Redata Repositorio de datos abiertos de investigación de Uruguay
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Dataset - Green solution synthesis of Bi19S27I3 nanostructures - engineering morphology through polyethylene glycol and photocatalytic reduction of Cr (VI)
No full textDataset del artículo Green solution synthesis of Bi19S27I3 nanostructures - engineering morphology through polyethylene glycol and photocatalytic reduction of Cr (VI), enviado para su publicación en Journal of Materials Chemistry C
Simulador Android de Robotito Básico
No full textAplicación que simula el Robotito Básico en un entorno de trabajo
Vídeo para difundir los resultados del proyecto ANII FSED_2_2021_1_169697
No full textVídeo que describe los antecedentes, la metodología y los resultados del proyecto "Estudio y desarrollo de la interacción niño-robot en contexto de aula en la educación inicial: mejoras en el diseño de Robotito para aumentar su inserción y apropiación" financiado por la ANII
Effects of MSC on Psychometric tests and an Empathy for pain task in Uruguayan teachers.
No full textThis research base includes:
1) self-reported data from the following tests:
i) Five-facet Mindfulness Questionnaire (FFMQ; Baer et al, 2006; Cebolla et al., 2012; Quintana et al., 2017),
ii) Scale self-pity (SCS; Neff, 2003a; García-Campayo, 2014);
iii) Interpersonal Reactivity Index (IRI; Davis, 1980, 1983; Fernández et al., 2011),
iv) Perceived Stress Scale (PSS; Cohen et al., 1983; Tapia et al., 2007); and
v) World Health Organization-5 Well-being Index (WHO-5; World Health Organization, 1998; Topp et al., 2015).
2) data from the in-person pain empathy task, according to Baez et al. (2017).
These data were collected within the framework of a research project whose objectives were to evaluate the effects of virtual training in conscious self-compassion (MSC) on mindfulness, self-compassion, stress, well-being, and empathy in Uruguayan primary school teachers.
For this, a quasi-experimental, longitudinal study was carried out. The volunteer teachers were randomly assigned to virtual nine-week MSC or Kundalini Yoga (KY; active control) trainings.
They completed self-reports and performed the pain empathy task (PTT) before, after training, and at follow-up (3 months)
Conjunto de datos de: Packet information encoding in a cerebellum-like circuit.
No full textDATA FILES
These complementary files contain 3 sets of data (*.mat files) and 6 processing codes (*.m files) supporting our results. The data are binary files in “matlab/octave format”. The codes can be opened with any text reader (for example notepad of Windows). Two of these codes are ad hoc functions: JSdistance.m and comparison.m. The other 3 are the codes for data processing: analysis_without_object.m, makefig5.m, and step_analysis.m.
PROCEDURE FOR REPRODUCING THE RESULTS OUT OF THE POINT PROCESSES
Please after download, make a separate directory in your computer, move all data and code files to it.Then you can e run all processes typing 'automaticrun' in the prompt. The computer will do everything without any other required maneouver. Otherwise make two additional directories inside the main directory; naming them transient and figures.Then you can run the different codes separately as ‘m functions’ just typing in the console the names of the three main files mentioned above. This will let you to reproduce the results from the data files. It will also save different partially processed files in the folder 'transient', and save the draft figures (in Matlab/Octave) format in the folder 'figures' and it will also display tables and comments in the console. Content of the data files is explained next.
BASIC PROCESSING
Contains the file 'data_without_object.mat' that will be used by the function 'analysis_without_object.m' and will perform the cross-covariance, the hierarchical cluster analysis, and the analysis of unit location within the electrosensory lobe.
“Data_without_object.mat” contains two cells corresponding to the spike timestamps and the EOD timestamps and a variable containing the unit locations.
MOVEMENT PROCESSING
The file 'moving.mat' will be used by the function 'makefig5.m' and will show the figures for analysis of movement.
“Moving.mat” contains 11 cells and one variable with the titles of the figures. The cells Xforward, Yforward, Xbackward, Ybackward, contain the smoothed position of the plotter for each EOD when the object was moved from rostral to caudal or from caudal to rostral respectively. The cells distanceforward, and distancebackward contains the calculated distance traveled along the skin in either direction calculated using the Pithagoric rule (i.e. the distance traveled between two time stamps equal to the square root of the sum of the squares of the distances traveled along each axes of the plotter). The variable 'EODbaseline' is the timing of the EOD in the absence of objects. The cell Spike_Latency_backward and Spike_Latency_forward show the spike time stamps after every EOD when the object was moved in either direction and the cells Spike_Latency_baseline and EOD baseline correspond to these latencies when the fish is resting without object.
STEP PROCESSING
The file 'stepdata.mat' will be used by the function step_analysis.m and will show the figures in the folder figures and the tables for analysis of the effects of steps in amplitude on spike rate and spike postEOD distribution. Contains two cells and two variables. The cells UP and DO contain the files corresponding to 32 units explored with increasing and decreasing steps respectively. The cell for each unit is a matrix with one dimension of 51 corresponding to the EOD order from -25 EODs before the step increment to the 25 EODS after the step increment. The other dimension contains the spike time stamps corresponding to each ordinal EOD in any trial (i.e. time stamps corresponding to every ordinal EOD pooled from all trials). N corresponds to the number of trials and Type correspond to the unit type in the same order as in the other variables. In Type, the code 1 corresponds to sharp monomodal units, 2 corresponds to deeply inhibited units, 3 corresponds to broad monomodal units, 4 corresponds to mildly inhibited units, 5 correspond to bimodal units and 6 corresponds to trimodal units
A Congruence-based Approach to Active Automata Learning from Neural Language Models code repository
No full textRepository for the experiments performed for the paper: "A Congruence-based Approach to Active Automata Learning from Neural Language Models" ICGI 202
Actividad de Twitter durante la Marcha del Silencio en Uruguay 2020
No full textConjunto de datos del artículo "La batalla virtual por la memoria: Un análisis de las memorias en disputa en Twitter durante la Marcha del Silencio del 2020 en Uruguay"
Revista Teknokultura
Año de publicación 202
Web logs de DVWA
No full textWeb logs de accesos normales y de ataques generados con sqlmap contra el sitio DVWA. Se puede diferenciar entre los siguientes tipos de ataques: B: Boolean-based blind, E: Error-based, U: Union query-based, T: Time-based blind, Q: Inline queries.
Los datos fueron generados usando el framework descrito en el documento "Framework para la generación automática de logs para el entrenamiento de modelos de aprendizaje automático" disponible en el siguiente enlace https://hdl.handle.net/20.500.12381/464
Conjunto de datos de: Relaciones Odontométricas en Odontología Forense
No full textSe trata de un análisis odontométrico de 1005 modelos de yeso de sujetos del sexo masculino y femenino, pacientes con edades comprendidas entre 18 y 60 años, asistidos en una clínica de ortodoncia de la ciudad de Montevideo, Uruguay. En el estudio se registraron varios atributos y en la tabla que queda disponible en el repositorio solo se consignan las mediciones del diámetro mesiodistal y altura gingivoincisal de los caninos y la distancia intercanina, además del sexo y del tipo de maxilar
Base de datos del Informe de Opinión Pública para Salud 2030
No full textLa Base de datos fue elaborada en base a una encuesta realizada mediante telefonía celular. La encuesta se realizó entre el 7 y el 19 de diciembre de 2022. El universo de estudio fueron todas las personas de 18 años y más, residentes en todo el territorio nacional y usuarios de telefonía celular. Las personas encuestadas fueron seleccionadas del total en base a una muestra probabilística y seleccionadas de forma probabilística utilizando RDD (Random Digit Dialing)
El tamaño muestral efectivo del sistema regular fue de 600 casos, con lo que el margen de error máximo esperado para las estimaciones es de +- 4.0%, dentro de un intervalo de confianza del 95%. Los resultados fueron ajustados según voto anterior, región, nivel educativo, edad, sexo y condición de ocupación de los encuestados.
La tasa de respuesta (RR1): 9%. Corresponde a la tasa de respuesta "RR1" según los criterios de AAPOR (The American Association for Public Opinion Research).
La duración promedio de la encuesta fue de 21 minutos. La base contiene el cuestionario y libro de código