Welcome to our latest edition on everything related to impact data. In this newsletter, we explore how societies withstand crisis, how deep learning monitors our oceans, the outcomes of global climate negotiations, and where to find the most reliable global development data.
In this newsletter, we cover:
- Interesting Variable: Community Resilience
- Interesting Tool: Automated Oil Spill Detection
- Interesting Read: COP30 Outcomes
- Interesting Dataset: World Bank Data
Interesting Variable: Community Resilience
A paper titled “A longitudinal study of societal resilience and its predictors during the Israel-Gaza war” has interesting insights in how societies function in war times.
Researchers tracked a cohort of 1,613 adults at two intervals: five days after the initial October 2023 assault and one month later. While overall societal resilience showed a decline between the two points, the data highlighted community resilience as a primary predictor of stability.
Through regression analysis, community resilience ranked alongside hope as the strongest predictor of societal resilience. The study found that community resilience operates synergistically. It is not merely the aggregate of individual strengths but acts as a base for stability. A community’s collective capacity to recover and maintain function proved more predictive than individual coping mechanisms alone.
This concept is defined by three aspects: the capacity for recovery and adaptation, the role of social interaction, and the maintenance of essential functions. It suggests that measuring the connections between people, such as social capital and collective action, is as important as measuring the status of individuals. As noted in the study, a collection of resilient individuals does not guarantee a resilient community because the connections between them define the outcome.
Interesting tool: Automated oil spill
There is an interesting paper titled “Automated oil spill detection using deep learning and SAR satellite data for the northern entrance of the Suez Canal” published in Scientific Report. It explores the use of Deep Learning and Synthetic Aperture Radar (SAR) satellite data to detect oil spills at the northern entrance of the Suez Canal. The study leveraged the DeepLabv3+ model to analyze surface roughness variations. The researchers compared two training strategies: a generalized approach using a European dataset (EMSA-CSN) and a localized approach using an Egyptian dataset.
The results highlighted a trade-off between specificity and generalizability. The localized model outperformed the generalized model in aggregate metrics, achieving 98.14% accuracy. This confirms that domain-specific training is effective for segmentation quality in known environments.
However, performance is not uniform across all samples. Figure 19 illustrates specific prediction cases where the generalized model outperformed the specialized Egyptian model. These cases occurred primarily when the oil spill was small relative to the scene frame. While the localized model captured large regional patterns, it missed fine-grained targets that the generalized model picked up. This indicates that diverse training data remains necessary for outlier detection.
Interesting read: COP30
The COP30 climate negotiations, held in Belém, Brazil, concluded on November 22, 2025, with the adoption of the Belém Package. The 195 Parties involved agreed to a set of 29 decisions regarding climate finance and adaptation. Key outcomes included a recommendation to triple adaptation finance by 2035, the finalization of indicators for the Global Goal on Adaptation, and a framework to scale climate finance flows to at least USD 1.3 trillion annually. Additionally, the Tropical Forests Forever Facility was launched to mobilize funds for conservation.
Despite these financial milestones, the outcomes faced mixed reactions due to the lack of a binding commitment to phase out fossil energy. In response, COP30 President André Corrêa do Lago announced two roadmaps to build future momentum for a planned global transition. This signals that while the financial architecture is being built, the political agreement for a full energy transition remains incomplete.
The following infographic was created with Google’s new image generation tool Nano Banana.
Interesting dataset: World Bank Data
For data scientists seeking robust, globally-scoped datasets, the World Bank DataBank is a useful resource. It offers access to collections of time-series data for development economics, policy analysis, and modeling. The platform allows users to build queries and generate visualizations across topics ranging from Climate Change and External Debt to Health and Gender.
Among its offerings is the World Development Indicators (WDI), a collection providing harmonized, internationally recognized metrics. Other datasets include Education Statistics and the Global Economic Monitor. With recent updates to their statistical capacity indicators, the DataBank serves as a gateway for assessing data quality in developing countries and performing global analysis.