Policy Registry Encourage data sharing via public repository List of repositories Advice and support 1 Sharing with researchers required Availability statement encouraged Public sharing recommended List of repositories Advice and support 2 Sharing with researchers required Availability statement required Public sharing recommended List of repositories Advice and support 3 Public sharing required Availability statement required Public sharing recommended Data included in peer review List of repositories Advice and support 4
  12. 12. The springer approach (3) » Based on Scientific Data journal list. » “In general, data should be submitted to discipline- specific, community-recognised repositories where possible, or to generalist repositories if no suitable community resource is available” »Available on figshare for reuse with attribution: https://figshare.com/articles/Scientific_Data_recommended_repositories_June_2015/1434640 The standard list of repositories 14/09/2016 Jisc Journal Research Data Policy Registry
  13. 13. Key questions » Does this approach help you? (as a research manager/ researcher/ information professional) »What other information would you need to quickly understand the requirements of a given journal? » Are there other ideas that we should be considering? For discussion: 14/09/2016 Jisc Journal Research Data Policy Registry
  14. 14. Follow the project » blog: https://journalrdregistry.jiscinvolve.org » webpage: http://www.jisc.ac.uk/rd/projects/journal-research-data-policy-registry-pilot (all images fromWikipedia page on the Julia Set: https://en.wikipedia.org/wiki/Julia_set )

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Research data network, September 2016, David Kernohan, Jisc.

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