Lacuna Fund - Open Data Sets & Use Cases

Under this initiative, a novel approach is employed by leveraging citizen scientists and IoT-based low-cost sensors to collect hyperlocal air quality data. This data is used to identify pollution sources and risk zones, facilitating targeted actions by regulatory authorities.To showcase data outreach, the project features the VAYU Android-based application and the VAYU citizen portal digital stack, which support targeted interventions and customized solutions backed by AI/ML algorithms. These tools potentially develop new approaches in air pollution management while reducing public investment costs.

The NaijaSenti dataset is the first large-scale human-annotated Twitter sentiment dataset for Hausa, Igbo, Nigerian-Pidgin, and Yorùbá, the four most widely spoken languages in Nigeria. It consists of around 30,000 annotated tweets per language (except for Nigerian-Pidgin), including a significant fraction of code-mixed tweets. These datasets are useful not only for sentiment analysis but also for hate speech detection. The open-source package consists of datasets, trained models, sentiment lexicons, and code. With more than 200 million people and 522 native languages, Nigeria is the most populous and linguistically diverse country in Africa, as well as the third most multilingual country in the world. The majority of the population speaks either Hausa, Igbo, Yorùbá, or Nigerian-Pidgin.

This dataset contributes to improved understanding and mitigation of the impacts of oil palm cultivation on forests and climate change. It can also serve to support sustainable forest management practices and empower local communities in forest resource management. The project created open and accessible training and evaluation datasets for machine learning applications focused on forest cover and change in Indonesia. It contains collections of polygons of uniform land cover, with labels indicating land cover type and, possibly, information on land cover change over time. To label land cover patches, the team has reviewed high-resolution satellite imagery facilitated by Collect Earth Online (CEO) and augmented it with field visits to difficult-to-classify areas. It has prioritized areas with significant landcover diversity with a preference for areas with significant smallholder production of oil palm, coconut, and other commodities - given the well-known difficulties of distinguishing oil palm from coconut palm with lower resolution imagery. Possible users of these outputs are local communities engaged in community forestry in Indonesia, as well as researchers, policymakers, and civil society organizations. The dataset also contributes to social forestry, which recognizes that people who depend on local forests are best placed to look after them, and that allowing communities to manage and use forest resources can have positive social, environmental and economic impacts. Social forestry is a broad term and has different names in different places. Some examples are community forestry, village forestry, participatory forestry, community-based forest management and people-centred forestry.

An AI-Driven Dataset for Nature-Positive Livelihoods and Forest Restoration in Eastern Himalayas.This open-access dataset and digital MRV (Monitoring, Reporting, and Verification) toolkit was developed to help communities, NGOs and policymakers in the Eastern Himalayas measure and manage the links between forests, livelihoods and climate resilience. The project was created in response to a persistent data gap in the Himalayan region where limited open data and difficult terrain have hindered effective monitoring of forest degradation and the impact of restoration programs. Using locally calibrated AI models trained on Indian biomass data, the system combines satellite observations with ground level socio-economic surveys to measure forest dependency, identify degradation hotspots and track restoration outcomes. All datasets ranging from community level livelihood surveys in Sikkim and Mizoram to forest biomass and land use layers in Assam are openly available. Together they form a replicable, open-source “digital public good” that can be used to train local AI models for forest monitoring, biodiversity accounting or impact assessment. By providing a scalable, context-aware dataset, this initiative enables indigenous start-ups, environmental NGOs and impact investors to quantify nature positive outcomes, align with IRIS+ indicators, and guide conservation investments. The goal is to make environmental monitoring inclusive and data driven, empowering local actors to track changes in forest carbon, identify erosion risks and measure the socio-economic benefits of restoration. Ultimately, this approach helps governments, researchers and entrepreneurs build locally adapted AI systems that support both ecosystem regeneration and sustainable livelihoods in fragile mountain regions.

This project aims to develop a longitudinal household-level electricity dataset by integrating repeated survey data with high-frequency smart meter & non-smart meter data. The survey component captures detailed information about household demographics, dwelling characteristics, appliance ownership, and energy-related behaviors, enabling these attributes to be linked to observed electricity consumption patterns. The resulting dataset is meant to facilitate analysis around experimentation with behavioral nudges, tariff reforms, and other demand-side management solutions relevant to energy affordability, efficiency, and policy in Sri Lanka.