cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.05156
title: A dataset of 1km Grid Comprehensive index of land use degree Density in China(2020）
title: 2020年中国1km格网土地利用综合程度数据集

description: Comprehensive index of land use degree quantitatively reflects the degree of exploitation and utilization of ecological environment by human activities from the aspect of land use, and is an important index to measure the depth and breadth of regional land use. The data set of comprehensive degree index of land use of 1km grid in China in 2020 is based on the 30-meter grid data of the national change survey in 2020. Considering the influence of social factors, the land use is divided into four levels: unused land, forest, grass and water land, agricultural land and urban settlement land. The degree of human intervention in regional land use is comprehensively calculated to quantitatively reflect the degree of land use in the study area. Meanwhile, in order to ensure the correctness and accuracy of the data, unified and standardized data processing methods, sample collection rules and quality are adopted. This data set can be used for land space planning, regional development and development evaluation, main functional area evaluation, coupling analysis of human-land relationship, etc.
description: Comprehensive index of land use degree quantitatively reflects the degree of exploitation and utilization of ecological environment by human activities from the aspect of land use, and is an important index to measure the depth and breadth of regional land use. The data set of comprehensive degree index of land use of 1km grid in China in 2020 is based on the 30-meter grid data of the national change survey in 2020. Considering the influence of social factors, the land use is divided into four levels: unused land, forest, grass and water land, agricultural land and urban settlement land. The degree of human intervention in regional land use is comprehensively calculated to quantitatively reflect the degree of land use in the study area. Meanwhile, in order to ensure the correctness and accuracy of the data, unified and standardized data processing methods, sample collection rules and quality are adopted. This data set can be used for land space planning, regional development and development evaluation, main functional area evaluation, coupling analysis of human-land relationship, etc.
DOI: 10.57760/sciencedb.06521
title: A dataset of 1km Grid Cultivated Land Fragmentation in China(2020）
title: 2020年中国1km格网耕地破碎度数据集

description: Cultivated land fragmentation is a quantitative description of cultivated land fragmentation. Cultivated land fragmentation means that due to natural or human factors, the number of cultivated land patches increases, the size of patches decreases, and the degree of isolation increases, showing a scattered and disorderly pattern. Fragmentation will not only affect the structure and function of the ecosystem, but also be detrimental to improving the utilization efficiency of cultivated land resources and the scale effect of grain production. If land property rights are not considered, the impact of human activities on cultivated land is mainly concentrated on the landscape level, and the landscape pattern index can largely summarize the information of land use pattern and reflect the characteristics of land spatial allocation. Therefore, this data set is based on the grid data of the 2020 annual change survey of the whole country (except Hong Kong, Macao and Taiwan), and three landscape pattern indexes, namely patch density index, separation index and separation index of landscape cultivated land types, are used to characterize the fragmentation characteristics of regional cultivated land, providing spatial decision support for the protection and sustainable utilization of regional cultivated land resources.
description: Cultivated land fragmentation is a quantitative description of cultivated land fragmentation. Cultivated land fragmentation means that due to natural or human factors, the number of cultivated land patches increases, the size of patches decreases, and the degree of isolation increases, showing a scattered and disorderly pattern. Fragmentation will not only affect the structure and function of the ecosystem, but also be detrimental to improving the utilization efficiency of cultivated land resources and the scale effect of grain production. If land property rights are not considered, the impact of human activities on cultivated land is mainly concentrated on the landscape level, and the landscape pattern index can largely summarize the information of land use pattern and reflect the characteristics of land spatial allocation. Therefore, this data set is based on the grid data of the 2020 annual change survey of the whole country (except Hong Kong, Macao and Taiwan), and three landscape pattern indexes, namely patch density index, separation index and separation index of landscape cultivated land types, are used to characterize the fragmentation characteristics of regional cultivated land, providing spatial decision support for the protection and sustainable utilization of regional cultivated land resources.
DOI: 10.57760/sciencedb.o00119.00030
title: A dataset of vegetation classification characteristics and background information of long-term ecological plots in Chinese Ecosystem Research Network (CERN)
title: CERN生物长期样地背景和植被分类特征本底数据集

description: This dataset is a comprehensive integration of 95 long-term plots from 22 natural ecosystem stations. We first sorted out the background information such as the geographical location, establishment time, area and representativeness of the long-term plot of ecological station. Drawing from the survey data of plant communities at the beginning of the plot establishment and adhering to the latest Chinese vegetation classification system, we then meticulously delineated the vegetation formation group, vegetation formation and vegetation subformation of each sample plot’s plant community. Based on the list of dominant species found in each sample plot, we identified and named the alliance and association of the vegetation in the plot. The construction of this dataset went through multiple rounds of expert review-station verification-expert review-station acceptance, etc. The dataset comprises station code, station name, plot code, plot name, plot type, plot representativeness, geographical location, altitude, plot area and shape, plot establishment time and design service life, vegetation formation group, vegetation formation, vegetation subformation, alliance, association, dominant species and remarks. It is expected to provide data support for the studies on flora, vegetation resource distribution, and biodiversity conservation.
description: This dataset is a comprehensive integration of 95 long-term plots from 22 natural ecosystem stations. We first sorted out the background information such as the geographical location, establishment time, area and representativeness of the long-term plot of ecological station. Drawing from the survey data of plant communities at the beginning of the plot establishment and adhering to the latest Chinese vegetation classification system, we then meticulously delineated the vegetation formation group, vegetation formation and vegetation subformation of each sample plot’s plant community. Based on the list of dominant species found in each sample plot, we identified and named the alliance and association of the vegetation in the plot. The construction of this dataset went through multiple rounds of expert review-station verification-expert review-station acceptance, etc. The dataset comprises station code, station name, plot code, plot name, plot type, plot representativeness, geographical location, altitude, plot area and shape, plot establishment time and design service life, vegetation formation group, vegetation formation, vegetation subformation, alliance, association, dominant species and remarks. It is expected to provide data support for the studies on flora, vegetation resource distribution, and biodiversity conservation.
DOI: 10.15468/dl.rahg5u
title: Occurrence Download

description: A dataset containing 444 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Scaura tenuis (Ducke, 1916)"
 ]
}

 The dataset includes 444 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001424-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.07320
title: Data for graphs
title: Data for graphs

description: The 66 general graphs are from SuiteSparse Matrix Collection and Gunrock benchmark datasets.
description: The 66 general graphs are from SuiteSparse Matrix Collection and Gunrock benchmark datasets.
DOI: 10.57760/sciencedb.07670
title: DORS0.25°: A High-Resolution Global Ocean Subsurface Temperature Dataset Using Remote Sensing Data and In Situ Observations
title: DORS0.25°: A High-Resolution Global Ocean Subsurface Temperature Dataset Using Remote Sensing Data and In Situ Observations

description: This product utilized Deep Forest algorithm to reconstruct a high spatial resolution (0.25° × 0.25°) global ocean subsurface (30–2000 m) temperature dataset for the period 1993–2023 (named DORS0.25°). It combined multisource surface remote sensing data (sea surface temperature [SST], absolute dynamic altitude [ADT], and the sea surface wind field, which can be decomposed into northward and eastward components [USSW and VSSW]) with georeferenced information and in situ temperature profile observations. The DORS0.25º dataset shows high correlation with EN4-profile and ARMOR3D datasets, achieving an average R2 of 0.980 and an RMSE of 0.567°C relative to the EN4-profile. In addition, the geographical pattern of DORS0.25º is consistent with Argo gridded dataset, but captures more detailed signals. The results indicate that the reconstruction of DOR0.25º dataset in time-series and geographical pattern is reliable, and can provide data support for the study of mesoscale ocean interior dynamics and global climate change. Time Range: 1993.01-2023.12Region: GlobalLongitude: 180°W~180°ELatitude: 78.375°S~78.375°NParameters:TemperatureHorizontal Resolution: 0.25° × 0.25°Vertical Resolution: 23 levels (30-2000 m)Temporal Resolution: monthlyStorage Format: netcdf
description: This product utilized Deep Forest algorithm to reconstruct a high spatial resolution (0.25° × 0.25°) global ocean subsurface (30–2000 m) temperature dataset for the period 1993–2023 (named DORS0.25°). It combined multisource surface remote sensing data (sea surface temperature [SST], absolute dynamic altitude [ADT], and the sea surface wind field, which can be decomposed into northward and eastward components [USSW and VSSW]) with georeferenced information and in situ temperature profile observations. The DORS0.25º dataset shows high correlation with EN4-profile and ARMOR3D datasets, achieving an average R2 of 0.980 and an RMSE of 0.567°C relative to the EN4-profile. In addition, the geographical pattern of DORS0.25º is consistent with Argo gridded dataset, but captures more detailed signals. The results indicate that the reconstruction of DOR0.25º dataset in time-series and geographical pattern is reliable, and can provide data support for the study of mesoscale ocean interior dynamics and global climate change. Time Range: 1993.01-2023.12Region: GlobalLongitude: 180°W~180°ELatitude: 78.375°S~78.375°NParameters:TemperatureHorizontal Resolution: 0.25° × 0.25°Vertical Resolution: 23 levels (30-2000 m)Temporal Resolution: monthlyStorage Format: netcdf
DOI: 10.15468/dl.p2b8b8
title: Occurrence Download

description: A dataset containing 2558 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Piptocarpha rotundifolia (Less.) Baker"
 ]
}

 The dataset includes 2558 records from 150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001425-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.02316
title: CLIMBra - Climate Change Dataset for Brazil
title: CLIMBra - Climate Change Dataset for Brazil

description: This dataset comprises a bias-corrected data set of 6 climate change variables at gridded (0.25° X 0.25°) and catchment scales within the Brazilian territory, following the second and fifth Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). We used Quantile Delta Mapping to correct bias in 19 General Circulation/Earth System Models from the Coupled Model Intercomparison Project 6 for six variables at a daily basis: precipitation, maximum and minimun temperature, radiation, near-surface wind speed and relative humidity. The bias-corrected dataset is available for both historical (1980-2013) and future (2015-2100) periods. The catchment-scale dataset that comprises point-based daily series for more than 700 Brazilian catchments. Our Dataset comprehends important variables commonly used in environmental/hydrological studies on climate change impacts at regional scales.
description: This dataset comprises a bias-corrected data set of 6 climate change variables at gridded (0.25° X 0.25°) and catchment scales within the Brazilian territory, following the second and fifth Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). We used Quantile Delta Mapping to correct bias in 19 General Circulation/Earth System Models from the Coupled Model Intercomparison Project 6 for six variables at a daily basis: precipitation, maximum and minimun temperature, radiation, near-surface wind speed and relative humidity. The bias-corrected dataset is available for both historical (1980-2013) and future (2015-2100) periods. The catchment-scale dataset that comprises point-based daily series for more than 700 Brazilian catchments. Our Dataset comprehends important variables commonly used in environmental/hydrological studies on climate change impacts at regional scales.
DOI: 10.57760/sciencedb.psych.00170
title: Data and code form "Prolonged visual perceptual changes induced by short-term dyadic training: The roles of confidence and autistic traits in social learning"
title: Data and code form "Prolonged visual perceptual changes induced by short-term dyadic training: The roles of confidence and autistic traits in social learning"

description: This dataset includes the original data, codes for data curation, and the summarized data for further analysis. Drift diffusion model fitting was performed in Python 3 using the hierarchical drift-diffusion model (HDDM) toolbox.
description: This dataset includes the original data, codes for data curation, and the summarized data for further analysis. Drift diffusion model fitting was performed in Python 3 using the hierarchical drift-diffusion model (HDDM) toolbox.
DOI: 10.57760/sciencedb.07812
title: Annual Report Readability and Trade Credit Financing in China
title: Annual Report Readability and Trade Credit Financing in China

description: Annual report is essential for building trust and transparency between a company and its suppliers and customers. And easy-to-understand text in the report can help to develop long-term, mutually beneficial relationships. This study examines how annual report readability impacts the trade credit financing of listed companies in China.
description: Annual report is essential for building trust and transparency between a company and its suppliers and customers. And easy-to-understand text in the report can help to develop long-term, mutually beneficial relationships. This study examines how annual report readability impacts the trade credit financing of listed companies in China.
DOI: 10.57760/sciencedb.12150
title: The Psychological Health Guard for Children and Adolescents Project of China Database
title: The Psychological Health Guard for Children and Adolescents Project of China Database

description: Unprecedented increases in risks of depressive disorders and resultant suicide have become the overarching menace for children/adolescent health. Despite global consensus to instigate psychological healthcare policy for them, their effects remain largely unclear neither from a small amount of official data nor from small-scale scientific studies. More importantly, in those underprivileged children/adolescents at lower-middle-economic-status countries/areas, the data collection may be not equally accessible as same as developed ones, thus resulting in underrepresented observations. To address these challenges, we released a living large-scale cohort dataset showing effects of primary psychological healthcare on decreasing depression and suicide ideation in underprivileged conditions, including unattended children/adolescents, orphan, children/adolescents in especially difficult circumstance, “left-behind” and “single-parenting” children/adolescents. 
description: Unprecedented increases in risks of depressive disorders and resultant suicide have become the overarching menace for children/adolescent health. Despite global consensus to instigate psychological healthcare policy for them, their effects remain largely unclear neither from a small amount of official data nor from small-scale scientific studies. More importantly, in those underprivileged children/adolescents at lower-middle-economic-status countries/areas, the data collection may be not equally accessible as same as developed ones, thus resulting in underrepresented observations. To address these challenges, we released a living large-scale cohort dataset showing effects of primary psychological healthcare on decreasing depression and suicide ideation in underprivileged conditions, including unattended children/adolescents, orphan, children/adolescents in especially difficult circumstance, “left-behind” and “single-parenting” children/adolescents.
DOI: 10.57760/sciencedb.ecodb.00148
title: A remote sensing classification dataset of Beidagang Wetland in Tianjin from 2019 to 2022
title: 2019-2022年天津北大港湿地遥感分类数据集

description: The remote sensing classification dataset of Beidagang Wetland in Tianjin from 2019 to 2022 is based on Google Earth Engine, using the random forest model to classify Sentinel-1 and Sentinel-2 images. This dataset processed and analyzed 313 Sentinel-1 images from May to October in each year from 2019 to 2022, and 275 Sentinel-2 images with cloud coverage less than 30%. In the random forest classification, the sample points are obtained from field sampling and UAV images, and the classification features comprised of NDWI, MNDWI, EVI, VH, and the phenological index of NDVI. There are 5 classes: permanent water, temporary water, perennial vegetation, annual vegetation, and barren. The dataset includes 4 TIF images of remote sensing classification in Beidagang Wetland from 2019 to 2022.
description: The remote sensing classification dataset of Beidagang Wetland in Tianjin from 2019 to 2022 is based on Google Earth Engine, using the random forest model to classify Sentinel-1 and Sentinel-2 images. This dataset processed and analyzed 313 Sentinel-1 images from May to October in each year from 2019 to 2022, and 275 Sentinel-2 images with cloud coverage less than 30%. In the random forest classification, the sample points are obtained from field sampling and UAV images, and the classification features comprised of NDWI, MNDWI, EVI, VH, and the phenological index of NDVI. There are 5 classes: permanent water, temporary water, perennial vegetation, annual vegetation, and barren. The dataset includes 4 TIF images of remote sensing classification in Beidagang Wetland from 2019 to 2022.
DOI: 10.57760/sciencedb.psych.00125
title: Data from: Multi-level processing of emotions in life motion signals revealed through pupil responses
title: Data from: Multi-level processing of emotions in life motion signals revealed through pupil responses

description: The ability to intuitively and reflexively interpret emotions through the motion pattern of living entities is essential for social interactions and human survival. Here we demonstrated that emotional information conveyed by point-light biological motion (BM) triggered automatic physiological responses as reflected in pupil size. A total of 120 participants (44 males, 76 females) ranging from 18 to 29 years old (M ± SD = 23.1 ± 2.5) were recruited. They were all naïve to the purpose of the experiments. Participants were seated at a viewing distance of 60 cm to the computer screen with their heads on a chinrest to minimize their head movements. Their pupil size and eye position of the left eye were recorded using a video-based iView X Hi-Speed system (SMI, Berlin, Germany) at 500 Hz. At the end of all experiments, participants were required to complete the 50-point Autism-spectrum Questionnaire (AQ), which measured the degree of autistic traits in the normal population (Baron-Cohen et al., 2001).
description: The ability to intuitively and reflexively interpret emotions through the motion pattern of living entities is essential for social interactions and human survival. Here we demonstrated that emotional information conveyed by point-light biological motion (BM) triggered automatic physiological responses as reflected in pupil size. A total of 120 participants (44 males, 76 females) ranging from 18 to 29 years old (M ± SD = 23.1 ± 2.5) were recruited. They were all naïve to the purpose of the experiments. Participants were seated at a viewing distance of 60 cm to the computer screen with their heads on a chinrest to minimize their head movements. Their pupil size and eye position of the left eye were recorded using a video-based iView X Hi-Speed system (SMI, Berlin, Germany) at 500 Hz. At the end of all experiments, participants were required to complete the 50-point Autism-spectrum Questionnaire (AQ), which measured the degree of autistic traits in the normal population (Baron-Cohen et al., 2001).
DOI: 10.57760/sciencedb.11745
title: Chemically Engineered Antibodies for Autophagy-based Receptor Degradation.
title: Chemically Engineered Antibodies for Autophagy-based Receptor Degradation.

description: Raw data for the manuscript entitled “Chemically Engineered Antibodies for Autophagy-based Receptor Degradation”.
description: Raw data for the manuscript entitled “Chemically Engineered Antibodies for Autophagy-based Receptor Degradation”.
DOI: 10.6084/m9.figshare.28232987
title: Size-Dependent Growth and Mortality Models for Japanese Tree Species

description: This is an interdisciplinary data set, including longevity Indices and climate data for Japanese tree species. Please refer to Kobayashi and Akasaka (2025) Nature Ecology and Evolution for details on data collection and processing.Supplementary Data 1: Potential maximum diameter of 87 tree species.Supplementary Data 2: Life table from age 0 to the potential maximum lifespan at 1 cm diameter for 53 tree species.Supplementary Data 3: Longevity indices for 53 tree species.Supplementary Data 4: Diameter data of Carpinus laxiflora in natural forests.Supplementary Data 5: Tree monitoring data of Carpinus laxiflora across all forest types.Supplementary Code 1: R script for calculating longevity indices.Supplementary Code 2: R script for statistical analysis of the main results.