Resultados de investigación

Data are stored in the following folder: ./ATIC_Database_Flauta ---------------------- The audio files in the data set are named following a descriptive convention to allow simple visual identification of their content as well as easy file indexing. The general structure of the name of each audio file follows this convention: FLT_###_EEE_DDD_XXX-$-###-$-%%%.wav where #, $, % represent integer numbers, E, D stand for defined character strings and X can represent both numbers and characters according to the table bellow. The file names have been coded in such a way that it will be possible to code all the transverse flute techniques currently in use. Next, the different fields, their meaning, their value range and what those values mean are explained. ------------------------------------------------------------------------------------------------------- | Field description | Value and Range | Current values in use ------------------------------------------------------------------------------------------------------- FLT | Transverse Flute | FLT | ------------------------------------------------------------------------------------------------------- ### |Flautist/Flute | 000 to 999 | 000, 001, 002, 005, 006, 007 ------------------------------------------------------------------------------------------------------- EEE | Technique | ARM: Harmonics | ARM,NOR,VBR | NOR: Regular technique | | VBR: Vibrato | | FRU: Frulatto | | WHT: Whisper tones | | EDT: Trumpet mouthpiece | | CYT: Sing while play | | MTF: Multiphonics | | PZZ: Pizzicato | | RLL: Key noise | | TGR: Tongue Ram | | SEO: Wind sounds | | GLS: Glissando | | JWT: Jet whistle | ------------------------------------------------------------------------------------------------------- DDD | Dynamics | DPP: Pianissimo | DMF | DDP: Piano | | DMF: Mezzoforte | | DDF: Forte | | DFF: Fortissimo | | DDD: Don’t apply | ------------------------------------------------------------------------------------------------------- XXX | Specific characteristics | ARM/MTF/RLL/TGR case: 000 to 028 (fingering, see [2]) | of every technique | VBR case: SSS Slow vibrato, FFF fast vibrato and | | XXX undetermined ------------------------------------------------------------------------------------------------------- $ | Additional note alteration, | 0: Flat of don't apply | 0 | in case ir exists | 1: Sharp | | 2: Bemol | | 3: Double sharp | | 4: Double bemol | ------------------------------------------------------------------------------------------------------- ### | MIDI number of the additional note | Number 059 to 102 notes | 000 | 000 if only one note is played | ------------------------------------------------------------------------------------------------------- $ | Sounding note alteration | 0: Flat | or final note (only for glissando) | 1: Sharp | 2: Bemol | 3: Double Sharp | 4: Double bemol ------------------------------------------------------------------------------------------------------- %%% | MIDI number or the sounding note | 000 and 059 to 102 ------------------------------------------------------------------------------------------------------- Note that the case the harmonic notes is a special one. In this case, it is necessary to include not only the note number (MIDI number) but also the fingering used by the flutist. This corresponds to the field denoted XXX: Specific characteristics of every technique. The notation employed follows the descriptions in [2], where the first (top left) fingering is assigned the number 000 and the last (bottom right) fingering is assigned the number 028. One of the identifying parameters of the flute samples is the ‘Flautist/Flute’ number. This number ranges from 000 to 999 to account for a wide range of data samples. Specifically, the flutes employed in each of those samples are: • Flute 000: Yamaha 371. • Flute 001: Yamaha YFL2215. • Flute 002: unknown model. • Flute 005: Powell Boston 2100. • Flute 006: Muramatsu EX-III-RCE Flute. • Flute 007: Muramatsu EX-III-RCE Flute. Note that the flutist remains unidentified; each combination of flute and flutist receives a unique number for identification., Acknowledgement: This work was possible thanks to the work and flute recordings provided by Mariana Fernández-Astaburuaga, María del Carmen Rodríguez-Martínez, Carlos Samuel Villalba and German Romero Alba. The authors are grateful to the selfless collaborating flutists for their valuable contribution to this work. This publication is part of project PID2021-123207NB-I00, funded by MCIN/AEI/10.13039/501100011033/FEDER, UE, and PID2024-156798OB-I00., This database contains audio recordings of flute notes played with regular style and with vibrato and harmonic techniques. These recordings were made by four different flute players, all of whom have completed a professional degree in transverse flute. A total of 246 flute notes samples have been obtained. With regard to recording conditions, a quiet home environment was used and the built-in microphone in each flautist’s laptop was used. Audacity was employed as recording software, configured with a sampling frequency of 44100Hz, 16 bits, and monophonic. If these data are used for any publication, it should be cited as follows: : Ana M. Barbancho, Lorenzo J. Tardón, Isabel Barbancho, "ATIC Database for Analysis and Detection of Flute Techniques", ATIC Research Group, ITIS Software, Universidad de Málaga, 2026., MCIN/AEI/10.13039/501100011033/FEDER, UE, Research Group TIC208, This publication is part of project PID2021-123207NB-I00, funded by MCIN/AEI/10.13039/501100011033/FEDER, UE, and PID2024-156798OB-I00. This work was done at Universidad de Málaga, Research Group TIC208 (Universidad de Málaga, Junta de Andalucía, FEDER UE: TIC208-G-FEDER), Campus de Excelencia Internacional Andalucía Tech.

Variable / Descripción ID Código identificador de la jugadora FNAC Fecha de nacimiento ALTURA (m) Estatura en metros PESO (kg) Peso corporal en kilogramos IMC Índice de Masa Corporal KGMUSC Kilogramos de masa muscular KGGRASA Kilogramos de masa grasa %GRASA Porcentaje de grasa corporal POSICIÓN Posición de juego (Portera, Extrema, 1ª línea, etc.) DOMINANCIA Lateralidad dominante (Izquierda/Derecha), La base de datos BBDD_Handball.xlsx contiene información de 33 jugadoras de balonmano e incluye un total de 68 variables organizadas en dos grandes bloques: variables descriptivas y variables electromiográficas. El primer bloque recoge datos identificativos y antropométricos como ID, fecha de nacimiento, altura (m), peso (kg), índice de masa corporal (IMC), kilogramos de masa muscular (KGMUSC), kilogramos de masa grasa (KGGRASA), porcentaje de grasa corporal (%GRASA), posición de juego y dominancia lateral. El segundo bloque corresponde a registros electromiográficos (EMG), donde se evalúa la actividad muscular mediante valores RMS (Root Mean Square) y MVC (máxima contracción voluntaria) en cuatro músculos (aductor derecho (AHD), aductor izquierdo (AHI), glúteo derecho (GD) y glúteo izquierdo (GI)) bajo siete condiciones experimentales: isometría máxima voluntaria, sentadilla sin cuñas sin biofeedback, sentadilla sin cuñas con biofeedback, sentadilla con cuñas de 10° sin y con biofeedback, y sentadilla con cuñas de 30° sin y con biofeedback. Las variables son mayoritariamente cuantitativas continuas (antropometría y EMG), mientras que ID, posición y dominancia son cualitativas, permitiendo analizar la activación muscular en función de las condiciones experimentales y las características físicas y deportivas de las jugadoras.

The raw dataset as well as the refined comparisons among experimental conditions are distributed in one EXCEL file (hippocampal_proteomics_results_MSJUL22.xlsx). Volcano plots for every comparison can also be found distributed in the pdf file (volcano_plots_hippocampal_proteomics_MSJUL22.pdf)., Proteomic analysis was performed on left hippocampal tissue from C57BL/6J mice subjected to maternal separation during infancy (MS), acute restriction stress in adulthood (RS), or combined MS+RS, compared to controls (CTRL). N=5-6 per group, males and females separately. Hippocampi were collected 24h post RS stress (P62-P65), homogenized in RIPA buffer, quantified by BCA, and digested via gel-assisted proteolysis with trypsin. Samples underwent UHPLC-HRMS on a Q-Exactive HF-X , acquiring MS/MS spectra (SCAI-Universidad de Málaga). Raw data were searched in Proteome Discoverer 2.5 against Mus musculus UniProt (2025-02), yielding 2,895 high-confidence proteins (FDR<1%, ≥2 peptides, master proteins). Label-free quantification used precursor intensities, normalized by total peptide amount; ANOVA identified upregulated (p<0.05, log2 FC≥1) and downregulated proteins (p<0.05, log2FC ≤-1) in the following comparisons: RS vs CTRL, MS vs CTRL, MS+RS vs CTRL, RS vs MS+RS, MS vs MS+RS (males and females, separately). Raw proteomic data as well as volcano plots are distributed in RIUMA (Universidad de Málaga)., Ministerio de Ciencia, Innovación y Universidades (MCIN) de España y Agencia Estatal de Investigación (AEI): MCIN/AEI/10.13039/501100011033, Consejería de Universidad, Investigación e Innovación de la Junta de Andalucía.

The dataset's structure and preliminary behavioral analyses are detailed, and specialized visualization methods illustrate how different presentation modalities influence cross-modal affective perception. Cortical analyses reveal increased power in parieto-occipital and temporal regions, which are known to mediate visual-emotional processing and multisensory integration, thereby corroborating established neurophysiological findings. The audiovisual condition produced superadditive cortical activation relative to a synthetic additive baseline, confirming genuine multisensory convergence. Overall, this dataset offers a robust large-scale baseline that researchers can employ to evaluate computational emotion models and to advance affective neuroscience using authentic artistic stimuli. The dataset can be downloaded from the following link: http://u.uma.es/h3S/EEG_ART_dataset, This open-access electroencephalography (EEG) dataset is designed to examine how artistic stimuli elicit emotional responses. The experimental protocol involved 20 artworks selected by domain experts and presented in three modalities (visual, auditory, and audiovisual) to systematically vary sensory input. The stimuli were chosen to cover a broad range of valence-arousal coordinates within Russell’s circumplex model, resulting in 60 distinct trials that included authentic pieces of art and musical pieces. High-density 64-channel EEG data were collected from 65 healthy participants, who also reported the discrete emotions they felt and rated the intensity of each experience after every trial., MCIU/AEI, FEDER, UE

Base de datos en la que se basa el artículo de investigación: Mental State Language in Children’s Persuasive Writing: Effects of Age and Audience., La persuasión implica la capacidad de influir en los estados mentales de otras personas para modificar su conducta y requiere habilidades lingüísticas y sociocognitivas que se desarrollan durante la infancia. Este estudio examinó el efecto de la edad del emisor y del tipo de relación con el destinatario en el uso de lenguaje de estado mental (LEM) en textos persuasivos escritos por niños/as en edad escolar. Participaron 117 niños/as de 7 a 12 años de Quito (Ecuador), quienes redactaron cuatro textos breves dirigidos a distintos interlocutores con el objetivo de convencerlos sobre una misma solicitud. Los términos de estado mental (cognitivos, volitivos, emocionales y morales) se codificaron siguiendo a Pinto et al. (2017) y se analizaron mediante ANOVA factorial mixto. Los resultados mostraron un aumento significativo del uso de LEM con la edad, especialmente en las categorías volitiva y moral. Aunque el destinatario no influyó en la frecuencia total de LEM, se observaron diferencias cualitativas asociadas a la simetría, familiaridad y autoridad del interlocutor. Estos hallazgos evidencian un desarrollo progresivo de la sensibilidad pragmática en la persuasión infantil.

This dataset contains responses collected from 450 preservice teachers through a structured questionnaire developed to investigate the factors influencing the perceived usefulness of Generative Artificial Intelligence (GenAI) tools in instructional material design. Specifically, the dataset includes measures of metacognitive skills, prior knowledge, problem-solving skills, intrinsic cognitive load, extraneous cognitive load, germane cognitive load, and perceived usefulness, together with sociodemographic information. All questionnaire items were measured using 7-point Likert-type scales. Each row represents one participant, and each column represents one variable.

Encuesta realizada por Soluciones Netquest de Investigación para Proyecto, Encuesta "Comportamiento de la audiencia ante las crisis alimentarias", Soluciones Netquest de Investigación, S.L.U., para el proyecto "Los grandes retos de la comunicación en el sector agroalimentario andaluz". (PPRO-B3-2023-19 (JA.B3-20), Junta de Andalucía, Plan Propio Universidad de Málaga

Datos del artículo Moreno-Ostos el al. (2026) The Albuferas de Adra in crisis. A comprehensive review demanding urgent conservation action, aceptado para su publicación en la revista Ecosistemas, 2026., Datos del artículo Moreno-Ostos el al. (2026) The Albuferas de Adra in crisis. A comprehensive review demanding urgent conservation action, aceptado para su publicación en la revista Ecosistemas, 2026.

General description This workbook contains the curated supplementary dataset associated with the comparative analysis of the T3SS-independent secretome (T3-IS) across three strains/pathovars of the Pseudomonas syringae complex: - Psv: Pseudomonas savastanoi pv. savastanoi NCPPB 3335 - Pto: Pseudomonas syringae pv. tomato DC3000 - Pph: Pseudomonas syringae pv. phaseolicola 1448A

This dataset contains EEG recordings affected by a periodic, extrinsic square‑wave artifact caused by the unintended activation of a hardware test function during acquisition. The dataset has been curated to support the evaluation and development of EEG artifact removal methods. Both raw (artifact-contaminated) and cleaned EEG data are provided, enabling direct comparison between corrupted and corrected signals. The dataset can be downloaded from the following link: http://u.uma.es/hQr/EEG_extrinsic_artifact_dataset/, This repository provides an EEG dataset for the removal of periodic extrinsic square-wave artifacts. The EEG data were recorded at a sampling rate of 2500 Hz using an actiCHamp-Plus amplifier and a 64-channel BrainVision actiCAP system (Brain Products). The recordings are inadvertently contaminated by a self-induced, experiment-related artifact caused by the unintended activation of a built-in hardware “test function” in the BrainVision Recorder software, resulting in a square-like signal at 1 Hz with an amplitude of approximately 200 µVpp superimposed on the EEG signals. Both corrupted and cleaned EEG signals are provided to support the evaluation and development of artifact removal methods., MICIU/AEI, FEDER, UE