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Data responsibility: a new social good for the information age

Stefaan G. Verhulst in The Conversation Global and at TedX MidAtlantic: “…What is less discussed, however, is that most data remains locked up and proprietary, the private property of companies, governments and other organisations. This limits its public benefits.
Data responsibility can help organisations break down these private barriers and share their proprietary data for the public good. In the case of the private sector, in particular, it represents a type of corporate social responsibility for the 21st century….
Such examples show us that data can improve and even save lives. But in order to fully harness the potential of data, three conditions must be fulfilled. They comprise the three pillars of data responsibility.
1. A duty to share
This is perhaps the most evident duty: to share private data when it’s clear that it will serve the public good. Secondary use is not always popular among data holders (often for good reasons) but when done correctly, data sharing can have powerful social benefits, as illustrated above.
2. A duty to protect
Sharing does involve risks, notably to privacy, security and other individual rights. So it is imperative that organisations share responsibly, with every effort to protect both the data itself and the individuals who have surrendered their data (even if often unwittingly).
The consequences of failing to protect data have now been well-documented. The most obvious problems occur when data is not properly anonymised before it is shared, or when de-anonymised data otherwise leaks into the public domain.
Ostensibly anonymised data may itself also be susceptible to de-anonymisation, wherein information released for the public good ends up causing individual harm.For example,…
Thus the good intentions guiding data releases must be accompanied by a powerful sense of responsibility at every stage of the information chain, from data collection, processing and analysing to sharing and use.
3. A duty to act
For released data to serve the public good, officials and others must also adopt policies and interventions based on insights gained from its release. Without action, the potential remains just that — potential….(Full post)

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GovLab Blog

Making Open Data more evidence-based: Toward a user-centric and interdisciplinary research agenda to advance open data.

Reposted from IODC2016 (Spanish version) – by Stefaan G. Verhulst and Danny Lämmerhirt
Key take-aways from the Measurement Action Track at IODC 2016
During the IODC 2016, the “Action Track: Measurement and Increasing Impact” sought to review the need and role of research for (scaling) open data practice and policy. The track was informed by the various sessions and workshops that took place at the Open Data Research Symposium prior to the Conference.
In what follows, we summarize what we heard throughout the conference.
Headline message that emerged from our engagement with the community at IODC: To realize its potential there is a need for more evidence on the full life cycle of open data – within and across settings and sectors.
Many participants acknowledged and shared progress toward gathering evidence on developments, actors and conditions that impact open data. Yet, a consensus emerged that more systematic research is still needed. An “evidence-based and user-centric open data” approach is necessary to drive adoption, implementation, and use.
In particular, three substantive areas were identified that could benefit from interdisciplinary and comparative research:
Demand and use: First, many expressed a need to become smarter about the demand and use-side of open data. Much of the focus, given the nascent nature of many initiatives around the world, has been on the supply-side of open data. Yet to be more responsive and sustainable more insight needs to be gained to the demand and/or user needs.
Conversations repeatedly emphasized that we should differentiate between open data demand and use. Open data demand and use can be analyzed from multiple directions: 1) top-down, starting from a data provider, to intermediaries, to the end users and/or audiences; or 2) bottom-up, studying the data demands articulated by individuals (for instance, through FOIA requests), and how these demands can be taken up by intermediaries and open data providers to change what is being provided as open data.
Research should scrutinize each stage (provision, intermediation, use and demand) on its own, but also examine the interactions between stages (for instance, how may open data demand inform data supply, and how does data supply influence intermediation and use?).
Several research questions were proposed including the following: What is the demand for open data – and do interest groups understand the potential value open data conveys for them? If so, how to study these interest groups? Who are the audiences of open data? What are the different types of users (and users of users)? What are their needs? What are the problems or opportunities current and potential users seek to address using open data? When do users become producers of data and vice-versa? What is the role of data intermediaries in providing and using open data? How to study and establish feedback loops between open data users, intermediaries, and providers that can help make open data more relevant to users?  Do we need professional standards for different types of users – such as, for instance, data journalists?
Unfortunately – besides traditional UX research methods – no method exists for data holders and/or users to assess demand and use in a manner that can inform design and policy requirements.
Next steps:

  • Toward that end, it was suggested to create a collaborative effort to develop a “diagnostic tool or method” to map and analyze the ecosystem of open data toward better understanding the needs, interests as well as power relations of different stakeholders, users, non-users and other audiences.
  • In addition, to be more deductive, explanatory, and generate insights that are operational (for instance, with regard to which users to prioritize) several IODC participants recommended to expand the development and exchange of “demand and use” case studies based on interdisciplinary perspectives (and going beyond a descriptive collection of examples).

Informing data supply and infrastructure: Second, we heard on numerous occasions, a call upon researchers and domain experts to help in identifying “key data” and inform the government data infrastructure needed to provide them. Principle 1 of the International Open Data Charter states that governments should provide key data “open by default”, yet the questions remains in how to identify “key” data (e.g., would that mean data relevant to society at large?).
Which governments (and other public institutions) should be expected to provide key data and which information do we need to better understand government’s role in providing key data? How can we evaluate progress around publishing these data coherently if countries organize the capture, collection, and publication of this data differently?
Next Steps: Several steps were suggested to enable policy and decision makers in prioritizing data sets and allocating resources to do so, including:

  • Develop decision trees that compare and integrate evidence on the demand, benefits and risks of data-sets;
  • Identify and analyze “data deserts” – where no or little data is collected and made available;
  • Develop and provide assessment frameworks for National Statistical Offices on the potential value of certain data-sets.

Impact: In addition to those two focus areas – covering the supply and demand side –  there was also a call to become more sophisticated about impact. Too often impact gets confused with outputs, or even activities. Given the embryonic and iterative nature of many open data efforts, signals of impact are limited and often preliminary. In addition, different types of impact (such as enhancing transparency versus generating innovation and economic growth) require different indicators and methods. At the same time, to allow for regular evaluations of what works and why there is a need for common assessment methods that can generate comparative and directional insights.
Next steps: Joint efforts were recommended to develop

  • Data-value chain assessment mechanisms that can identify and illustrate how value gets generated (if at all), at what stage and under which conditions;
  • A conceptual framework that can accommodate the (e)-valuation of data as an infrastructure or “commons” (similar to other public interest resources such as green spaces or air quality).

Research Networking: Several researchers identified a need for better exchange and collaboration among the research community. This would allow to tackle the research questions and challenges listed above, as well as to identify gaps in existing knowledge, to develop common research methods and frameworks and to learn from each other. Key questions posed involved: how to nurture and facilitate networking among researchers and (topical) experts from different disciplines, focusing on different issues or using different methods? How are different sub-networks related or disconnected with each other (for instance how connected are the data4development; freedom of information or civic tech research communities)? In addition, an interesting discussion emerged around how researchers can also network more with those part of the respective universe of analysis – potentially generating some kind of participatory research design.
Next steps: To enable networking and increased matching of expertise, needs and interests, resources and efforts must be directed toward:

  • A collaborative (and dynamic) mapping of the current open data research eco-system – identifying both the supply and demand for research; and how research questions and methods of different research disciplines already intersect and could cross-pollinate each other;
  • Network analysis of the open data research universe to identify gaps and hubs of expertise (including, for instance, possible correlation analysis of participants of different open data-related conferences);
  • Experimentation with participatory research design – not only to study “the user”, but to study open data “with the user”;
  • Experimentation and evaluation of different networking and collaboration platforms. This may increase our understanding of the usability and usefulness of existing research networks.

To conclude, the different papers that were submitted and presented at the Open Data Research Symposium (all downloadable from odresearch.org) and the growing literature on open data (see for instance ogrx.org) indicates that much progress has been made toward an enhanced understanding of open data –its suppliers, users and practices. Yet as the Open Data community matures, more evidence is needed to guide future investments and uses. Ultimately the open data community should “walk the talk” and become more data-driven – which means that more investment is needed to support research and network the evidence and expertise that already exists.
This blogpost intends to start a conversation how we can better research open data. We invite everyone interested in this important area to discuss with us the possible research topics proposed above.
How can we operationalize the topics described above?
Are any important research areas missing?
Please feel free to use established venues including the Network of Innovators and the Open Knowledge Discuss Forum for Research and Policy. This allows us to create central discussion channels that will bring interdisciplinary research interests together.
See also: Open Data Research Symposium

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GovLab Blog GovLab Index Open Data Open Data 500

The GovLab Index: Open Data – 2016 Edition

By Robert Montano and Prianka Srinivasan
Please find below the latest installment in The GovLab Index series, inspired by Harper’s Index. “The GovLab Index: Open Data” provides an update on our previous Open Data installment, and highlights global trends in Open Data and the release of public sector information.
Previous installments of the Index include Prizes and Challenges, Measuring Impact with Evidence, The Data Universe, Participation and Civic Engagement and Trust in Institutions. Please share any additional statistics and research findings on the intersection of technology in governance with us by emailing shruti at thegovlab.org.
Value and Impact

Public Views on and Use of Open Government Data in the US

  • Percentage of Americans surveyed that used the internet to find data or information pertaining to government in 2015  according to Pew Research study: 65%
  • How many Americans think the federal government shares data very or somewhat effectively with the public: 44%
  • How many Americans “could think of an example where local government did not provide enough useful information about data and information to the public”: 19%
  • Percentage of Americans who have “used government sources to find information about student or teacher performance”: 20%
    • Those who have used government sources “to look for information on the performance of hospitals or health care providers”: 17%
    • To find out about contracts between governmental agencies and external firms: 7%
  • Percentage of Americans with a smartphone who have used open data: 84%
  • Percentage of Americans surveyed who think governments are very effective in sharing data to the public according to Pew Research study: 5%

Efforts and Involvement

  • Countries participating in the Open Government Partnership today: 70
    • In 2011? 8
  • Countries with open data portals: 52
    • In 2013? Approximately 40
  • Percentage of governments that share open data on the performance of public education: 12% 
  • Percentage of governments that release open data on health services: 7%
  • Number of cities globally that participated in 2016 International Open Data Hackathon Day: 84
  • Percentage of “open data readiness” assessed by European Data Portal: 59%
  • Number of U.S. cities with Open Data Sites in 2016: 119
  • Number of governments who have adopted the International Open Data Charter: 35
  • Number of non-state organizations who have endorsed the International Open Data Charter: 30
  • Number of places analyzed by the Open Data Index: 122
    • In 2014? 97
  • Top 5 countries in Open Data Barometer rankings: UK, US, Sweden, France, New Zealand
  • Percentage of countries, out of 122 assessed, that open their election results: 58%
    • In Sub-Saharan Africa? 42%
    • In Asia? 41%
    • In Eastern Europe? 71%
    • In Latin America? 71%
  • Number of cities participating in the Open Data Census: 39
  • Latin American countries with the highest number of open-data driven companies surveyed by the World Bank: Mexico, Chile and Brazil
  • Asian countries with the highest number of open-data driven companies surveyed by the World Bank: India, Indonesia, Philippines and Malaysia
  • Average amount of equity and quasi-equity investment needed to finance data-driven companies in Latin America and Asia: $2 and $3 million
  • Savings through real-time transport data in London, UK: £15-58 million each year 
  • Rate of completion on coordination mechanism commitments among OGP members: 71%
  • Rate of completion on sub-national open data commitments among OGP members: 75%

Examining Datasets

  • Number of datasets available through data.gov: 189,814
  • Number of datasets available through data.gov.uk: 39, 710
  • Number of datasets available through data.gov.au: 23,270
  • Number of datasets available via the Open Data Index: 156
  • Countries assessed by the Open Data Barometer (ODB) that release data on government spending: 8%
  • Number of datasets classed as “open” by the Open Data Index: 9% (down from 12% in 2014)
  • Percentage of countries surveyed by ODB (92) with open data initiatives in place: 55%
  • Percentage of data available online in ODB survey: 76%
  • Percentage of civil societies/tech communities utilizing data in ODB survey: 93%
  • ODB Government data updated at regular intervals: 73%
  • Average ranking of 92 countries by ODB with some form of open data policy (scaled 0-100): 33
  • Percentage of datasets found by ODB in top 10 ranked countries: 50%
  • Percentage of open datasets in Australia, according to Open Data Census: 30%
  • Number of datasets in the Caribbean according to Open Data Census: 27
  • Percentage of open datasets in the Caribbean, according to Open Data Census: 7%

 
Sources
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Aligning Supply and Demand for Better Governance.” Independence Reporting Mechanism Report of the Open Government Partnership. 2015.
Americans’ Views on Open Government Data.” Pew Research Center. April 2015.
Characterization study of the Infomediary Sector”. Datos.gov.es, July 2012.
Creating Value through Open Data,” European Data Portal, European Commission. 2015.

Data Will Only Get Us So Far. We Need it to be Open.” World Economic Forum. January 16, 2016.
Datasets of the United Kingdom
The Economic Benefits of Commercial GPS Use in the U.S. and The Costs of Potential Disruption.” NPD Report. Nam D. Pham. 2011.

The Economic Impact of Open Data”, Socrata. February 27, 2014.

“The economic impact of open data: what do we already know?”, International Trade Forum, December 2015.
European Data Portal, accessed September, 2016.
International Open Data Hackathon” Open Data Day, accessed September 2016.
Investment in Open Data Challenge Series could see 5 to 10-fold return to UK economy over 3 years” Open Data Institute News,October 2015.
Landsat Benefited U.S. Economy by $1.8 Billion in 2011.” NASA Landsat Science. August 30, 2015.
Making sense of US$3 trillion – Estimating the value of Open Data for Small Developing Economies”, IODC Blog. May, 2015.  
New Development: Leveraging Big Data Analytics in the Public Sector.” Pandula Gamage. Public Money and Management. June 2016.
New Surveys Reveal Dynamism, Challenges of Open Data-Driven Businesses in Developing Countries”, Ala Morrison, Data Blog of the World Bank. December 15, 2014.
New Research Shows the Impact of Open Data in Agriculture and Nutrition”, Global Open Data for Agriculture and Nutrition. May 28, 2015.
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Open for Business: How Open Data Can Help Achieve the G20 Growth Target” Omidyar Network, June 2014.
The Open Data Economy Unlocking Economic Value by Opening Government and Public Data” by Dinand Tinholt, Capgemini Consulting. February 2013.  
Open Data for Economic Growth”, Report of the World Bank. June 25, 2014.
Open Data in the United States”, data.gov, accessed September 2016.
Permission granted: The economic value of data assets under alternative policy regimes”, 2016 Report. Open Data Institute.
Policy in the Data Age: Data Enablement for the Common Good.” Karim Tadjeddine and Martin Lundqvist. McKinsey and Company. August 2016.
Shakespeare Review: An Independent Review of Public Sector Information”, Commissioned by the UK Government. May 2013.
Researching the Economics of Data to Help Government make Better Choices, Jack Hardinges, Jeni Tennison and Peter Wells
Review of recent studies on PSI reuse and related market developments.” European Commission. 2011.
“Tracking the state of government Open Data” Global Open Data Index, accessed September 2016.
URBAN MOBILITY IN THE SMART CITY AGE, Schneider Group, ARUP, The Climate Group. 2016
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What is the Economic Impact of Geo Services?”, Oxera Report. 2013.