Resultados de investigación

Se trata del conjunto de datos del artículo Análisis documental sobre los riesgos y las posibilidades de internet para los menores. Pautas educativas dirigidas a familias https://doi.org/10.6018/riite.430341, Item en revisión. Pendiente de cumplimentar metadatos.

The S3 dataset contains the behavior (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones. All attributes of the different kinds of data are writed in a vector. The dataset contains the fellow vectors: Sensors: This type of vector contains data belonging to smartphone sensors (accelerometer and gyroscope) that has been acquired in a given windows of time. Each vector is obtained every 20 seconds, and the monitored features are: - Average of accelerometer and gyroscope values. - Maximum and minimum of accelerometer and gyroscope values. - Variance of accelerometer and gyroscope values. - Peak-to-peak (max-min) of X, Y, Z coordinates. - Magnitude for gyroscope and accelerometer. Statistics: These vectors contain data about the different applications used by the user recently. Each vector of statistics is calculated every 60 seconds and contains : - Foreground application counters (number of different and total apps) for the last minute and the last day. - Most common app ID and the number of usages in the last minute and the last day. - ID of the currently active app. - ID of the last active app prior to the current one. - ID of the application most frequently utilized prior to the current application. - Bytes transmitted and received through the network interfaces. Voice: This kind of vector is generated when the microphone is active in a call o voice note. The speaker vector is an embedding, extracted from the audio, and it contains information about the user's identity. This vector, is usually named "x-vector" in the Speaker Recognition field, and it is calculated following the steps detailed in "egs/sitw/v2" for the Kaldi library, with the models available for the extraction of the embedding. A summary of the details of the collected database. - Users: 21 - Sensors vectors: 417.128 - Statistics app's usage vectors: 151.034 - Speaker vectors: 2.720 - Call recordings: 629 - Voice messages: 2.091

Base de datos utilizada en la elaboración del artículo Intervention for oral language comprehension skills in preschoolers with Developmental Language Disorder, publicado en International Journal of Language & Communication Disorders. Database used in the preparation of the article Intervention for oral language comprehension skills in preschoolers with Developmental Language Disorder, published in the International Journal of Language & Communication Disorders.

These experimental materials and the reaction times (and accuracy) correspond to the article titled "The frequency of word gender as a variable for lexical access in Spanish" under review at the journal "Linguistics: An Interdisciplinary Journal of the Language Sciences". The excel file contains a sheet for the stimuli, another for the experimental RT and errors obtained by third grade children of primary school, another sheet for sixth grade children of primary school and the last one for adults.

Datos de investigación asociados al artículo: Endophytic colonization by the entomopathogenic fungus Beauveria Bassiana affects plant volatile emissions in the presence or absence of chewing and sap-sucking insects, disponible en: http://hdl.handle.net/10396/32965

The PFuji-Size dataset [2] includes the 3D point clouds of 6 Fuji apple trees containing a total of 615 apples and an additional 25 apples scanned in laboratory conditions. Structure-from-motion and multi-view stereo techniques were used to generate the 3D point clouds of the captured scene. Apple locations and ground truth diameter annotations are provided for assessing fruit detection and size estimation algorithms.