Where is the opportunity for libraries in the collaborative data infrastructure?
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Where is the opportunity for libraries in the collaborative data infrastructure?

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Presentation by Susan Reilly at Bibsys2013 on the opportunties for libraries and their role in the collaborative data infrastructure. Looks at data sharing, authentication, preservation and advocacy.

Presentation by Susan Reilly at Bibsys2013 on the opportunties for libraries and their role in the collaborative data infrastructure. Looks at data sharing, authentication, preservation and advocacy.

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  • this figure suggests, in the broadest possible terms, how different actors, data types and services should interrelate in a global einfrastructure for science. Data generators and users gather, capture, transfer and process data - often, across the globe, in virtual research environments. they draw upon support services in their specific scientific communities - tools to help them find remote data, work with it, annotate it or interpret it. the support services, specific to each scientific domain and provided by institutes or companies, draw on a broad set of common data services that cut across the global system; these include systems to store and identify data, authenticate it, execute tasks, and mine it for unexpected insights. At every layer in the system, there are appropriate provisions to curate data - and to ensure its trustworthiness.
  • Libraries and data centres must support data publishing as a prerequisite for data availability, including persistent identification/citation of datasets, and solutions for data description and retrieval, which together facilitate findability. They must also ensure that data is properly documented as a condition for data interpretability and re-usability and prepare for long-term data archiving including data curation and preservation.
  • this figure suggests, in the broadest possible terms, how different actors, data types and services should interrelate in a global einfrastructure for science. Data generators and users gather, capture, transfer and process data - often, across the globe, in virtual research environments. they draw upon support services in their specific scientific communities - tools to help them find remote data, work with it, annotate it or interpret it. the support services, specific to each scientific domain and provided by institutes or companies, draw on a broad set of common data services that cut across the global system; these include systems to store and identify data, authenticate it, execute tasks, and mine it for unexpected insights. At every layer in the system, there are appropriate provisions to curate data - and to ensure its trustworthiness.

Transcript

  • 1. Where is the opportunity forlibraries in the collaborativedata infrastructure?Susan ReillyProject ManagerLIBERsusan.reilly@kb.nl@skreilly
  • 2. Contents About LIBER Some context What is the collaborative data infrastructure? Introducing the researcher to the CDI Introducing the CDI to the researcher Now and next?
  • 3. LIBER: reinventing the library of the future Largest network of European reseach libraries: 450 in over 40 countries Mission: To provide an information infrastructure to enable research in LIBER institutions to be world class
  • 4. Key performance areas Scholarly communication and research infrastructures Reshaping the research library Advocacy
  • 5. LIBER Projects Reshaping The research library Scholarly Communication Advocacy & Research Infrastructure
  • 6. So why am I here? Reshaping Collaborative data The infrastructure research library Scholarly Communication Advocacy & Research Infrastructure
  • 7. What is the collaborative data infrastructure(scientific data infrastructure)? …it’s about data
  • 8. Not just the 20+ petabytes that the LHC at CERN produces every year
  • 9. Libraries in the data deluge Increasing amount of digitised and born digital content in libraries Increasing emphasis on open access publications and data: mandates, institutional repositories Demand for data management support
  • 10. What is the collaborative data infrastructure? “a broad, conceptual framework for how different companies, institutes, universities, governments and individuals would interact with the system – what types of data, privileges, authentication or performance metrics should be planned. This framework would ensure the trustworthiness of data, provide for its curation, and permit an easy interchange among the generators and users of data”
  • 11. Now and Next Authentication & authorisation New skills
  • 12. Introducing the researcher to the CDI Current situation ODE & linking data to publications Demand for data management support Advocacy
  • 13. Opportunities for data exchange (ODE) identify, collate, interpret and deliver evidence of emerging best practices in sharing, re-using, preserving and citing data, the drivers for these changes and barriers impeding progress, in forms suited to each audience policy makers, funders, infrastructure operators, data centres, data providers and users, libraries and publishers
  • 14. Steps to creating the conditions for data sharing Understand data sharing today  Collection of "success stories”, “near misses” and “honourable failures” in data sharing, re-use and preservation Data & scholarly communications  Integrating data and publications  Best practice in data citation  New roles Identify drivers and barriers  Interviews with stakeholder to seek consensus Foto "Bell", Noordewierweg 116, Amersfoort.
  • 15. Hypotheses “Without the infrastructure that helps scientists manage their data in a convenient and efficient way, no culture of data sharing will evolve.” Stefan Winkler-Nees (German Research Foundation, DFG)
  • 16. Hypotheses by Category4.Attitudes6.Policies8.Infrastructure10.DMPs,Citability11.Dependency ondiscipline
  • 17. The DataPublication Pyramid (1) Data contained and explained within the article (2) Further data explanations in any kind of supplementary (3) Data files to articles referenced from the article and held in data centers and (4) Data repositoriespublications, describing available datasets (5) Data in drawers and on disks at the institute
  • 18. The Pyramid’s likely short term reality: (1) Top of the pyramid is stable but small (2) Risk that supplements to articles turn into Data Dumping (3) Too many places disciplines lack a community endorsed data archive (4) Estimates are that at least 75 % of research data is never made openly avaiable 21
  • 19. (1) More integration of text and data, viewers and seamless links to interactive datasets The Ideal Pyramid (2) Only if data cannot be integrated in (3) Seamless links article, and only (bi-directional) relevant extra between explanations publications and data, interactive(4) More Data viewers within the Journals that articles describedatasets, datamgt plans anddata methods 22
  • 20. Issues for researchers Researchers need somewhere to put data and make it safe for reuse Researchers need to control its sharing and access Researchers need the ability to integrate data and publication Researchers need to get creditfor data as a first class researchobject Researchers need someone topay for the costs of data availabilityand re-use
  • 21. Library support for the researcherLibraries and data centres must support… data as first class research object: Availability publishing, persistent identification/citation of datasets data description, metadata, standards Findability documentation and retrieval proper documentation of data Interpretability long-term data archiving including data curation and preservation Re-usability
  • 22. Implications for librariesLevel of integration Implication for libraryData contained within the article  Prepare for adequate preservation strategiesData published in supplementary files to  Presentation and preservationarticles mechanisms  Persistent linkDatasets referenced from the articles  Citability of dataset  Persistent link  Perpetual access to datasetData published independently from written  Support publication processpublications (“data publication”)  Curation of datasets  Metadata and documentationData in drawers and on disks at the  Engage in data managementinstitute planning
  • 23. Demand for data management support
  • 24. Advocacy “Many researchers do not appear to see the value and benefits of data citation. There is a gap, which could be filled by libraries, in advocacy for data sharing, the use of subject specific repositories, and best practice in data citation. These, if filled, would increase the number of researchers sharing and reusing data.”http://www.alliancepermanentaccess.org/wp-content/plugins/download-monitor/downlo
  • 25. Introducing the CDI to the researcher Scoping the researcher’s requirements Collaboration & policy development
  • 26. The AAA Study: a research passport“evaluate the feasibility of delivering an integrated Authentication and Authorisation Infrastructure, AAI, to help the emergence of a robust platform for access to and preservation of scientific information within a Scientific Data Infrastructure (SDI)”
  • 27. Now and Next Authentication & authorisation New skills
  • 28. Methodology
  • 29. The Google Generation
  • 30. Collaboration “Networked science is on the rise, the researcher is no longer working alone in his office, he is working virtually with other researchers from around the world. For them it is important that they can use the same software and share and reuse the same content related objects, in a trusted environment.” Heinke Neuroth, Head of Innovation, Goettingen State & University Library
  • 31. Use Cases1. Creating Data2. Processing Data3. Sharing Data4. Preserving Data5. Multi-disciplinary Data Services6. Analysing Data7. Accessing Data8. Accessing Experiments and Data
  • 32. Requirements… Tracking of provenance, authenticity, integrity of the material Integration of researcher ID with institutional credentials Researchers’ self registration Securely linking researcher and data identifiers for tracking provenance Delegation of identity management to home institute Attribute provisioning for users participating in specific research projects managed by the specific research groups (VOs) Attribute aggregation Unification and homogenisation of identity federations´ attributes and agreed levels of assurance in order to facilitate authorisation Accreditation of trusted identity Providers (IdPs), based on international standards, depending on the required level of assurance Entitlement management to minimise the occurrence of events where license monies are being paid twice without necessity (e.g., for access to scientific journals).
  • 33. Technical infrastructure
  • 34. Legal Recommendations Need to protect the user
  • 35. Collaboration & policy development Policies for data sharing  Values & Ecosystems  Infrastructure & Technology  Legal & Ethical  Institutional Support http://recodeproject.eu/
  • 36. Now & next What should our priorities be?LIBER ten recommendations:http://www.libereurope.eu/news/ten-recommendations-for-libraries-to-get-started-with-research-data
  • 37. 1. Identify & develop skills
  • 38. 2.Collaborate Alliance for Permanent Access to the Record of Science in Europe Network (APARSEN)  look across the excellent work in digital preservation which is carried out in Europe and to try to bring it together under a common vision Trust! Sustainability! Usability! Access! http://www.alliancepermanentaccess.org/
  • 39. Engage
  • 40. Thank you! Any questions?