cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.j00001.00335
title: 2020 Ecological Sensitivity Evaluation Dataset of Sanya City
title: 2020年三亚市生态环境敏感性评价数据集

description: Ecological sensitivity refers to the sensitivity of the ecological environment to the natural phenomena and human activities in the region. It reflects the possibility of ecological imbalance and ecological environmental problems. It is one of the main methods to analyze the stability of the regional ecological environment. It is important for regional ecological protection and decision-making. As the central city in the southern part of Hainan Province, Sanya City, improving and protecting the ecological environment plays an important role in the sustainable development of the region. This paper selects Sanya City as the study area to make a 2020 Sanya City Ecological Environment Sensitivity Evaluation Data Set. The dataset uses 9 factors, including elevation, slope, aspect, vegetation coverage, water body, land use type, soil erosion intensity, population density distribution, and road accessibility distance as the ecological sensitivity evaluation indicators. And we adopt the analytic hierarchy process ( AHP) to calculate the weight of each evaluation index, and divide the study area into five levels: non-sensitive, low-sensitive, medium-sensitive, high-sensitive, and extremely high-sensitive through GIS spatial analysis and calculation. This dataset reflects the ecological sensitivity of Sanya City, and provides a reference data source for ecological environmental protection, resource development and land planning in Sanya City.
description: Ecological sensitivity refers to the sensitivity of the ecological environment to the natural phenomena and human activities in the region. It reflects the possibility of ecological imbalance and ecological environmental problems. It is one of the main methods to analyze the stability of the regional ecological environment. It is important for regional ecological protection and decision-making. As the central city in the southern part of Hainan Province, Sanya City, improving and protecting the ecological environment plays an important role in the sustainable development of the region. This paper selects Sanya City as the study area to make a 2020 Sanya City Ecological Environment Sensitivity Evaluation Data Set. The dataset uses 9 factors, including elevation, slope, aspect, vegetation coverage, water body, land use type, soil erosion intensity, population density distribution, and road accessibility distance as the ecological sensitivity evaluation indicators. And we adopt the analytic hierarchy process ( AHP) to calculate the weight of each evaluation index, and divide the study area into five levels: non-sensitive, low-sensitive, medium-sensitive, high-sensitive, and extremely high-sensitive through GIS spatial analysis and calculation. This dataset reflects the ecological sensitivity of Sanya City, and provides a reference data source for ecological environmental protection, resource development and land planning in Sanya City.
DOI: 10.57760/sciencedb.j00001.20002
title: A carbon and water fluxes dataset over farmland ecosystem of winter wheat and summer corn in Yucheng (2003–2010)
title: 2003–2010年禹城冬小麦夏玉米农田生态系统碳水通量观测数据集

description: Yucheng Comprehensive Experimental Station (YCES) is located in the lower Yellow River irrigation district where is one of the major planting area of winter wheat and summer maize. YCES is one of the oldest flux station in China. This dataset is the collection of ecosystem carbon and water fluxes from 2003 to 2010 in YCES. A standardized dataset of ecosystem carbon water fluxes and key meteorological elements were processed based on the data processing system of ChinaFLUX. This dataset is of great significance for studying important parameters such as farmland productivity and water consumption in North China, as well as for accurately evaluating the status and role of carbon-water fluxes in the regional carbon-water cycle of winter wheat and summer corn farmland ecosystems in North China.The dataset consists of the flux and meteorological measurement including half-hour, daily, monthly and yearly data products. The half-hour dataset of flux measurement includes CO2 flux, latent heat flux, sensible heat flux, air temperature, soil temperature, precipitation, global radiation and photosynthetic active radiation.
description: Yucheng Comprehensive Experimental Station (YCES) is located in the lower Yellow River irrigation district where is one of the major planting area of winter wheat and summer maize. YCES is one of the oldest flux station in China. This dataset is the collection of ecosystem carbon and water fluxes from 2003 to 2010 in YCES. A standardized dataset of ecosystem carbon water fluxes and key meteorological elements were processed based on the data processing system of ChinaFLUX. This dataset is of great significance for studying important parameters such as farmland productivity and water consumption in North China, as well as for accurately evaluating the status and role of carbon-water fluxes in the regional carbon-water cycle of winter wheat and summer corn farmland ecosystems in North China.The dataset consists of the flux and meteorological measurement including half-hour, daily, monthly and yearly data products. The half-hour dataset of flux measurement includes CO2 flux, latent heat flux, sensible heat flux, air temperature, soil temperature, precipitation, global radiation and photosynthetic active radiation.
DOI: 10.57760/sciencedb.j00076.00076
title: The utilization status of urban green spcae in China
title: The utilization status of urban green spcae in China

description: Combined with land use data and POI big data, we constructed three indicators for assessing the actual utilization of urban green space (UGS). First, Stock indicator: the total area of UGS within the administrative boundary of each city. Second, Actual Utilization indicator: the area of actual used UGS within the administrative boundary of each city. Third, Actual Utilization rate indicator. The ratio of the used UGS area and the total UGS area in each city. In the shapefile, the Stock, Actual Utilization, and Actual Utilization rate indicator was represented by fieldname “Stock”, "AU", and "AUR", respectively.Then, Stock, Actual Utilization and Actual Utilization rate were calculated for the urban area green space and peri-urban green space. For the urban area green space, the Stock, Actual Utilization, and Actual Utilization rate indicator was represented by fieldname “Incitys”, "IncityAU", and "IncityAUR", respectively. For the peri-urban green space, the Stock, Actual Utilization, and Actual Utilization rate indicator was represented by fieldname “Outcitys”, "OutcityAU", and "OutcityAUR", respectively.
description: Combined with land use data and POI big data, we constructed three indicators for assessing the actual utilization of urban green space (UGS). First, Stock indicator: the total area of UGS within the administrative boundary of each city. Second, Actual Utilization indicator: the area of actual used UGS within the administrative boundary of each city. Third, Actual Utilization rate indicator. The ratio of the used UGS area and the total UGS area in each city. In the shapefile, the Stock, Actual Utilization, and Actual Utilization rate indicator was represented by fieldname “Stock”, "AU", and "AUR", respectively.Then, Stock, Actual Utilization and Actual Utilization rate were calculated for the urban area green space and peri-urban green space. For the urban area green space, the Stock, Actual Utilization, and Actual Utilization rate indicator was represented by fieldname “Incitys”, "IncityAU", and "IncityAUR", respectively. For the peri-urban green space, the Stock, Actual Utilization, and Actual Utilization rate indicator was represented by fieldname “Outcitys”, "OutcityAU", and "OutcityAUR", respectively.
DOI: 10.57760/sciencedb.j00104.00026
title: User Profiling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction
title: User Profiling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction

description: Six tables, five figures and one algorithm of this paper. Table 1 shows candidate phrases. Table 2 demonstrates performance of different types of features. Table 3 summarizes notations used in this paper. Table 4 shows the statistics of data sets. Table 5 compares scores of methods. Table 6 shows performance of models. Algorithm 1 shows semi-supervised regression algorithm. Figure 1 shows candidate phrase generation procedure. Figure 2 is the framework of a two-phase model. Figure 3 depicts the architecture of the neural network. Figure 4 shows the architecture of user growth value prediction. Figure 5 presents bagging of primary model.
description: Six tables, five figures and one algorithm of this paper. Table 1 shows candidate phrases. Table 2 demonstrates performance of different types of features. Table 3 summarizes notations used in this paper. Table 4 shows the statistics of data sets. Table 5 compares scores of methods. Table 6 shows performance of models. Algorithm 1 shows semi-supervised regression algorithm. Figure 1 shows candidate phrase generation procedure. Figure 2 is the framework of a two-phase model. Figure 3 depicts the architecture of the neural network. Figure 4 shows the architecture of user growth value prediction. Figure 5 presents bagging of primary model.
DOI: 10.57760/sciencedb.j00001.00290
title: A rainstorm dataset in China during 2001-2019
title: 2001-2019年中国暴雨数据集

description: This rainstorm dataset is extracted from a long time series (January 2001 to December 2019) of Global Precipitation Measurement (GPM) product. According to the rainstorm threshold defined by the China Meteorological Administration, rainstorm events are extracted from time series GPM data using Python programming. The dataset covers the whole China with a spatial resolution of 0.1°×0.1°. Derived from long-term sequence of satellite precipitation products, with high-precision and high-resolution, this rainstorm dataset is able to reflect the spatial distribution characteristics of extreme precipitation in China. Understanding the temporal and spatial characteristics of rainstorms across the country can be helpful for preventing and alleviating disasters. This dataset provides important support for the development of social economy and ecological environment. It is of important theoretical and practical application value.
description: This rainstorm dataset is extracted from a long time series (January 2001 to December 2019) of Global Precipitation Measurement (GPM) product. According to the rainstorm threshold defined by the China Meteorological Administration, rainstorm events are extracted from time series GPM data using Python programming. The dataset covers the whole China with a spatial resolution of 0.1°×0.1°. Derived from long-term sequence of satellite precipitation products, with high-precision and high-resolution, this rainstorm dataset is able to reflect the spatial distribution characteristics of extreme precipitation in China. Understanding the temporal and spatial characteristics of rainstorms across the country can be helpful for preventing and alleviating disasters. This dataset provides important support for the development of social economy and ecological environment. It is of important theoretical and practical application value.
DOI: 10.57760/sciencedb.j00001.00304
title: Dataset of precipitation and groundwater elements for water resources carrying capacity in North China (2005–2016)
title: 基于水资源承载力的华北地区降水与地下水要素数据集（2005–2016年）

description: Based on the observation data of groundwater in North China from 2005 to 2016 and meteorological data related to water resources, this dataset established a data set of water resources carrying capacity elements in North China through the process of element extraction, quality control and data processing. This could provide reliable data support for the development of rainfed forest and grass vegetation construction in this region according to local conditions and the realization of a stable and efficient sustainable ecosystem.
description: Based on the observation data of groundwater in North China from 2005 to 2016 and meteorological data related to water resources, this dataset established a data set of water resources carrying capacity elements in North China through the process of element extraction, quality control and data processing. This could provide reliable data support for the development of rainfed forest and grass vegetation construction in this region according to local conditions and the realization of a stable and efficient sustainable ecosystem.
DOI: 10.57760/sciencedb.j00001.00336
title: Microscopic image data set of Xujiahe gas reservoir in northeast Sichuan
title: 川东北地区须家河组气藏储层显微图像数据集

description: Xujiahe Formation gas reservoir in northeast Sichuan Basin has good exploration and development potential. In this paper, the microscopic image data of the clastic reservoir of xujiahe Formation gas reservoir in the study area are collected and sorted, and the petrological characteristics, diagenesis, reservoir space and physical properties of xujiahe Formation reservoir are further studied. The reservoir rocks are lithic sandstone, lithic arkose sandstone and quartz sandstone. There are intergranular solution pores, residual intergranular solution pores, intragranular solution pores, clay mineral intergranular (solution) pores and various microfractures in the reservoir space. Through the observation of the cast thin section, the pores are relatively undeveloped, and some thin sections (micro-cracks) are relatively developed. Through thin section identification and observation, the sandstone reservoirs of Xujiahe Formation in the study area have undergone compaction, cementation, dissolution and tectonic fracture. The establishment of this data set can provide a direct way for the comparative analysis and reference of sandstone image data, and can also be used as a standard for the intelligent identification of lithology.
description: Xujiahe Formation gas reservoir in northeast Sichuan Basin has good exploration and development potential. In this paper, the microscopic image data of the clastic reservoir of xujiahe Formation gas reservoir in the study area are collected and sorted, and the petrological characteristics, diagenesis, reservoir space and physical properties of xujiahe Formation reservoir are further studied. The reservoir rocks are lithic sandstone, lithic arkose sandstone and quartz sandstone. There are intergranular solution pores, residual intergranular solution pores, intragranular solution pores, clay mineral intergranular (solution) pores and various microfractures in the reservoir space. Through the observation of the cast thin section, the pores are relatively undeveloped, and some thin sections (micro-cracks) are relatively developed. Through thin section identification and observation, the sandstone reservoirs of Xujiahe Formation in the study area have undergone compaction, cementation, dissolution and tectonic fracture. The establishment of this data set can provide a direct way for the comparative analysis and reference of sandstone image data, and can also be used as a standard for the intelligent identification of lithology.
DOI: 10.57760/sciencedb.j00001.00349
title: Mongolian speech corpus IMUT-MC
title: 蒙古语语音语料库IMUT-MC

description: Mongolian, as a minority language, lacks a large-scale speech corpus accessible to researchers for experimental support, because its users are scattered and it is difficult to collect and label the speech sounds, which hinders the further development of Mongolian speech recognition. This research group has constructed a speech corpus IMUT-MC for Mongolian speech recognition tasks, which contains about 212 hours of reading speech recorded by 417 speakers, and is committed to advancing Mongolian speech recognition research.
description: Mongolian, as a minority language, lacks a large-scale speech corpus accessible to researchers for experimental support, because its users are scattered and it is difficult to collect and label the speech sounds, which hinders the further development of Mongolian speech recognition. This research group has constructed a speech corpus IMUT-MC for Mongolian speech recognition tasks, which contains about 212 hours of reading speech recorded by 417 speakers, and is committed to advancing Mongolian speech recognition research.
DOI: 10.57760/sciencedb.j00053.00012
title: 引力波探测领域WOS数据
title: 引力波探测领域WOS数据

description: 对全球引力波探测领域的研究论文进行检索，检索数据库为科睿唯安的Web of science数据库，检索时间到2021年10月，获得引力波探测领域研究论文数据13114篇，基于该数据集合，对引力波探测领域的发展以及科学研究进行分析，包括研究主题、主要地区以及重点研究机构等信息。
description: 对全球引力波探测领域的研究论文进行检索，检索数据库为科睿唯安的Web of science数据库，检索时间到2021年10月，获得引力波探测领域研究论文数据13114篇，基于该数据集合，对引力波探测领域的发展以及科学研究进行分析，包括研究主题、主要地区以及重点研究机构等信息。
DOI: 10.57760/sciencedb.j00076.00012
title: Water use efficiency data from 2000 to 2019 in measuring progress towards SDGs in Central Asia
title: Water use efficiency data from 2000 to 2019 in measuring progress towards SDGs in Central Asia

description: This dataset aims to evaluate the water use efficiency in Central Asia for monitoring indicator 6.4.1 Change in water-use efficiency over time in the context of the Sustainable Development Goals (SDGs) report. It consists of two sub datasets. One is the summer WUE dataset from 2000 to 2019 and the other is the water use statistics, i.e., water use amount per 10,000 USD GDP for the five countries from 1997 to 2017. The domain of the WUE dataset covers the land areas of the five Central Asian countries, which ranges from 35°07'N to 55°26'N in latitude and from 46°29'E to 87°18'E in longitude, covering an area of 402.8 × 104 km2 at a spatial resolution of 500 m. This dataset reflects the long term changes in summer time WUE. Each raster file corresponds to one year. For example, the file WUE-CentralAsia2000.tif refers to summer WUE in Central Asia in 2000. The nation-scale water use efficiency for the five countries was represented using total water use amount per 10,000 USD GDP. It could reflect the overall water use efficiency of all sectors in Central Asia. In addition, the agricultural area is produced based on the MCD12Q1 land cover data and the global irrigation dataset version 5.
description: This dataset aims to evaluate the water use efficiency in Central Asia for monitoring indicator 6.4.1 Change in water-use efficiency over time in the context of the Sustainable Development Goals (SDGs) report. It consists of two sub datasets. One is the summer WUE dataset from 2000 to 2019 and the other is the water use statistics, i.e., water use amount per 10,000 USD GDP for the five countries from 1997 to 2017. The domain of the WUE dataset covers the land areas of the five Central Asian countries, which ranges from 35°07'N to 55°26'N in latitude and from 46°29'E to 87°18'E in longitude, covering an area of 402.8 × 104 km2 at a spatial resolution of 500 m. This dataset reflects the long term changes in summer time WUE. Each raster file corresponds to one year. For example, the file WUE-CentralAsia2000.tif refers to summer WUE in Central Asia in 2000. The nation-scale water use efficiency for the five countries was represented using total water use amount per 10,000 USD GDP. It could reflect the overall water use efficiency of all sectors in Central Asia. In addition, the agricultural area is produced based on the MCD12Q1 land cover data and the global irrigation dataset version 5.
DOI: 10.57760/sciencedb.j00076.00085
title: Sentinel-1 EW mode dataset for Antarctica from 2014-2020 produced by the CASEarth Cloud Service Platform
title: Sentinel-1 EW mode dataset for Antarctica from 2014-2020 produced by the CASEarth Cloud Service Platform

description: Antarctica plays an important role in research on global change, and its unique geography, ocean, climate, and environment provide an ideal place for humankind to understand Earth’s evolution. Remote sensing provides an effective means to monitor and observe large-scale processes on the continent. Synthetic aperture radar (SAR) in particular provides the capability for all-weather Earth observation. The Sentinel-1A and Sentinel-1B SAR satellites have ideal ground coverage and imaging frequency for observing Antarctica. This study developed a dataset of 69,586 Sentinel-1 EW mode satellite images of the Antarctic ice sheet from October 2014 to December 2020. The dataset was processed with the European Space Agency Sentinel Application Platform (SNAP) and a Python batch scheduling tool on the Big Earth Data Cloud Service Platform of the Chinese Academy of Sciences Big Earth Data Science Engineering Program (CASEarth). Several data processing operations were implemented to process the raw dataset, including radiometric calibration, invalid edge removal, geocoding, data re-projection to an Antarctic projection, data compression to TIFF format, and construction of image pyramids.
description: Antarctica plays an important role in research on global change, and its unique geography, ocean, climate, and environment provide an ideal place for humankind to understand Earth’s evolution. Remote sensing provides an effective means to monitor and observe large-scale processes on the continent. Synthetic aperture radar (SAR) in particular provides the capability for all-weather Earth observation. The Sentinel-1A and Sentinel-1B SAR satellites have ideal ground coverage and imaging frequency for observing Antarctica. This study developed a dataset of 69,586 Sentinel-1 EW mode satellite images of the Antarctic ice sheet from October 2014 to December 2020. The dataset was processed with the European Space Agency Sentinel Application Platform (SNAP) and a Python batch scheduling tool on the Big Earth Data Cloud Service Platform of the Chinese Academy of Sciences Big Earth Data Science Engineering Program (CASEarth). Several data processing operations were implemented to process the raw dataset, including radiometric calibration, invalid edge removal, geocoding, data re-projection to an Antarctic projection, data compression to TIFF format, and construction of image pyramids.