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Absell-Federico-Tena World Trade Historical Database 1948-2020 : Thailand
No full textProject developed by Christopher Absell (University Gothenburg and Instituto Figuerola) Giovanni Federico (New York University Dubai) and Antonio Tena Junguito (Universidad Carlos III de Madrid and Instituto Figuerola). Dataset: Thailan
Supplementary material for the article "Are Educational Escape Rooms More Effective Than Traditional Lectures for Teaching Software Engineering? A Randomized Controlled Trial"
No full textThis dataset contains the data related to the article "Are Educational Escape Rooms More Effective Than Traditional Lectures for Teaching Software Engineering? A Randomized Controlled Trial" with DOI 10.1109/TE.2024.3403913.
This dataset contains data of 326 software engineering students: 162 in the control group and 164 in the experimental group. The 164 students belonging to the experimental group learned software modeling by playing an educational escape room whereas the 162 students belonging to the control group learned the same subject matter through a traditional lecture.
The dataset is provided through an XLSX file with two sheets: 1) ExperimentalGroup. This sheet contains the data related to the experimental group. 2) ControlGroup. This sheet contains the data related to the control group.
The dataset contains the following data for each student in the experimental group:
* Pre-test and post-test scores (on a scale from 0 to 10).
* Learning gains, calculated as the difference between the post-test and the pre-test score.
* Results of a perceptions questionnaire with 16 items. Items 1-14 are 5-point Likert items scored from 1 (strongly disagree) to 5 (strongly agree), whereas items 14 and 15 are yes/no questions.
The dataset contains the following data for each student in the control group:
* Pre-test and post-test scores (on a scale from 0 to 10).
* Learning gains, calculated as the difference between the post-test and the pre-test score.
All data is anonymized so that the identity of the participants cannot be ascertained.
Additional details are provided in the aforementioned article, which can be openly accessed at https://www.doi.org/10.1109/TE.2024.3403913
Green nanomaterials for sorbentbased extraction techniques in food analysis samples
No full textThis database includes the information of the relevant applications of green nanomaterials in sorbent-based extraction methods for food analysi
Supplementary code for the article: Fast Evacuation Method: using an effective dynamic floor field based on efficient pedestrian assignment
No full textThe problem of pedestrian evacuation can be addressed through cellular automata incorporating a floor field that indicates promising movements to pedestrians. The two main types of floor field are the static, which represents the shortest path from each cell to an exit (and is usually combined with dynamic measures such as the density or distribution of pedestrians), and the dynamic, which represents the quickest path from each cell to an exit. The second type has been widely used recently, since it gives rise to more efficient and realistic simulations of pedestrian dynamics. The goal of these two types of floor field is to minimize the travel time for each pedestrian; however, this paper tackles the evacuation problem from a different perspective: The time taken by the whole evacuation process is optimized. For that purpose, a floor field is constructed by assigning pedestrians to exits such that the estimated time for complete evacuation is minimized. An experimental evaluation is conducted to compare the new fast evacuation method with competitive methods using floor fields based on quickest paths: Flood Fill and the Fast Marching Method. The results show that the new method is effective in terms of the number of time steps for complete evacuation and efficient regarding the total simulation runtime.In order to execute the NetLogo model: FastEvacuationMethod.nlogo
you first need to install NetLogo, which can be freely downloaded from:
https://ccl.northwestern.edu/netlogo/download.shtm
Absell-Federico-Tena World Trade Historical Database 1948-2020 : Costa Rica
No full textProject developed by Christopher Absell (University Gothenburg and Instituto Figuerola) Giovanni Federico (New York University Dubai) and Antonio Tena Junguito (Universidad Carlos III de Madrid and Instituto Figuerola). Dataset: Costa Ric
Absell-Federico-Tena World Trade Historical Database 1948-2020 : Grenada
No full textProject developed by Christopher Absell (University Gothenburg and Instituto Figuerola) Giovanni Federico (New York University Dubai) and Antonio Tena Junguito (Universidad Carlos III de Madrid and Instituto Figuerola). Dataset: Grenad
Absell-Federico-Tena World Trade Historical Database 1948-2020 : Iraq
No full textProject developed by Christopher Absell (University Gothenburg and Instituto Figuerola) Giovanni Federico (New York University Dubai) and Antonio Tena Junguito (Universidad Carlos III de Madrid and Instituto Figuerola). Dataset: Ira
Fibreglass membrane chemically modified with amino-functionalised SBA-15 and its application in solid-phase extraction to determine macrolide antibiotics in eggs
No full textThis database includes the results of Box-Behnken design and response obtained for the extraction of 5 macrolides antibiotics in egg at 150 ng/g, recoveries (%) obtained with the optimised protocol using nonfunctionalised (FGM) and functionalised (FGM-SBA-15-NH2), analytical performance of developed methodology, description of the analysed egg samples, parameters of mass spectrometry, textural properties and functionalization degree of FGM-SBA-15-NH2 compared with SBA-15, preliminary assays, ANOVA report and mathematical equations, instrumental validation, reproducibility studies of the FGM-SBA-15-NH2, content of macrolide antibiotics and comparison of proposed methodology with other approache
The effect of programming on primary school students’ mathematical and scientific understanding: educational use of mBot
No full textEste estudio resalta la importancia de un diseño educativo que incluya la robótica y la programación a través de un lenguaje de programación visual como medio que permita a los estudiantes mejorar sustancialmente su comprensión de los elementos de la lógica y las matemáticas. La adquisición de una comprensión de los conceptos computacionales, así como un alto grado de participación y compromiso de los estudiantes enfatizan la efectividad de introducir la robótica y la programación visual basada en metodologías activas en la educación primaria. La implementación de este diseño brinda a los estudiantes de sexto grado de educación primaria actividades que integran la programación y la robótica en las ciencias y las matemáticas; Estas prácticas permiten a los estudiantes comprender la codificación, el movimiento, los motores, las secuencias y los condicionales. Se aplicó un diseño cuasiexperimental, análisis descriptivo y observación participante en diversas dimensiones a 93 estudiantes de sexto grado en cuatro escuelas de educación primaria. La programación y la robótica se integraron en una unidad didáctica de matemáticas y otra de ciencias. Se lograron mejoras estadísticamente significativas en la comprensión de conceptos matemáticos y en la adquisición de conceptos computacionales, a partir de una práctica pedagógica activa que infunde motivación, entusiasmo, compromiso, diversión e interés por el contenido estudiado
Subset of Quick, Draw! dataset for neural network pre-training / Subconjunto del conjunto de datos Quick, Draw! para pre-entrenamiento de redes neuronales
No full textDescription of the project
This dataset is the result of the research carried out in the project "A Benchmark for Rey-Osterrieth Complex Figure (ROCF) Test Automatic Scoring", whose main goal was to establish a baseline for the scoring task consisting of: a dataset with 528 ROCF and results obtained by several deep learning models, as well as, by a group of psychology experts.Description of the dataset
This dataset contains a folder with 414000 drawings from Quick, Draw! dataset.Methodology
The methodology used to collect the data consists of 4 steps:
Step 1. Downloading the Quick, Draw! images in binary format.
Step 2. Selection of 1200 images for each of the 345 classes (414000 elements in total).
Step 3. The image size is 256x256 and they are converted from vector format into pixel format.
Step 4. The 414000 images are divided into three sets: training (289800 elements), validation (62100 elements) and test (62100 elements).
Descripción del proyecto
El contenido de este dataset es resultado de la investigación llevada a cabo en el proyecto "Banco de Pruebas para la Puntuación Automática del Test de la Figura Compleja de Rey-Osterrieth (FCRO)", cuyo objetivo principal era el de establecer una línea base para dicha tarea que incluye: un dataset con 528 FCRO y resultados obtenidos por varios modelos de aprendizaje profundo, así como, por varios profesionales de la psicología.Descripción del dataset
Este dataset contiene una carpeta con 414000 dibujos del conjunto de datos Quick, Draw!Metolodogía
La metodología empleada para la obtención de los datos incluye 4 etapas:
Etapa 1. Descarga de las imágenes de Quick, Draw! en formato binario.
Etapa 2. Selección de 1200 imágenes por cada una de las 345 clases (414000 elementos en total).
Etapa 3. El tamaño de las imágenes es de 256x256 y son transformadas de formato vectorial a formato de píxel.
Etapa 4. Las 414000 imágenes son divididas en tres conjuntos: entrenamiento (289800 elementos), validación (62100 elementos) y test (62100 elementos).
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