cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.j00104.00005
title: The Computer Science Ontology (CSO)
title: The Computer Science Ontology (CSO)

description: the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 14K topics and 162K semantic relationships. It was created by applying the Klink-2 algorithm on a very large data set of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO, we have also released the CSO Classifier, a tool for automatically classifying research papers, and the CSO Portal, a Web application that enables users to download, explore, and provide granular feedback on CSO. Users can use the portal to navigate and visualize sections of the ontology, rate topics and relationships, and suggest missing ones. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.
description: the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 14K topics and 162K semantic relationships. It was created by applying the Klink-2 algorithm on a very large data set of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO, we have also released the CSO Classifier, a tool for automatically classifying research papers, and the CSO Portal, a Web application that enables users to download, explore, and provide granular feedback on CSO. Users can use the portal to navigate and visualize sections of the ontology, rate topics and relationships, and suggest missing ones. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.
DOI: 10.57760/sciencedb.j00104.00021
title: AMiner: Search and Mining of Academic Social Networks
title: AMiner: Search and Mining of Academic Social Networks

description: 8 figures of the paper. Figure 1 presents the architecture of AMiner. Figure 2 shows the schema of the researcher profile. Figure 3 is an example of researcher profile. Figure 4 is an overview of the name disambiguation framework in AMiner. Figure 5 is graphical representation of the three Author-Conference-Topic (ACT) models. Figure 6 shows an example result of experts found for “Data Mining”. Figure 7 is a model framework of DeepInf. Figure 8 shows an example of researcher ranking by sociability index.
description: 8 figures of the paper. Figure 1 presents the architecture of AMiner. Figure 2 shows the schema of the researcher profile. Figure 3 is an example of researcher profile. Figure 4 is an overview of the name disambiguation framework in AMiner. Figure 5 is graphical representation of the three Author-Conference-Topic (ACT) models. Figure 6 shows an example result of experts found for “Data Mining”. Figure 7 is a model framework of DeepInf. Figure 8 shows an example of researcher ranking by sociability index.
DOI: 10.57760/sciencedb.j00104.00028
title: MDKB-Bot: A Practical Framework for Multi-Domain Task-Oriented Dialogue System
title: MDKB-Bot: A Practical Framework for Multi-Domain Task-Oriented Dialogue System

description: Four tables and two figures of this paper. Table 1 shows the statistics of training corpus for domain classification. Table 2 summarizes the statistics of training corpus for slot filling. Table 3 is a comparison of labeling performance on NLU. Table 4 shows dialogue quality evaluation results between top 4 teams in the competition. Figure 1 depicts the overall framework of the model consists of three components: (1) NLU module, which predicts intent domain and gives slot value of user utterance, (2) DM module, which outputs the dialogue action to NLG module, and (3) NLG module, which generates the final response. Figure 2 presents BLSTM-CRF model for slot filling.
description: Four tables and two figures of this paper. Table 1 shows the statistics of training corpus for domain classification. Table 2 summarizes the statistics of training corpus for slot filling. Table 3 is a comparison of labeling performance on NLU. Table 4 shows dialogue quality evaluation results between top 4 teams in the competition. Figure 1 depicts the overall framework of the model consists of three components: (1) NLU module, which predicts intent domain and gives slot value of user utterance, (2) DM module, which outputs the dialogue action to NLG module, and (3) NLG module, which generates the final response. Figure 2 presents BLSTM-CRF model for slot filling.
DOI: 10.57760/sciencedb.j00001.00308
title: A dataset of lakes in Qinghai province with an area above 1.0 km2 in 2020
title: 2020年青海省面积≥1.0 km2湖泊边界数据集

description: Qinghai province (31°39′~39°19′N, 89°35′~103°04′E) is located in the northeastern part of the Qinghai-Tibetan Plateau. As the source region of the Yangtze River, the Yellow River and the Lantsang River, it is known as the China Water Tower. The contemporary investigation of lakes in Qinghai province is of great significance to understand the impacts of climate change and promote the ecological civilization construction. Based on Sentinel-2A/2B MSI images acquired in 2020, the pixels of water body were firstly extracted according to the normalized difference water index (NDWI) greater than the normalized difference vegetation index (NDVI). Then the vector dataset of lake boundary with an area above 1.0 km2 in Qinghai province was built after manual inspection and revision. Some attributes of the lake including its name, area, elevation, basin, administration and date were also attached. This dataset reflects the status of lakes in Qinghai province in 2020, and can be used as the data source and scientific basis for researches on lake ecological protection, rational use of regional water resources, disaster prevention and mitigation in Qinghai province.
description: Qinghai province (31°39′~39°19′N, 89°35′~103°04′E) is located in the northeastern part of the Qinghai-Tibetan Plateau. As the source region of the Yangtze River, the Yellow River and the Lantsang River, it is known as the China Water Tower. The contemporary investigation of lakes in Qinghai province is of great significance to understand the impacts of climate change and promote the ecological civilization construction. Based on Sentinel-2A/2B MSI images acquired in 2020, the pixels of water body were firstly extracted according to the normalized difference water index (NDWI) greater than the normalized difference vegetation index (NDVI). Then the vector dataset of lake boundary with an area above 1.0 km2 in Qinghai province was built after manual inspection and revision. Some attributes of the lake including its name, area, elevation, basin, administration and date were also attached. This dataset reflects the status of lakes in Qinghai province in 2020, and can be used as the data source and scientific basis for researches on lake ecological protection, rational use of regional water resources, disaster prevention and mitigation in Qinghai province.
DOI: 10.57760/sciencedb.j00001.00318
title: A dataset of soil chemical elements for the typical photovoltaic power plant in Xinjiang and wind farm in Hebei during 2020 – 2021
title: 新疆光伏电站和河北风电场2020–2021年土壤化学元素数据集

description: This data set uses drones equipped with multi-spectral sensors and X-ray fluorescence spectrometer to monitor a large onshore wind farm in Hebei and a photovoltaic power station in Xinjiang, and collects the GPS location data of the wind turbines. This data set will provide data and technological support for the coordinated development of green energy and environmental protection. The dataset consists of 4 subsets in total, among which: (1) PPP.zip is the boundary, DSM and NDVI data for drone monitoring of photovoltaic power plants, the data volume is 306 M; (2) WF.kml is the GPS data of wind power plants, The data volume is 0.2 M; (3) PPP_SOIL_XRAY.csv is the soil element concentration data of the photovoltaic power station, the data volume is 1.39 M; (4) WF_SOIL_XRAY.csv is the wind farm soil element concentration data, the data volume is 0.2 M；(5) at-wind_evp_gst_1981-2019_SD_SY.csv is the data of Shangdu and Shangyi weather stations around the wind farm, with a data volume of 0.2M.
description: This data set uses drones equipped with multi-spectral sensors and X-ray fluorescence spectrometer to monitor a large onshore wind farm in Hebei and a photovoltaic power station in Xinjiang, and collects the GPS location data of the wind turbines. This data set will provide data and technological support for the coordinated development of green energy and environmental protection. The dataset consists of 4 subsets in total, among which: (1) PPP.zip is the boundary, DSM and NDVI data for drone monitoring of photovoltaic power plants, the data volume is 306 M; (2) WF.kml is the GPS data of wind power plants, The data volume is 0.2 M; (3) PPP_SOIL_XRAY.csv is the soil element concentration data of the photovoltaic power station, the data volume is 1.39 M; (4) WF_SOIL_XRAY.csv is the wind farm soil element concentration data, the data volume is 0.2 M；(5) at-wind_evp_gst_1981-2019_SD_SY.csv is the data of Shangdu and Shangyi weather stations around the wind farm, with a data volume of 0.2M.
DOI: 10.57760/sciencedb.j00001.00357
title: Tibetan dialect speech synthesis dataset
title: 藏语方言语音合成数据集

description: The dataset is compressed into Tibetan Dialect Speech Synthesis Data Set.zip. It comprises 3 large-size folders (ad, kb, wz), each large folder comprises 4 middle-size folders (e.g. ad1, ad2, ad3, and ad4 under ad), and each multiple-size folder comprises multiple small folders (e.g. ad1-1, ad1-2... ad1-10 under ad1), in which *.wav is voice data, and *.txt is text data. In total, the dataset includes: (1) 4684 *.wav audio files, with a total length of more than 10 hours and a data volume of 8.01GB, including 2.45GB for Amdo, 2.13GB for Kangba and 3.34GB for Weizang; (2) 65 *.txt text files, with data volume of 1.631MB, and a total of 4684 sentences.
description: The dataset is compressed into Tibetan Dialect Speech Synthesis Data Set.zip. It comprises 3 large-size folders (ad, kb, wz), each large folder comprises 4 middle-size folders (e.g. ad1, ad2, ad3, and ad4 under ad), and each multiple-size folder comprises multiple small folders (e.g. ad1-1, ad1-2... ad1-10 under ad1), in which *.wav is voice data, and *.txt is text data. In total, the dataset includes: (1) 4684 *.wav audio files, with a total length of more than 10 hours and a data volume of 8.01GB, including 2.45GB for Amdo, 2.13GB for Kangba and 3.34GB for Weizang; (2) 65 *.txt text files, with data volume of 1.631MB, and a total of 4684 sentences.
DOI: 10.57760/sciencedb.j00053.00011
title: Database of China-CEEC Co-authorship Papers (2011-2020)
title: 2011-2020年我国与中东欧国家国际合作论文数据库

description: This database is co-authorship papers between China and central and Eastern European countries included in incites database from 2011 to 2020. The statistical literature types are article, review and proceedings paper. The statistical time window is 2011-2020, the data collection time is November 19, 2021, and the update time of incites database is October 29, 2021. China only counts co-authorship papers in the mainland, while central and Eastern European countries include Bosnia and Herzegovina, Czech Republic, Montenegro, Poland, Serbia, Albania, Croatia, Greece, Hungary, northern Macedonia, Slovakia, Bulgaria, Slovenia, Estonia, Latvia and Romania. The classification of thesis disciplines is based on the catalogue of disciplines for degree awarding and talent training (updated in April 2018) issued by the Academic Degrees Committee of the State Council and the Ministry of education.
description: This database is co-authorship papers between China and central and Eastern European countries included in incites database from 2011 to 2020. The statistical literature types are article, review and proceedings paper. The statistical time window is 2011-2020, the data collection time is November 19, 2021, and the update time of incites database is October 29, 2021. China only counts co-authorship papers in the mainland, while central and Eastern European countries include Bosnia and Herzegovina, Czech Republic, Montenegro, Poland, Serbia, Albania, Croatia, Greece, Hungary, northern Macedonia, Slovakia, Bulgaria, Slovenia, Estonia, Latvia and Romania. The classification of thesis disciplines is based on the catalogue of disciplines for degree awarding and talent training (updated in April 2018) issued by the Academic Degrees Committee of the State Council and the Ministry of education.
DOI: 10.57760/sciencedb.j00001.00396
title: A snow depth data set over northern China derived using observed data from a continuously operating GNSS network (2013-2020)
title: 中国北方GNSS气象观测业务站网积雪深度数据集（2013–2020）

description: Snow depth is the most critical parameter to provide snow equivalent. Its spatial and temporal distribution is the key variable of snowmelt runoff simulation, snow disaster monitoring in pastoral areas, and prediction model of the avalanche. The all-weather global navigation satellite system (GNSS) signal has been proven to retrieve snow depth with high spatial-temporal resolution and high accuracy. This paper develops a new snow depth data set (v1.0, 12 / 24 hours, ~ 1000 m2, 2013-2020) using the observed data from the GNSS meteorological observation network over northern China. The data set has an independent measurement principle, and the spatial resolution is between ground measurement (point-scale) and satellite remote sensing (tens of meters to tens of kilometers). The data set is a valuable supplement to the existing snow depth data, supporting scientific research and snow monitoring applications. The data set also helps to improve the potential value of the existing/future GNSS station networks in China.
description: Snow depth is the most critical parameter to provide snow equivalent. Its spatial and temporal distribution is the key variable of snowmelt runoff simulation, snow disaster monitoring in pastoral areas, and prediction model of the avalanche. The all-weather global navigation satellite system (GNSS) signal has been proven to retrieve snow depth with high spatial-temporal resolution and high accuracy. This paper develops a new snow depth data set (v1.0, 12 / 24 hours, ~ 1000 m2, 2013-2020) using the observed data from the GNSS meteorological observation network over northern China. The data set has an independent measurement principle, and the spatial resolution is between ground measurement (point-scale) and satellite remote sensing (tens of meters to tens of kilometers). The data set is a valuable supplement to the existing snow depth data, supporting scientific research and snow monitoring applications. The data set also helps to improve the potential value of the existing/future GNSS station networks in China.
DOI: 10.57760/sciencedb.j00053.00006
title: 3D生物打印技术论文数据集
title: 3D生物打印技术论文数据集

description: Based on the web of science core collection database as the statistical source, the bibliometric analysis of articles on 3D bioprinting technology included in the database was carried out. In the field of 3D bioprinting technology, 15736 papers were included in the web of science core collection database.
description: Based on the web of science core collection database as the statistical source, the bibliometric analysis of articles on 3D bioprinting technology included in the database was carried out. In the field of 3D bioprinting technology, 15736 papers were included in the web of science core collection database.
DOI: 10.57760/sciencedb.j00076.00071
title: Dataset of daily snow water equivalent product from 1980 to 2020 over China
title: Dataset of daily snow water equivalent product from 1980 to 2020 over China

description: The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system. This work presents a 40-yr (1980-2020) daily snow water equivalent product with a linear unmixing method through passive microwave radiometer data including the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) over China. The unbiased root mean square error (unRMSE) of snow depth is about 5-7cm, corresponding to 10-15 mm for the snow water equivalent (SWE), when compared with ground measurements of station snow depth and field snow course data.
description: The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system. This work presents a 40-yr (1980-2020) daily snow water equivalent product with a linear unmixing method through passive microwave radiometer data including the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) over China. The unbiased root mean square error (unRMSE) of snow depth is about 5-7cm, corresponding to 10-15 mm for the snow water equivalent (SWE), when compared with ground measurements of station snow depth and field snow course data.
DOI: 10.57760/sciencedb.j00076.00090
title: A dataset of sea surface temperature abnormal change process objects (GDPoSSTA V1.0), Global ocean 1982-2009
title: A dataset of sea surface temperature abnormal change process objects (GDPoSSTA V1.0), Global ocean 1982-2009

description: The GDPoSSTA includes three datasets and two relationship files with a time range from January 1982 to December 2009. Three datasets formatted in SHP are a dataset of process object-oriented SSTA, named DSPOSSTA, storing SSTA process objects, a dataset of sequence object-oriented SSTA, named DSSOSSTA, storing SSTA sequence objects, and a dataset of variation object-oriented SSTA, named DSVOSSTA, storing SSTA variation objects, respectively. And two relationship files formatted in CSV store the evolving behaviors among sequence objects of SSTA and variation objects of SSTA, respectively.
description: The GDPoSSTA includes three datasets and two relationship files with a time range from January 1982 to December 2009. Three datasets formatted in SHP are a dataset of process object-oriented SSTA, named DSPOSSTA, storing SSTA process objects, a dataset of sequence object-oriented SSTA, named DSSOSSTA, storing SSTA sequence objects, and a dataset of variation object-oriented SSTA, named DSVOSSTA, storing SSTA variation objects, respectively. And two relationship files formatted in CSV store the evolving behaviors among sequence objects of SSTA and variation objects of SSTA, respectively.
DOI: 10.57760/sciencedb.j00001.00309
title: A dataset of mangrove forests changes in Hainan Island based on GF-2 data during 2015-2019
title: 基于GF-2数据的海南岛红树林变化数据集（2015-2019）

description: Mangrove forests are wetland ecosystems in the coastal zone with important ecological and eco-economic service values. At the same time, mangrove forests are vulnerable ecosystems, being under threat from both natural and anthropogenic forces, so mangrove monitoring is a permanent task. The development of remote sensing technology has provided an efficient and convenient means for mangrove monitoring. In this study, a dataset of mangrove forest changes on Hainan Island from 2015 to 2019 was obtained using Gaofen-2 (GF-2) data in 2015, 2017 and 2019. The dataset was compiled through a support vector machine (SVM) classification method and field survey data. It can be used as the basic data for the analysis of spatial and temporal changes of mangrove forests, and can also provide decision support for the restoration, protection and management of mangrove wetland ecosystems, and provide basic data for the ecological environment supervision in Hainan Province.
description: Mangrove forests are wetland ecosystems in the coastal zone with important ecological and eco-economic service values. At the same time, mangrove forests are vulnerable ecosystems, being under threat from both natural and anthropogenic forces, so mangrove monitoring is a permanent task. The development of remote sensing technology has provided an efficient and convenient means for mangrove monitoring. In this study, a dataset of mangrove forest changes on Hainan Island from 2015 to 2019 was obtained using Gaofen-2 (GF-2) data in 2015, 2017 and 2019. The dataset was compiled through a support vector machine (SVM) classification method and field survey data. It can be used as the basic data for the analysis of spatial and temporal changes of mangrove forests, and can also provide decision support for the restoration, protection and management of mangrove wetland ecosystems, and provide basic data for the ecological environment supervision in Hainan Province.
DOI: 10.57760/sciencedb.j00001.00321
title: A dataset of boundary and length of glaciers in Qinghai province in 2020
title: 2020年青海省冰川边界及长度数据集

description: Qinghai province (31°39′~39°19′N, 89°35′~103°04′E) in the northeastern Qinghai-Tibet Plateau possesses numerous modern glaciers, which are widely developed in high altitude mountains such as Kunlun Mountains, Tanggula Mountains, Qilian Mountains, Altun Mountains and A'Nyêmaqên Mountains. Under global warming, the glaciers had been retreating in Qinghai province in recent years. The knowledge of the contemporary glaciers is the basis for scientific evaluation of the response pattern of glacier changes on climate change and the formulation of policies for rational use of water resources. Based on the GF-1/2/6 high resolution remote sensing images from 2018 to 2021 and SRTM DEM data, a vector dataset of boundary and length of glaciers in Qinghai province were produced using Deep Learning method and automatic extraction method of glacier centerline, the accuracy is 99.01% and 98.09%, respectively. The dataset can reflect the status of glaciers in Qinghai province in 2020 and provide data to support the research of glacier and climate change.
description: Qinghai province (31°39′~39°19′N, 89°35′~103°04′E) in the northeastern Qinghai-Tibet Plateau possesses numerous modern glaciers, which are widely developed in high altitude mountains such as Kunlun Mountains, Tanggula Mountains, Qilian Mountains, Altun Mountains and A'Nyêmaqên Mountains. Under global warming, the glaciers had been retreating in Qinghai province in recent years. The knowledge of the contemporary glaciers is the basis for scientific evaluation of the response pattern of glacier changes on climate change and the formulation of policies for rational use of water resources. Based on the GF-1/2/6 high resolution remote sensing images from 2018 to 2021 and SRTM DEM data, a vector dataset of boundary and length of glaciers in Qinghai province were produced using Deep Learning method and automatic extraction method of glacier centerline, the accuracy is 99.01% and 98.09%, respectively. The dataset can reflect the status of glaciers in Qinghai province in 2020 and provide data to support the research of glacier and climate change.