cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.j00001.00831
title: A dataset of distribution of toxic and harmful microalgae and algal toxins in China's coastal waters during 2019–2021
title: 2019–2021年中国近海有毒有害微藻和藻毒素分布数据集

description: China's coastal waters, including the Bohai Sea, the Yellow Sea, the East China Sea and the South China Sea, are rich in marine resources and of strategic importance to the country's economic development. In recent years, the problem of water safety caused by algae and algal toxins is becoming more and more serious, and the frequency and scale of harmful algal blooms are increasing in China's coastal waters. This dataset collected and organized the survey data of toxic and harmful microalgae and algal toxins in China's coastal waters from 2019 to 2021, it included 3429 microalgae data, 33 240 microalgae molecular species data, 489 phytoplankton algal toxin data, 76 seawater algal toxin data and 440 economic species algal toxin data. The research teams providing the data ensured the accuracy of the test data through inter-project and international collation. In order to display the distribution of toxic and harmful microalgae and algal toxins in China's coastal waters, 115 microalgae distribution maps and 29 algal toxin distribution maps were provided. This dataset can provide data support for the study of the spatial and temporal distribution of toxic and harmful microalgae and algal toxins in China's coastal waters, and for the prevention, warning and control of harmful algal blooms.
description: China's coastal waters, including the Bohai Sea, the Yellow Sea, the East China Sea and the South China Sea, are rich in marine resources and of strategic importance to the country's economic development. In recent years, the problem of water safety caused by algae and algal toxins is becoming more and more serious, and the frequency and scale of harmful algal blooms are increasing in China's coastal waters. This dataset collected and organized the survey data of toxic and harmful microalgae and algal toxins in China's coastal waters from 2019 to 2021, it included 3429 microalgae data, 33 240 microalgae molecular species data, 489 phytoplankton algal toxin data, 76 seawater algal toxin data and 440 economic species algal toxin data. The research teams providing the data ensured the accuracy of the test data through inter-project and international collation. In order to display the distribution of toxic and harmful microalgae and algal toxins in China's coastal waters, 115 microalgae distribution maps and 29 algal toxin distribution maps were provided. This dataset can provide data support for the study of the spatial and temporal distribution of toxic and harmful microalgae and algal toxins in China's coastal waters, and for the prevention, warning and control of harmful algal blooms.
DOI: 10.57760/sciencedb.j00001.00792
title: A dataset of the plant species list in Fengtai district natural protected areas during 2021–2022
title: 2021–2022年北京市丰台区典型自然保护地植物物种名录数据集

description: Plants are the core components of protected areas and are the basic resources for maintaining ecosystem functions such as biodiversity. y. In this paper, based on the results of field surveys of plants in 2021 and 2022, we sorted out a dataset of plant species list for typical protected areas (Nanyuan forest and wetland park, Lüdi park, Lotus pond park, Beigong national forest park) in Fengtai District, Beijing. This dataset includes 255 species of 179 genera in 73 families of tree, shrub, and herbaceous plant species. In addition, the dataset contains the information of the protected area name, family Chinese name, scientific name, genus Chinese name, genus scientific name, plant species name, and Latin name, etc. Through comprehensive mapping and monitoring of plant species in important protected areas in Fengtai District, the dataset provides data support for research on the spatial and temporal patterns of biodiversity and the response of plants to natural and anthropogenic disturbances in Beijing.
description: Plants are the core components of protected areas and are the basic resources for maintaining ecosystem functions such as biodiversity. y. In this paper, based on the results of field surveys of plants in 2021 and 2022, we sorted out a dataset of plant species list for typical protected areas (Nanyuan forest and wetland park, Lüdi park, Lotus pond park, Beigong national forest park) in Fengtai District, Beijing. This dataset includes 255 species of 179 genera in 73 families of tree, shrub, and herbaceous plant species. In addition, the dataset contains the information of the protected area name, family Chinese name, scientific name, genus Chinese name, genus scientific name, plant species name, and Latin name, etc. Through comprehensive mapping and monitoring of plant species in important protected areas in Fengtai District, the dataset provides data support for research on the spatial and temporal patterns of biodiversity and the response of plants to natural and anthropogenic disturbances in Beijing.
DOI: 10.57760/sciencedb.o00121.00001
title: A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020
title: A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020

description: We provide a remote sensing classification dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform using random forest classifier. Specifically, ground samples are carefully collected around China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated together with topographic features that are sensitive to PV locations including slope and aspect, aiming to construct a multi-dimensional feature space. Finally, the trained random forest model is adopted predict PV power stations of China on GEE. Above all, as the first publicly released 10-m national-scale distribution dataset of China’s ground-mounted PV power stations, it can provide data references for relevant researchers in fields such as energy, land, remote sensing and environmental sciences.
description: We provide a remote sensing classification dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform using random forest classifier. Specifically, ground samples are carefully collected around China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated together with topographic features that are sensitive to PV locations including slope and aspect, aiming to construct a multi-dimensional feature space. Finally, the trained random forest model is adopted predict PV power stations of China on GEE. Above all, as the first publicly released 10-m national-scale distribution dataset of China’s ground-mounted PV power stations, it can provide data references for relevant researchers in fields such as energy, land, remote sensing and environmental sciences.
DOI: 10.15468/dl.w7ht5t
title: Occurrence Download

description: A dataset containing 11879 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Month is August",
 "TaxonKey is Cuculus canorus Linnaeus, 1758",
 "Year 2004-2014"
 ]
}

 The dataset includes 11879 records from 161 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001380-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.03390
title: Convolutional Neural Network based Ground Reaction Forces and Center of Pressure Estimation During Stair Walking Based on Multi-level Kinematics
title: Convolutional Neural Network based Ground Reaction Forces and Center of Pressure Estimation During Stair Walking Based on Multi-level Kinematics

description: Ground reaction forces (GRFs) and center of pressure trajectories (CoPs) are required for a comprehensive biomechanical analysis. They are also important outcome measures in sports sciences or clinical areas. GRFs and CoPs are usually measured by force plate, which is rarely equipped on staircases in laboratories. We present a one-dimensional convolutional neural network for estimating GRFs and CoPs during stair ascent and descent using multi-level of kinematics as input. We collected a dataset of 3782 trials from 172 subjects for training and validating this model, involving healthy subjects and individuals with knee osteoarthritis or with moderate-to-high risk of cardiovascular diseases. Our model achieves the state-of-the-art estimating performance with nRMSE of 2.755%~7.633%, Pearson correlation of 0.950~0.996 on GRFs estimation, and with nRMSE of 5.519%~14.669%, Pearson correlation of 0.918~0.991 on CoPs estimation. With the proposed model, GRFs and CoPs during stair walking can be achieved without force-plate-embedded staircases. The network architecture, the estimation model as well as some demo data are provided here.
description: Ground reaction forces (GRFs) and center of pressure trajectories (CoPs) are required for a comprehensive biomechanical analysis. They are also important outcome measures in sports sciences or clinical areas. GRFs and CoPs are usually measured by force plate, which is rarely equipped on staircases in laboratories. We present a one-dimensional convolutional neural network for estimating GRFs and CoPs during stair ascent and descent using multi-level of kinematics as input. We collected a dataset of 3782 trials from 172 subjects for training and validating this model, involving healthy subjects and individuals with knee osteoarthritis or with moderate-to-high risk of cardiovascular diseases. Our model achieves the state-of-the-art estimating performance with nRMSE of 2.755%~7.633%, Pearson correlation of 0.950~0.996 on GRFs estimation, and with nRMSE of 5.519%~14.669%, Pearson correlation of 0.918~0.991 on CoPs estimation. With the proposed model, GRFs and CoPs during stair walking can be achieved without force-plate-embedded staircases. The network architecture, the estimation model as well as some demo data are provided here.
DOI: 10.57760/sciencedb.13300
title: the BRI
title: the BRI

description: From the World Bank Open Database 
description: From the World Bank Open Database
DOI: 10.57760/sciencedb.11388
title: Shenyang Xinsong Robot Co., Ltd. C5 Floor Dataset
title: 沈阳新松机器人股份有限公司绕C5楼数据集

description: This dataset was recorded using a Xinsong outdoor test vehicle with a moving speed of about 1m/s. The two datasets were recorded on April 29, 2022 and April 12, 2023, respectively. The vehicle was equipped with RS-LiDAR-16, Xsens MTi-30-2A8G4 IMU, and RTK-GPS. The LiDAR was installed at a height of 1.6m, with an effective detection distance of 150m, a vertical angle resolution of 2 °, a horizontal angle resolution of 0.2 °, and an IMU sampling frequency of 200Hz. The recorded data is the sensor data of various parts obtained by circling around C5 building 
description: This dataset was recorded using a Xinsong outdoor test vehicle with a moving speed of about 1m/s. The two datasets were recorded on April 29, 2022 and April 12, 2023, respectively. The vehicle was equipped with RS-LiDAR-16, Xsens MTi-30-2A8G4 IMU, and RTK-GPS. The LiDAR was installed at a height of 1.6m, with an effective detection distance of 150m, a vertical angle resolution of 2 °, a horizontal angle resolution of 0.2 °, and an IMU sampling frequency of 200Hz. The recorded data is the sensor data of various parts obtained by circling around C5 building
DOI: 10.57760/sciencedb.07590
title: Plant species composition and distribution in Caryopteris mongolica community in Northwestern China
title: Plant species composition and distribution in Caryopteris mongolica community in Northwestern China

description: Based on the analysis objects of Caryopteris mongolica communities distributed in typical steppe, desertified steppe and deserts, to investigate the relative effects of environmental factors on species distribution and diversity in C. mongolica communities and how they changed with environmental conditions, which provide a scientific basis for understanding how climate change affects biodiversity across regions. We selected a total of 34 representative C. mongolica community sites in northern China, and their community composition was studied using the sample plot survey method. Redundancy analysis (RDA), Pearson correlation and generalized summation models (GAMs) were used to explore the effects of environmental heterogeneity on species distribution and species diversity in the communities.
description: Based on the analysis objects of Caryopteris mongolica communities distributed in typical steppe, desertified steppe and deserts, to investigate the relative effects of environmental factors on species distribution and diversity in C. mongolica communities and how they changed with environmental conditions, which provide a scientific basis for understanding how climate change affects biodiversity across regions. We selected a total of 34 representative C. mongolica community sites in northern China, and their community composition was studied using the sample plot survey method. Redundancy analysis (RDA), Pearson correlation and generalized summation models (GAMs) were used to explore the effects of environmental heterogeneity on species distribution and species diversity in the communities.
DOI: 10.57760/sciencedb.07861
title: Dataset used for study "High diversity, close genetic relatedness, and favorable living conditions benefit species co-occurrence of gut microbiota in Brandt’s vole"
title: Dataset used for study "High diversity, close genetic relatedness, and favorable living conditions benefit species co-occurrence of gut microbiota in Brandt’s vole"

description: 16S gut microbiological data of Brandt's vole. The data have been double-ended spliced and resampled with a sampling threshold of 50,000.
description: 16S gut microbiological data of Brandt's vole. The data have been double-ended spliced and resampled with a sampling threshold of 50,000.
DOI: 10.15468/dl.2gkbe9
title: Occurrence Download

description: A dataset containing 2727 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Plebeia frontalis (Friese, 1911)"
 ]
}

 The dataset includes 2727 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001388-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sq2r6u
title: Occurrence Download

description: A dataset containing 195264 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is U.S.D.A. Forest Service, Redwood Sciences Laboratory - Lamna Point Count"
 ]
}

 The dataset includes 195264 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001387-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.12395
title: FY-4A REGC AGRI AOD
title: FY-4A REGC AGRI AOD

description: The dataset is a colltection of FY-4A REGC AGRI AOD retrieved with transfer learning method (see details in the paper:A deep-learning and transfer-learning hybrid aerosol retrieval algorithm for FY4-AGRI: Development and verification over Asia). The AGRI AOD is built with 5min/4km. The dataset in this category is provided in 1 hour.Due to the cloud mask before March 12th being derived from a simplified empirical algorithm, there is a difference in data quality between the AOD data before and after March 12th. If the author requires a dataset with a 5-minute time resolution, please contact Dr. Dong Fu (fudisong@mail.iap.ac.cn). If you use this dataset, please cite this dataset and the related article.
description: The dataset is a colltection of FY-4A REGC AGRI AOD retrieved with transfer learning method (see details in the paper:A deep-learning and transfer-learning hybrid aerosol retrieval algorithm for FY4-AGRI: Development and verification over Asia). The AGRI AOD is built with 5min/4km. The dataset in this category is provided in 1 hour.Due to the cloud mask before March 12th being derived from a simplified empirical algorithm, there is a difference in data quality between the AOD data before and after March 12th. If the author requires a dataset with a 5-minute time resolution, please contact Dr. Dong Fu (fudisong@mail.iap.ac.cn). If you use this dataset, please cite this dataset and the related article.
DOI: 10.15468/dl.58vpde
title: Occurrence Download

description: A dataset containing 194449 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Swiss National Databank of Mayflies, Stoneflies and Caddiesflies"
 ]
}

 The dataset includes 194449 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001390-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xp4z38
title: Occurrence Download

description: A dataset containing 194825 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Natural History Museum Aarhus Entomology Collection"
 ]
}

 The dataset includes 194825 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001389-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.06932
title: Insect Diversity Observation Data Set in Guangdong Greater Bay Area (2018 - 2022)
title: 2018–2022年广东大湾区昆虫多样性观测数据集

description: Insects are the largest animal group in the world, having a close relationship with human agricultural and forestry production and daily life. The forest in Qi’niang Mountain is luxuriant. There are many evergreen broad-leaved forests and a variety of shrubs and herb communities have been preserved without human destruction. In accordance with the Technical Guidelines for Biodiversity Observation of Large and Medium Soil Animals and the Shenzhen Biodiversity White Paper, Guangdong Shenzhen Environmental Monitoring Center cooperated with the Insect Laboratory of the School of Life Sciences of Shanghai Normal University to carry out long-term monitoring of insect diversity in Qi’niang Mountain from December 2018 to October 2022, through field collection, specimen identification and statistical sorting, there were 3511 insects belonging to 392 species in 132 families. This study will publicly report the insect data from 2018 to 2022, providing basic data for clarifying the city's insect resources and future long-term environmental monitoring services.
description: Insects are the largest animal group in the world, having a close relationship with human agricultural and forestry production and daily life. The forest in Qi’niang Mountain is luxuriant. There are many evergreen broad-leaved forests and a variety of shrubs and herb communities have been preserved without human destruction. In accordance with the Technical Guidelines for Biodiversity Observation of Large and Medium Soil Animals and the Shenzhen Biodiversity White Paper, Guangdong Shenzhen Environmental Monitoring Center cooperated with the Insect Laboratory of the School of Life Sciences of Shanghai Normal University to carry out long-term monitoring of insect diversity in Qi’niang Mountain from December 2018 to October 2022, through field collection, specimen identification and statistical sorting, there were 3511 insects belonging to 392 species in 132 families. This study will publicly report the insect data from 2018 to 2022, providing basic data for clarifying the city's insect resources and future long-term environmental monitoring services.
DOI: 10.57760/sciencedb.07961
title: Full-scale Annual N2O dataset in China (FAN2020)
title: Full-scale Annual N2O dataset in China (FAN2020)

description: FAN includes N2O emission of five sectors: agriculture, energy, industry, waste and natural ecosystem. Most sectors cover the temporal variations of N2O emissions from 1980 to 2020, and hence the version was named FAN v2020. For anthropogenic sources, FAN follows the categories of each sector in the IPCC-guided methodology, and includes 23 categories for all anthropogenic sources, which are estimated at the provincial level. Natural sources include N2O emission from forest and grassland ecosystem simulated with at a spatial resolution of 25 km×25 km.
description: FAN includes N2O emission of five sectors: agriculture, energy, industry, waste and natural ecosystem. Most sectors cover the temporal variations of N2O emissions from 1980 to 2020, and hence the version was named FAN v2020. For anthropogenic sources, FAN follows the categories of each sector in the IPCC-guided methodology, and includes 23 categories for all anthropogenic sources, which are estimated at the provincial level. Natural sources include N2O emission from forest and grassland ecosystem simulated with at a spatial resolution of 25 km×25 km.
DOI: 10.57760/sciencedb.13382
title: HDCO
title: 中国绿洲高分辨数据集

description: High-resolution Oasis maps are imperative for obtaining ecological and socio-economic development of arid regions. However, due to the late establishment and relatively niche nature of the oasis discipline system, there are no high-precision datasets related to oases in the world to date. To fill this gap, we conducted detailed visual interpretation of remote sensing images on Google Earth Professional or Sentinel II in summer 2020, and constructed for the first time a high-precision dataset of China's oases (abbreviation HDCO) with a resolution of 1 meter, covering 1,465 oases with an area larger than 0.01 km2. These oases collectively cover an expansive area of 275143.30 km², equivalent to approximately 8.12% of China's dryland area. The kappa coefficient for this dataset validated by the field survey was 0.8686 and the AUC value for the ROC curve was 0.935. The accuracy obtained from the validation using a random hexagonal grid is 95.73%. In addition, we added information on the geographic coordinates, climatic conditions, major landforms, and hydrological features of each oasis to the attribute table of the dataset. This dataset enables researches to quantitatively monitor location, area, and dynamic changes of oases, fosters explore the relationship between oases and human under climate change and urbanization.
description: High-resolution Oasis maps are imperative for obtaining ecological and socio-economic development of arid regions. However, due to the late establishment and relatively niche nature of the oasis discipline system, there are no high-precision datasets related to oases in the world to date. To fill this gap, we conducted detailed visual interpretation of remote sensing images on Google Earth Professional or Sentinel II in summer 2020, and constructed for the first time a high-precision dataset of China's oases (abbreviation HDCO) with a resolution of 1 meter, covering 1,465 oases with an area larger than 0.01 km2. These oases collectively cover an expansive area of 275143.30 km², equivalent to approximately 8.12% of China's dryland area. The kappa coefficient for this dataset validated by the field survey was 0.8686 and the AUC value for the ROC curve was 0.935. The accuracy obtained from the validation using a random hexagonal grid is 95.73%. In addition, we added information on the geographic coordinates, climatic conditions, major landforms, and hydrological features of each oasis to the attribute table of the dataset. This dataset enables researches to quantitatively monitor location, area, and dynamic changes of oases, fosters explore the relationship between oases and human under climate change and urbanization.