Pathways to Open Science

Geoffrey BoultonCODATA President University of Edinburgh

NISO/ICSTI Joint Webinar10 November 2015

Openness – the bedrock of science in the modern era

Henry Oldenburg

Scientific self correction

A crisis of credibility?

problems & opportunities

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The Data Deluge

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The Challenge: the “Data Deluge” is undermining “self correction”

THEN AND NOW

“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• 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

What open data meansFor 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-databut 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.

Linear regression

Cluster analysis

Dynamic/complex behaviour

Complex systemsNo mathematical pipeline

Glucose levels in Type II Diabetes

Simple relationshipsClassical statistics

Topological analysis

Valid reasoning: e.g. coping with complex data

Exploiting the Data Flux (Volume & velocity)

http://www.wired.co.uk/news/archive/2014-01/15/1000-dollar-genome/viewgallery/331679

Data acquistion: Cost down – Flux up

Looking at clouds

Daily Trajectory Observation Model

Ozone LevelsMicro-satellites

From “simple” science to complexity,from uncoupled to highly coupled systems

Uncoupledsystems

The behaviour of highly coupled systems

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

Semantic Web of Broad, Linked DataSubject – 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 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?

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

• 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

Openness to public scrutiny

… seizing the data opportunities depends on an ethos of data-sharing

e.g. a growing number of disciplinary communities

A national data-intensive system

International Research Data Collaboration

CODATA Policies & practice Frontiers of data

science Capacity Building

WDS• Data stewardship• Data standards

RDA• Interoperability

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 meetingSouth Africa, December 2015

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

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

Possible Regional Platforms for Open Science

AfricanPlatform

Asian Platform?

European

Platform?

AustralianPlatform

Shared investment in infrastructure; harvesting and circulating good ideas; spreading and supporting good practice; capacity building; promoting applications; linking to international programmes and standards.

?

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

Open ScienceData / Publications

Open Knowledge

Open ScienceData / Publications

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Open Knowledge

WHAT?

Open Knowledge

Open Science

OpenData/Open Access

WHO?

Stakeholders

Researchers

DISCIPLINE

Trans-

Inter-

Mono-Multi-

PURPOSE

PolicySolutions

Innovation

Nuts & Bolts of Reality

Some related concepts

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?

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?