Dataset
XingChen-AGI/Xing4.0-29B-Intent-oss
Text classification dataset published by XingChen-AGI. Languages: zh, en.
XingChen-AGI
apache-2.0View resource
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Ding0702/xingchen
Other / unspecified task dataset published by Ding0702.
Ding0702
cc-by-4.0View resource
Dataset
A dataset of the global potential for onshore field-scale solar photovoltaic systems
The deployment of solar photovoltaics (PV) can contribute to global decarbonization and mitigation of climate change[1,2]. A spatially explicit potential map of solar PV worldwide can facilitate decision-makers to plan PV projects in a sustainable way and provide important input for investigating the long-term impacts of PV expansion under different scenarios. Here we assess the land suitability for PV development using machine learning and estimate the potential of generating capacity for onshore field-scale solar PV (including commercial, industrial, and utility scales) worldwide. Our mapping is focused on the Earth’s surface between 65° N
Li, Wenkai, Liu, Hongliang, Hu, Xiaomei, Lu, Xingcheng, Tao, Shengli, Ma, Qin, Yang, Haitao, Liu, Yuanchi, Li, Mingxuan, Li, Tianhong, Guo, Qinghua
cc-by-4.0View resource
Dataset
Estimation of Ca2+ wet deposition in the Northern Hemisphere by use of CNN deep-learning model
A dataset to estimate long-term and high-resolution gridded Ca2+ wet deposition across the Northern Hemisphere from 2001-2022.
Wanying, Chen, Lu, Xingcheng, Xian, Chaofan, Sun, Xu, Chen, Yiang, Hu, Mingyun, Li, Geng, Fung, Jimmy C.H.
cc-by-4.0View resource
Dataset
High Spatiotemporal Resolution Estimation of Global Surface CO Concentrations Using a Deep Learning Model
A high-performance Convolutional Neural Network (CNN)-based Residual Network (ResNet) was developed for estimating daily worldwide CO concentrations at a high spatial resolution of 0.07 ° from June 2018 to May 2021, using the global TROPOMI Total Column of atmospheric CO (TCCO) product and reanalysis datasets. The proposed framework achieved a desirable estimation accuracy, with R -values (correlation coefficients) of 0.90 and 0.96 for daily and monthly predictions, respectively. The daily surface CO concentration dataset from our study is potentially useful for further relevant sustainable studies.
Hu, Mingyun, Lu, Xingcheng, Chen, Yiang, Chen, Wanying, Guo, Cui, Xian, Chaofan, Fung, Jimmy C.H.
Not specifiedView resource
Dataset
Dataset for the paper "Review on Transit Method and Artificial Intelligence for Detecting Long-Period Weak Signals"
This dataset is aimed at the comparative study of Kepler exoplanet transit signal detection and photometric noise. It collects traceable input data of key charts in the paper and author derived result data, and is divided into three categories: the first is publicly available archive download data (Class A), which includes the planetary system comprehensive catalog (PS) and KOI cumulative candidate catalog (cumulative, both fixed with file name timestamps on March 4, 2026) obtained from NASA Exoplanet Archive, as well as several Kepler target star light curve metadata downloaded from MAST (A4-A9); The second type is derived data (Class B) gen
Xingcheng, Yan, Qingtian, Liu, Jian, Ge, Zeyu, Ruan, Zhenghong, Liu
Creative Commons Attribution Share Alike 4.0 InternationalView resource
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