Big Data, My Data

Curated on Posted on by Stefaan Verhulst

Jane Sarasohn-Kahn  at iHealthBeat: “The routine operation of modern health care systems produces an abundance of electronically stored data on an ongoing basis,” Sebastian Schneeweis writes in a recent New England Journal of Medicine Perspective.
Is this abundance of data a treasure trove for improving patient care and growing knowledge about effective treatments? Is that data trove a Pandora’s black box that can be mined by obscure third parties to benefit for-profit companies without rewarding those whose data are said to be the new currency of the economy? That is, patients themselves?
In this emerging world of data analytics in health care, there’s Big Data and there’s My Data (“small data”). Who most benefits from the use of My Data may not actually be the consumer.
Big focus on Big Data. Several reports published in the first half of 2014 talk about the promise and perils of Big Data in health care. The Federal Trade Commission’s study, titled “Data Brokers: A Call for Transparency and Accountability,” analyzed the business practices of nine “data brokers,” companies that buy and sell consumers’ personal information from a broad array of sources. Data brokers sell consumers’ information to buyers looking to use those data for marketing, managing financial risk or identifying people. There are health implications in all of these activities, and the use of such data generally is not covered by HIPAA. The report discusses the example of a data segment called “Smoker in Household,” which a company selling a new air filter for the home could use to target-market to an individual who might seek such a product. On the downside, without the consumers’ knowledge, the information could be used by a financial services company to identify the consumer as a bad health insurance risk.
“Big Data and Privacy: A Technological Perspective,” a report from the President’s Office of Science and Technology Policy, considers the growth of Big Data’s role in helping inform new ways to treat diseases and presents two scenarios of the “near future” of health care. The first, on personalized medicine, recognizes that not all patients are alike or respond identically to treatments. Data collected from a large number of similar patients (such as digital images, genomic information and granular responses to clinical trials) can be mined to develop a treatment with an optimal outcome for the patients. In this case, patients may have provided their data based on the promise of anonymity but would like to be informed if a useful treatment has been found. In the second scenario, detecting symptoms via mobile devices, people wishing to detect early signs of Alzheimer’s Disease in themselves use a mobile device connecting to a personal couch in the Internet cloud that supports and records activities of daily living: say, gait when walking, notes on conversations and physical navigation instructions. For both of these scenarios, the authors ask, “Can the information about individuals’ health be sold, without additional consent, to third parties? What if this is a stated condition of use of the app? Should information go to the individual’s personal physicians with their initial consent but not a subsequent confirmation?”
The World Privacy Foundation’s report, titled “The Scoring of America: How Secret Consumer Scores Threaten Your Privacy and Your Future,” describes the growing market for developing indices on consumer behavior, identifying over a dozen health-related scores. Health scores include the Affordable Care Act Individual Health Risk Score, the FICO Medication Adherence Score, various frailty scores, personal health scores (from WebMD and OneHealth, whose default sharing setting is based on the user’s sharing setting with the RunKeeper mobile health app), Medicaid Resource Utilization Group Scores, the SF-36 survey on physical and mental health and complexity scores (such as the Aristotle score for congenital heart surgery). WPF presents a history of consumer scoring beginning with the FICO score for personal creditworthiness and recommends regulatory scrutiny on the new consumer scores for fairness, transparency and accessibility to consumers.
At the same time these three reports went to press, scores of news stories emerged discussing the Big Opportunities Big Data present. The June issue of CFO Magazine published a piece called “Big Data: Where the Money Is.” InformationWeek published “Health Care Dives Into Big Data,” Motley Fool wrote about “Big Data’s Big Future in Health Care” and WIRED called “Cloud Computing, Big Data and Health Care” the “trifecta.”
Well-timed on June 5, the Office of the National Coordinator for Health IT’s Roadmap for Interoperability was detailed in a white paper, titled “Connecting Health and Care for the Nation: A 10-Year Vision to Achieve an Interoperable Health IT Infrastructure.” The document envisions the long view for the U.S. health IT ecosystem enabling people to share and access health information, ensuring quality and safety in care delivery, managing population health, and leveraging Big Data and analytics. Notably, “Building Block #3” in this vision is ensuring privacy and security protections for health information. ONC will “support developers creating health tools for consumers to encourage responsible privacy and security practices and greater transparency about how they use personal health information.” Looking forward, ONC notes the need for “scaling trust across communities.”
Consumer trust: going, going, gone? In the stakeholder community of U.S. consumers, there is declining trust between people and the companies and government agencies with whom people deal. Only 47% of U.S. adults trust companies with whom they regularly do business to keep their personal information secure, according to a June 6 Gallup poll. Furthermore, 37% of people say this trust has decreased in the past year. Who’s most trusted to keep information secure? Banks and credit card companies come in first place, trusted by 39% of people, and health insurance companies come in second, trusted by 26% of people.
Trust is a basic requirement for health engagement. Health researchers need patients to share personal data to drive insights, knowledge and treatments back to the people who need them. PatientsLikeMe, the online social network, launched the Data for Good project to inspire people to share personal health information imploring people to “Donate your data for You. For Others. For Good.” For 10 years, patients have been sharing personal health information on the PatientsLikeMe site, which has developed trusted relationships with more than 250,000 community members…”

Selected Readings on Crowdsourcing Tasks and Peer Production

Curated on Posted on by Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of crowdsourcing was originally published in 2014.

Technological advances are creating a new paradigm by which institutions and organizations are increasingly outsourcing tasks to an open community, allocating specific needs to a flexible, willing and dispersed workforce. “Microtasking” platforms like Amazon’s Mechanical Turk are a burgeoning source of income for individuals who contribute their time, skills and knowledge on a per-task basis. In parallel, citizen science projects – task-based initiatives in which citizens of any background can help contribute to scientific research – like Galaxy Zoo are demonstrating the ability of lay and expert citizens alike to make small, useful contributions to aid large, complex undertakings. As governing institutions seek to do more with less, looking to the success of citizen science and microtasking initiatives could provide a blueprint for engaging citizens to help accomplish difficult, time-consuming objectives at little cost. Moreover, the incredible success of peer-production projects – best exemplified by Wikipedia – instills optimism regarding the public’s willingness and ability to complete relatively small tasks that feed into a greater whole and benefit the public good. You can learn more about this new wave of “collective intelligence” by following the MIT Center for Collective Intelligence and their annual Collective Intelligence Conference.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Benkler, Yochai. The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale University Press, 2006. http://bit.ly/1aaU7Yb.

  • In this book, Benkler “describes how patterns of information, knowledge, and cultural production are changing – and shows that the way information and knowledge are made available can either limit or enlarge the ways people can create and express themselves.”
  • In his discussion on Wikipedia – one of many paradigmatic examples of people collaborating without financial reward – he calls attention to the notable ongoing cooperation taking place among a diversity of individuals. He argues that, “The important point is that Wikipedia requires not only mechanical cooperation among people, but a commitment to a particular style of writing and describing concepts that is far from intuitive or natural to people. It requires self-discipline. It enforces the behavior it requires primarily through appeal to the common enterprise that the participants are engaged in…”

Brabham, Daren C. Using Crowdsourcing in Government. Collaborating Across Boundaries Series. IBM Center for The Business of Government, 2013. http://bit.ly/17gzBTA.

  • In this report, Brabham categorizes government crowdsourcing cases into a “four-part, problem-based typology, encouraging government leaders and public administrators to consider these open problem-solving techniques as a way to engage the public and tackle difficult policy and administrative tasks more effectively and efficiently using online communities.”
  • The proposed four-part typology describes the following types of crowdsourcing in government:
    • Knowledge Discovery and Management
    • Distributed Human Intelligence Tasking
    • Broadcast Search
    • Peer-Vetted Creative Production
  • In his discussion on Distributed Human Intelligence Tasking, Brabham argues that Amazon’s Mechanical Turk and other microtasking platforms could be useful in a number of governance scenarios, including:
    • Governments and scholars transcribing historical document scans
    • Public health departments translating health campaign materials into foreign languages to benefit constituents who do not speak the native language
    • Governments translating tax documents, school enrollment and immunization brochures, and other important materials into minority languages
    • Helping governments predict citizens’ behavior, “such as for predicting their use of public transit or other services or for predicting behaviors that could inform public health practitioners and environmental policy makers”

Boudreau, Kevin J., Patrick Gaule, Karim Lakhani, Christoph Reidl, Anita Williams Woolley. “From Crowds to Collaborators: Initiating Effort & Catalyzing Interactions Among Online Creative Workers.” Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 14-060. January 23, 2014. https://bit.ly/2QVmGUu.

  • In this working paper, the authors explore the “conditions necessary for eliciting effort from those affecting the quality of interdependent teamwork” and “consider the the role of incentives versus social processes in catalyzing collaboration.”
  • The paper’s findings are based on an experiment involving 260 individuals randomly assigned to 52 teams working toward solutions to a complex problem.
  • The authors determined the level of effort in such collaborative undertakings are sensitive to cash incentives. However, collaboration among teams was driven more by the active participation of teammates, rather than any monetary reward.

Franzoni, Chiara, and Henry Sauermann. “Crowd Science: The Organization of Scientific Research in Open Collaborative Projects.” Research Policy (August 14, 2013). http://bit.ly/HihFyj.

  • In this paper, the authors explore the concept of crowd science, which they define based on two important features: “participation in a project is open to a wide base of potential contributors, and intermediate inputs such as data or problem solving algorithms are made openly available.” The rationale for their study and conceptual framework is the “growing attention from the scientific community, but also policy makers, funding agencies and managers who seek to evaluate its potential benefits and challenges. Based on the experiences of early crowd science projects, the opportunities are considerable.”
  • Based on the study of a number of crowd science projects – including governance-related initiatives like Patients Like Me – the authors identify a number of potential benefits in the following categories:
    • Knowledge-related benefits
    • Benefits from open participation
    • Benefits from the open disclosure of intermediate inputs
    • Motivational benefits
  • The authors also identify a number of challenges:
    • Organizational challenges
    • Matching projects and people
    • Division of labor and integration of contributions
    • Project leadership
    • Motivational challenges
    • Sustaining contributor involvement
    • Supporting a broader set of motivations
    • Reconciling conflicting motivations

Kittur, Aniket, Ed H. Chi, and Bongwon Suh. “Crowdsourcing User Studies with Mechanical Turk.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 453–456. CHI ’08. New York, NY, USA: ACM, 2008. http://bit.ly/1a3Op48.

  • In this paper, the authors examine “[m]icro-task markets, such as Amazon’s Mechanical Turk, [which] offer a potential paradigm for engaging a large number of users for low time and monetary costs. [They] investigate the utility of a micro-task market for collecting user measurements, and discuss design considerations for developing remote micro user evaluation tasks.”
  • The authors conclude that in addition to providing a means for crowdsourcing small, clearly defined, often non-skill-intensive tasks, “Micro-task markets such as Amazon’s Mechanical Turk are promising platforms for conducting a variety of user study tasks, ranging from surveys to rapid prototyping to quantitative measures. Hundreds of users can be recruited for highly interactive tasks for marginal costs within a timeframe of days or even minutes. However, special care must be taken in the design of the task, especially for user measurements that are subjective or qualitative.”

Kittur, Aniket, Jeffrey V. Nickerson, Michael S. Bernstein, Elizabeth M. Gerber, Aaron Shaw, John Zimmerman, Matthew Lease, and John J. Horton. “The Future of Crowd Work.” In 16th ACM Conference on Computer Supported Cooperative Work (CSCW 2013), 2012. http://bit.ly/1c1GJD3.

  • In this paper, the authors discuss paid crowd work, which “offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale.” However, they caution that, “it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework.”
  • The authors argue that seven key challenges must be met to ensure that crowd work processes evolve and reach their full potential:
    • Designing workflows
    • Assigning tasks
    • Supporting hierarchical structure
    • Enabling real-time crowd work
    • Supporting synchronous collaboration
    • Controlling quality

Madison, Michael J. “Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo.” In Convening Cultural Commons, 2013. http://bit.ly/1ih9Xzm.

  • This paper explores a “case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place, Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis on the Internet. In the second place, Galaxy Zoo is a highly successful example of peer production, some times known as crowdsourcing…In the third place, is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies.”
  • Madison concludes that the success of Galaxy Zoo has not been the result of the “character of its information resources (scientific data) and rules regarding their usage,” but rather, the fact that the “community was guided from the outset by a vision of a specific organizational solution to a specific research problem in astronomy, initiated and governed, over time, by professional astronomers in collaboration with their expanding universe of volunteers.”

Malone, Thomas W., Robert Laubacher and Chrysanthos Dellarocas. “Harnessing Crowds: Mapping the Genome of Collective Intelligence.” MIT Sloan Research Paper. February 3, 2009. https://bit.ly/2SPjxTP.

  • In this article, the authors describe and map the phenomenon of collective intelligence – also referred to as “radical decentralization, crowd-sourcing, wisdom of crowds, peer production, and wikinomics – which they broadly define as “groups of individuals doing things collectively that seem intelligent.”
  • The article is derived from the authors’ work at MIT’s Center for Collective Intelligence, where they gathered nearly 250 examples of Web-enabled collective intelligence. To map the building blocks or “genes” of collective intelligence, the authors used two pairs of related questions:
    • Who is performing the task? Why are they doing it?
    • What is being accomplished? How is it being done?
  • The authors concede that much work remains to be done “to identify all the different genes for collective intelligence, the conditions under which these genes are useful, and the constraints governing how they can be combined,” but they believe that their framework provides a useful start and gives managers and other institutional decisionmakers looking to take advantage of collective intelligence activities the ability to “systematically consider many possible combinations of answers to questions about Who, Why, What, and How.”

Mulgan, Geoff. “True Collective Intelligence? A Sketch of a Possible New Field.” Philosophy & Technology 27, no. 1. March 2014. http://bit.ly/1p3YSdd.

  • In this paper, Mulgan explores the concept of a collective intelligence, a “much talked about but…very underdeveloped” field.
  • With a particular focus on health knowledge, Mulgan “sets out some of the potential theoretical building blocks, suggests an experimental and research agenda, shows how it could be analysed within an organisation or business sector and points to possible intellectual barriers to progress.”
  • He concludes that the “central message that comes from observing real intelligence is that intelligence has to be for something,” and that “turning this simple insight – the stuff of so many science fiction stories – into new theories, new technologies and new applications looks set to be one of the most exciting prospects of the next few years and may help give shape to a new discipline that helps us to be collectively intelligent about our own collective intelligence.”

Sauermann, Henry and Chiara Franzoni. “Participation Dynamics in Crowd-Based Knowledge Production: The Scope and Sustainability of Interest-Based Motivation.” SSRN Working Papers Series. November 28, 2013. http://bit.ly/1o6YB7f.

  • In this paper, Sauremann and Franzoni explore the issue of interest-based motivation in crowd-based knowledge production – in particular the use of the crowd science platform Zooniverse – by drawing on “research in psychology to discuss important static and dynamic features of interest and deriv[ing] a number of research questions.”
  • The authors find that interest-based motivation is often tied to a “particular object (e.g., task, project, topic)” not based on a “general trait of the person or a general characteristic of the object.” As such, they find that “most members of the installed base of users on the platform do not sign up for multiple projects, and most of those who try out a project do not return.”
  • They conclude that “interest can be a powerful motivator of individuals’ contributions to crowd-based knowledge production…However, both the scope and sustainability of this interest appear to be rather limited for the large majority of contributors…At the same time, some individuals show a strong and more enduring interest to participate both within and across projects, and these contributors are ultimately responsible for much of what crowd science projects are able to accomplish.”

Schmitt-Sands, Catherine E. and Richard J. Smith. “Prospects for Online Crowdsourcing of Social Science Research Tasks: A Case Study Using Amazon Mechanical Turk.” SSRN Working Papers Series. January 9, 2014. http://bit.ly/1ugaYja.

  • In this paper, the authors describe an experiment involving the nascent use of Amazon’s Mechanical Turk as a social science research tool. “While researchers have used crowdsourcing to find research subjects or classify texts, [they] used Mechanical Turk to conduct a policy scan of local government websites.”
  • Schmitt-Sands and Smith found that “crowdsourcing worked well for conducting an online policy program and scan.” The microtasked workers were helpful in screening out local governments that either did not have websites or did not have the types of policies and services for which the researchers were looking. However, “if the task is complicated such that it requires ongoing supervision, then crowdsourcing is not the best solution.”

Shirky, Clay. Here Comes Everybody: The Power of Organizing Without Organizations. New York: Penguin Press, 2008. https://bit.ly/2QysNif.

  • In this book, Shirky explores our current era in which, “For the first time in history, the tools for cooperating on a global scale are not solely in the hands of governments or institutions. The spread of the Internet and mobile phones are changing how people come together and get things done.”
  • Discussing Wikipedia’s “spontaneous division of labor,” Shirky argues that the process is like, “the process is more like creating a coral reef, the sum of millions of individual actions, than creating a car. And the key to creating those individual actions is to hand as much freedom as possible to the average user.”

Silvertown, Jonathan. “A New Dawn for Citizen Science.” Trends in Ecology & Evolution 24, no. 9 (September 2009): 467–471. http://bit.ly/1iha6CR.

  • This article discusses the move from “Science for the people,” a slogan adopted by activists in the 1970s to “’Science by the people,’ which is “a more inclusive aim, and is becoming a distinctly 21st century phenomenon.”
  • Silvertown identifies three factors that are responsible for the explosion of activity in citizen science, each of which could be similarly related to the crowdsourcing of skills by governing institutions:
    • “First is the existence of easily available technical tools for disseminating information about products and gathering data from the public.
    • A second factor driving the growth of citizen science is the increasing realisation among professional scientists that the public represent a free source of labour, skills, computational power and even finance.
    • Third, citizen science is likely to benefit from the condition that research funders such as the National Science Foundation in the USA and the Natural Environment Research Council in the UK now impose upon every grantholder to undertake project-related science outreach. This is outreach as a form of public accountability.”

Szkuta, Katarzyna, Roberto Pizzicannella, David Osimo. “Collaborative approaches to public sector innovation: A scoping study.” Telecommunications Policy. 2014. http://bit.ly/1oBg9GY.

  • In this article, the authors explore cases where government collaboratively delivers online public services, with a focus on success factors and “incentives for services providers, citizens as users and public administration.”
  • The authors focus on six types of collaborative governance projects:
    • Services initiated by government built on government data;
    • Services initiated by government and making use of citizens’ data;
    • Services initiated by civil society built on open government data;
    • Collaborative e-government services; and
    • Services run by civil society and based on citizen data.
  • The cases explored “are all designed in the way that effectively harnesses the citizens’ potential. Services susceptible to collaboration are those that require computing efforts, i.e. many non-complicated tasks (e.g. citizen science projects – Zooniverse) or citizens’ free time in general (e.g. time banks). Those services also profit from unique citizens’ skills and their propensity to share their competencies.”

Statistics and Open Data: Harvesting unused knowledge, empowering citizens and improving public services

Curated on Posted on by Stefaan Verhulst

House of Commons Public Administration Committee (Tenth Report):
“1. Open data is playing an increasingly important role in Government and society. It is data that is accessible to all, free of restrictions on use or redistribution and also digital and machine-readable so that it can be combined with other data, and thereby made more useful. This report looks at how the vast amounts of data generated by central and local Government can be used in open ways to improve accountability, make Government work better and strengthen the economy.

2. In this inquiry, we examined progress against a series of major government policy announcements on open data in recent years, and considered the prospects for further development. We heard of government open data initiatives going back some years, including the decision in 2009 to release some Ordnance Survey (OS) data as open data, and the Public Sector Mapping Agreement (PSMA) which makes OS data available for free to the public sector.  The 2012 Open Data White Paper ‘Unleashing the Potential’ says that transparency through open data is “at the heart” of the Government’s agenda and that opening up would “foster innovation and reform public services”. In 2013 the report of the independently-chaired review by Stephan Shakespeare, Chief Executive of the market research and polling company YouGov, of the use, re-use, funding and regulation of Public Sector Information urged Government to move fast to make use of data. He criticised traditional public service attitudes to data before setting out his vision:

    • To paraphrase the great retailer Sir Terry Leahy, to run an enterprise without data is like driving by night with no headlights. And yet that is what Government often does. It has a strong institutional tendency to proceed by hunch, or prejudice, or by the easy option. So the new world of data is good for government, good for business, and above all good for citizens. Imagine if we could combine all the data we produce on education and health, tax and spending, work and productivity, and use that to enhance the myriad decisions which define our future; well, we can, right now. And Britain can be first to make it happen for real.

3. This was followed by publication in October 2013 of a National Action Plan which sets out the Government’s view of the economic potential of open data as well as its aspirations for greater transparency.

4. This inquiry is part of our wider programme of work on statistics and their use in Government. A full description of the studies is set out under the heading “Statistics” in the inquiries section of our website, which can be found at www.parliament.uk/pasc. For this inquiry we received 30 pieces of written evidence and took oral evidence from 12 witnesses. We are grateful to all those who have provided evidence and to our Specialist Adviser on statistics, Simon Briscoe, for his assistance with this inquiry.”

Table of Contents:

Summary
1 Introduction
2 Improving accountability through open data
3 Open Data and Economic Growth
4 Improving Government through open data
5 Moving faster to make a reality of open data
6 A strategic approach to open data?
Conclusion
Conclusions and recommendations

Open Data (Updated and Expanded)

Curated on Posted on by Andrew Young

As part of an ongoing effort to build a knowledge base for the field of opening governance by organizing and disseminating its learnings, the GovLab Selected Readings series provides an annotated and curated collection of recommended works on key opening governance topics. We start our series with a focus on Open Data. To suggest additional readings on this or any other topic, please email biblio@thegovlab.org.

Data and its uses for GovernanceOpen data refers to data that is publicly available for anyone to use and which is licensed in a way that allows for its re-use. The common requirement that open data be machine-readable not only means that data is distributed via the Internet in a digitized form, but can also be processed by computers through automation, ensuring both wide dissemination and ease of re-use. Much of the focus of the open data advocacy community is on government data and government-supported research data. For example, in May 2013, the US Open Data Policy defined open data as publicly available data structured in a way that enables the data to be fully discoverable and usable by end users, and consistent with a number of principles focused on availability, accessibility and reusability.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)
Fox, Mark S. “City Data: Big, Open and Linked.” Working Paper, Enterprise Integration Laboratory (2013). http://bit.ly/1bFr7oL.

  • This paper examines concepts that underlie Big City Data using data from multiple cities as examples. It begins by explaining the concepts of Open, Unified, Linked, and Grounded data, which are central to the Semantic Web. Fox then explore Big Data as an extension of Data Analytics, and provide case examples of good data analytics in cities.
  • Fox concludes that we can develop the tools that will enable anyone to analyze data, both big and small, by adopting the principles of the Semantic Web:
    • Data being openly available over the internet,
    • Data being unifiable using common vocabularies,
    • Data being linkable using International Resource Identifiers,
    • Data being accessible using a common data structure, namely triples,
    • Data being semantically grounded using Ontologies.

Foulonneau, Muriel, Sébastien Martin, and Slim Turki. “How Open Data Are Turned into Services?” In Exploring Services Science, edited by Mehdi Snene and Michel Leonard, 31–39. Lecture Notes in Business Information Processing 169. Springer International Publishing, 2014. http://bit.ly/1fltUmR.

  • In this chapter, the authors argue that, considering the important role the development of new services plays as a motivation for open data policies, the impact of new services created through open data should play a more central role in evaluating the success of open data initiatives.
  • Foulonneau, Martin and Turki argue that the following metrics should be considered when evaluating the success of open data initiatives: “the usage, audience, and uniqueness of the services, according to the changes it has entailed in the public institutions that have open their data…the business opportunity it has created, the citizen perception of the city…the modification to particular markets it has entailed…the sustainability of the services created, or even the new dialog created with citizens.”

Goldstein, Brett, and Lauren Dyson. Beyond Transparency: Open Data and the Future of Civic Innovation. 1 edition. (Code for America Press: 2013). http://bit.ly/15OAxgF

  • This “cross-disciplinary survey of the open data landscape” features stories from practitioners in the open data space — including Michael Flowers, Brett Goldstein, Emer Colmeman and many others — discussing what they’ve accomplished with open civic data. The book “seeks to move beyond the rhetoric of transparency for transparency’s sake and towards action and problem solving.”
  • The book’s editors seek to accomplish the following objectives:
    • Help local governments learn how to start an open data program
    • Spark discussion on where open data will go next
    • Help community members outside of government better engage with the process of governance
    • Lend a voice to many aspects of the open data community.
  • The book is broken into five sections: Opening Government Data, Building on Open Data, Understanding Open Data, Driving Decisions with Data and Looking Ahead.

Granickas, Karolis. “Understanding the Impact of Releasing and Re-using Open Government Data.” European Public Sector Information Platform, ePSIplatform Topic Report No. 2013/08, (2013). http://bit.ly/GU0Nx4.

  • This paper examines the impact of open government data by exploring the latest research in the field, with an eye toward enabling  an environment for open data, as well as identifying the benefits of open government data and its political, social, and economic impacts.
  • Granickas concludes that to maximize the benefits of open government data: a) further research is required that structure and measure potential benefits of open government data; b) “government should pay more attention to creating feedback mechanisms between policy implementers, data providers and data-re-users”; c) “finding a balance between demand and supply requires mechanisms of shaping demand from data re-users and also demonstration of data inventory that governments possess”; and lastly, d) “open data policies require regular monitoring.”

Gurin, Joel. Open Data Now: The Secret to Hot Startups, Smart Investing, Savvy Marketing, and Fast Innovation, (New York: McGraw-Hill, 2014). http://amzn.to/1flubWR.

  • In this book, GovLab Senior Advisor and Open Data 500 director Joel Gurin explores the broad realized and potential benefit of Open Data, and how, “unlike Big Data, Open Data is transparent, accessible, and reusable in ways that give it the power to transform business, government, and society.”
  • The book provides “an essential guide to understanding all kinds of open databases – business, government, science, technology, retail, social media, and more – and using those resources to your best advantage.”
  • In particular, Gurin discusses a number of applications of Open Data with very real potential benefits:
    • “Hot Startups: turn government data into profitable ventures;
    • Savvy Marketing: understanding how reputational data drives your brand;
    • Data-Driven Investing: apply new tools for business analysis;
    • Consumer Information: connect with your customers using smart disclosure;
    • Green Business: use data to bet on sustainable companies;
    • Fast R&D: turn the online world into your research lab;
    • New Opportunities: explore open fields for new businesses.”

Jetzek, Thorhildur, Michel Avital, and Niels Bjørn-Andersen. “Generating Value from Open Government Data.” Thirty Fourth International Conference on Information Systems, 5. General IS Topics 2013. http://bit.ly/1gCbQqL.

  • In this paper, the authors “developed a conceptual model portraying how data as a resource can be transformed to value.”
  • Jetzek, Avital and Bjørn-Andersen propose a conceptual model featuring four Enabling Factors (openness, resource governance, capabilities and technical connectivity) acting on four Value Generating Mechanisms (efficiency, innovation, transparency and participation) leading to the impacts of Economic and Social Value.
  • The authors argue that their research supports that “all four of the identified mechanisms positively influence value, reflected in the level of education, health and wellbeing, as well as the monetary value of GDP and environmental factors.”

Kassen, Maxat. “A promising phenomenon of open data: A case study of the Chicago open data project.” Government Information Quarterly (2013). http://bit.ly/1ewIZnk.

  • This paper uses the Chicago open data project to explore the “empowering potential of an open data phenomenon at the local level as a platform useful for promotion of civic engagement projects and provide a framework for future research and hypothesis testing.”
  • Kassen argues that “open data-driven projects offer a new platform for proactive civic engagement” wherein governments can harness “the collective wisdom of the local communities, their knowledge and visions of the local challenges, governments could react and meet citizens’ needs in a more productive and cost-efficient manner.”
  • The paper highlights the need for independent IT developers to network in order for this trend to continue, as well as the importance of the private sector in “overall diffusion of the open data concept.”

Keen, Justin, Radu Calinescu, Richard Paige, John Rooksby. “Big data + politics = open data: The case of health care data in England.” Policy and Internet 5 (2), (2013): 228–243. http://bit.ly/1i231WS.

  • This paper examines the assumptions regarding open datasets, technological infrastructure and access, using healthcare systems as a case study.
  • The authors specifically address two assumptions surrounding enthusiasm about Big Data in healthcare: the assumption that healthcare datasets and technological infrastructure are up to task, and the assumption of access to this data from outside the healthcare system.
  • By using the National Health Service in England as an example, the authors identify data, technology, and information governance challenges. They argue that “public acceptability of third party access to detailed health care datasets is, at best, unclear,” and that the prospects of Open Data depend on Open Data policies, which are inherently political, and the government’s assertion of property rights over large datasets. Thus, they argue that the “success or failure of Open Data in the NHS may turn on the question of trust in institutions.”

Kulk, Stefan and Bastiaan Van Loenen. “Brave New Open Data World?” International Journal of Spatial Data Infrastructures Research, May 14, 2012. http://bit.ly/15OAUYR.

  • This paper examines the evolving tension between the open data movement and the European Union’s privacy regulations, especially the Data Protection Directive.
  • The authors argue, “Technological developments and the increasing amount of publicly available data are…blurring the lines between non-personal and personal data. Open data may not seem to be personal data on first glance especially when it is anonymised or aggregated. However, it may become personal by combining it with other publicly available data or when it is de-anonymised.”

Kundra, Vivek. “Digital Fuel of the 21st Century: Innovation through Open Data and the Network Effect.” Joan Shorenstein Center on the Press, Politics and Public Policy, Harvard College: Discussion Paper Series, January 2012, http://hvrd.me/1fIwsjR.

  • In this paper, Vivek Kundra, the first Chief Information Officer of the United States, explores the growing impact of open data, and argues that, “In the information economy, data is power and we face a choice between democratizing it and holding on to it for an asymmetrical advantage.”
  • Kundra offers four specific recommendations to maximize the impact of open data: Citizens and NGOs must demand open data in order to fight government corruption, improve accountability and government services; Governments must enact legislation to change the default setting of government to open, transparent and participatory; The press must harness the power of the network effect through strategic partnerships and crowdsourcing to cut costs and provide better insights; and Venture capitalists should invest in startups focused on building companies based on public sector data.

Noveck, Beth Simone and Daniel L. Goroff. “Information for Impact: Liberating Nonprofit Sector Data.” The Aspen Institute Philanthropy & Social Innovation Publication Number 13-004. 2013. http://bit.ly/WDxd7p.

  • This report is focused on “obtaining better, more usable data about the nonprofit sector,” which encompasses, as of 2010, “1.5 million tax-exempt organizations in the United States with $1.51 trillion in revenues.”
  • Toward that goal, the authors propose liberating data from the Form 990, an Internal Revenue Service form that “gathers and publishes a large amount of information about tax-exempt organizations,” including information related to “governance, investments, and other factors not directly related to an organization’s tax calculations or qualifications for tax exemption.”
  • The authors recommend a two-track strategy: “Pursuing the longer-term goal of legislation that would mandate electronic filing to create open 990 data, and pursuing a shorter-term strategy of developing a third party platform that can demonstrate benefits more immediately.”

Robinson, David G., Harlan Yu, William P. Zeller, and Edward W. Felten, “Government Data and the Invisible Hand.” Yale Journal of Law & Technology 11 (2009), http://bit.ly/1c2aDLr.

  • This paper proposes a new approach to online government data that “leverages both the American tradition of entrepreneurial self-reliance and the remarkable low-cost flexibility of contemporary digital technology.”
  • “In order for public data to benefit from the same innovation and dynamism that characterize private parties’ use of the Internet, the federal government must reimagine its role as an information provider. Rather than struggling, as it currently does, to design sites that meet each end-user need, it should focus on creating a simple, reliable and publicly accessible infrastructure that ‘exposes’ the underlying data.”
Ubaldi, Barbara. “Open Government Data: Towards Empirical Analysis of Open Government Data Initiatives.” OECD Working Papers on Public Governance. Paris: Organisation for Economic Co-operation and Development, May 27, 2013. http://bit.ly/15OB6qP.

  • This working paper from the OECD seeks to provide an all-encompassing look at the principles, concepts and criteria framing open government data (OGD) initiatives.
  • Ubaldi also analyzes a variety of challenges to implementing OGD initiatives, including policy, technical, economic and financial, organizational, cultural and legal impediments.
  • The paper also proposes a methodological framework for evaluating OGD Initiatives in OECD countries, with the intention of eventually “developing a common set of metrics to consistently assess impact and value creation within and across countries.”

Worthy, Ben. “David Cameron’s Transparency Revolution? The Impact of Open Data in the UK.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, November 29, 2013. http://bit.ly/NIrN6y.

  • In this article, Worthy “examines the impact of the UK Government’s Transparency agenda, focusing on the publication of spending data at local government level. It measures the democratic impact in terms of creating transparency and accountability, public participation and everyday information.”
  • Worthy’s findings, based on surveys of local authorities, interviews and FOI requests, are disappointing. He finds that:
    • Open spending data has led to some government accountability, but largely from those already monitoring government, not regular citizens.
    • Open Data has not led to increased participation, “as it lacks the narrative or accountability instruments to fully bring such effects.”
    • It has also not “created a new stream of information to underpin citizen choice, though new innovations offer this possibility. The evidence points to third party innovations as the key.
  • Despite these initial findings, “Interviewees pointed out that Open Data holds tremendous opportunities for policy-making. Joined up data could significantly alter how policy is made and resources targeted. From small scale issues e.g. saving money through prescriptions to targeting homelessness or health resources, it can have a transformative impact. “

Zuiderwijk, Anneke, Marijn Janssen, Sunil Choenni, Ronald Meijer and Roexsana Sheikh Alibaks. “Socio-technical Impediments of Open Data.” Electronic Journal of e-Government 10, no. 2 (2012). http://bit.ly/17yf4pM.

  • This paper to seeks to identify the socio-technical impediments to open data impact based on a review of the open data literature, as well as workshops and interviews.
  • The authors discovered 118 impediments across ten categories: 1) availability and access; 2) find-ability; 3) usability; 4) understandability; 5) quality; 6) linking and combining data; 7) comparability and compatibility; 8) metadata; 9) interaction with the data provider; and 10) opening and uploading.

Zuiderwijk, Anneke and Marijn Janssen. “Open Data Policies, Their Implementation and Impact: A Framework for Comparison.” Government Information Quarterly 31, no. 1 (January 2014): 17–29. http://bit.ly/1bQVmYT.

  • In this article, Zuiderwijk and Janssen argue that “currently there is a multiplicity of open data policies at various levels of government, whereas very little systematic and structured research [being] done on the issues that are covered by open data policies, their intent and actual impact.”
  • With this evaluation deficit in mind, the authors propose a new framework for comparing open data policies at different government levels using the following elements for comparison:
    • Policy environment and context, such as level of government organization and policy objectives;
    • Policy content (input), such as types of data not publicized and technical standards;
    • Performance indicators (output), such as benefits and risks of publicized data; and
    • Public values (impact).

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Did we miss anything? Please submit reading recommendations to biblio@thegovlab.org or in the comments below.

From Crowds to Collaborators: Initiating Effort and Catalyzing Interactions Among Online Creative Workers

Curated on Posted on by Stefaan Verhulst

Harvard Business School Paper by Kevin J. Boudreau, Patrick Gaule, Karim R. Lakhani, Christoph Riedl, and Anita Williams Woolley: “Online “organizations” are becoming a major engine for knowledge development in a variety of domains such as Wikipedia and open source software development. Many online platforms involve collaboration and coordination among members to reach common goals. In this sense, they are collaborative communities. This paper asks: What factors most inspire online teams to begin to collaborate and to do so creatively and effectively? The authors analyze a data set of 260 individuals randomly assigned to 52 teams tasked with developing working solutions to a complex innovation problem over 10 days, with varying cash incentives. Findings showed that although cash incentives stimulated a significant boost of effort per se, cash incentives did not transform the nature of the work process or affect the level of collaboration. In addition, at a basic yet striking level, the likelihood that an individual chooses to participate depended on whether teammates were themselves active. Moreover, communications among teammates led to more communications, and communications among teammates also stimulated greater continuous levels of effort. Overall, the study sheds light on how perspectives on incentives, predominant in economics, and perspectives on social processes and interactions, predominant in research on organizational behavior and teams, can be better understood. Key concepts include:

  • An individual’s likelihood of being active in online collaboration increases by about 41 percent with each additional active teammate.
  • Management could provide communications channels to make the efforts of other members more visible. This is important in the design of systems for online work as it helps members to confirm that others are actively contributing.

Full Working Paper Text”
 

Innovation in Multi-Stakeholder Engagement

Curated on Posted on by Stefaan Verhulst

New INSEAD working paper by Mahroum, Sami, Bell, Simon and Yassin, Nasser: “This paper is concerned with the multitude of interleaving issues which emerge when engaging multiple stakeholders in decision making. Whilst recognising the intrinsic values of group work (including shared views, wide option selection, public spirited focus, legitimacy of decisions and improved intellectual content) and keeping in mind the numerous issues which confuse and obscure clear findings from group work (including multiple roles for participants, bias due to domination and distortion emerging from uneven group inputs) this paper uses an innovative methodology – the Triple Task – to propose a new framework for organising multi-stakeholder consultations. The Triple Task methodology was applied to test the new framework on multi-stakeholders in the context of education in Abu Dhabi, where various small groups were tasked and assessed using the methodology. The results indicate that moving participants from heterogeneous to homogenous groups results on these groups becoming more focused in their outcomes with greater clarity in the thinking of group members”

Transparency 2.0: The Fundamentals of Online Open Government

Curated on Posted on by Stefaan Verhulst
White Paper by Granicus: “Open government is about building transparency, trust, and engagement with the public. Today, with 80% of the North American public on the Internet, it is becoming increasingly clear that building open government starts online. Transparency 2.0 not only provides public information, but also develops civic engagement, opens the decision-making process online, and takes advantage of today’s technology trends.
Citizen ideation & feedback. While open data comprised much of what online transparency used to be, today, government agencies have expanded openness to include public records, legislative data, decision-making workflow, and citizen ideation and feedback.
This paper outlines the principles of Transparency 2.0, the fundamentals and best practices for creating the most advanced and comprehensive online open government that over a thousand state, federal, and local government agencies are now using to reduce information requests, create engagement, and improve efficiency.”

Prizes and Productivity: How Winning the Fields Medal Affects Scientific Output

Curated on Posted on by Stefaan Verhulst

New NBER working paper by George J. Borjas and Kirk B. Doran: “Knowledge generation is key to economic growth, and scientific prizes are designed to encourage it. But how does winning a prestigious prize affect future output? We compare the productivity of Fields medalists (winners of the top mathematics prize) to that of similarly brilliant contenders. The two groups have similar publication rates until the award year, after which the winners’ productivity declines. The medalists begin to “play the field,” studying unfamiliar topics at the expense of writing papers. It appears that tournaments can have large post-prize effects on the effort allocation of knowledge producers.”

The Three Worlds of Governance: Arguments for a Parsimonious Theory of Quality of Government.

Curated on Posted on by Stefaan Verhulst
New Working Paper by Bo Rothstein for the Quality of Governance Institute: “It is necessary to conceptualize and provide better measures of good governance because in contrast to democratization, empirical studies show that it has strong positive effects on measures of human well-being, social trust, life satisfaction, peace and political legitimacy. A central problem is that the term “governance” is conceptualized differently in three main approaches to governance which has led to much confusion. To avoid this, the term quality of government (QoG) is preferred.
This paper argues for a parsimonious conceptualization of QoG built the “Rawls-Machiavelli pro-gramme”. This is a combination of the Rawlsian understanding of what should be seen as a just political order and the empirical strategy used by Machiavelli stating what is possible to implement. It is argued that complex definitions are impossible to operationalize and that such a strategy would leave political science without a proper conceptualization as well as measures of the part of the state that is most important for humans’ well-being and political legitimacy. The theory proposed is that impartiality in the exercise of public power should be the basic norm for how QoG should be defined. The advantage with this strategy is that it does not include in the definition of QoG what we want to explain (efficiency, prosperity, administrative capacity and other “good outcomes”) and that recent empirical research shows that this theory can be operationalized and used to measure QoG in ways that have the predicted outcomes.”

City Data: Big, Open and Linked

Curated on Posted on by Stefaan Verhulst

Working Paper by Mark S. Fox (University of Toronto): “Cities are moving towards policymaking based on data. They are publishing data using Open Data standards, linking data from disparate sources, allowing the crowd to update their data with Smart Phone Apps that use Open APIs, and applying “Big Data” Techniques to discover relationships that lead to greater efficiencies.
One Big City Data example is from New York City (Schönberger & Cukier, 2013). Building owners were illegally converting their buildings into rooming houses that contained 10 times the number people they were designed for. These buildings posed a number of problems, including fire hazards, drugs, crime, disease and pest infestations. There are over 900,000 properties in New York City and only 200 inspectors who received over 25,000 illegal conversion complaints per year. The challenge was to distinguish nuisance complaints from those worth investigating where current methods were resulting in only 13% of the inspections resulting in vacate orders.
New York’s Analytics team created a dataset that combined data from 19 agencies including buildings, preservation, police, fire, tax, and building permits. By combining data analysis with expertise gleaned from inspectors (e.g., buildings that recently received a building permit were less likely to be a problem as they were being well maintained), the team was able to develop a rating system for complaints. Based on their analysis of this data, they were able to rate complaints such that in 70% of their visits, inspectors issued vacate orders; a fivefold increase in efficiency…
This paper provides an introduction to the concepts that underlie Big City Data. It explains the concepts of Open, Unified, Linked and Grounded data that lie at the heart of the Semantic Web. It then builds on this by discussing Data Analytics, which includes Statistics, Pattern Recognition and Machine Learning. Finally we discuss Big Data as the extension of Data Analytics to the Cloud where massive amounts of computing power and storage are available for processing large data sets. We use city data to illustrate each.”