City of Copenhagen launches data marketplace

Curated on Posted on by Stefaan Verhulst

Sarah Wray at TMForum: “The City of Copenhagen has launched its City Data Exchange to make public and private data accessible to power innovation.

The City Data Exchange is a new service to create a ‘marketplace for data’ from public and private data providers and allow monetization. The platform has been developed by Hitachi Insight Group.

“Data is the fuel powering our digital world, but in most cities it is unused,” said Hans Lindeman, Senior Vice President, Hitachi Insight Group, EMEA. “Even where data sits in public, freely accessible databases, the cost of extracting and processing it can easily outweigh the benefits.”

The City of Copenhagen is using guidelines for a data format that is safe, secure, ensures privacy and makes data easy to use. The City Data Exchange will only accept data that has been fully anonymized by the data supplier, for example.

According to Hitachi Insight Group, “All of this spares organizations the trouble and cost of extracting and processing data from multiple sources. At the same time, proprietary data can now become a business resource that can be monetized outside an organization.”

As a way to demonstrate how data from the City Data Exchange could be used in applications, Hitachi Insight Group is developing two applications:

  • Journey Insight, which helps citizens in the region to track their transportation usage over time and understand the carbon footprint of their travel
  • Energy Insight, which allows both households and businesses to see how much energy they use.

Both are set for public launch later this year.

Another example of how data marketplaces can enable innovation is the Mind My Business mobile app, developed by Vizalytics. It brings together all the data that can affect a retailer — from real-time information on how construction or traffic issues can hurt the footfall of a business, to timely reminders about taxes to pay or new regulations to meet. The “survival app for shopkeepers” makes full use of all the relevant data sources brought together by the City Data Exchange.

The platform will offer data in different categories such as: city life, infrastructure, climate and environment, business data and economy, demographics, housing and buildings, and utilities usage. It aims to meet the needs of local government, city planners, architects, retailers, telecoms networks, utilities, and all other companies and organizations who want to understand what makes Copenhagen, its businesses and its citizens tick.

“Smart cities need smart insights, and that’s only possible if everybody has all the facts at their disposal. The City Data Exchange makes that possible; it’s the solution that will help us all to create better public spaces and — for companies in Copenhagen — to offer better services and create jobs,” said Frank Jensen, the Lord Mayor of Copenhagen.

The City Data Exchange is currently offering raw data to its customers, and later this year will add analytical tools. The cost of gathering and processing the data will be recovered through subscription and service fees, which are expected to be much lower than the cost any company or city would face in performing the work of extracting, collecting and integrating the data by themselves….(More)”

Are we too obsessed with data?

Curated on Posted on by Stefaan Verhulst

Lauren Woodman of Nethope:” Data: Everyone’s talking about it, everyone wants more of it….

Still, I’d posit that we’re too obsessed with data. Not just us in the humanitarian space, of course, but everyone. How many likes did that Facebook post get? How many airline miles did I fly last year? How many hours of sleep did I get last week?…

The problem is that data by itself isn’t that helpful: information is.

We need to develop a new obsession, around making sure that data is actionable, that it is relevant in the context in which we work, and on making sure that we’re using the data as effectively as we are collecting it.

In my talk at ICT4D, I referenced the example of 7-Eleven in Japan. In the 1970s, 7-Eleven in Japan became independent from its parent, Southland Corporation. The CEO had to build a viable business in a tough economy. Every month, each store manager would receive reams of data, but it wasn’t effective until the CEO stripped out the noise and provided just four critical data points that had the greatest relevance to drive the local purchasing that each store was empowered to do on their own.

Those points – what sold the day before, what sold the same day a year ago, what sold the last time the weather was the same, and what other stores sold the day before – were transformative. Within a year, 7-Eleven had turned a corner, and for 30 years, remained the most profitable retailer in Japan. It wasn’t about the Big Data; it was figuring out what data was relevant, actionable and empowered local managers to make nimble decisions.

For our sector to get there, we need to do the front-end work that transforms our data into information that we can use. That, after all, is where the magic happens.

A few examples provide more clarity as to why this is so critical.

We know that adaptive decision-making requires access to real-time data. By knowing what is happening in real-time, or near-real-time, we can adjust our approaches and interventions to be most impactful. But to do so, our data has to be accessible to those that are empowered to make decisions. To achieve that, we have to make investments in training, infrastructure, and capacity-building at the organizational level.  But in the nonprofit sector, such investments are rarely supported by donors and beyond the limited unrestricted funding available to most most organizations. As a result, the sector has, so far, been able to take only limited steps towards effective data usage, hampering our ability to transform the massive amounts of data we have into useful information.

Another big question about data, and particularly in the humanitarian space, is whether it should be open, closed or somewhere in between. Privacy is certainly paramount, and for types of data, the need for close protection is very clear. For many other data, however, the rules are far less clear. Every country has its own rules about how data can and cannot be used or shared, and more work is needed to provide clarity and predictability so that appropriate data-sharing can evolve.

And perhaps more importantly, we need to think about not just the data, but the use cases.  Most of us would agree, for example, that sharing information during a crisis situation can be hugely beneficial to the people and the communities we serve – but in a world where rules are unclear, that ambiguity limits what we can do with the data we have. Here again, the context in which data will be used is critically important.

Finally, all of in the sector have to realize that the journey to transforming data into information is one we’re on together. We have to be willing to give and take. Having data is great; sharing information is better. Sometimes, we have to co-create that basis to ensure we all benefit….(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.

Soon Your City Will Know Everything About You

Curated on Posted on by Stefaan Verhulst
Philip N. Howard at Yahoo Tech: “They call it “the Internet of Things” — the rapidly growing network of everyday objects equipped with sensors, tiny power supplies, and internet addresses. Within a few years, we will be immersed in a world of these connected devices. The best estimatessuggest that there will be about 60 billion of them by the year 2020. We’ve already seen internet-accessible sensors implanted in dolls, cars, and cows.

Currently, the biggest users of these sensor arrays are in cities, where city governments use them to collect large amounts of policy-relevant data. In Los Angeles, the crowdsourced traffic and navigation app Waze collects data that helps residents navigate the city’s choked highway networks. In Chicago, an ambitious program makes public data available to startups eager to build apps for residents. The city’s 49th ward has been experimenting with participatory budgeting and online votingto take the pulse of the community on policy issues. Chicago has also been developing the “Array of Things,” a network of sensors that track, among other things, the urban conditions that affect bronchitis.

Edmonton uses the cloud to track the condition of playground equipment. And a growing number of countries have purpose-built smart cities, like South Korea’s high tech utopia city of Songdo, where pervasive sensor networks and ubiquitous computing generate immense amounts of civic data for public services.

The drive for smart cities isn’t restricted to the developed world. Rio de Janeiro coordinates the information flows of 30 different city agencies. In Beijing and Da Nang (Vietnam), mobile phone data is actively tracked in the name of real-time traffic management. Urban sensor networks, in other words, are also developing in countries with few legal protections governing the usage of data.

These services are promising and useful. But you don’t have to look far to see why the Internet of Things has serious privacy implications. Public data is used for “predictive policing” in at least 75 cities across the U.S., including New York City, where critics maintain that using social media or traffic data to help officers evaluate probable cause is a form of digital stop-and-frisk. In Los Angeles, the security firm Palantir scoops up publicly generated data on car movements, merges it with license plate information collected by the city’s traffic cameras, and sells analytics back to the city so that police officers can decide whether or not to search a car. In Chicago, concern is growing about discriminatory profiling because so much information is collected and managed by the police department — an agency with a poor reputation for handling data in consistent and sensitive ways. In 2015, video surveillance of the police shooting Laquan McDonald outside a Burger King was erased by a police employee who ironically did not know his activities were being digitally recorded by cameras inside the restaurant.

Since most national governments have bungled privacy policy, cities — which have a reputation for being better with administrative innovations — will need to fill this gap. A few countries, such as Canada and the U.K., have independent “privacy commissioners” who are responsible for advocating for the public when bureaucracies must decide how to use or give out data. It is pretty clear that cities need such advocates too.

What would Urban Privacy Commissioners do? They would teach the public — and other government staff — about how policy algorithms work. They would evaluate the political context in which city agencies make big data investments. They would help a city negotiate contracts that protect residents’ privacy while providing effective analysis to policy makers and ensuring that open data is consistently serving the public good….(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)”

All European scientific articles to be freely accessible by 2020

Curated on Posted on by Stefaan Verhulst

EU Presidency: “All scientific articles in Europe must be freely accessible as of 2020. EU member states want to achieve optimal reuse of research data. They are also looking into a European visa for foreign start-up founders.

And, according to the new Innovation Principle, new European legislation must take account of its impact on innovation. These are the main outcomes of the meeting of the Competitiveness Council in Brussels on 27 May.

Sharing knowledge freely

Under the presidency of Netherlands State Secretary for Education, Culture and Science Sander Dekker, the EU ministers responsible for research and innovation decided unanimously to take these significant steps. Mr Dekker is pleased that these ambitions have been translated into clear agreements to maximise the impact of research. ‘Research and innovation generate economic growth and more jobs and provide solutions to societal challenges,’ the state secretary said. ‘And that means a stronger Europe. To achieve that, Europe must be as attractive as possible for researchers and start-ups to locate here and for companies to invest. That calls for knowledge to be freely shared. The time for talking about open access is now past. With these agreements, we are going to achieve it in practice.’

Open access

Open access means that scientific publications on the results of research supported by public and public-private funds must be freely accessible to everyone. That is not yet the case. The results of publicly funded research are currently not accessible to people outside universities and knowledge institutions. As a result, teachers, doctors and entrepreneurs do not have access to the latest scientific insights that are so relevant to their work, and universities have to take out expensive subscriptions with publishers to gain access to publications.

Reusing research data

From 2020, all scientific publications on the results of publicly funded research must be freely available. It also must be able to optimally reuse research data. To achieve that, the data must be made accessible, unless there are well-founded reasons for not doing so, for example intellectual property rights or security or privacy issues….(More)”

Time for sharing data to become routine: the seven excuses for not doing so are all invalid

Curated on Posted on by Stefaan Verhulst

Paper by Richard Smith and Ian Roberts: “Data are more valuable than scientific papers but researchers are incentivised to publish papers not share data. Patients are the main beneficiaries of data sharing but researchers have several incentives not to share: others might use their data to get ahead in the academic rat race; they might be scooped; their results might not be replicable; competitors may reach different conclusions; their data management might be exposed as poor; patient confidentiality might be breached; and technical difficulties make sharing impossible. All of these barriers can be overcome and researchers should be rewarded for sharing data. Data sharing must become routine….(More)”

If you build it… will they come?

Curated on Posted on by Stefaan Verhulst

Laura Bacon at Omidyar Network: “What do datasets on Danish addresses, Indonesian elections, Singapore Dengue Fever, Slovakian contracts, Uruguayan health service provision, and Global weather systems have in common? Read on to learn more…

On May 12, 2016, more than 40 nations’ leaders gathered in London for an Anti-Corruption Summit, convened by UK Prime Minister David Cameron. Among the commitments made, 40 countries pledged to make their procurement processes open by default, with 14 countries specifically committing to publish to the Open Contracting Data Standard.

This conference and these commitments can be seen as part of a larger global norm toward openness and transparency, also embodied by the Open Government Partnership, Open Data Charter, and increasing numbers of Open Data Portals.

As government data is increasingly published openly in the public domain, valid questions have been raised about what impact the data will have: As governments release this data, will it be accessed and used? Will it ultimately improve lives, root out corruption, hold answers to seemingly intractable problems, and lead to economic growth?*

Omidyar Network — having supported several Open Data organizations and platforms such as Open Data Institute, Open Knowledge, and Web Foundation — sought data-driven answers to these questions. After a public call for proposals, we selected NYU’s GovLab to conduct research on the impact open data has already had. Not the potential or prospect of impact, but past proven impact. The GovLab research team, led by Stefaan Verhulst, investigated a variety of sectors — health, education, elections, budgets, contracts, etc. — in a variety of locations, spanning five continents.

Their findings are promising and exciting, demonstrating that open data is changing the world by empowering people, improving governance, solving public problems, and leading to innovation. A summary is contained in thisKey Findings report, and is accompanied by many open data case studies posted in this Open Data Impact Repository.

Of course, stories such as this are not 100% rosy, and the report is clear about the challenges ahead. There are plenty of cases in which open data has had minimal impact. There are cases where there was negative impact. And there are obstacles to open data reaching its full potential: namely, open data projects that don’t respond to citizens’ questions and needs, a lack of technical capacity on either the data provider and data user side, inadequate protections for privacy and security, and a shortage of resources.

But this research holds good news: Danish addresses, Indonesian elections,Singapore Dengue Fever, Slovakian contracts, Uruguayan health service provision, Global weather systems, and others were all opened up. And all changed the world by empowering citizens, improving governance, solving public problems, and leading to innovation. Please see this report for more….(More)”

See also odimpact.org

Smart crowds in smart cities: real life, city scale deployments of a smartphone based participatory crowd management platform

Curated on Posted on by Stefaan Verhulst

Tobias Franke, Paul Lukowicz and Ulf Blanke at the Journal of Internet Services and Applications: “Pedestrian crowds are an integral part of cities. Planning for crowds, monitoring crowds and managing crowds, are fundamental tasks in city management. As a consequence, crowd management is a sprawling R&D area (see related work) that includes theoretical models, simulation tools, as well as various support systems. There has also been significant interest in using computer vision techniques to monitor crowds. However, overall, the topic of crowd management has been given only little attention within the smart city domain. In this paper we report on a platform for smart, city-wide crowd management based on a participatory mobile phone sensing platform. Originally, the apps based on this platform have been conceived as a technology validation tool for crowd based sensing within a basic research project. However, the initial deployments at the Notte Bianca Festival1 in Malta and at the Lord Mayor’s Show in London2 generated so much interest within the civil protection community that it has gradually evolved into a full-blown participatory crowd management system and is now in the process of being commercialized through a startup company. Until today it has been deployed at 14 events in three European countries (UK, Netherlands, Switzerland) and used by well over 100,000 people….

Obtaining knowledge about the current size and density of a crowd is one of the central aspects of crowd monitoring . For the last decades, automatic crowd monitoring in urban areas has mainly been performed by means of image processing . One use case for such video-based applications can be found in, where a CCTV camera-based system is presented that automatically alerts the staff of subway stations when the waiting platform is congested. However, one of the downsides of video-based crowd monitoring is the fact that video cameras tend to be considered as privacy invading. Therefore,  presents a privacy preserving approach to video-based crowd monitoring where crowd sizes are estimated without people models or object tracking.

With respect to the mitigation of catastrophes induced by panicking crowds (e.g. during an evacuation), city planners and architects increasingly rely on tools simulating crowd behaviors in order to optimize infrastructures. Murakami et al. presents an agent based simulation for evacuation scenarios. Shendarkar et al. presents a work that is also based on BSI (believe, desire, intent) agents – those agents however are trained in a virtual reality environment thereby giving greater flexibility to the modeling. Kluepfel et al. on the other hand uses a cellular automaton model for the simulation of crowd movement and egress behavior.

With smartphones becoming everyday items, the concept of crowd sourcing information from users of mobile application has significantly gained traction. Roitman et al. presents a smart city system where the crowd can send eye witness reports thereby creating deeper insights for city officials. Szabo et al. takes this approach one step further and employs the sensors built into smartphones for gathering data for city services such as live transit information. Ghose et al. utilizes the same principle for gathering information on road conditions. Pan et al. uses a combination of crowd sourcing and social media analysis for identifying traffic anomalies….(More)”.

Case Studies of Government Use of Big Data in Latin America: Brazil and Mexico

Curated on Posted on by Stefaan Verhulst

Chapter by Roberto da Mota Ueti, Daniela Fernandez Espinosa, Laura Rafferty, Patrick C. K. Hung in Big Data Applications and Use Cases: “Big Data is changing our world with masses of information stored in huge servers spread across the planet. This new technology is changing not only companies but governments as well. Mexico and Brazil, two of the most influential countries in Latin America, are entering a new era and as a result, facing challenges in all aspects of public policy. Using Big Data, the Brazilian Government is trying to decrease spending and use public money better by grouping public information with stored information on citizens in public services. With new reforms in education, finances and telecommunications, the Mexican Government is taking on a bigger role in efforts to channel the country’s economic policy into an improvement of the quality of life of their habitants. It is known that technology is an important part for sub-developed countries, who are trying to make a difference in certain contexts such as reducing inequality or regulating the good usage of economic resources. The good use of Big Data, a new technology that is in charge of managing a big quantity of information, can be crucial for the Mexican Government to reach the goals that have been set in the past under Peña Nieto’s administration. This article focuses on how the Brazilian and Mexican Governments are managing the emerging technologies of Big Data and how it includes them in social and industrial projects to enhance the growth of their economies. The article also discusses the benefits of these uses of Big Data and the possible problems that occur related to security and privacy of information….(More)’