From Open Data to Open Science: Enabling Reproducibility, Innovation and Solutions
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1st LEARN Workshop. Embedding Research Data as part of the research cycle. 29 Jan 2016. Presentation by Geoffrey Boulton, University of Edinburgh & CODATA
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From Open Data to Open Science: Enabling Reproducibility, Innovation and Solutions
- 1. From Open Data to Open Science Geoffrey Boulton University of Edinburgh & CODATA “Learn” Workshop University College, London January 2016
- 2. Knowledge and understanding - the engines of material progress depend on technologies that enable their accumulation and communication 1454 2002
- 3. Openness – the bedrock of science in the modern era Henry Oldenburg
- 5. /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 Storm” is undermining “self correction” THEN AND NOW
- 6. A crisis of reproducibility and credibility? Why such low levels of reproducibility? • Misconduct/fraud • Invalid reasoning • Absent or inadequate data and/or metadata
- 7. 19 Exabytes280Exabytes Based on: http://www.martinhilbert.net/WorldOnfoCapacity.html 1 Exabyte=1018 bytes The digital revolution Global information storage capacity In optimally compressed bytes Digital Storage Analogue Storage Explosion of the Digital revolution 1986 1993 2000 2007 2014-4000Exabytes
- 8. http://www.wired.co. uk/news/archive/201 4-01/15/1000-dollar- genome/viewgallery/3 31679 Data acquistion: Cost down – Flux up
- 9. Information: how much is crystallised into knowledge?
- 10. Reinventing reproducibility for the digital age How do we retain an essential principle? The data providing the evidence for a published concept MUST be concurrently published, together with necessary metadata and computer code. To do otherwise is scientific MALPRACTICE
- 12. Ozone Levels Four key drivers of change for science • Big data • Semantically-linked data • Open data • Cost reduction Micro-satellite Looking at clouds
- 13. Pillars of the Digital Revolution Big Data Volume Velocity Variety Linked Open Data Many databases Semantic Relations Deeper meaning Foundations : Openness Machine analysis & learning Text and data mining
- 14. The opportunity: data from “simple” to complex systems from uncoupled to highly coupled behaviour Uncoupled systems Simulating behaviour of highly coupled systems
- 15. Simulating system dynamics Mapping a complex state Image of brain cells in a rat Emergent behaviour of a specific 6-component coupled system • patterns not hitherto seen • unsuspected relationship • complex systems e.g. complexity: dynamic evolution and system state Scientific opportunities
- 16. Satellite observation Surface monitoring The opportunity: data-modelling: iterative integration Initial conditions Model forecast Model-data iteration - forecast correction
- 17. Linear regression Cluster analysis Dynamic/complex behaviour Complex systems No mathematical pipeline Simple relationships Classical statistics System characterisations: from simple to complex Glucose in type II diabetes Topological analysis
- 18. 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?
- 19. 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
- 20. The Open Data Iceberg The Technical Challenge The Consent Challenge The Ecosystem Challenge The Funding Challenge The Support Challenge The Skills Challenge The Incentives Challenge The Mindset Challenge Processes & Organisation People motivation and ethos. Developed from: Deetjen, U., E. T. Meyer and R. Schroeder (2015). A National Infrastructure Technology
- 21. The “Science International” Accord: principles of open data (www.icsu.org/science-international) Responsibilities 1-2. Scientists 3. Research institutions & universities 4. Publishers 5. Funding agencies 6. Scholarly societies and academies 7. Libraries & repositories 8. Boundaries of openness Enabling practices 9. Citation and provenance 10. Interoperability 11. Non-restrictive re-use 12. Linkability
- 22. Responsibilities Scientists 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. This should permit 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 well-managed and trusted repositories with low access barriers.
- 23. CODATACODATA II SS UU African Open Data/Open Science Platform Platform Forum Coordination Government Priority setting Funders Funding Incentives Capacity Building Training and Skills Infrastructure Roadmaps Flagship Co-Designed Data Intensive Projects International Standards Programmes Shared infrastructure investment; shared good practice; capacity building; system development
- 24. 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 Disciplinary communities can lead the way e.g. Elixir programme in life sciences/bio-informatics
- 25. 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. S. American Platform?
- 26. Inputs Outputs 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 “science as a public enterprise” Collecting the data Doing research Doing science openly Researchers - Govt & Public sector - Businesses - Citizens - Citizen scientists (communication/dialogue – joint production of knowledge) Stakeholders • Communication/dialogue must be audience-sensitive • Is it – with all stakeholder groups?
- 27. Open Science Data / Publications Researchers Mono/MultiInterTransdisciplinary Stakeholders RigourInnovationPolicySolutions Open Knowledge
- 28. 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
- 29. 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
- 30. 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?
- 31. • 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
Editor's Notes
- 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.
- 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.
- 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”.
- 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.
- 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).