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.

Three Things Great Data Storytellers Do Differently

Curated on Posted on by Stefaan Verhulst

Jake Porway at Stanford Social Innovation Review: “…At DataKind, we use data science and algorithms in the service of humanity, and we believe that communicating about our work using data for social impact is just as important as the work itself. There’s nothing worse than findings gathering dust in an unread report.

We also believe our projects should always start with a question. It’s clear from the questions above and others that the art of data storytelling needs some demystifying. But rather than answering each question individually, I’d like to pose a broader question that can help us get at some of the essentials: What do great data storytellers do differently and what can we learn from them?

1. They answer the most important question: So what?

Knowing how to compel your audience with data is more of an art than a science. Most people still have negative associations with numbers and statistics—unpleasant memories of boring math classes, intimidating technical concepts, or dry accounting. That’s a shame, because the message behind the numbers can be so enriching and enlightening.

The solution? Help your audience understand the “so what,” not the numbers. Ask: Why should someone care about your findings? How does this information impact them? My strong opinion is that most people actually don’t want to look at data. They need to trust that your methods are sound and that you’re reasoning from data, but ultimately they just want to know what it all means for them and what they should do next.

A great example of going straight to the “so what” is this beautiful, interactive visualization by Periscopic about gun deaths. It uses data sparingly but still evokes a very clear anti-gun message….

2. They inspire us to ask more questions.

The best data visualization helps people investigate a topic further, instead of drawing a conclusion for them or persuading them to believe something new.

For example, the nonprofit DC Action for Children was interested in leveraging publicly available data from government agencies and the US Census, as well as DC Action for Children’s own databases, to help policymakers, parents, and community members understand the conditions influencing child well-being in Washington, DC. We helped create a tool that could bring together data in a multitude of forms, and present it in a way that allowed people to delve into the topic themselves and uncover surprising truths, such as the fact that one out of every three kids in DC lives in a neighborhood without a grocery store….

3. They use rigorous analysis instead of just putting numbers on a page.

Data visualization isn’t an end goal; it’s a process. It’s often the final step in a long manufacturing chain, along which we poke, prod, and mold data to create that pretty graph.

Years ago, the New York City Department of Parks & Recreation (NYC Parks) approached us—armed with data about every single tree in the city, including when it was planted and how it was pruned—and wanted to know: Does pruning trees in one year reduce the number of hazardous tree conditions in the following year? This is one of the first things our volunteer data scientists came up with:

Visualization of NYC Parks’ Department data showing tree density in New York City.

This is a visualization of tree density New York—and it was met with oohs and aahs. It was interactive! You could see where different types of trees lived! It was engaging! But another finding that came out of this work arguably had a greater impact. Brian D’Alessandro, one of our volunteer data scientists, used statistical modeling to help NYC Parks calculate a number: 22 percent. It turns out that if you prune trees in New York, there are 22 percent fewer emergencies on those blocks than on the blocks where you didn’t prune. This number is helping the city become more effective by understanding how to best allocate its resources, and now other urban forestry programs are asking New York how they can do the same thing. There was no sexy visualization, no interactivity—just a rigorous statistical model of the world that’s shaping how cities protect their citizens….(More)”

Searching for Someone: From the “Small World Experiment” to the “Red Balloon Challenge,” and beyond

Curated on Posted on by Stefaan Verhulst

Essay by Manuel Cebrian, Iyad Rahwan, Victoriano Izquierdo, Alex Rutherford, Esteban Moro and Alex (Sandy) Pentland: “Our ability to search social networks for people and information is fundamental to our success. We use our personal connections to look for new job opportunities, to seek advice about what products to buy, to match with romantic partners, to find a good physician, to identify business partners, and so on.

Despite living in a world populated by seven billion people, we are able to navigate our contacts efficiently, only needing a handful of personal introductions before finding the answer to our question, or the person we are seeking. How does this come to be? In folk culture, the answer to this question is that we live in a “small world.” The catch-phrase was coined in 1929 by the visionary author Frigyes Karinthy in his Chain-Links essay, where these ideas are put forward for the first time.

Let me put it this way: Planet Earth has never been as tiny as it is now. It shrunk — relatively speaking of course — due to the quickening pulse of both physical and verbal communication. We never talked about the fact that anyone on Earth, at my or anyone’s will, can now learn in just a few minutes what I think or do, and what I want or what I would like to do. Now we live in fairyland. The only slightly disappointing thing about this land is that it is smaller than the real world has ever been. — Frigyes Karinthy, Chain-Links, 1929

Then, it was just a dystopian idea reflecting the anxiety of living in an increasingly more connected world. But there was no empirical evidence that this was actually the case, and it took almost 30 years to find any.

Six Degrees of Separation

In 1967, legendary psychologist Stanley Milgram conducted a ground-breaking experiment to test this “small world” hypothesis. He started with random individuals in the U.S. midwest, and asked them to send packages to people in Boston, Massachusetts, whose address was not given. They must contribute to this “search” only by sending the package to individuals known on a first-name basis. Milgram expected that successful searches (if any!) would require hundreds of individuals along the chain from the initial sender to the final recipient.

Surprisingly, however, Milgram found that the average path length was somewhere between five point five and six individuals, which made social search look astonishingly efficient. Although the experiment raised some methodological criticisms, its findings were profound. However, what it did not answer is why social networks have such short paths in the first place. The answer was not obvious. In fact, there were reasons to suspect that short paths were just a myth: social networks are very cliquish. Your friends’ friends are likely to also be your friends, and thus most social paths are short and circular. This “cliquishness” suggests that our search through the social network can easily get “trapped” within our close social community, making social search highly inefficient.

Architectures for Social Search

Again, it took a long time — more than 40 years — before this riddle was solved. In a 1998 seminal paper in Nature, Duncan Watts & Steven Strogatzcame up with an elegant mathematical model to explain the existence of these short paths. They started from a social network that is very cliquish, i.e., most of your friends are also friends of one another. In this model, the world is “large” since the social distance among individuals is very long. However, if we take only a tiny fraction of these connections (say one out of every hundred links), and rewire them to random individuals in the network, that same world suddenly becomes “small.” These random connections allow individuals to jump to faraway communities very quickly — using them as social network highways — thus reducing average path length in a dramatic fashion.

While this theoretical insight suggests that social networks are searchable due to the existence of short paths, it does not yet say much about the “procedure” that people use to find these paths. There is no reason, a priori, that we should know how to find these short chains, especially since there are many chains, and no individuals have knowledge of the network structure beyond their immediate communities. People do not know how the friends of their friends are connected among themselves, and therefore it is not obvious that they would have a good way of navigating their social network while searching.

Soon after Watts and Strogatz came up with this model at Cornell University, a computer scientist across campus, Jon Kleinberg, set out to investigate whether such “small world” networks are searchable. In a landmark Nature article, “Navigation in a Small World,” published in 200o, he showed that social search is easy without global knowledge of the network, but only for a very specific value of the probability of long-range connectivity (i.e., the probability that we know somebody far removed from us, socially, in the social network). With the advent of a publicly available social media dataset such as LiveJournal, David Liben-Nowell and colleagues showed that real-world social networks do indeed have these particular long-range ties. It appears the social architecture of the world we inhabit is remarkably fine-tuned for searchability….

The Tragedy of the Crowdsourcers

Some recent efforts have been made to try and disincentivize sabotage. If verification is also rewarded along the recruitment tree, then the individuals who recruited the saboteurs would have a clear incentive to verify, halt, and punish the saboteurs. This theoretical solution is yet to be tested in practice, and it is conjectured that a coalition of saboteurs, where saboteurs recruit other saboteurs pretending to “vet” them, would make recursive verification futile.

If we are to believe in theory, theory does not shed a promising light on reducing sabotage in social search. We recently proposed the “Crowdsourcing Dilemma.” In it, we perform a game-theoretic analysis of the fundamental tradeoff between the potential for increased productivity of social search and the possibility of being set back by malicious behavior, including misinformation. Our results show that, in competitive scenarios, such as those with multiple social searches competing for the same information, malicious behavior is the norm, not an anomaly — a result contrary to conventional wisdom. Even worse: counterintuitively, making sabotage more costly does not deter saboteurs, but leads all the competing teams to a less desirable outcome, with more aggression, and less efficient collective search for talent.

These empirical and theoretical findings have cautionary implications for the future of social search, and crowdsourcing in general. Social search is surprisingly efficient, cheap, easy to implement, and functional across multiple applications. But there are also surprises in the amount of evildoing that the social searchers will stumble upon while recruiting. As we get deeper and deeper into the recruitment tree, we stumble upon that evil force lurking in the dark side of the network.

Evil mutates and regenerates in the crowd in new forms impossible to anticipate by the designers or participants themselves. Crowdsourcing and its enemies will always be engaged in an co-evolutionary arms race.

Talent is there to be searched and recruited. But so are evil and malice. Ultimately, crowdsourcing experts need to figure out how to recruit more of the former, while deterring more of the later. We might be living on a small world, but the cost and fragility of navigating it could harm any potential strategy to leverage the power of social networks….

Being searchable is a way of being closely connected to everyone else, which is conducive to contagion, group-think, and, most crucially, makes it hard for individuals to differentiate from each other. Evolutionarily, for better or worse, our brain makes us mimic others, and whether this copying of others ends up being part of the Wisdom of the Crowds, or the “stupidity of many,” it is highly sensitive to the scenario at hand.

Katabasis, or the myth of the hero that descends to the underworld and comes back stronger, is as old as time and pervasive across ancient cultures. Creative people seem to need to “get lost.” Grigori Perelman, Shinichi Mochizuki, and Bob Dylan all disappeared for a few years to reemerge later as more creative versions of themselves. Others like J. D. Salinger and Bobby Fisher also vanished, and never came back to the public sphere. If others cannot search and find us, we gain some slack, some room to escape from what we are known for by others. Searching for our true creative selves may rest on the difficulty of others finding us….(More)”

Fan Favorites

Curated on Posted on by Stefaan Verhulst

Erin Reilly at Strategy + Business: “…In theory, new technological advances such as big data and machine learning, combined with more direct access to audience sentiment, behaviors, and preferences via social media and over-the-top delivery channels, give the entertainment and media industry unprecedented insight into what the audience actually wants. But as a professional in the television industry put it, “We’re drowning in data and starving for insights.” Just as my data trail didn’t trace an accurate picture of my true interest in soccer, no data set can quantify all that consumers are as humans. At USC’s Annenberg Innovation Lab, our research has led us to an approach that blends data collection with a deep understanding of the social and cultural context in which the data is created. This can be a powerful practice for helping researchers understand the behavior of fans — fans of sports, brands, celebrities, and shows.

A Model for Understanding Fans

Marketers and creatives often see audiences and customers as passive assemblies of listeners or spectators. But we believe it’s more useful to view them as active participants. The best analogy may be fans. Broadly characterized, fans have a continued connection with the property they are passionate about. Some are willing to declare their affinity through engagement, some have an eagerness to learn more about their passion, and some want to connect with others who share their interests. Fans are emotionally linked to the object of their passion, and experience their passion through their own subjective lenses. We all start out as audience members. But sometimes, when the combination of factors aligns in just the right way, we become engaged as fans.

For businesses, the key to building this engagement and solidifying the relationship is understanding the different types of fan motivations in different contexts, and learning how to turn the data gathered about them into actionable insights. Even if Jane Smith and her best friend are fans of the same show, the same team, or the same brand, they’re likely passionate for different reasons. For example, some viewers may watch the ABC melodrama Scandal because they’re fashionistas and can’t wait to see the newest wardrobe of star Kerry Washington; others may do so because they’re obsessed with politics and want to see how the newly introduced Donald Trump–like character will behave. And those differences mean fans will respond in varied ways to different situations and content.
Though traditional demographics may give us basic information about who fans are and where they’re located, current methods of understanding and measuring engagement are missing the answers to two essential questions: (1) Why is a fan motivated? and (2) What triggers the fan’s behavior? Our Innovation Lab research group is developing a new model called Leveraging Engagement, which can be used as a framework when designing media strategy….(More)”

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)”.

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)”

Value and Vulnerability: The Internet of Things in a Connected State Government

Curated on Posted on by Stefaan Verhulst

Pressrelease: “The National Association of State Chief Information Officers (NASCIO) today released a policy brief on the Internet of Things (IoT) in state government. The paper focuses on the different ways state governments are using IoT now and in the future and the policy considerations involved.

“In NASCIO’s 2015 State CIO Survey, we asked state CIOs to what extent IoT was on their agenda. Just over half said they were in informal discussions, however only one in five had moved to the formal discussion phase. We believe IoT needs to be a formal part of each state’s policy considerations,” explained NASCIO Executive Director Doug Robinson.

The paper encourages state CIOs to make IoT part of the enterprise architecture discussions on asset management and risk assessment and to develop an IoT roadmap.

“Cities and municipalities have been working toward the designation of ‘smart city’ for a while now,” said Darryl Ackley, cabinet secretary for the New Mexico Department of Information Technology and NASCIO president. “While states provide different services than cities, we are seeing a lot of activity around IoT to improve citizen services and we see great potential for growth. The more organized and methodical states can be about implementing IoT, the more successful and useful the outcomes.”

Read the policy brief at www.NASCIO.org/ValueAndVulnerability “

Is civic technology the killer app for democracy?

Curated on Posted on by Stefaan Verhulst

 at TechCrunch: “Smartphone apps have improved convenience for public transportation in many urban centers. In Washington, DC, riders can download apps to help them figure out where to go, when to show up and how long to wait for a bus or train. However, the problem with public transport in DC is not the lack of modern, helpful and timely information. The problem is that the Metro subway system is onfire. 

Critical infrastructure refers to the vital systems that connect us. Like the water catastrophe in Flint, Michigan and our crumbling roads, bridges and airports, the Metro system in DC is experiencing a systems failure. The Metro’s problems arise from typical public challenges like  poor management and deferred maintenance.

Upgrades of physical infrastructure are not easy and nimble like a software patch or an agile design process. They are slow, expensive and subject to deliberation and scrutiny. In other words, they are the fundamental substance of democratic decision-making: big decisions with long-term implications that require thoughtful strategy, significant investment, political leadership and public buy-in.

A killer app is an application you love so much you buy into a whole new way of doing things. Email and social media are good examples of killer apps. The killer app for Metro would have to get political leaders to look beyond their narrow, short-term interests and be willing to invest in modern public transportation for our national capital region.

The same is true for fixing our critical infrastructure throughout the nation. The killer apps for the systems on which we rely daily won’t be technical, they will be human. It will be Americans working together to a build a technology-enabled resilient democracy —one that is inclusive, responsive and successful in the Information Age.

In 2007, the I-35 bridge in Minneapolis collapsed into the Mississippi river. During his presidential bid, Senator John McCain used this event as an example of the failure of our leaders to make trade-offs for common national purpose. Case in point, an extravagantly expensive congressionally funded Alaskan “bridge to nowhere” that served just a handful of people on an island. But how many apps to nowhere are we building?.

In DC, commuters who can afford alternatives will leave Metro. They’ll walk, drive, ordera car service or locate a bikeshare. The people who suffer from the public service risk and imbalance of the current Metro system are those who have no choice.

So here’s the challenge: Modern technology needs to create an inclusive society. Our current technical approach too often means that we’re prioritizing progress or profit for the few over the many. This pattern defeats the purpose of both the technology revolution and American democracy. Government and infrastructure are supposed to serve everyone, but technology thus far has made it so that public failures affect some Americans more than others. …

For democracy to succeed in the Information Age, we’ll need some new rules of engagement with technology. The White House recently released its third report on data and its implications for society. The 2016 report pays special attention to the ethics of machine automation and algorithms. The authors stress the importance of ethical analytics and propose the principle of “equal opportunity by design.” It’s an excellent point of departure as we recalibrate old systems and build new bridges to a more resilient, inclusive and prosperous nation….(more)”

Twiplomacy Study 2016

Curated on Posted on by Stefaan Verhulst

Executive Summary: “Social media has become diplomacy’s significant other. It has gone from being an afterthought to being the very first thought of world leaders and governments across the globe, as audiences flock to their newsfeeds for the latest news. This recent worldwide embrace of online channels has brought with it a wave of openness and transparency that has never been experienced before. Social media provides a platform for unconditional communication, and has become a communicator’s most powerful tool. Twitter in particular, has even become a diplomatic ‘barometer, a tool used to analyze and forecast international relations.

There is a vast array of social networks for government communicators to choose from. While some governments and foreign ministries still ponder the pros and cons of any social media engagement, others have gone beyond Twitter, Facebook and Instagram to reach their target audiences, even embracing emerging platforms such as Snapchat, WhatsApp and Telegram where communications are under the radar and almost impossible to track.

Burson-Marsteller’s 2016 Twiplomacy study has been expanded to include other social media platforms such as Facebook, Instagram and YouTube, as well as more niche digital diplomacy platforms such as Snapchat, LinkedIn, Google+,Periscope and Vine.

There is a growing digital divide between governments that are active on social media with dedicated teams and those that see digital engagement as an afterthought and so devote few resources to it. There is still a small number of government leaders who refuse to embrace the new digital world and, for these few, their community managers struggle to bring their organizations into the digital century.

Over the past year, the most popular world leaders on social media have continued to increase their audiences, while new leaders have emerged in the Twittersphere. Argentina’s Mauricio Macri, Canada’s Justin Trudeau and U.S. President Barack Obama have all made a significant impact on Twitter and Facebook over the past year.

Obama’s social media communication has become even more personal through his @POTUS Twitter account and Facebook page, and the first “president of the social media age” will leave the White House in January 2017 with an incredible 137 million fans, followers and subscribers. Beyond merely Twitter and Facebook, world leaders such as the Argentinian President have also become active on new channels like Snapchat to reach a younger audience and potential future voters. Similarly, a number of governments, mainly in Latin America, have started to use Periscope, a cost-effective medium to live-stream their press conferences.

We have witnessed occasional public interactions between leaders, namely the friendly fighting talk between the Obamas, the Queen of England and Canada’s Justin Trudeau. Foreign ministries continue to expand their diplomatic and digital networks by following each other and creating coalitions on specific topics, in particular the fight against ISIS….

A number of world leaders, including the President of Colombia and Australia’s Julie Bishop, also use emojis to brighten up their tweets, creating what can be described as a new diplomatic sign language. The Foreign Ministry in Finland has even produced its own set of 49 emoticons depicting summer and winter in the Nordic country.

We asked a number of digital leaders of some of the best connected foreign ministries and governments to share their thoughts on their preferred social media channel and examples of their best campaigns on our blog. You will learn:

Here is our list of the #Twiplomacy Top Twenty Twitterati in 2016….(More)”

#OpenZika project

Curated on Posted on by Stefaan Verhulst

World Community Grid: “In February 2016, the World Health Organization declared the Zika virus to be a global public health emergency due to its rapid spread and new concerns about its link to a rise in neurological conditions.

The virus is rapidly spreading in new geographic areas such as the Americas, where people have not been previously exposed to the disease and therefore have little immunity to it. In April 2016, the Centers for Disease Control announced that a rise in severe neurological disorders, especially in children, has been linked to the Zika virus. Some pregnant women who have contracted the Zika virus have given birth to infants with a condition called microcephaly, which results in brain development issues typically leading to severe mental deficiencies. In other cases, paralysis and other neurological problems can occur, even in adults.

Problem

Currently, there is no vaccine to provide immunity to the disease and no antiviral drug for curing Zika, although various efforts are underway. Even though the virus was first identified in 1947, there has been little research since then, because the symptoms of the infection are usually mild. However, new data on links between Zika and microcephaly or other neurological issues have revealed that the disease may not be so benign, prompting the need for intensified research efforts.

Proposed Solution

The OpenZika project on World Community Grid aims to identify drug candidates to treat the Zika virus in someone who has been infected. The project will target proteins that the Zika virus likely uses to survive and spread in the body, based on what is known from similar diseases, such as dengue virus and yellow fever. In order to develop an anti-Zika drug, researchers need to identify which of millions of chemical compounds might be effective at interfering with these key proteins. The effectiveness of each compound will be tested in virtual experiments, called “docking calculations,” performed on World Community Grid volunteers’ computers and Android devices. These calculations would help researchers focus on the most likely compounds that may eventually lead to an antiviral medicine….(More)”