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Open Science Governance and Regulation/Simon Hodson

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Presented during the SA-EU Science Workshop

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Open Science Governance and Regulation/Simon Hodson

  1. 1. Open Science Governance and Regulation Simon Hodson, Executive Director, CODATA www.codata.org SA-EU Open Science Dialogue Workshop Birchwood Hotel & OR Tambo Conference Centre Johannesburg, South Africa 30 November 2017
  2. 2. Open Science  What is Open Science:  Open access to research literature.  Data that is as Open as possible, as closed as necessary.  FAIR Data (Findable, Accessible, Interoperable, Reusable).  A shop window and repository of all research outputs.  A culture and methodology of open discussion and enquiry (including methodology, lab notebooks, pre-prints)  Engagement with society and the economy in research activities (citizen science, co-design / transdisciplinary research, interface between research, development and innovation).  Research institutions have an opportunity to build their reputation around research specialisation: and this requires data specialisation and FAIR data collections.  The Open Science ethos and co-design helps build collaboration between research institutions, government agencies and industry.
  3. 3. The Open Data Iceberg The Technical Challenge The Ecosystem Challenge The Funding Challenge The Support Challenge The Skills Challenge The Incentives Challenge The Mindset Challenge Processes & Organisation People Geoffrey Boulton (CODATA) - developed from an idea by Deetjen, U., E. T. Meyer and R. Schroeder (2015). OECD Digital Economy Papers, No. 246, OECD Publishing. A(n Inter)National Infrastructure Technology
  4. 4. Boundaries of Open  For data created with public funds or where there is a strong demonstrable public interest, Open should be the default.  As Open as Possible as Closed as Necessary.  Proportionate exceptions for:  Legitimate commercial interests (sectoral variation)  Privacy (‘safe data’ vs Open data – the anonymisation problem)  Public interest (e.g. endangered species, archaeological sites)  Safety, security and dual use (impacts contentious)  All these boundaries are fuzzy and need to be understood better!  A great deal of data is not affected by these issues and can and should be open.  There is a need to evolve policies, practices and ethics around closed, shared, and open data.
  5. 5. What are data? When are data? Big Data Biggish Data Small Data Broad Data Monothematic Axis Polythematic/ Interdisciplinary Axis Complex pa erns In nature & society Patterns in space & time Low volumes Batch velocities Structured varieties Petabyte volumes High velocities Multistructured Global byte/year Yo abytes1024 Zettabytes1021 Now Exabytes 1018 Petabytes 1015 Terabytes1012 Gigabytes109 Megabytes106 Kilobytes 103  Data is not the new oil. They are far more valuable because they are renewable.  Big Data poses many challenges and opportunities: how do we manage it and develop the data science to extract information?  Small data poses many challenges, because often they require detailed human-crafted metadata.  Often still our challenge is a lack of data, no data.  Data can be very varied: poses challenges of storage, management, analysis.  Many disciplines have raw data, processed data, science ready data or data products.  Important to understand which of these can and / or should be preserved and made available.
  6. 6. Research data, public data…  Typology of data that are in scope of research data policies:  Data underpinning research conclusions presented the literature must be published.  Significant data produced by research projects should be made available where possible.  Data resulting from major data creation projects for monitoring and research.  Data created by public activities where there are research and development opportunities.  Private sector data where there is a public good case or mutual benefit in data sharing.  Research data should be as open as possible, as closed as possible.  All research data should be well managed and FAIR.
  7. 7. Criteria for appraisal, selection, preservation?  Need for more work on criteria and guidelines for appraisal, selection, preservation.  A lot of the criteria has to come from the disciplines, but informed by some general principles.  Major data collection/creation exercises with evident multiple uses (census, Hubble etc).  Unrepeatable observations, measurements in nature or society? (preserve and publish)  Data created by in vitro experiments that can be reproduced and for which the instruments are being improved (perhaps very limited reasons for preserving)  Data collected for a given public or private purpose (traffic management, customer relations, ships logs) but which could be used for research…  Need for collaborative approach (with researchers) to clarifying the criteria to keeping, publishing and discarding data.  While being mindful of potential for other extra- and inter-disciplinary uses.  Qualified by the knowledge that some disciplines have to discard data because of the sheer volume.  What is important is that we start actively exercising these processes.  DCC Guidelines: http://www.dcc.ac.uk/resources/how-guides/appraise-select-data  NERC Data Value Checklist: http://www.nerc.ac.uk/research/sites/data/policy/data-value- checklist/
  8. 8. Barriers to Data Sharing Researchers concerns:  Concern that data may be misused or misunderstood.  Concern that will lose scientific edge if sharing before fully exploited.  Desire to retain control of a professional asset.  Culture in particular research disciplines; availability of infrastructure.  Concern that will not be credited.  Lack of career rewards for data publication.  Fundamentally, researchers are reluctant to expend effort sharing data because they do not feel that data is adequately exposed or credited.  See ODE report, using Parse.Insight findings: http://www.alliancepermanentaccess.org/wp- content/uploads/downloads/2011/11/ODE- ReportOnIntegrationOfDataAndPublications-1_1.pdf Nature special issue on data sharing: http://www.nature.com/news/specials/da tasharing/index.html
  9. 9. Benefits of Data Sharing  The Value of Open Data Sharing, report for GEO http://dx.doi.org/10.5281/zenodo.33830  Tend to be expressed as systemic, macro-level benefits.  What are the benefits for individual institutions, researchers?  Will Open Science, building expertise in RDM, in transdisciplinary research make institutions more competitive?  Will data collections serve as a shop window?  Some evidence of a citation advantage for articles with open data?  To what extent are we seeing data citation?
  10. 10. Open Science Governance and Regulation  What governance, regulation and guidelines are needed to advance Open Science, while respecting other imperatives?  Open Science and intellectual property, patents, innovation?  Open Data and data protection (Freedom of Information vs ZA Protection of Personal Information (2013) / EU General Data Protection Regulation (May 2018)  What governance does Open Science require? I.e. what set of policies, principles, norms and what mechanisms for compliance checking?  Global governance: governance of international projects and access to SA data.  Governance, roles and responsibilities among key actors (ministry, institutions, national initiatives)?  Responsibilities and rewards for researchers, projects, institutions?  3-5 concrete recommendations; risks and opportunities, opportunity costs; outline time frames, be realistic; international aspects are important.
  11. 11. Simon Hodson Executive Director CODATA www.codata.org http://lists.codata.org/mailman/listinfo/codata-international_lists.codata.org Email: simon@codata.org Twitter: @simonhodson99 Tel (Office): +33 1 45 25 04 96 | Tel (Cell): +33 6 86 30 42 59 CODATA (ICSU Committee on Data for Science and Technology), 5 rue Auguste Vacquerie, 75016 Paris, Thank you for your attention!

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