Paper by Sille Obelitz Søe & Jens-Erik Mai: “Given the concerns about big tech’s hoarding of data, creation of profiles, mining of data, and extrapolation of new knowledge from their data warehouses, there is a need and interest in devising policies and regulations that better shape big tech’s influence on people and their lives. One such proposal is to create data commons. In this paper, we examine the idea of data commons as well as the concept of sharing in relation to the concept of personal data. We argue that personal data are different in nature from the objects of classical commons wherefore the logic of “sharing is caring” is flawed. We, therefore, develop an ethics of sharing taking privacy into account as well as the idea that sometimes the right thing to do is not sharing. This ethics of sharing is based in a proposal to conceptualize data commons as MacIntyrean practices and Wittgensteinian forms of life…(More)”.
Public Sector Use of Private Sector Personal Data: Towards Best Practices
Paper by Teresa Scassa: “Governments increasingly turn to the private sector as a source of data for various purposes. In some cases, the data that they seek to use is personal data. The public sector use of private sector personal data raises several important law and public policy concerns. These include the legal authority for such uses; privacy and data protection; ethics; transparency; and human rights. Governments that use private sector personal data without attending to the issues that such use raises may breach existing laws, which in some cases may not be well-adapted to evolving data practices. They also risk undermining public trust.
This paper uses two quite different recent examples from Canada where the use of private sector personal data by public sector actors caused considerable backlash and led to public hearings and complaints to the Privacy Commissioner. The examples are used to tease out the complex and interwoven law and policy issues. In some cases, the examples reveal issues that are particular to the evolving data society and that are not well addressed by current law or practice. The paper identifies key issues and important gaps and makes recommendations to address these. Although the examples discussed are Canadian and depend to some extent on Canadian law and institutions, the practices at issue are ones that are increasingly used around the world, and many of the issues raised are broadly relevant…(More)”.
Primer on Data Sharing
Primer by John Ure: “…encapsulates insights gleaned from the Inter-Modal Transport Data Sharing Programme, a collaborative effort known as Data Trust 1.0 (DT1), conducted in Hong Kong between 2020 and 2021. This initiative was a pioneering project that explored the feasibility of sharing operational data between public transport entities through a Trusted Third Party. The objective was to overcome traditional data silos and promote evidence-based public transport planning.
DT1, led by the ‘HK Team’ in conjunction with Dr. Jiangping Zhou and colleagues from the University of Hong Kong, successfully demonstrated that data sharing between public transport companies, both privately-owned and government-owned, was viable. Operational data, anonymised and encrypted, were shared with a Trusted Third Party and aggregated for analysis, supported by a Transport Data Analytics Service Provider. The data was used solely for analysis purposes, and confidentiality was maintained throughout.
The establishment of the Data Trust was underpinned by the creation of a comprehensive Data Sharing Framework (DSF). This framework, developed collaboratively, laid the groundwork for future data sharing endeavours. The DSF has been shared internationally, fostering the exchange of knowledge and best practices across diverse organisations and agencies. The Guide serves as a repository of lessons learned, accessible studies, and references, aimed at facilitating a comprehensive understanding of data sharing methodologies.
The central aim of the Guide is twofold: to promote self-learning and to offer clarity on intricate approaches related to data sharing. Its intention is to encourage researchers, governmental bodies, commercial enterprises, and civil society entities, including NGOs, to actively engage in data sharing endeavours. By combining data sets, these stakeholders can glean enhanced insights and contribute to the common good…(More)”.
Interested but Uncertain: Carbon Markets and Data Sharing among U.S. Crop Farmers
Paper by Guang Han and Meredith T. Niles: “The potential for farmers and agriculture to sequester carbon and contribute to global climate change goals is widely discussed. However, there is currently low participation in agricultural carbon markets and a limited understanding of farmer perceptions and willingness to participate. Furthermore, farmers’ concerns regarding data privacy may complicate participation in agricultural carbon markets, which necessitates farmer data sharing with multiple entities. This study aims to address research gaps by assessing farmers’ willingness to participate in agricultural carbon markets, identifying the determinants of farmers’ willingness regarding carbon markets participation, and exploring how farmers’ concerns for data privacy relate to potential participation in agricultural carbon markets. Data were collected through a multistate survey of 246 farmers and analyzed using descriptive statistics, factor analysis, and multinomial regression models. We find that the majority of farmers (71.8%) are aware of carbon markets and would like to sell carbon credits, but they express high uncertainty about carbon market information, policies, markets, and cost impacts. Just over half of farmers indicated they would share their data for education, developing tools and models, and improving markets and supply chains. Farmers who wanted to participate in carbon markets were more likely to have higher farm revenues, more likely to share their data overall, more likely to share their data with private organizations, and more likely to change farming practices and had more positive perceptions of the impact of carbon markets on farm profitability. In conclusion, farmers have a general interest in carbon market participation, but more information is needed to address their uncertainties and concerns…(More)”.
Data Collaboratives: Enabling a Healthy Data Economy Through Partnerships
Paper by Stefaan Verhulst (as Part of the Digital Revolution and New Social Contract Program): “…Overcoming data silos is key to addressing these data asymmetries and promoting a healthy data economy. This is equally true of silos that exist within sectors as it is of those among sectors (e.g., between the public and private sectors). Today, there is a critical mismatch between data supply and demand. The data that could be most useful rarely gets applied to the social, economic, cultural, and political problems it could help solve. Data silos, driven in large part by deeply entrenched asymmetries and a growing sense of “ownership,” are stunting the public good potential of data.
This paper presents a framework for responsible data sharing and reuse that could increase sharing between the public and private sectors to address some of the most entrenched asymmetries. Drawing on theoretical and empirical material, we begin by outlining how a period of rapid datafication—the Era of the Zettabyte—has led to data asymmetries that are increasingly deleterious to the public good. Sections II and III are normative. Having outlined the nature and scope of the problem, we present a number of steps and recommendations that could help overcome or mitigate data asymmetries. In particular, we focus on one institutional structure that has proven particularly promising: data collaboratives, an emerging model for data sharing between sectors. We show how data collaboratives could ease the flow of data between the public and private sectors, helping break down silos and ease asymmetries. Section II offers a conceptual overview of data collaboratives, while Section III provides an approach to operationalizing data collaboratives. It presents a number of specific mechanisms to build a trusted sharing ecology….(More)”.
Patients are Pooling Data to Make Diabetes Research More Representative
Blog by Tracy Kariuki: “Saira Khan-Gallo knows how overwhelming managing and living healthily with diabetes can be. As a person living with type 1 diabetes for over two decades, she understands how tracking glucose levels, blood pressure, blood cholesterol, insulin intake, and, and, and…could all feel like drowning in an infinite pool of numbers.
But that doesn’t need to be the case. This is why Tidepool, a non-profit tech organization composed of caregivers and other people living with diabetes such as Gallo, is transforming diabetes data management. Its data visualization platform enables users to make sense of the data and derive insights into their health status….
Through its Big Data Donation Project, Tidepool has been supporting the advancement of diabetes research by sharing anonymized data from people living with diabetes with researchers.
To date, more than 40,000 individuals have chosen to donate data uploaded from their diabetes devices like blood glucose meters, insulin pumps and continuous glucose monitors, which is then shared by Tidepool with students, academics, researchers, and industry partners — Making the database larger than many clinical trials. For instance, Oregon Health and Science University have used datasets collected from Tidepool to build an algorithm that predicts hypoglycemia, which is low blood sugar, with the goal of advancing closed loop therapy for diabetes management…(More)”.
What prevents us from reusing medical real-world data in research
Paper by Julia Gehrmann, Edit Herczog, Stefan Decker & Oya Beyan: “Recent studies show that Medical Data Science (MDS) carries great potential to improve healthcare. Thereby, considering data from several medical areas and of different types, i.e. using multimodal data, significantly increases the quality of the research results. On the other hand, the inclusion of more features in an MDS analysis means that more medical cases are required to represent the full range of possible feature combinations in a quantity that would be sufficient for a meaningful analysis. Historically, data acquisition in medical research applies prospective data collection, e.g. in clinical studies. However, prospectively collecting the amount of data needed for advanced multimodal data analyses is not feasible for two reasons. Firstly, such a data collection process would cost an enormous amount of money. Secondly, it would take decades to generate enough data for longitudinal analyses, while the results are needed now. A worthwhile alternative is using real-world data (RWD) from clinical systems of e.g. university hospitals. This data is immediately accessible in large quantities, providing full flexibility in the choice of the analyzed research questions. However, when compared to prospectively curated data, medical RWD usually lacks quality due to the specificities of medical RWD outlined in section 2. The reduced quality makes its preparation for analysis more challenging…(More)”.
Unleashing the power of data for electric vehicles and charging infrastructure
Report by Thomas Deloison: “As the world moves toward widespread electric vehicle (EV) adoption, a key challenge lies ahead: deploying charging infrastructure rapidly and effectively. Solving this challenge will be essential to decarbonize transport, which has a higher reliance on fossil fuels than any other sector and accounts for a fifth of global carbon emissions. However, the companies and governments investing in charging infrastructure face significant hurdles, including high initial capital costs and difficulties related to infrastructure planning, permitting, grid connections and grid capacity development.
Data has the power to facilitate these processes: increased predictability and optimized planning and infrastructure management go a long way in easing investments and accelerating deployment. Last year, members of the World Business Council for Sustainable Development (WBCSD) demonstrated that digital solutions based on data sharing could reduce carbon emissions from charging by 15% and unlock crucial grid capacity and capital efficiency gains.
Exceptional advances in data, analytics and connectivity are making digital solutions a potent tool to plan and manage transport, energy and infrastructure. Thanks to the deployment of sensors and the rise of connectivity, businesses are collecting information faster than ever before, allowing for data flows between physical assets. Charging infrastructure operators, automotive companies, fleet operators, energy providers, building managers and governments collect insights on all aspects of electric vehicle charging infrastructure (EVCI), from planning and design to charging experiences at the station.
The real value of data lies in its aggregation. This will require breaking down siloes across industries and enabling digital collaboration. A digital action framework released by WBCSD, in collaboration with Arcadis, Fujitsu and other member companies and partners, introduces a set of recommendations for companies and governments to realize the full potential of digital solutions and accelerate EVCI deployments:
- Map proprietary data, knowledge gaps and digital capacity across the value chain to identify possible synergies. The highest value potential from digital solutions will lie at the nexus of infrastructure, consumer behavior insights, grid capacity and transport policy. For example, to ensure the deployment of charging stations where they will be most needed and at the right capacity level, it is crucial to plan investments within energy grid capacity, spatial constraints and local projected demand for EVs.
- Develop internal data collection and storage capacity with due consideration for existing structures for data sharing. A variety of schemes allow actors to engage in data sharing or monetization. Yet, their use is limited by mismatched use of data standards and specification and process uncertainty. Companies must build a strong understanding of these structures internally by providing internal training and guidance, and invest in sound data collection, storage and analysis capacity.
- Foster a policy environment that supports digital collaboration across sectors and industries. Digital policies must provide incentives and due diligence frameworks to guide data exchanges across industries and support the adoption of common standards and protocols. For instance, it will be crucial to integrate linkages with energy systems and infrastructure beyond roads in the rollout of the European mobility data space…(More)”.
Questions as a Device for Data Responsibility: Toward a New Science of Questions to Steer and Complement the Use of Data Science for the Public Good in a Polycentric Way
Paper by Stefaan G. Verhulst: “We are at an inflection point today in our search to responsibly handle data in order to maximize the public good while limiting both private and public risks. This paper argues that the way we formulate questions should be given more consideration as a device for modern data responsibility. We suggest that designing a polycentric process for co-defining the right questions can play an important role in ensuring that data are used responsibly, and with maximum positive social impact. In making these arguments, we build on two bodies of knowledge—one conceptual and the other more practical. These observations are supplemented by the author’s own experience as founder and lead of “The 100 Questions Initiative.” The 100 Questions Initiative uses a unique participatory methodology to identify the world’s 100 most pressing, high-impact questions across a variety of domains—including migration, gender inequality, air quality, the future of work, disinformation, food sustainability, and governance—that could be answered by unlocking datasets and other resources. This initiative provides valuable practical insights and lessons into building a new “science of questions” and builds on theoretical and practical knowledge to outline a set of benefits of using questions for data responsibility. More generally, this paper argues that, combined with other methods and approaches, questions can help achieve a variety of key data responsibility goals, including data minimization and proportionality, increasing participation, and enhancing accountability…(More)”.
Weather Warning Inequity: Lack of Data Collection Stations Imperils Vulnerable People
Article by Chelsea Harvey: “Devastating floods and landslides triggered by extreme downpours killed hundreds of people in Rwanda and the Democratic Republic of Congo in May, when some areas saw more than 7 inches of rain in a day.
Climate change is intensifying rainstorms throughout much of the world, yet scientists haven’t been able to show that the event was influenced by warming.
That’s because they don’t have enough data to investigate it.
Weather stations are sparse across Africa, making it hard for researchers to collect daily information on rainfall and other weather variables. The data that does exist often isn’t publicly available.
“The main issue in some countries in Africa is funding,” said Izidine Pinto, a senior researcher on weather and climate at the Royal Netherlands Meteorological Institute. “The meteorological offices don’t have enough funding.”
There’s often too little money to build or maintain weather stations, and strapped-for-cash governments often choose to sell the data they do collect rather than make it free to researchers.
That’s a growing problem as the planet warms and extreme weather worsens. Reliable forecasts are needed for early warning systems that direct people to take shelter or evacuate before disasters strike. And long-term climate data is necessary for scientists to build computer models that help make predictions about the future.
The science consortium World Weather Attribution is the latest research group to run into problems. It investigates the links between climate change and individual extreme weather events all over the globe. In the last few months alone, the organization has demonstrated the influence of global warming on extreme heat in South Asia and the Mediterranean, floods in Italy, and drought in eastern Africa.
Most of its research finds that climate change is making weather events more likely to occur or more intense.
The group recently attempted to investigate the influence of climate change on the floods in Rwanda and Congo. But the study was quickly mired in challenges.
The team was able to acquire some weather station data, mainly in Rwanda, Joyce Kimutai, a research associate at Imperial College London and a co-author of the study, said at a press briefing announcing the findings Thursday. But only a few stations provided sufficient data, making it impossible to define the event or to be certain that climate model simulations were accurate…(More)”.