Science as open enterprise


Published on

Palestra apresentada à CONFOA 2013 (Universidade de São Paulo, São Paulo, Brasil, de 06 a 08 de outubro de 2013) na Mesa III - A ciência aberta e a gestão de dados de pesquisa - pelo Prof. Dr. Peter Elias – REINO UNIDO - The Royal Society of UK.

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Science as open enterprise

  1. 1. Science as an open enterprise: open data for an open science Peter Elias 4th Luso-Brazilian Conference on Open Access University of São Paulo October 6 - 9, 2013
  2. 2. Open communication of data: the source of a scientific revolution and of scientific progress Henry Oldenburg
  3. 3. The challenge to Oldenburg’s principle: a crisis of replicability and credibility? The data providing the evidence for a published concept MUST be concurrently published, together with the metadata
  4. 4. The challenge: the data produced today
  5. 5. Working Group • Professor Geoffrey Boulton FRS FRSE (Chair) Regius Professor of Geology Emeritus at the University of Edinburgh • Dr Philip Campbell Editor in Chief, Nature • Professor Brian Collins CB FREng Professor of Engineering Policy, University College London • Professor Peter Elias CBE Institute for Employment Research, University of Warwick • Dame Wendy Hall FRS FREng Professor of Computer Science , University of Southampton • Professor Graeme Laurie FRSE Professor of Medical Jurisprudence, University of Edinburgh • Baroness Onora O’Neill FRS FBA FMedSci Professor of Philosophy, University of Cambridge • Sir Michael Rawlins FMedSci Chairman, National Institute for Health and Clinical Excellence • Professor Janet Thornton FRS CBE Director, European Bioinformatics Institute • Professor Patrick Vallance FMedSci Senior Vice President, Medicines discovery and development, GlaxoSmithKline • Sir Mark Walport FRS FMedSci Director, Wellcome Trust
  6. 6. Data, information, knowledge Data from human or machine observation are numbers, characters or images that refer to an attribute of a phenomenon. In order to be interpretable, data usually require metadata, which are data about the data, for example details of the context in which the data were collected. Data only become information when analysed in ways that reveal patterns in the phenomenon under investigation. Information yields knowledge when it supports non-trivial, true claims about a phenomenon.
  7. 7. Open data is more than disclosure To be open, and to be communicated effectively, data on which scientific knowledge is built must be accessible and readily located. Data must be intelligible to those who wish to scrutinise them. They must be assessable so that judgments can be made about their reliability and the competence of those who created them. And they must be usable by others. Only when these four criteria are fulfilled are data properly open.
  8. 8. Gastro-intestinal infection in Hamburg - 2011 • E-coli outbreak spread through several countries affecting 4000 people • Strain analysed and genome released under an open data license. • Two dozen reports in a week with interest from 4 continents • Crucial information about strain’s virulence and resistance made available to public health authorities
  9. 9. …. and the economic implications •
  10. 10. A new ways of sharing in science
  11. 11. “Scientific fraud is rife: it's time to stand up for good science” “Science is broken” Examples:  psychology academics making up data,  anaesthesiologist Yoshitaka Fujii with 172 faked articles  Nature - rise in biomedical retraction rates overtakes rise in published papers Cause: Rewards and pressures promote extreme behaviours, and normalise malpractice (e.g. selective publication of positive novel findings) Cures: Open data for replication Transparent peer review Not just personal integrity – but system integrity
  12. 12. Openness of data per se has no value. Open science is more than disclosure For effective communication, replication and re-purposing we need intelligent openness. Data and meta-data must be: • • • • Accessible Intelligible Assessable Re-usable Only when these four criteria are fulfilled are data properly open Scientific data rarely fits neatly into an EXCEL spreadsheet!
  13. 13. But, intelligently open to whom? To “taxpayers who are paying for that research will want to see something back. Directly – through open access to results and data.” Neelie Kroes, Vice President of the European Commission 27.03.13 Effective communication must be audience-sensitive – “data dumping” is ineffectual – a waste of time and effort. We must prioritise. How? We need to target public interest science, not with ex cathedra statements, but intelligently open data, arguments, uncertainties and options. (climate change, energy, Earth resources, infectious disease, obesity, novel technologies etc) The Commission’s policy should be more nuanced.
  14. 14. …. and, intelligently open to citizen scientists Examples: Collecting the Data: professional community • Galaxy Zoo: Hubble • Solar Storm Watch working with citizens in a different way • Old Weather • Whale FM • Ancient Lives • Fold It (creating protein molecules) • SETI (extra terrestrial intelligence) Benefits: • Collaboration • Scale • Statistical power and changing the social dynamics of science?
  15. 15. Boundaries of openness? Openness should be the default position, with proportional exceptions for: • Legitimate commercial interests (sectoral variation) • Privacy (completely anonymised data is impossible) • Safety and security (impacts contentious) All these boundaries are fuzzy
  16. 16. Recommendations (1) Open data should be the default, not the exception. It is part of the professional responsibility of scientists to communicate data. Universities and research institutes should support the ability of scientists to communicate data through investment in skills training and infrastructure. Research assessment should include metrics for open data on the same scale as journal articles. These metrics should recognise those who maximise usability and good communication of their data. The costs of preparing data and metadata for curation are part of the normal cost of the research process. Data that underpin major claims in a publication should be traceable and usable from information in the article, within practical limits.
  17. 17. Recommendations (2) Businesses should actively consider opportunities for the use and commercial exploitation of freely available data and information. Governments should recognise the potential of open data and open science to enhance the productivity and excellence of the national science base. Appropriate sharing of research data and information should be recognised to be in the public interest. Restrictions on sharing should be proportionate and risk based. Secure practices should be introduced as part of scientists’ training and codes of conduct as they evolve.
  18. 18. The cascade of responsibility Funders of research: - mandate intelligent openness - accept diverse outputs - cost of open data is a cost of science Scientists: - changing the mindset Learned Societies: - influencing their communities Universities/Institutes: - Research assessors: - recognise diversity of contribution Publishers: - mandate concurrent open deposition accept responsibility strategies management processes incentives & promotion criteria proactive, not just compliant
  19. 19. A realizable aspiration: all scientific literature online, all data online, and for them to interoperate … but, this is a process, not an event!