Resultados de investigación

This dataset contains the responses collected from teachers through a structured questionnaire designed to analyse their perceptions, attitudes, and behavioural intentions regarding the use of Generative Artificial Intelligence (GenAI) tools in educational contexts. The dataset includes sociodemographic variables and questionnaire items measured using Likert-type scales.

Supplementary vesicles dataset: curated proteomic tables of extracellular vesicle-associated proteins from Pseudomonas savastanoi pv. savastanoi, Pseudomonas syringae pv. tomato, and Pseudomonas syringae pv. phaseolicola

These data derived from the proyect PID2020-115954RB-I00: Interacción entre la virulencia de nodavirus y la respuesta inmune innata en dorada y lubina (INTERNODASS), and they have been published in: Moreno P, J Gemez-Mata, D Alvarez-Torres, E Garcia-Rosado, J Bejar, MC Alonso (2023) Genomic characterization and transcription analysis of European sea bass (Dicentrarchus labrax) rtp3 genes. Molecular Immunology, 163, 243-248. (doi: 10.1016/j.molimm.2023.10.009) Moreno P, D Alvarez-Torres, E Garcia-Rosado, J Bejar, MC Alonso (2026) Functional assessment of European sea bass RTP3 proteins and their effect on RGNNV replication. Fish and Shellfish Immunology, 168, 111002 (https://doi.org/10.1016/j.fsi.2025.111002), European sea bass rtp3 genes: in vivo transcriptional study and in vitro functional characterization

This dataset contains aerial images of cellular base stations captured using an Unmanned Aerial Vehicle (UAV). The images are used to train and evaluate YOLO-based object detection models for automatic identification of base station infrastructure.

Este archivo contiene el código en R (script) necesario para reproducir el análisis metodológico del estudio sobre los predictores contextuales del rendimiento académico (matemáticas, lectura y ciencias) y el pensamiento creativo en la muestra española de PISA 2022. El pipeline analítico está diseñado específicamente para manejar arquitecturas de datos de evaluaciones internacionales a gran escala e incluye tres fases principales: 1. Imputación múltiple no paramétrica de valores perdidos mediante el algoritmo missForest. 2. Selección de características (cribado) mediante regresión LASSO con validación cruzada, iterada sobre 10 Valores Plausibles (Plausible Values) para garantizar la selección por estabilidad. 3. Clasificación de importancia de variables (Permutation Importance) mediante modelos de aprendizaje automático Random Forest, incorporando los pesos de muestreo oficiales de los estudiantes de la OCDE (W_FSTUWT) para asegurar la representatividad poblacional. Requisitos técnicos: • Lenguaje: R (versión 4.0 o superior) • Librerías requeridas: missForest, glmnet, ranger, dplyr, doParallel. Notas de uso: Por motivos de licencias y políticas de distribución, la base de datos original no se adjunta con este código. Para ejecutar el script, los investigadores deben descargar la base de datos pública de PISA 2022 desde el repositorio oficial de la OCDE (https://www.oecd.org/pisa/data/2022database/) y ajustar la ruta del archivo de entrada en el código. Palabras clave: PISA 2022, Pensamiento Creativo, Machine Learning, Random Forest, LASSO, Valores Plausibles, Educación. Replication Code: Predictors of Academic Performance and Creative Thinking in PISA 2022 Spain via LASSO and Random Forest Analyses. This file contains the R code (script) required to replicate the methodological analysis of the study examining contextual predictors of academic performance (mathematics, reading, and science) and creative thinking in the Spanish PISA 2022 sample. The analytical pipeline is specifically tailored to handle complex data structures from international large-scale assessments and comprises three main stages: 1. Non-parametric multiple imputation of missing values using the missForest algorithm. 2. Feature selection (screening) via LASSO regression with cross-validation, iterated across 10 Plausible Values to ensure stability selection. 3. Variable importance ranking (Permutation Importance) using Random Forest machine learning models, incorporating the official OECD student sampling weights (W_FSTUWT) to ensure population representativeness. Technical Requirements: • Language: R (version 4.0 or higher) • Required packages: missForest, glmnet, ranger, dplyr, doParallel. Usage Notes: Due to licensing and distribution policies, the original dataset is not bundled with this code. To execute the script, researchers must download the public PISA 2022 database from the official OECD repository (https://www.oecd.org/pisa/data/2022database/) and specify the input file path in the script. Keywords: PISA 2022, Creative Thinking, Machine Learning, Random Forest, LASSO, Plausible Values, Education.

“You are not the person you used to be…”. A descriptive phenomenological study on sexual desire in heterosexual women with fibromyalgia síndrome

Actividad de enzimas asociadas al tracto gastrointestinal de doradas alimentadas con dietas en las que parcialmente se sustituyó la harina de pescado con harina de insecto (Hermetia illucens) procesada según diferentes protocolos en lo referente a las etapas de sacrificio, desecado y desgrasado.

This dataset contains the experimental data collected for a study investigating the psychological and attentional effects of biophilic urban interventions using immersive Virtual Reality (VR). The data was obtained from a within-subjects experimental design involving 16 participants who viewed fixed-viewpoint 360° video footage of urban environments in Almería (Spain). The stimuli represent three distinct socioeconomic statuses (Low, Medium, and High SES), presented under two experimental conditions: Original (baseline) and Bio-aesthetically Enhanced. The dataset includes processed variables suitable for Repeated Measures ANOVA (RM-ANOVA) and correlational analysis, specifically: Psychometric/Subjective Measures: Positive and Negative Affect (PANAS); Perceived Stress and Relaxation ratings; Aesthetic Evaluation and Subjective Liking scores. Oculomotor/Eye-Tracking Metrics: Fixation Count and Average Fixation Duration; Saccadic Velocity and Amplitude; Time to First Fixation (TTFF) and Average Dwell Time; Area of Interest (AOI) Coverage. These data support the findings regarding the "equigenic" potential of biophilic design and the "nature gaze" mechanism in immersive environments. The files are provided in XLSX format.