cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28684120
title: Spatiotemporal patterns of human mobility during the COVID-19 pandemic in China

description: The outbreak of the COVID-19 pandemic has significantly reshaped population mobility, exerting a sustained impact on the patterns and dynamics of population mobility in China over the next three years. To comprehend the changes in population mobility patterns during the early stages of the COVID-19 outbreak, as well as standard epidemic prevention and control measures, we conducted an analysis using data from Baidu Huiyan’s migration scale index. This data was used to examine the characteristics of population movement in China during the Spring Festival and National Day from 2020 to 2022. We employed the Louvain algorithm and SVD decomposition to examine the spatiotemporal patterns of population movement. In addition, we calculated the response speed of urban population arrival flow to the pandemic using the Pearson correlation coefficient. Furthermore, we analyzed the factors influencing this correlation and response speed using random forest eigenvalues. The findings suggest that daily commuting and holiday travel patterns were not significantly altered by the pandemic. Over the past three years, there has been a trend in population mobility toward quicker responses to the pandemic, influenced primarily by economic, policy, medical conditions, and population density. Areas with higher population density and greater structural complexity exhibit increased sensitivity of population mobility to the severity of the pandemic. Examining population movement patterns and influencing factors against the backdrop of the COVID-19 pandemic can offer valuable insights for devising more targeted and effective prevention and control measures. Ultimately, this endeavor contributes to enhancing health-related urban resilience and sustainability.
DOI: 10.6084/m9.figshare.28684120.v1
title: Spatiotemporal patterns of human mobility during the COVID-19 pandemic in China

description: The outbreak of the COVID-19 pandemic has significantly reshaped population mobility, exerting a sustained impact on the patterns and dynamics of population mobility in China over the next three years. To comprehend the changes in population mobility patterns during the early stages of the COVID-19 outbreak, as well as standard epidemic prevention and control measures, we conducted an analysis using data from Baidu Huiyan’s migration scale index. This data was used to examine the characteristics of population movement in China during the Spring Festival and National Day from 2020 to 2022. We employed the Louvain algorithm and SVD decomposition to examine the spatiotemporal patterns of population movement. In addition, we calculated the response speed of urban population arrival flow to the pandemic using the Pearson correlation coefficient. Furthermore, we analyzed the factors influencing this correlation and response speed using random forest eigenvalues. The findings suggest that daily commuting and holiday travel patterns were not significantly altered by the pandemic. Over the past three years, there has been a trend in population mobility toward quicker responses to the pandemic, influenced primarily by economic, policy, medical conditions, and population density. Areas with higher population density and greater structural complexity exhibit increased sensitivity of population mobility to the severity of the pandemic. Examining population movement patterns and influencing factors against the backdrop of the COVID-19 pandemic can offer valuable insights for devising more targeted and effective prevention and control measures. Ultimately, this endeavor contributes to enhancing health-related urban resilience and sustainability.
DOI: 10.6084/m9.figshare.28452453
title: How coarse analysis can obscure key trends - a multi-scale study using very high-resolution intra-urban land data

description: Land use change (LUC) mechanisms are vital for understanding change processes and policy formulation. However, most previous studies neglected intra-urban land use evolution and the influence of spatial resolution. This study designed a multi-scale LUC driver analysis framework to analyze the drivers of intra-urban land use expansion in selected cities in Hunan Province, China, and explored the impact of spatial resolution on driver analysis. First, very high-resolution intra-urban land use survey data were used to explore the driving mechanisms based on Random Forest and Spearman’s correlation analysis at 2 m resolution. Then, results from other resolutions (5 m, 10 m, 30 m, 60 m, 90 m, and 120 m) were compared to the 2 m resolution. A scale effect assessment strategy based on Spearman’s correlation analysis, Sen’s slope, and Mann-Kendall (MK) trend test was devised to analyze the patterns of change in the driver mining results with spatial resolution. Findings reveal that night light brightness, elevation, residential density, population density, distance to transportation facilities, and distance to industrial parks predominantly drive intra-urban land use expansion, with different categories emphasizing specific factors. Analyzing intra-urban land use expansion mechanisms at low spatial resolutions (below 30 m) can significantly distort results and render the ranking of driving elements unstable. The loss of patch information due to reduced spatial resolution is the primary cause of distorted driving factor analysis results. It is recommended that related studies use data with a resolution of 30 m or higher. These findings offer valuable insights for optimizing land use policies and serve as a reference for selecting appropriate data resolutions in future studies.
DOI: 10.6084/m9.figshare.28452453.v1
title: How coarse analysis can obscure key trends - a multi-scale study using very high-resolution intra-urban land data

description: Land use change (LUC) mechanisms are vital for understanding change processes and policy formulation. However, most previous studies neglected intra-urban land use evolution and the influence of spatial resolution. This study designed a multi-scale LUC driver analysis framework to analyze the drivers of intra-urban land use expansion in selected cities in Hunan Province, China, and explored the impact of spatial resolution on driver analysis. First, very high-resolution intra-urban land use survey data were used to explore the driving mechanisms based on Random Forest and Spearman’s correlation analysis at 2 m resolution. Then, results from other resolutions (5 m, 10 m, 30 m, 60 m, 90 m, and 120 m) were compared to the 2 m resolution. A scale effect assessment strategy based on Spearman’s correlation analysis, Sen’s slope, and Mann-Kendall (MK) trend test was devised to analyze the patterns of change in the driver mining results with spatial resolution. Findings reveal that night light brightness, elevation, residential density, population density, distance to transportation facilities, and distance to industrial parks predominantly drive intra-urban land use expansion, with different categories emphasizing specific factors. Analyzing intra-urban land use expansion mechanisms at low spatial resolutions (below 30 m) can significantly distort results and render the ranking of driving elements unstable. The loss of patch information due to reduced spatial resolution is the primary cause of distorted driving factor analysis results. It is recommended that related studies use data with a resolution of 30 m or higher. These findings offer valuable insights for optimizing land use policies and serve as a reference for selecting appropriate data resolutions in future studies.
DOI: 10.6084/m9.figshare.28577278
title: 3D spatial evolutionary particle swarm algorithm based emergency communication spatial deployment optimization

description: Building emergency communication in disaster areas is a key problem that emergency response needs to solve. Ad hoc Network (ANET) can quickly establish communication networks when public communication infrastructure is disrupted. At present, ground emergency communication deployment based on ANET usually relies on the operator’s experience, which struggles to ensure high-quality deployment in complex urban environments. This paper proposes an ANET nodes spatial optimization deployment algorithm based on 3D Spatial Evolutionary Particle Swarm Optimization (3DSEPSO). A wireless communication transmission rate model that depends on surface buildings and trees is constructed using ground truth communication data. The algorithm incorporates a fitness evaluation model, particle chromosome structure, and an evolutionary mechanism to intelligently deploy ANET nodes. By considering the spatial distribution of buildings and trees, the algorithm can achieve optimal data transmission quality by using a given number of ANET nodes. Experimental results demonstrate that the proposed algorithm significantly outperforms empirical approaches and traditional methods in terms of data transmission rates and quality. Thus, the algorithm provides better support for emergency rescue teams by facilitating more effective and reliable emergency communication.
DOI: 10.6084/m9.figshare.28577278.v1
title: 3D spatial evolutionary particle swarm algorithm based emergency communication spatial deployment optimization

description: Building emergency communication in disaster areas is a key problem that emergency response needs to solve. Ad hoc Network (ANET) can quickly establish communication networks when public communication infrastructure is disrupted. At present, ground emergency communication deployment based on ANET usually relies on the operator’s experience, which struggles to ensure high-quality deployment in complex urban environments. This paper proposes an ANET nodes spatial optimization deployment algorithm based on 3D Spatial Evolutionary Particle Swarm Optimization (3DSEPSO). A wireless communication transmission rate model that depends on surface buildings and trees is constructed using ground truth communication data. The algorithm incorporates a fitness evaluation model, particle chromosome structure, and an evolutionary mechanism to intelligently deploy ANET nodes. By considering the spatial distribution of buildings and trees, the algorithm can achieve optimal data transmission quality by using a given number of ANET nodes. Experimental results demonstrate that the proposed algorithm significantly outperforms empirical approaches and traditional methods in terms of data transmission rates and quality. Thus, the algorithm provides better support for emergency rescue teams by facilitating more effective and reliable emergency communication.
DOI: 10.34740/kaggle/dsv/14654204
title: Garbage Dataset
DOI: 10.6084/m9.figshare.28582553
title: Improved estimation of soil organic carbon stock in subtropical cropland of Southern China based on digital soil mapping and multi-sources data

description: Accurate information on spatial patterns of Soil Organic Carbon (SOC) stock in cropland is crucial for soil quality management, agriculture production, and carbon cycling regulation. This research aims to produce the finest map (30 m) of SOC Density (SOCD) and estimate SOC stock in the plow layer of the cropland of Jiangxi Province, which is an important grain-production region in China but its knowledge of the quantity and spatial pattern of SOC stock majorly remain unclear. To fill this gap, we used the recursive feature elimination to choose the best predictors for mapping SCOD. Then we constructed a random forest model to map the SOCD in cropland across Jiangxi Province, quantified relative importance of predictors, and calculated the SOC stock using soil type method and the digital soil mapping method. Finally, structural equation model was adopted to analyze the impacts of various factors on SCOD. We hypothesized that using machine learning method and incorporating various covariates cover soil properties, terrain, climate, biota, lithology, and soil management policies can well capture the spatial variability of SOCD in the cropland. Our results indicating the averaged SOCD in cropland’s plow layer of Jiangxi Province is 2.95 kg m<sup>−2</sup>. Our model explained 54% of the variance. The terrain factors had the largest contribution to mapping SOCD. The total SOC stock in the plow layer of the survey region is 9148.33 × 10<sup>4</sup> tons. Soil properties, soil management practices, and lithological factors affect SOCD mainly through direct way while terrain, climate, and biota mainly via indirect way. Additionally, the mean value of SOCD significantly decreased from 3.23 in the 1980s to 2.95 kg m<sup>−2</sup> in the 2010s. Our results offer critical information on the spatial pattern in SOCD and its potential dominators, which enable us to make climate-smarter agricultural policies when making strategies for cropland management.
DOI: 10.6084/m9.figshare.28582553.v1
title: Improved estimation of soil organic carbon stock in subtropical cropland of Southern China based on digital soil mapping and multi-sources data

description: Accurate information on spatial patterns of Soil Organic Carbon (SOC) stock in cropland is crucial for soil quality management, agriculture production, and carbon cycling regulation. This research aims to produce the finest map (30 m) of SOC Density (SOCD) and estimate SOC stock in the plow layer of the cropland of Jiangxi Province, which is an important grain-production region in China but its knowledge of the quantity and spatial pattern of SOC stock majorly remain unclear. To fill this gap, we used the recursive feature elimination to choose the best predictors for mapping SCOD. Then we constructed a random forest model to map the SOCD in cropland across Jiangxi Province, quantified relative importance of predictors, and calculated the SOC stock using soil type method and the digital soil mapping method. Finally, structural equation model was adopted to analyze the impacts of various factors on SCOD. We hypothesized that using machine learning method and incorporating various covariates cover soil properties, terrain, climate, biota, lithology, and soil management policies can well capture the spatial variability of SOCD in the cropland. Our results indicating the averaged SOCD in cropland’s plow layer of Jiangxi Province is 2.95 kg m<sup>−2</sup>. Our model explained 54% of the variance. The terrain factors had the largest contribution to mapping SOCD. The total SOC stock in the plow layer of the survey region is 9148.33 × 10<sup>4</sup> tons. Soil properties, soil management practices, and lithological factors affect SOCD mainly through direct way while terrain, climate, and biota mainly via indirect way. Additionally, the mean value of SOCD significantly decreased from 3.23 in the 1980s to 2.95 kg m<sup>−2</sup> in the 2010s. Our results offer critical information on the spatial pattern in SOCD and its potential dominators, which enable us to make climate-smarter agricultural policies when making strategies for cropland management.
DOI: 10.25574/cdc.534
title: Chandra X-ray Observatory Chandra Data Collection for request #534

description: Chandra Data Collection for request #534
DOI: 10.15468/dl.jhtn77
title: Occurrence Download

description: A dataset containing 475 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Clathrotropis brunnea"
 ]
}

 The dataset includes 475 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003737-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6zesfn
title: Occurrence Download

description: A dataset containing 186638 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Procyon"
 ]
}

 The dataset includes 186638 records from 790 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003639-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v3fpfq
title: Occurrence Download

description: A dataset containing 54345 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Vascular Plant Herbarium: Herbarium Generale",
 "Country is Sweden"
 ]
}

 The dataset includes 54345 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003708-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b29276
title: Occurrence Download

description: A dataset containing 334 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Malus",
 "OccurrenceStatus is Present",
 "Geometry POLYGON((8.4 44.6, 11.5 44.6, 11.5 46.7, 8.4 46.7, 8.4 44.6))"
 ]
}

 The dataset includes 334 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003739-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v953rc
title: Occurrence Download

description: A dataset containing 454 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prionotus punctatus",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLatitude is less than 90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 454 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003740-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wgtbfa
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hovea longifolia",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003741-260126135527185/datasets/export for details.
DOI: 10.25574/00914
title: Chandra X-ray Observatory ObsId 00914

description: THE CLUSTER X-RAY TEMPERATURE FUNCTION AT Z&gt;0.6
DOI: 10.25574/03199
title: Chandra X-ray Observatory ObsId 03199

description: OBSERVATION OF MACS CLUSTERS, Z&gt;0.5
DOI: 10.25574/03249
title: Chandra X-ray Observatory ObsId 03249

description: OBSERVATION OF BCS CLUSTERS, FIRST SET
DOI: 10.25574/03254
title: Chandra X-ray Observatory ObsId 03254

description: OBSERVATION OF MACS CLUSTERS, 0.45
DOI: 10.25574/03594
title: Chandra X-ray Observatory ObsId 03594

description: X-RAY/SZ DETERMINATION OF COSMOLOGICAL PARAMETERS
DOI: 10.15468/dl.7834qr
title: Occurrence Download

description: A dataset containing 73397 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Liriodendron tulipifera"
 ]
}

 The dataset includes 73397 records from 643 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003653-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hq6nw8
title: Occurrence Download

description: A dataset containing 28 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (CHN.6.4_1, CHN.6.15_1, CHN.6.21_1, CHN.6.3_1, CHN.6.6_1, CHN.6.2_1, CHN.6.20_1, CHN.6.7_1, CHN.6.19_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Saundersilarus saundersi",
 "Year 2020-2025"
 ]
}

 The dataset includes 28 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025707-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c83zv8
title: Occurrence Download

description: A dataset containing 3653 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (West Nile virus)",
 "BasisOfRecord is one of (Specimen, Living Specimen, Material sample, Literature Occurrence, Machine Observation, Occurrence evidence, Human Observation)",
 "Year is greater than or equal to 1960",
 "HasCoordinate is false",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3653 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016474-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vgvamu
title: Occurrence Download

description: A dataset containing 88 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Parthenocissus heterophylla",
 "Year 1970-2000"
 ]
}

 The dataset includes 88 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010789-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wtjdvq
title: Occurrence Download

description: A dataset containing 6683 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhincodon typus"
 ]
}

 The dataset includes 6683 records from 131 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010790-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t3sd2z
title: Occurrence Download

description: A dataset containing 103 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Synotis alata",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 103 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010791-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bpnr7h
title: Occurrence Download

description: A dataset containing 396 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Arachnospila consobrina"
 ]
}

 The dataset includes 396 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010793-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dfwxwu
title: Occurrence Download

description: A dataset containing 9250 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Western Sahara",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 9250 records from 143 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010792-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b82jpa
title: Occurrence Download

description: A dataset containing 2247 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tagetes minuta",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 2247 records from 252 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010794-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.