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    Contra la bibliometría “rápida y sucia”: aspectos para valorar la complejidad en los análisis bibliométricos
    (Escuela de Comunicación - CampCM, 2025-06-30)
    Rafael Repiso
    ;
    Álvaro Cabezas-Clavijo
    El presente trabajo denuncia el auge indiscriminado de estudios bibliométricos de baja calidad que, aprovechando la accesibilidad de bases de datos como Web of Science o Scopus y el uso de herramientas automatizadas, proliferan en revistas científicas de diversas disciplinas. Esta tendencia, facilitada por la ausencia de conocimientos especializados de los revisores de revista y por el interés en incrementar rápidamente la productividad académica, ha derivado en la publicación de investigaciones metodológicamente pobres, con escasa elaboración conceptual y valor analítico limitado. Frente a este panorama, los autores reivindican la bibliometría como un campo de alta especialización que exige comprensión teórica, competencia metodológica e interpretación crítica de los datos. Con base en estos principios, se propone una aproximación metodológica para valorar la complejidad de los estudios bibliométricos a partir de seis dimensiones: tamaño de la población, origen y fuente de los datos, forma de recolección, grado de normalización, tipos de análisis empleados y herramientas utilizadas. Esta perspectiva metodológica pretende ofrecer a editores e investigadores un marco para identificar investigaciones sustantivas y distinguirlas de aquellas realizadas con poco esfuerzo, criterio y contexto teórico.
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    The effectiveness of curriculum standardization in data analysis and tools proficiency for undergraduate education: a case study
    (Frontiers Media SA, 2025)
    DelaTorre-Diaz, Lorena  
    ;
    Ramírez Pérez, Héctor Xavier  
    ;
    Escobar Castillejos, David  
    Introduction: The rapid evolution of technology necessitates the development of advanced computing and data analysis skills in undergraduate education. Standardizing curricula is a strategy to ensure consistent learning outcomes and align educational objectives with industry requirements. This study investigates the impact of a standardized curriculum on students' academic performance and professional certification outcomes. Methods: A quasi-experimental design was used to analyze 1,597 students enrolled in a data analysis course before and after implementing a standardized curriculum at a private university in Mexico City. The study assessed course grades and certification exam scores to evaluate the effectiveness of standardization. Parametric and non-parametric tests were applied to ensure robust analysis. Results: Implementing the standardized curriculum resulted in a slight decrease in average course grades but significantly improved certification exam scores, exceeding the threshold for certification. The findings highlight enhanced proficiency in data analysis tools and consistency in achieving educational objectives across groups. Discussion: The results suggest that curriculum standardization effectively addresses teaching methodologies and assessment criteria discrepancies. While increased curriculum difficulty temporarily impacted grades, the improved certification outcomes demonstrate the value of standardization in preparing students for industry demands. These insights provide a foundation for future curriculum development to align academic instruction with the evolving requirements of a technology-driven workforce. ©The authors ©Frontiers Media.
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    Sales predictive analysis for improving supply chain drug sample
    (Elsevier BV, 2025)
    Téllez-Ballesteros, Susana Casy
    ;
    Torres-Mendoza, Ricardo
    ;
    Marmolejo-Saucedo, José Antonio
    ;
    Rodríguez Aguilar, Román  
    The delivery of drug samples allows increasing sales of pharmaceutical products [6]. However, we discovered some problems that can be improved in the supply chain that delivers drug samples (used for the treatment of excess glucose). Databases were integrated; then we apply data extraction and transformation; and finally we apply multiple regression analysis to explain drug sales. The first analysis evaluates the integration of regional data and the second analysis refers to data dis-aggregated by region. We identify the region with the greatest impact on sales and the impact of the delivery of drug samples in the Mexican market. ©The authors ©Elsevier ©Procedia Computer Science.
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    Application of parametric activation function A string in the task of multimodal data analysis
    (AIP Publishing, 2023)
    Verina, Yana V.
    ;
    Tolstoukhov, Denis E.
    ;
    Pérez-Daniel, Karina Ruby
    ;
    Egorov, Dobroslav P.
    ;
    Kravchenko, Oleg V.
    Data analysis is a dynamically developing field, currently. One of the actual tasks of data analysis is the task of classification. The problem of dividing a specific group of objects into a predetermined number of groups united in various ways is also important. On the other hand, computational performance increases and the volume of observed data increases, therefore, assigning them to certain subgroups becomes more complicated. In this paper, the binary classification problem is solved and a new parametric activation function for the machine learning model under consideration is analyzed. An important difference between the proposed classifier, for example, from standard classifiers based on logistic regression, is the connection with infinitely differentiable splines, the so-called atomic functions. At the same time, it is of interest to study the dependence of the classifier quality on the value of the variable parameter of the activation function. By changing the parameter from the activation function, you can make dependencies on the quality of the presented classifier. The comparison of quality indicators with various parameters of the activation function is considered. As data for model training, cross-validation and testing, an open multimodal data set MELD was used, consisting of a parallel set of videos and their textual interpretation. It is worth noting that MELD is a pre– labeled data corpus. The data were divided into two categories of sentiment analysis: positive and negative. A comparison of the work of a classifier based on parametric AString and logistic regression is given.
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