e-cienciaDatos
Not a member yet
1765 research outputs found
Sort by
Effort Project. Child experimental data and associated survey data (multiple-imputed data, m = 10)
No full textChild experimental data and associated survey data (multiple-imputed data, m = 10): An R list of ten dataframes that contain imputed values for the principal explanatory variables of the analysis conducted for Radl et al. (2025). Missing data exist for some students because surveys were occasionally returned incomplete or illegible. Multiple imputation was conducted using multilevel joint modeling using the jomo.smc wrapper function in the R package jomo (v.2.7.6) (Quartagno and Carpenter 2023). See Radl et al. (2025) for more specification on how the imputation was conducted
Smart Rural IoT Lab - Dataset de Tráfico Benigno IoT # Fase 1 #
No full textEl dataset contiene exclusivamente tráfico de red benigno, capturado durante el funcionamiento normal del Smart Rural IoT Lab,
sin inyección de ataques ni generación de tráfico malicioso.
Está compuesto por 13 sesiones de captura independientes, correspondientes a diferentes jornadas de operación normal del laboratorio.
Cada sesión se almacena en una carpeta separada, siguiendo una nomenclatura basada en la fecha de captura.
Características principales:
- Tráfico real.
- Backend remoto (CPD universitario).
- Entorno controlado.
- Tráfico exclusivamente benigno.
- Dataset de referencia (baseline)
High school students' views on cinema-based EFL instruction: Quantitative and qualitative data from the questionnaire at the pre- and post- test times, and analyses performed
No full textSe presentan los datos recogidos de estudiantes de educación secundaria (n=48) con el objetivo de explorar su actitud acerca de la implementación del cine en las clases de inglés. Los datos se obtuvieron mediante un cuestionario, que incluía características demográficas, una escala Likert, y una parte de respuesta abierta debajo de cada item para que los participantes justificasen sus selecciones de la escala. El cuestionario se administró dos veces, antes y después de la intervención. Se presentan varios documentos: dos documentos Excel y un archivo SPSS con los datos, un documento de Nvivo con la información cualitativa, y un documento word con los análisis estadísticos realizados
Dataset Descripciones de Imágenes en Arqueología Griega
No full textEste dataset se enmarca dentro del trabajo "Creación de un Modelo de Descripciones de Imágenes Especializado
en Arqueología Griega". Un modelo de descripción de imágenes especializado en arqueología griega, que combina técnicas avanzadas de Computer Vision y Natural Language Processing para generar descripciones más precisas y contextualizadas en un ámbito altamente especializado. El proyecto abre la puerta a nuevas aplicaciones de la IA en el estudio, documentación y divulgación del patrimonio cultural
Dataset on the literature surrounding the use of BARS instruments in education
No full textThis dataset is linked to a research project that conducts a systematic literature review on the use of BARS-type instruments to measure efficiency in various work contexts, and subsequently, specifically, to assess teacher efficiency in educational settings.
Variables: Publication Type, Authors, Article Title, Source Title, Publication Year, ISSN/eISSN, Volume, Issue, DOI, DOI Link, UT (Unique Web of Science ID)
Dataset y Benchmark reproducible de lenguaje abusivo en español (IID vs OOD-LODO)
No full text
Benchmark reproducible (español) + harness de evaluación
Este repositorio proporciona un benchmark reproducible para detección de lenguaje abusivo en español,
diseñado para comparar modelos bajo protocolos cerrados y con trazabilidad por artefactos.
El foco es medir robustez cross-dataset (IID vs. LODO) de forma auditable, evitando decisiones implícitas.
Construido sobre
Dataset unificado (data/all.csv) con contrato explícito:
esquema canónico, procedencia (dataset_id, dialect_region) y mapeo trazable
de raw_label a un objetivo binario y.
Protocolos versionados como especificaciones:
P1 (IID, split fijo) y P2 (LODO, Leave-One-Dataset-Out por dataset_id).
Manifests deterministas (manifests/) que materializan splits y folds de forma estable.
Runs auditables (runs/<run_id>/) y reportes generados
leyendo artefactos (sin recomputación ad hoc de métricas).
No es
No es un producto de moderación desplegable.
No es un benchmark para inferir causalidad dialectal:
dialect_region es colineal con dataset_id en este release y se usa como descriptor operacional.
</section
Adaptation of Proactive Career Behavior Questionnaire to distance undergraduate students
No full textThis dataset contains responses from 934 Spanish distance higher education students who completed the adapted version of the Proactive Career Behavior Questionnaire (Strauss et al., 2012). The study aimed to validate the questionnaire in this context, analyzing its factorial structure, reliability, and gender invariance. The dataset includes demographic variables (gender and year of birth) and measures related to proactive career behavior, resilience, future orientation, and personal initiative. These variables were examined in relation to proactive career behaviors such as career planning, skill development, career consultation, and network building
Dataset on student evaluations of a BARS questionnaire designed for face-to-face teaching modalities
No full textThis dataset is linked to a research project analyzing the validity and reliability of a BARS questionnaire designed to measure teaching effectiveness in face-to-face teaching modalities, using a sample of students from three Spanish universities.
Variables: Age, Gender, University, 1. Introduction to the subject, 2. Description of the assessment system, 3. Time management, 4. General availability, 5. Organizational coherence, 6. Implementation of the assessment system, 7. Answering questions, 8. Explanatory capacity, 9. Ease of follow-up, 10. Overall satisfaction
Green and Emerging Microextraction Strategies for Bioanalytical Determination of Hormones: Trends, Challenges, and Applications
No full textAccurate and sensitive determination of hormones in biological matrices is essential for
clinical diagnostics, therapeutic monitoring, and endocrine research. However, hormone
determination presents significant challenges due to their typically low concentrations,
complex sample matrices, and structural diversity. In recent years, microextraction techniques
have emerged as strategic tools in bioanalytical chemistry, offering advantages in
terms of miniaturization, enhanced selectivity, and compatibility with the principles of
green analytical chemistry (GAC). This review provides a comprehensive overview of
green and emerging microextraction approaches for the determination of steroidal, thyroid,
peptide, and other hormones in biological samples. Key techniques such as solid-phase microextraction
(SPME) and dispersive liquid–liquid microextraction (DLLME), followed by
high-performance liquid chromatography (HPLC) coupled to diode array detectors (DADs)
or mass spectrometry (MS), are critically discussed. Special emphasis is placed on the use of
environmentally friendly solvents, such as deep eutectic solvents (DESs), supramolecular
solvents (SUPRASs), and advanced sorbents including molecularly imprinted polymers
(MIPs) and nanostructured magnetic phases. Applications across various bioanalytical
matrices (urine, plasma, serum, saliva, tissues. . .) are examined in terms of sensitivity, selectivity,
and validation parameters. Finally, current challenges, method development gaps,
and future directions are highlighted to support the continued advancement of sustainable
hormone determination in complex biological systems
LAN-SDN-NIDS: A Multi-Class Dataset for OpenFlow Attack Detection
No full textSynthetic, tabular data for benign (normal) traffic and five attack-types (link fabrication, host hijacking, host injection, ddos, and portscan) in a LAN-SDN, emulated with mininet and the OpenDaylight controller. The data has 36 features, including OpenFlow-dependent values. There are a total of six CSV files, corresponding to five different experiments —each with a different topology— and a sixth file combining four topologies, which we recommend for training a machine learning model. The combination (LAN_SDN_NIDS.csv) includes all the experiments except the star topology