Data Exchange, Data Citation: An overview of some community work
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Data Exchange, Data Citation: An overview of some community work
- 1. Data Exchange, Data Citation: An overview of some community work Todd Carpenter Executive Director, NISO May 20, 2012 Council of Science Editors
- 2. National Information Standards Organization Non-profit industry trade association accredited by ANSI 110+ Organizational Members Divided roughly in thirds among publishers, libraries and support service providers Mission of developing and maintaining technical standards related to information, documentation, discovery and distribution of published materials and media Volunteer driven organization: 400+ contributors who May 20, 2012 CSE: Data Standards and Data Citation 1 Issues
- 3. Standards are familiar, even if you don‟t notice Image: DanTaylor Image: Joel Washing May 20, 2012 CSE: Data Standards and Data Citation 2 Issues
- 4. Data Publishing May 20, 2012 CSE: Data Standards and Data Citation 3 Issues
- 5. Large Scale Data-Driven Science Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. May 20, 2012 CSE: Data Standards and Data Citation 4 Issues
- 6. Science data isn‟t what it used to be Image: Walters Art Museum Image: Domenico, Caron, Davis, et al. May 20, 2012 CSE: Data Standards and Data Citation 5 Issues
- 7. Challenges of Data Publishing/Sharing Data – is massive in scale. – can be constantly changing. – is difficult to describe/catalog. – is often difficult to integrate with other data. – can be complex to analyze (tools required). – is hard to peer review. – is challenging to curate. – is challenging to preserve. May 20, 2012 CSE: Data Standards and Data Citation 6 Issues
- 8. Data – Complexities Storage Provenance Legal Issues Technical Interoperability Metadata Privacy Identification May Citation 20, 2012 CSE: Data Standards and Data Citation 7 Issues
- 9. Problem with data, e.g.: mutability The fact data sets are constantly changing poses the following problems (not exhaustive, BTW) – How does someone rerun your experiment, when the data set isn‟t now what it was when you ran your experiment – For citation – You have to cite the subset of the data that existed on XXX date when you ran your analysis – How can you preserve a snapshot of a dataset? – How do you describe the dataset at that point in time? – Problems of managing the metadata of each data deposit May 20, 2012 CSE: Data Standards and Data Citation 8 – Problems of synchronization across repositories Issues
- 10. Data Complexity – Provenance • How can you be certain that the data set you are looking at hasn‟t been altered or changed? • Can you trust the data source? • Has the data been updated/added to/or appended since the analysis was initially run • Dealing with issues of abstraction May 20, 2012 CSE: Data Standards and Data Citation Issues 9
- 11. Data Citation May 20, 2012 CSE: Data Standards and Data Citation 10 Issues
- 12. We all know what a citation looks like? Right? May 20, 2012 CSE: Data Standards and Data Citation 11 Issues
- 13. But what about citing a data set? Source: Citations for SEER Databases Source: International Polar Year Source: Global Land Cover Facility Source: The Economist Source: ICPSR May 20, 2012 CSE: Data Standards and Data Citation 12 Issues
- 14. CODATA Group on Data Citation • Approved by the CODATA 27th General Assembly in Cape Town in October, 2010 TASKS – Survey existing literature and existing data citation initiatives. – Obtain input from stakeholders in library, academic, publishing, and research communities. – Hold at least one meeting and a workshop to help establish a solid foundation of the state of the art and practices in this area. – Work with the ISO and major regional and national standards organizations to develop formal data citation standards and good practices. May 20, 2012 CSE: Data Standards and Data Citation 13 Issues
- 15. Issues Requiring Attention Technical Sustainability • Metadata Standards Identifiers Scientific • DOI, PURL, ARC • Publication and peer review Legal/Intellectual Institutional Property Rights • Promotion & Tenure • Accessibility and reuse rights • Reward infrastructure Socio-cultural Financial • Culture of citing data • Infrastructure support • Proper attribution May 20, 2012 CSE: Data Standards and Data Citation 14 Issues
- 16. Accomplishments so far • Compiled a significant bibliography of 200+ references • Conducted a survey for repository managers, publishers, and funding organizations. – More than 60 narrative responses • Upcoming meeting in Copenhagen in June • Final meeting in Taipei in October with final report to follow. May 20, 2012 CSE: Data Standards and Data Citation 15 Issues
- 17. BRDI Meeting in Berkeley • Two-day meeting in Berkeley, CA on data citation issues in August 2011 • Covered topics critical to data citation – Administration -- Legal – Publishing -- Policy – Identification -- P&T – Funding support -- Provenance National Academies report to be published in June May 20, 2012 CSE: Data Standards and Data Citation 16 Issues
- 18. What does the future hold? May 20, 2012 CSE: Data Standards and Data Citation 17 Issues
- 19. Standards for Data: Work areas? Many potential areas for work in sharing of scientific data including: • Author/Contributor disambiguation & other issues • Data Equivalence – How does one know that this thing and that are equivalent (i.e., contain same data)? • Systemic metadata What is the form of this information? What are its structural components? • Archival issues Storage, physical level, but also migration issues • Bibliographic information for discovery, delivery and reuse • Rights issues – Ownership, recognition, sharing, privacy May 20, 2012 CSE: Data Standards and Data Citation Issues 18
- 20. ORCID – Author disambiguation Founded by CrossRef, Thomson-Reuters, Nature in 2009 Now 328 participant organizations, 50 of which have provided sponsorship funding Prototype technology Full launch in Fall 2012 May 20, 2012 CSE: Data Standards and Data Citation Issues 19
- 21. International Standard Name ID • ISO 27729:2012 Information & Documentation -- International Standard Name Identifier • Another identifier for public identity of parties • Application for institutions (NISO I2) • Launched in Spring May 20, 2012 CSE: Data Standards and Data Citation Issues 20 „12
- 22. Data Equivalence • Creation of a high- level conceptual model of data description • A “FRBR” for data • Defines the distinctions between states & transformations of data • Basis for identification May 20, 2012 CSE: Data Standards and Data Citation Issues 21 & description
- 23. Too many players, doing too many things? Source: XKCD May 20, 2012 CSE: Data Standards and Data Citation Issues 22
- 24. Other Initiatives & Links • CODATA/ICSTI – Task Group on Data Citation Standards and Practices • DataCite – datacite.org • NISO/NFAIS Group on Supplemental Journal Materials • National Academies Board on Research Data and Information – For Attribution: Developing Data Attribution and Citation Practices and Standards in Berkely, CA. August 2011 Report due in January • Cite Datasets and Link to Publicationsby Digital Curation Centre • ORCID - about.orcid.org • International Standard Name ID – www.isni.org • ScienceCommons - Scholar‟s Copyright Project • International Council for Scientific and Technical Information (ICSTI) – Multimedia Search and Retrieval, and Interactive Journal Articles Projects • Dublin Core Metadata Initiative - Science and Metadata Community • DataNet projects funded by NSF, launched in 2009 • NISO/OAI ResourceSyncproject funded by Alfred P. Sloan Foundation May 20, 2012 CSE: Data Standards and Data Citation 23 Issues
- 25. Information Standards Quarterly (ISQ) Summer 2010 Issue of ISQ Special issue on Enhanced Journal Articles Article-level Enhancements in the Humanities and Social Sciences Hosting Supplemental Materials Archiving Supplemental Materials DOIs - Linking and Beyond Supplemental Materials Survey Report on NISO/NFAIS project NOW AVAILABLE OPEN ACCESS www.niso.org/publications/is q May 20, 2012 CSE: Data Standards and Data Citation 24 Issues
- 26. Questions? Todd Carpenter Executive Director tcarpenter@niso.org National Information Standards Organization (NISO) One North Charles Street, Suite 1905 Baltimore, MD 21201 USA +1 (301) 654-2512 www.niso.org May 20, 2012 CSE: Data Standards and Data Citation 25 Issues
Editor's Notes
- A great deal has been written of late about the growing prevalence of data, data analysis and data sharing in science. This is just one of those works, but one I highly recommend, edited by Tony Hey, who is head of Microsoft Research, and others – its available freely online and worth the time to go though.
- And as science has changed, so too have the ways in which scholarship is communicated and distributed. Increasingly paper is no longer the main medium of distributing findings. Most academic journals have already moved online and many are ceasing print publication altogether. Increasingly, the limitations of physical distribution are also falling away. We are no longer tied simply to text or static images. Science can be communicated in moving images, data visualizations, programs, and even datasets themselves, which can be separately analyzed, reprocessed and reused.
- Massive in scale – terabytes orpetabytes of data is difficult to manage, store
- This is just one example of the problems surrounding data publishing and use in science, on one of the vectors which I’ve just mentioned
- Finally, another critical question about data is provenance.
- So I’m going to talk about two projects of the many, many, many initiatives that are underway related to data that I and NISO are directly involved in related to data and data management. The first is data citation.
- CODATA = International Council for Science (ICSU) : Committee on Data for Science and Technology –
- Unfortunately, my crystal ball doesn’t work terribly well.
- Each community is doing its own things, for its own reasons to support their own needs. This isn’t wrong. It’s completely understandable. However, it will increasingly cause problems when we start talking about Machines talking to Machines and interoperability, which is how access to data can be so valuable. Sure a human might be able to decipher the differences between biology and ecology, population science from voting results. However, computers can’t. Just as one example, we can’t do anything if we don’t have the right metadata. Data tables are filled with figures that are just numbers. Without context, a 22 is as useless a figure as as 356,000, when one is tons and one is grams.