November 10, 2015 NISO/ICSTI Joint Webinar: A Pathway from Open Access and Data Sharing to Open Science in Practice
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Pathways to Open Science Geoffrey Boulton – University of Edinburgh, Royal Society, and President of CODATA – UK
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November 10, 2015 NISO/ICSTI Joint Webinar: A Pathway from Open Access and Data Sharing to Open Science in Practice
- 1. Pathways to Open Science Geoffrey Boulton CODATA President University of Edinburgh NISO/ICSTI Joint Webinar 10 November 2015
- 2. Openness – the bedrock of science in the modern era Henry Oldenburg
- 4. A crisis of credibility?
- 5. problems & opportunities 1020 bytes Available storage The Data Deluge
- 6. /var/folders/ls/nv6g47p94ks4d11f1p72h2ch00 00gn/T/com.apple.Preview/com.apple.Preview .PasteboardItems/rutford_avo_afi_ed_july201 0 (dragged).pdf The Challenge: the “Data Deluge” is undermining “self correction” THEN AND NOW
- 7. “Scientists like to think of science as self-correcting. To an alarming degree, it is not.”
- 8. A crisis of replicability and credibility? Pre-clinical oncology – 89% not reproducible Reasons • Fraudulent behaviour • Invalid reasoning • Absent or inadequate data and/or metadata Causes? • Pressure to publish • Pressure to make excessive claims • Data hoarding • Poor data science Solutions: Open data & Valid Methods
- 9. What open data means For effective communication, replication and re-purposing we need intelligent openness. Data, meta-data and, increasingly software/machine codes must be: • Discoverable • Accessible • Intelligible • Assessable • Re-usable Only with these criteria are are data properly open. & not only the data & meta-data but also the software used to manipulate it The data providing the evidence for a published concept MUST be concurrently published. To do otherwise should come to be regarded by all, including journals, as scientific MALPRACTICE.
- 10. Linear regression Cluster analysis Dynamic/complex behaviour Complex systems No mathematical pipeline Glucose levels in Type II Diabetes Simple relationships Classical statistics Topological analysis Valid reasoning: e.g. coping with complex data
- 11. Exploiting the Data Flux (Volume & velocity)
- 12. http://www.wired.co. uk/news/archive/201 4-01/15/1000-dollar- genome/viewgallery/3 31679 Data acquistion: Cost down – Flux up
- 13. Looking at clouds Daily Trajectory Observation Model Ozone LevelsMicro-satellites
- 14. From “simple” science to complexity, from uncoupled to highly coupled systems Uncoupled systems The behaviour of highly coupled systems
- 15. Simulating a complex system Characterising a complex system Image of brain cells in a ratEmergent behaviour of a specific 6-component coupled system Complex systems
- 16. Semantic Web of Broad, Linked Data Subject – Predicate – Object - e.g. A Rice Ontology
- 17. 1. Maintaining “self-correction” 2. Open knowledge is creative & productive “If you have an apple and I have an apple and we exchange these apples, then you and I will still each have one apple. But if you have an idea and I have an idea and we exchange these ideas, then each of us will have two ideas.” 3. Open data enables semantic linking George Bernard Shaw Why openness & sharing?
- 18. Mathematics related discussions Tim Gowers - crowd-sourced mathematics An unsolved problem posed on his blog. 32 days – 27 people – 800 substantive contributions Emerging contributions rapidly developed or discarded Problem solved! “Its like driving a car whilst normal research is like pushing it” What inhibits such processes? - The criteria for credit and promotion – ALTMETRICS THE ANSWER? New modes of technology- enabled creativity: e.g Crowd-sourcing
- 19. • Openly collected science is already helping policy makers. • AshTag app allows users to submit photos and locations of sightings to a team who will refer them on to the Forestry Commission, which is leading efforts to stop the disease's spread with the Department for Environment, Food and Rural Affairs (Defra). Chalara spread: 1992-2012 Citizen Science
- 20. Openness to public scrutiny
- 21. EMBL-EBI services Labs around the world send us their data and we… Archive it Classify it Share it with other data providers Analyse, add value and integrate it …provide tools to help researchers use it A collaborative enterprise … seizing the data opportunities depends on an ethos of data-sharing e.g. a growing number of disciplinary communities
- 22. Ins tu onal management and support Na onal policies & e-infrastructure Open Research Data Big Data Analy cs Knowledge Output EXPLOITING THE DATA REVOLUTION Scien fic inference Ins tu onal management & support Na onal policies & e-infrastructure A national data-intensive system
- 23. CODATACODATA II SS UU International Research Data Collaboration CODATACODATA II SS UU CODATA Policies & practice Frontiers of data science Capacity Building WDS • Data stewardship • Data standards RDA • Interoperability
- 24. Science International: international voice of policy for science International Council for Science (ICSU), International Social Science Council (ISSC), The World Academy of Science (TWAS), Inter-Academy Partnership (IAP) Agenda: • An International Accord: Open Data in a Big Data World (principles) • African Data Science Capacity Mobilisation Initiative First, action-oriented meeting South Africa, December 2015
- 25. Responsibilities • Scientists • Research Institutions and universities • Publishers • Funding agencies • Professional associations, scholarly societies & academies • Libraries, archives & repositories Boundaries of openness Enabling practices • Citation & provenance • Interoperability • Non-restrictive reuse • Linkability Science International Principles for Open Data
- 26. i. Publicly funded scientists have a responsibility to contribute to the public good through the creation and communication of new knowledge, of which associated data are intrinsic parts. They should make such data openly available to others as soon as possible after their production in ways that permit them to be re-used and re-purposed. ii. The data that provide evidence for published scientific claims should be made concurrently and publicly available in an intelligently open form in a way that permits the logic of the link between data and claim to be rigorously scrutinised and the validity of the data to be tested by replication of experiments or observations. To the extent possible, data should be deposited in managed repositories with low access barriers. Principles for Open Data Responsibilities of scientists
- 27. Possible Regional Platforms for Open Science African Platform Asian Platform? ? Australian Platform Shared investment in infrastructure; harvesting and circulating good ideas; spreading and supporting good practice; capacity building; promoting applications; linking to international programmes and standards. ?
- 28. A taxonomy of open science (research) - a journey towards science as a public enterprise Inputs Outputs & open engagement with Open access Administrative data (held by public authorities e.g. prescription data) Public Sector Research data (e.g. Met Office weather data) Research Data (e.g. CERN, generated in universities) Research publications (i.e. papers in journals) Open data Open science Collecting the data Doing research Doing science openly Researchers - Govt & Public sector - Businesses - Citizens - Citizen scientists Co-production of knowledge Information & knowledge as public not private goods
- 29. Open Science Data / Publications Open Knowledge
- 30. Open Science Data / Publications Mono/MultiInterTransdisciplinary Open Knowledge
- 31. Open Science Data / Publications Researchers Mono/MultiInterTransdisciplinary Stakeholders Open Knowledge
- 32. Open Science Data / Publications Researchers Mono/MultiInterTransdisciplinary Stakeholders RigourInnovationPolicySolutions Open Knowledge
- 33. WHAT? Open Knowledge Open Science Open Data/Open Access WHO? Stakeholders Researchers DISCIPLINE Trans- Inter- Mono- Multi- PURPOSE Policy Solutions Innovation Nuts & Bolts of Reality Some related concepts
- 34. A barrier to openness? - Analytic overload. E.g. - Global Earth Observation System of Systems • What is the human role? • Can we analyse & scrutinise what is in the black box? - &who owns the box? • What does it mean to be a researcher in a data intensive age? A disconnect between machine analysis & human cognition?
- 35. A barrier to openness? - Analytic overload. E.g. - Global Earth Observation System of Systems • What is the human role? • Can we analyse & scrutinise what is in the black box? - &who owns the box? • What does it mean to be a researcher in a data intensive age? A disconnect between machine analysis & human cognition?
Notes de l'éditeur
- The material advance of human society has been based on the acquisition and use of knowledge and science, as it has been practised in the last 300 years has proved to be the most effective way of gaining reliable knowledge. I want to talk about the processes whereby science is done and how they need to adapt to a novel environment in which we are able to acquire, store, manipulate and communicate data of unprecedented volume and complexity. What challenges does this environment offer to the essential processes of science, how can we exploit the opportunities that it offers and what barriers inhibit necessary changes. This is not about openness for itself – but open processes in the doing of science) Open science is not new. It was the bedrock on which the extraordinary scientific revolutions of the 18th and 19th centuries were built. But we do need to reinvent it for a data-rich era. So let us start with a little history.
- This is Henry Oldenberg, the first secretary of the newly formed Royal Society in the early 1660s. Henry was an inveterate correspondent, with those we would now call scientists both in Europe and beyond. Rather than keep this correspondence private, he thought it would be a good idea to publish it, and persuaded the new Society to do so by creating the Philosophical Transactions, which remains a top-flight journal to the present day. But he demanded two things of his correspondents: that they should submit in the vernacular and not Latin; and that evidence (data) that supported a concept must be published together with the concept. It permitted others to scrutinize the logic of the concept, the extent to which it was supported by the data and permitted replication and re-use. Open publication of concept and evidence is the basis of “scientific self-correction”, which historians of science argue were the crucial building blocks on which the scientific revolution of the 18th and 19th centuries was built and remain fundamental to the progress of science. Openness to scrutiny by scientific peers is the most powerful form of peer review.
- But Oldenberg’s world has changed. The last 20 years have seen an unprecedented data storm, which poses both challenges and opportunities to the way science is done.
- The fundamental challenge is to scientific self-correction. Journals can no longer contain the data, and neither scientists nor journals have taken the obvious step of having data relevant to a publication concurrently available in an electronic database. (example of last year’s Nature paper revealing that only 11% of results in 50 benchmark papers in pre-clinical oncology were replicable. If lack of Oldenburg’s rigour in presenting evidence is widespread, a failure of replicability risks undermines science as a reliable way of acquiring knowledge and can therefore undermines its credibility.
- Openness of itself has no value unless it is “intelligent openness”, where data are: Accessible – they can be found Intelligible – they can be understood Assessable – e.g. does the creator have an interest in a particular outcome? Re-useable – sufficient meta-data to permit re-use and re-purposing. These should be standard criteria for an open data regime. But we must recognise that the amount of meta and background data required for intelligent openness to fellow citizens is usually far greater than that required for openness to scientific peers. If all data were to be intelligently open to fellow citizens on the basis that that have ultimately paid for it, science would stop tomorrow. A way forward would be to make a much greater effort to make data intelligently open in what we could call “public interest science”, including those issues that frequently arise in public debate or concern.
- Ash dieback, caused by the fungus Chalara fraxinea, was found in the UK in October outside of plantations and nurseries in East Anglia, raising fears of a repeat of Dutch elm disease which killed 25 million mature elms in the 1970s and 80s. In an attempt to map and help prevent the spread of the disease across the country, a team of developers and academics worked through the weekend to create an app that smartphone owners can use to report suspected cases of infection. Infected ash trees are recognisable by lesions on their bark, dieback of leaves at the tree's crown, and leaves turning brown – though experts say the arrival of autumn makes the latter harder to accurately spot. zThe AshTag app for IOS and Android devices allows users to submit photos and locations of sightings to a team who will refer them on to the Forestry Commission, which is leading efforts to stop the disease's spread with the Department for Environment, Food and Rural Affairs (Defra).
- Human and technical requirements for a sustainable data infrastructure. Network of world data centres. Data policies and data science: bringing data experts together with research scientists.
- Lots of interchangeable and fluid terms but many shared principles. The word “science” is used to mean the systematic organisation of knowledge that can be rationally explained and reliably applied. It is not exclusively restricted to “natural science”.
- The material advance of human society has been based on the acquisition and use of knowledge and science, as it has been practised in the last 300 years has proved to be the most effective way of gaining reliable knowledge. I want to talk about the processes whereby science is done and how they need to adapt to a novel environment in which we are able to acquire, store, manipulate and communicate data of unprecedented volume and complexity. What challenges does this environment offer to the essential processes of science, how can we exploit the opportunities that it offers and what barriers inhibit necessary changes. This is not about openness for itself – but open processes in the doing of science) Open science is not new. It was the bedrock on which the extraordinary scientific revolutions of the 18th and 19th centuries were built. But we do need to reinvent it for a data-rich era. So let us start with a little history.