Science 2.0: discussing the best available evidence

David OsimoOpen Evidence

based on a study for DG RTD

24th January 2013

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Three stories• Galaxyzoo: Galaxyzoo let users classify galaxies – 150K

volunteers had already classified more than 10 million images of galaxies. “as accurate as that done by astronomers“. 25+ scientific articles by Galaxy Zoo project (from 2009)

• Synaptic Leap: to find an alternative drug treatment for schistosomiasis with fewer side effect. All data and experiments published on Electronic Lab Notebook; social network activated. About 30 people, half from industry, participated. Identified new process and resolving agent.

• Excel-gate: Reinhart & Rogoff, 2010: “as countries see debt/GDP going above 90%, growth slows dramatically”. Paper was used as main theoretical justification for austerity. 2013: after getting the original excel file, Herndon et al. discover coding error + data gaps + unconventional weighting. 2

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Science outside academia

Mass collaboration

CrowdsourcingSensors

Open data, Article mining

Science 2.0: much more than Open Access

Open access

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Open access

Scientific blogs

Collaborative bibliographies

Alternative Reputation

systems

Citizens science Open

code

Open labbooks / wflows

Open annotati

on

Open data

Pre-print

Data-intensive

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Open access

Scientific blogs

Collaborative bibliographies

Alternative Reputation

systems

Citizens science Open

code

Open labbooks / wflows

Open annotati

on

Open data

Pre-print

Data-intensive

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Datadryad.org

Myexperiment.org

Runmycode.org

ArXiv

Sci-starter.com

Openannotation.orgAltmetric.com

Mendeley.comResearchgate.com

Figshare.com

Roar.eprints.org

An emerging ecosystem of services

and standards

Growing at different speedTrend Status DataPre-print Mature 694.000 articles in arXiv

Open access Fast growing Exponential growth of OA journals. 8/10% of scientific output is OA

Data intensive Fast growing 52% of science authors deals with datasets larger than 1Gb

Citizen scientist Medium growth 650K Zoouniverse users500 similar projects on SciStarter

Open data Medium growth 20% scientists share data15% journals require data sharing

Reference sharing

Medium growth 2 Million users of Mendeley reference-sharing tools

Open code Sketchy growth 21% of JASA articles make code available7% journals require code

Open Notebook Sketchy growth Isolated projects

Natural sciences outrank social science across all trends 7

> 50 My Papers2 M scientists

2 M papers/year

Where The Data Goes Now:

Majority of data(90%?) is stored

on local hard drivesDryad:

7,631 filesDataverse:

0.6 M

Datacite: 1.5 M

Some data (8%?) stored in large,

generic data repositories

MiRB: 25k

PetDB: 1,5 k

TAIR: 72,1 k

PDB: 88,3 k

SedDB: 0.6 k

A small portion of data (1-2%?) stored in small,

topic-focuseddata repositories

Source: Anita De Waard 2013http://www.slideshare.net/anitawaard/making-data-sharing-happen

Deep implications• New scientific outputs and players: nanopublications,

data and code; vertical disintegration of the value chain

• Greater role for inductive methods: everything becomes a Genome Project

• Scaling serendipity: Big linked data, collaborative annotation, social networking and knowledge mining detect unexpected correlations on a massive scale

• Better science: reproducible and truly falsifiable research findings; earlier uncovering of mistakes

• More productive science: reusing data and products, crowdsourcing work, reduce time-to-publication

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Europe can lead• European scientific publishers are leading on

experimentation with new kind of open and data-intensive services

E.g. “Article of the Future project, AppsForScience competition (Elsevier) Thieme ( a small German publisher) data integration

• Home to world class science 2.0 startups: Mendeley and ResearchGate are global players in social networking

for scientists, Digital Science that recently acquired FigShareMendeley used by about 2 million researchers, covering 65 million

documents vs 49 by commercial databases by Thomson Reuters. Elsevier just bought Mendeley for 50 M Euros.

• Home to top citizen science initiatives (GalaxyZoo was launched in Oxford, ExCiteS group and Citizen

Cyberscience Centre)• Funding agencies are active in new mandates on openness

(e.g. Wellcome Trust, FP7) – open access, open data10

BUT the institutional framework is a bottleneck

• Researchers are reluctant to share data and code [1], and to provide open peer review

• Current career mechanisms are “publish or perish”. No reward for sharing.

• Publishing data and code requires additional work

• Publishing intermediate products can actually hinder publication/patenting: sharing is difficult in patent-intensive domains

• Funding mechanisms are too rigid, roadmap-based and evaluated on articles and patents

[1] Wicherts et al., 2011 ; Research Information Network, 2008 ; Campbell , 200211

Institutional failure and the case for public intervention

BENEFITS Individual Researchers

Institutions Business Publishers Societal benefits

Open access ++ + + -- ++

Open data -- -- -- + ++

Open code -- -- -- = ++

Citizen science

+ = + = +

Alternative reputation systems

+ - + - +

Data-intensive

+ + + + ++

Social media + = = = + 12

• Contradictions emerge between individuals’ and societal benefits • Research funders (and publishers) have high leverage on scientific institutions

How to grasp this opportunity?

• It’s not about adding a science 2.0 top-down roadmap-based initiative in existing programmes

• It’s not about simply letting a thousand flowers flourish bottom-up

• It’s about nudging the right institutional re-arrangement (Perez) and right system of incentives for the scientific value chain

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Towards research policy 2.0Recommendation Inspiring example

Adopt more flexible reputation mechanisms for scientists

From 2013, NSF requires PI to list research “products” rather than “publications”

Encourage sharing by regulation Wellcome Trust mandatory data planCover the costs of sharing intermediate output such as data

Gold access publication costs to be covered in Horizon2020

Develop Innovative infrastructure, tools , methods and standards

Alternative reputation system, Openannotation, Datadryad

Make IPR more flexible Innocentive.com, Peertopatent.comIncrease open-ended funding system

FET open, UK Arts council, Inducement prizes

Collect better evidence Dedicated data-gathering exercise (a’ la PEW) 14

Thanks

• Continue the discussion at science20study.wordpress.com

• Collect evidence and cases at groups.diigo.com/group/science-20

• Contact david.osimo@tech4i2.com ; katarzyna.szkuta@tech4i2.com ; @osimod

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Backup

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Emerging impact: a) more productive science

– using the same data sets for multiple research. 50% of Hubble papers came from data re-users [1].

– Crowdsourcing work: “thousands recruited in months versus years and billions of data points per person, potential novel discovery in the patterns of large data sets, and the possibility of near real-time testing and application of new medical findings.” [2].

– “cut down the time it takes to go from lab to medicine by 10 15 ‐years with Open Notebook Science”. “because of poor literature analysis tools 20-25% of the work done in his synthetic chemistry lab is unnecessary duplication or could be predicted to fail” [3]

– Faster circulaton of high-quality ideas: 70% of publications discussed in blogs are from high-impact journals

– Open research solved one-third of a sample of problems that large and well-known R & D-intensive firms had been unsuccessful in solving internally [4]

[1] http://archive.stsci.edu/hst/bibliography/pubstat.html[2] http://www.jmir.org/2012/2/e46/[3] http://science.okfn.org/category/pubs/ [4] Lakhani et al., 2007) 17

b) Better science• Greater falsifiability (Popper): move towards reproducible

science thanks to publishing data + code in addition to article,

• Rapidly uncover mistaken findings (Climategate 2009 or microarray-based clinical trials underway at Duke University)

• Data sharing is associated with greater robustness of findings [1]. Sharing data and notes applies to failures, as well as successes

• Especially important for computational science “Computational science cannot be elevated to a third branch of the scientific method until it generates routinely verifiable knowledge” [2]

[1] Wicherts et al., 2011[2] Donoho, Stodden, et al. 2009 18

c) Greater role of inductive methods

• “The end of theory”: “Here’s the evidence, now what is the hypothesis?”

• All science becomes computational. 38% of scientists spend more than 1/5 of their time developing software (Merali, 2010).

• Greater availability of data collection and datasets increases the utility of inductive methods. Genome project as new paradigm

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d) Scaling serendipity• From penicillin to theory of relativity, serendipity has

always been a core component of science• Big linked data, collaborative annotation and knowledge

mining of OA articles allow to detect unexpected correlation on a massive scale. Mendeley manages the bibliographies of 2 Million scientists and uses them for suggest further reading.

• Emerging evidence that for scholars recommendation is more important than search for references. Social networking and recommendation systems allow scientists to “stumble upon” new evidence

• Open research successful solvers solved problems at the boundary or outside of their fields of expertise [1]

[1] Lakhani et al., 2007 20

e) New outputs and players #beyondthepdf

• Nanopublications, datasets, code• Integration of data and code with articles• Reproducible papers and books

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Emerging policies• Funders and publishers have high leverage on researchers• Increasing push towards Open Access from funders• Journals and funding agencies increasingly require data

submission and data management plans• From 14 January 2013, NSF grants forms requires PI to list

research “products” rather than “publications”• Alternative metrics emerge such as altmetrics and

download statistics

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Towards research policy 2.0

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Features • Simplified proposals• Rewarding solutions, not proposal• Multi-stage• Open priorities• Flexible and open ended (allowing for • serendipity)• Peer-selection Reputation-based • (funding not to the proposal but to the person) • Multidisciplinarity by design• Flexible IPR• Short project time• Accepting failure • transparency (open monitoring)• Based on social network analysis

Examples• Inducement prizes e.g.

http://www.heritagehealthprize.com• Seed Capital

http://www.ibbt.be/en/istart/our-istart• toolbox/iventure)• ERC http://erc.europa.eu• SBIR http://www.sbir.gov• FET OPEN • http://cordis.europa.eu/fp7/ict/fetopen/

home_en.html• SME • htt://cordis.europa.eu/fetch?

CALLER=PROGLINK_PARTNERS&AC• TION=D&DOC=1&CAT=PROG&QUERY=012e7c32

4da6:39b1:49a0• 957c&RCN=862• IBBT www.ibbt.be• Arts council

http://www.artscouncil.org.uk/funding/grants

• arts• Banca dell’innovazione / Innovation Bank• http://italianvalley.wired.it/news/altri/

perche-ci• serve-una-banca-nazionale-dell-

innovazione.html

• Last year researchers at one biotech firm, Amgen, found they could reproduce just six of 53 “landmark” studies in cancer research.

• Earlier, a group at Bayer, a drug company, managed to repeat just a quarter of 67 similarly important papers.

• A leading computer scientist frets that three-quarters of papers in his subfield are bunk.

• In 2000-10 roughly 80,000 patients took part in clinical trials based on research that was later retracted because of mistakes or improprieties.

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• Conversely, failures to prove a hypothesis are rarely even offered for publication, let alone accepted. “Negative results” now account for only 14% of published papers, down from 30% in 1990. Yet knowing what is false is as important to science as knowing what is true. The failure to report failures means that researchers waste money and effort exploring blind alleys already investigated by other scientists.

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• When a prominent medical journal ran research past other experts in the field, it found that most of the reviewers failed to spot mistakes it had deliberately inserted into papers, even after being told they were being tested.

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