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Data cite


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Presentation at DataCite Summer Meeting, Hannover, Germany on 7th June 2010

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  1. 1. GLOBAL BIODIVERSITY INFORMATION FACILITY Dr Vishwas Chavan Senior Programme Officer for DIGIT [email_address] WWW.GBIF.ORG Towards Data Publishing Framework for primary biodiversity data Building the Biodiversity Informatics Commons DataCite Summer Meeting 7-8 June 2010, Hannover
  2. 2. GBIF: an intergovernmental initiative to share biodiversity information Currently 5 4 countries; 4 4 International Organisations…
  3. 3. GBIF’s Mandate ” To facilitate free and open access to biodiversity data worldwide, via the Internet, to underpin scientific research, conservation and sustainable development.” GBIF is govt-initiated, and govt. funded, in response to government agency needs in biodiversity information access and management; GBIF is in service to science, as a global ‘public good’
  4. 4. Data shared online via GBIF (> 20 1 m biodiversity records mapped to a 1 X 1 degree grid) Data Publishers: 316 Data Resources: 9900
  5. 5. GBIF facilitates access/exchange of data <ul><li>GBIF-mediated data on the ‘India’ </li></ul>
  6. 6. GBIF, Global Information Infrastructure for Biodiversity <ul><li>Global Infrastructure </li></ul><ul><li>Tools, Standards, and Processes </li></ul><ul><li>Strategies and Policy Framework </li></ul><ul><li>Outreach and Capacity Building </li></ul>
  7. 7. Global Biodiversity Research Infrastructure
  8. 8. In summary…GBIF’s Informatics Improved access to Names, Metadata and Primary Biodiversity Data Distributed GBIF informatics architecture Faster and easier publishing of data
  9. 9. Primary biodiversity data and information effectively available Data and information that have been produced but are not easy to find, access, and use (i.e not effectively available!) - a gigantic task of mobilising billions of data is still needed, as well as integrating new data. Biological collections Scientific publications Observations Reports Gray literature Data Bases Geography
  10. 10. <ul><li>Broadening Data Types </li></ul><ul><li>Data Resources Discovery </li></ul><ul><li>Innovative Approaches to Data Mobilisation </li></ul><ul><li>Data Mobilisation Strategy Discussions </li></ul><ul><li>Data Publishing Framework </li></ul>Improving mobilisation and Cultural Changes
  11. 11. <ul><li>Recognition </li></ul><ul><li>Opportunities </li></ul><ul><li>Investment </li></ul>Why should I publish data? What is there for me?
  12. 12. Data Publishing Framework <ul><li>Cultural change towards ‘ free and open access ’ to biodiversity data </li></ul><ul><li>Addresses social, technical, and policy concerns </li></ul><ul><li>Answer ‘ What is there for me?’ for ALL </li></ul>
  13. 13. Chavan and Ingwersen (2009) , BMC Bioinformatics, 10 (Suppl. 14): S2
  14. 14. DPF: Core Technical Components Chavan and Ingwersen (2009) , BMC Bioinformatics, 10 (Suppl. 14): S2
  15. 15. Occurrence Data KML file Data Publication together with scholarly publication: ZooKeys experience Penev, (2009). ZooKeys, 11: 1-8.
  16. 16. Persistent Identifiers Journal System Submission Acceptance Revision Peer Review Publication DoI Distributed Metadata Catalogues Metadata Authors auto conversion to manuscript GBIF Metadata Repository Current Biology PhytoKeys Indian J. Mar. Sci. Data Paper: Recognising Data Discovery Registry GBRDS
  17. 17. Data Citation Mechanism & Service <ul><li>Deep data citation mechanism </li></ul><ul><ul><li>Recognise ALL with their roles </li></ul></ul><ul><ul><li>Multilayer citation – producer, publisher, aggregator </li></ul></ul><ul><ul><li>Citations within citations </li></ul></ul><ul><li>Data Citation Service </li></ul><ul><ul><li>Resolve citation any time </li></ul></ul><ul><ul><li>Discover the underlined data </li></ul></ul><ul><li>Under development </li></ul>
  18. 18. Data Usage Index (DUI): Why? <ul><li>To demonstrate to data publishers that their biodiversity efforts do have impact </li></ul><ul><li>To encourage … </li></ul><ul><ul><li>Increase of high quality data discovery and mobilisation </li></ul></ul><ul><ul><li>Further usage of biodiversity data and information in scientific work </li></ul></ul><ul><ul><li>Formal citation behavior in research papers of dataset </li></ul></ul><ul><ul><li>Standardisation of dataset information </li></ul></ul>
  19. 19. Data Usage Index (DUI): What is it? <ul><li>As set of indicators operating on data concerned with: </li></ul><ul><ul><li>Unique Visits </li></ul></ul><ul><ul><li>Loyal Visits (repeated visits by same IP address) </li></ul></ul><ul><ul><li>Download of datasets & dataset records </li></ul></ul><ul><ul><li>Volume and (rank) distributions of dataset records </li></ul></ul><ul><li>per visit, visitor, dataset provider (institution, country, region, world, theme) & period </li></ul><ul><li>Indicators to be normalised (by records or MB), relative (to world, theme) and weighted (according to provider profile of species/taxa/themes) </li></ul>GBIF Indicators Chavan, June 2009
  20. 20. Global DUIs Natl., Regional, Thematic DUIs Local DUIs UNIVERSAL DUI LDUI LDUI Implementation of DUI Data Flow type Digitisation Bottom – Top Top – Bottom Mirror Mirror GDUI GDUI GDUI Aggregator Aggregator Aggregator RDUI TDUI TDUI Aggregator Aggregator Aggregator NDUI NDUI Publishing Toolkit Publishing Toolkit Publishing Toolkit Publishing Toolkit LDUI
  21. 21. Data Usage Index (DUI) implementation Data Usage Index (DUI) Phase I Phase III Phase II Access Use Management Data Life Cycle Improving the relevance of Data Usage Index
  22. 22. DPF: Challenges Chavan and Ingwersen (2009) , BMC Bioinformatics, 10 (Suppl. 14): S2 Policy & Political Uptake Cultural & Social Acceptance <ul><li>Individual Researcher </li></ul><ul><li>Scientific and Academic Institutions </li></ul><ul><li>Funding and Donor Agencies </li></ul><ul><li>Traditional Publishing Industry </li></ul>
  23. 23. Impact of Data Publishing Framework Funding Agencies Project Data Creation, Collection Analysis, Interpretation Scholarly Publishing Data Management, & Archival Data Publishing Increased Data Usage Knowledge Dissemination support results in Inspires another results in requires provide feedback on gaps and strategies for leads to Metadata facilitate facilitate results in Improves data quality and fitness facilitate encourages Existing cycle Complementary Expected cycle Impact Factor Data Usage Index Data Discovery Incentivisation through Data Paper leads to leads to leads to results in Source: BMC Bioinformatics 2009, 10 (Suppl 14) : S2, doi:10.1186/1471-2105-10-S14-S2
  24. 24. Primary Data and Scholarly Publications <ul><li>Seamless, embedded interconnections between data & paper </li></ul><ul><li>Unconventional use of data </li></ul><ul><li>Improving reliability & credibility </li></ul>
  25. 25. Email: [email_address] Data Publishing together with Scholarly Publishing!