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Saimiri cassiquiarensis cassiquiarensis (Cebidae) is a primate subspecies with a wide distribution in the Amazonian region of Brazil, Colombia, and Venezuela. However, the boundaries of its geographic range remain poorly defined. This study presents new occurrence localities for this subspecies and updates its distribution using a compiled data set of 140 occurrence records based on literature, specimens vouchered in scientific collections, and new field data to produce model‐based range maps. After cleaning our data set, we updated the subspecies' extent of occurrence, which was used in model calibration. We then modeled the subspecies' range using a maximum entropy algorithm (MaxEnt). The final model was adjusted using a fixed threshold, and we revised this polygon based on known geographic barriers and parapatric congeneric ranges. Our findings indicate that this subspecies is strongly associated with lowland areas, with consistently high daily temperatures. We propose modifications to all range boundaries and estimate that 3% of the area of occupancy (AOO, as defined by IUCN) has already been lost due to deforestation, resulting in a current range of 224,469 km2. We also found that 54% of their AOO is currently covered by protected areas (PAs). Based on these results, we consider that this subspecies is currently properly classified as Least Concern, because it occupies an extensive range, which is relatively well covered by PAs, and is currently experiencing low rates of deforestation. Research Highlights: We used a model‐based approach to estimate Saimiri cassiquiarensis cassiquiarensis range. This subspecies is a small Amazonian cebid that ranges over wide areas in Brazil, Colombia, and Venezuela. However, the geographic range of this taxon remains poorly defined.The MaxEnt model indicates that the taxon is strongly linked to lowland regions with consistently high daily temperatures. The suitable habitat for this taxon only accounts for 24% of its extent of occurrence.We proposed changes to all range limits and estimated that 3% of the area has already been deforested. This results in a current range of 224,469 km2, with half of this range being encompassed by protected areas. [ABSTRACT FROM AUTHOR]

The application of data science (DS) techniques has become increasingly essential in various fields, including epidemiology and climatology in agricultural production systems. In this sector, traditionally large amounts of data are acquired, but not well-managed and -analyzed as a basis for evidence-based decision-making processes. Here, we present a comprehensive step-by-step guide that explores the use of DS in managing epidemiological and climatological data within rice production systems under tropical conditions. Our work focuses on using the multi-temporal dataset associated with the monitoring of diseases and climate variables in rice in Colombia during eight years (2012–2019). The study comprises four main phases: (I) data cleaning and organization to ensure the integrity and consistency of the dataset; (II) data management involving web-scraping techniques to acquire climate information from free databases, like WordClim and Chelsa, validation against in situ weather stations, and bias removal to enrich the dataset; (III) data visualization techniques to effectively represent the gathered information, and (IV) a basic analysis related to the clustering and climatic characterization of rice-producing areas in Colombia. In our work, a process of evaluation and the validation of climate data are conducted based on errors (r, R2, MAE, RSME) and bias evaluation metrics. In addition, in phase II, climate clustering was conducted based on a PCA and K-means algorithm. Understanding the association of climatic and epidemiological data is pivotal in predicting and mitigating disease outbreaks in rice production areas. Our research underscores the significance of DS in managing epidemiological and climatological data for rice production systems. By applying a protocol responsible for DS tools, our study provides a solid foundation for further research into disease dynamics and climate interactions in rice-producing regions and other crops, ultimately contributing to more informed decision-making processes in agriculture. [ABSTRACT FROM AUTHOR]

The Saber 5° tests seek to contribute to improving the quality of education in Colombia. Their application is periodic, they evaluate students' basic skills, and they analyze the factors that affect their achievements. This paper presents one of the results of a research work whose objective was to apply data mining techniques to detect patterns of academic performance regarding the mathematics component of the Saber 5 tests in the year 2017. This test was taken by the fifth-grade students of Colombian Primary Education institutions. To meet this objective, the CRISP-DM methodology was used. Socioeconomic, academic, and institutional information was obtained from the ICFES databases. This information was preprocessed using data cleaning and transformation techniques. The decision tree classification model was selected, as it allows easily interpreting patterns. Among the most important factors in the patterns associated with good or poor academic performance in mathematics are the nature and location of the school and whether or not the student had failed a grade. The knowledge generated in this research constitutes quality information for decision-making by the Ministry of National Education, education secretariats, and the executives of Primary Education institutions with regard to the definition of improvement plans that result in the quality of Primary Education in Colombia. [ABSTRACT FROM AUTHOR]

The supply of the country's road infrastructure and the demand of the vehicles that travel through it, must maintain a balance, which allows the trips generated by road users to be the shortest in distance and as soon as possible in time, also taking into account the comfort and safety of users. Achieving this goal is not an easy task, because there are many factors involved in the design and construction of this type of infrastructure project. One of the factors that must be taken into account to achieve infrastructure projects that respond to current and future needs, corresponds to the volumes of vehicles that will use the roads, for which it is of the utmost importance to know the past and current conditions of the vehicular flows that have occurred in the project areas or in sectors with similar conditions. The purpose of this work is to carry out a descriptive analysis of the distribution of vehicular traffic for road concessions Cartagena-Barranquilla and Santa Marta-Riohacha-Paraguachón, located on the roads of the Caribbean region of Colombia, during the period between the years 2010 - 2019. The information related to traffic was obtained from the website Datos Abiertos of the Gobierno Nacional de Colombia and was provided by the Agencia Nacional de Infraestructura (ANI). The data consulted contains information about the different types of vehicles (Cars, Buses and Trucks), as well as the different types of Trucks (C2P, C2G, C3-C4, C5 and C6) that transit through the two concessions that are made up of by six toll stations, three in each. With the information studied, a dataset was built, after carrying out the processing, ordering and cleaning of the data and with them different descriptive graphs were made that helped to draw the conclusions of the work. To carry out these tasks, tools such as Jupyter Notbooks and its data analysis libraries and Microsoft Excel were used. After this, an analysis of the main findings was carried out, among which a notable difference in the distribution of Cars, Buses and Trucks can be highlighted for the two road corridors studied, as well as the discrepancies between the vehicular flows of trucks, which supports the importance of carrying out this type of study to know the general conditions of a road section under study and determine what could be the possible demands of vehicles that may appear in them, to carry out road infrastructure designs according to the needs of each road section. [ABSTRACT FROM AUTHOR]

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