BUSCADOR RECOLECTA

Entrevista a Nemesio González y Ascensión Nieto en Castromil. Diferencias dentro de la aldea y con otras localidades. ¿Hasta dónde se habla gallego? ¿Entiende mejor a un gallego o a un portugués? ¿Cómo hablan con los portugueses? ¿Y ellos con los de Castromil?, Diferenças dentro da aldeia e com outras localidades. Quais são os limites da área onde se fala galego? Percebe melhor um galego ou um português? Como falam com os portugueses? E eles com os de Castromil?, Differences between the village and other localities. Which are the limits of the area where Galician is spoken? Do you understand better Galician or Portuguese people? How do you speak with Portuguese people? And how do they speak with you?

Child experimental data and associated survey data: The dataset contains socioeconomic data and experimental measures of cognitive effort stemming from fifth-grade students and their parents. Among the socioeconomic variables that can be found in the dataset are the gender, age, and country of birth of both parents and students. Furthermore, the level of education, occupation, employment status and other socioeconomic variables of the parents are also present. A wide range of variables regarding the student in the school are included, such as grades, involvement, or homework. As effort is the main variable of interest, a series of non-cognitive (personality) variables that are conceptually close were measured, namely, Locus of Control, the Big Five and Need for Cognition. The students also completed a Raven matrices style IQ test (fluid intelligence). A few variables reflect the basic characteristics of their teacher and the school that the individuals attend.

Parent experimental data and associated survey data: The dataset contains socioeconomic data and experimental measures of cognitive effort stemming from parents of a subset of the fifth-grade students who participated in the laboratory experiments. Effort-related non-cognitive (personality) variables that were collected for children were also collected for the parents, including Locus of Control, the Big Five, and Need for Cognition. The parents also completed a Raven matrices style IQ test. Data is also collected on parental perceptions of child's non-cognitive (personality) traits, how parents reward children for good grades, and Covid-related information including how parents and children were navigating the situation at work and school. It should be noted that many questions on this second survey that participating parents completed were repeated from the first parental survey administered when children were completing the experiments. If the same question was asked on first and second parental surveys, missing responses on second survey were filled in with non-missing responses from first survey. Responses may vary for certain questions depending on whether it was a different parent who answered the first versus second survey. Therefore, it is required that it was the same parent was answering in order for these missing responses to be imputed this way.

Para poder aplicar técnicas de PLN y aprendizaje profundo capaces de reconocer de forma automática las menciones de las enfermedades raras y sus manifestaciones clínicas en textos es imprescindible contar con un dataset (colección de textos anotados con enfermedades y síntomas / signos) que pueda ser utilizado par entrenar y evaluar los distintos modelos. Con ese objetivo, dentro del proyecto NLP4RARE-CM-UC3M, creamos el corpus RAre4Dis, formado por 1.041 textos seleccionados de una de las principales bases de datos con información sobre enfermedades raras, Orphanet (https://www.orpha.net/consor/cgi-bin/index.php). Una vez definidas una metodología y guías de anotación, se anotaron las entidades (enfermedad, enfermedad rara, síntoma, signo y anáfora). El corpus de RareDis contiene más de 5.000 enfermedades raras y unas 6.000 manifestaciones clínicas. Para garantizar la calidad del corpus, se midió el acuerdo entre anotadores, alcanzando una medida F1 de 83.5%. El dataset, que es distribuido con particiones para entrenamiento, validación y evaluación, puede ser utilizado para entrenar y evaluar enfoques basados en aprendizaje automático y dirigido al reconocimiento de este tipo de entidades en textos médicos. A continuación, se presentan algunas tablas con información del número de textos y entidades más importantes del dataset:
Training Validación Test Total
Textos 729 - 104 - 208 - 1.041
Oraciones 6.451 - 903 - 1.787 - 9.141
Tokens 135.656 - 18.492 - 37.893 - 192.041

Training Validación Test Total
Disease 1.647 - 230 - 471 - 2.348
Rare Disease 3.608 - 525 - 1.088 - 5.221
Symptom 319 - 24 - 53 - 396
Sign 3.744 - 528 - 1.061 - 5.333
produces 4.106 - 556 - 1.131 - 5.793
increase-risk-of 169 - 22 - 54 - 245

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

Entrevista a informante 1 en Esperança. La siega y el ciclo del cereal. Molinos y molienda. Hornos y fabricación del pan. Vendimia y preparación del vino. Aceituna y aceite; la mecanización de los olivares ha alterado su fisionomía y todo el proceso., A ceifa e o ciclo do cereal. Moinhos e moagem. Fornos e panificação. Vendima e preparação de vinho. Azeitona e azeite; a mecanização dos olivais alterou a sua fisionomia e todo o processo., The harvest and the cereal cycle. Mills and grinding. Ovens and bread making. Harvest and wine preparation. Olive and oil; the mechanization of the olive groves has altered its physiognomy and the whole process.

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Crete

Project developed by Giovanni Federico (New York University Abu Dhabi) and Antonio Tena Junguito (Universidad Carlos III de Madrid). Dataset: Danish Virgin Islands

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Eritrea

Entrevista a Informante 1 en Segura. Personajes e historias de Segura. Pesca en el río; hidrónimos. La mora de la Cova da Moura. Juegos., Personagens e histórias de Segura. Pesca no rio. Hidrónimos. A moura da Cova da Moura. Jogos., Characters and stories of Segura. Fishing in the river. Hydronims. The moor of the Cova da Moura. Games.

Se hacía al terminar la aceituna. Cebado del animal. Colocación del cerdo encima del “poio” y modo de darle muerte. Retirada de la sangre. Chamuscado. Retirada de la hiel, limpieza de las tripas (mujeres) y colgadura del cerdo (hombres). Por la tarde se picaba., Fazia-se quando a azeitona estava pronta. Engorda do animal. Colocação do porco em cima do "poio" e forma de o matar. Retirada de sangue. Chamuscar. Remoção da fel, limpeza das tripas (mulheres) e suspensão do porco (homens). À tarde picava-se., It was done when the olive was finished. Fattening of the animal. Placing the pig on top of the "poio" and way to kill it. Withdrawal of blood. Singing. Removal of the gall, cleaning of the guts (women) and hanging of the pig (men). In the afternoon it was chopped.