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Realising the value of open data: some disciplinary perspectives


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Presentation fro the CIRCE workshop on ISS data preservation and use. Presents finding from the RECODE project on the value of making data open from the perspective of different research disciplines.

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Realising the value of open data: some disciplinary perspectives

  1. 1. Realising the value of open data: Some disciplinary perspective Susan Reilly, LIBER Projects Manager @skreilly
  2. 2. Overview • Introduction: Policy RECommendations for Open access to research Data in Europe (RECODE) • The open research data agenda • Case studies: drivers and barriers • The way forward
  3. 3. Project ReCODE The project will leverage existing networks, communities and projects to address challenges within the open access and data dissemination and preservation sector and produce policy recommendations for open access to research data based on existing good practice.
  4. 4. Project ReCODE Objectives • Reduce stakeholder fragmentation • Identify stakeholder values and interrelationships • Identify gaps, tensions and good practices • Produce a set of guidelines for the sharing of scientific data • Engagement of stakeholders • Use 5 cases from different disciplines
  5. 5. By Ken Lund (Flickr: Why, Arizona (2)) [CC-BY-SA-2.0 (
  6. 6. Clear benefits of open data But if we really want researchers to open their data, maybe we should move from the general to the specific
  7. 7. Because there are barriers too… • • • • • • Cultural differences Definition of research data Lack of skills/education Poorly defined roles and responsibilities Lack of infrastructure Lack of career incentives
  8. 8. 5 case studies • • • • • Particle physics Clinical science Human physiology Enviromental science Archeology and related disciplines
  9. 9. Particle Physics • Practice – Large scale collaborative – Numerical data, complex analysis software and hardware – Long time scale – Grid anlysis • Motivation – Access for comparision, error testing, less duplication of effort
  10. 10. Particle physics • Barriers – Size of data – Relevance – Cost of openness – Complexity – Needs context (metadata) – Culture of collaboration + competition
  11. 11. Health Science • Practices – Interdisciplinary – Different data types and sources – Many stakeholders (commercial, government, practice) • Motivations – Faster advancement, more reliable results, access to negative result, duplication, understand genome
  12. 12. Health Science • Barriers – Anonymisation – Commericial interests (competition) – Variety of formats – Quality metadata
  13. 13. Archeology • Practice – Highly individual, fieldwork – Lots of data formats – Lacks standardisation in language, terminology and measurement • Motivations – Not replicable, cumulative knowledge, creating narrative
  14. 14. Archeology • Barriers – Legacy data – Not digital – Context is key- metadata, interoperability – Unclear research parameters – Specific skill sets needed (e.g. coding) – Cost
  15. 15. How do we define open access to research data? • We can define ‘open access’ (see Berlin Declaration): license to copy, use, distribute and display material subject to proper attribution of authorship and appropriate standard format, online repository, enable unrestricted distribution,interoperability, and long-term archiving. • But how do we define research data? Data underlying publications, all experimental data? Disciplines need to define what data should be made open
  16. 16. The entire data lifecycle must be addressed • Open access to data extends across the life cycle of the production of knowledge, from ethical concerns about data collection, characteristics of data collection, data analysis, data management, access to findings, and the status of findings. • Although some developments are shared across research practices, these are adapted within specific disciplines
  17. 17. Stakeholder fragmentation • What is the real cost of open data? • Universities, publishers, public and private research organizations, software developers, libraries, funding bodies and repositories within national, world regions and global science ecosystems • High interdependency, but lack of clarity around roles and Responsibilities By Oneblackline (Own work) [GFDL ( or CC-BY-3.0 (], via Wikimedia Commons
  18. 18. Infrastructure & technologies • Interoperability • Scalability • Data quality • Automatically executable policies By Anonymous (Guillaume Blanchard, Juillet 2004, Fujifilm S6900.) [CC-BY-SA-2.5-2.0-1.0 (, GFDL (, CC-BY-SA-3.0 ( or FAL], via Wikimedia Commons
  19. 19. Legal and ethical issues • Intellectual property – the database directive, copyright agreements with publishers, can we (libraries/repositories) change the format of data? • Data protection – right to be forgotten
  20. 20. A word on the long tail of research data… • Data that does not fall within the scope of discipline/government repositories •
  21. 21. Thank you from the ReCODE partners!