The Waterproofing Data project explored how to build communities’ resilience to flooding, by engaging them in the process of generating the data used to predict when floods will occur. The project team developed a functional citizen-science mobile app prototype and a model school curriculum, which has been successfully co-produced and trialled with more than 300 students from over 20 schools and civil protection agencies of five Brazilian states (Acre, Mato Grosso, Pernambuco, Santa Catarina and Sao Paulo). The app and curriculum enabled the communities involved to democratise flood data, raise awareness of flood risks, and co-design new initiatives to reduce disaster risks to communities. Aiming to understand better the impact of our project, we gathered further quantitative evidence of improvement of knowledge about floods through an evaluation survey with school students and community resident volunteers after our data generation activities using the mobile app. This data set compiles the answers to an evaluation survey aiming at evaluating the knowledge improvement about floods derived from the Waterproofing Data Project. The data groups questions in 8 groups that cover flooding aspects related to housing, neighbourhoods, communication, and flooding features. Our analysis indicates the positive impact of data generation activities on the flood risk knowledge of participants. We analysed the group of users before (baseline, n=292) and after they participated in data generation activities (treatment, n=216). The percentage of participants who declared to have “sufficient”, “good” or “very good” flood risk knowledge after our pedagogical intervention (48%) was higher than before their participation (32%) and this difference was statistically significant (Wilcoxon Signed Ranks Test, p < 0.0005, effect size, r = 0.324). No significant change has been observed in a control group of non-participants. The questionnaire and answers are written in Portuguese. This dataset is complementary data for our publication “Dialogic data innovations for sustainability transformations and flood resilience: The case for waterproofing data” available here https://doi.org/10.1016/j.gloenvcha.2023.102730Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to scientific "centres of expertise", and then flood-related alerts and interventions flow back down through local governments and into communities. Rethinking how flood-related data is produced, and how it flows, can help build sustainable, flood resilient communities. To this end, this project develops three innovative methods around data practices, across different sites and scales: 1) we will make visible existing flows of flood-related data through tracing data; 2) generate new types of data at the local level by engaging citizens through the creation of multi-modal interfaces, which sense, collect and communicate flood data, and; 3) integrate citizen-generated data with other data using geo-computational techniques. These methodological interventions will transform how flood-related data is produced and flows, creating new governance arrangements between citizens, governments and flood experts and, ultimately, increased community resilience related to floods in vulnerable communities of Sao Paulo and Acre, Brazil. The project will be conducted by a highly skilled international team of researchers with multiple disciplinary backgrounds from Brazil, Germany and the UK, in close partnership with researchers, stakeholders and publics of a multi-site case study on flood risk management in Brazil. Furthermore, the methods and results of this case study will be the basis for a transcultural dialogue with government organisations and local administration involved in flood risk management in Germany and the United Kingdom.

Survey population corresponded to residents of the impoverished M’Boi Mirim district in São Paulo, Brazil. In particular, we invited school students and community resident volunteers that a) have contributed to data generation activities using the mobile app or b) have not been part of any project activities. No specific sampling method nor stratification was applied for collecting data. The sample size was 70 subjects.