Resultados de investigación

The data refer to items from the constructs of the structural equation research model associated with the article. The items define constructs related to experience quality, perceived value, emotions, and visitor satisfaction in the cities of York (United Kingdom) and Seville.

The PaGER-Sync ADICVIDEO dataset is a multimodal, temporally synchronized repository of physiological and facial expression data recorded during controlled, immersive video game sessions designed to simulate realistic home gaming environments. It integrates biosignals from the Empatica E4 wristband —including Electrodermal Activity (EDA), Blood Volume Pulse (BVP), and Skin Temperature (TEMP) — with facial expression features extracted from video recordings using FaceReader software. Additionally, the dataset includes scores from pre-session psychometric questionnaires (Gaming Addiction Scale, Scale of Positive and Negative Experience, Emotion Regulation Questionnaire) and demographic gender data, providing psychological and individual difference context. A summary file detailing the two strongest emotions expressed by each participant with their respective percentages is included. A total of 25 participants played three commercial video games (Tetris, Sonic Racing, and Fall Guys) under controlled conditions, while their physiological responses were continuously recorded and their facial expressions captured on video for subsequent analysis. All data streams were precisely aligned using a common video-based timestamp, enabling frame-level synchronization across modalities, and the data were segmented by game. The dataset supports a wide range of research applications in affective computing, human-computer interaction, and behavioral analysis, and is particularly well-suited for the development and evaluation of multimodal affect detection models, as well as for exploring the interplay between psychological traits and real-time emotional responses., The PaGER-Sync dataset is meticulously organized into .xlsx spreadsheet files, structured to facilitate both general overview and detailed per-participant and per-trial analysis. Raw video recordings of participants are not provided due to privacy restrictions stipulated by the ethical committee approval. The dataset comprises the following key components: _Video_datetime_UTC.xlsx: A global file providing session-level metadata for all trials: Participant: An anonymous ID consisting of two letters and three numbers, self-assigned. Trial: A numerical identifier for each specific gameplay session. Game: A unique identifier for the video game played in that session (values: "Tetris", "Sonic", "FallG" for Fall Guys). Video_UTC_dateTime: Exact UTC start timestamp of the video recording (DD/MM/YYYY HH:MM:SS). This timestamp serves as a reference for synchronizing all modalities. Raw Physiological Data Files (per Participant): For each participant, separate .xlsx files contain the raw, unsynchronized values of their physiological signals across all their trials. These files include: Participant_eda.xlsx: Electrodermal Activity (EDA) values with corresponding UTC timestamps Participant_bvp.xlsx: Blood Volume Pulse (BVP) readings with UTC timestamps. Participant_temp.xlsx: Skin Temperature (TEMP) measurements with UTC timestamps. Synchronized Emotion Video Data Files (per Trial): For each individual trial (gameplay session) of a participant, a comprehensive Participant_Trial_Video_Data.xlsx file is generated. This file represents the core of the synchronized dataset, containing: Timestamp: Seconds from the start of the video recording. State: Dominant emotion classified by FaceReader 9.1 (e.g., "Happy", "Sad", "Neutral"), validated by expert review. Valence and Arousal: Continuous numerical scores. Facial Action Units (AUs): Intensity values for the 18 specific Action Units detected by OpenFace 2.2, at 20 FPS. _Emotions_Summary.xlsx: Participant (Alphanumeric): An anonymous ID consisting of two letters and three numbers, self-assigned, allowing this file to be linked with existing multimodal data. Emotion_1 (Categorical): The most dominant or frequently expressed emotion by the participant throughout their gaming sessions, according to FaceReader analysis. Emotion_1_Ratio (Numeric): The percentage of time or intensity with which Top_Emotion_1 was expressed for that participant. Emotion_2 (Categorical): The second most dominant or frequently expressed emotion by the participant. Emotion_2_Ratio (Numeric): The percentage of time or intensity with which Top_Emotion_2 was expressed for that participant. _Psychometric and Demographic Data.xlsx: Participant (Alphanumeric): An anonymous ID consisting of two letters and three numbers, self-assigned, allowing this file to be linked with existing multimodal data. Gender (Categorical): Self-reported gender of the participant (Male or Female). GASA_Total_Score (Numeric): Total score from the Gaming Addiction Scale (GAS), a 7-item questionnaire assessing problematic video game use. GASA_Problematic_Use_Indicator (Binary): Binary indicator (0 or 1) derived from the GAS score, where 1 typically denotes problematic or addictive use (a score of 3 or higher on at least four items). SPANE_Positive_Affect_Score (Numeric): Score from the Positive Affect subscale of the Scale of Positive and Negative Experience (SPANE-P), measuring the frequency of positive feelings. Range: 6 to 30. SPANE_Negative_Affect_Score (Numeric): Score from the Negative Affect subscale of the Scale of Positive and Negative Experience (SPANE-N), measuring the frequency of negative feelings. Range: 6 to 30. SPANE_Affect_Balance_Score (Numeric): Score from the Affect Balance subscale of the Scale of Positive and Negative Experience (SPANE-B), calculated as SPANE-P minus SPANE-N. Range: -24 to 24 ERQ_Cognitive_Reappraisal_Score (Numeric): Score from the Cognitive Reappraisal subscale of the Emotion Regulation Questionnaire (ERQ), measuring the tendency to change one's thinking about a situation to regulate emotions ERQ_Expressive_Suppression_Score (Numeric): Score from the Expressive Suppression subscale of the Emotion Regulation Questionnaire (ERQ), measuring the tendency to inhibit the behavioral expression of emotions.

Los datos se han obtenido mediante fotointerpretación directa de las fuentes de datos para toda el área de estudio a escala 1:2.500. La información temática deriva del modelo de datos de línea de costa del Grupo de Investigación RNM177 “Ordenación del Litoral y Tecnologías de Información Territorial” y consta de una clasificación tipológica jerárquica de la línea de costa, presencia y tipología de infraestructuras costeras y base toponímica oficial. Las tasas de erosión han sido calculadas mediante la herramienta Digital Shoreline Analysis System -DSAS- (v. 5.1) desarrollada por el US Geological Survey, utilizando transectos con un muestreo espacial de 50 m., Base de datos geográfica puntual en formato de texto (.csv) con información temática de la línea de costa y el resultado de las tasas de erosión (m/año) de la costa expuesta de Andalucía para el período general 2001-2019 y para los subperíodos 2001-2001 y 2011-2019. Para su correcta representación, se adjuntan campos con las coordenadas XY de cada punto de muestreo. Cumpliendo con el Real Decreto 1071/2007, las coordenadas vienen expresadas en ETRS89, proyectadas en UTM para el Huso 30 norte.

Datos brutos correspondientes a los experimentos 1, 2 y 3 del artículo titulado "Anxiolytic and Sedative Effects of Sodium Valproate with Different Experimental Paradigms in Male and Female Rats"., Raw data correspondent to experiments 1, 2 y 3 and from the paper entitled "Anxiolytic and Sedative Effects of Sodium Valproate with Different Experimental Paradigms in Male and Female Rats".

Los datos recopilan los registros de diferentes contaminantes a lo largo del experimento

This dataset contains a set of anonymized and labeled HTTP requests (selected fields) from the logs of a real-in-production web server at the library of the University of Seville during 6.5 months in 2017. The dataset has been sanitized using a supervised methodology as proposed in: - Díaz-Verdejo, Jesús E.; Estepa, Antonio; Estepa, Rafael; Madinabeitia, German; Muñoz-Calle, Javier, "A methodology for conducting efficient sanitization of HTTP training datasets", Future Generation Computer Systems, vol. 109, pp. 67–82, 2020. https://doi.org/10.1016/j.future.2020.03.033., The dataset is organized in a tree structure (subdirectories) each containing different types of files or sets. As provided, 5 sets of files and two partitioning schemes are considered. The partition files are not directly provided but can be generated from the files using the provided script. The following sets of files (subdirs) are included: - RAW files: Initial registers (obtained after preprocessing and anonymization of real captured files). - LABEL files: Labels assigned during analysis. - CLEAN files: Registers considered as clean after sanitization. This is the full dataset to be used as normal traffic. - SID files: Information about SIDs triggered by used SIDS tools. - ATTACK files: Registers classified as attack (only LVL1 -indubituous- attacks). Registers in each set are organized in daily bins (files) named as biblio-2017--

., being the number of the month,

the day and an extension related to the type of content: - .raw for RAW files - .lbl for LBL files - .cl for CLEAN files - .sid for SID files - .att for ATTACK files

Los datos hacen referencia a los ítems de los constructos del modelo de investigación de ecuaciones estructurales asociado al artículo. Los ítems definen los constructos relativos a la autenticidad objetiva, autenticidad existencial y a la satisfacción de visitantes en la ciudad de York (Reino Unido), The data refer to items from the constructs of the structural equation model associated with the article. The items define constructs related to objective authenticity, existential authenticity, and visitor satisfaction in the city of York (United Kingdom).

La evaluación en niños suele basarse en informes, por lo que el diagnóstico es controvertido. Para resolver esta cuestión, el objetivo de este estudio fue desarrollar una nueva herramienta para detectar posibles problemas de atención y conducta impulsiva en niños de 4 y 5 años. Esta herramienta, desarrollada como aplicación para Android, podría utilizarse como indicador temprano de posibles problemas de desarrollo futuros. Se seleccionó aleatoriamente una muestra de 103 niños (48 niñas y 55 niños) de escuelas primarias y se evaluó mediante Pinky-Piggy, una aplicación de videojuego basada en un paradigma clásico de la psicología experimental. Los datos de esta aplicación se compararon con un Cuestionario de Madurez Neuropsicológica Infantil. Los sujetos mostraron diferentes patrones de respuesta al jugar a un juego muy sencillo llamado Pinky-Piggy. La aplicación discriminó entre los participantes con alta y baja respuesta. Los resultados mostraron una relación entre estos dos perfiles y los niveles de atención y neurodesarrollo en cada grupo. La herramienta pudo identificar diferentes tipos de perfiles y demostró su potencial para evaluar endofenotipos y predecir problemas de atención relacionados con la conducta impulsiva. Además, requirió menos tiempo y menos pruebas para identificar posibles poblaciones en riesgo, lo que ayudó en el diagnóstico clínico.

En este dataset se recogen los modelos 3D de células de cáncer de mama de la línea MDA-MB-231 embebidas en hidrogeles de colágeno de diferente composición y concentración. Estos modelos 3D se crearon mediante el software Paraview, a partir de imágenes tomadas en un microscopio confocal (Leica Stellaris 8) en un stack de Z., Se incluyen archivos de paraview de una célula en cada tipo de hidrogel (colágeno bovino, de rata o mezcla de ambos) y cada concentración (0.8, 1.5 y 2.3 mg/ml).

La base de datos recoge los ítems de los constructos implicados en el modelo de investigación del artículo asociado. Los datos fueron recogidos de hoteles chinos en 2019 En la pestaña Series se describen los ítems de los constructos. En la pestaña DATOS, están recogido los datos de los indicadores correspondientes al año 2019. El modelo de investigación permite identificar la influencia de la percepción de CSR, reputación del hotel sobre los distintos tipos de performance (Accounting-based performance; Market-based performance; Non-financial performance)