The fragmentation of public data in Brazil, coupled with inconsistent standards and limited interoperability, hinders effective research, evidence-based policymaking and access to data-driven insights. To address these issues, we introduce Brazil Data Commons, a platform that unifies various Brazilian datasets under a common semantic framework, enabling the seamless discovery, integration and visualization of information from different domains. By adopting globally recognized ontologies and interoperable data standards, Brazil Data Commons aligns with the principles of the broader Data Commons ecosystem and places Brazilian data in a global context. Through user-friendly interfaces, straightforward query mechanisms and flexible data access options, the platform democratizes data use and enables researchers, policy makers, and the public to gain meaningful insights and make informed decisions. This paper illustrates how Brazil Data Commons transforms scattered datasets into an integrated and easily navigable resource that allows a deeper understanding of Brazil's complex social, economic and environmental landscape.
This paper proposes an empirical, replicable, and interpretable framework to decompose, in basis points (bps), daily changes in Brazil's 5-year DI futures rate (DI5Y). The approach combines three building blocks: (i) macroeconomic and fiscal expectations from the Central Bank of Brazil Focus survey, converted into daily changes; (ii) a supervised macro factor built with Partial Least Squares (PLS) that summarizes changes in expectations together with a high-frequency macro "surprise" indicator; and (iii) a decomposition of sovereign risk using Brazil CDS into global and domestic components, obtained by regressing CDS on external financial conditions (DXY, CRB, VIX, and the US 10-year yield). The final step maps these drivers into daily bps contributions through a linear regression of the daily change in DI5Y on the three factors, producing a cumulative decomposition that adds up with an intercept and a residual. In the final sample (2015-01-13 to 2025-12-12; 2,741 observations), the model explains about 22.45% of the daily variance in DI5Y changes. The explained share is dominated by domestic risk, with a smaller but statistically significant contribution from the macro factor. The
This research introduces LegalScore, a specialized index for assessing how generative artificial intelligence models perform in a selected range of career exams that require a legal background in Brazil. The index evaluates fourteen different types of artificial intelligence models' performance, from proprietary to open-source models, in answering objective questions applied to these exams. The research uncovers the response of the models when applying English-trained large language models to Brazilian legal contexts, leading us to reflect on the importance and the need for Brazil-specific training data in generative artificial intelligence models. Performance analysis shows that while proprietary and most known models achieved better results overall, local and smaller models indicated promising performances due to their Brazilian context alignment in training. By establishing an evaluation framework with metrics including accuracy, confidence intervals, and normalized scoring, LegalScore enables systematic assessment of artificial intelligence performance in legal examinations in Brazil. While the study demonstrates artificial intelligence's potential value for exam preparation an
Water research in Brazil largely overlooks the widespread damming of small streams for agricultural uses such as watering cattle, farm-scale hydropower, irrigation, and aquaculture. These ubiquitous dams and their reservoirs can alter water temperature, stream connectivity, aquatic habitats, greenhouse gas emissions, and evaporative water losses. Mapping small reservoirs is challenging because it requires reliably detecting small water bodies and distinguishing artificial reservoirs from natural lakes. As a result, most regional and global datasets exclude them. To address this gap, we trained a deep learning computer vision model to accurately segment small ($< 1 km^2$), stream-fed, surface water reservoirs in Brazil leveraging data from Landsat 5-9. Applying our model from 1984 to 2025, we created annual reservoir maps for the entire country to evaluate how their count, size, and distribution have changed over time. The number of detected reservoirs grew nearly fourfold from 263,913 to 996,245, while their total surface area increased from 3510 $km^2$ to 8550 $km^2$. To our knowledge, this is the first country-wide annual dataset representing the evolution of small reservoirs
The paradigm of global weather forecasting is rapidly shifting with the emergence of Machine Learning Weather Prediction models (MLWP). While these data-driven architectures demonstrate remarkable global skill, regional benchmarks in the Global South remain scarce, leaving their efficacy in complex, highly convective environments largely unverified. This study evaluates the performance of GraphCast operational against the deterministic ECMWF IFS HRES as baseline across four distinct Brazilian climatic sub-regions. Utilizing a scalable, cloud-native pipeline and the WeatherBench-X framework for benchmarking weather models, we assess selected tropospheric variables ($T_{850}$, $Q_{850}$, $Z_{500}$) over four selected seasonal windows, employing the operational IFS analysis as the ground truth to calculate the statistical metrics for both models. Results reveal a regime-dependent skill profile. During the austral winter, GraphCast underperforms in the medium range (lead days 2-7) for $Z_{500}$ when resolving fast-propagating baroclinic systems over southern Brazil, but regains an advantage in the extended range, where its inherent smoothing of chaotic small-scale variability becomes b
An important consequence of human induced climate change is the increase in extreme weather events. This study contributes to the understanding of Brazil's climate change by examining historical temperature and precipitation patterns. Extreme events of temperature and precipitation are identified using data from the Brazilian Institute of Meteorology, which includes records from 634 meteorological stations operating intermittently since 1961. Using the first 30 years (1961 to 1990) as the reference period, our results show a significant increase in warm days and a corresponding decrease in cold days over the last 30 years (1991 to 2020), in agreement with previous works. In terms of precipitation, it indicates a trend toward drier conditions in the Northeast region of Brazil, whereas the South is experiencing wetter conditions, with an increase in the number of heavy precipitation days in South and in the extremely dry periods in the Northeast. These results have been verified for consistency with several extreme climate indices measured in this study. Additionally, data from S2iD is analyzed, an official database that records natural disasters in Brazil, to estimate their impact i
Background. Dengue outbreaks are a major public health issue, with Brazil reporting 71% of global cases in 2024. Purpose. This study aims to describe the profile of severe dengue patients admitted to Brazilian Intensive Care units (ICUs) (2012-2024), assess trends over time, describe new onset complications while in ICU and determine the risk factors at admission to develop complications during ICU stay. Methods. We performed a prospective study of dengue patients from 253 ICUs across 56 hospitals. We used descriptive statistics to describe the dengue ICU population, logistic regression to identify risk factors for complications during the ICU stay, and a machine learning framework to predict the risk of evolving to complications. Visualisations were generated using ISARIC VERTEX. Results. Of 11,047 admissions, 1,117 admissions (10.1%) evolved to complications, including non-invasive (437 admissions) and invasive ventilation (166), vasopressor (364), blood transfusion (353) and renal replacement therapy (103). Age>80 (OR: 3.10, 95% CI: 2.02-4.92), chronic kidney disease (OR: 2.94, 2.22-3.89), liver cirrhosis (OR: 3.65, 1.82-7.04), low platelets (<50,000 cells/mm3; OR: OR: 2.2
[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unrealistic customer expectations and vagueness in ML problem specifications. Literature suggests that Requirements Engineering (RE) practices and tools may help to alleviate these issues, yet there is insufficient understanding of RE's practical application and its perception among practitioners. [Goal] This study aims to investigate the application of RE in developing ML-enabled systems in Brazil, creating an overview of current practices, perceptions, and problems in the Brazilian industry. [Method] To this end, we extracted and analyzed data from an international survey focused on ML-enabled systems, concentrating specifically on responses from practitioners based in Brazil. We analyzed RE-related answers gathered from 72 practitioners involved in data-driven projects. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative studies on the reported problems involving open and axial coding procedures. [Results] Our findings highlig
Starting from the perspective of reports published in Brazilian newspapers at the time, as well as letters exchanged between scientists who worked in Brazil and North American colleagues and documents from the symposium on cosmic rays, a chronological sequence of how the so-called Compton mission in Brazil took place and was perceived by the literate public will be presented.
The influence of climate on mosquito-borne diseases like dengue and chikungunya is well-established, but comprehensively tracking long-term spatial and temporal trends across large areas has been hindered by fragmented data and limited analysis tools. This study presents an unprecedented analysis, in terms of breadth, estimating the SIR transmission parameters from incidence data in all 5,570 municipalities in Brazil over 14 years (2010-2023) for both dengue and chikungunya. We describe the Episcanner computational pipeline, developed to estimate these parameters, producing a reusable dataset describing all dengue and chikungunya epidemics that have taken place in this period, in Brazil. The analysis reveals new insights into the climate-epidemic nexus: We identify distinct geographical and temporal patterns of arbovirus disease incidence across Brazil, highlighting how climatic factors like temperature and precipitation influence the timing and intensity of dengue and chikungunya epidemics. The innovative Episcanner tool empowers researchers and public health officials to explore these patterns in detail, facilitating targeted interventions and risk assessments. This research offe
This survey explores the impact perceived by employers and employees of GenAI in their work activities in Brazil. Generative AI (GenAI) is gradually transforming Brazil workforce, particularly in micro and small businesses, though its adoption remains uneven. This survey examines the perceptions of employers and employees across five sectors: Sales, Customer Service, Graphic Design or Photography, Journalism or Content Production, and Software Development or Coding. The results are analyzed in light of six key dimensions of workforce impact. The findings reveal a mix of optimism, apprehension, and untapped potential in the integration of AI tools. This study serves as a foundation for developing inclusive strategies that maximize AI's benefits while safeguarding workers' rights. The IIA-LNCC supports open research and remains committed to shaping a future where technology and human potential progress together.
According to WHO (2013), in general 30% of all women worldwide who have been in a relationship have experienced physical and/or sexual violence by their intimate partner. However, only a small percentage of intimate partner violence (IPV) victims report it to the police. This phenomenon of under-reporting is known as ``dark figure''. This paper aims to investigate the factors associated with the reporting decision of IPV victims to the police in Brazil using the third wave of the ``Pesquisa de Condições Socioeconômicas e Violência Doméstica e Familiar contra a Mulher ($PCSVDF^{Mulher}$)''. Using a bivariate probit regression model with sample selection, we found that older white women, those who do not tolerate domestic violence, and women who have experienced physical violence are more likely to report IPV to the police. In contrast, married women, those with partners who abuse alcohol and those who witnessed or knew that their mothers had experienced IPV, are less likely to report it to law enforcement.
We use administrative panel data on the universe of Brazilian formal workers to investigate the labor market effects of the Venezuelan crisis in Brazil, focusing on the border state of Roraima. The results using difference-in-differences show that the monthly wages of Brazilians in Roraima increased by around 2 percent, which was mostly driven by those working in sectors and occupations with no refugee involvement. The study finds negligible job displacement for Brazilians but finds evidence of native workers moving to occupations without immigrants. We also find that immigrants in the informal market offset the substitution effects in the formal market.
The objective of this paper is to investigate a more efficient cross-border payment and document handling process for the export of Indian goods to Brazil. The paper is structured into two sections: first, to explain the problems unique to the India-Brazil international trade corridor by highlighting the obstacles of compliance, speed, and payments; and second, to propose a digital solution for India-brazil trade utilizing Supernets, focusing on the use case of Indian exports. The solution assumes that stakeholders will be onboarded as permissioned actors (i.e. nodes) on a Polygon Supernet. By engaging trade and banking stakeholders, we ensure that the digital solution results in export benefits for Indian exporters, and a lawful channel to receive hard currency payments. The involvement of Brazilian and Indian banks ensures that Letter of Credit (LC) processing time and document handling occur at the speed of blockchain technology. The ultimate goal is to achieve faster settlement and negotiation period while maintaining a regulatory-compliant outcome, so that the end result is faster and easier, yet otherwise identical to the real-world process in terms of export benefits and com
Hydroelectricity accounted for roughly 61.4% of Brazil's total generation in 2024 and addressed most of the intermittency of wind and solar generation. Thus, inflow forecasting plays a critical role in the operation, planning, and market in this country, as well as in any other hydro-dependent power system. These forecasts influence generation schedules, reservoir management, and market pricing, shaping the dynamics of the entire electricity sector. The objective of this paper is to measure and present empirical evidence of a systematic optimistic bias in the official inflow forecast methodology, which is based on the PAR(p)-A model. Additionally, we discuss possible sources of this bias and recommend ways to mitigate it. By analyzing 14 years of historical data from the Brazilian system through rolling-window multistep (out-of-sample) forecasts, results indicate that the official forecast model exhibits statistically significant biases of 1.28, 3.83, 5.39, and 6.73 average GW for 1-, 6-, 12-, and 24-step-ahead forecasts in the Southeast subsystem, and 0.54, 1.66, 2.32, and 3.17 average GW in the Northeast subsystem. These findings uncover the limitations of current inflow forecast
Agriculture is impacted by multiple variables such as weather, soil, crop, stocks, socioeconomic context, cultural aspects, supply and demand, just to name a few. Hence, understanding this domain and identifying challenges faced by stakeholders is hard to scale due to its highly localized nature. This work builds upon six months of field research and presents challenges and opportunities for stakeholders acting in the rural credit ecosystem in Brazil, highlighting how small farmers struggle to access higher values in credit. This study combined two methods for understanding challenges and opportunities in rural credit ecosystem in Brazil: (1) a study that took place in a community of farmers in Brazil and it was based on participatory observations of their work processes and interactions of 20 informants (bank employees and farmers); (2) design thinking workshops with teams from 3 banks, counting on 15-20 participants each. The results show that key user experience challenges are tightly connected to the heterogeneity of farmer profiles and contexts of use involving technology available, domain skills, level of education, and connectivity, among others. In addition to presenting da
Brazil rose as a global powerhouse producer of soybeans and corn over the past 15 years has fundamentally changed global markets in these commodities. This is arguably due to the development of varieties of soybean and corn adapted to climates within Brazil, allowing farmers to double-crop corn after soybeans in the same year. Corn and soybean market participants increasingly look to Brazil for fundamental price information, and studies have shown that the two markets have become cointegrated. However little is known about how much volatility from each market spills over to the other. In this article we measure volatility spillover ratios between U.S. and Brazilian first crop corn, second crop corn, and soybeans. We find that linkages between the two countries increased after double cropping corn after soybeans expanded, volatility spillover magnitudes expanded, and the direction of volatility spillovers flipped from U.S. volatility spilling over to Brazil before double cropping, to Brazil spilling over to U.S. after double cropping.
This paper presents a mathematical model to investigate co-infection with HIV/AIDS and zika virus (ZIKV) in Colombia and Brazil, where the first cases were reported in 2015-2016. The model considers the sexual transmission dynamics of both viruses and vector-host interactions. We begin by exploring the qualitative behaviour of each model separately. Then, we analyze the dynamics of the co-infection model using the thresholds and results defined separately for each model. The model also considers the impact of intervention strategies, such as, personal protection, antiretroviral therapy (ART), and sexual protection (condoms use). Using available parameter values for Colombia and Brazil, the model is calibrated to predict the potential effect of implementing combinations of those intervention strategies on the co-infection spread. According to these findings, transmission through sexual contact is a determining factor in the long-term behaviour of these two diseases. Furthermore, it is important to note that co-infection with HIV and ZIKV may result in higher rates of HIV transmission and an increased risk of severe congenital disabilities linked to ZIKV infection. As a result, contr
Research on productive structures has shown that economic complexity conditions economic growth. However, little is known about which type of complexity, e.g., export or industrial complexity, matters more for regional economic growth in a large emerging country like Brazil. Brazil exports natural resources and agricultural goods, but a large share of the employment derives from services, non-tradables, and within-country manufacturing trade. Here, we use a large dataset on Brazil's formal labor market, including approximately 100 million workers and 581 industries, to reveal the patterns of export complexity, industrial complexity, and economic growth of 558 micro-regions between 2003 and 2019. Our results show that export complexity is more evenly spread than industrial complexity. Only a few -- mainly developed urban places -- have comparative advantages in sophisticated services. Regressions show that a region's industrial complexity is a significant predictor for 3-year growth prospects, but export complexity is not. Moreover, economic complexity in neighboring regions is significantly associated with economic growth. The results show export complexity does not appropriately d
In this paper, we use a Bayesian method to estimate the effective reproduction number (R(t)), in the context of monitoring the time evolution of the COVID-19 pandemic in Brazil at different geographic levels. The focus of this study is to investigate the similarities between the trends in the evolution of such indicators at different subnational levels with the trends observed nationally. The underlying question addressed is whether national surveillance of such variables is enough to provide a picture of the epidemic evolution in the country or if it may hide important localized trends. This is particularly relevant in the scenario where health authorities use information obtained from such indicators in the design of non-pharmaceutical intervention policies to control the epidemic. A comparison between R(t) estimates and the moving average (MA) of daily reported infections is also presented, which is another commonly monitored variable. The analysis carried out in this paper is based on the data of confirmed infected cases provided by a public repository. The correlations between the time series of R(t) and MA in different geographic levels are assessed. Comparing national with s