cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.16944
title: Dynamic modeling and analysis of brucellosis on metapopulation network: Heilongjiang as cases
title: Dynamic modeling and analysis of brucellosis on metapopulation network: Heilongjiang as cases

description: This program takes Heilongjiang Province as an example to study the spread of brucellosis in a metapopulation network and conducts numerical simulations.Additionally, for Heilongjiang Province, we constructed BA networks, ER networks, and uniform mixing natworks. We applied the least squares method to fit the cumulative human brucellosis cases in Heilongjiang from 2010 to 2020, based on the brucellosis transmission model on the constructed ensemble networks, estimating unknown parameters. Then, we constructed 1000 BA networks and 1000 ER networks to study the impact of network average degree on brucellosis transmission, and used cubic spline interpolation functions for smoothing treatment. Finally, we conducted a sensitivity analysis of the basic reproduction number parameters.
description: This program takes Heilongjiang Province as an example to study the spread of brucellosis in a metapopulation network and conducts numerical simulations.Additionally, for Heilongjiang Province, we constructed BA networks, ER networks, and uniform mixing natworks. We applied the least squares method to fit the cumulative human brucellosis cases in Heilongjiang from 2010 to 2020, based on the brucellosis transmission model on the constructed ensemble networks, estimating unknown parameters. Then, we constructed 1000 BA networks and 1000 ER networks to study the impact of network average degree on brucellosis transmission, and used cubic spline interpolation functions for smoothing treatment. Finally, we conducted a sensitivity analysis of the basic reproduction number parameters.
DOI: 10.57760/sciencedb.j00001.01173
title: A dataset of spatial risk assessment of tropical cyclone disaster in global marine areas（1950–2023）
title: 全球海域热带气旋灾害空间风险评估数据集（1950–2023）

description: Tropical cyclones are among the most destructive natural disasters, The scientific assessment of the spatial risk distribution of tropical cyclone disasters in global marine areas is of importance for the effective management of disaster risks and the promotion of coastal cities' economic development. This dataset establishes a spatial risk assessment dataset of tropical cyclone disasters in global marine areas from 1950 to 2023, based on the best track data released by the National Oceanic and Atmospheric Administration (NOAA), using ArcGIS tools and big data analysis methods, and integrating the lifecycle attributes and movement trajectories of tropical cyclones. It provides detailed information on the spatial distribution of the impacts of tropical cyclone disasters across global marine areas, with a spatial resolution of 20 km, a geospatial extent of global waters, a spatial coordinate system of the WGS 1984 Mercator projection, and a data format of TIF. The dataset file is named ‘Spatial Risk Assessment Data for Tropical Cyclone Hazards in Global Seas 1950-2023.gdb’, and can be viewed by ArcGIS, QGIS.
description: Tropical cyclones are among the most destructive natural disasters, The scientific assessment of the spatial risk distribution of tropical cyclone disasters in global marine areas is of importance for the effective management of disaster risks and the promotion of coastal cities' economic development. This dataset establishes a spatial risk assessment dataset of tropical cyclone disasters in global marine areas from 1950 to 2023, based on the best track data released by the National Oceanic and Atmospheric Administration (NOAA), using ArcGIS tools and big data analysis methods, and integrating the lifecycle attributes and movement trajectories of tropical cyclones. It provides detailed information on the spatial distribution of the impacts of tropical cyclone disasters across global marine areas, with a spatial resolution of 20 km, a geospatial extent of global waters, a spatial coordinate system of the WGS 1984 Mercator projection, and a data format of TIF. The dataset file is named ‘Spatial Risk Assessment Data for Tropical Cyclone Hazards in Global Seas 1950-2023.gdb’, and can be viewed by ArcGIS, QGIS.
DOI: 10.57760/sciencedb.16940
title: Identification of a novel Klebsiella phage containing a new combination of K2 and K30 depolymerases and therapeutic application
title: Identification of a novel Klebsiella phage containing a new combination of K2 and K30 depolymerases and therapeutic application

description: Statistical data
description: Statistical data
DOI: 10.57760/sciencedb.iga.00997
title: Remote sensing simulation data set of water yield at 1km resolution in Songnen Plain (2015)
title: 松嫩平原1km分辨率产水量遥感模拟数据集（2015年）

description: a. Data content (data file/table name, observation index content included)
This data set includes the 2015 Songnen Plain 1km resolution water yield remote sensing simulation spatial data set, which can provide data support for the Songnen Plain biodiversity protection and flood regulation and storage.
b. Construction purpose
The concept of water yield is broad, and its main manifestations include: rainfall retention by ecosystem, rainfall impact, water purification, etc. The water yield of different ecosystems is different, including the difference of water yield between different forest and grassland species and within various groups. To understand the water yield of Songnen Plain and provide data support for ecological protection and ecological monitoring.
c. Service object
It can widely serve scientific researchers and students in ecological protection, ecological monitoring and other related disciplines.
d. Time range of data
2015
e. Spatial range of data
Songnen Plain
f. Projection mode of data
Projected Coordinate System：Krasovsky_1940_Albers
Geographic Coordinate System：GCS_Krasovsky_1940
g. Subject scope of data
Biology&gt;ecology&gt;Regional Ecology
h. Amount of data
The total data volume is about 4.82mb
description: a. Data content (data file/table name, observation index content included)
This data set includes the 2015 Songnen Plain 1km resolution water yield remote sensing simulation spatial data set, which can provide data support for the Songnen Plain biodiversity protection and flood regulation and storage.
b. Construction purpose
The concept of water yield is broad, and its main manifestations include: rainfall retention by ecosystem, rainfall impact, water purification, etc. The water yield of different ecosystems is different, including the difference of water yield between different forest and grassland species and within various groups. To understand the water yield of Songnen Plain and provide data support for ecological protection and ecological monitoring.
c. Service object
It can widely serve scientific researchers and students in ecological protection, ecological monitoring and other related disciplines.
d. Time range of data
2015
e. Spatial range of data
Songnen Plain
f. Projection mode of data
Projected Coordinate System：Krasovsky_1940_Albers
Geographic Coordinate System：GCS_Krasovsky_1940
g. Subject scope of data
Biology&gt;ecology&gt;Regional Ecology
h. Amount of data
The total data volume is about 4.82mb
DOI: 10.57760/sciencedb.iga.00998
title: Remote sensing simulation data set of carbon storage at 1km resolution in Songnen Plain (2015)
title: 松嫩平原1km分辨率碳储量遥感模拟数据集（2015年）

description: a. Data content (data file/table name, observation index content included)
This data includes the remote sensing monitoring and simulation spatial data set of carbon storage in Songnen Plain with 1km resolution in 2015, which can provide data support for the ecological carbon sequestration project in Songnen Plain.
b. Construction purpose
Carbon storage is the result of long-term carbon accumulation in the ecosystem and the sum of the existing vegetation biomass organic carbon, litter organic carbon and total soil organic carbon fixation capacity of the ecosystem. Understanding the carbon storage of Songnen Plain and formulating ecological carbon sequestration projects suitable for Songnen Plain are of great significance to improve the carbon management of Songnen Plain and its position in global greenhouse gas emission reduction.
c. Service object
It can widely serve scientific researchers and students in ecological protection, ecological monitoring and other related disciplines.
d. Time range of data
2015
e. Spatial range of data
Songnen Plain
f. Projection mode of data
Projected Coordinate System：Krasovsky_1940_Albers
Geographic Coordinate System： GCS_Krasovsky_1940
g. Subject scope of data
Biology&gt;ecology&gt;Regional Ecology
h. Amount of data
The total data volume is about 4.82mb
description: a. Data content (data file/table name, observation index content included)
This data includes the remote sensing monitoring and simulation spatial data set of carbon storage in Songnen Plain with 1km resolution in 2015, which can provide data support for the ecological carbon sequestration project in Songnen Plain.
b. Construction purpose
Carbon storage is the result of long-term carbon accumulation in the ecosystem and the sum of the existing vegetation biomass organic carbon, litter organic carbon and total soil organic carbon fixation capacity of the ecosystem. Understanding the carbon storage of Songnen Plain and formulating ecological carbon sequestration projects suitable for Songnen Plain are of great significance to improve the carbon management of Songnen Plain and its position in global greenhouse gas emission reduction.
c. Service object
It can widely serve scientific researchers and students in ecological protection, ecological monitoring and other related disciplines.
d. Time range of data
2015
e. Spatial range of data
Songnen Plain
f. Projection mode of data
Projected Coordinate System：Krasovsky_1940_Albers
Geographic Coordinate System： GCS_Krasovsky_1940
g. Subject scope of data
Biology&gt;ecology&gt;Regional Ecology
h. Amount of data
The total data volume is about 4.82mb
DOI: 10.57760/sciencedb.iga.00999
title: 1km resolution remote sensing simulation data set of food supply in Songnen Plain (2015)
title: 松嫩平原1km分辨率食物供给遥感模拟数据集（2015年）

description: a. Data content (data file/table name, observation index content included)
This data includes a 1km resolution remote sensing monitoring and simulation spatial data set of food supply in Songnen Plain in 2015, which can provide data support for food security in Songnen Plain.
b. Construction purpose
Grain production is a major strategic issue for national economic development and social stability. In recent years, China has attached great importance to grain production. Scientific and technological progress, policy support, climate conditions and other factors have provided favorable conditions for China to increase grain production. Songnen Plain is one of the three major plains in Northeast China. It is one of the important grain producing areas in China and one of the important distribution areas of swamps and wetlands. It is very important for national and regional food security and ecological security.
c. Service object
It can widely serve scientific researchers and students in food security, ecological monitoring and other related disciplines.
d. Time range of data
2015
e. Spatial range of data
Songnen Plain
f. Projection mode of data
Projected Coordinate System：Krasovsky_1940_Albers
Geographic Coordinate System： GCS_Krasovsky_1940
g. Subject scope of data
Biology&gt;ecology&gt;Regional Ecology
h. Amount of data
The total data volume is about 4.82mb
description: a. Data content (data file/table name, observation index content included)
This data includes a 1km resolution remote sensing monitoring and simulation spatial data set of food supply in Songnen Plain in 2015, which can provide data support for food security in Songnen Plain.
b. Construction purpose
Grain production is a major strategic issue for national economic development and social stability. In recent years, China has attached great importance to grain production. Scientific and technological progress, policy support, climate conditions and other factors have provided favorable conditions for China to increase grain production. Songnen Plain is one of the three major plains in Northeast China. It is one of the important grain producing areas in China and one of the important distribution areas of swamps and wetlands. It is very important for national and regional food security and ecological security.
c. Service object
It can widely serve scientific researchers and students in food security, ecological monitoring and other related disciplines.
d. Time range of data
2015
e. Spatial range of data
Songnen Plain
f. Projection mode of data
Projected Coordinate System：Krasovsky_1940_Albers
Geographic Coordinate System： GCS_Krasovsky_1940
g. Subject scope of data
Biology&gt;ecology&gt;Regional Ecology
h. Amount of data
The total data volume is about 4.82mb
DOI: 10.57760/sciencedb.iga.01000
title: 10m resolution soil organic matter content data set in Songnen Plain (2020)
title: 松嫩平原地区10m分辨率土壤有机质含量数据集（2020年）

description: a. Data content (data file/table name, observation index content included)
The soil organic matter data of Songnen Plain in 2020 reflect the soil organic matter content (g/kg) of Songnen Plain in Northeast China.
b. Construction purpose
Through the identification of rice planting distribution, provide data support for various agricultural research.
c. Service object
It can widely serve agricultural managers, producers and scientific researchers in agronomy, ecology, climate change science and other related disciplines.
d. Time range of data
2020
e. Spatial range of data
Songnen Plain
f. Projection mode of data
WGS_1984_UTM_Zone_52N
g. Subject scope of data
Surveying and Mapping Science and Technology&gt;photogrammetry and remote sensing technology
h. Amount of data
The total data volume is about 18.2gb
description: a. Data content (data file/table name, observation index content included)
The soil organic matter data of Songnen Plain in 2020 reflect the soil organic matter content (g/kg) of Songnen Plain in Northeast China.
b. Construction purpose
Through the identification of rice planting distribution, provide data support for various agricultural research.
c. Service object
It can widely serve agricultural managers, producers and scientific researchers in agronomy, ecology, climate change science and other related disciplines.
d. Time range of data
2020
e. Spatial range of data
Songnen Plain
f. Projection mode of data
WGS_1984_UTM_Zone_52N
g. Subject scope of data
Surveying and Mapping Science and Technology&gt;photogrammetry and remote sensing technology
h. Amount of data
The total data volume is about 18.2gb
DOI: 10.57760/sciencedb.iga.01001
title: Soil sand content data set with 10m resolution in Songnen Plain (2020)
title: 松嫩平原地区10m分辨率土壤砂粒含量数据集（2020年）

description: a. Data content (data file/table name, observation index content included)
The soil sand content data set with 10m resolution in Songnen Plain in 2020 reflects the distribution of soil sand content in Songnen Plain in China.
b. Construction purpose
The distribution of soil sand content provides data support for understanding soil physical properties and various agricultural research.
c. Service object
It can widely serve agricultural managers, producers and scientific researchers in agronomy, ecology, climate change science and other related disciplines.
d. Time range of data
2020
e. Spatial range of data
Songnen Plain
f. Projection mode of data
WGS 84
g. Subject scope of data
Surveying and Mapping Science and Technology&gt;photogrammetry and remote sensing technology
h. Amount of data
The total data volume is about 16 GB
description: a. Data content (data file/table name, observation index content included)
The soil sand content data set with 10m resolution in Songnen Plain in 2020 reflects the distribution of soil sand content in Songnen Plain in China.
b. Construction purpose
The distribution of soil sand content provides data support for understanding soil physical properties and various agricultural research.
c. Service object
It can widely serve agricultural managers, producers and scientific researchers in agronomy, ecology, climate change science and other related disciplines.
d. Time range of data
2020
e. Spatial range of data
Songnen Plain
f. Projection mode of data
WGS 84
g. Subject scope of data
Surveying and Mapping Science and Technology&gt;photogrammetry and remote sensing technology
h. Amount of data
The total data volume is about 16 GB
DOI: 10.57760/sciencedb.iga.01002
title: Soil silt content data set with 10m resolution in Songnen Plain (2020)
title: 松嫩平原地区10m分辨率土壤粉粒含量数据集（2020年）

description: a. Data content (data file/table name, observation index content included)
The data set of soil silt content at 10m resolution in Songnen Plain in 2020 reflects the distribution of soil silt content in Songnen Plain in China.
b. Construction purpose
The distribution of soil silt content provides data support for understanding soil physical properties and various agricultural research.
c. Service object
It can widely serve agricultural managers, producers and scientific researchers in agronomy, ecology, climate change science and other related disciplines.
d. Time range of data
2020
e. Spatial range of data
Songnen Plain
f. Projection mode of data
WGS 84
g. Subject scope of data
Surveying and Mapping Science and Technology&gt;photogrammetry and remote sensing technology
h. Amount of data
The total data volume is about 16 GB
description: a. Data content (data file/table name, observation index content included)
The data set of soil silt content at 10m resolution in Songnen Plain in 2020 reflects the distribution of soil silt content in Songnen Plain in China.
b. Construction purpose
The distribution of soil silt content provides data support for understanding soil physical properties and various agricultural research.
c. Service object
It can widely serve agricultural managers, producers and scientific researchers in agronomy, ecology, climate change science and other related disciplines.
d. Time range of data
2020
e. Spatial range of data
Songnen Plain
f. Projection mode of data
WGS 84
g. Subject scope of data
Surveying and Mapping Science and Technology&gt;photogrammetry and remote sensing technology
h. Amount of data
The total data volume is about 16 GB
DOI: 10.57760/sciencedb.iga.01003
title: Soil clay content data set with 10m resolution in Songnen Plain (2020)
title: 松嫩平原地区10m分辨率土壤黏粒含量数据集（2020年）

description: a. Data content (data file/table name, observation index content included)
The soil clay content data set with 10m resolution in Songnen Plain in 2020 reflects the distribution of soil clay content in Songnen Plain in China.
b. Construction purpose
The distribution of soil clay content provides data support for understanding soil physical properties and various agricultural research.
c. Service object
It can widely serve agricultural managers, producers and scientific researchers in agronomy, ecology, climate change science and other related disciplines.
d. Time range of data
2020
e. Spatial range of data
Songnen Plain
f. Projection mode of data
WGS 84
g. Subject scope of data
Surveying and Mapping Science and Technology&gt;photogrammetry and remote sensing technology
h. Amount of data
The total data volume is about 16 GB
description: a. Data content (data file/table name, observation index content included)
The soil clay content data set with 10m resolution in Songnen Plain in 2020 reflects the distribution of soil clay content in Songnen Plain in China.
b. Construction purpose
The distribution of soil clay content provides data support for understanding soil physical properties and various agricultural research.
c. Service object
It can widely serve agricultural managers, producers and scientific researchers in agronomy, ecology, climate change science and other related disciplines.
d. Time range of data
2020
e. Spatial range of data
Songnen Plain
f. Projection mode of data
WGS 84
g. Subject scope of data
Surveying and Mapping Science and Technology&gt;photogrammetry and remote sensing technology
h. Amount of data
The total data volume is about 16 GB
DOI: 10.57760/sciencedb.16958
title: Data on Japanese REITs
title: Data on Japanese REITs

description: Data on the prices of Japanese Real Estate Investment Trusts
description: Data on the prices of Japanese Real Estate Investment Trusts
DOI: 10.57760/sciencedb.16959
title: Effect of dietary supplementation with Chlorella vulgaris and lysozyme on the microflora of the colon of weaned piglets
title: 饲粮添加小球藻和溶菌酶对断奶仔猪结肠微生物的影响

description:  The experiment was carried out from December 2023 to February 2024 at the Institute of Subtropical Agricultural Ecology of the Chinese Academy of Sciences, and 30 Xiangcun black pigs weighing 11.52 ± 1.13 kg, half males and half females, were randomly divided into the control group, the Chlorella group, and the Chlorella + lysozyme group, and were fed with the basal diet, the basal diet + 5% Chlorella, the basal diet + 5% Chlorella + 100 mg /kg lysozyme, with 10 replicates in each group and 1 pig in each replicate. The pre-test period was 3 d and the formal period was 56 d. One pig died in each of the three groups, so there were 9 samples in each group, in which CRTL represented the control group, CHV represented the Chlorella group, and LYS represented the Chlorella+lysozyme group, and 540 samples of each of the top 20 phyla and phyla were selected at both phylum and genus levels, and 27 samples of each of the α and β diversity, and 1336 samples of each of the top 20 phyla and phyla were also selected for LEfSe analysis. 27 samples totaling 1336 data.
description:  The experiment was carried out from December 2023 to February 2024 at the Institute of Subtropical Agricultural Ecology of the Chinese Academy of Sciences, and 30 Xiangcun black pigs weighing 11.52 ± 1.13 kg, half males and half females, were randomly divided into the control group, the Chlorella group, and the Chlorella + lysozyme group, and were fed with the basal diet, the basal diet + 5% Chlorella, the basal diet + 5% Chlorella + 100 mg /kg lysozyme, with 10 replicates in each group and 1 pig in each replicate. The pre-test period was 3 d and the formal period was 56 d. One pig died in each of the three groups, so there were 9 samples in each group, in which CRTL represented the control group, CHV represented the Chlorella group, and LYS represented the Chlorella+lysozyme group, and 540 samples of each of the top 20 phyla and phyla were selected at both phylum and genus levels, and 27 samples of each of the α and β diversity, and 1336 samples of each of the top 20 phyla and phyla were also selected for LEfSe analysis. 27 samples totaling 1336 data.
DOI: 10.57760/sciencedb.nbsdc.00131
title: Deep Learning-Guided Redesign of a Miniaturized Myoglobin for Enhanced Catalysis
title: Deep Learning-Guided Redesign of a Miniaturized Myoglobin for Enhanced Catalysis

description: Data for the paper
description: Data for the paper