cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7779984
title: Multi-view emotional expressions dataset

description: Multi-view emotional expressions dataset (MEED) using 2D pose estimation.
DOI: 10.5281/zenodo.7779985
title: Multi-view emotional expressions dataset

description: Multi-view emotional expressions dataset (MEED) using 2D pose estimation.
DOI: 10.5281/zenodo.8185369
title: Multi-view emotional expressions dataset

description: Multi-view emotional expressions dataset (MEED) using 2D pose estimation.
DOI: 10.5281/zenodo.8217315
title: Scholes African Ecoregions

description: <strong>Ecoregion description</strong> We separated Africa into five functionally different ecoregions in order to assist with the interpretation of Greenhouse Gas flux estimates for the Regional Carbon Cycle and Assessment Phase 2 (RECCAP2) project (https://www.globalcarbonproject.org/reccap/). For the synthesis paper (in prep), the analyses therefore included Africa as a whole and where possible separate estimates for the ecoregions. The Scholes African Ecoregion Map was delineated by regrouping and smoothing the vegetation classification of the UNESCO/AETFAT/UNSO (White's) Vegetation Map of Africa (White, 1983) in accordance with the delineations of the distributions of Mean Annual Precipitation-determined (‘stable’) and Disturbance-determined (‘unstable’) savannas in Africa by Sankaran et al. (2005). In honour of the late Prof. Robert J. Scholes (Bob), who encouraged the creation of the map, we named it the “Scholes African Ecoregion Map”. The spatial maps are available as a shapefile and as grids in netcdf format at 1° and 0.05° spatial resolution. <strong>References</strong> White, Frank; 1983; Vegetation of Africa - a descriptive memoir to accompany the Unesco/AETFAT/UNSO vegetation map of Africa; Natural Resources Research Report XX; U. N. Educational, Scientific and Cultural Organization; 7 Place de Fontenoy, 75700 Paris, France; 356 pages. Sankaran, M., Hanan, N., Scholes, R. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005). https://doi.org/10.1038/nature04070
DOI: 10.5281/zenodo.8217314
title: Scholes African Ecoregions

description: <strong>Ecoregion description</strong> We separated Africa into five functionally different ecoregions in order to assist with the interpretation of Greenhouse Gas flux estimates for the Regional Carbon Cycle and Assessment Phase 2 (RECCAP2) project (https://www.globalcarbonproject.org/reccap/). For the synthesis paper (in prep), the analyses therefore included Africa as a whole and where possible separate estimates for the ecoregions. The Scholes African Ecoregion Map was delineated by regrouping and smoothing the vegetation classification of the UNESCO/AETFAT/UNSO (White's) Vegetation Map of Africa (White, 1983) in accordance with the delineations of the distributions of Mean Annual Precipitation-determined (‘stable’) and Disturbance-determined (‘unstable’) savannas in Africa by Sankaran et al. (2005). In honour of the late Prof. Robert J. Scholes (Bob), who encouraged the creation of the map, we named it the “Scholes African Ecoregion Map”. The spatial maps are available as a shapefile and as grids in netcdf format at 1° and 0.05° spatial resolution. <strong>References</strong> White, Frank; 1983; Vegetation of Africa - a descriptive memoir to accompany the Unesco/AETFAT/UNSO vegetation map of Africa; Natural Resources Research Report XX; U. N. Educational, Scientific and Cultural Organization; 7 Place de Fontenoy, 75700 Paris, France; 356 pages. Sankaran, M., Hanan, N., Scholes, R. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005). https://doi.org/10.1038/nature04070
DOI: 10.3886/e181861
title: Direct and interactive effects of TMT and board task-related faultlines on innovation performance: The moderating role of environmental dynamism

description: In this study, we address this problem by drawing on the categorization-elaboration model and the role theory perspective to explain how task-related faultlines in top management team (TMT) and board of directors (board) affect innovation performance. Evidence from the Chinese listed firms shows that TMT task-related faultlines and board task-related faultlines are positively related to innovation performance. Our results also indicate that the interaction between TMT task-related faultlines and board task-related faultlines (TMT-board task-related faultlines) positively influences innovation performance. Furthermore, our findings demonstrate that environmental dynamism strengthens the positive relationship between TMT task-related faultlines and innovation performance but weakens the positive effects of board task-related faultlines and TMT-board task-related faultlines on innovation performance.
DOI: 10.3886/e181861v1
title: How do faultlines in top management team and board of directors affect innovation performance? The moderating role of environmental dynamism

description: In this study, we address this problem by drawing on the categorization-elaboration model and the role theory perspective to explain how task-related faultlines in top management team (TMT) and board of directors (board) affect innovation performance. Evidence from the Chinese listed firms shows that TMT task-related faultlines and board task-related faultlines are positively related to innovation performance. Our results also indicate that the interaction between TMT task-related faultlines and board task-related faultlines (TMT-board task-related faultlines) positively influences innovation performance. Furthermore, our findings demonstrate that environmental dynamism strengthens the positive relationship between TMT task-related faultlines and innovation performance but weakens the positive effects of board task-related faultlines and TMT-board task-related faultlines on innovation performance.
DOI: 10.3886/e181861v2
title: Direct and interactive effects of TMT and board task-related faultlines on innovation performance: The moderating role of environmental dynamism

description: In this study, we address this problem by drawing on the categorization-elaboration model and the role theory perspective to explain how task-related faultlines in top management team (TMT) and board of directors (board) affect innovation performance. Evidence from the Chinese listed firms shows that TMT task-related faultlines and board task-related faultlines are positively related to innovation performance. Our results also indicate that the interaction between TMT task-related faultlines and board task-related faultlines (TMT-board task-related faultlines) positively influences innovation performance. Furthermore, our findings demonstrate that environmental dynamism strengthens the positive relationship between TMT task-related faultlines and innovation performance but weakens the positive effects of board task-related faultlines and TMT-board task-related faultlines on innovation performance.
DOI: 10.6084/m9.figshare.22775435
title: Supplemental Appendix

description: Details for the search that was conducted on PubMed and on EMBASE
DOI: 10.6084/m9.figshare.22775435.v1
title: Supplemental Appendix

description: Details for the search that was conducted on PubMed and on EMBASE
DOI: 10.5281/zenodo.8217324
title: The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material

description: These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. <strong>Code</strong> <strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP. <strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. <strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper. <strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>. <strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions. <strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong> <strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour. <strong>Files</strong> <strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) <strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy. <strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model. <strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>. <strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022
DOI: 10.5281/zenodo.8217325
title: The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material

description: These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. <strong>Code</strong> - <strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP.<br> - <strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. <br> - <strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.<br> - <strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>.<br> - <strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions.<br> - <strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong><br> - <strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour. ## Files - <strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) <br> - <strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy.<br> - <strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model.<br> - <strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>.<br> - <strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022
DOI: 10.5281/zenodo.8217356
title: The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material

description: These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. <strong>Code</strong> <strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP. <strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. <strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper. <strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>. <strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions. <strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong> <strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour. <strong>Files</strong> <strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) <strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy. <strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model. <strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>. <strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022
DOI: 10.6084/m9.figshare.23892741
title: Protein domain overrepresentation analysis

description: Results of the protein domain overrepresentation analysis for manuscript "Conserved non-coding elements evolve around the same genes throughout Metazoan evolution"
DOI: 10.6084/m9.figshare.23892741.v1
title: Protein domain overrepresentation analysis

description: Results of the protein domain overrepresentation analysis for manuscript "Conserved non-coding elements evolve around the same genes throughout Metazoan evolution"
DOI: 10.34740/kaggle/dsv/6252989
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6252992
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/6252995
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.34740/kaggle/dsv/6253012
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6253040
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6253060
title:  OpenAssistant-oasst1-fa
DOI: 10.18112/openneuro.ds003521.v2.2.0
title: FridayNightLights_Study2
DOI: 10.34740/kaggle/dsv/6253084
title:  OpenAssistant-oasst1-fa
DOI: 10.5281/zenodo.8217377
title: The export of African mineral dust across the Atlantic and its impact over the Amazon Basin

description: the codes used to analyze the model simulations in the paper
DOI: 10.5281/zenodo.8217376
title: The export of African mineral dust across the Atlantic and its impact over the Amazon Basin

description: the codes used to analyze the model simulations in the paper
DOI: 10.34740/kaggle/dsv/6253102
title:  OpenAssistant-oasst1-fa
DOI: 10.5281/zenodo.7901290
title: Global tree-canopy cover over three decades

description: Losses and gains in canopy cover of the world’s tree canopies affect carbon stocks, species habitats, water cycles, and human livelihoods. Consistent and multi-decadal global data on tree-canopy cover dynamics are needed for modelling climate scenarios, tracking progress towards restoration targets, and diverse other research, management and policy applications. However, most data only map binary ‘forest’/‘non forest’ distinctions that are regionally restricted or biassed by data gaps, and those mapping tree-canopy cover are limited to the 21st century. Here, we present an annual and global time-series of tree-canopy cover between 1992 and 2018. To develop these data, we integrated complementary products, using their respective strengths to compensate for weaknesses, and exploiting path dependencies in change processes to derive predictions into the data-sparse 1990s. Our model validation indicates we can accurately map tree-canopy cover (r2=0.95 [±0.01], RMSE=6.75% [±0.08], F1-score=0.97 [±0.0]) and our time-series agree well with national forest statistics (r2=0.94 [±0.0]).<br> <br> This repository contains the Global Tree-Canopy Cover Change dataset (GTCCC), which consists of a global time-series on per-pixel tree-canopy covers estimated at a 300-m resolution between 1992 and 2018. The repository contains the following: <em>GTCCC_canopyDensity.tar.gz</em> - Annual GeoTiffs on per-piel tree-canopy cover estimates (EPSG:4326) <em>GTCCC_uncertainty.tar.gz </em>- Annual GeotiFFs with per-pixel estimates of the 95% confidence interval of the the predictions of each RFReg decision tree. <em>GTCCC_change.tar.gz</em> - Multiple outputs describing changes in tree-canopy cover between 1992 and 2018. The contents are described in a README.txt file found within. <em>modelling_infrastructure.tar.gz </em>- Infrastructure to generate the GTCCC dataset, including code, and some intermediary outputs, such as reference samples and the predictive model. The contents are described in a README.txt file found within. The predictors used to generate the GTCCC are provided separately given large volume, and can be reached by clicking here.
DOI: 10.5281/zenodo.7901289
title: Global tree-canopy cover over three decades

description: Losses and gains in canopy cover of the world’s tree canopies affect carbon stocks, species habitats, water cycles, and human livelihoods. Consistent and multi-decadal global data on tree-canopy cover dynamics are needed for modelling climate scenarios, tracking progress towards restoration targets, and diverse other research, management and policy applications. However, most data only map binary ‘forest’/‘non forest’ distinctions that are regionally restricted or biassed by data gaps, and those mapping tree-canopy cover are limited to the 21st century. Here, we present an annual and global time-series of tree-canopy cover between 1992 and 2018. To develop these data, we integrated complementary products, using their respective strengths to compensate for weaknesses, and exploiting path dependencies in change processes to derive predictions into the data-sparse 1990s. Our model validation indicates we can accurately map tree-canopy cover (r2=0.95 [±0.01], RMSE=6.75% [±0.08], F1-score=0.97 [±0.0]) and our time-series agree well with national forest statistics (r2=0.94 [±0.0]).<br> <br> This repository contains the Global Tree-Canopy Cover Change dataset (GTCCC), which consists of a global time-series on per-pixel tree-canopy covers estimated at a 300-m resolution between 1992 and 2018. The repository contains the following: <em>GTCCC_canopyDensity.tar.gz</em> - Annual GeoTiffs on per-piel tree-canopy cover estimates (EPSG:4326) <em>GTCCC_uncertainty.tar.gz </em>- Annual GeotiFFs with per-pixel estimates of the 95% confidence interval of the the predictions of each RFReg decision tree. <em>GTCCC_change.tar.gz</em> - Multiple outputs describing changes in tree-canopy cover between 1992 and 2018. The contents are described in a README.txt file found within. <em>modelling_infrastructure.tar.gz </em>- Infrastructure to generate the GTCCC dataset, including code, and some intermediary outputs, such as reference samples and the predictive model. The contents are described in a README.txt file found within. The predictors used to generate the GTCCC are provided separately given large volume, and can be reached by clicking here.
DOI: 10.34740/kaggle/dsv/6253126
title:  OpenAssistant-oasst1-fa
DOI: 10.5281/zenodo.8146967
title: BLAB_Roboshoulder_dataset

description: This dataset contains thorax, clavicle, scapula, and humerus kinematics from 5 cadaveric specimens (10 shoulders) during abduction in the scapular plane, flexion, internal/external rotation at 0° of abduction, horizontal abduction, vertical traction, and horizontal compression. Three repetitions of these motions were induced by a robotic manipulator. All bony segments were instrumented with reflective markers fixed on bone pins, and their 3D trajectories were tracked by an optoelectronic system sampled at 100 Hz. For all bony segments, 3D models were constructed from segmentation of specimen CT-scan together with a set of bony and marker landmarks. For all joints of the shoulder girdle, the 6-DoF kinematics was computed for every shoulder and motion.
description: Only data related to shoulder RS001_L uploaded yet (version 1).
DOI: 10.5281/zenodo.8146966
title: BLAB_Roboshoulder_dataset

description: This dataset contains thorax, clavicle, scapula, and humerus kinematics from 10 shoulders (5 cadaveric specimens) during abduction in the scapular plane, flexion, internal/external rotation at 0° of abduction, horizontal abduction, vertical traction, and horizontal compression. Three repetitions of these motions were induced by a robotic manipulator. All bony segments were instrumented with reflective markers fixed on bone pins, and their 3D trajectories were tracked by an optoelectronic system sampled at 100 Hz. For all bony segments, 3D models were constructed from segmentation of specimen CT-scan together with a set of landmarks. For all joints of the shoulder girdle, the 6-DoF kinematics was computed for every shoulder and motion.
DOI: 10.5281/zenodo.8217257
title: BLAB_Roboshoulder_dataset

description: This dataset contains thorax, clavicle, scapula, and humerus kinematics from 10 shoulders (5 cadaveric specimens) during abduction in the scapular plane, flexion, internal/external rotation at 0° of abduction, horizontal abduction, vertical traction, and horizontal compression. Three repetitions of these motions were induced by a robotic manipulator. All bony segments were instrumented with reflective markers fixed on bone pins, and their 3D trajectories were tracked by an optoelectronic system sampled at 100 Hz. For all bony segments, 3D models were constructed from segmentation of specimen CT-scan together with a set of landmarks. For all joints of the shoulder girdle, the 6-DoF kinematics was computed for every shoulder and motion.
DOI: 10.34740/kaggle/dsv/6253144
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6253168
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6253183
title:  OpenAssistant-oasst1-fa
DOI: 10.6084/m9.figshare.22316641.v4
title: scIBD Website Data

description: Data files used in scIBD (www.scibd.cn) is also available here. Gene expression matrix Integrated gene expression matrix of scIBD (Anndata object in H5AD format) (scIBD.gex_matrix.h5ad) Integrated gene expression matrix of scIBD (Seurat object in RDS format) (scIBD.gex_matrix.rds) Meta data of scIBD (scIBD_sample_meta_data.txt.gz) Differential expressed genes Differential expressed genes across cell subtypes within each major cluster (scIBD.deg_by_major_cluster.rds) Differential activated regulons Differential regulons between healthy and disease in each major cluster (scIBD.diff_regulon_by_major_cluster.rds)
DOI: 10.34740/kaggle/dsv/6253206
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6253217
title:  OpenAssistant-oasst1-fa