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Where is the opportunity for
libraries in the collaborative
data infrastructure?
Susan Reilly
Project Manager
LIBER

susan.reilly@kb.nl
@skreilly
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?
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
Key performance areas

 Scholarly communication and research infrastructures
 Reshaping the research library
 Advocacy
LIBER Projects



                   Reshaping
                      The
                 research library


                            Scholarly
                         Communication
           Advocacy
                                &
                            Research
                          Infrastructure
So why am I here?



               Reshaping Collaborative data
                  The         infrastructure
             research library

                       Scholarly
                    Communication
        Advocacy           &
                       Research
                     Infrastructure
What is the collaborative data infrastructure
(scientific data infrastructure)?




                 …it’s about data
Not just the 20+ petabytes that the LHC at CERN
 produces every year
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
Where is the opportunity for libraries in the collaborative data infrastructure?
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”
Now and Next

 Authentication & authorisation
 New skills
Introducing the researcher to the CDI

 Current situation
 ODE & linking data to publications
 Demand for data management support
 Advocacy
Where is the opportunity for libraries in the collaborative data infrastructure?
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
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.
Where is the opportunity for libraries in the collaborative data infrastructure?
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)
Hypotheses by Category



4.Attitudes
6.Policies
8.Infrastructure
10.DMPs,
Citability
11.Dependency on
discipline
The Data
Publication 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
                                repositories
publications,
 describing
  available
  datasets
                                   (5) Data in
                                 drawers and on
                                   disks at the
                                     institute
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
(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
   describe
datasets, data
mgt plans and
data methods




                                                      22
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 credit
for data as a first class research
object
 Researchers need someone to
pay for the costs of data availability
and re-use
Library support for the researcher


Libraries 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
Implications for libraries

Level of integration                        Implication for library

Data contained within the article               Prepare for adequate preservation
                                                 strategies
Data published in supplementary files to        Presentation and preservation
articles                                         mechanisms
                                                Persistent link

Datasets referenced from the articles           Citability of dataset
                                                Persistent link
                                                Perpetual access to dataset

Data published independently from written      Support publication process
publications (“data publication”)              Curation of datasets
                                               Metadata and documentation

Data in drawers and on disks at the            Engage in data management
institute                                       planning
Demand for data management support
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
Introducing the CDI to the researcher

 Scoping the researcher’s requirements
 Collaboration & policy development
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)”
Now and Next

 Authentication & authorisation
 New skills
Methodology
The Google Generation
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
Use Cases

1.   Creating Data
2.   Processing Data
3.   Sharing Data
4.   Preserving Data
5.   Multi-disciplinary Data Services
6.   Analysing Data
7.   Accessing Data
8.   Accessing Experiments and Data
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).
Technical infrastructure
Legal Recommendations
                        Need to
                         protect
                        the user
Collaboration & policy development


 Policies for data sharing
    Values & Ecosystems
    Infrastructure & Technology
    Legal & Ethical
    Institutional Support


   http://recodeproject.eu/
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
1. Identify & develop skills
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/
Engage
Thank you!

 Any questions?

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

  • 1. Where is the opportunity for libraries in the collaborative data infrastructure? Susan Reilly Project Manager LIBER susan.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
  • 11. 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”
  • 12. Now and Next  Authentication & authorisation  New skills
  • 13. Introducing the researcher to the CDI  Current situation  ODE & linking data to publications  Demand for data management support  Advocacy
  • 15. 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
  • 16. 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.
  • 18. 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)
  • 20. The Data Publication 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 repositories publications, describing available datasets (5) Data in drawers and on disks at the institute
  • 21. 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
  • 22. (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 describe datasets, data mgt plans and data methods 22
  • 23. 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 credit for data as a first class research object  Researchers need someone to pay for the costs of data availability and re-use
  • 24. Library support for the researcher Libraries 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
  • 25. Implications for libraries Level of integration Implication for library Data contained within the article  Prepare for adequate preservation strategies Data published in supplementary files to  Presentation and preservation articles mechanisms  Persistent link Datasets referenced from the articles  Citability of dataset  Persistent link  Perpetual access to dataset Data published independently from written  Support publication process publications (“data publication”)  Curation of datasets  Metadata and documentation Data in drawers and on disks at the  Engage in data management institute planning
  • 26. Demand for data management support
  • 27. 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
  • 28. Introducing the CDI to the researcher  Scoping the researcher’s requirements  Collaboration & policy development
  • 29. 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)”
  • 30. Now and Next  Authentication & authorisation  New skills
  • 33. 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
  • 34. Use Cases 1. Creating Data 2. Processing Data 3. Sharing Data 4. Preserving Data 5. Multi-disciplinary Data Services 6. Analysing Data 7. Accessing Data 8. Accessing Experiments and Data
  • 35. 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).
  • 37. Legal Recommendations Need to protect the user
  • 38. Collaboration & policy development  Policies for data sharing  Values & Ecosystems  Infrastructure & Technology  Legal & Ethical  Institutional Support http://recodeproject.eu/
  • 39. 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
  • 40. 1. Identify & develop skills
  • 41. 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/
  • 43. Thank you!  Any questions?

Notas del editor

  1. 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.
  2. 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.
  3. 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.