cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5594497
title: Harter Pictorial Scale / Escala Pictórica de Harter // HARTER-P_Data_IC_T1-Pre

description: Answers given by children in the first grade of Primary Education to the 24 items of the Pictorial Scale of Perceived Competence and Social Acceptance for Young Children by Harter and Pike (1984), translated into Spanish by García Bacete et al. (2014). Data collected in public schools in Castellón (Spain) in the autumn of the school year 2010-11. The items are written in two gendered versions, one for boys and the other for girls. The scale is administered individually. The calculation of the 4 Factors is included: Cognitive competence, Physical competence, Peer acceptance and Parents acceptance. To learn more about our research and access to other datasets of this or other measures, follow the link GREI Longitudinal Project. Respuestas que los niños/as de primer curso de Educación Primaria dan a los 24 ítems de la Escala Pictórica de Competencia Autopercibida de Harter and Pike (1984), traducida al español por García Bacete et al. (2014). Datos recogidos en centros públicos de Castellón (España) en otoño del curso escolar 2010-11. Los ítems están redactados en versión para chicos y en versión para chicas. Escala se administra de forma individual Se incluye el cálculo de los 4 Factores: Competencia cognitiva, competencia física, aceptación de los iguales y aceptación de los padres. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace Proyecto Longitudinal GREI.
description: GRANTS / SUBVENCIONES: Ministerio de Economía y Competitividad (EDU2012-35930); Ministerio de Ciencia e Innovación (PSI2008-00541/PSIC); Generalitat Valenciana (11I028); Universitat Jaume I (UJI-B2019-29; P1-1A2012-04; P1-1B2009-33) y Fundación Dávalos-Fletcher REQUEST / RUEGO: Researchers using this dataset are kindly asked to inform the authors / Se agradecería a los investigadores que usen este dataset que informen a los autores.
DOI: 10.5281/zenodo.5594490
title: Harter Pictorial Scale / Escala Pictórica de Harter // HARTER-P_Data_CC-CS_T2-Post

description: Answers given by children in the second grade of Primary Education to the 24 items of the Pictorial Scale of Perceived Competence and Social Acceptance for Young Children by Harter and Pike (1984), translated into Spanish by García Bacete et al. (2014). Data collected in public schools in Castellón and Seville (Spain) in the spring of the school year 2010-11. The items are written in two gendered versions, one for boys and the other for girls. The scale is administered individually. The calculation of the 4 Factors is included: Cognitive competence, Physical competence, Peer acceptance and Parents acceptance. To learn more about our research and access to other datasets of this or other measures, follow the link GREI Longitudinal Project. Respuestas que los niños/as de segundo curso de Educación Primaria dan a los 24 ítems de la Escala Pictórica de Competencia Autopercibida de Harter and Pike (1984), traducida al español por García Bacete et al. (2014). Datos recogidos en centros públicos de Castellón y Sevilla (España) en primavera del curso escolar 2010-11. Los ítems están redactados en versión para chicos y en versión para chicas. Escala se administra de forma individual Se incluye el cálculo de los 4 Factores: Competencia cognitiva, competencia física, aceptación de los iguales y aceptación de los padres. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace Proyecto Longitudinal GREI.
description: GRANTS / SUBVENCIONES: Ministerio de Economía y Competitividad (EDU2012-35930); Ministerio de Ciencia e Innovación (PSI2008-00541/PSIC); Generalitat Valenciana (11I028); Universitat Jaume I (UJI-B2019-29; P1-1A2012-04; P1-1B2009-33) y Fundación Dávalos-Fletcher REQUEST / RUEGO: Researchers using this dataset are kindly asked to inform the authors / Se agradecería a los investigadores que usen este dataset que informen a los autores.
DOI: 10.5281/zenodo.5594491
title: Harter Pictorial Scale / Escala Pictórica de Harter // HARTER-P_Data_CC-CS_T2-Post

description: Answers given by children in the second grade of Primary Education to the 24 items of the Pictorial Scale of Perceived Competence and Social Acceptance for Young Children by Harter and Pike (1984), translated into Spanish by García Bacete et al. (2014). Data collected in public schools in Castellón and Seville (Spain) in the spring of the school year 2010-11. The items are written in two gendered versions, one for boys and the other for girls. The scale is administered individually. The calculation of the 4 Factors is included: Cognitive competence, Physical competence, Peer acceptance and Parents acceptance. To learn more about our research and access to other datasets of this or other measures, follow the link GREI Longitudinal Project. Respuestas que los niños/as de segundo curso de Educación Primaria dan a los 24 ítems de la Escala Pictórica de Competencia Autopercibida de Harter and Pike (1984), traducida al español por García Bacete et al. (2014). Datos recogidos en centros públicos de Castellón y Sevilla (España) en primavera del curso escolar 2010-11. Los ítems están redactados en versión para chicos y en versión para chicas. Escala se administra de forma individual Se incluye el cálculo de los 4 Factores: Competencia cognitiva, competencia física, aceptación de los iguales y aceptación de los padres. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace Proyecto Longitudinal GREI.
description: GRANTS / SUBVENCIONES: Ministerio de Economía y Competitividad (EDU2012-35930); Ministerio de Ciencia e Innovación (PSI2008-00541/PSIC); Generalitat Valenciana (11I028); Universitat Jaume I (UJI-B2019-29; P1-1A2012-04; P1-1B2009-33) y Fundación Dávalos-Fletcher REQUEST / RUEGO: Researchers using this dataset are kindly asked to inform the authors / Se agradecería a los investigadores que usen este dataset que informen a los autores.
DOI: 10.5281/zenodo.5592124
title: Harter Pictorial Scale / Escala Pictórica de Harter // HARTER-P_Data_CC-CS_T1-Pre-Rev

description: Answers given by children in the first grade of Primary Education to the 24 items of the Pictorial Scale of Perceived Competence and Social Acceptance for Young Children by Harter and Pike (1984), translated into Spanish by García Bacete et al. (2014). Data collected in public schools in Castellón and Seville (Spain) in the autumn of the school year 2009-10. The items are written in two gendered versions, one for boys and the other for girls. The scale is administered individually. The calculation of the 4 Factors is included: Cognitive competence, Physical competence, Peer acceptance and Parents acceptance. To learn more about our research and access to other datasets of this or other measures, follow the link GREI Longitudinal Project. Respuestas que los niños/as de primer curso de Educación Primaria dan a los 24 ítems de la Escala Pictórica de Competencia Autopercibida de Harter and Pike (1984), traducida al español por García Bacete et al. (2014). Datos recogidos en centros públicos de Castellón y Sevilla (España) en otoño del curso escolar 2009-10. Los ítems están redactados en versión para chicos y en versión para chicas. Escala se administra de forma individual Se incluye el cálculo de los 4 Factores: Competencia cognitiva, competencia física, aceptación de los iguales y aceptación de los padres. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace Proyecto Longitudinal GREI.
description: GRANTS / SUBVENCIONES: Ministerio de Economía y Competitividad (EDU2012-35930); Ministerio de Ciencia e Innovación (PSI2008-00541/PSIC); Generalitat Valenciana (11I028); Universitat Jaume I (UJI-B2019-29; P1-1A2012-04; P1-1B2009-33) y Fundación Dávalos-Fletcher REQUEST / RUEGO: Researchers using this dataset are kindly asked to inform the authors / Se agradecería a los investigadores que usen este dataset que informen a los autores.
DOI: 10.5281/zenodo.5592125
title: Harter Pictorial Scale / Escala Pictórica de Harter // HARTER-P_Data_CC-CS_T1-Pre-Rev

description: Answers given by children in the first grade of Primary Education to the 24 items of the Pictorial Scale of Perceived Competence and Social Acceptance for Young Children by Harter and Pike (1984), translated into Spanish by García Bacete et al. (2014). Data collected in public schools in Castellón and Seville (Spain) in the autumn of the school year 2009-10. The items are written in two gendered versions, one for boys and the other for girls. The scale is administered individually. The calculation of the 4 Factors is included: Cognitive competence, Physical competence, Peer acceptance and Parents acceptance. To learn more about our research and access to other datasets of this or other measures, follow the link GREI Longitudinal Project. Respuestas que los niños/as de primer curso de Educación Primaria dan a los 24 ítems de la Escala Pictórica de Competencia Autopercibida de Harter and Pike (1984), traducida al español por García Bacete et al. (2014). Datos recogidos en centros públicos de Castellón y Sevilla (España) en otoño del curso escolar 2009-10. Los ítems están redactados en versión para chicos y en versión para chicas. Escala se administra de forma individual Se incluye el cálculo de los 4 Factores: Competencia cognitiva, competencia física, aceptación de los iguales y aceptación de los padres. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace Proyecto Longitudinal GREI.
description: GRANTS / SUBVENCIONES: Ministerio de Economía y Competitividad (EDU2012-35930); Ministerio de Ciencia e Innovación (PSI2008-00541/PSIC); Generalitat Valenciana (11I028); Universitat Jaume I (UJI-B2019-29; P1-1A2012-04; P1-1B2009-33) y Fundación Dávalos-Fletcher REQUEST / RUEGO: Researchers using this dataset are kindly asked to inform the authors / Se agradecería a los investigadores que usen este dataset que informen a los autores.
DOI: 10.6084/m9.figshare.22193443
title: Auxiliary online material to Brands et al. (2023): A global climate model performance atlas for the Southern Hemisphere extratropics based on regional atmospheric circulation patterns

description: This is the auxiliary online material to the research article: Brands et al. (2023): <pre><code>A global climate model performance atlas for the Southern Hemisphere extratropics based on regional atmospheric circulation patterns</code></pre> For further informaion, please see the README.txt file contained herein or contact Swen Brands, swen.brands@gmail.com
DOI: 10.5281/zenodo.7221630
title: Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers

description: In order to ensure transparency and reproducibility, we have made everything available publicly here, including the Code, Models, Datasets and more. All the files and their functionality used in this paper are explained clearly in the <strong>README.md</strong> file. Background: Technical Debt (TD) needs to be controlled and tracked during software development. Support to automatically track TD in issue trackers is limited. Aim: We explore the usage of a large dataset of developer-labeled TD issues in combination with cutting-edge Natural Language Processing (NLP) approaches to automatically classify TD in issue trackers. Method: We mine and analyze more than 160GB of textual data from GitHub projects, collecting over 55,600 TD issues and consolidating them into a large dataset (GTD dataset). We use such datasets to train and test Transformer ML models. Then we test the model's generalization ability by testing them on six unseen projects. Finally, we re-train the models including part of the TD issues from the target project to test their adaptability. Results and Conclusion: (i) We create and release the GTD dataset, a comprehensive dataset including TD issues from 6,401 public repositories with various contexts; (ii) By training Transformers using the GTD dataset, we achieve performance metrics that are promising; (iii) Our results are a significant step forward towards supporting the automatic classification of TD in issue trackers, especially when the models are adapted to the context of unseen projects after fine-tuning.
DOI: 10.5281/zenodo.7221631
title: Technical Debt identification in Issue Trackers using Natural Language Processing based on Transformers

description: In order to ensure transparency and reproducibility, we have made everything available publicly here, including the Code, Models, Datasets and more. All the files and their functionality used in this paper are explained clearly in the <strong>README.md</strong> file. Background: Technical Debt (TD) needs to be controlled and tracked during software development. Current support, such as static analysis tools and even ML-based automatic tagging, is still ineffective, especially for context-dependent TD. Aim: We study the usage of a large TD dataset in combination with cutting-edge Natural Language Processing (NLP) approaches to classify TD automatically in issue trackers, allowing the identification and tracking of informal TD conversations. Method: We mine and analyse more than 160GB of textual data from GitHub projects, collecting over 55,600 TD issues and consolidating them into a large dataset (GTD-dataset). We then use our dataset to train state-of-the-art Transformer ML models, before performing a quantitative case study on three projects and evaluating the performance metrics during inference. Additionally, we study the adaptation of our model to classify context-dependent TD in an unseen project, by retraining the model including different percentages of the TD issues in the target project. Results: (i) We provide GTD- dataset, the most comprehensive datasets of TD issues to date, including issues from 6,401 unique public repositories with various contexts; (ii) By training state-of-the-art Transformers using the GTD-dataset, we achieve performance metrics that outperform previous approaches; (iii) We show that our model can provide a relatively reliable tool to classify automatically TD in issue trackers, especially when adapted to unseen projects where the training includes a small portion of TD issues in the new project. Conclusion: Our results indicate that we have taken significant steps towards closing the gap to practically and semi-automatically track TD issues in issue trackers.<br>
DOI: 10.5281/zenodo.7225077
title: Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers

description: In order to ensure transparency and reproducibility, we have made everything available publicly here, including the Code, Models, Datasets and more. All the files and their functionality used in this paper are explained clearly in the <strong>README.md</strong> file. Background: Technical Debt (TD) needs to be controlled and tracked during software development. Support to automatically track TD in issue trackers is limited. Aim: We explore the usage of a large dataset of developer-labeled TD issues in combination with cutting-edge Natural Language Processing (NLP) approaches to automatically classify TD in issue trackers. Method: We mine and analyze more than 160GB of textual data from GitHub projects, collecting over 55,600 TD issues and consolidating them into a large dataset (GTD dataset). We use such datasets to train and test Transformer ML models. Then we test the model's generalization ability by testing them on six unseen projects. Finally, we re-train the models including part of the TD issues from the target project to test their adaptability. Results and Conclusion: (i) We create and release the GTD dataset, a comprehensive dataset including TD issues from 6,401 public repositories with various contexts; (ii) By training Transformers using the GTD dataset, we achieve performance metrics that are promising; (iii) Our results are a significant step forward towards supporting the automatic classification of TD in issue trackers, especially when the models are adapted to the context of unseen projects after fine-tuning.
DOI: 10.34740/kaggle/dsv/5087295
title: YouTube Daily
DOI: 10.26165/juelich-data/pv2ly1
title: Replication Data for: Robustness of individualized inferences from longitudinal resting state EEG dynamics

description: This dataset contains EEG scalp recordings of 27 young healthy participants (11 female, 27.9 +- 3.4 years, 22-34 years). Scalp-EEG was acquired with a 64-channel active Ag/AgCl electrode system (actiCap, Brain Products, Germany) with a standard 10–20 spherical array layout (ground electrode at AFz, reference electrode on the left mastoid). Electrooculographic (EOG) activity evoked by horizontal and vertical eye movements was recorded with three electrodes (FT9, FT10 and TP10) placed at the left and right lateral canthi and below the left eye respectively. During acquisition, measured voltages (0.1 μV resolution) were amplified, filtered (low cut-off: DC, high cut-off: 250 Hz) and digitized at a sampling rate of 2.5 kHz (BrainAmp DC, BrainProducts GmbH, Germany). Subjects were measured on five consecutive days (ses-D1 to ses-D5) performing four tasks in the following order: 1. Resting State (RS1) - 5 minutes of eyes open resting state 2. Tapping Task (Tap) - 14 minutes of 2s repititive finger tapping every 10-14s 3. Resting State (RS2) - 5 minutes of eyes open resting state 4. Sequence Task (sequence) - 14 minutes of complex finger movements (sequences) every 10-14s. For further details see Hommelsen et al. 2022
DOI: 10.6084/m9.figshare.22191538
title: Importance of Orographic Gravity Waves over the Tibetan Plateau on the Spring Rainfall in East Asia

description: These files are monthly data averaged from 6-hourly outputs from different experiments conducted by WRF model. Variables include precipitation, geopotential height, relative humidity, u component of wind, v component of wind, u component of gravity wave drag, and v component of gravity wave drag. Orographic data are invariant.
DOI: 10.6084/m9.figshare.22191538.v2
title: Importance of Orographic Gravity Waves over the Tibetan Plateau on the Spring Rainfall in East Asia

description: These files are monthly data averaged from 6-hourly outputs from different experiments conducted by WRF model. Variables include precipitation, geopotential height, relative humidity, u component of wind, v component of wind, u component of gravity wave drag, and v component of gravity wave drag. Orographic data are invariant.
DOI: 10.6084/m9.figshare.22191538.v1
title: Importance of Orographic Gravity Waves over the Tibetan Plateau on the Spring Rainfall in East Asia

description: These files are monthly data averaged from 6-hourly WRF model outputs from different experiments. Variables include precipitation, geopotential height, relative humidity, u component of wind, v component of wind, u component of gravity wave drag, and v component of gravity wave drag. Orographic data are invariant.
DOI: 10.5281/zenodo.7391072
title: Competences and Needs of Students and Support Received / Competencias y Necesidades de los Alumnos y Apoyos que Reciben // STUDENT-INFORM_Data_IC_T6-Pre

description: Information provided by the teacher on the competencies, needs and individual supports of the students in his/her classroom. The teacher estimates the competence in 5 areas (Social, Mathematics, Spanish Language, Valencian Language and Physical Education), the presence of educational needs (motor, intellectual, hearing, vision, hearing and language, learning, behavioral, developmental, and high abilities), and the type of support received and with what periodicity (therapeutic pedagogy, compensatory education, hearing and language and educational reinforcement). The information was collected through interviews with tutor teachers from public schools in Castellón (Spain) in the fall of the school year 2015-16 and compiled in this Descriptive Data, Competencies and Educational Needs of the Student Report (STUDENT-INFORM) (García Bacete et al., 2014). The dataset also includes information on the worries that some students generate in the teacher in different areas (relational, behavioral, and academic) and the specific reasons why these students worry the teacher, compiled in the Class Group and Teacher Report (CLASSROOM-INFORM) (García Bacete et al., 2014). To learn more about our research and access to other datasets of this or other measures, follow the link GREI Longitudinal Project. Información proporcionada por el profesor sobre las competencias, necesidades y apoyos individuales de los alumnos de su aula. El profesor estima la competencia en 5 áreas (Social, Matemáticas, Lengua Española, Lengua Valenciana y Educación Física), la presencia de necesidades educativas (motrices, intelectuales, auditivas, visuales, de lenguaje, de aprendizaje, de conducta, de desarrollo y de altas capacidades), y el tipo de apoyo recibido y con qué periodicidad (pedagogía terapéutica, educación compensatoria, audición y lenguaje y refuerzo educativo). La información se recogió a través de entrevistas a profesores tutores de colegios públicos de Castellón (España) en el otoño del curso escolar 2015-16 y se recopiló en este Informe de Datos Descriptivos, Competencias y Necesidades Educativas del Alumno (STUDENT-INFORM) (García Bacete et al., 2014). El fichero de datos también incluye información sobre las preocupaciones que algunos alumnos generan en el profesor en diferentes ámbitos (relacional, conductual y académico) y los motivos concretos por los que estos alumnos preocupan al profesor, recopilados en el Informe del grupo de clase y del profesor (CLASSROOM-INFORM) (García Bacete et al., 2014). Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace Proyecto Longitudinal GREI.