cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.iga.00309
title: Russian Far East 30m bare land spatial dataset (2015)
title: 俄罗斯远东30m裸地空间数据集(2015年)

description: a. Data content (data file/table name, including observation index content)

The 30m bare land spatial dataset in the Russian Far East (2015) reflects the spatial distribution of bare land in the Russian Far East region in 2015.

b. Construction purpose

By extracting information from remote sensing image data in 2015, the distribution of bare land in the Far East region of Russia was analyzed nationwide, providing data support and theoretical basis for urban planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and urban planning.

d. Time range of data

2015

e. The spatial range of data

russian far east
description: a. Data content (data file/table name, including observation index content)

The 30m bare land spatial dataset in the Russian Far East (2015) reflects the spatial distribution of bare land in the Russian Far East region in 2015.

b. Construction purpose

By extracting information from remote sensing image data in 2015, the distribution of bare land in the Far East region of Russia was analyzed nationwide, providing data support and theoretical basis for urban planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and urban planning.

d. Time range of data

2015

e. The spatial range of data

russian far east
DOI: 10.57760/sciencedb.agriculture.00016
title: A Dataset for Constructing Agricultural Knowledge Graph
title: 农业知识图谱构建数据集

description: This dataset utilizes web crawler technology to mine data from publicly available agricultural websites. Through automated or semi-automatic data cleaning, denoising, and other processes, unstructured data is recombined into structured data, which is ultimately stored in the form of a knowledge graph. The constructed agricultural knowledge graph includes item data for 11 agricultural categories, such as grain crops, cash crops, fruits, vegetables, etc. Specifically, it includes 461 types of grain crops, 2208 types of cash crops, 1294 types of fruits, 257 types of vegetables, 118 types of edible fungi, 1161 types of flowers and trees, 142 types of aquatic products, 113 types of pesticides, 1605 types of crop diseases and pests, 519 types of veterinary drugs, and 603 types of Chinese herbal medicines, totaling 8481 subcategories. The constructed agricultural knowledge graph can provide basic data support for the development of human-machine interactive intelligent applications such as agricultural knowledge Q&A and recommendation systems.
description: This dataset utilizes web crawler technology to mine data from publicly available agricultural websites. Through automated or semi-automatic data cleaning, denoising, and other processes, unstructured data is recombined into structured data, which is ultimately stored in the form of a knowledge graph. The constructed agricultural knowledge graph includes item data for 11 agricultural categories, such as grain crops, cash crops, fruits, vegetables, etc. Specifically, it includes 461 types of grain crops, 2208 types of cash crops, 1294 types of fruits, 257 types of vegetables, 118 types of edible fungi, 1161 types of flowers and trees, 142 types of aquatic products, 113 types of pesticides, 1605 types of crop diseases and pests, 519 types of veterinary drugs, and 603 types of Chinese herbal medicines, totaling 8481 subcategories. The constructed agricultural knowledge graph can provide basic data support for the development of human-machine interactive intelligent applications such as agricultural knowledge Q&A and recommendation systems.
DOI: 10.57760/sciencedb.iga.00310
title: Russian Far East 30m bare land spatial dataset (1990)
title: 俄罗斯远东30m裸地空间数据集(1990年)

description: a. Data content (data file/table name, including observation index content)

The 30m bare land spatial dataset in the Russian Far East (1990) reflects the spatial distribution of bare land in the Russian Far East region in 1990.

b. Construction purpose

By extracting information from remote sensing image data in 1990, the distribution of bare land in the Far East region of Russia was analyzed nationwide, providing data support and theoretical basis for urban planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and urban planning.

d. Time range of data

1990

e. The spatial range of data

russian far east
description: a. Data content (data file/table name, including observation index content)

The 30m bare land spatial dataset in the Russian Far East (1990) reflects the spatial distribution of bare land in the Russian Far East region in 1990.

b. Construction purpose

By extracting information from remote sensing image data in 1990, the distribution of bare land in the Far East region of Russia was analyzed nationwide, providing data support and theoretical basis for urban planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and urban planning.

d. Time range of data

1990

e. The spatial range of data

russian far east
DOI: 10.57760/sciencedb.iga.00311
title: Russian Far East 30m Naked Land Increase Spatial Dataset (1990-2015)
title: 俄罗斯远东30m裸地增加空间数据集(1990-2015年)

description: a. Data content (data file/table name, including observation index content)

The 30m bare land increase spatial dataset in the Russian Far East (1990-2015) reflects the spatial distribution of bare land increase in the Russian Far East region from 1990 to 2015.

b. Construction purpose

By comparing remote sensing image data from two periods in 1990 and 2015, this study analyzes the changes in bare land in the Far East region of Russia, providing data support and theoretical basis for urban planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and urban planning.

d. Time range of data

From 1990 to 2015

e. The spatial range of data

russian far east
description: a. Data content (data file/table name, including observation index content)

The 30m bare land increase spatial dataset in the Russian Far East (1990-2015) reflects the spatial distribution of bare land increase in the Russian Far East region from 1990 to 2015.

b. Construction purpose

By comparing remote sensing image data from two periods in 1990 and 2015, this study analyzes the changes in bare land in the Far East region of Russia, providing data support and theoretical basis for urban planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and urban planning.

d. Time range of data

From 1990 to 2015

e. The spatial range of data

russian far east
DOI: 10.57760/sciencedb.j00001.00904
title: Mechanical response observation dataset of semi-rigid asphalt pavement at RIOHTrack of National Field Scientific Observation and Research Station of Beijing Dadushe Highway Material Corrosion and Engineering Safety from 2017 to 2018
title: 2017–2018年北京足尺路面环道中心站半刚性基层沥青路面结构力学响应观测数据集

description: Structural mechanical response data is an important indicator of road service performance, which can effectively reflect the internal state of road structures, calculate road service performance and service life. It has significant research significance in the field of road engineering, especially continuous and stable long-term actual vehicle load response data, which is difficult to obtain by conventional research methods at home and abroad. This dataset includes continuous real vehicle loading response data from three semi rigid structural sections of the Beijing Dadushe Highway Long Term Performance Scientific Observation and Research Station from December 2016 to December 2018. It can provide basic data for the mechanical calculation of asphalt pavement structure, evaluation of asphalt pavement performance, and establishment of asphalt pavement design models.
description: Structural mechanical response data is an important indicator of road service performance, which can effectively reflect the internal state of road structures, calculate road service performance and service life. It has significant research significance in the field of road engineering, especially continuous and stable long-term actual vehicle load response data, which is difficult to obtain by conventional research methods at home and abroad. This dataset includes continuous real vehicle loading response data from three semi rigid structural sections of the Beijing Dadushe Highway Long Term Performance Scientific Observation and Research Station from December 2016 to December 2018. It can provide basic data for the mechanical calculation of asphalt pavement structure, evaluation of asphalt pavement performance, and establishment of asphalt pavement design models.
DOI: 10.57760/sciencedb.iga.00312
title: Russian Far East 30m bare land reduction spatial dataset (1990-2015)
title: 俄罗斯远东30m裸地减少空间数据集(1990-2015年)

description: a. Data content (data file/table name, including observation index content)

The Russian Far East 30m bare land reduction spatial dataset (1990-2015) reflects the spatial distribution of bare land reduction in the Russian Far East region from 1990 to 2015.

b. Construction purpose

By comparing remote sensing image data from two periods in 1990 and 2015, this study analyzes the changes in bare land in the Far East region of Russia, providing data support and theoretical basis for urban planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and urban planning.

d. Time range of data

From 1990 to 2015

e. The spatial range of data

russian far east
description: a. Data content (data file/table name, including observation index content)

The Russian Far East 30m bare land reduction spatial dataset (1990-2015) reflects the spatial distribution of bare land reduction in the Russian Far East region from 1990 to 2015.

b. Construction purpose

By comparing remote sensing image data from two periods in 1990 and 2015, this study analyzes the changes in bare land in the Far East region of Russia, providing data support and theoretical basis for urban planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and urban planning.

d. Time range of data

From 1990 to 2015

e. The spatial range of data

russian far east
DOI: 10.57760/sciencedb.iga.00313
title: Russian Far East 30m Grassland Spatial Dataset (2015)
title: 俄罗斯远东30m草地空间数据集(2015年)

description: a. Data content (data file/table name, including observation index content)

The 30m grassland spatial dataset in the Far East of Russia (2015) reflects the spatial distribution of grasslands in the Far East region of Russia in 2015.

b. Construction purpose

By extracting information from remote sensing image data in 2015, the distribution of grasslands in the Far East region of Russia was analyzed, providing data support and theoretical basis for agricultural planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and agriculture.

d. Time range of data

2015

e. The spatial range of data

russian far east
description: a. Data content (data file/table name, including observation index content)

The 30m grassland spatial dataset in the Far East of Russia (2015) reflects the spatial distribution of grasslands in the Far East region of Russia in 2015.

b. Construction purpose

By extracting information from remote sensing image data in 2015, the distribution of grasslands in the Far East region of Russia was analyzed, providing data support and theoretical basis for agricultural planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and agriculture.

d. Time range of data

2015

e. The spatial range of data

russian far east
DOI: 10.57760/sciencedb.10843
title: High ytterbium concentration Yb/Al/P/Ce co-doped silica fiber for 1-μm ultra-short cavity fiber laser application
title: High ytterbium concentration Yb/Al/P/Ce co-doped silica fiber for 1-μm ultra-short cavity fiber laser application

description: We demonstrate a high ytterbium concentration Yb/Al/P/Ce co-doped silica fiber by conventional modified chemical vapor deposition (MCVD) technology and solution doping process. The fiber has a Yb concentration of about 2.5 wt%, and the corresponding core absorption coefficient is measured to be ~1400 dB/m at 976 nm. The gain coefficient was measured to be approximately 1.0 dB/cm. It is found that the Yb/Al/P/Ce co-doped silica shows a lower photodarkening-induced equilibrium loss of 52 dB/m at 633nm than the Yb/Al/P co-doped silica fiber of 117 dB/m. Using the heavily Yb3+-doped silica fiber, a compact and robust ultrashort cavity single-frequency fiber laser was achieved with a maximum output power of 75 mW and a linewidth of 14 kHz. Furthermore, a compact passively mode-locked fiber laser (MLFL) with a repetition rate of 1.23 GHz was also proposed using our developed Yb-doped fiber. The laser properties of the proposed lasers were systematically investigated, demonstrating the superior performance of this fiber in terms of photodarkening resistance and ultrashort-cavity laser application. Furthermore, utilizing an all-fiber structure based on silica-based fiber offers the significant advantage of high stability and reliability.
description: We demonstrate a high ytterbium concentration Yb/Al/P/Ce co-doped silica fiber by conventional modified chemical vapor deposition (MCVD) technology and solution doping process. The fiber has a Yb concentration of about 2.5 wt%, and the corresponding core absorption coefficient is measured to be ~1400 dB/m at 976 nm. The gain coefficient was measured to be approximately 1.0 dB/cm. It is found that the Yb/Al/P/Ce co-doped silica shows a lower photodarkening-induced equilibrium loss of 52 dB/m at 633nm than the Yb/Al/P co-doped silica fiber of 117 dB/m. Using the heavily Yb3+-doped silica fiber, a compact and robust ultrashort cavity single-frequency fiber laser was achieved with a maximum output power of 75 mW and a linewidth of 14 kHz. Furthermore, a compact passively mode-locked fiber laser (MLFL) with a repetition rate of 1.23 GHz was also proposed using our developed Yb-doped fiber. The laser properties of the proposed lasers were systematically investigated, demonstrating the superior performance of this fiber in terms of photodarkening resistance and ultrashort-cavity laser application. Furthermore, utilizing an all-fiber structure based on silica-based fiber offers the significant advantage of high stability and reliability.
DOI: 10.57760/sciencedb.iga.00314
title: Russian Far East 30m Grassland Spatial Dataset (1990)
title: 俄罗斯远东30m草地空间数据集(1990年)

description: a. Data content (data file/table name, including observation index content)

The 30m grassland spatial dataset from the Far East of Russia (1990) reflects the spatial distribution of grasslands in the Far East region of Russia in 1990.

b. Construction purpose

By extracting information from remote sensing image data in 1990, the distribution of grasslands in the Far East of Russia was analyzed, providing data support and theoretical basis for agricultural planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and agriculture.

d. Time range of data

1990

e. The spatial range of data

russian far east
description: a. Data content (data file/table name, including observation index content)

The 30m grassland spatial dataset from the Far East of Russia (1990) reflects the spatial distribution of grasslands in the Far East region of Russia in 1990.

b. Construction purpose

By extracting information from remote sensing image data in 1990, the distribution of grasslands in the Far East of Russia was analyzed, providing data support and theoretical basis for agricultural planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and agriculture.

d. Time range of data

1990

e. The spatial range of data

russian far east
DOI: 10.57760/sciencedb.iga.00315
title: Russian Far East 30m Grassland Increase Spatial Dataset (1990-2015)
title: 俄罗斯远东30m草地增加空间数据集(1990-2015年)

description: a. Data content (data file/table name, including observation index content)

The spatial dataset of 30m grassland increase in the Russian Far East (1990-2015) reflects the spatial distribution of grassland increase in the Russian Far East region from 1990 to 2015.

b. Construction purpose

By comparing remote sensing image data from 1990 and 2015, the grassland changes in the Far East region of Russia were analyzed, providing data support and theoretical basis for agricultural planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and agricultural planning.

d. Time range of data

From 1990 to 2015

e. The spatial range of data

russian far east
description: a. Data content (data file/table name, including observation index content)

The spatial dataset of 30m grassland increase in the Russian Far East (1990-2015) reflects the spatial distribution of grassland increase in the Russian Far East region from 1990 to 2015.

b. Construction purpose

By comparing remote sensing image data from 1990 and 2015, the grassland changes in the Far East region of Russia were analyzed, providing data support and theoretical basis for agricultural planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and agricultural planning.

d. Time range of data

From 1990 to 2015

e. The spatial range of data

russian far east
DOI: 10.57760/sciencedb.iga.00316
title: Russian Far East 30m Grassland Reduction Spatial Dataset (1990-2015)
title: 俄罗斯远东30m草地减少空间数据集(1990-2015年)

description: a. Data content (data file/table name, including observation index content)

The 30m grassland reduction spatial dataset in the Russian Far East (1990-2015) reflects the spatial distribution of grassland reduction in the Russian Far East region from 1990 to 2015.

b. Construction purpose

By comparing remote sensing image data from 1990 and 2015, the grassland changes in the Far East region of Russia were analyzed, providing data support and theoretical basis for agricultural planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and agriculture.

d. Time range of data

From 1990 to 2015

e. The spatial range of data

russian far east
description: a. Data content (data file/table name, including observation index content)

The 30m grassland reduction spatial dataset in the Russian Far East (1990-2015) reflects the spatial distribution of grassland reduction in the Russian Far East region from 1990 to 2015.

b. Construction purpose

By comparing remote sensing image data from 1990 and 2015, the grassland changes in the Far East region of Russia were analyzed, providing data support and theoretical basis for agricultural planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and agriculture.

d. Time range of data

From 1990 to 2015

e. The spatial range of data

russian far east
DOI: 10.57760/sciencedb.iga.00317
title: Russian Far East 30m Cultivated Land Spatial Dataset (2015)
title: 俄罗斯远东30m耕地空间数据集(2015年)

description: a. Data content (data file/table name, including observation index content)

The 30m arable land spatial dataset in the Russian Far East (2015) reflects the spatial distribution of arable land in the Russian Far East region in 2015.

b. Construction purpose

By extracting information from remote sensing image data in 2015, we analyzed the distribution of arable land in the Far East region of Russia, providing data support and theoretical basis for agricultural planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and agriculture.

d. Time range of data

2015

e. The spatial range of data

russian far east
description: a. Data content (data file/table name, including observation index content)

The 30m arable land spatial dataset in the Russian Far East (2015) reflects the spatial distribution of arable land in the Russian Far East region in 2015.

b. Construction purpose

By extracting information from remote sensing image data in 2015, we analyzed the distribution of arable land in the Far East region of Russia, providing data support and theoretical basis for agricultural planning and management.

c. Service object

It can widely serve scientific researchers in related disciplines such as natural geography and agriculture.

d. Time range of data

2015

e. The spatial range of data

russian far east
DOI: 10.57760/sciencedb.10846
title: Effects of Astragalus polysaccharides on metabolomics and microbial composition in EAE mice
title: Effects of Astragalus polysaccharides on metabolomics and microbial composition in EAE mice

description: We applied untargeted and targeted metabolomics anaiyses to serum and feces and used 16s rRNA gene sequencing to detecr the fecal microbiome. Cluster analysis of metabolites in feces and plasma detected by non-targeted metabolome showed that APS significantly upregulated taurochenodeoxycholate-7-sulfate and N-palmitoyl aspartic acid in feces and plasma, both of which belong to bile acid metabolites and participate in bile acid metabolism. In addition, the targeted metabolomics anaiyses showed that the short-chain fatty acids (SCFA) were not regulated by APS in EAE. The 16s rDNA gene sequencing revealed that APS had a regulatory effect on the levels of Lactobacillus_johnsonii, Lactobacillus_murinus, and Akkermansia_muciniphila in EAE.
description: We applied untargeted and targeted metabolomics anaiyses to serum and feces and used 16s rRNA gene sequencing to detecr the fecal microbiome. Cluster analysis of metabolites in feces and plasma detected by non-targeted metabolome showed that APS significantly upregulated taurochenodeoxycholate-7-sulfate and N-palmitoyl aspartic acid in feces and plasma, both of which belong to bile acid metabolites and participate in bile acid metabolism. In addition, the targeted metabolomics anaiyses showed that the short-chain fatty acids (SCFA) were not regulated by APS in EAE. The 16s rDNA gene sequencing revealed that APS had a regulatory effect on the levels of Lactobacillus_johnsonii, Lactobacillus_murinus, and Akkermansia_muciniphila in EAE.
DOI: 10.57760/sciencedb.10840
title: Supplement material for "A feature-aided multiple model algorithm for maneuvering target tracking""
title: Supplement material for "A feature-aided multiple model algorithm for maneuvering target tracking""

description: The supplement material of the letter "A feature-aided multiple model algorithm for maneuvering target tracking", including figures and some descriptions.
description: The supplement material of the letter "A feature-aided multiple model algorithm for maneuvering target tracking", including figures and some descriptions.
DOI: 10.57760/sciencedb.10852
title: parallel ship-borne platform
title: 并联平台

description: 一个 具有全状态约束的有限时间容错控制方法是 用于解决位置和方向稳定控制问题 带模型的三自由度平行船载平台 本文中的不确定性、外部干扰和执行器故障。
description: 一个 具有全状态约束的有限时间容错控制方法是 用于解决位置和方向稳定控制问题 带模型的三自由度平行船载平台 本文中的不确定性、外部干扰和执行器故障。