cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.03398
title: A dataset of 1km Grid orchard density in China(2020）
title: 2020年中国1km格网果园密度数据集

description: Orchard is one of the most important types of plantations in China, which has important economic value and is of great significance for rural revitalization and farmers' income increase. The spatial distribution of orchards reflects the endowment of agricultural resources, which can provide a reference for the development of regional agricultural resources and the formulation of special agricultural product planning, and is of great significance for the sustainable development of rural areas. This paper uses the 30 meter land use grid data of the National Land Change Survey in 2020, uses the kilometer grid production software, and calculates the grid space to obtain the 1km grid orchard density data set of China in 2020. This data set can be used in the research of land spatial planning, rural revitalization planning, rural characteristic agricultural product development, etc. 
description: Orchard is one of the most important types of plantations in China, which has important economic value and is of great significance for rural revitalization and farmers' income increase. The spatial distribution of orchards reflects the endowment of agricultural resources, which can provide a reference for the development of regional agricultural resources and the formulation of special agricultural product planning, and is of great significance for the sustainable development of rural areas. This paper uses the 30 meter land use grid data of the National Land Change Survey in 2020, uses the kilometer grid production software, and calculates the grid space to obtain the 1km grid orchard density data set of China in 2020. This data set can be used in the research of land spatial planning, rural revitalization planning, rural characteristic agricultural product development, etc.
DOI: 10.57760/sciencedb.03479
title: A dataset of 1km Grid Arbor forest land density in China(2020）
title: 2020年中国1km格网乔木林地密度数据集

description: Arbor forest land is an important natural resource and strategic resource of the country, and it is also the foundation of forest survival and development. Arbor forest land plays a core role in ensuring the supply of wood and forest products and maintaining the national ecological security, and also plays a special role in coping with global climate change. The grid density of arbor forest land, as the basic indicator for evaluating the regional high-quality forest resource endowment, is of great significance for coordinating the land use for wood and forest products production and improving the forest management level by dynamically monitoring it. This paper uses the 30 meter land use grid data of the 2020 National Land Change Survey, uses the kilometer grid production software, and calculates the grid space to obtain the data set of China's 1km grid tree forest land density in 2020. This data set can be used for research on land spatial planning, forestry resource planning, ecological asset value estimation, forest carbon sink calculation, remote sensing sample database construction, etc. 
description: Arbor forest land is an important natural resource and strategic resource of the country, and it is also the foundation of forest survival and development. Arbor forest land plays a core role in ensuring the supply of wood and forest products and maintaining the national ecological security, and also plays a special role in coping with global climate change. The grid density of arbor forest land, as the basic indicator for evaluating the regional high-quality forest resource endowment, is of great significance for coordinating the land use for wood and forest products production and improving the forest management level by dynamically monitoring it. This paper uses the 30 meter land use grid data of the 2020 National Land Change Survey, uses the kilometer grid production software, and calculates the grid space to obtain the data set of China's 1km grid tree forest land density in 2020. This data set can be used for research on land spatial planning, forestry resource planning, ecological asset value estimation, forest carbon sink calculation, remote sensing sample database construction, etc.
DOI: 10.15468/dl.q6ugdj
title: Occurrence Download

description: A dataset containing 194388 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is LBBG_ADULT - Lesser black-backed gulls (Larus fuscus, Laridae) breeding in Belgium"
 ]
}

 The dataset includes 194388 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001381-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.02986
title: How to submit Paper Related Data to ScienceDB
title: 科研人员如何提交论文关联数据到ScienceDB

description: In order to help the researchers of Chinese Academy of Sciences complete the data archiving work and carry out the independent archiving and initial work of paper related data in a regular manner in the future, we wrote a user manuals for scientific researcher, which details how to submit data. In addition, we recorded 3 videos.
description: In order to help the researchers of Chinese Academy of Sciences complete the data archiving work and carry out the independent archiving and initial work of paper related data in a regular manner in the future, we wrote a user manuals for scientific researcher, which details how to submit data. In addition, we recorded 3 videos.
DOI: 10.57760/sciencedb.o00119.00077
title: A dataset of annual gross primary productivity over Chinese terrestrial ecosystems during 2000-2020
title: 2000–2020年中国陆地生态系统年总初级生产力数据集

description: The dataset of annual gross primary productivity over Chinese terrestrial ecosystems was constructed based on Chinese Flux Observation and Research Network (ChinaFLUX) measurements and public datasets. Combined with biological, climatic, and soil factors, the random forest regression tree was used to construct the assessment model of China AGPP by simulating the AGPP of unit leaf area. The dataset of annual gross primary productivity over Chinese terrestrial ecosystems during 2000-2020 was generated with a spatial resolution of 30arcsecond and a data format of tiff. This data set includes one site observation AGPP data set and 21 regional AGPP data. The AGPP data set of site observation consists of 166 sites and 872 annual observation results, which is composed of 20 columns, including site name, ecosystem type, observation year, AGPP value, climate, and soil biological variables, references, etc.
description: The dataset of annual gross primary productivity over Chinese terrestrial ecosystems was constructed based on Chinese Flux Observation and Research Network (ChinaFLUX) measurements and public datasets. Combined with biological, climatic, and soil factors, the random forest regression tree was used to construct the assessment model of China AGPP by simulating the AGPP of unit leaf area. The dataset of annual gross primary productivity over Chinese terrestrial ecosystems during 2000-2020 was generated with a spatial resolution of 30arcsecond and a data format of tiff. This data set includes one site observation AGPP data set and 21 regional AGPP data. The AGPP data set of site observation consists of 166 sites and 872 annual observation results, which is composed of 20 columns, including site name, ecosystem type, observation year, AGPP value, climate, and soil biological variables, references, etc.
DOI: 10.57760/sciencedb.o00009.00360
title: Polar/UVI Image Sequence during the Substorm Expansion Phase in 1996-2000
title: Polar/UVI Image Sequence during the Substorm Expansion Phase in 1996-2000

description: This is a dataset of Polar/UVI aurora image sequence during the substorm expansion phase in 1996-2000. The time interval between each frame is about 3 minutes. Each file is a substorm case, which contains the images during the expansion phase.
description: This is a dataset of Polar/UVI aurora image sequence during the substorm expansion phase in 1996-2000. The time interval between each frame is about 3 minutes. Each file is a substorm case, which contains the images during the expansion phase.
DOI: 10.15468/dl.dsmfyf
title: Occurrence Download

description: A dataset containing 196219 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Australian Tropical Herbarium (CNS) AVH data"
 ]
}

 The dataset includes 196219 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001382-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qf94zj
title: Occurrence Download

description: A dataset containing 195638 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Academy of Natural Sciences of Philadelphia Ornithology Collection"
 ]
}

 The dataset includes 195638 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001383-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.o00119.00024
title: A dataset of plant community composition and dynamics of kettle peatlands of Xingkai Lake (2012-2018)
title: 2012-2018年兴凯湖"浮毯"沼泽植物组成动态数据集

description: The formation of kettle peatlands is a typical vertical peatland terrestrialization. There are few studies on the plant composition and dynamics of kettle peatlands, especially in freshwater lake wetlands, which lack long-term observations. In this paper, the plant community composition of typical kettle peatlands was observed in the Xingkai Lake area, Heilongjiang and we made strict rules for field investigation, recording and laboratory analysis. This dataset contained the plant community composition, dominant plants, above- and belowground biomass, and plant density in the kettle peatlands from 2012 to 2018. Users can search the data by survey year and plant community composition. On the one hand, this shared the long-term dataset about plant community in kettle peatlands could enrich the peatland plant community dataset. On the other hand, the data can be used as a support for students and researchers working in physical geography and ecology, and as a reference for ecological restoration and sustainable management of wetlands.
description: The formation of kettle peatlands is a typical vertical peatland terrestrialization. There are few studies on the plant composition and dynamics of kettle peatlands, especially in freshwater lake wetlands, which lack long-term observations. In this paper, the plant community composition of typical kettle peatlands was observed in the Xingkai Lake area, Heilongjiang and we made strict rules for field investigation, recording and laboratory analysis. This dataset contained the plant community composition, dominant plants, above- and belowground biomass, and plant density in the kettle peatlands from 2012 to 2018. Users can search the data by survey year and plant community composition. On the one hand, this shared the long-term dataset about plant community in kettle peatlands could enrich the peatland plant community dataset. On the other hand, the data can be used as a support for students and researchers working in physical geography and ecology, and as a reference for ecological restoration and sustainable management of wetlands.
DOI: 10.57760/sciencedb.j00001.00778
title: Mongolia 30m resolution grass yield estimation dataset (2017-2021)
title: 蒙古国30米分辨率产草量估算数据集（2017−2021年）

description: Deep neural network training: use the GEE cloud computing platform to obtain the NDVI, surface temperature and precipitation data corresponding to the measured grass yield coordinates. The obtained data and the measured grass yield were normalized, and a three-layer neural network model was built. Fitting the relationship between the grass yield and the other three influencing factors, build the grass yield estimation model. Due to the small number of training samples, the method of cross validation is adopted. 70% of the training samples are randomly selected as the training set and 30% of the data as the validation set. The grass yield estimation model is obtained through three cross validation.Grass yield estimation: based on the improvement of model estimation accuracy, the three grass yield estimation models trained are loaded into the GEE platform by calling the Python language API interface provided by GEE. Complete a series of steps such as calling and processing the basic remote sensing data in the GEE cloud platform, take the average of the retrieval results of the three grass yield models, and obtain the estimation data of Mongolia's domestic grass yield.Resampling and re-projection of remote sensing data are completed on the GEE platform.
description: Deep neural network training: use the GEE cloud computing platform to obtain the NDVI, surface temperature and precipitation data corresponding to the measured grass yield coordinates. The obtained data and the measured grass yield were normalized, and a three-layer neural network model was built. Fitting the relationship between the grass yield and the other three influencing factors, build the grass yield estimation model. Due to the small number of training samples, the method of cross validation is adopted. 70% of the training samples are randomly selected as the training set and 30% of the data as the validation set. The grass yield estimation model is obtained through three cross validation.Grass yield estimation: based on the improvement of model estimation accuracy, the three grass yield estimation models trained are loaded into the GEE platform by calling the Python language API interface provided by GEE. Complete a series of steps such as calling and processing the basic remote sensing data in the GEE cloud platform, take the average of the retrieval results of the three grass yield models, and obtain the estimation data of Mongolia's domestic grass yield.Resampling and re-projection of remote sensing data are completed on the GEE platform.
DOI: 10.57760/sciencedb.07043
title: The data of non-native fishes recorded at PAR and non-PAR sites in Guangdong Province (WebData 03)
title: The data of non-native fishes recorded at PAR and non-PAR sites in Guangdong Province (WebData 03)

description: WebData 03: Non-native fishes recorded from PAR and field surveys in Guangdong Province.Sheet 1: Non-native fishes observed in PAR activities.Sheet 2: Non-native fishes collected from PAR and non-PAR sites.
description: WebData 03: Non-native fishes recorded from PAR and field surveys in Guangdong Province.Sheet 1: Non-native fishes observed in PAR activities.Sheet 2: Non-native fishes collected from PAR and non-PAR sites.
DOI: 10.57760/sciencedb.07252
title: A dataset of atmospheric silicon wet deposition in China’s terrestrial ecosystems (2013–2020)
title: 2013-2020年中国典型生态系统大气硅湿沉降数据集

description: Silicon (Si) is a "quasi essential" nutrient element for plants and an essential nutrient for some phytoplankton. Atmospheric deposition has gradually become an important supplement way for silicon nutrients to enter the ecosystem. At present, there is no national observation data report on atmospheric silicon deposition. In 2013, China Wet Deposition Observation Network (ChinaWD) was established based on the Chinese Ecosystem Research Network (CERN) and some stations. The observation network has continued since 2013, including 43 stations nationwide. These sites are evenly distributed in each ecological region, and almost cover the main ecosystems in China. This paper focuses on the compilation and collation of atmospheric silicon wet deposition flux data of 43 stations in ChinaWD network from 2013 to 2020. ChinaWD network has standard quality control specifications. During the test, standard solution and blank samples are used for testing and calibration, and repeated processing is used to ensure the accuracy of data. All relevant parameters demonstrate the accuracy and reliability of test results. Our data make up for the deficiency of regional silicon deposition data, can provide data support for the follow-up regional, national and global silicon biogeochemical cycle research, and provide unique scientific data for comprehensive evaluation of the element proportion and its ecological effects of nutrient elements (nitrogen, phosphorus, silicon).
description: Silicon (Si) is a "quasi essential" nutrient element for plants and an essential nutrient for some phytoplankton. Atmospheric deposition has gradually become an important supplement way for silicon nutrients to enter the ecosystem. At present, there is no national observation data report on atmospheric silicon deposition. In 2013, China Wet Deposition Observation Network (ChinaWD) was established based on the Chinese Ecosystem Research Network (CERN) and some stations. The observation network has continued since 2013, including 43 stations nationwide. These sites are evenly distributed in each ecological region, and almost cover the main ecosystems in China. This paper focuses on the compilation and collation of atmospheric silicon wet deposition flux data of 43 stations in ChinaWD network from 2013 to 2020. ChinaWD network has standard quality control specifications. During the test, standard solution and blank samples are used for testing and calibration, and repeated processing is used to ensure the accuracy of data. All relevant parameters demonstrate the accuracy and reliability of test results. Our data make up for the deficiency of regional silicon deposition data, can provide data support for the follow-up regional, national and global silicon biogeochemical cycle research, and provide unique scientific data for comprehensive evaluation of the element proportion and its ecological effects of nutrient elements (nitrogen, phosphorus, silicon).
DOI: 10.57760/sciencedb.j00186.00003
title: A dataset of application of VITAS to the C5G7-TD pin-resolved benchmark problem
title: A dataset of application of VITAS to the C5G7-TD pin-resolved benchmark problem

description: This dataset contains results of the C5G7-TD benchmark problems calculated by transient solvers of VITAS. VITAS is a high-fidelity neutron transport solver, whose transient solvers include both direct-SCM and PCQ-SCM. The results in the dataset describe how reactor power changes with time in different cases of C5G7-TD benchmark problems.The dataset has three parts, 'Direct-SCM', 'PCQ-SCM' and 'Steady-state'. 'Direct-SCM' contains results (time, power and amplitude frequency) run by the direct-SCM solver in C5G7-TD0~TD5 problems. 'PCQ-SCM' contains comparisons of PCQ-SCM and direct-SCM solvers in TD0-5, TD3-4 and TD5-1 problems. Also, it presents kinetics parameters (reactivity, neutron generation time, delayed neutron fraction and precursor decay constant) of TD3-4 and TD5-1 problems. 'Steady-state' includes the pin-wise flux and fission distribution in the steady calculations, which can be visualized by ParaView.
description: This dataset contains results of the C5G7-TD benchmark problems calculated by transient solvers of VITAS. VITAS is a high-fidelity neutron transport solver, whose transient solvers include both direct-SCM and PCQ-SCM. The results in the dataset describe how reactor power changes with time in different cases of C5G7-TD benchmark problems.The dataset has three parts, 'Direct-SCM', 'PCQ-SCM' and 'Steady-state'. 'Direct-SCM' contains results (time, power and amplitude frequency) run by the direct-SCM solver in C5G7-TD0~TD5 problems. 'PCQ-SCM' contains comparisons of PCQ-SCM and direct-SCM solvers in TD0-5, TD3-4 and TD5-1 problems. Also, it presents kinetics parameters (reactivity, neutron generation time, delayed neutron fraction and precursor decay constant) of TD3-4 and TD5-1 problems. 'Steady-state' includes the pin-wise flux and fission distribution in the steady calculations, which can be visualized by ParaView.
DOI: 10.57760/sciencedb.o00119.00062
title: A dataset of carbon and water flux observations in the agricultural ecosystem of spring maize in Jinzhou (2005–2014)
title: 2005–2014年锦州春玉米农田生态系统碳水通量观测数据集

description: Jinzhou agricultural Ecosystem Field Observation Station (Jinzhou Station) is one of the member stations of Chinese FLUX Observation and Research Network (ChinaFLUX), located in Jinzhou City, Liaoning Province, which belongs to the main spring maize producing area of China. Based on EC technique the observation of carbon and water fluxes of spring maize agricultural ecosystem has been carried out in Jinzhou Station since May 2004. Following the ChinaFLUX data processing protocols, this dataset collected the carbon and water fluxes and auxiliary meteorological environment observations of spring maize agricultural ecosystem in Jinzhou from 2005 to 2014, forming a standardized dataset with four time scales of 30 minutes, day, month and year. Among them, the flux data products include ecosystem net carbon exchange (NEE), ecosystem respiration (Reco), ecosystem photosynthesis (GEE), sensible heat flux (Hs), and latent heat flux (LE), etc.; meteorological data products include air temperature, humidity, wind speed, wind direction, solar radiation, photosynthetically active radiation, soil temperature, soil moisture, and rainfall, etc.
description: Jinzhou agricultural Ecosystem Field Observation Station (Jinzhou Station) is one of the member stations of Chinese FLUX Observation and Research Network (ChinaFLUX), located in Jinzhou City, Liaoning Province, which belongs to the main spring maize producing area of China. Based on EC technique the observation of carbon and water fluxes of spring maize agricultural ecosystem has been carried out in Jinzhou Station since May 2004. Following the ChinaFLUX data processing protocols, this dataset collected the carbon and water fluxes and auxiliary meteorological environment observations of spring maize agricultural ecosystem in Jinzhou from 2005 to 2014, forming a standardized dataset with four time scales of 30 minutes, day, month and year. Among them, the flux data products include ecosystem net carbon exchange (NEE), ecosystem respiration (Reco), ecosystem photosynthesis (GEE), sensible heat flux (Hs), and latent heat flux (LE), etc.; meteorological data products include air temperature, humidity, wind speed, wind direction, solar radiation, photosynthetically active radiation, soil temperature, soil moisture, and rainfall, etc.
DOI: 10.57760/sciencedb.02924
title: A dataset of desertification degree monitoring in Inner Mongolia from 2001 to 2021
title: 中国内蒙古自治区荒漠化程度监测数据集（2001–2021年）

description: This dataset includes the distribution data of desertification degree in Inner Mongolia Autonomous Region from 2001 to 2021. The dataset consists of 21 subsets in total. And each folder contains the annual data of four desertification climate types: semi-arid area, arid area, superarid area; subhumid area. The data type is shp, and the data volume is about 226MB.
description: This dataset includes the distribution data of desertification degree in Inner Mongolia Autonomous Region from 2001 to 2021. The dataset consists of 21 subsets in total. And each folder contains the annual data of four desertification climate types: semi-arid area, arid area, superarid area; subhumid area. The data type is shp, and the data volume is about 226MB.