Download to read offline
![3. Existing modes of recognition – Journal Policies
» ”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](https://image.slidesharecdn.com/1706byorkchriskeene-170628125054/85/Recognising-data-sharing-13-320.jpg)
Uploaded byJisc RDM
Recognising data sharing
1. Metrics are being developed to track downloads and reuse of research data to understand impact and reassure researchers. A new service called IRUS for Data will provide metrics for data repositories across different platforms. 2. There is debate around what data citations mean and how they should be used and understood. Projects are working to develop best practices and encourage responsible use of citation metrics for data. 3. Ensuring research data sharing is recognized in existing systems like journal policies is challenging due to lack of standards. Initiatives are working with publishers and repositories to develop guidance and implement principles for data citation.
More Related Content
Similar to Recognising data sharing
Recognising data sharing
- 2. Chris Keene –Jisc Iain Hrynaszkiewicz – Springer Nature Recognising Data Sharing
- 3. Recognition Slide: M Humphries;Photo: C Ingram
- 4. 3 key strands »1. Data download metrics »2. Critically engaging with the debates around data citation » 3. Ensuring that data sharing is recognised within existing modes of recognition
- 5. 1. Data DownloadMetrics » There is a demand for an accurate understanding of how data is reused and at what level. » This supports the development of RDM services and curation and storage planning. » It also reassures researchers that their shared data is being accessed and used. » Research data downloads need to be analysed in different ways to article downloads.
- 6. From IRUS-UK… » OffersCOUNTER compliant download metrics for institutional repositories » 127 institutions use this popular Jisc service, users are very positive about it » stable and scalable architecture » Connects to common article repository systems.
- 7. … to IRUSfor Data » Ability to return statistics at file and item levels » Connectability to data specific and bespoke repositories » COUNTER for data » More modern back-end architecture » A fully featured API » A new, API driven, visual user interface » 20+ test sites, using multiple platforms On the way On the way On the way
- 8. 24 live testsites… 28/06/2017 Jisc RDN Recogising data sharing Repository Software Platform Aston Data Explorer Eprints Brighton Research Data Eprints Cranfield figshare figshare Apollo – University of Cambridge Repositiry DSpace data.bris Research Data Repository CKAN DataSTORRE: Stirling Online Repository for Research Data DSpace Edinburgh DataShare DSpace Lincoln Repository Eprints Loughborough figshare figshare Loughborough University Institutional Repository DSpace LSE Research Online Eprints LSHTM Data Compass Eprints Open Research Exeter (ORE) DSpace Research Data Leeds Eprints Salford figshare figshare Sheffield figshare figshare UK Data Service – ReShare Eprints University of Bath Research Data Archive Eprints University of Birmingham ePapers Repository Eprints University of Huddersfield Repository Eprints University of Hull, Hydra Fedora University of Reading Research Data Archive Eprints University of Southampton - ePrints Soton Eprints WRAP: Warwick Research Archive Portal Eprints … 5 platforms (and counting)
- 9. We don’t fundamentallyunderstand what citations mean, a problem that as a community we tend to brush under the carpet. This is a problem because it turns out they actually mean nothing at all… - Cameron Neylon http://repository.jisc.ac.uk/6553/ 2. Critically engaging with data citation 28/06/2017 Jisc RDN Recogising data sharing
- 10. The issues » Citationmetrics are widely used for purposes for which they have not been designed » Understanding of the limitations of citation metrics, especially amongst managers, is poor. » Dominant citation metrics are not transparent. 28/06/2017 Jisc RDN Recogising data sharing
- 11. And… » these issuesalready apply to “traditional” citations. » How can we be sure these do not apply to the emerging world of data citations?
- 12. Open Metrics projectaims 28/06/2017 To encourage and facilitate experiments around alternative research metrics To highlight and promote the benefits of the responsible use of research metrics. Jisc RDN Recogising data sharing
- 13. 3. Existing modesof recognition – Journal Policies » ”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
- 14. The story sofar… Initial Aim To develop templates and guidance for journal publishers around research data policies future Looking at how Sherpa can include information to support data policies, and other information to support open science
- 15. Collaboration »Linked to ongoingNIH-supported BioCaddie project, hosted by the Force11Collaboration »Aimed at developing an implementation pathway for the JDDCP principles… The Data Citation Implementation Pilot (DCIP) 28/06/2017 Jisc RDN Recogising data sharing
- 16. The Joint Declarationof 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)
- 17. The work ofthe 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 (JournalArticleTag Suite) https://www.force11.org/group/dcip
- 18. DCIP EG3 Outputs »“Authors can be encouraged to cite data or can be required to cite data. Authors should provide details of previously published major datasets used and also major datasets generated by the work of the paper.The policy should specify which datasets to cite (e.g., underlying data versus relevant data not used for analysis) and how to format data citations. It is recommended if at all possible that data citation occurs either in the standard reference list or (less preferable) in a separate list of cited data, formatted similarly to standard literature references. But regardless of where citations appear in the manuscript, they should be in readily parsable form.” » A preprint of “Data Citation Roadmap for Scientific Publishers” is available on BioRxiv: https://doi.org/10.1101/100784
- 19. Recognising data sharing »Datausage metrics »How should data be cited »Exploring new scholarly metrics and indicators, including data citation »Funder and journal policies
Editor's Notes
- #3 Iain, Journal policies Springer Nature, and RDA group: data policy standardisation and implementation.
- #4 recognition of compliance (funder policies etc) and recognition of use (downloads, citation, altmetrics...) Mark Humphries
- #14 Jisc Journal Research Data Policy Registry Data v supplementary material Sherpa software functionality Looking at how it can incorporate data, software equipment etc for open science.
- #16 A set of initiatives have developed at the community level. Other relevant initiatives include the NISO Altmetrics Initiative which touches on some of the same issues, the Making Data Count project (a collaboration of PLOS, California Digital Library, and DataONE), and work at Europe Pubmed Central on identifying references to biomedical data resources in the full text of scholarly literature. The FORCE11 has been awarded supplemental funding as part of the NIH BD2K bioCADDIE project to extend the work of the Data Citation Implementation Group by organizing a Data Citation Implementation Pilot (DCIP). Members of this FORCE11 community have been participating in NIH meetings and bioCADDIE workshops and contributed substantial materials to the bioCADDIE white paper outlining the vision for a Data Discovery Index produced by bioCADDIE. Concrete plans were formulated by members of the community to conduct a pilot project on data citation with international partners based on the FORCE2015 bioCADDIE data citation workshop and the joint Elixr-BD2K workshop held in January. At both of these workshops, significant support was expressed for testing the proposed implementation of the Joint Declaration of Data Citation Principles (JDDCP), developed by the FORCE11 Data Citation Implementation Group. The joint Elixr-BD2K workshop recommended a data citation pilot project as one of two outcomes of the meeting.
- #17 A set of principles for Data Citation arose from a Birds of Feather session at the Beyond the PDF meeting in Amsterdam in 2013. The initial set of principles that came out of this session were circulated and a broader group convened through the FORCE11 organisation to harmonise a broader set of recommendations from the many activities occurring in this space. The Data Citation principles have been endorsed by 107 organisations and 236 individuals making them the best consensus statement on data citation.
- #20 This article presents a practical roadmap for scholarly publishers to implement data citation in accordance with the Joint Declaration of Data Citation Principles (JDDCP), a synopsis and harmonization of the recommendations of major science policy bodies. It was developed by the Publishers Early Adopters Expert Group as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH BioCADDIE program. The structure of the roadmap presented here follows the 'life of a paper' workflow and includes the categories Pre-submission, Submission, Production, and Publication. The roadmap is intended to be publisher-agnostic so that all publishers can use this as a starting point when implementing JDDCP-compliant data citation.
- #21 This article presents a practical roadmap for scholarly publishers to implement data citation in accordance with the Joint Declaration of Data Citation Principles (JDDCP), a synopsis and harmonization of the recommendations of major science policy bodies. It was developed by the Publishers Early Adopters Expert Group as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH BioCADDIE program. The structure of the roadmap presented here follows the 'life of a paper' workflow and includes the categories Pre-submission, Submission, Production, and Publication. The roadmap is intended to be publisher-agnostic so that all publishers can use this as a starting point when implementing JDDCP-compliant data citation.