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Pathways to Open Science
Geoffrey Boulton
CODATA President
University of Edinburgh
NISO/ICSTI Joint Webinar
10 November 20...
Openness – the bedrock of science in the
modern era
Henry Oldenburg
Scientific self correction
A crisis of credibility?
problems & opportunities
1020 bytes
Available storage
The Data Deluge
/var/folders/ls/nv6g47p94ks4d11f1p72h2ch00
00gn/T/com.apple.Preview/com.apple.Preview
.PasteboardItems/rutford_avo_afi_ed_...
“Scientists like to think of science as
self-correcting. To an alarming degree, it is not.”
A crisis of replicability and credibility?
Pre-clinical oncology – 89% not reproducible
Reasons
• Fraudulent behaviour
• I...
What open data means
For effective communication, replication and re-purposing
we need intelligent openness. Data, meta-da...
Linear regression
Cluster analysis
Dynamic/complex behaviour
Complex systems
No mathematical pipeline
Glucose levels in Ty...
Exploiting the Data Flux (Volume & velocity)
http://www.wired.co.
uk/news/archive/201
4-01/15/1000-dollar-
genome/viewgallery/3
31679
Data acquistion: Cost down – Flux...
Looking at clouds
Daily Trajectory Observation Model
Ozone LevelsMicro-satellites
From “simple” science to complexity,
from uncoupled to highly coupled systems
Uncoupled
systems
The behaviour of
highly co...
Simulating a complex system Characterising a complex system
Image of brain cells in a ratEmergent behaviour of a specific
...
Semantic Web of Broad, Linked Data
Subject – Predicate – Object - e.g. A Rice Ontology
1. Maintaining “self-correction”
2. Open knowledge is creative & productive
“If you have an apple and I have an apple and ...
Mathematics related discussions
Tim Gowers
- crowd-sourced mathematics
An unsolved problem posed on
his blog.
32 days – 27...
• Openly collected science is already helping policy
makers.
• AshTag app allows users to submit photos and
locations of s...
Openness to public scrutiny
EMBL-EBI services
Labs around the
world send us
their data and
we…
Archive it
Classify it
Share it with
other data
provide...
Ins tu onal
management and support
Na onal policies
& e-infrastructure
Open
Research
Data
Big Data
Analy cs
Knowledge
Outp...
CODATACODATA
II
SS
UU
International Research Data Collaboration
CODATACODATA
II
SS
UU
CODATA
 Policies & practice
 Front...
Science International:
international voice of policy for science
International Council for Science (ICSU), International S...
Responsibilities
• Scientists
• Research Institutions and universities
• Publishers
• Funding agencies
• Professional asso...
i. Publicly funded scientists have a responsibility to contribute
to the public good through the creation and communicatio...
Possible Regional Platforms for Open Science
African
Platform
Asian
Platform?
?
Australian
Platform
Shared investment in i...
A taxonomy of open science (research)
- a journey towards science as a public enterprise
Inputs Outputs & open engagement ...
Open Science
Data / Publications
Open Knowledge
Open Science
Data / Publications
Mono/MultiInterTransdisciplinary
Open Knowledge
Open Science
Data / Publications
Researchers
Mono/MultiInterTransdisciplinary
Stakeholders
Open Knowledge
Open Science
Data / Publications
Researchers
Mono/MultiInterTransdisciplinary
Stakeholders
RigourInnovationPolicySolu...
WHAT?
Open
Knowledge
Open
Science
Open
Data/Open
Access
WHO?
Stakeholders
Researchers
DISCIPLINE
Trans-
Inter-
Mono-
Multi...
A barrier to openness? - Analytic overload.
E.g. - Global Earth Observation System of Systems
• What is the human role?
• ...
A barrier to openness? - Analytic overload.
E.g. - Global Earth Observation System of Systems
• What is the human role?
• ...
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November 10, 2015 NISO/ICSTI Joint Webinar: A Pathway from Open Access and Data Sharing to Open Science in Practice

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. 1. Pathways to Open Science Geoffrey Boulton CODATA President University of Edinburgh NISO/ICSTI Joint Webinar 10 November 2015
  2. 2. Openness – the bedrock of science in the modern era Henry Oldenburg
  3. 3. Scientific self correction
  4. 4. A crisis of credibility?
  5. 5. problems & opportunities 1020 bytes Available storage The Data Deluge
  6. 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. 7. “Scientists like to think of science as self-correcting. To an alarming degree, it is not.”
  8. 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. 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. 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. 11. Exploiting the Data Flux (Volume & velocity)
  12. 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. 13. Looking at clouds Daily Trajectory Observation Model Ozone LevelsMicro-satellites
  14. 14. From “simple” science to complexity, from uncoupled to highly coupled systems Uncoupled systems The behaviour of highly coupled systems
  15. 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. 16. Semantic Web of Broad, Linked Data Subject – Predicate – Object - e.g. A Rice Ontology
  17. 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. 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. 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. 20. Openness to public scrutiny
  21. 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. 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. 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. 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. 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. 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. 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. 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. 29. Open Science Data / Publications Open Knowledge
  30. 30. Open Science Data / Publications Mono/MultiInterTransdisciplinary Open Knowledge
  31. 31. Open Science Data / Publications Researchers Mono/MultiInterTransdisciplinary Stakeholders Open Knowledge
  32. 32. Open Science Data / Publications Researchers Mono/MultiInterTransdisciplinary Stakeholders RigourInnovationPolicySolutions Open Knowledge
  33. 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. 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. 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?

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