cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.17901
title: Forest aboveground biomass product of the Qinghai-Tibet Plateau in 2013-2023
title: 青藏高原2013-2023年森林地上生物量产品

description: The accurate estimation of aboveground biomass (AGB) reserves on the Qinghai-Tibet Plateau serves as a crucial ecological security measure within China. It is of vital importance to monitor and protect the ecosystem, study the carbon cycle, estimate the carbon storage and reveal the interaction between forest and climate on the Plateau. The estimation of forest AGB in the Qinghai-Tibet Plateau is a challenging endeavour, due to the Plateau's high altitude, complex climate and varied topography, which exhibit significant spatial heterogeneity. This study employed the random forest regression tree model to analyse forest AGB samples collected on the ground. Potential driving factors, including surface reflectance, vegetation index, meteorological data and topographic data, were considered. Feature selection was used to enhance model accuracy, with the optimal model achieving an R2 of 0.78 and an RMSE of 33.79 t/ha. The high spatial resolution (30 m) forest AGB product for the Qinghai-Tibet Plateau in 2013-2023 have been generated. The product can reflect the temporal and spatial distribution pattern of forest biomass in the Qinghai-Tibet Plateau, and provide scientific reference for the formulation of forest protection and management policies in the region.
description: The accurate estimation of aboveground biomass (AGB) reserves on the Qinghai-Tibet Plateau serves as a crucial ecological security measure within China. It is of vital importance to monitor and protect the ecosystem, study the carbon cycle, estimate the carbon storage and reveal the interaction between forest and climate on the Plateau. The estimation of forest AGB in the Qinghai-Tibet Plateau is a challenging endeavour, due to the Plateau's high altitude, complex climate and varied topography, which exhibit significant spatial heterogeneity. This study employed the random forest regression tree model to analyse forest AGB samples collected on the ground. Potential driving factors, including surface reflectance, vegetation index, meteorological data and topographic data, were considered. Feature selection was used to enhance model accuracy, with the optimal model achieving an R2 of 0.78 and an RMSE of 33.79 t/ha. The high spatial resolution (30 m) forest AGB product for the Qinghai-Tibet Plateau in 2013-2023 have been generated. The product can reflect the temporal and spatial distribution pattern of forest biomass in the Qinghai-Tibet Plateau, and provide scientific reference for the formulation of forest protection and management policies in the region.
DOI: 10.57760/sciencedb.10057
title: A daily hydrological dataset of Guangdong Nanling Forest Ecosystem National Field Scientific Observation and Research Station during 2021–2023
title: 2021-2023年广东南岭站日尺度水文数据集

description: The dataset is reorganized based on the instrumental observations at Nanling subtropical evergreen broad-leaved forest from the Guangdong Nanling Forest Ecosystem National Field Scientific Observation and Research Station. After data processing, quality control and evaluation, the observation results including the volumetric soil moisture at a range of depths (5, 10, 15, 20, 40, 60, 100 cm), atmospheric precipitation, throughtfall precipiation, groundwater depth, surface runoff, valley runoff, stemflow, and evapotranspiration are published at daily scale, respectively. It provides data support for further understanding of the hydrological processes and mechanisms of Nanling mountainous forest, and serves as a theoretical reference for the protection and utilization of water resources in mountainous forest basins.
description: The dataset is reorganized based on the instrumental observations at Nanling subtropical evergreen broad-leaved forest from the Guangdong Nanling Forest Ecosystem National Field Scientific Observation and Research Station. After data processing, quality control and evaluation, the observation results including the volumetric soil moisture at a range of depths (5, 10, 15, 20, 40, 60, 100 cm), atmospheric precipitation, throughtfall precipiation, groundwater depth, surface runoff, valley runoff, stemflow, and evapotranspiration are published at daily scale, respectively. It provides data support for further understanding of the hydrological processes and mechanisms of Nanling mountainous forest, and serves as a theoretical reference for the protection and utilization of water resources in mountainous forest basins.
DOI: 10.57760/sciencedb.16194
title: Source code for NMP-Hydro 1.0
title: Source code for NMP-Hydro 1.0

description: NMP-Hydro is a hydrological model based on Noah-MP but coded using the CSharp (C#) language. This model was crafted by creating a framework and accurately translating the original Noah-MP LSM code from WRF-Hydro 3.0, coupled with a Muskingum method-based river routing model(Liu et al., 2023). CSharp, recognized for its modern and object-oriented approach, is widely used for software development across various platforms, particularly on the Windows operating system. NMP-Hydro offers several advantages over the original Noah-MP and WRF-Hydro. Unlike the original version that requires compiling for each computer and predominantly relies on Unix-like systems, NMP-Hydro can seamlessly run on Windows systems supporting the Microsoft Dotnet Framework. The executable files, once compiled, can be easily packaged and distributed to other Windows computers, providing convenience for users less familiar with Unix-like operations. The utilization of the CSharp language facilitates advanced software programs for code visualization and analysis, enhancing user convenience for code reading and modification. The model's design aligns with the input datasets and settings in the 'namelist' file, ensuring compatibility with WRF-Hydro 3.0. Both the translated Noah-MP LSM simulation and the river routing simulation in NMP-Hydro support parallel execution on common personal computers.
description: NMP-Hydro is a hydrological model based on Noah-MP but coded using the CSharp (C#) language. This model was crafted by creating a framework and accurately translating the original Noah-MP LSM code from WRF-Hydro 3.0, coupled with a Muskingum method-based river routing model(Liu et al., 2023). CSharp, recognized for its modern and object-oriented approach, is widely used for software development across various platforms, particularly on the Windows operating system. NMP-Hydro offers several advantages over the original Noah-MP and WRF-Hydro. Unlike the original version that requires compiling for each computer and predominantly relies on Unix-like systems, NMP-Hydro can seamlessly run on Windows systems supporting the Microsoft Dotnet Framework. The executable files, once compiled, can be easily packaged and distributed to other Windows computers, providing convenience for users less familiar with Unix-like operations. The utilization of the CSharp language facilitates advanced software programs for code visualization and analysis, enhancing user convenience for code reading and modification. The model's design aligns with the input datasets and settings in the 'namelist' file, ensuring compatibility with WRF-Hydro 3.0. Both the translated Noah-MP LSM simulation and the river routing simulation in NMP-Hydro support parallel execution on common personal computers.
DOI: 10.57760/sciencedb.14696
title: A daily average dataset of temperature and salinity vertical profile based on No. 5 buoy of Yellow Sea observation station during 2009–2012
title: 2009–2012年黄海站05号浮标温盐垂直剖面日均值数据集

description: Temperature, salinity and depth are important elements of ocean hydrology observation, and also the basic elements of studying the temporal and spatial distribution characteristics of ocean phenomena. In order to obtain the observation data of the offshore ocean of the North Yellow Sea, the Institute of Oceanology of the Chinese Academy of Sciences, relying on the Yellow Sea Station of the China Offshore Ocean Observation and Research Network, deployed the vertical profile buoy system No. 05 in the waters near Changhai County, Dalian in 2009. So far, real-time observation data have been obtained for 14 years in the area (38º57 'N, 122º45' E). In order to make better use of these valuable observational data, based on the time series data of the temperature-salinity-depth of buoy No. 05 from December 2009 to July 2012, this study adopted standardized data processing and quality control system to produce the daily average dataset of temperature and salinity at 14 vertical depths.
description: Temperature, salinity and depth are important elements of ocean hydrology observation, and also the basic elements of studying the temporal and spatial distribution characteristics of ocean phenomena. In order to obtain the observation data of the offshore ocean of the North Yellow Sea, the Institute of Oceanology of the Chinese Academy of Sciences, relying on the Yellow Sea Station of the China Offshore Ocean Observation and Research Network, deployed the vertical profile buoy system No. 05 in the waters near Changhai County, Dalian in 2009. So far, real-time observation data have been obtained for 14 years in the area (38º57 'N, 122º45' E). In order to make better use of these valuable observational data, based on the time series data of the temperature-salinity-depth of buoy No. 05 from December 2009 to July 2012, this study adopted standardized data processing and quality control system to produce the daily average dataset of temperature and salinity at 14 vertical depths.
DOI: 10.15468/dl.u5u27s
title: Occurrence Download

description: A dataset containing 200187 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is inatura - Erlebnis Naturschau Dornbirn"
 ]
}

 The dataset includes 200187 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001358-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.j00001.01162
title: A spatial distribution of elderly institutions in the Yangtze River Delta (Up to 2023)
title: 长三角养老机构空间分布数据集（截至2023年）

description: Based on the information provided by the integrated civil affairs service platform for elderly care institutions in the Yangtze River Delta region, multi-source data is used for information supplementation and attribute enrichment. The main source of data is from the civil affairs bureaus (departments) of each province (city), supplemented by web information sources such as Qichacha, Tianyancha, and elderly care websites. A spatial distribution dataset of elderly care institutions in the Yangtze River Delta region as of 2023 and relevant attribute information of elderly care institutions in various provinces and cities within each region have been established. This dataset integrates information from 8462 elderly care institutions in the Yangtze River Delta region, including 3278 in Anhui Province, 2244 in Jiangsu Province, 920 in Shanghai, and 2020 in Zhejiang Province. The dataset includes data on elderly care institutions in various provinces (cities) of the Yangtze River Delta and spatial distribution vector data of elderly care institutions in the Yangtze River Delta, with vector data coordinates being WGS84. 
description: Based on the information provided by the integrated civil affairs service platform for elderly care institutions in the Yangtze River Delta region, multi-source data is used for information supplementation and attribute enrichment. The main source of data is from the civil affairs bureaus (departments) of each province (city), supplemented by web information sources such as Qichacha, Tianyancha, and elderly care websites. A spatial distribution dataset of elderly care institutions in the Yangtze River Delta region as of 2023 and relevant attribute information of elderly care institutions in various provinces and cities within each region have been established. This dataset integrates information from 8462 elderly care institutions in the Yangtze River Delta region, including 3278 in Anhui Province, 2244 in Jiangsu Province, 920 in Shanghai, and 2020 in Zhejiang Province. The dataset includes data on elderly care institutions in various provinces (cities) of the Yangtze River Delta and spatial distribution vector data of elderly care institutions in the Yangtze River Delta, with vector data coordinates being WGS84.
DOI: 10.57760/sciencedb.16238
title: code used by 'Two Novel Improvement Strategies: Distance-Based Adaptive Inertia Weight and Entropy Crossover Strategy—A Case Study on the Seahorse Optimization Algorithm"
title: code used by 'Two Novel Improvement Strategies: Distance-Based Adaptive Inertia Weight and Entropy Crossover Strategy—A Case Study on the Seahorse Optimization Algorithm"

description: “两种新颖的改进策略：基于距离的自适应惯性权重和熵交叉策略——海马优化算法的案例研究”使用的代码
description: “两种新颖的改进策略：基于距离的自适应惯性权重和熵交叉策略——海马优化算法的案例研究”使用的代码
DOI: 10.57760/sciencedb.14148
title: 'Time Series Prediction with Transformer Neural Network Optimized by IFS and Hunger-Driven DMOA'support code
title: 'Time Series Prediction with Transformer Neural Network Optimized by IFS and Hunger-Driven DMOA'support code

description: 'Time Series Prediction with Transformer Neural Network Optimized by IFS and Hunger-Driven DMOA'support code
description: 'Time Series Prediction with Transformer Neural Network Optimized by IFS and Hunger-Driven DMOA'support code
DOI: 10.57760/sciencedb.04389
title: Circular Polarization in two Active Repeating Fast Radio Bursts
title: Circular Polarization in two Active Repeating Fast Radio Bursts

description: Polarization, a fundamental property of electromagnetic signals, often carries critical information about the radiation processes, the environment, and the intervening medium of FRBs. We report circular polarization detections of two active repeating FRBs, namely FRBs 20121102A and 20190520B, with the Five-hundred-meter Aperture Spherical radio Telescope. The data set includes 15 bursts with significant circular polarization. Raw data and calibration files are provided.
description: Polarization, a fundamental property of electromagnetic signals, often carries critical information about the radiation processes, the environment, and the intervening medium of FRBs. We report circular polarization detections of two active repeating FRBs, namely FRBs 20121102A and 20190520B, with the Five-hundred-meter Aperture Spherical radio Telescope. The data set includes 15 bursts with significant circular polarization. Raw data and calibration files are provided.
DOI: 10.57760/sciencedb.10471
title: Mechanism underlying soil microbial necromass nitrogen accumulation in response to increasing plant species diversity in a subtropical forest
title: Mechanism underlying soil microbial necromass nitrogen accumulation in response to increasing plant species diversity in a subtropical forest

description: This dataset offers detailed results from a study conducted in a subtropical forest, which explores the influence of plant species diversity on soil microbial necromass nitrogen accumulation. The study involves 45 plots with a gradient of plant species diversity. The dataset encompasses key metrics as follows:(1) Plant species diversity: Quantified using the Shannon Index of trees(2) Fine root biomass: A measure of belowground productivity(3) Amino sugars: Including glucosamine and muramic acid(4) Microbial necromass nitrogen and its contribution to total nitrogen(5) Microbial nitrogen metabolism: Encompassing processes such as microbial nitrogen growth, gross nitrogen mineralization, and microbial nitrogen use efficiency(6) Soil properties: Including pH, soil organic carbon, total nitrogen, dissolved organic carbon, total dissolved nitrogen, exchangeable calcium, exchangeable magnesium, and soil texture(7) Soil microbial traits: Including microbial biomass carbon, microbial biomass nitrogen, and microbial life-history strategy.
description: This dataset offers detailed results from a study conducted in a subtropical forest, which explores the influence of plant species diversity on soil microbial necromass nitrogen accumulation. The study involves 45 plots with a gradient of plant species diversity. The dataset encompasses key metrics as follows:(1) Plant species diversity: Quantified using the Shannon Index of trees(2) Fine root biomass: A measure of belowground productivity(3) Amino sugars: Including glucosamine and muramic acid(4) Microbial necromass nitrogen and its contribution to total nitrogen(5) Microbial nitrogen metabolism: Encompassing processes such as microbial nitrogen growth, gross nitrogen mineralization, and microbial nitrogen use efficiency(6) Soil properties: Including pH, soil organic carbon, total nitrogen, dissolved organic carbon, total dissolved nitrogen, exchangeable calcium, exchangeable magnesium, and soil texture(7) Soil microbial traits: Including microbial biomass carbon, microbial biomass nitrogen, and microbial life-history strategy.
DOI: 10.57760/sciencedb.j00001.01063
title: Chinese-Mongolian bilingual legal field question and answer corpus dataset
title: 汉蒙双语法律领域问答语料数据集

description: With the development of large model technology, intelligent question answering is more and more widely used in people's work and life. However, due to the limitation of data resources, the intelligent question answering system of low-resource languages such as Mongolian can not meet the application needs of people. This study uses the existing Chinese question and answer(Q&A) corpus, constructs 50,000 pairs of Chinese-Mongolian bilingual Q&A corpus data and corresponding classification labels through the steps of rule screening, Chinese-Mongolian translation and manual correction. This dataset can provide researchers with rich, accurate question-answering samples for training and evaluating the performance of intelligent question-answering systems, as well as for tasks such as machine translation and text classification. The manual evaluation verifies that 92% of the corpus conforms to the Q&A in the field of Chinese-Mongolian bilingual law. Therefore, the data set has important usage value for promoting the research of Chinese and Mongolian multi-language intelligent question answering.
description: With the development of large model technology, intelligent question answering is more and more widely used in people's work and life. However, due to the limitation of data resources, the intelligent question answering system of low-resource languages such as Mongolian can not meet the application needs of people. This study uses the existing Chinese question and answer(Q&A) corpus, constructs 50,000 pairs of Chinese-Mongolian bilingual Q&A corpus data and corresponding classification labels through the steps of rule screening, Chinese-Mongolian translation and manual correction. This dataset can provide researchers with rich, accurate question-answering samples for training and evaluating the performance of intelligent question-answering systems, as well as for tasks such as machine translation and text classification. The manual evaluation verifies that 92% of the corpus conforms to the Q&A in the field of Chinese-Mongolian bilingual law. Therefore, the data set has important usage value for promoting the research of Chinese and Mongolian multi-language intelligent question answering.
DOI: 10.57760/sciencedb.16441
title: Dataset of Thin Film Lithium Niobate Electro-optic Isolator Fabricated by photolithography assisted chemo-mechanical etching (PLACE)
title: Thin Film Lithium Niobate Electro-optic Isolator Fabricated by photolithography assisted chemo-mechanical etching (PLACE) 的数据集

description: The dataset collects the necessary data from the article "Thin Film Lithium Niobate Electro-optic Isolator Fabricated by Photolithography Assisted Chemo-Mechanical Etching (PLACE)"
description: The dataset collects the necessary data from the article "Thin Film Lithium Niobate Electro-optic Isolator Fabricated by Photolithography Assisted Chemo-Mechanical Etching (PLACE)"
DOI: 10.57760/sciencedb.ecodb.00130
title: A 1km monthly precipitation dataset for the Qinghai-Tibetan Plateau using machine learning spatial interpolation
title: A 1km monthly precipitation dataset for the Qinghai-Tibetan Plateau using machine learning spatial interpolation

description: High-quality precipitation products with high spatial and temporal resolution play a key role in the study of the hydrological cycle at global and regional scales. Site-based interpolated datasets provide irreplaceable products for monitoring the spatial and temporal variability characteristics of precipitation, especially in areas with harsh natural conditions and lack of information. To this end, month-by-month precipitation data for the Tibetan Plateau were generated using station data from the National Meteorological Information Centre of the China Meteorological Administration (CMA) through a gated convolutional neural network with the introduction of an attention mechanism. The spatial resolution of this data is 0.0083333° (about 1 km), the temporal range is from January 1980 to December 2020, and the data format is TIF. Moreover, validation was carried out using data from independent meteorological observation sites, and the results proved that the dataset is credible. The unit of precipitation in the dataset is 1 mm. The TIF data can be opened for mapping by ArcMAP software , and it is recommended to use the WGS84 coordinate system.
description: High-quality precipitation products with high spatial and temporal resolution play a key role in the study of the hydrological cycle at global and regional scales. Site-based interpolated datasets provide irreplaceable products for monitoring the spatial and temporal variability characteristics of precipitation, especially in areas with harsh natural conditions and lack of information. To this end, month-by-month precipitation data for the Tibetan Plateau were generated using station data from the National Meteorological Information Centre of the China Meteorological Administration (CMA) through a gated convolutional neural network with the introduction of an attention mechanism. The spatial resolution of this data is 0.0083333° (about 1 km), the temporal range is from January 1980 to December 2020, and the data format is TIF. Moreover, validation was carried out using data from independent meteorological observation sites, and the results proved that the dataset is credible. The unit of precipitation in the dataset is 1 mm. The TIF data can be opened for mapping by ArcMAP software , and it is recommended to use the WGS84 coordinate system.