Funded by the Mozilla Foundation, Data-Pop Alliance developed a multimodal dataset of Brazilian Sign Language (Libras), comprising a collection of video recordings and associated metadata,
This project will develop a scalable application to automate the generation and continuous updating of key climate change mitigation and adaptation indicators under the
Technology-facilitated Gender-Based Violence (TF-GBV) against women in politics is an increasing, global concern for democracies. It is situated within the rising anti-gender and anti-feminist
The Book and Movie Club “(Re)Imagining Technologies: Paths to the Future” [(Re)Imaginando Tecnologias: Caminhos para o Futuro] by DPA, Instituto da Hora and Eureka explored
EmpoderaData built upon the success of the “Quantitative Step” (Q-Step) program, which was developed as a strategic response to the shortage of quantitatively-skilled social
In November 2019, Data-Pop Alliance, in partnership with ECLAC and the Brazilian Institute of Geography and Statistics (IBGE), conducted its first technical workshop in
In partnership with the United Nations Economic Commission for Latin America and the Caribbean (ECLAC), DPA offered a series of workshops focused on “Big
With over 30,000 confirmed cases, Brazil is currently the country most affected by COVID-19 in Latin America, and ranked 12th worldwide. Despite all evidence,
Criteria, a project led by Prosperia with funding from the Inter-American Development Bank (IDB) and support from Data-Pop Alliance, is an interactive decision support
This report analyzed Alipay’s Ant Forest’s exponential growth, its trajectory, and vision to unlock the power of individual behavior change for climate change mitigation.
This project, developed in partnership with UNIDAS and GIZ Data Lab, leveraged traditional and non-traditional data sources to assess the reporting capability of women
This project leveraged novel ‘non-traditional’ data for mapping and monitoring multidimensional poverty in informal urban settings, while taking into account and reflecting context-specific nuances.
