Paper by Francisco Mendonca, Giovanna DiMarzo, and Nabil Abdennadher: “Data cooperatives offer a new model for fair data governance, enabling individuals to collectively control, manage, and benefit from their information while adhering to cooperative principles such as democratic member control, economic participation, and community concern. This paper reviews data cooperatives, distinguishing them from models like data trusts, data commons, and data unions, and defines them based on member ownership, democratic governance, and data sovereignty. It explores applications in sectors like healthcare, agriculture, and construction. Despite their potential, data cooperatives face challenges in coordination, scalability, and member engagement, requiring innovative governance strategies, robust technical systems, and mechanisms to align member interests with cooperative goals. The paper concludes by advocating for data cooperatives as a sustainable, democratic, and ethical model for the future data economy…(More)”.
The Future of Health Is Preventive — If We Get Data Governance Right
Article by Stefaan Verhulst: “After a long gestation period of three years, the European Health Data Space (EHDS) is now coming into effect across the European Union, potentially ushering in a new era of health data access, interoperability, and innovation. As this ambitious initiative enters the implementation phase, it brings with it the opportunity to fundamentally reshape how health systems across Europe operate. More generally, the EHDS contains important lessons (and some cautions) for the rest of the world, suggesting how a fragmented, reactive model of healthcare may transition to one that is more integrated, proactive, and prevention-oriented.
For too long, health systems–in the EU and around the world–have been built around treating diseases rather than preventing them. Now, we have an opportunity to change that paradigm. Data, and especially the advent of AI, give us the tools to predict and intervene before illness takes hold. Data offers the potential for a system that prioritizes prevention–one where individuals receive personalized guidance to stay healthy, policymakers access real-time evidence to address risks before they escalate, and epidemics are predicted weeks in advance, enabling proactive, rapid, and highly effective responses.
But to make AI-powered preventive health care a reality, and to make the EHDS a success, we need a new data governance approach, one that would include two key components:
- The ability to reuse data collected for other purposes (e.g., mobility, retail sales, workplace trends) to improve health outcomes.
- The ability to integrate different data sources–clinical records and electronic health records (EHRS), but also environmental, social, and economic data — to build a complete picture of health risks.
In what follows, we outline some critical aspects of this new governance framework, including responsible data access and reuse (so-called secondary use), moving beyond traditional consent models to a social license for reuse, data stewardship, and the need to prioritize high-impact applications. We conclude with some specific recommendations for the EHDS, built from the preceding general discussion about the role of AI and data in preventive health…(More)”.
Unlocking Public Value with Non-Traditional Data: Recent Use Cases and Emerging Trends
Article by Adam Zable and Stefaan Verhulst: “Non-Traditional Data (NTD)—digitally captured, mediated, or observed data such as mobile phone records, online transactions, or satellite imagery—is reshaping how we identify, understand, and respond to public interest challenges. As part of the Third Wave of Open Data, these often privately held datasets are being responsibly re-used through new governance models and cross-sector collaboration to generate public value at scale.
In our previous post, we shared emerging case studies across health, urban planning, the environment, and more. Several months later, the momentum has not only continued but diversified. New projects reaffirm NTD’s potential—especially when linked with traditional data, embedded in interdisciplinary research, and deployed in ways that are privacy-aware and impact-focused.
This update profiles recent initiatives that push the boundaries of what NTD can do. Together, they highlight the evolving domains where this type of data is helping to surface hidden inequities, improve decision-making, and build more responsive systems:
- Financial Inclusion
- Public Health and Well-Being
- Socioeconomic Analysis
- Transportation and Urban Mobility
- Data Systems and Governance
- Economic and Labor Dynamics
- Digital Behavior and Communication…(More)”.
Exploring Human Mobility in Urban Nightlife: Insights from Foursquare Data
Article by Ehsan Dorostkar: “In today’s digital age, social media platforms like Foursquare provide a wealth of data that can reveal fascinating insights into human behavior, especially in urban environments. Our recent study, published in Cities, delves into how virtual mobility on Foursquare translates into actual human mobility in Tehran’s nightlife scenes. By analyzing user-generated data, we uncovered patterns that can help urban planners create more vibrant and functional nightlife spaces…
Our study aimed to answer two key questions:
- How does virtual mobility on Foursquare influence real-world human mobility in urban nightlife?
- What spatial patterns emerge from these movements, and how can they inform urban planning?
To explore these questions, we focused on two bustling nightlife spots in Tehran—Region 1 (Darband Square) and Region 6 (Valiasr crossroads)—where Foursquare data indicated high user activity.
Methodology
We combined data from two sources:
- Foursquare API: To track user check-ins and identify popular nightlife venues.
- Tehran Municipality API: To contextualize the data within the city’s urban framework.
Using triangulation and interpolation techniques, we mapped the “human mobility triangles” in these areas, calculating the density and spread of user activity…(More)”.
Data Collaborations How-to-Guide
Resource by The Data for Children Collaborative: “… excited to share a consolidation of 5 years of learning in one handy how-to-guide. Our ethos is to openly share tools, approaches and frameworks that may benefit others working in the Data for Good space. We have developed this guide specifically to support organisations working on complex challenges that may have data-driven solutions. The How-to-Guide provides advice and examples of how to plan and execute collaboration on a data project effectively…
Data collaboration can provide organisations with high-quality, evidence-based insights that drive policy and practice while bringing together diverse perspectives to solve problems. It also fosters innovation, builds networks for future collaboration, and ensures effective implementation of solutions on the ground…(More)”.
From Insights to Action: Amplifying Positive Deviance within Somali Rangelands
Article by Basma Albanna, Andreas Pawelke and Hodan Abdullahi: “In every community, some individuals or groups achieve significantly better outcomes than their peers, despite having similar challenges and resources. Finding these so-called positive deviants and working with them to diffuse their practices is referred to as the Positive Deviance approach. The Data-Powered Positive Deviance (DPPD) method follows the same logic as the Positive Deviance approach but leverages existing, non-traditional data sources, in conjunction with traditional data sources to identify and scale the solutions of positive deviants. The UNDP Somalia Accelerator Lab was part of the first cohort of teams that piloted the application of DPPD trying to tackle the rangeland health problem in the West Golis region. In this blog post we’re reflecting on the process we designed and tested to go from the identification and validation of successful practices to helping other communities adopt them.
Uncovering Rangeland Success
Three years ago we embarked on a journey to identify pastoral communities in Somaliland that demonstrated resilience in the face of adversity. Using a mix of traditional and non-traditional data sources, we wanted to explore and learn from communities that managed to have healthy rangelands despite the severe droughts of 2016 and 2017.
We engaged with government officials from various ministries, experts from the University of Hargeisa, international organizations like the FAO and members of agro-pastoral communities to learn more about rangeland health. We then selected the West Golis as our region of interest with a majority pastoral community and relative ease of access. Employing the Soil-Adjusted Vegetation Index (SAVI) and using geospatial and earth observation data allowed us to identify an initial group of potential positive deviants illustrated as green circles in Figure 1 below.
Following the identification of potential positive deviants, we engaged with 18 pastoral communities from the Togdheer, Awdal, and Maroodijeex regions to validate whether the positive deviants we found using earth observation data were indeed doing better than the other communities.
The primary objective of the fieldwork was to uncover the existing practices and strategies that could explain the outperformance of positively-deviant communities compared to other communities. The research team identified a range of strategies, including soil and water conservation techniques, locally-produced pesticides, and reseeding practices as summarized in Figure 2.
Data-Powered Positive Deviance is not just about identifying outperformers and their successful practices. The real value lies in the diffusion, adoption and adaptation of these practices by individuals, groups or communities facing similar challenges. For this to succeed, both the positive deviants and those learning about their practices must take ownership and drive the process. Merely presenting the uncommon but successful practices of positive deviants to others will not work. The secret to success is in empowering the community to take charge, overcome challenges, and leverage their own resources and capabilities to effect change…(More)”.
Integrating Social Media into Biodiversity Databases: The Next Big Step?
Article by Muhammad Osama: “Digital technologies and social media have transformed ecology and conservation biology data collection. Traditional biodiversity monitoring often relies on field surveys, which can be time-consuming and biased toward rural habitats.
The Global Biodiversity Information Facility (GBIF) serves as a key repository for biodiversity data, but it faces challenges such as delayed data availability and underrepresentation of urban habitats.
Social media platforms have become valuable tools for rapid data collection, enabling users to share georeferenced observations instantly, reducing time lags associated with traditional methods. The widespread use of smartphones with cameras allows individuals to document wildlife sightings in real-time, enhancing biodiversity monitoring. Integrating social media data with traditional ecological datasets offers significant advancements, particularly in tracking species distributions in urban areas.
In this paper, the authors evaluated the Jersey tiger moth’s habitat usage by comparing occurrence data from social media platforms (Instagram and Flickr) with traditional records from GBIF and iNaturalist. They hypothesized that social media data would reveal significant JTM occurrences in urban environments, which may be underrepresented in traditional datasets…(More)”.
Bridging Digital Divides: How PescaData is Connecting Small-Scale Fishing Cooperatives to the Blue Economy
Article by Stuart Fulton: “In this research project, we examine how digital platforms – specifically PescaData – can be leveraged to connect small-scale fishing cooperatives with impact investors and donors, creating new pathways for sustainable blue economy financing, while simultaneously ensuring fair data practices that respect data sovereignty and traditional ecological knowledge.
PescaData emerged as a pioneering digital platform that enables fishing communities to collect more accurate data to ensure sustainable fisheries. Since then, PescaData has evolved to provide software as a service to fishing cooperatives and to allow fishers to document their solutions to environmental and economic challenges. Since 2022, small-scale fishers have used it to document nearly 300 initiatives that contribute to multiple Sustainable Development Goals.
Respecting Data Sovereignty in the Digital Age
One critical aspect of our research acknowledges the unique challenges of implementing digital tools in traditional cooperative settings. Unlike conventional tech implementations that often extract value from communities, PescaData´s approach centers on data sovereignty – the principle that fishing communities should maintain ownership and control over their data. As the PescaData case study demonstrates, a humanity-centric rather than merely user-centric approach is essential. This means designing with compassion and establishing clear governance around data from the very beginning. The data generated by fishing cooperatives represents not just information, but traditional knowledge accumulated over generations of resource management.
The fishers themselves have articulated clear principles for data governance in a cooperative model:
- Ownership: Fishers, as data producers, decide who has access and under what conditions.
- Transparency: Clear agreements on data use.
- Knowledge assessment: Highlighting fishers’ contributions and placing them in decision-making positions.
- Co-design: Ensuring the platform meets their specific needs.
- Security: Protecting collected data…(More)”.
Can Real-Time Metrics Fill China’s Data Gap?
Case-study by Danielle Goldfarb: “After Chinese authorities abruptly reversed the country’s zero-COVID policy in 2022, global policymakers needed a clear and timely picture of the economic and health fallout.
China’s economy is the world’s second largest and the country has deep global links, so an accurate picture of its trajectory mattered for global health, growth and inflation. Getting a solid read was a challenge, however, since official health and economic data not only were not timely, but were widely viewed as unreliable.
There are now vast amounts and varied types of digital data available, from satellite images to social media text to online payments; these, along with advances in artificial intelligence (AI), make it possible to collect and analyze digital data in ways previously impossible.
Could these new tools help governments and global institutions refute or confirm China’s official picture and gather more timely intelligence?..(More)”.
Vetted Researcher Data Access
Coimisiún na Meán: “Article 40 of the Digital Services Act (DSA) makes provision for researchers to access data from Very Large Online Platforms (VLOPs) or Very Large Online Search Engines (VLOSEs) for the purposes of studying systemic risk in the EU and assessing mitigation measures. There are two ways that researchers that are studying systemic risk in the EU can get access to data under Article 40 of the DSA.
Non-public data, known as “vetted researcher data access”, under Article 40(4)-(11). This is a process where a researcher, who has been vetted or assessed by a Digital Services Coordinator to have met the criteria as set out in DSA Article 40(8), can request access to non-public data held by a VLOP/VLOSE. The data must be limited in scope and deemed necessary and proportionate to the purpose of the research.
Public data under Article 40(12). This is a process where a researcher who meets the relevant criteria can apply for data access directly from a VLOP/VLOSE, for example, access to a content library or API of public posts…(More)”.