Smart openness: endavant y seny!

David Osimo, #icitycamp

We don’t live perfect times

Why open

Source: Open Evidence / UNDP

Thematic knowledge: peer to patent

Decision rests with gov(USPTO)

Geographic coverage

User experience

IT skills

Many eyes and many hands

Networks and contacts

2003 2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

mySociety and MICRO-level initiatives

Web 2.0

MESO-level initiatives (barcamps, apps contests, open SAAS)

Obama election: the MACRO-level

Malmo Declaration

OpenGov Partnership

Lisbon conference

Revision directive open data

The emergence of an ecosystemHorizon 2020: open access + open data

Take a look at the dark side

“with the ideal of naked transparency alone--our democracy, like the music industry and print journalism generally, is doomed. The Web will show us every possible influence. The most cynical will be the most salient. Limited attention span will assure that the most salient is the most stable. Unwarranted conclusions will be drawn, careers will be destroyed, alienation will grow.”

Lawrence Lessig, 2009. Against Transparency

> 50 My Papers2 M scientists

2 M papers/year

Where The Data Goes Now:

Majority of data(90%?) is stored

on local hard drives Dryad: 7,631 files

Dataverse: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

Design

Implement

Evaluate

Set agenda

Brainstorming solutions

Drafting proposals Revising

proposals

Induce behavioural change

Collaborative action

Ensure Buy-in

Monitor executionCollect

feedback

Identify problems

Collect evidence

Set priorities

Analyze data

Uservoice, ideascale

EtherpadCo-ment.com

Social networks

Persuasive technologies

Challenge.gov

Open data

Participatry sensing

Open Data visualization

Evidencechallenge.com

Collaborative

visualization

Open discussion

Policy cycle

Simulate impact of options

Model and simulation

DECISION

Source: CROSSOVER roadmap

Analysis

Publication

ReviewConceptualisation

Data gathering

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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Research cycle

• “There are more smart people outside government than within it” (Bill Joy)

• “A problem shared is a problem halved ...and a pressure group created” (Paul Hodgkin – PatientOpinion.com)

• “it’s about pressure points, chinks in the armour where improvements might be possible, whether with the consent of government or not” (Tom Steinberg, Mysociety.org)

• “many participants in the process dilute the effect of bad apples or unconstructive participants” (Beth Noveck, Peertopatent.org)

Doesn’t matter how many, but who

Ignore

Read

Commen

1

10

100

1000

Open data

Reuse

1 reuser can be enough

Source: www.bbc.co.uk/news/magazine-22223190

Lessons learnt

Source: UNDP – Open Evidence

It’s not about “total citizens”• DAE Mid Term Review: More insightful than

representative

It’s doesn’t have to be totally open to the crowd

Open Declaration on European Public Services

Open to all

Digital Agenda Mid Term review Open to all, 2000 comments received, 1500 participants

Pledge Tracker Only to those organisations committing to the Grand Coalition for Digital Jobs

OpenIdeo Members of the OpenIdeo communityDaeimplementation Collaborative platform for EU MS

representativeYoung Advisors to VP Neelie Kroes Appointed Young Advisors

Need for restricted online spaces

Not all the time open

Fuente: http://ebiinterfaces.wordpress.com/2010/11/29/ux-people-autumn-2010-talks/

Open brainstorming

Small groups drafting

Open commenting

Small group re-drafting

Open endorsement

EU Open Declaration:

A new way to do public innovation

• Innovation without permission

• Fast development (week-end)

• Perpetual beta• Planning for

emergence• Government not only a

player but a platform

New government assets

• Innovation spaces • Innovation teams

Different types of citizen/gov collaboration

Ongoing work: an evaluation framework

Source: UNDP – Open Evidence

Current status of evidence

• Focus on definition, drivers and barriers, not on impact

• Largely via qualitative case studies, no systematic evaluation or quantitative evidence

Value for money

Cost per comment (EUR)

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There’s elite and elite: who benefits?

Usual suspects No problem

Not interested/interesting

Missed opportunity

Low quality of ideas High quality of ideas

Don’t participate in policy debate

Participate in policy debate

Source: adapted from Kublai evaluation

Application of logical framework to EU Community project

Before joining Kublai... Significant correlation between experience and benefit received

ExperienceY N

Bene

fit

Remember the debate on universal right to vote

• Perversity: it will reinforce the power of the elites• Futility: people won’t participate anyway• Jeopardy: it will lead to a rise in populism

• BUT: participation has educational effects, as the worker through political discussion opens his mind beyond the limitations of the factory, understands the relation between personal interests and faraway events, and becomes member of the community

Hirschmann, The Rethorics of Reaction

Locke quoted in Bobbio, The Future of Democracy

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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What is needed

Expertise to decide when and how to

open up

High quality implementation skills,

in terms of design, usability, technology

Robust methods to evaluate input, output

and outcome

Thanks

• dosimo@open-evidence.com• @osimod• Egov20.wordpress.com• www.open-evidence.com

Back up

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/2012/03/21/a-researchers-response-to-ipo-consultation-on-text-mining-copyright-exception/ [4] Lakhani et al., 2007) 37

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 38

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 40

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=012e7c324d

a6:39b1:49a0• 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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What is Policy 2.0

TOOLS• Open data• Social networks and

crowdsourcing• Visualisation• Big data simulation• Serious gaming

VALUES• Open up to external contributions

earlier in the process• Enable peer-to-peer collaboration

between participants• Design for unexpected

questions/contributions (Raw data, open questions)

• Be very clear and usable when you ask for help

• Account for real humans not simplified abstract entities

A reality check: policy-making 2.0 still more promising than impactful

• 2050 PATHWAYS : high usage (16K pathways created, 200 stakeholders involved in the building phase). Higher awareness by citizens. Output used by govt to back up the Carbon Strategy.

• GLEAM: adopted by mainstream gov’t agency to anticipate disease spread through transportation. Adopted also for educational purposes

• OPINION SPACE 3.0: significant participation (5K individuals) , endorsement at top level (Secretary of State Clinton)

• URBANSIM: High usage by US local gov’tLack of systematic robust evaluation of different policy-methods.

Initial evidence points to the potential impact, but very far from counterfactual / RCT approach available to date.

Open questions

– Does Policy 2.0 favour the participation of people beyond the “usual suspects”? Is it only for the elite?

– Does it bring new relevant ideas useful for policy-making?

– Does it actually lead to better policies?

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We should design gov 2.0 for Bart, not only for Lisa

Hat tip: Carter and Dance, Nytimes.com