cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4538745
title: Data from: Functional biogeography of weeds reveals how anthropogenic management blurs trait-climate relationships

description: The dataset gathers information on the 954 cropland and 5619 grassland plant assemblages studied in the research article <em>Functional biogeography of weeds reveals how anthropogenic management blurs trait-climate relationships</em> by Bourgeois, B., Munoz, F., Gaba, S., Denelle, P., Fried, G., Storkey, J. and Violle, C. published in the <em>Journal of Vegetation Science</em>. A R script for analyzing data is also provided. Data include: <strong>SURVEY.ID</strong>: unique identifier of the plant survey; <strong>HABITAT</strong>: type of habitat (2 levels: cropland - grassland); <strong>CROP TYPE</strong>: type of annual crop cultivated for croplands (4 levels: maize - oilseed - sunflower - winter cereal); <strong>HERBICIDE TREATMENT</strong>: herbicide treatment for croplands (2 levels: herbicide-free - herbicide-sprayed); <strong>CWM SLA</strong>: Community-Weighted Mean of Specific Leaf Area (in m<sup>2 </sup>/ kg); <strong>CWM LDMC</strong>: Community-Weighted Mean of Leaf Dry Matter Content (in mg / kg); <strong>CWM LNC</strong>: Community-Weighted Mean of Leaf Nitrogen Content (in mg N / kg); <strong>CWV SLA</strong>: Community-Weighted Variance of Specific Leaf Area (in m<sup>2 </sup>/ kg); <strong>CWV LDMC</strong>: Community-Weighted Variance of Leaf Dry Matter Content (in mg N / kg); <strong>CWV LNC</strong>: Community-Weighted Variance of Leaf Nitrogen Content (in mg / kg); <strong>GSLtw</strong>: Growing Season Length (in days) accounting for both temperature and soil water limitations; <strong>Xcoord, Ycoord</strong>: coordinates of the plant survey. Please contact the authors for further information.
DOI: 10.5281/zenodo.4538744
title: Data from: Functional biogeography of weeds reveals how anthropogenic management blurs trait-climate relationships

description: The dataset gathers information on the 954 cropland and 5619 grassland plant assemblages studied in the research article <em>Functional biogeography of weeds reveals how anthropogenic management blurs trait-climate relationships</em> by Bourgeois, B., Munoz, F., Gaba, S., Denelle, P., Fried, G., Storkey, J. and Violle, C. published in the <em>Journal of Vegetation Science</em>. A R script for analyzing data is also provided. Data include: <strong>SURVEY.ID</strong>: unique identifier of the plant survey; <strong>HABITAT</strong>: type of habitat (2 levels: cropland - grassland); <strong>CROP TYPE</strong>: type of annual crop cultivated for croplands (4 levels: maize - oilseed - sunflower - winter cereal); <strong>HERBICIDE TREATMENT</strong>: herbicide treatment for croplands (2 levels: herbicide-free - herbicide-sprayed); <strong>CWM SLA</strong>: Community-Weighted Mean of Specific Leaf Area (in m<sup>2 </sup>/ kg); <strong>CWM LDMC</strong>: Community-Weighted Mean of Leaf Dry Matter Content (in mg / kg); <strong>CWM LNC</strong>: Community-Weighted Mean of Leaf Nitrogen Content (in mg N / kg); <strong>CWV SLA</strong>: Community-Weighted Variance of Specific Leaf Area (in m<sup>2 </sup>/ kg); <strong>CWV LDMC</strong>: Community-Weighted Variance of Leaf Dry Matter Content (in mg N / kg); <strong>CWV LNC</strong>: Community-Weighted Variance of Leaf Nitrogen Content (in mg / kg); <strong>GSLtw</strong>: Growing Season Length (in days) accounting for both temperature and soil water limitations; <strong>Xcoord, Ycoord</strong>: coordinates of the plant survey. Please contact the authors for further information.
DOI: 10.5281/zenodo.4541509
title: PP-ind: A Repository of Industrial Pair Programming Research Data

description: PP-ind is a repository of research data on industrial pair programming sessions. Since 2007, our research group has collected audio-video-recordings and questionnaire data in 13 companies. A total of 57 developers worked together (mostly in groups of two, but also three or four) in 67 sessions with a mean length of 1:35 hours. A separate tech report provides many details on how this data was collected. While we cannot share the original video recordings due to confidentiality agreements, we <em>do </em>provide transcripts of the pairs' dialog in this data set. Since we perform our analyses directly on the video material, we only transcribe our data on an <em>is-needed</em> basis, e.g., in preparation for a publication. This data set will therefore contain only few and partial transcripts, which may be amended in future versions. Files named <code>session-<strong>&lt;ID&gt;</strong>-transcript.txt</code> contain original quotations in the language spoken by the recorded developers. For non-English sessions, we also provide non-authoritative <code>session-<strong>&lt;ID&gt;</strong>-transcript_translated.txt</code> files (following the same is-needed rule for translating the originals). All our analyses, however, are performed on the raw data as reflected in the original transcripts. See file <code>transcription-notation.txt</code> for details on the special notation we use.
DOI: 10.5281/zenodo.4529142
title: PP-ind: A Repository of Industrial Pair Programming Research Data

description: PP-ind is a repository of research data on industrial pair programming sessions. Since 2007, our research group has collected audio-video-recordings and questionnaire data in 13 companies. A total of 57 developers worked together (mostly in groups of two, but also three or four) in 67 sessions with a mean length of 1:35 hours. A separate tech report provides many details on how this data was collected. While we cannot share the original video recordings due to confidentiality agreements, we <em>do </em>provide transcripts of the pairs' dialog in this data set. Since we perform our analyses directly on the video material, we only transcribe our data on an <em>is-needed</em> basis, e.g., in preparation for a publication. This data set will therefore contain only few and partial transcripts, which may be amended in future versions. Files named <code>session-<strong>&lt;ID&gt;</strong>-transcript.txt</code> contain original quotations in the language spoken by the recorded developers. For non-English sessions, we also provide non-authoritative <code>session-<strong>&lt;ID&gt;</strong>-transcript_translated.txt</code> files (following the same is-needed rule for translating the originals). All our analyses, however, are performed on the raw data as reflected in the original transcripts. See file <code>transcription-notation.txt</code> for details on the special notation we use.
DOI: 10.5281/zenodo.4541593
title: Psychosocial Risk Level Teachers School Repository 2016-2017

description: The database was created with 5443 records of psychosocial risk level colombian teachers school using physiological variables from May 2016 to December 2017 in five municipalities of a metropolitan area of city in Colombia. The application of physiological variables was made to the people who voluntarily participated in the study. The names and personal data were kept by the researcher.
description: The repository contains two datasets, one of the total population and a sample of the population to which associated physiological variables were evaluated. 1. The First dataset: -- Number of Instances: 5444 -- Number of Attributes: 115 (114 predictive attributes, 1 class) The second dataset --Number of instances: 481 --Number of attributes: 120 (119 predictive attributes, 1 class)
description: {"references": ["Mosquera, R., Parra-Osorio, L., & Castrill\u00f3n, O. D. (2018). Support Vector Machines, Na\u00efve Bayes Classifier and Genetic Algorithms for the Prediction of Psychosocial Risks in Teachers of Colombian Public Schools. Inf. tecnol. vol.29, n.6, pp.153-162. http://dx.doi.org/10.4067/S0718-07642018000600153.", "Mosquera, R., Parra-Osorio, L., & Castrill\u00f3n, O. D. (2018). Prediction of Psychosocial Risks in Colombian Teachers of Public Schools using Machine Learning Techniques. Inf. tecnol. vol.29, n.4, pp.267-280. http://dx.doi.org/10.4067/S0718-07642018000400267.", "Mosquera, R., Parra-Osorio, L., & Castrill\u00f3n, O. D. (2016). Methodology for Predicting the Psychosocial Risk Level on Colombian Teachers using Data Mining Techniques. Inf. tecnol. vol.27, n.6, pp.259-272. http://dx.doi.org/10.4067/S0718-07642016000600026.", "Mosquera Navarro, R., Castrill\u00f3n, O. D. G., Osorio, L. P., & Garc\u00eda, A. C. (2018). Classification system for the predicting of psychosocial risk level in public-school teachers based on Artificial Intelligence. XVIII Conferencia de La Asociaci\u00f3n Espa\u00f1ola Para La Inteligencia Artificial (CAEPIA), 1367-1372.", "Mosquera, R, Castrill\u00f3n G\u00f3mez, O. D., & Parra-Osorio, L. (2019a). A new method based on Physical Surface Tension-Neural Net for the Prediction of Psychosocial-risk Level in Public School Teachers. (Versi\u00f3n 1) [Matlab]. Recuperado de https://doi.org/10.24433/CO.4268666.v1", "Mosquera, R, Castrill\u00f3n G\u00f3mez, O. D., & Parra-Osorio, L. (2019b). Aplicaci\u00f3n del modelo hibrido k-nearest neighbors-Support Vector Machine para la predicci\u00f3n del riesgo psicosocial en docentes de colegios p\u00fablicos colombianos. Presentado en 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: \"Industry, Innovation, And Infrastructure for Sustainable Cities and Communities\", Jamaica. Recuperado de http://www.laccei.org/index.php/publications/laccei-proceedings"]}
DOI: 10.5281/zenodo.1298609
title: Psychosocial Risk Level Teachers School Repository 2016-2017

description: The database was created with 5443 records of psychosocial risk level colombian teachers school using physiological variables from May 2016 to December 2017 in five municipalities of a metropolitan area of city in Colombia. The application of physiological variables was made to the people who voluntarily participated in the study. The names and personal data were kept by the researcher.
description: The repository contains two datasets, one of the total population and a sample of the population to which associated physiological variables were evaluated. 1. The First dataset: -- Number of Instances: 5444 -- Number of Attributes: 115 (114 predictive attributes, 1 class) The second dataset --Number of instances: 481 --Number of attributes: 120 (119 predictive attributes, 1 class)
description: {"references": ["Mosquera, R., Parra-Osorio, L., & Castrill\u00f3n, O. D. (2018). Support Vector Machines, Na\u00efve Bayes Classifier and Genetic Algorithms for the Prediction of Psychosocial Risks in Teachers of Colombian Public Schools. Inf. tecnol. vol.29, n.6, pp.153-162. http://dx.doi.org/10.4067/S0718-07642018000600153.", "Mosquera, R., Parra-Osorio, L., & Castrill\u00f3n, O. D. (2018). Prediction of Psychosocial Risks in Colombian Teachers of Public Schools using Machine Learning Techniques. Inf. tecnol. vol.29, n.4, pp.267-280. http://dx.doi.org/10.4067/S0718-07642018000400267.", "Mosquera, R., Parra-Osorio, L., & Castrill\u00f3n, O. D. (2016). Methodology for Predicting the Psychosocial Risk Level on Colombian Teachers using Data Mining Techniques. Inf. tecnol. vol.27, n.6, pp.259-272. http://dx.doi.org/10.4067/S0718-07642016000600026.", "Mosquera Navarro, R., Castrill\u00f3n, O. D. G., Osorio, L. P., & Garc\u00eda, A. C. (2018). Classification system for the predicting of psychosocial risk level in public-school teachers based on Artificial Intelligence. XVIII Conferencia de La Asociaci\u00f3n Espa\u00f1ola Para La Inteligencia Artificial (CAEPIA), 1367-1372.", "Mosquera, R, Castrill\u00f3n G\u00f3mez, O. D., & Parra-Osorio, L. (2019a). A new method based on Physical Surface Tension-Neural Net for the Prediction of Psychosocial-risk Level in Public School Teachers. (Versi\u00f3n 1) [Matlab]. Recuperado de https://doi.org/10.24433/CO.4268666.v1", "Mosquera, R, Castrill\u00f3n G\u00f3mez, O. D., & Parra-Osorio, L. (2019b). Aplicaci\u00f3n del modelo hibrido k-nearest neighbors-Support Vector Machine para la predicci\u00f3n del riesgo psicosocial en docentes de colegios p\u00fablicos colombianos. Presentado en 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: \"Industry, Innovation, And Infrastructure for Sustainable Cities and Communities\", Jamaica. Recuperado de http://www.laccei.org/index.php/publications/laccei-proceedings"]}
DOI: 10.6084/m9.figshare.14035761
title: Processed data from Limited vocal compensation for elevated ambient noise in bearded seals: implications for an industrializing Arctic ocean

description: Data used for statistical analysis, data summary, and modeling
DOI: 10.6084/m9.figshare.14035761.v1
title: Processed data from Limited vocal compensation for elevated ambient noise in bearded seals: implications for an industrializing Arctic ocean

description: Data used for statistical analysis, data summary, and modeling
DOI: 10.20351/29sg20110630
title: PROMARES Cruise, RV Sarmiento de Gamboa

description: PROMARES Cruise (29SG20110630) carried out on the Research Vessel Sarmiento de Gamboa in 2011
DOI: 10.21343/z7sn-pq96
title: Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #2, 2015-2018)

description: NDVI, GCC, soil and surface temperature, and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
DOI: 10.21343/9cvd-1b32
title: Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #3, 2015-2018)

description: NDVI, GCC, soil and surface temperature, and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
DOI: 10.20351/29sg20090914
title: FAMOSO-3 Cruise, RV Sarmiento de Gamboa

description: FAMOSO-3 Cruise (29SG20090914) carried out on the Research Vessel Sarmiento de Gamboa in 2009
DOI: 10.21343/czvx-vg37
title: Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #4, 2015-2018)

description: NDVI, GCC, soil and surface temperature, and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
DOI: 10.21343/50tc-5f33
title: Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #5, 2015-2018)

description: NDVI, GCC, soil and surface temperature, and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
DOI: 10.21343/ef5z-rr59
title: Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #6, 2016-2018)

description: NDVI, GCC, soil temperature and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
DOI: 10.21343/p5k0-8a77
title: Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #7, 2016-2018)

description: NDVI, GCC, soil temperature and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
DOI: 10.6084/m9.figshare.11295383.v3
title: Dataset_Judo.xlsx

description: Data includes Participants information: Age, Gender, Group, Experience, Hours<br>Scores in PMA verbal<br>Spatial tasks and wayfinding inclinations scores:4 measures of spatial abilities (MRT, OPT, WLT, EFT)<br>6 measures of wayfinding inclinations (sub-scales)<br>Scores in navigation tasks2 measures of virtual reality navigation<br>
DOI: 10.21343/02yn-7a03
title: Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #8, 2016-2018)

description: NDVI, GCC, soil temperature and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
DOI: 10.21343/nk1q-5p29
title: Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #9, 2016-2018)

description: NDVI, GCC, soil temperature and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
DOI: 10.6084/m9.figshare.11984361
title: Feraco et al. 2020 data: "Using Mobile Devices in Teaching Large University Classes: How Does It Affect Exam Success?"

description: Data includes information about 294 undergraduates about:Session = exam dateCohort <br>GenderCourse_activity = number of out-of-class activities completedQuiz_activity = number of lesson in which Top Hat has been used by the participant<br>Participation = percentage of quizzes answeredCorrectness = percentage of right answers on the totalITQ = total for the theory of intelligence questionnaireSRSQ = total for the self-regulated strategies questionnaireExam_performance<br>
DOI: 10.6084/m9.figshare.11984361.v2
title: Feraco et al. 2020 data: "Using Mobile Devices in Teaching Large University Classes: How Does It Affect Exam Success?"

description: Data includes information about 294 undergraduates about:Session = exam dateCohort <br>GenderCourse_activity = number of out-of-class activities completedQuiz_activity = number of lesson in which Top Hat has been used by the participant<br>Participation = percentage of quizzes answeredCorrectness = percentage of right answers on the totalITQ = total for the theory of intelligence questionnaireSRSQ = total for the self-regulated strategies questionnaireExam_performance<br>
DOI: 10.6084/m9.figshare.11984361.v1
title: SRS_data

description: Data includes information about 294 undergraduates about:Session = exam dateCohort <br>GenderCourse_activity = number of out-of-class activities completedQuiz_activity = number of lesson in which Top Hat has been used by the participant<br>Participation = percentage of quizzes answeredCorrectness = percentage of right answers on the totalITQ = total for the theory of intelligence questionnaireSRSQ = total for the self-regulated strategies questionnaireExam_performance<br>
DOI: 10.21343/evzg-7d07
title: Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #10, 2016-2018)

description: NDVI, GCC, soil temperature and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.