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.

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.”

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.

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.

The authors of this Overseas Development Institute 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 report explores solutions focused 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.

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.

This essay, included in the Harvard Berkman Center’s 2014 Internet Monitor, describes 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 discussed in the piece 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.

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.

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.”

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.

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.”

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.”

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.

This journal article 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.

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.

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.

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.”

This 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.

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).

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.

• In light of the significant challenges and opportunities presented by sharing clinical trial data, the IOM assembled a team of ad-hoc experts to create guiding principles for data sharing intended to maximize the scientific value of data sharing while also protecting the privacy of those involved.
• All stakeholders - including participants, sponsors, regulators, investigators, research institutions, journals, and professional societies - have a role and responsibility to ensure that data is shared responsibly and in accordance with ethical values.
• The report has four main recommendations for stakeholders that will share data: 1) that stakeholders should encourage and foster a professional environment in which the responsible sharing of data is the norm; 2) that data should be shared within certain timeframes specified within the report; 3) that stakeholders should employ operational strategies designed to mitigate risk and maximize benefits; and 4) that the sponsors of the IOM study ought to take the lead in creating an international framework and infrastructure related to responsible sharing of clinical trial data.

In this article for the Stanford Social Innovation Review, Stefaan Verhulst analyses how an increasing number of companies are using their data for social good, evidence for a new concept of “data responsibility” where data and information is used to reach positive public ends. Data Responsibility is defined as steps companies can take to open their proprietary data to external bodies, who can use this information to confront humanitarian emergencies and public problems. Central to this is the creation of “Data Collaboratives”—data-sharing models between corporations and public institutions, NGOs, and academic bodies. The article goes on to detail the “Three Pillars of Data Responsibility” which include: • Share: the duty for data-holders to share their data. • Protect: the need to protect data through adequate anonymization techniques. • Act: the need for policy makers and leaders to act upon the information and data shared. The article ends by calling on a “culture shift” to incorporate concepts of data responsibility into day-to-day company activities, and outlying four practical steps to make this happen.

This articles provides non profits with three guiding principles on how to effectively incorporate data science into their operations. (1) Collaborate with data science experts to define your project. (2) Collaborate across your organization to “build with, not for.” (3) Collaborate across your sector to move the needle.

– “The report reviews the challenges the field is currently facing and discusses a range of issues preventing mobile data from being used for social good. – It continues by providing a set of recommendations intended to move beyond short-term and ad hoc projects to more systematic and institutionalized implementations that are scalable, replicable, sustainable and focused on impact. – Finally, the report proposes a roadmap for 2018 calling all stakeholders to work on developing a scalable and impactful demonstration project that will help to establish the value of mobile data for social good. – The report includes examples of innovation projects and ways in which mobile data is already being used to inform development and humanitarian work. It is intended to inspire social impact organizations and mobile network operators (MNOs) to collaborate in the exploration and application of new data sources, methods and technologies.”

– “The report seeks to provide insights about why companies share data with academic researchers, how they make that data available for research, the perceived risks from sharing data, and the strategies that companies employ to address those risks. – Participants that provided information for the report came from 19 companies across diverse sectors, such as high-tech manufacturing, workforce, education, healthcare, telecommunucations, real estate, ecommerce, data-related services, transportation, consumer genetics testing, and online services. – Future of Privacy Forum researchers use the insights gathered from interviews with these companies to identify opportunities for private data sharing. Some recommendations include the potential to enhance a positive public profile of a company sharing data, to increase peer-to-peer knowledge sharing networks, to create a clearinghouse to match academics to companies with the research that they need, and to develop safeguards to mitigate perceived risks.”

Purpose In “data collaboratives”, private and public organizations coordinate their activities to leverage data to address a societal challenge. This paper aims to focus on analyzing challenges and coordination mechanisms of data collaboratives.

Design/methodology/approach This study uses coordination theory to identify and discuss the coordination problems and coordination mechanisms associated with data collaboratives. The authors also use a taxonomy of data collaborative forms from a previous empirical study to discuss how different forms of data collaboratives may require different coordination mechanisms.

Findings The study analyzed data collaboratives from the perspective of organizational and task levels. At the organizational level, the authors argue that data collaboratives present an example of the bazaar form of coordination. At the task level, the authors identified five coordination problems and discussed potential coordination mechanisms to address them, such as coordination by negotiation, by third party, by standardization, to name a few.

Research limitations/implications This study is one of the first few to systematically analyze the phenomenon of “data collaboratives”.

Practical implications This study can help practitioners better understand the coordination challenges they may face when initiating a data collaborative and to develop successful data collaboratives by using coordination mechanisms to mitigate these challenges.

Originality/value Data collaboratives are a novel form of data-driven initiatives which have seen rapid experimentation lately. This study draws attention to this concept in the academic literature and highlights some of the complexities of organizing data collaboratives in practice.

The mission of the academic social sciences is to understand and ameliorate society’s greatest challenges. The data held by private companies holds vast potential to further this mission. Yet, because of its interaction with highly politicized issues, customer privacy, proprietary content, and differing goals of firms and academics, these data are often inaccessible to university researchers. We propose here a new model for industry-academic partnerships that addresses these problems via a novel organizational structure: Respected scholars form a commission which, as a trusted third party, receives access to all relevant firm information and systems, and then recruits independent academics to do research in specific areas following standard peer review protocols organized and funded by nonprofit foundations. We also report on a partnership we helped forge under this model to make data available about the extremely visible and highly politicized issues surrounding the impact of social media on elections and democracy. In our partnership, Facebook will provide privacy-preserving data and access; seven major politically and substantively diverse nonprofit foundations will fund the research; and the Social Science Research Council will oversee the peer review process for funding and data access.

• Seabold and Coppola obtained the consumer price data from the statistical office of each country, and also Google Trends data on a number of search keywords and found that the Google Trends data improved nowcasts of price, compared to an ARMA model and other respective benchmarks
• They conclude by saying that “the study of the manifestation of consumer sentiment via Internet search behavior is very much still in its infancy. It certainly presents a number of challenges, but the potential insights and use cases are varied and exciting. It may be tempting to dismiss this excitement as hype. All the same, it is difficult to deny the possible benefits of real-time consumer sentiment to future economics research and forecasting studies.”

The purpose of this Staff Working Document is “to provide a toolbox for companies that are data holders, data users, or both at the same time. For this purpose, it contains a “How to” guide on legal, business and technical aspects of data sharing that can be used in practice when considering and preparing data transfers between companies coming from the same or different sectors.”

This Staff Working Document defines the following principles to ensure fair markets: transparency, shared value creation, respect for each other’s commercial interests, ensure undistorted competition, minimised data lock-in.

Additionally, it also provides a guide of data sharing for businesses and a guide on how to make data sharing successful and fruitful.

In building towards a common data space, which is “a seamless digital area with the scale that will enable the development of new products and services based on data,” the European Commission proposes the following steps:

• “a proposal for a review of the Directive on the re-use of public sector information (PSI Directive);
• “an update of the Recommendation on access to and preservation of scientific information; and
• “guidance on sharing private sector data.”

• Big data has demonstrated to provide useful insights that can be used to address problems ranging from health, disaster, mobility, and many others. This paper suggests that data sharing needs to be formalize in order for it to be beneficial for the wider public.
• This paper seeks to answer “how to best “institutionalize” data sharing for public good within the private sector and in collaboration with public sector and philanthropic actors?”
• It also argues that not sharing data poses more risks than sharing, and that we should ponder on the risks of not treating data as a public good.
• It proposes an approach called “stewardship”, which is “intended to convey a fiduciary level of responsibility toward the data” and uses the EU General Data Protection Regulation (GDPR) as a good example of framework that can be use to regulate data sharing practices.

The CDE has created a platform for both public and private organizations to sell and purchase data in an effort to create a data exchange between the two sectors. One of the datasets in highest demand is what they call people movement patterns data, which is how people in a given area move around at different times and places.

In this article, Popkin examines a number of examples of technology companies initiating data collaboratives, including:

• “Facebook would fund one of his graduate students to use the company’s technology to study how economic data from public surveys correlated with the visual characteristics of buildings represented in the satellite data. Facebook, in turn, could potentially gain a sharper view of the socio-economic characteristics of rural areas, whose residents are least likely to have Internet connections.”
• “Microsoft’s AI for Earth, which launched in late 2017, has enabled more than 60 research groups from more than 20 countries to analyse remote-sensing data sets from Esri, a mapping and geospatial-analysis company in Redlands, California, using Microscoft’s artificial intelligence (AI) algorithms and computing power.”
• “Amazon Web Services, the cloud-computing branch of the e-commerce giant Amazon, started hosting the Landsat archive in early 2015. In September 2016, the company launched its Earth on AWS programme, through which it hosts around 15 data sets, including imagery, weather data from the US National Oceanic and Atmospheric Administration, and air-quality data from the non-profit organization OpenAQ in Washington DC. Although anyone can pay to analyse the data using Amazon’s computers, scientists can apply for donations of computing time; applications must include a description of the research problem and plans for dissemination of the results.”
• “Google now hosts more than 600 public satellite, weather, population and other Earth and environmental data sets through its Earth Engine platform. More than 70,000 users — most of them researchers — have created free accounts on the platform, says Rebecca Moore, Earth Engine’s director of engineering.”

From the abstract: “This research explores and explains why the private sector participates in crisis response data collaboratives.

“Through secondary literature analysis, and primary survey and interview analysis of three case studies, this research provides new insights into data collaborative objectives, the private sector’s activities, the incentives and risks these collaboratives present for the private sector, and how it mitigates such risks.

“The research concludes that the private sector enters crisis response data collaboratives to help the public sector address one or more of its obstacles to creating datadriven solutions to societal problems, and occasionally to achieve additional objectives for the public good.

“Although the private sector is motivated by various incentives, sufficient mitigation of presented risks, especially risks to data subjects’ privacy and security, is a precondition to joining a crisis response data collaborative.”

In this report, the authors examine social media data to study migration patterns:

• “Innovative sources offer data that are timely, have a wide coverage, can be accessible at limited cost, and can potentially include information that may not always be provided by traditional migration data sources. As of April 2018, the Facebook Network (i.e. Facebook,Instagram, Messenger and the Audience Network) reported more than 2.15 billion monthly active users. The digital traces that Internet users are actively or passively generating can potentially be exploited for studying migration-related phenomena.”
• “The potential of new data sources (e.g.Facebook) to provide policy-relevant information on international migration is currently being explored by international bodies and initiatives such as the Big Data for Migration Alliance (BD4M), recently launched by the European Commission Knowledge Centre on Migration and Demography (KCMD) and IOM’s Global Migration Data Analysis Centre (GMDAC), following a dedicated workshop on the topic.
• “The inherent bias of social media data described above, coupled with the risk of a lack of control on how the data are derived and processed cause uncertainties regarding the possibility of effectively using such data for demographic research. This requires the development of appropriate methods for quantifying and mitigating such bias as well as the active collaboration with the social media companies themselves.”
• “The paper shows that social media applications can be a timely, low-cost, and almost globally available data source for estimating stocks of international migrants, based on estimates of users who are classified as ‘expats.’ In the European context, it can be used for quantifying intra-EU mobility patterns of specific demographic groups, such as students,as well as for measuring migration movements that may not be captured in as timely a fashion by official statistics.”

From the abstract: “Much of the recent excitement around data, especially ‘Big Data,’ focuses on the potential commercial or economic value of data. How that data will affect people isn’t much discussed. People know that smart cities will deploy Internet-based monitoring and that flows of the collected data promise to produce new values. Less considered is that smart cities will be sites of new forms of citizen action—enabled by an ‘economy’ of data that will lead to new methods of collectivization, accountability, and control which, themselves, can provide both positive and negative values to the citizenry. Therefore, smart city design needs to consider not just measurement and publication of data but also the implications of city-wide deployment, data openness, and the possibility of unintended consequences if data leave the city.”

From the abstract: “The problem of unicity and reidentifiability of records in large-scale databases has been studied in different contexts and approaches, with focus on preserving privacy or matching records from different data sources. With an increasing number of service providers nowadays routinely collecting location traces of their users on unprecedented scales, there is a pronounced interest in the possibility of matching records and datasets based on spatial trajectories. Extending previous work on reidentifiability of spatial data and trajectory matching, we present the first large-scale analysis of user matchability in real mobility datasets on realistic scales, i.e. among two datasets that consist of several million people’s mobility traces, coming from a mobile network operator and transportation smart card usage. We extract the relevant statistical properties which influence the matching process and analyze their impact on the matchability of users. We show that for individuals with typical activity in the transportation system (those making 3-4 trips per day on average), a matching algorithm based on the co-occurrence of their activities is expected to achieve a 16.8% success only after a one-week long observation of their mobility traces, and over 55% after four weeks. We show that the main determinant of matchability is the expected number of co-occurring records in the two datasets. Finally, we discuss different scenarios in terms of data collection frequency and give estimates of matchability over time. We show that with higher frequency data collection becoming more common, we can expect much higher success rates in even shorter intervals.”

• The authors point out that the high penetration of mobile phone creates an abundance of data which can be useful in informing policymaking process, but also poses privacy risk to individuals that the data is associated with.
• The authors argue that standard de-identification methodology for data analysis is “not sufficient to prevent users from being re-identified in mobile phone data”.
• Furthermore, “[th]hese privacy concerns are, in our opinion, due to the failures of the traditional de-identification model and the lack of a modern and agreed upon framework for the privacy-conscientious use of mobile phone data by third-parties especially in the context of the EU General Data Protection Regulation (GDPR).”
• In this paper, the authors propose four models for the privacy-conscientious use of mobile phone data to address this issue.

Full abstract: “Driven by the technological capabilities that ICTs offer, data enable new ways to generate value for both society and the parties that own or offer the data. This article looks at the idea of data collaboratives as a form of cross-sector partnership to exchange and integrate data and data use to generate public value. The concept thereby bridges data-driven value creation and collaboration, both current themes in the field. To understand how data collaboratives can add value in a public governance context, we exploratively studied the qualitative longitudinal case of an infomobility platform. We investigated the ability of a data collaborative to produce results while facing significant challenges and tensions between the goals of parties, each having the conflicting objectives of simultaneously retaining control whilst allowing for generativity. Taken together, the literature and case study findings help us to understand the emergence and viability of data collaboratives. Although limited by this study’s explorative nature, we find that conditions such as prior history of collaboration and supportive rules of the game are key to the emergence of collaboration. Positive feedback between trust and the collaboration process can institutionalise the collaborative, which helps it survive if conditions change for the worse.”

• The author points out the increasingly vast amount of data being collected, stashed, and reused for deeper analysis by tech companies, driven by the rise of the Internet of Things.
• These companies own the control of data and restrict access to it on legal as well as technical grounds, rendering inefficiency and preventing innovation which might be useful for the society.
• In other instance, companies may share data with each other in order to “exclude competitors or to abuse their market position.”
• “This article will discuss what implications combining data in data pools by firms might have on competition, and when competition law should be applicable. It develops the idea that data pools harbour great opportunities, whilst acknowledging that there are still risks to take into consideration, and to regulate.”

Abstract:

Data platform companies (such as Facebook, Google, or Twitter) amass and process immense amounts of data that is generated by their users. These companies primarily use the data to advance their commercial interests, but there is a growing public dismay regarding the adverse and discriminatory impacts of their algorithms on society at large. The regulation of data platform companies and their algorithms has been hotly debated in the literature, but current approaches often neglect the value of data collection, defy the logic of algorithmic decision-making, and exceed the platform companies’ operational capacities.

This Article suggests a different approach — an open, collaborative, and incentives-based stance toward data platforms that takes full advantage of the tremendous societal value of user-generated data. It contends that this data shall be recognized as a “global commons,” and access to it shall be made available to a wide range of independent stakeholders — research institutions, journalists, public authorities, and international organizations. These external actors would be able to utilize the data to address a variety of public challenges, as well as observe from within the operation and impacts of the platforms’ algorithms.

After making the theoretical case for the “global commons of data,” the Article explores the practical implementation of this model. First, it argues that a data commons regime should operate through a spectrum of data sharing and usage modalities that would protect the commercial interests of data platforms and the privacy of data users. Second, it discusses regulatory measures and incentives that can solicit the collaboration of platform companies with the commons model. Lastly, it explores the challenges embedded in this approach.

• This paper presents a legal-technical framework that will ensure that accountability and transparency that are acquired through data sharing do not harm individual privacy.
• This proposed solution seeks to address the challenge often faced when a set of data is too sensitive to be “open”, hindering the creation of a more effective research and policy that affect the public.
• The authors propose a “third-party public-private data trust”, which will act as an intermediary who will ensure “a) strong privacy guarantees, b) removal of signals that could expose competitive advantage for the data providers, and c) removal of biases that could reinforce discriminatory policies”.

• The authors posit that governments are looking to balance the interest of the public and private individuals surrounding the use of data, but the mechanism is yet to be developed.
• The authors propose ‘data trust’, a concept which repurposes the term ‘fiduciary trust’, which has been “used to govern and maintain shared resources such as public lands [and] pension funds.”
• Further “[w]hen used for governance, data trusts can steward, maintain and manage how data is used and shared — from who is allowed access to it, and under what terms, to who gets to define the terms, and how. They can involve a number of approaches to solving a range of problems, creating different structures to experiment with governance models and solutions in an agile way.”

Abstract: “Thanks to appropriate data algorithms, firms, especially those on-line, are able to extract detailed knowledge about consumers and markets. This raises the question of the essential facility character of data. Moreover, the features of digital markets lead to a concentration of this core input in the hands of few big “superstars” and arouse legitimate economic and societal concerns. In a more and more data-driven society, one could ask if data openness is a solution to deal with power derived from data concentration. We conclude that only a case-by-case approach should be followed. Mandatory open data policy should be conditioned on an ex-ante cost-benefit analysis proving that the benefits of disclosure exceed its costs.”

Abstract:

“Background: India is home to 20% of the world’s suicide deaths. Although statistics regarding suicide in India are distressingly high, data and cultural issues likely contribute to a widespread underreporting of the problem. Social stigma and only recent decriminalization of suicide are among the factors hampering official agencies’ collection and reporting of suicide rates.

“Objective: As the product of a data collaborative, this paper leverages private-sector search engine data toward gaining a fuller, more accurate picture of the suicide issue among young people in India. By combining official statistics on suicide with data generated through search queries, this paper seeks to: add an additional layer of information to more accurately represent the magnitude of the problem, determine whether search query data can serve as an effective proxy for factors contributing to suicide that are not represented in traditional datasets, and consider how data collaboratives built on search query data could inform future suicide prevention efforts in India and beyond.

“Methods: We combined official statistics on demographic information with data generated through search queries from Bing to gain insight into suicide rates per state in India as reported by the National Crimes Record Bureau of India. We extracted English language queries on “suicide,” “depression,” “hanging,” “pesticide,” and “poison”. We also collected data on demographic information at the state level in India, including urbanization, growth rate, sex ratio, internet penetration, and population. We modeled the suicide rate per state as a function of the queries on each of the 5 topics considered as linear independent variables. A second model was built by integrating the demographic information as additional linear independent variables.

“Results: Results of the first model fit (R2) when modeling the suicide rates from the fraction of queries in each of the 5 topics, as well as the fraction of all suicide methods, show a correlation of about 0.5. This increases significantly with the removal of 3 outliers and improves slightly when 5 outliers are removed. Results for the second model fit using both query and demographic data show that for all categories, if no outliers are removed, demographic data can model suicide rates better than query data. However, when 3 outliers are removed, query data about pesticides or poisons improves the model over using demographic data.

“Conclusions: In this work, we used search data and demographics to model suicide rates. In this way, search data serve as a proxy for unmeasured (hidden) factors corresponding to suicide rates. Moreover, our procedure for outlier rejection serves to single out states where the suicide rates have substantially different correlations with demographic factors and query rates.”

Abstract: “In their report on the development of the UK AI industry, Wendy Hall and Jérôme Pesenti recommend the establishment of data trusts, “proven and trusted frameworks and agreements” that will “ensure exchanges [of data] are secure and mutually beneficial” by promoting trust in the use of data for AI. This paper defends the following thesis: A data trust works within the law to provide ethical, architectural and governance support for trustworthy data processing. Data trusts are therefore both constraining and liberating. They constrain: they respect current law, so they cannot render currently illegal actions legal. They are intended to increase trust, and so they will typically act as further constraints on data processors, adding the constraints of trustworthiness to those of law. Yet they also liberate: if data processors are perceived as trustworthy, they will get improved access to data. The paper addresses the areas of: trust and trustworthiness; ethics; architecture; legal status.”

• The Ontario Brain Institute (OBI) developed Brain-CODE, which is a platform that “supports the capture, storage, federation, sharing, and analysis of different data types across several brain disorders.”
• The platform is made possible by “a robust and scalable data governance structure which allows for the flexibility to advance scientific understanding, while protecting the privacy of research participants.”
• This paper presents key of lessons learned from the implementation of this initiative.

From the introduction: “Coming out of ten months of applied, participatory, and multidisciplinary research, this journal presents:

A. “One case study developed to document and explain how the program was conceived, designed, and implemented, with the goal of offering lessons for scalability at New Lab and replicability in other cities around the world. The key questions explored in the case study are:

• “What is The Circular City?
• “What is Circular City Data?
• “How might we build, scale-up, and replicate a circular city data program for smarter economic development, mobility, and resilience and sustainability?

B. “Three research papers developed to investigate three urban challenges:

• “What is the value of this program for economic development in New York City?
• “What is the value of this program for mobility in New York City?
• “What is the value of this program for resilience and sustainability in New York City?
• “What are the conditions of success for this value creation?
• “What are the risks and the corresponding mitigation strategies?”

• The author argues that the “system of federal economic statistics developed in the 20th century has served the country well, but the current methods for collecting and disseminating these data products are unsustainable. These statistics are heavily reliant on the great 20th century measurement innovation: sample surveys. Recently, however, response rates for both household and business surveys have declined, increasing costs and threatening quality.”
• He further points out that existing “statistical measures, many developed decades ago, may also miss important aspects of our rapidly evolving economy; moreover, they may not be sufficiently accurate, timely, or granular to meet the increasingly complex needs of data users. Meanwhile, the rapid proliferation of online data and more powerful computation make privacy and confidentiality protections more challenging.”
• He concludes that “current measurement programs are not keeping pace with the changing economy, and current methods for collecting and disseminating statistical information are not sustainable. A process is underway in which government statisticians will take greater advantage of the proliferation of new data sources that can be utilized for economic measurement, as well as new tools for processing, analyzing, and publishing improved economic statistics.”

“This paper argues that new institutions—an ecosystem of trust—are needed to ensure that uses of data are trusted and trustworthy. It advocates the creation of different kinds of data trust to fill this gap. It argues:

• “That we need, but currently lack, institutions that are good at thinking through, discussing, and explaining the often complex trade-offs that need to be made about data.
• “That the task of creating trust is different in different fields. Overly generic solutions will be likely to fail.
• “That trusts need to be accountable—in some cases to individual members where there is a direct relationship with individuals giving consent, in other cases to the broader public.
• “That we should expect a variety of types of data trust to form—some sharing data; some managing synthetic data; some providing a research capability; some using commercial data and so on. The best analogy is finance which over time has developed a very wide range of types of institution and governance.”

Abstract: “Indigenous communities and scholars have been influencing a shift in participation and inclusion in academic and agency research over the past two decades. As a response, Indigenous peoples are increasingly asking research questions and developing their own studies rooted in their cultural values. They use the study results to rebuild their communities and to protect their lands. This process of Indigenous-driven research has led to partnering with academic institutions, establishing research review boards, and entering into data sharing agreements to protect environmental data, community information, and local and traditional knowledges.Data sharing agreements provide insight into how Indigenous nations are addressing the key areas of data collection, ownership, application, storage, and the potential for data reuse in the future. By understanding this mainstream data governance mechanism, how they have been applied, and how they have been used in the past, we aim to describe how Indigenous nations and communities negotiate data protection and control with researchers.

“The project described here reviewed publicly available data sharing agreements that focus on research with Indigenous nations and communities in the United States. We utilized qualitative analysis methods to identify specific areas of focus in the data sharing agreements, whether or not traditional or cultural values were included in the language of the data sharing agreements, and how the agreements defined data. The results detail how Indigenous peoples currently use data sharing agreements and potential areas of expansion for language to include in data sharing agreements as Indigenous peoples address the research needs of their communities and the protection of community and cultural data.”

• The authors point out that “health and genetic data are considered special categories of personal data and are subject to relatively stringent rules for processing.”
• Furthermore, according to the GDPR, “personal data may be collected only for specific, explicit, and legitimate purposes. The GDPR considers further use of these data for scientific research purposes to be compatible with the original rationale for collection. Yet, it also recognizes the right of study participants to object to this use of their data, to withdraw consent if they wish, and to be informed of any data breaches. To exercise their rights, individuals must be informed about the processing of their data in the research repository. However, the way some repositories are set up might make it difficult for researchers to comply with the law. One such challenge centers on the oversight of data access.”
• The authors propose that developing “solutions that support data deposition while ensuring legal compliance is necessary for research. Adopting the right solutions will lead to greater transparency and accountability in data sharing, as well as a more efficient and equitable scientific practice.”

• In his testimony before NYC COPIC, Stefaan Verhulst pointed out that the challenges facing communities in the 21st century “need not only new solutions but also new methods for arriving at solutions.”
• Verhulst argued for the potential use of data to address these problems, by posing four key questions: “First, why should NYC care about data and data sharing? Second, if you build a data-sharing framework, will they come? Third, how can we best engage the private sector when it comes to sharing and using their data? And fourth, is technology the main (or best) answer?”

• According to this paper, of “the 232 SDG indicators, only 93 are classified as Tier 1, meaning that the indicator is conceptually clear, has internationally established methodology and standards, and data are regularly compiled for at least 50 per cent of the countries. The remaining indicators are Tier 2 (72 indicators) meaning the indicator is conceptually clear but the data are not regularly produced by countries or Tier 3 (62 indicators), meaning that no internationally established methodology or standards are yet available.”
• The author points out that the potential of big data has generated much excitement and argument, but it also has many legal, ethical, technical, and reputational risks.
• According to the author many “big data are proprietary, that is, data that are commercially or privately owned and not publicly available. Consequently, many big data are not currently accessible by NSOs, either because costs are prohibitive or proprietary ownership makes it impossible. For example, data generated from the use of credit cards, search engines, social media, mobile phones, and store loyalty cards are all proprietary and are often not accessible. In many cases there are also legal impediments to accessing big data. MacFeely and Barnat (2017) have argued that to futureproof statistical legislation, changes may be required to give NSOs or NSSs access to big data sources.”
• Two challenges arise from this problem: one technical and one of perception. “The technical challenge arises from the availability of large, linkable datasets which make anonymization of individual data very difficult. … The changing nature of perception is arguably a trickier problem. … There appear to be inter-generational differences in opinion vis-a-vis privacy and confidentiality, where those ‘born digital’ (roughly those born since 1990) are less concerned about disclosing personal information than older generations (European Commission, 2011).”

From the abstract:

• “The problem of unicity and reidentifiability of records in large-scale databases has been studied in different contexts and approaches, with focus on preserving privacy or matching records from different data sources.
• “With an increasing number of service providers nowadays routinely collecting location traces of their users on unprecedented scales, there is a pronounced interest in the possibility of matching records and datasets based on spatial trajectories.
• “Extending previous work on reidentifiability of spatial data and trajectory matching, we present the first large-scale analysis of user matchability in real mobility datasets on realistic scales, i.e. among two datasets that consist of several million people’s mobility traces, coming from a mobile network operator and transportation smart card usage.
• “We extract the relevant statistical properties which influence the matching process and analyze their impact on the matchability of users.
• “We show that for individuals with typical activity in the transportation system (those making 3-4 trips per day on average), a matching algorithm based on the co-occurrence of their activities is expected to achieve a 16.8% success only after a one-week long observation of their mobility traces, and over 55% after four weeks.
• “We show that the main determinant of matchability is the expected number of co-occurring records in the two datasets. Finally, we discuss different scenarios in terms of data collection frequency and give estimates of matchability over time.
• “We show that with higher frequency data collection becoming more common, we can expect much higher success rates in even shorter intervals.”

• According to the authors, mobile phone penetration rate is at 90% and the communications activity made possible with a mobile phone generates a large amount of data that is highly valuable especially in answering humanitarian questions. However, this benefit comes with some risks, most notably privacy.
• Anonymization has been used in many statistical projects to try to address this privacy issues, but the authors point out how recent studies have shown that “pseudonymization and standard de-identification are not sufficient to prevent uers from being re-identified in mobile phone data.”
• The authors argue that these privacy concerns are “due to the failures of the traditional de-identification model and the lack of a modern and agreed upon framework for the privacy-conscientious use of mobile phone data by third-parties especially in the context of the EU General Data Protection Regulation (GDPR).”
• Thus, the authors propose four models for the privacy-conscientious use of mobile phone data, which they call 1) limited release, 2) pre-computed indicators and synthetic data, 3) remote access, and 4) question-and-answer.

Recommendations:

1. Improve funder policies: 1a. Align on policies for data sharing in PHEs to require sharing of quality assured interim and fi nal data in real time (wherever feasible). 1b. Where policies cannot be altered, align grant/contract conditions to require sharing of quality assured interim and fi nal data in real time. 1c. Define appropriate thresholds for activation of rapid data sharing. 1d. Align funding policies to ensure that data sets and pre-publications are all included within assessment of researcher outputs (in accordance with the San Francisco Declaration).
2. Align tools and strengthen capacity: 2a. Align to improve guidance for Data Management Plans in grant applications. 2b. Support the development and uptake of standardised tools and approaches to support international research collaborations & data sharing, including MOUs, MTAs & DTAs, data standards & data collection templates. 2c. Fund capacity strengthening in data management and analysis (linked to 3a to enable equity).
3. Build trust 3a. Fund improved equitable, multi-disciplinary, multinational, disease networks in advance of PHEs (linked to 2c. and 4a.) with real time external data sharing requirements (aligned with 1.). 3b. Facilitate coordination between established research networks.
4. Influence 4a. Collaborate within GloPID-R to align with and influence other stakeholders: national funders, Ministries of Health, Ministries of Science & Technology, commercial companies, publishers, university hierarchy, policy makers (especially WHO) & humanitarian sector. 4b. Communicate the PH benefits of data sharing. Continue to publish and advocate on the benefits.
5. Strengthen systems 5a. Support existing and expanded data sharing platforms for priority pathogens with agreed governance mechanisms and data security systems. 5b. Support an overarching unifi ed governance structure for international data sharing within which existing platforms can be embedded and through which platforms for novel pathogens can be developed.