Open access: All human knowledge is there—so why can’t everybody access it?

Curated on Posted on by Stefaan Verhulst

 at ArsTechnica: “In 1836, Anthony Panizzi, who later became principal librarian of the British Museum, gave evidence before a parliamentary select committee. At that time, he was only first assistant librarian, but even then he had an ambitious vision for what would one day became the British Library. He told the committee:

I want a poor student to have the same means of indulging his learned curiosity, of following his rational pursuits, of consulting the same authorities, of fathoming the most intricate inquiry as the richest man in the kingdom, as far as books go, and I contend that the government is bound to give him the most liberal and unlimited assistance in this respect.

He went some way to achieving that goal of providing general access to human knowledge. In 1856, after 20 years of labour as Keeper of Printed Books, he had helped boost the British Museum’s collection to over half a million books, making it the largest library in the world at the time. But there was a serious problem: to enjoy the benefits of those volumes, visitors needed to go to the British Museum in London.

Imagine, for a moment, if it were possible to provide access not just to those books, but to all knowledge for everyone, everywhere—the ultimate realisation of Panizzi’s dream. In fact, we don’t have to imagine: it is possible today, thanks to the combined technologies of digital texts and the Internet. The former means that we can make as many copies of a work as we want, for vanishingly small cost; the latter provides a way to provide those copies to anyone with an Internet connection. The global rise of low-cost smartphones means that group will soon include even the poorest members of society in every country.

That is to say, we have the technical means to share all knowledge, and yet we are nowhere near providing everyone with the ability to indulge their learned curiosity as Panizzi wanted it.

What’s stopping us? That’s the central question that the “open access” movement has been asking, and trying to answer, for the last two decades. Although tremendous progress has been made, with more knowledge freely available now than ever before, there are signs that open access is at a critical point in its development, which could determine whether it will ever succeed in realising Panizzi’s plan.

Table of Contents

Digital Government: overcoming the systemic failure of transformation

Curated on Posted on by Stefaan Verhulst

Paul Waller and Vishanth Weerakkody: “This Working Paper contains propositions regarding the use of digital technology to “transform” government that significantly conflict with received wisdom in academia and governments across the world. It counters assertions made in countless political, official and commercial statements and reports produced over past decades….

The “transformation of government” has often been proposed as an objective of e-government; frequently presented as a phase in stage models following the provision online of information and transactions. Yet in literature or official documents there is no established definition of transformation as applied to government. Implicitly or explicitly, it mostly refers to a change in organisational form, signalled by the terms “joining-up” or “integration”, of government. In some work,

In some work, transformation is limited to changing processes or “services”— though “services” is a term unhelpfully applied to a multitude of entities. There is in academic or other literature little evidence of any type of “transformation” achieved beyond a change in an administrative process, nor a robust framework of benefits one might deliver. This begs the questions of what it actually means in reality and why it might be a desired goal.

In essence, what we aim to do in this paper is to develop a structured frame of reference for making sense of how information and communications technologies (ICT), in all their forms, really fit within the world of government and public administration — exactly the challenge set by Professor Christopher Hood in his 2007 paper:

“But we need to have a way of assessing current developments in administrative technologies with those of other eras, such as development of telephones, cars, radios, and fingerprinting in police work in the early part of the twentieth century, or of exact methods of measurement on excise tax collection in the eighteenth century. And if the analysis of the changes such developments bring is to amount to anything more than a breathless tour d’horizon of the latest technological gizmos in public policy (much though governments themselves have a liking for that sort of approach), it needs to be related to some foundational analysis that is, in some way, technology-free and rooted in the nature of government as a social and legal phenomenon.”

After a brief historical review, the paper starts by considering what governments and public administrations actually do: specifically, policy design and implementation through policy instruments. It redefines transformation in terms of changing the policy instrument set chosen to implement policy and sets out broad rationales for how and why ICT can enable this. It proposes a frame of reference of terminology, concepts and objects that enable the examination of not only such transformation, but e-government in general as it has developed over two decades. …(More)”

Selected Readings on Data Collaboratives

Curated on Posted on by Andrew Young

By Neil Britto, David Sangokoya, Iryna Susha, Stefaan Verhulst and 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 data collaboratives was originally published in 2017.

The term data collaborative refers to a new form of collaboration, beyond the public-private partnership model, in which participants from different sectors (including private companies, research institutions, and government agencies ) can exchange data to help solve public problems. Several of society’s greatest challenges — from addressing climate change to public health to job creation to improving the lives of children — require greater access to data, more collaboration between public – and private-sector entities, and an increased ability to analyze datasets. In the coming months and years, data collaboratives will be essential vehicles for harnessing the vast stores of privately held data toward the public good.

Selected Reading List (in alphabetical order)

Annotated Selected Readings List (in alphabetical order)

Agaba, G., Akindès, F., Bengtsson, L., Cowls, J., Ganesh, M., Hoffman, N., . . . Meissner, F. “Big Data and Positive Social Change in the Developing World: A White Paper for Practitioners and Researchers.” 2014. http://bit.ly/25RRC6N.

  • This white paper, produced by “a group of activists, researchers and data experts” explores the potential of big data to improve development outcomes and spur positive social change in low- and middle-income countries. Using examples, the authors discuss four areas in which the use of big data can impact development efforts:
    • Advocating and facilitating by “opening[ing] up new public spaces for discussion and awareness building;
    • Describing and predicting through the detection of “new correlations and the surfac[ing] of new questions;
    • Facilitating information exchange through “multiple feedback loops which feed into both research and action,” and
    • Promoting accountability and transparency, especially as a byproduct of crowdsourcing efforts aimed at “aggregat[ing] and analyz[ing] information in real time.
  • The authors argue that in order to maximize the potential of big data’s use in development, “there is a case to be made for building a data commons for private/public data, and for setting up new and more appropriate ethical guidelines.”
  • They also identify a number of challenges, especially when leveraging data made accessible from a number of sources, including private sector entities, such as:
    • Lack of general data literacy;
    • Lack of open learning environments and repositories;
    • Lack of resources, capacity and access;
    • Challenges of sensitivity and risk perception with regard to using data;
    • Storage and computing capacity; and
    • Externally validating data sources for comparison and verification.

Ansell, C. and Gash, A. “Collaborative Governance in Theory and Practice.” Journal of Public Administration Research and  Theory 18 (4), 2008. http://bit.ly/1RZgsI5.

  • This article describes collaborative arrangements that include public and private organizations working together and proposes a model for understanding an emergent form of public-private interaction informed by 137 diverse cases of collaborative governance.
  • The article suggests factors significant to successful partnering processes and outcomes include:
    • Shared understanding of challenges,
    • Trust building processes,
    • The importance of recognizing seemingly modest progress, and
    • Strong indicators of commitment to the partnership’s aspirations and process.
  • The authors provide a ‘’contingency theory model’’ that specifies relationships between different variables that influence outcomes of collaborative governance initiatives. Three “core contingencies’’ for successful collaborative governance initiatives identified by the authors are:
    • Time (e.g., decision making time afforded to the collaboration);
    • Interdependence (e.g., a high degree of interdependence can mitigate negative effects of low trust); and
    • Trust (e.g. a higher level of trust indicates a higher probability of success).

Ballivian A, Hoffman W. “Public-Private Partnerships for Data: Issues Paper for Data Revolution Consultation.” World Bank, 2015. Available from: http://bit.ly/1ENvmRJ

  • This World Bank report provides a background document on forming public-prviate partnerships for data with the private sector in order to inform the UN’s Independent Expert Advisory Group (IEAG) on sustaining a “data revolution” in sustainable development.
  • The report highlights the critical position of private companies within the data value chain and reflects on key elements of a sustainable data PPP: “common objectives across all impacted stakeholders, alignment of incentives, and sharing of risks.” In addition, the report describes the risks and incentives of public and private actors, and the principles needed to “build[ing] the legal, cultural, technological and economic infrastructures to enable the balancing of competing interests.” These principles include understanding; experimentation; adaptability; balance; persuasion and compulsion; risk management; and governance.
  • Examples of data collaboratives cited in the report include HP Earth Insights, Orange Data for Development Challenges, Amazon Web Services, IBM Smart Cities Initiative, and the Governance Lab’s Open Data 500.

Brack, Matthew, and Tito Castillo. “Data Sharing for Public Health: Key Lessons from Other Sectors.” Chatham House, Centre on Global Health Security. April 2015. Available from: http://bit.ly/1DHFGVl

  • The Chatham House report provides an overview on public health surveillance data sharing, highlighting the benefits and challenges of shared health data and the complexity in adapting technical solutions from other sectors for public health.
  • The report describes data sharing processes from several perspectives, including in-depth case studies of actual data sharing in practice at the individual, organizational and sector levels. Among the key lessons for public health data sharing, the report strongly highlights the need to harness momentum for action and maintain collaborative engagement: “Successful data sharing communities are highly collaborative. Collaboration holds the key to producing and abiding by community standards, and building and maintaining productive networks, and is by definition the essence of data sharing itself. Time should be invested in establishing and sustaining collaboration with all stakeholders concerned with public health surveillance data sharing.”
  • Examples of data collaboratives include H3Africa (a collaboration between NIH and Wellcome Trust) and NHS England’s care.data programme.

de Montjoye, Yves-Alexandre, Jake Kendall, and Cameron F. Kerry. “Enabling Humanitarian Use of Mobile Phone Data.” The Brookings Institution, Issues in Technology Innovation. November 2014. Available from: http://brook.gs/1JxVpxp

  • Using Ebola as a case study, the authors describe the value of using private telecom data for uncovering “valuable insights into understanding the spread of infectious diseases as well as strategies into micro-target outreach and driving update of health-seeking behavior.”
  • The authors highlight the absence of a common legal and standards framework for “sharing mobile phone data in privacy-conscientious ways” and recommend “engaging companies, NGOs, researchers, privacy experts, and governments to agree on a set of best practices for new privacy-conscientious metadata sharing models.”

Eckartz, Silja M., Hofman, Wout J., Van Veenstra, Anne Fleur. “A decision model for data sharing.” Vol. 8653 LNCS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014. http://bit.ly/21cGWfw.

  • This paper proposes a decision model for data sharing of public and private data based on literature review and three case studies in the logistics sector.
  • The authors identify five categories of the barriers to data sharing and offer a decision model for identifying potential interventions to overcome each barrier:
    • Ownership. Possible interventions likely require improving trust among those who own the data through, for example, involvement and support from higher management
    • Privacy. Interventions include “anonymization by filtering of sensitive information and aggregation of data,” and access control mechanisms built around identity management and regulated access.  
    • Economic. Interventions include a model where data is shared only with a few trusted organizations, and yield management mechanisms to ensure negative financial consequences are avoided.
    • Data quality. Interventions include identifying additional data sources that could improve the completeness of datasets, and efforts to improve metadata.
    • Technical. Interventions include making data available in structured formats and publishing data according to widely agreed upon data standards.

Hoffman, Sharona and Podgurski, Andy. “The Use and Misuse of Biomedical Data: Is Bigger Really Better?” American Journal of Law & Medicine 497, 2013. http://bit.ly/1syMS7J.

  • This journal articles explores the benefits and, in particular, the risks related to large-scale biomedical databases bringing together health information from a diversity of sources across sectors. Some data collaboratives examined in the piece include:
    • MedMining – a company that extracts EHR data, de-identifies it, and offers it to researchers. The data sets that MedMining delivers to its customers include ‘lab results, vital signs, medications, procedures, diagnoses, lifestyle data, and detailed costs’ from inpatient and outpatient facilities.
    • Explorys has formed a large healthcare database derived from financial, administrative, and medical records. It has partnered with major healthcare organizations such as the Cleveland Clinic Foundation and Summa Health System to aggregate and standardize health information from ten million patients and over thirty billion clinical events.
  • Hoffman and Podgurski note that biomedical databases populated have many potential uses, with those likely to benefit including: “researchers, regulators, public health officials, commercial entities, lawyers,” as well as “healthcare providers who conduct quality assessment and improvement activities,” regulatory monitoring entities like the FDA, and “litigants in tort cases to develop evidence concerning causation and harm.”
  • They argue, however, that risks arise based on:
    • The data contained in biomedical databases is surprisingly likely to be incorrect or incomplete;
    • Systemic biases, arising from both the nature of the data and the preconceptions of investigators are serious threats the validity of research results, especially in answering causal questions;
  • Data mining of biomedical databases makes it easier for individuals with political, social, or economic agendas to generate ostensibly scientific but misleading research findings for the purpose of manipulating public opinion and swaying policymakers.

Krumholz, Harlan M., et al. “Sea Change in Open Science and Data Sharing Leadership by Industry.” Circulation: Cardiovascular Quality and Outcomes 7.4. 2014. 499-504. http://1.usa.gov/1J6q7KJ

  • This article provides a comprehensive overview of industry-led efforts and cross-sector collaborations in data sharing by pharmaceutical companies to inform clinical practice.
  • The article details the types of data being shared and the early activities of GlaxoSmithKline (“in coordination with other companies such as Roche and ViiV”); Medtronic and the Yale University Open Data Access Project; and Janssen Pharmaceuticals (Johnson & Johnson). The article also describes the range of involvement in data sharing among pharmaceutical companies including Pfizer, Novartis, Bayer, AbbVie, Eli Llly, AstraZeneca, and Bristol-Myers Squibb.

Mann, Gideon. “Private Data and the Public Good.” Medium. May 17, 2016. http://bit.ly/1OgOY68.

    • This Medium post from Gideon Mann, the Head of Data Science at Bloomberg, shares his prepared remarks given at a lecture at the City College of New York. Mann argues for the potential benefits of increasing access to private sector data, both to improve research and academic inquiry and also to help solve practical, real-world problems. He also describes a number of initiatives underway at Bloomberg along these lines.    
  • Mann argues that data generated at private companies “could enable amazing discoveries and research,” but is often inaccessible to those who could put it to those uses. Beyond research, he notes that corporate data could, for instance, benefit:
      • Public health – including suicide prevention, addiction counseling and mental health monitoring.
    • Legal and ethical questions – especially as they relate to “the role algorithms have in decisions about our lives,” such as credit checks and resume screening.
  • Mann recognizes the privacy challenges inherent in private sector data sharing, but argues that it is a common misconception that the only two choices are “complete privacy or complete disclosure.” He believes that flexible frameworks for differential privacy could open up new opportunities for responsibly leveraging data collaboratives.

Pastor Escuredo, D., Morales-Guzmán, A. et al, “Flooding through the Lens of Mobile Phone Activity.” IEEE Global Humanitarian Technology Conference, GHTC 2014. Available from: http://bit.ly/1OzK2bK

  • This report describes the impact of using mobile data in order to understand the impact of disasters and improve disaster management. The report was conducted in the Mexican state of Tabasco in 2009 as a multidisciplinary, multi-stakeholder consortium involving the UN World Food Programme (WFP), Telefonica Research, Technical University of Madrid (UPM), Digital Strategy Coordination Office of the President of Mexico, and UN Global Pulse.
  • Telefonica Research, a division of the major Latin American telecommunications company, provided call detail records covering flood-affected areas for nine months. This data was combined with “remote sensing data (satellite images), rainfall data, census and civil protection data.” The results of the data demonstrated that “analysing mobile activity during floods could be used to potentially locate damaged areas, efficiently assess needs and allocate resources (for example, sending supplies to affected areas).”
  • In addition to the results, the study highlighted “the value of a public-private partnership on using mobile data to accurately indicate flooding impacts in Tabasco, thus improving early warning and crisis management.”

* Perkmann, M. and Schildt, H. “Open data partnerships between firms and universities: The role of boundary organizations.” Research Policy, 44(5), 2015. http://bit.ly/25RRJ2c. 

  • This paper discusses the concept of a “boundary organization” in relation to industry-academic partnerships driven by data. Boundary organizations perform mediated revealing, allowing firms to disclose their research problems to a broad audience of innovators and simultaneously minimize the risk that this information would be adversely used by competitors.
  • The authors identify two especially important challenges for private firms to enter open data or participate in data collaboratives with the academic research community that could be addressed through more involvement from boundary organizations:
    • First is a challenge of maintaining competitive advantage. The authors note that, “the more a firm attempts to align the efforts in an open data research programme with its R&D priorities, the more it will have to reveal about the problems it is addressing within its proprietary R&D.”
    • Second, involves the misalignment of incentives between the private and academic field. Perkmann and Schildt argue that, a firm seeking to build collaborations around its opened data “will have to provide suitable incentives that are aligned with academic scientists’ desire to be rewarded for their work within their respective communities.”

Robin, N., Klein, T., & Jütting, J. “Public-Private Partnerships for Statistics: Lessons Learned, Future Steps.” OECD. 2016. http://bit.ly/24FLYlD.

  • This working paper acknowledges the growing body of work on how different types of data (e.g, telecom data, social media, sensors and geospatial data, etc.) can address data gaps relevant to National Statistical Offices (NSOs).
  • Four models of public-private interaction for statistics are describe: in-house production of statistics by a data-provider for a national statistics office (NSO), transfer of data-sets to NSOs from private entities, transfer of data to a third party provider to manage the NSO and private entity data, and the outsourcing of NSO functions.
  • The paper highlights challenges to public-private partnerships involving data (e.g., technical challenges, data confidentiality, risks, limited incentives for participation), suggests deliberate and highly structured approaches to public-private partnerships involving data require enforceable contracts, emphasizes the trade-off between data specificity and accessibility of such data, and the importance of pricing mechanisms that reflect the capacity and capability of national statistic offices.
  • Case studies referenced in the paper include:
    • A mobile network operator’s (MNO Telefonica) in house analysis of call detail records;
    • A third-party data provider and steward of travel statistics (Positium);
    • The Data for Development (D4D) challenge organized by MNO Orange; and
    • Statistics Netherlands use of social media to predict consumer confidence.

Stuart, Elizabeth, Samman, Emma, Avis, William, Berliner, Tom. “The data revolution: finding the missing millions.” Overseas Development Institute, 2015. Available from: http://bit.ly/1bPKOjw

  • The authors of this report highlight the need for good quality, relevant, accessible and timely data for governments to extend services into underrepresented communities and implement policies towards a sustainable “data revolution.”
  • The solutions focused on this recent report from the Overseas Development Institute focus on capacity-building activities of national statistical offices (NSOs), alternative sources of data (including shared corporate data) to address gaps, and building strong data management systems.

Taylor, L., & Schroeder, R. “Is bigger better? The emergence of big data as a tool for international development policy.” GeoJournal, 80(4). 2015. 503-518. http://bit.ly/1RZgSy4.

  • This journal article describes how privately held data – namely “digital traces” of consumer activity – “are becoming seen by policymakers and researchers as a potential solution to the lack of reliable statistical data on lower-income countries.
  • They focus especially on three categories of data collaborative use cases:
    • Mobile data as a predictive tool for issues such as human mobility and economic activity;
    • Use of mobile data to inform humanitarian response to crises; and
    • Use of born-digital web data as a tool for predicting economic trends, and the implications these have for LMICs.
  • They note, however, that a number of challenges and drawbacks exist for these types of use cases, including:
    • Access to private data sources often must be negotiated or bought, “which potentially means substituting negotiations with corporations for those with national statistical offices;”
    • The meaning of such data is not always simple or stable, and local knowledge is needed to understand how people are using the technologies in question
    • Bias in proprietary data can be hard to understand and quantify;
    • Lack of privacy frameworks; and
    • Power asymmetries, wherein “LMIC citizens are unwittingly placed in a panopticon staffed by international researchers, with no way out and no legal recourse.”

van Panhuis, Willem G., Proma Paul, Claudia Emerson, John Grefenstette, Richard Wilder, Abraham J. Herbst, David Heymann, and Donald S. Burke. “A systematic review of barriers to data sharing in public health.” BMC public health 14, no. 1 (2014): 1144. Available from: http://bit.ly/1JOBruO

  • The authors of this report provide a “systematic literature of potential barriers to public health data sharing.” These twenty potential barriers are classified in six categories: “technical, motivational, economic, political, legal and ethical.” In this taxonomy, “the first three categories are deeply rooted in well-known challenges of health information systems for which structural solutions have yet to be found; the last three have solutions that lie in an international dialogue aimed at generating consensus on policies and instruments for data sharing.”
  • The authors suggest the need for a “systematic framework of barriers to data sharing in public health” in order to accelerate access and use of data for public good.

Verhulst, Stefaan and Sangokoya, David. “Mapping the Next Frontier of Open Data: Corporate Data Sharing.” In: Gasser, Urs and Zittrain, Jonathan and Faris, Robert and Heacock Jones, Rebekah, “Internet Monitor 2014: Reflections on the Digital World: Platforms, Policy, Privacy, and Public Discourse (December 15, 2014).” Berkman Center Research Publication No. 2014-17. http://bit.ly/1GC12a2

  • This essay describe a taxonomy of current corporate data sharing practices for public good: research partnerships; prizes and challenges; trusted intermediaries; application programming interfaces (APIs); intelligence products; and corporate data cooperatives or pooling.
  • Examples of data collaboratives include: Yelp Dataset Challenge, the Digital Ecologies Research Partnerhsip, BBVA Innova Challenge, Telecom Italia’s Big Data Challenge, NIH’s Accelerating Medicines Partnership and the White House’s Climate Data Partnerships.
  • The authors highlight important questions to consider towards a more comprehensive mapping of these activities.

Verhulst, Stefaan and Sangokoya, David, 2015. “Data Collaboratives: Exchanging Data to Improve People’s Lives.” Medium. Available from: http://bit.ly/1JOBDdy

  • The essay refers to data collaboratives as a new form of collaboration involving participants from different sectors exchanging data to help solve public problems. These forms of collaborations can improve people’s lives through data-driven decision-making; information exchange and coordination; and shared standards and frameworks for multi-actor, multi-sector participation.
  • The essay cites four activities that are critical to accelerating data collaboratives: documenting value and measuring impact; matching public demand and corporate supply of data in a trusted way; training and convening data providers and users; experimenting and scaling existing initiatives.
  • Examples of data collaboratives include NIH’s Precision Medicine Initiative; the Mobile Data, Environmental Extremes and Population (MDEEP) Project; and Twitter-MIT’s Laboratory for Social Machines.

Verhulst, Stefaan, Susha, Iryna, Kostura, Alexander. “Data Collaboratives: matching Supply of (Corporate) Data to Solve Public Problems.” Medium. February 24, 2016. http://bit.ly/1ZEp2Sr.

  • This piece articulates a set of key lessons learned during a session at the International Data Responsibility Conference focused on identifying emerging practices, opportunities and challenges confronting data collaboratives.
  • The authors list a number of privately held data sources that could create positive public impacts if made more accessible in a collaborative manner, including:
    • Data for early warning systems to help mitigate the effects of natural disasters;
    • Data to help understand human behavior as it relates to nutrition and livelihoods in developing countries;
    • Data to monitor compliance with weapons treaties;
    • Data to more accurately measure progress related to the UN Sustainable Development Goals.
  • To the end of identifying and expanding on emerging practice in the space, the authors describe a number of current data collaborative experiments, including:
    • Trusted Intermediaries: Statistics Netherlands partnered with Vodafone to analyze mobile call data records in order to better understand mobility patterns and inform urban planning.
    • Prizes and Challenges: Orange Telecom, which has been a leader in this type of Data Collaboration, provided several examples of the company’s initiatives, such as the use of call data records to track the spread of malaria as well as their experience with Challenge 4 Development.
    • Research partnerships: The Data for Climate Action project is an ongoing large-scale initiative incentivizing companies to share their data to help researchers answer particular scientific questions related to climate change and adaptation.
    • Sharing intelligence products: JPMorgan Chase shares macro economic insights they gained leveraging their data through the newly established JPMorgan Chase Institute.
  • In order to capitalize on the opportunities provided by data collaboratives, a number of needs were identified:
    • A responsible data framework;
    • Increased insight into different business models that may facilitate the sharing of data;
    • Capacity to tap into the potential value of data;
    • Transparent stock of available data supply; and
    • Mapping emerging practices and models of sharing.

Vogel, N., Theisen, C., Leidig, J. P., Scripps, J., Graham, D. H., & Wolffe, G. “Mining mobile datasets to enable the fine-grained stochastic simulation of Ebola diffusion.” Paper presented at the Procedia Computer Science. 2015. http://bit.ly/1TZDroF.

  • The paper presents a research study conducted on the basis of the mobile calls records shared with researchers in the framework of the Data for Development Challenge by the mobile operator Orange.
  • The study discusses the data analysis approach in relation to developing a situation of Ebola diffusion built around “the interactions of multi-scale models, including viral loads (at the cellular level), disease progression (at the individual person level), disease propagation (at the workplace and family level), societal changes in migration and travel movements (at the population level), and mitigating interventions (at the abstract government policy level).”
  • The authors argue that the use of their population, mobility, and simulation models provide more accurate simulation details in comparison to high-level analytical predictions and that the D4D mobile datasets provide high-resolution information useful for modeling developing regions and hard to reach locations.

Welle Donker, F., van Loenen, B., & Bregt, A. K. “Open Data and Beyond.” ISPRS International Journal of Geo-Information, 5(4). 2016. http://bit.ly/22YtugY.

  • This research has developed a monitoring framework to assess the effects of open (private) data using a case study of a Dutch energy network administrator Liander.
  • Focusing on the potential impacts of open private energy data – beyond ‘smart disclosure’ where citizens are given information only about their own energy usage – the authors identify three attainable strategic goals:
    • Continuously optimize performance on services, security of supply, and costs;
    • Improve management of energy flows and insight into energy consumption;
    • Help customers save energy and switch over to renewable energy sources.
  • The authors propose a seven-step framework for assessing the impacts of Liander data, in particular, and open private data more generally:
    • Develop a performance framework to describe what the program is about, description of the organization’s mission and strategic goals;
    • Identify the most important elements, or key performance areas which are most critical to understanding and assessing your program’s success;
    • Select the most appropriate performance measures;
    • Determine the gaps between what information you need and what is available;
    • Develop and implement a measurement strategy to address the gaps;
    • Develop a performance report which highlights what you have accomplished and what you have learned;
    • Learn from your experiences and refine your approach as required.
  • While the authors note that the true impacts of this open private data will likely not come into view in the short term, they argue that, “Liander has successfully demonstrated that private energy companies can release open data, and has successfully championed the other Dutch network administrators to follow suit.”

World Economic Forum, 2015. “Data-driven development: pathways for progress.” Geneva: World Economic Forum. http://bit.ly/1JOBS8u

  • This report captures an overview of the existing data deficit and the value and impact of big data for sustainable development.
  • The authors of the report focus on four main priorities towards a sustainable data revolution: commercial incentives and trusted agreements with public- and private-sector actors; the development of shared policy frameworks, legal protections and impact assessments; capacity building activities at the institutional, community, local and individual level; and lastly, recognizing individuals as both produces and consumers of data.

Your City Needs a Local Data Intermediary Now

Curated on Posted on by Stefaan Verhulst

Matt Lawyue and Kathryn Pettit at Next City: “Imagine if every community nationwide had access to their own data — data on which children are missing too many days of school, which neighborhoods are becoming unaffordable, or where more mothers are getting better access to prenatal care.

This is a reality in some areas, where neighborhood data is analyzed to evaluate community health and to promote development. Cleveland is studying cases of lead poisoning and the impact on school readiness and educational outcomes for children. Detroit is tracking the extent of property blight and abandonment.

But good data doesn’t just happen.

These activities are possible because of local intermediaries, groups that bridge the gap between data and local stakeholders: nonprofits, government agencies, foundations and residents. These groups access data that are often confidential and indecipherable to the public and make them accessible and useful. And with the support of the National Neighborhood Indicators Partnership (NNIP), groups around the country are championing community development at the local level.

Without a local data intermediary in Baltimore, we might know less about what happened there last year and why.

Freddie Gray’s death prompted intense discussion about police brutality and discrimination against African-Americans. But the Baltimore Neighborhood Indicators Alliance (BNIA) helped root this incident and others like it within a particular place, highlighting what can happen when disadvantage is allowed to accumulate over decades.

BNIA, an NNIP member, was formed in 2000 to help community organizations use data shared by government agencies. By the time of Gray’s death, BNIA had 15 years of data across more than 150 indicators that demonstrated clear socioeconomic disadvantages for residents of Gray’s neighborhood, Sandtown-Winchester. The neighborhood had a 34 percent housing vacancy rate and 23 percent unemployment. The neighborhood lacks highway access and is poorly served by public transit, leaving residents cut off from jobs and services.

With BNIA’s help, national and local media outlets, including the New York Times,MSNBC and the Baltimore Sun portrayed a community beset by concentrated poverty, while other Baltimore neighborhoods benefited from economic investment and rising incomes. BNIA data, which is updated yearly, has also been used to develop policy ideas to revitalize the neighborhood, from increasing the use of housing choice vouchers to tackling unemployment.

Local data intermediaries like BNIA harness neighborhood data to make underserved people and unresolved issues visible. They work with government agencies to access raw data (e.g., crime reports, property records, and vital statistics) and facilitate their use to improve quality of life for residents.

But it’s not easy. Uncovering useful, actionable information requires trust, technical expertise, knowledge of the local context and coordination among multiple stakeholders.

This is why the NNIP is vital. NNIP is a peer network of more than two dozen local data intermediaries and the Urban Institute, working to democratize data by building local capacity and planning joint activities. Before NNIP’s founding partners, there were no advanced information systems documenting and tracking neighborhood indicators. Since 1996, NNIP has been a platform for sharing best practices, providing technical assistance, managing cross-site projects and analysis, and expanding the outreach of local data intermediaries to national networks and federal agencies. The partnership continues to grow. In order to foster this capacity in more places, NNIP has just released a guide for local communities to start a data intermediary….(More)”

Open Data For Social Good: The Case For Better Transport Services

Curated on Posted on by Stefaan Verhulst

 at TechWeek Europe: “The growing focus on data protection, driven partly by stronger legislation and partly by consumer pressure, has put the debate on the benefits of open data somewhat on the back burner.

The continuing spate of high-profile data breaches and the abuse of public trust in the form of constant bombardment of automated calls, spam emails and clumsily ‘personalised’ advertising has done little to further the open data agenda. In fact it left many consumers feeling lukewarm about the prospects of organisations opening up their data feeds, even at a promise of a better service in return.

That’s a worrying trend. In many industries effective use of open data can lead to development of solutions that address some of the major challenges populations are faced with today, allowing for faster innovation and adaptability to change. There are significant ways in which individuals, and society as a whole could benefit from open data, if organisations and governments get data sharing right.

Open data for transport

A good example is city transportation. Many metropolises face a major challenge – growing populations are placing pressure on current infrastructure systems, leading to congestion and inefficiency.

An open data system, where commuters use a single travel account for all travel transactions and information – whether that’s public transport, walking, using the bike, using Uber, and so on, would give the city unprecedented insight into how people commute and what’s behind their travel choices.

The key to engaging the public with this is the condition that data is used responsibly and for the greater good. Currently, Transport for London (TfL) operates a meet-in-the-middle model. Consumers can travel anonymously on the TfL network, with only the point of entry and point of exit being recorded, and the company provides that anonymised data to third-party app developers who can then use it to release useful travel applications.

TfL doesn’t profit from sharing consumer data but it does enjoy the benefits that come with it. Third-party travel applications make it easier for commuters to use TfL’s network and make the service itself appear more efficient – in short, everyone benefits.

Mutual benefit

Let’s now imagine a scenario that takes this mutually beneficial relationship a step forward, with consumers willingly giving up some information about themselves to the responsible parties (in this case, the city) and receiving personalised service in return. In this scenario, the more information commuters can provide to the system, the more useful the system can be to them.

Apart from providing personalised travel information and recommendations, such a system would have one more important benefit – it would enable cities to encourage greater social responsibility, extending the benefits from the individual to the community as a whole….(More)”

Big Data Quality: a Roadmap for Open Data

Curated on Posted on by Stefaan Verhulst

Paper by Paolo Ciancarini, Francesco Poggi and Daniel Russo: “Open Data (OD) is one of the most discussed issue of Big Data which raised the joint interest of public institutions, citizens and private companies since 2009. In addition to transparency in public administrations, another key objective of these initiatives is to allow the development of innovative services for solving real world problems, creating value in some positive and constructive way. However, the massive amount of freely available data has not yet brought the expected effects: as of today, there is no application that has exploited the potential provided by large and distributed information sources in a non-trivial way, nor any service has substantially changed for the better the lives of people. The era of a new generation applications based on open data is far to come. In this context, we observe that OD quality is one of the major threats to achieving the goals of the OD movement. The starting point of this study is the quality of the OD released by the five Constitutional offices of Italy. W3C standards about OD are widely known accepted in Italy by the Italian Digital Agency (AgID). According to the most recent Italian Laws the Public Administration may release OD according to the AgID standards. Our exploratory study aims to assess the quality of such releases and the real implementations of OD. The outcome suggests the need of a drastic improvement in OD quality. Finally we highlight some key quality principles for OD, and propose a roadmap for further research….(more)”

Why Didn’t E-Gov Live Up To Its Promise?

Curated on Posted on by Stefaan Verhulst

Excerpt from the report Delivering on Digital: The Innovators and Technologies that are Transforming Government” by William Eggers: “Digital is becoming the new normal. Digital technologies have quietly and quickly pervaded every facet of our daily lives, transforming how we eat, shop, work, play and think.

An aging population, millennials assuming managerial positions, budget shortfalls and ballooning entitlement spending all will significantly impact the way government delivers services in the coming decade, but no single factor will alter citizens’ experience of government more than the pure power of digital technologies.

Ultimately, digital transformation means reimagining virtually every facet of what government does, from headquarters to the field, from health and human services to transportation and defense.

By now, some of you readers with long memories can’t be blamed for feeling a sense of déjà vu.

After all, technology was supposed to transform government 15 years ago; an “era of electronic government” was poised to make government faster, smaller, digitized and increasingly transparent.

Many analysts (including yours truly, in a book called “Government 2.0”) predicted that by 2016, digital government would already long be a reality. In practice, the “e-gov revolution” has been an exceedingly slow-moving one. Sure, technology has improved some processes, and scores of public services have moved online, but the public sector has hardly been transformed.

What initial e-gov efforts managed was to construct pretty storefronts—in the form of websites—as the entrance to government systems stubbornly built for the industrial age. Few fundamental changes altered the structures, systems and processes of government behind those websites.

With such halfhearted implementation, the promise of cost savings from information technology failed to materialize, instead disappearing into the black hole of individual agency and division budgets. Government websites mirrored departments’ short-term orientation rather than citizens’ long-term needs. In short, government became wired—but not transformed.

So why did the reality of e-gov fail to live up to the promise?

For one thing, we weren’t yet living in a digitized economy—our homes, cars and workplaces were still mostly analog—and the technology wasn’t as far along as we thought; without the innovations of cloud computing and open-source software, for instance, the process of upgrading giant, decades-old legacy systems proved costly, time-consuming and incredibly complex.

And not surprisingly, most governments—and private firms, for that matter—lacked deep expertise in managing digital services. What we now call “agile development”—an iterative development model that allows for constant evolution through recurrent testing and evaluation—was not yet mainstreamed.

Finally, most governments explicitly decided to focus first on the Hollywood storefront and postpone the bigger and tougher issues of reengineering underlying processes and systems. When budgets nosedived—even before the recession—staying solvent and providing basic services took precedence over digital transformation.

The result: Agencies automated some processes but failed to transform them; services were put online, but rarely were they focused logically and intelligently around the citizen.

Given this history, it’s natural to be skeptical after years of hype about government’s amazing digital future. But conditions on the ground (and in the cloud) are finally in place for change, and citizens are not only ready for digital government—many are demanding it.

Digital-native millennials are now consumers of public services, and millions of them work in and around government; they won’t tolerate balky and poorly designed systems, and they’ll let the world know through social media. Gen Xers and baby boomers, too, have become far more savvy consumers of digital products and services….(More)”

While governments talk about smart cities, it’s citizens who create them

Curated on Posted on by Stefaan Verhulst

Carlo Ratti at the Conversation: “The Australian government recently released an ambitious Smart Cities Plan, which suggests that cities should be first and foremost for people:

If our cities are to continue to meet their residents’ needs, it is essential for people to engage and participate in planning and policy decisions that have an impact on their lives.

Such statements are a good starting point – and should probably become central to Australia’s implementation efforts. A lot of knowledge has been collected over the past decade from successful and failed smart cities experiments all over the world; reflecting on them could provide useful information for the Australian government as it launches its national plan.

What is a smart city?

But, before embarking on such review, it would help to start from a definition of “smart city”.

The term has been used and abused in recent years, so much so that today it has lost meaning. It is often used to encompass disparate applications: we hear people talk and write about “smart city” when they refer to anything from citizen engagement to Zipcar, from open data to Airbnb, from smart biking to broadband.

Where to start with a definition? It is a truism to say the internet has transformed our lives over the past 20 years. Everything in the way we work, meet, mate and so on is very different today than it was just a few decades ago, thanks to a network of connectivity that now encompasses most people on the planet.

In a similar way, we are today at the beginning of a new technological revolution: the internet is entering physical space – the very space of our cities – and is becoming the Internet of Things; it is opening the door to a new world of applications that, as with the first wave of the internet, can incorporate many domains….

What should governments do?

In the above technological context, what should governments do? Over the past few years, the first wave of smart city applications followed technological excitement.

For instance, some of Korea’s early experiments such as Songdo City were engineered by the likes of Cisco, with technology deployment assisted by top-down policy directives.

In a similar way, in 2010, Rio de Janeiro launched the Integrated Centre of Command and Control, engineered by IBM. It’s a large control room for the city, which collects real-time information from cameras and myriad sensors suffused in the urban fabric.

Such approaches revealed many shortcomings, most notably the lack of civic engagement. It is as if they thought of the city simply as a “computer in open air”. These approaches led to several backlashes in the research and academic community.

A more interesting lesson can come from the US, where the focus is more on developing a rich Internet of Things innovation ecosystem. There are many initiatives fostering spaces – digital and physical – for people to come together and collaborate on urban and civic innovations….

That isn’t to say that governments should take a completely hands-off approach to urban development. Governments certainly have an important role to play. This includes supporting academic research and promoting applications in fields that might be less appealing to venture capital – unglamorous but nonetheless crucial domains such as municipal waste or water services.

The public sector can also promote the use of open platforms and standards in such projects, which would speed up adoption in cities worldwide.

Still, the overarching goal should always be to focus on citizens. They are in the best position to determine how to transform their cities and to make decisions that will have – as the Australian Smart Cities Plan puts it – “an impact on their lives”….(more)”

Private Data and the Public Good

Curated on Posted on by Stefaan Verhulst

Gideon Mann‘s remarks on the occasion of the Robert Khan distinguished lecture at The City College of New York on 5/22/16: and opportunities about a specific aspect of this relationship, the broader need for computer science to engage with the real world. Right now, a key aspect of this relationship is being built around the risks and opportunities of the emerging role of data.

Ultimately, I believe that these relationships, between computer science andthe real world, between data science and real problems, hold the promise tovastly increase our public welfare. And today, we, the people in this room,have a unique opportunity to debate and define a more moral dataeconomy….

The hybrid research model proposes something different. The hybrid research model, embeds, as it were, researchers as practitioners.The thought was always that you would be going about your regular run of business,would face a need to innovate to solve a crucial problem, and would do something novel. At that point, you might choose to work some extra time and publish a paper explaining your innovation. In practice, this model rarely works as expected. Tight deadlines mean the innovation that people do in their normal progress of business is incremental..

This model separated research from scientific publication, and shortens thetime-window of research, to what can be realized in a few year time zone.For me, this always felt like a tremendous loss, with respect to the older so-called “ivory tower” research model. It didn’t seem at all clear how this kindof model would produce the sea change of thought engendered byShannon’s work, nor did it seem that Claude Shannon would ever want towork there. This kind of environment would never support the freestanding wonder, like the robot mouse that Shannon worked on. Moreover, I always believed that crucial to research is publication and participation in the scientific community. Without this engagement, it feels like something different — innovation perhaps.

It is clear that the monopolistic environment that enabled AT&T to support this ivory tower research doesn’t exist anymore. .

Now, the hybrid research model was one model of research at Google, butthere is another model as well, the moonshot model as exemplified byGoogle X. Google X brought together focused research teams to driveresearch and development around a particular project — Google Glass and the Self-driving car being two notable examples. Here the focus isn’t research, but building a new product, with research as potentially a crucial blocking issue. Since the goal of Google X is directly to develop a new product, by definition they don’t publish papers along the way, but they’re not as tied to short-term deliverables as the rest of Google is. However, they are again decidedly un-Bell-Labs like — a secretive, tightly focused, non-publishing group. DeepMind is a similarly constituted initiative — working, for example, on a best-in-the-world Go playing algorithm, with publications happening sparingly.

Unfortunately, both of these approaches, the hybrid research model and the moonshot model stack the deck towards a particular kind of research — research that leads to relatively short term products that generate corporate revenue. While this kind of research is good for society, it isn’t the only kind of research that we need. We urgently need research that is longterm, and that is undergone even without a clear financial local impact. Insome sense this is a “tragedy of the commons”, where a shared public good (the commons) is not supported because everyone can benefit from itwithout giving back. Academic research is thus a non-rival, non-excludible good, and thus reasonably will be underfunded. In certain cases, this takes on an ethical dimension — particularly in health care, where the choice ofwhat diseases to study and address has a tremendous potential to affect human life. Should we research heart disease or malaria? This decisionmakes a huge impact on global human health, but is vastly informed by the potential profit from each of these various medicines….

Private Data means research is out of reach

The larger point that I want to make, is that in the absence of places where long-term research can be done in industry, academia has a tremendous potential opportunity. Unfortunately, it is actually quite difficult to do the work that needs to be done in academia, since many of the resources needed to push the state of the art are only found in industry: in particular data.

Of course, academia also lacks machine resources, but this is a simpler problem to fix — it’s a matter of money, resources form the government could go to enabling research groups building their own data centers or acquiring the computational resources from the market, e.g. Amazon. This is aided by the compute philanthropy that Google and Microsoft practice that grant compute cycles to academic organizations.

But the data problem is much harder to address. The data being collected and generated at private companies could enable amazing discoveries and research, but is impossible for academics to access. The lack of access to private data from companies actually is much more significant effects than inhibiting research. In particular, the consumer level data, collected by social networks and internet companies could do much more than ad targeting.

Just for public health — suicide prevention, addiction counseling, mental health monitoring — there is enormous potential in the use of our online behavior to aid the most needy, and academia and non-profits are set-up to enable this work, while companies are not.

To give a one examples, anorexia and eating disorders are vicious killers. 20 million women and 10 million men suffer from a clinically significant eating disorder at some time in their life, and sufferers of eating disorders have the highest mortality rate of any other mental health disorder — with a jaw-dropping estimated mortality rate of 10%, both directly from injuries sustained by the disorder and by suicide resulting from the disorder.

Eating disorders are particular in that sufferers often seek out confirmatory information, blogs, images and pictures that glorify and validate what sufferers see as “lifestyle” choices. Browsing behavior that seeks out images and guidance on how to starve yourself is a key indicator that someone is suffering. Tumblr, pinterest, instagram are places that people host and seek out this information. Tumblr has tried to help address this severe mental health issue by banning blogs that advocate for self-harm and by adding PSA announcements to query term searches for queries for or related to anorexia. But clearly — this is not the be all and end all of work that could be done to detect and assist people at risk of dying from eating disorders. Moreover, this data could also help understand the nature of those disorders themselves…..

There is probably a role for a data ombudsman within private organizations — someone to protect the interests of the public’s data inside of an organization. Like a ‘public editor’ in a newspaper according to how you’ve set it up. There to protect and articulate the interests of the public, which means probably both sides — making sure a company’s data is used for public good where appropriate, and making sure the ‘right’ to privacy of the public is appropriately safeguarded (and probably making sure the public is informed when their data is compromised).

Next, we need a platform to make collaboration around social good between companies and between companies and academics. This platform would enable trusted users to have access to a wide variety of data, and speed process of research.

Finally, I wonder if there is a way that government could support research sabbaticals inside of companies. Clearly, the opportunities for this research far outstrip what is currently being done…(more)”

Big data: Issues for an international political sociology of the datafication of worlds

Curated on Posted on by Stefaan Verhulst

Paper by Madsen, A.K.; Flyverbom, M.; Hilbert, M. and Ruppert, Evelyn: “The claim that big data can revolutionize strategy and governance in the context of international relations is increasingly hard to ignore. Scholars of international political sociology have mainly discussed this development through the themes of security and surveillance. The aim of this paper is to outline a research agenda that can be used to raise a broader set of sociological and practice-oriented questions about the increasing datafication of international relations and politics. First, it proposes a way of conceptualizing big data that is broad enough to open fruitful investigations into the emerging use of big data in these contexts. This conceptualization includes the identification of three moments contained in any big data practice. Secondly, it suggests a research agenda built around a set of sub-themes that each deserve dedicated scrutiny when studying the interplay between big data and international relations along these moments. Through a combination of these moments and sub-themes, the paper suggests a roadmap for an international political sociology of the datafication of worlds….(more)”