BUSCADOR RECOLECTA

Project 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: Estonia

Conjunto de datos sobre restos arqueológicos del culto al dios Mitra en la Hispania romana a los que se hace referencia en: Mitra en Hispania, recurso electrónico. Autor: Jaime Alvar Ezquerra

Los bailes. Músicos e instrumentos. Iban directos del baile a las tareas agrícolas, y viceversa. Cantinas y tabernas. Los carboneros. Vestidos para las fiestas. Personas con las que iban a los bailes y lugares en los que se celebraban., Os bailes. Músicos e instrumentos. Passaram directamente da dança à agricultura, e vice-versa. Cantinas e tabernas. Os carvoeiros. Trajes para as festas. Pessoas com as quais iam aos bailes e lugares onde se realizavam., The dances. Musicians and instruments. They went straight from dancing to agricultural work, and vice versa. Canteens and taverns. The coalmen. Costumes for the festivities. People with whom they went to the dances and places where they were held.

Semantic Textual Similarity (also known as Semantic Short-text Similarity) is a research problem that aims to calculate the similarity among text units (phrases, sentences, paragraphs or texts) focusing on the semantic content. The importance of Semantic Similarity in Natural Language Processing has increased in the last years due to its relevance in many tasks and applications, such as Automatic Summarization, Machine Translation, Question Answering or Semantic Indexing. UB-NER is a self-contained Java software library for benchmarking state-of-the-art STS measures in the biomedical domain. It allows to define and execute a set of experiments combining different measures and preprocessing methods. This dataset contains the reproducibility framework and dependencies, whose aim is to allow the exact replication of unsupervised named entity recognition experiment in the biomedical domain as detailed in "ReproductionProtocol.pdf" file.

Todo el mundo hablaba ferrereño. Los jóvenes ya no saben. El papel del maestro como represor. Él fue algo a la escuela. Hizo el servicio militar en Melilla, y ya entonces sabía hablar castellano. El dialecto de Casalinho (Cedillo). Conversación con unos paseantes sobre el ferrereño y la relación con Malpica., Todos falavam ferrerenho. Os jovens já não sabem. O papel do mestre como repressor. Ele foi para a escola. Fez o serviço militar em Melilla, e já sabia falar espanhol. O dialeto de Casalinho (Cedillo). Conversação com transeuntes sobre o ferrerenho e a relação com Malpica., Everyone spoke Ferrereño. Young people no longer know. The role of the teacher as repressor. He went to school. He did his military service in Melilla, and he already knew how to speak Spanish. The dialect of Casalinho (Cedillo). Conversation with passers-by about the Ferrereño and the relationship with Malpica.

Project developed by Giovanni Federico (New York University Abu Dhabi) and Antonio Tena Junguito (Universidad Carlos III de Madrid). Dataset: Dutch East Indies (Indonesia)

The dataset is stored in one spreadsheet file with two sheets. The sheet named "Teamwork" contains data regarding the measurement of the teamwork soft skill acquisition. The sheet named "Knowledge" contains data regarding the measurement of the knowledge acquisition about human-computer interaction. -Columns of the sheet "Teamwork"- Timestamp_PRE. Timestamp of the pretest. CodGroup. Three possible values: Early childhood education degree (1), Primary education degree (2), and Computer science (3) Gender. Two possible values: Male(M) and female (F). Age. Age range: 18, 19-20, 21-22, 23-24, and 25-30 AcadYear. Academic year of the degree where the student is enrolled: First (1), second (2), second (3), and fourth (4) WORK_PRE. Completing assigned tasks within the deadline as group member. Measured in the prestest. Being 1 the worst case and 5 the best case. PARTI_PRE. Participating actively in team meetings, sharing information, knowledge, and experiences. Measured in the prestest. Being 1 the worst case and 5 the best case. ORGA_PRE. Collaborating in defining, organizing, and distributing group tasks. Measured in the prestest. Being 1 the worst case and 5 the best case. COHE_PRE. Focusing on and being committed to agreement and shared objectives. Measured in the prestest. Being 1 the worst case and 5 the best case. SOCIAL_PRE. Social value of activity, taking into account the points of view of others and giving constructive feedback. Measured in the prestest. Being 1 the worst case and 5 the best case. TEAMW_PRE. Aggregates the five previous columns. Being 1 the worst case and 5 the best case. Timestamp_POS. Timestamp of the postest WORK_POS. Completing assigned tasks within the deadline as group member. Measured in the postest. Being 1 the worst case and 5 the best case. PARTI_POS. Participating actively in team meetings, sharing information, knowledge, and experiences. Being 1 the worst case and 5 the best case.Measured in the postest. ORGA_POS. Collaborating in defining, organizing, and distributing group tasks. Measured in the postest. Being 1 the worst case and 5 the best case. COHE_POS. Focusing on and being committed to agreement and shared objectives. Measured in the postest. Being 1 the worst case and 5 the best case. SOCIAL_POS. Social value of activity, taking into account the points of view of others and giving constructive feedback. Measured in the postest. Being 1 the worst case and 5 the best case. TEAMW_POS. Aggregates the five previous columns. Being 1 the worst case and 5 the best case. KNOWLEDGE. Knowledge acquisition regarding human-computer interaction concepts -Columns of the sheet "Knowledge"- Group. Two possible values: Control group (0) and experimental group (1) Know. Measurement of the knowledge acquisition as a numeric value in the range [0-10], being 0 the worst case and 10 the best case.

Project 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: Colombia

This database includes the results of evolution in the number of food alerts that have noted high levels of pyrrolizidine alkaloids (PAs), tropane alkaloids (TAs) and opium alkaloids (OAs) from 2012 to 2022, distribution of food alerts based on the food type contaminated, overview of the number of works currently available in the literature addressing the effect of different food processing and culinary preparations, thermal treatment effects, fermentation effects on the content of PAs, TAs and OAs, and transfer rate of PAs, TAs and Oas during brewing processes according to literature data