Vegetation is an important component of the terrestrial ecosystem. The changes of vegetation are assumed to well indicate the dynamic changes of the ecosystem. However, the changing global climate and the intensifying human activities have a great effect on vegetation growth, which particularly highlights the implications to monitor and assess vegetation changes. Vegetation changes are usually measured by vegetation indexes. The normalized difference vegetation index (NDVI) based on remote sensing is widely used in the studies on vegetation changes and climate impact. In this study, we used the spectral reflectance data product (MOD09Q1) of the Moderate Resolution Imaging Spectrometer (MODIS) from 2000 to 2018 to calculate the NDVI (with a spatial resolution of 250m and temporal step of 8 days). The S-G filtering method of the TIMESAT3.2 software is applied to remove the noise in the NDVI time series for the reconstruction of time series. In this way, we finally obtained this dataset, which is open to the public for sharing and downloading. It is expected to support further studies on the dynamic changes of vegetation in the Three-river Headwaters.
{"title":"A dataset of normalized difference vegetation index in the Three-river Headwaters during 2000-2018","authors":"Peixia Liu, Junbang Wang, Meng Wang, Xiaofang Sun, Duoping Zhu","doi":"10.11922/11-6035.csd.2022.0059.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0059.zh","url":null,"abstract":"Vegetation is an important component of the terrestrial ecosystem. The changes of vegetation are assumed to well indicate the dynamic changes of the ecosystem. However, the changing global climate and the intensifying human activities have a great effect on vegetation growth, which particularly highlights the implications to monitor and assess vegetation changes. Vegetation changes are usually measured by vegetation indexes. The normalized difference vegetation index (NDVI) based on remote sensing is widely used in the studies on vegetation changes and climate impact. In this study, we used the spectral reflectance data product (MOD09Q1) of the Moderate Resolution Imaging Spectrometer (MODIS) from 2000 to 2018 to calculate the NDVI (with a spatial resolution of 250m and temporal step of 8 days). The S-G filtering method of the TIMESAT3.2 software is applied to remove the noise in the NDVI time series for the reconstruction of time series. In this way, we finally obtained this dataset, which is open to the public for sharing and downloading. It is expected to support further studies on the dynamic changes of vegetation in the Three-river Headwaters.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46897446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.noda.2022.0004.zh
Yesen Liu, Shengzi Chen, Yuanyuan Liu, Min Liu, Hancheng Ren
This paper provides a dataset of monthly river pollution index from April 2012 to October 2021 in China based on the published HydroSHEDS dataset and the monthly composite data of NPP-VIIRS night light. Firstly, we extracted the river sections in China from HydroSHEDS, and corrected unreasonable river sections in accordance with the authoritative river data. Secondly, we overlyed the river sections layer and NPP-VIIRS grid to identify the pixels flowing through the water system, and extracted the value of each pixel from the NPP-VIIRS grid. Then, taking the 10 × 10 km grid as the unit, comprehensively considering the flow, brightness and river length of each unit, we designed the river light pollution indexes, and calculated the monthly river light pollution index of each unit. Finally, we obtained a dataset of monthly light pollution index of rivers with the resolution of 10 × 10 km. As the first dataset of river light pollution, this dataset reflects the temporal and spatial distribution and evolution pattern of river light pollution in China, and it can provide reference for river development degree and interference degree evaluation, light pollution analysis and other research.
{"title":"A dataset of monthly light pollution indexes of rivers in China","authors":"Yesen Liu, Shengzi Chen, Yuanyuan Liu, Min Liu, Hancheng Ren","doi":"10.11922/11-6035.noda.2022.0004.zh","DOIUrl":"https://doi.org/10.11922/11-6035.noda.2022.0004.zh","url":null,"abstract":"This paper provides a dataset of monthly river pollution index from April 2012 to October 2021 in China based on the published HydroSHEDS dataset and the monthly composite data of NPP-VIIRS night light. Firstly, we extracted the river sections in China from HydroSHEDS, and corrected unreasonable river sections in accordance with the authoritative river data. Secondly, we overlyed the river sections layer and NPP-VIIRS grid to identify the pixels flowing through the water system, and extracted the value of each pixel from the NPP-VIIRS grid. Then, taking the 10 × 10 km grid as the unit, comprehensively considering the flow, brightness and river length of each unit, we designed the river light pollution indexes, and calculated the monthly river light pollution index of each unit. Finally, we obtained a dataset of monthly light pollution index of rivers with the resolution of 10 × 10 km. As the first dataset of river light pollution, this dataset reflects the temporal and spatial distribution and evolution pattern of river light pollution in China, and it can provide reference for river development degree and interference degree evaluation, light pollution analysis and other research.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41326981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.nasdc.2022.0005.zh
Xuyang Guo, Shanshan Cao, Rui Man, Yiming Zeng, Yi Wang, Gulimila Kezierbieke, Wei Sun
The intelligent detection of offshore maritime targets using computer vision technology can provide a scientific basis for marine administrative management, marine environmental supervision and management as well as the formulation of marine environmental protection policies, providing a powerful environmental information reference for the steady development of the economy. The dataset includes the data collected from Sanduao Harbor in the southeast of Ningde City, Fujian Province, China, with Google Earth serving as the primary data source and a time span from 2019 to 2021. This dataset comprises 1,761 visible light remote sensing images acquired under different seasons, backgrounds and illumination conditions, and corresponding horizontal object detection labels, rotational object detection labels and semantic segmentation labels, covering three types of offshore maritime targets, namely ships, fish row cage culture areas, and raft culture areas. After screening and correction, it can meet the current mainstream deep learning model training needs. This dataset can provide basic data for the semantic segmentation, horizontal object detection, rotational object detection and other research fields of offshore maritime target images.
{"title":"A dataset of the visible light remote sensing images for offshore maritime targets in Sanduao from 2019 to 2021","authors":"Xuyang Guo, Shanshan Cao, Rui Man, Yiming Zeng, Yi Wang, Gulimila Kezierbieke, Wei Sun","doi":"10.11922/11-6035.nasdc.2022.0005.zh","DOIUrl":"https://doi.org/10.11922/11-6035.nasdc.2022.0005.zh","url":null,"abstract":"The intelligent detection of offshore maritime targets using computer vision technology can provide a scientific basis for marine administrative management, marine environmental supervision and management as well as the formulation of marine environmental protection policies, providing a powerful environmental information reference for the steady development of the economy. The dataset includes the data collected from Sanduao Harbor in the southeast of Ningde City, Fujian Province, China, with Google Earth serving as the primary data source and a time span from 2019 to 2021. This dataset comprises 1,761 visible light remote sensing images acquired under different seasons, backgrounds and illumination conditions, and corresponding horizontal object detection labels, rotational object detection labels and semantic segmentation labels, covering three types of offshore maritime targets, namely ships, fish row cage culture areas, and raft culture areas. After screening and correction, it can meet the current mainstream deep learning model training needs. This dataset can provide basic data for the semantic segmentation, horizontal object detection, rotational object detection and other research fields of offshore maritime target images.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48536947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urban park data have been widely applied to urban planning and management. The availability of urban park has also been viewed as one of the evaluation indicators of the UN’s sustainable development goals. However, currently there is still a lack of urban park datasets that are open to the public. To fill this gap, this study aims to produce a dataset of major urban parks of Wuhan in 2021. This dataset was produced based on multi-source data, including OpenStreetMap, POI and Google Earth image, with the official Statistical Table of Major Urban Parks of Wuhan in 2021 as a reference. This dataset is in the format of ESRI shapefile, covering the name, area, latitude and longitude coordinates and address of the city parks in the year of 2021. We found that the correlation coefficient between the areas of urban parks for our dataset and the official statistic results is up to 0.96, which confirms the reliability and accuracy of our dataset. The approach of using multi-source data for acquiring urban park data boasts the advantage in reducing time-consuming and labor-intensive manual work; more importantly, it may also be used as a reference in acquiring urban park data of other cities.
{"title":"A dataset of major urban park of Wuhan in 2021","authors":"Yiming Liao, ShuZhu Wang, K. Chang, Chang Qin, Zhuoying Deng, Zheng Lv, Qi Zhou","doi":"10.11922/11-6035.noda.2022.0005.zh","DOIUrl":"https://doi.org/10.11922/11-6035.noda.2022.0005.zh","url":null,"abstract":"Urban park data have been widely applied to urban planning and management. The availability of urban park has also been viewed as one of the evaluation indicators of the UN’s sustainable development goals. However, currently there is still a lack of urban park datasets that are open to the public. To fill this gap, this study aims to produce a dataset of major urban parks of Wuhan in 2021. This dataset was produced based on multi-source data, including OpenStreetMap, POI and Google Earth image, with the official Statistical Table of Major Urban Parks of Wuhan in 2021 as a reference. This dataset is in the format of ESRI shapefile, covering the name, area, latitude and longitude coordinates and address of the city parks in the year of 2021. We found that the correlation coefficient between the areas of urban parks for our dataset and the official statistic results is up to 0.96, which confirms the reliability and accuracy of our dataset. The approach of using multi-source data for acquiring urban park data boasts the advantage in reducing time-consuming and labor-intensive manual work; more importantly, it may also be used as a reference in acquiring urban park data of other cities.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44077337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paddy fields play an important role in the study of agricultural land use and global carbon cycle. As one of the main rice producing areas in Northeast China, the Liaohe River Delta provides a good experimental platform for the study of carbon and water cycle in Paddy fields. However, due to the insufficient study of long-term carbon and water fluxes in the paddy areas of the Liaohe River Delta, there is an urgent need of long-term data monitoring and sorting. This dataset collects the observations of the flux data of the paddy field ecosystem in the Liaohe River Delta from 2018 to 2020 from the Panjin Paddy Field Research Station of the Northeast Ecological and Agrometeorological Field Experimental Base of China Meteorological Administration. Based on the data processing system of the China Flux Observation and Research Network (ChinaFLUX), we prepared standardized data files for the dataset of water fluxes of ecosystem carbon and key meteorological elements, including data files at hourly, daily, monthly and yearly scales. This dataset is of great significance for the accurate evaluation of the position and role of carbon and water fluxes of paddy field ecosystems in the regional and global carbohydrate circle in the Liaohe River Delta.
{"title":"A dataset of the observations of carbon and water fluxes in the paddy fields of Panjin (2018–2020)","authors":"Q. Jia, Rihong Wen, Li Zhou, Guangsheng Zhou, Yanbing Xie, Qiong Wu","doi":"10.11922/11-6035.csd.2023.0003.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0003.zh","url":null,"abstract":"Paddy fields play an important role in the study of agricultural land use and global carbon cycle. As one of the main rice producing areas in Northeast China, the Liaohe River Delta provides a good experimental platform for the study of carbon and water cycle in Paddy fields. However, due to the insufficient study of long-term carbon and water fluxes in the paddy areas of the Liaohe River Delta, there is an urgent need of long-term data monitoring and sorting. This dataset collects the observations of the flux data of the paddy field ecosystem in the Liaohe River Delta from 2018 to 2020 from the Panjin Paddy Field Research Station of the Northeast Ecological and Agrometeorological Field Experimental Base of China Meteorological Administration. Based on the data processing system of the China Flux Observation and Research Network (ChinaFLUX), we prepared standardized data files for the dataset of water fluxes of ecosystem carbon and key meteorological elements, including data files at hourly, daily, monthly and yearly scales. This dataset is of great significance for the accurate evaluation of the position and role of carbon and water fluxes of paddy field ecosystems in the regional and global carbohydrate circle in the Liaohe River Delta.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46613553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.csd.2023.0012.zh
Fa-wei Zhang, Mengke Si, Xiaowei Guo, G. Cao, Zhenhua Zhang
Covering an area of 5.04×105 km2 on the Qinghai-Tibetan Plateau, Alpine meadow is essential to the plateau ecological barrier function and regional sustainable development. Since May 2014, Haibei National Field Research Station for Alpine Grassland (Haibei Station hereafter) has been accumulating amounts of valuable data by employing eddy covariance techniques to continuously measure carbon and water cycles and energy exchanges between an alpine Graminoid-Kobresia meadow ecosystem and the atmosphere. In the following of data processing such as outlier removal and flux data gaps filled by boosted regression tree model, Haibei Station plans to publish a dataset of the continuous observations of carbon, water, and heat fluxes of the alpine meadow from 2015 to 2010. This dataset consists of the subsets of carbon, water, and heat fluxes data (i.e. net ecosystem CO2 exchange, ecosystem CO2 respiration, gross ecosystem CO2 exchange, latent heat flux, and sensible heat flux) and the subsets of routine meteorological data (i.e. air temperature, air relative humidity, total solar radiation, net radiation, photosynthetically active radiation, precipitation, soil temperature, volumetric soil moisture content). The temporal resolutions of the dataset are half-hourly, daily, monthly, and yearly scales. This dataset can be used to validate the parameters of processes-based ecological models of carbon and water cycles and to evaluate the spatiotemporal patterns and evolution trends in ecological functions of carbon sequestration and water-holding capacity in alpine meadow ecosystems.
{"title":"A dataset of the observations of carbon, water and heat fluxes over an alpine meadow in Haibei (2015–2020)","authors":"Fa-wei Zhang, Mengke Si, Xiaowei Guo, G. Cao, Zhenhua Zhang","doi":"10.11922/11-6035.csd.2023.0012.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0012.zh","url":null,"abstract":"Covering an area of 5.04×105 km2 on the Qinghai-Tibetan Plateau, Alpine meadow is essential to the plateau ecological barrier function and regional sustainable development. Since May 2014, Haibei National Field Research Station for Alpine Grassland (Haibei Station hereafter) has been accumulating amounts of valuable data by employing eddy covariance techniques to continuously measure carbon and water cycles and energy exchanges between an alpine Graminoid-Kobresia meadow ecosystem and the atmosphere. In the following of data processing such as outlier removal and flux data gaps filled by boosted regression tree model, Haibei Station plans to publish a dataset of the continuous observations of carbon, water, and heat fluxes of the alpine meadow from 2015 to 2010. This dataset consists of the subsets of carbon, water, and heat fluxes data (i.e. net ecosystem CO2 exchange, ecosystem CO2 respiration, gross ecosystem CO2 exchange, latent heat flux, and sensible heat flux) and the subsets of routine meteorological data (i.e. air temperature, air relative humidity, total solar radiation, net radiation, photosynthetically active radiation, precipitation, soil temperature, volumetric soil moisture content). The temporal resolutions of the dataset are half-hourly, daily, monthly, and yearly scales. This dataset can be used to validate the parameters of processes-based ecological models of carbon and water cycles and to evaluate the spatiotemporal patterns and evolution trends in ecological functions of carbon sequestration and water-holding capacity in alpine meadow ecosystems.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44072271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.csd.2023.0037.zh
Renxue Fan, Xianjin Zhu, Zhi Chen, Guirui Yu, Weikang Zhang, Lang Han, Qiufeng Wang, Shiping Chen, Shaomin Liu, Huimin Wang, Junhua Yan, Junlei Tan, Fa-wei Zhang, F. Zhao, Ying-nian Li, Yiping Zhang, P. Shi, Jiaojun Zhu, Jiabing Wu, Zhong‐Hui Zhao, Y. Hao, L. Sha, Yucui Zhang, Shicheng Jiang, Fengxue Gu, Zhixiang Wu, Yang-jian Zhang, Li Zhou, Yakun Tang, B. Jia, Yuqiang Li, Q. Song, G. Dong, Y. Gao, Zheng Jiang, Dan-Dan Sun, Jianlin Wang, Qihua He, Xinhu Li, Fei Wang, Wenxue Wei, Z. Deng, X. Hao, Yan Li, Xiaoli Liu, Xifeng Zhang, Zhilin Zhu
The annual gross primary productivity (AGPP) is the basis of food production and carbon sequestration in terrestrial ecosystems. An accurate assessment of regional AGPP can provide a theoretical basis for analyzing the spatiotemporal variation of AGPP and ensuring regional food security and mitigating climate change trends. Based on Chinese Flux Observation and Research Network (ChinaFLUX) measurements and public datasets, we produced a dataset of annual gross primary productivity over China’s terrestrial ecosystems was constructed. In combination with biological, climatic, and soil factors, we used the random forest regression tree to construct the assessment model of China AGPP by simulating the AGPP of unit leaf area. The dataset of annual gross primary productivity over China’s terrestrial ecosystems during 2000-2020 was generated with a spatial resolution of 30arcsecond and a data format of tiff. The dataset can provide validation data for model simulation, as well as data support for regional productivity, ecological quality, and assessment and management of terrestrial carbon sinks.
{"title":"A dataset of annual gross primary productivity in China’s terrestrial ecosystems during 2000-2020","authors":"Renxue Fan, Xianjin Zhu, Zhi Chen, Guirui Yu, Weikang Zhang, Lang Han, Qiufeng Wang, Shiping Chen, Shaomin Liu, Huimin Wang, Junhua Yan, Junlei Tan, Fa-wei Zhang, F. Zhao, Ying-nian Li, Yiping Zhang, P. Shi, Jiaojun Zhu, Jiabing Wu, Zhong‐Hui Zhao, Y. Hao, L. Sha, Yucui Zhang, Shicheng Jiang, Fengxue Gu, Zhixiang Wu, Yang-jian Zhang, Li Zhou, Yakun Tang, B. Jia, Yuqiang Li, Q. Song, G. Dong, Y. Gao, Zheng Jiang, Dan-Dan Sun, Jianlin Wang, Qihua He, Xinhu Li, Fei Wang, Wenxue Wei, Z. Deng, X. Hao, Yan Li, Xiaoli Liu, Xifeng Zhang, Zhilin Zhu","doi":"10.11922/11-6035.csd.2023.0037.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0037.zh","url":null,"abstract":"The annual gross primary productivity (AGPP) is the basis of food production and carbon sequestration in terrestrial ecosystems. An accurate assessment of regional AGPP can provide a theoretical basis for analyzing the spatiotemporal variation of AGPP and ensuring regional food security and mitigating climate change trends. Based on Chinese Flux Observation and Research Network (ChinaFLUX) measurements and public datasets, we produced a dataset of annual gross primary productivity over China’s terrestrial ecosystems was constructed. In combination with biological, climatic, and soil factors, we used the random forest regression tree to construct the assessment model of China AGPP by simulating the AGPP of unit leaf area. The dataset of annual gross primary productivity over China’s terrestrial ecosystems during 2000-2020 was generated with a spatial resolution of 30arcsecond and a data format of tiff. The dataset can provide validation data for model simulation, as well as data support for regional productivity, ecological quality, and assessment and management of terrestrial carbon sinks.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44892036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.csd.2023.0039.zh
Jing Wang, H. Xiong, Qilegele, Yi Du, Yuanchun Zhou
Myths is an important carrier of traditional Chinese culture. There are numerous myths of different ethnic minorities, rich in content and diverse in form. Many mythological characters have become precious memories in the course of human history and Chinese civilization. With the analysis of mythical figures as the starting point, we take relevant mythological texts and narratives as research topics and important data sources in the field of humanities and social sciences, which is of great cultural significance and academic value in promoting the construction of big data in humanities, deepening the analysis of traditional Chinese cultural data, and innovating new methods of humanities and social science research. In this paper, we took the origin narrative motif of 50 core mythological characters in China as the research object, and collected 1,620 valid data from them, including the standard fields of motif, example, nationality, place of spread and source of literature. Meanwhile, we systematically analyzed and explained the data sources, data collection methods, data classification and structure. Moreover, we described the data samples in detail; and on the basis of listing relevant dataset examples, we further carried out a multi-dimensional statistical analysis of the way of generation, ethnic groups and geographical location, etc. Finally, we provided some suggestions on the application of the dataset in exploring the connotation of China’s multi-ethnic culture, cross regional cultural research and analysis of Chinese traditional culture.
{"title":"A dataset of origin motifs of major Chinese mythological characters","authors":"Jing Wang, H. Xiong, Qilegele, Yi Du, Yuanchun Zhou","doi":"10.11922/11-6035.csd.2023.0039.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0039.zh","url":null,"abstract":"Myths is an important carrier of traditional Chinese culture. There are numerous myths of different ethnic minorities, rich in content and diverse in form. Many mythological characters have become precious memories in the course of human history and Chinese civilization. With the analysis of mythical figures as the starting point, we take relevant mythological texts and narratives as research topics and important data sources in the field of humanities and social sciences, which is of great cultural significance and academic value in promoting the construction of big data in humanities, deepening the analysis of traditional Chinese cultural data, and innovating new methods of humanities and social science research. In this paper, we took the origin narrative motif of 50 core mythological characters in China as the research object, and collected 1,620 valid data from them, including the standard fields of motif, example, nationality, place of spread and source of literature. Meanwhile, we systematically analyzed and explained the data sources, data collection methods, data classification and structure. Moreover, we described the data samples in detail; and on the basis of listing relevant dataset examples, we further carried out a multi-dimensional statistical analysis of the way of generation, ethnic groups and geographical location, etc. Finally, we provided some suggestions on the application of the dataset in exploring the connotation of China’s multi-ethnic culture, cross regional cultural research and analysis of Chinese traditional culture.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43875573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alpine shrubland is one of the important vegetation types on the Qinghai-Tibet Plateau, which mainly lies in the shady or semi-shady slope of snowpack mountains or the high-altitude alluvium and diluvium on plains. It plays a crucial role in carbon sequestration, water conservation and climate regulation. Since 2002, Haibei National Field Research Station for Alpine Grassland (Haibei Station) has been using eddy covariance techniques to continuously observe the carbon, water and heat exchange between an alpine Potentilla fruticosa shrubland ecosystem and the atmosphere and has accumulated nearly 20-year data. On the basis of the previous publication of relevant data from 2003 to 2010, the carbon, we further released water and heat fluxes of the alpine shrubland and supplementary meteorological data from 2011 to 2020. This dataset consists of the subsets of meteorological factors, covering air temperature, air relative humidity, water vapor pressure, wind speed, wind direction, ambient pressure, total solar radiation, net radiation, photosynthetically active radiation, precipitation, soil temperature, and soil moisture, as well as net ecosystem CO2 exchange, ecosystem respiration, gross ecosystem CO2 exchange, latent heat flux, and sensible heat flux. The temporal resolutions of the dataset include half-hourly, daily, monthly, and yearly scales. This dataset can not only be used to scientifically evaluate the environmental drivers and evolution trends of the ecological functions of carbon, water and heat in alpine shrub ecosystems, but also provide ground data support for parameter validation and optimization of remote sensing-based ecological process models.
{"title":"A dataset of the observations of carbon, water and heat fluxes over an alpine shrubland in Haibei (2011–2020)","authors":"Fa-wei Zhang, Hong-qin Li, Leiming Zhang, Jiexia Li, Yongsheng Yang, Guirui Yu, Ying-nian Li","doi":"10.11922/11-6035.csd.2023.0013.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0013.zh","url":null,"abstract":"Alpine shrubland is one of the important vegetation types on the Qinghai-Tibet Plateau, which mainly lies in the shady or semi-shady slope of snowpack mountains or the high-altitude alluvium and diluvium on plains. It plays a crucial role in carbon sequestration, water conservation and climate regulation. Since 2002, Haibei National Field Research Station for Alpine Grassland (Haibei Station) has been using eddy covariance techniques to continuously observe the carbon, water and heat exchange between an alpine Potentilla fruticosa shrubland ecosystem and the atmosphere and has accumulated nearly 20-year data. On the basis of the previous publication of relevant data from 2003 to 2010, the carbon, we further released water and heat fluxes of the alpine shrubland and supplementary meteorological data from 2011 to 2020. This dataset consists of the subsets of meteorological factors, covering air temperature, air relative humidity, water vapor pressure, wind speed, wind direction, ambient pressure, total solar radiation, net radiation, photosynthetically active radiation, precipitation, soil temperature, and soil moisture, as well as net ecosystem CO2 exchange, ecosystem respiration, gross ecosystem CO2 exchange, latent heat flux, and sensible heat flux. The temporal resolutions of the dataset include half-hourly, daily, monthly, and yearly scales. This dataset can not only be used to scientifically evaluate the environmental drivers and evolution trends of the ecological functions of carbon, water and heat in alpine shrub ecosystems, but also provide ground data support for parameter validation and optimization of remote sensing-based ecological process models.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42160466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.csd.2022.0075.zh
X. Duan
With the rise of artificial intelligence, the booming application of convolutional neural networks to the classification and identification of fossils has attracted more and more attention. According to our survey, it is found that the species classified by previous authors basically belong to different genera, families or higher biological taxonomic units. However, in fact, the identification of fossils between species within a genus is often the focus and challenge for the identification task, which means that the previously trained classifiers may not be suitable for actual fossil identification. On this basis, in this paper, we built a dataset covering 12 species of the conodont genus Hindeodus by means of literature collection, while providing an augmented dataset of the original data. Since the dataset is fine-grained, users can train it by using convolutional neural network combined with fine-grained image feature extraction technology. In view of the deficiencies of the dataset such as small amount of data and unbalanced classes, it is suggested that users use stratified K-fold cross-validation, transfer learning and weighted loss function in the training task to solve the above problems. The dataset is aimed to add a fine-grained fossil dataset to the field of intelligent identification of biological fossils, which can be used as an experimental dataset for intelligent identification of fine-grained (species-level) fossils by convolutional neural networks. The fine-grained primitive followed by this dataset can also be used as a reference for the establishment of other fossil datasets.
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