In keeping with China’s President Xi Jinping’s “Chinese Dream,” China has set a goal of becoming a world-class innovator by 2050. China’s higher education Science, Technology, Engineering, and Math (STEM) research environment will play a pivotal role in influencing whether China is successful in transitioning from a manufacturing-based economy to an innovation-driven, knowledge-based economy. Past studies on China’s research environment have been primarily qualitative in nature or based on anecdotal evidence. In this study, we surveyed STEM faculty from China’s top 25 universities to get a clearer understanding of how faculty members view China’s overall research environment. We received 731 completed survey responses, 17% of which were from individuals who received terminal degrees from abroad and 83% of which were from individuals who received terminal degrees from domestic institutions of higher education. We present results on why returnees decided to study abroad, returnees’ decisions to return to China, and differences in perceptions between returnees and domestic degree holders on the advantages of having a foreign degree. The top five challenges to China’s research environment identified by survey respondents were: a promotion of short-term thinking and instant success (37% of all respondents); research funding (33%); too much bureaucratic or governmental intervention (31%); the evaluation system (27%); and a reliance on human relations (26%). Results indicated that while China has clearly made strides in its higher education system, there are numerous challenges that must be overcome before China can hope to effectively produce the kinds of innovative thinkers that are required if it is to achieve its ambitious goals. We also raise questions about the current direction of education and inquiry in China, particularly indications that government policy is turning inward, away from openness that is central to innovative thinking.

This fileset contains data associated with the following publication: China's STEM Research Environment: A Snapshot. Accessible here: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195347All identifying information have been removed from the following data sets to protect the privacy of our survey respondents. Original survey questions (in both Mandarin Chinese and English) can be found as supplemental material under a forthcoming publication. If you would like access to the original survey questions prior to the publication or have any questions regarding the fileset, please contact Xueying Han at xhan@ida.org.There are five files in this fileset:1. "All surveys combined.csv" Raw survey data from the 731 completed surveys. Does not include free response answers.2. "Translation and coding for challenges to research environment.xlsx" Free response answers (original text + English translation) to survey question #51 (What challenges, if any, do you think exist in China's current research environment/culture?) File also provides manual coding of each free response answer: 1 = challenge was identified by a survey respondent, 0 = challenge was not identified by a survey respondent. Twelve challenge themes were identified. Each challenge theme consists of a subset of smaller challenges that contributes to the larger challenge. (N = 466 responses) 3. "Identified challenges to China's scientific environnment.csv" Contains presence/absence data (1/0) for the 12 challenge themes identified in data set #2. File to be used in R.4. "Translation and coding for opportunities for improvement.xlsx" Free response answers (original text + English translation) to survey question #52 (What changes, if any, do you think would improve the research environment/culture in China?) File also provides manual coding of each free response answer: 1 = recommendation to improve the research environment was identified by a survey respondent, 0 = recommendation was not identified by a survey respondent. Ten overencompassing themes were identified. Each theme consists of a subset of recommendations to improve China's research environment. (N = 443 responses)5. "Opportunities for improving China's research environment.csv" Contains presence/absence data (1/0) for the 10 themes on how to improve China's research environment identified in data set #4. File to be used in R.

This dataset is about universities in China. It has 357 rows. It features 5 columns: city, total students, foundation year, and graduate students.

This chapter estimates dynamic panel data models on a sample comprising 53 STPs in China for 2008 – 2018 and finds evidence for beta- and sigma-convergence of labor productivity and beta-, but not sigma-convergence of innovation performance. FDI and human capital have a significant impact on labor productivity and innovation of STPs, and explain differences in performance among them.

CHCP Overview:The human behavior and brain are shaped by genetic, environmental and cultural interactions. Recent advances in neuroimaging integrate multimodal imaging data from a large population and start to explore the large-scale structural and functional connectomic architectures of the human brain. One of the major pioneers is the Human Connectome Project (HCP) that developed sophisticated imaging protocols and has built a collection of high-quality multimodal neuroimaging, behavioral and genetic data from US population. A large-scale neuroimaging project parallel to the HCP, but with a focus on the East Asian population, will allow comparisons of brain-behavior associations across different ethnicities and cultures. The Chinese Human Connectome Project (CHCP) is launched in 2017 and led by Professor Jia-Hong GAO at Peking University, Beijing, China. CHCP aims to provide large sets of multimodal neuroimaging, behavioral and genetic data on the Chinese population that are comparable to the data of the HCP. The CHCP protocols were almost identical to those of the HCP, including the procedure for 3T MRI scanning, the data acquisition parameters, and the task paradigms for functional brain imaging. The CHCP also collected behavioral and genetic data that were compatible with the HCP dataset. The first public release of the CHCP dataset is in 2022. CHCP dataset includes high-resolution structural MR images (T1W and T2W), resting-state fMRI (rfMRI), task fMRI (tfMRI), and high angular resolution diffusion MR images (dMRI) of the human brain as well as behavioral data based on Chinese population. The unprocessed "raw" images of CHCP dataset (about 1.85 TB) have been released on the platform and can be downloaded. Considering our current cloud-storage service, sharing full preprocessed images (up to 70 TB) requires further construction. We will be actively cooperating with researchers who contact us for academic request, offering case-by-case solution to access the preprocessed data in a timely manner, such as by mailing hard disks or a third-party trusted cloud-storage service. V2 Release (Date: January 16, 2023):Here, we released the seven major domains task fMRI EVs files, including: 1) visual, motion, somatosensory, and motor systems; 2) category specific representations; 3) working memory/cognitive control systems; 4) language processing (semantic and phonological processing); 5) social cognition (Theory of Mind); 6) relational processing; and 7) emotion processing.V3 Release (Date: January 12, 2024):This version of data release primarily discloses the CHCP raw MRI dataset that underwent “HCP minimal preprocessing pipeline”, located in CHCP_ScienceDB_preproc folder (about 6.90 TB). In this folder, preprocessed MRI data includes T1W, T2W, rfMRI, tfMRI, and dMRI modalities for all young adulthood participants, as well as partial results for middle-aged and older adulthood participants in the CHCP dataset. Following the data sharing strategy of the HCP, we have eliminated some redundant preprocessed data, resulting in a final total size of the preprocessed CHCP dataset is about 6.90 TB in zip files. V4 Release (Date: December 4, 2024):In this update, we have fixed the issue with the corrupted compressed file of preprocessed data for subject 3011, and removed the incorrect preprocessed results for subject 3090. Additionally, we have updated the subject file information list. Additionally, this release includes the update of unprocessed "raw" images of the CHCP dataset in CHCP_ScienceDB_unpreproc folder (about 1.85 TB), addressing the previously insufficient anonymization of T1W and T2W modalities data for some older adulthood participants in versions V1 and V2. For more detailed information, please refer to the data descriptions in versions V1 and V2.CHCP Summary:Subjects:366 healthy adults (Chinese Han)Imaging Scanner:3T MR (Siemens Prisma)Institution:Peking University, Beijing, ChinaFunding Agencies:Beijing Municipal Science & Technology CommissionChinese Institute for Brain Research (Beijing)National Natural Science Foundation of ChinaMinistry of Science and Technology of China CHCP Citations:Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from CHCP data should contain the following wording in the acknowledgments section: "Data were provided [in part] by the Chinese Human Connectome Project (CHCP, PI: Jia-Hong Gao) funded by the Beijing Municipal Science & Technology Commission, Chinese Institute for Brain Research (Beijing), National Natural Science Foundation of China, and the Ministry of Science and Technology of China."

Update notice

JHU is currently using a GitHub repo to store its data instead of a web api. I'll need to update my scraping methodology. Apologies for not being able to provide most up-to-date coronavirus dataset.

Alternatively, if you're very excited about the dataset, JHU already updates their dataset daily on this link: https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

Context

The 2019-nCoV is a contagious coronavirus that hailed from Wuhan, China. This new strain of virus has striked fear in many countries as cities are quarantined and hospitals are overcrowded. This dataset will help us understand how 2019-nCoV is spread aroud the world.

Acknowledgements

I would like to acknowledge Johns Hopkins University for open-sourcing their dataset. Their dataset is transformed into a format that is easier for kaggle to handle.

Inspiration

I believe that epidemic data should be openly available and easily accessible for health professionals and data scientists. This dataset would serve as a starting point for people to gather more data about epidemics, not just statistics, but also new stories, government responses etc.

This list presents China's science and technology agreements and related agreements collected in December 2021. It is not an exhaustive list. Anyone who has agreements to add to the list, please contact Caroline Wagner wagner.911@osu.edu

The USTC-TFC2016 dataset is mainly used for network traffic classification research, including malicious traffic and normal application traffic, and is jointly completed by the University of Science and Technology of China and the Institute of Acoustics of the Chinese Academy of Sciences. The data comes from two sources: one is 10 types of malicious traffic selected from the CTU dataset, which were collected by researchers from the Czech CTU University from real environments between 2011 and 2015; The second type is the 10 normal application traffic generated by network instrument simulation. This dataset consists of 20 types of traffic, corresponding to 20 data files, all in pcap format. In order to save space, some pcap files are compressed and uploaded. After decompression, the total size of each pcap file is 3.71GB. For more information about this dataset, please refer to: 1) Wei Wang， Ming Zhu， Xuewen Zeng， Xiaozhou Ye and Yiqiang Sheng， “Malware traffic classification using convolutional neural network for representation learning”ICOIN 2017，pp712-717; 2) Wang Wei, Research on Network Traffic Classification and Anomaly Detection Methods Based on Deep Learning, Ph.D. Thesis, University of Science and Technology of China, 2018. This dataset and preprocessing tool were released in 2018 https://github.com/echowei/ Many domestic and foreign researchers are using this dataset. Due to bandwidth and capacity constraints, it is often unable to download. Upload it to the "Science Database" website of the Chinese Academy of Sciences for long-term storage and easy download. At the same time, we look forward to relevant researchers uploading and sharing new malicious traffic and encrypted traffic using domestic cryptographic protocols, as well as expanding this dataset to include more types of malicious and normal traffic (such as 100 each), forming a richer and more comprehensive dataset to approach the actual network traffic situation.

Blended Learning has been widely used in current basic education as a new teaching model

This paper provides a remote sensing derived dataset for agricultural plastic greenhouses in China of 2019, which has spatial resolution of 30 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform, where random forest classification model is utilized to classify the Sentinel-2 remote sensing images. Specifically, ground-truth samples are collected through field survey and visual interpretation, and then are randomly divided into training set and test set. Afterwards, feature extraction is performed such as spectral and texture features to construct a multi-dimensional feature space. Finally, the trained random forest model is utilized to classify national-scale remote sensing images through parallel computing to acquire the spatial distribution of China’s agricultural plastic greenhouses. The accuracy evaluation shows that the average classification accuracy is 87.45%, which indicates that the proposed dataset can accurately reflect the spatial distribution of agricultural plastic greenhouses across the whole country of China. In addition, in order to better visualize the national greenhouse distribution data, this paper also calculates the proportion of the greenhouse area in the 5km grid. Above all, this data set is the first publicly released thematic data on the spatial distribution of China’s agricultural plastic greenhouses across the country, which can provide data references for scientific researchers in related fields.

FoodShotImages1_5

## Overview

FoodShotImages1_5 is a dataset for object detection tasks - it contains Food annotations for 1,798 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Important Notice: These old dataset versions (V2.0.x) have been superseded. A new version, CHM_PRE V2.1, is now available and is recommended for all users (https://doi.org/10.5281/zenodo.14632156). The updated version (V2.1) extends the data coverage to 2024 and incorporates adjusted precipitation values for the southern foothills of the Himalayas.

The CHM_PRE V2 dataset is a new high-precision, long-term, daily gridded precipitation dataset for mainland China.

The long-term daily observation from 3,476 gauges and incorporated 11 related precipitation variables were utilized to characterize the correlations of precipitation. Then, the dataset was developed by employing an improved inverse distance weighting method combined with the machine learning-based light gradient boosting machine (LGBM) algorithm.

CHM_PRE V2 demonstrates strong spatiotemporal consistency with existing gridded precipitation datasets, including CHM_PRE V1, GSMaP, IMERG, PERSIANN-CDR, and GLDAS. Validation against 63,397 high-density gauges confirms its high accuracy in both precipitation values and events. The dataset achieves a mean absolute error of 1.48 mm/day and a Kling-Gupta efficiency coefficient of 0.88. In terms of event detection capability, CHM_PRE V2 achieves a Heidke skill score of 0.68 and a false alarm ratio of 0.24. Overall, CHM_PRE V2 significantly enhances precipitation measurement accuracy and reduces the overestimation of precipitation events, providing a reliable foundation for hydrological modeling and climate assessments.

The CHM_PRE V2 dataset provides daily precipitation data with a resolution of 0.1°, covering the entire mainland China (18°N–54°N, 72°E–136°E). This dataset covers the period of 1960–2023, and will be continuously updated annually. The daily precipitation data is provided in NetCDF format, and for the convenience of users, we also offer annual and monthly total precipitation data in both NetCDF and GeoTIFF formats.

References:
1. Hu, J., Miao, C., Su, J., Zhang, Q., Gou, J., and Sun, Q.: A new upgraded high-precision gridded precipitation dataset considering spatiotemporal and physical correlations for mainland China, Earth System Science Data Discussions. [preprint], https://doi.org/10.5194/essd-2025-20, in review, 2025

2. Zhang, Q., Miao, C., Su, J., Gou, J., Hu, J., Zhao, X., & Xu, Y. (2025). A new high-resolution multi-drought-index dataset for mainland China. Earth System Science Data, 17(3), 837–853. https://doi.org/10.5194/essd-17-837-2025

3. Han, J., Miao, C., Gou, J., Zheng, H., Zhang, Q., & Guo, X. (2023). A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations. Earth System Science Data, 15(7), 3147–3161. https://doi.org/10.5194/essd-15-3147-2023

This is a demo and partial dataset of a full dataset of paper- patent pair from chinese chemical scholars. The full dataset covers the published English papers and the paired patents by some of the scholars of the chemical and pharmaceutical field who received the Distinguished Young Scholars of the National Science Foundation of China (NSFC) during 2009–2018. Data related to the papers are obtained from the China National Knowledge Infrastructure (CNKI) and WoS; data related to the scholars are obtained from the NSFC website, the Ministry of Science and Technology of China (MSTC) website, and the scholars' home pages; and data related to the patents are obtained from the CNIPA website. The dataset also covers the data on the average number of patent infringement cases at the prefecture level from China Judgements Online (CJO) using crawlers and text mining techniques to reflect the degree of local intellectual property protection (IPR). Besides the paper information, the dataset also contains the corresponding scholars' funding information and the paired patents[1] information gathered through “patent–paper pairs.”

This demo datasets covers about 1/15 samples of the full datast. The detail variable explanation will be found in the "label" and "Notes" column after every variable name in the DTA data file. As a series of related papers are published, we will gradually publish the full data set.

[1] A small number of papers may be paired with multiple patents. We deal with the situation of multiple paired patents when constructing variables. See “Variable” section of original paper for detailed illustrations.

Abstract: Northwest China is deeply inland, and has a dry climate. The variation of lake area can reflect the temporal and spatial distribution characteristics of regional water resources to a certain extent. This data set is based on the comprehensive analysis of meteorological data and the actual coverage of Landsat series satellite images to determine the interpretation time. Referring to the lake data set of "Lake data set of 1:250000 above 1km2 in China（2005-2006）", the 113 lakes were selected as vectorization objects, which are in natural conditions, larger than 10 km2 and non-dry salt lakes. The lake boundary vector data of 15 periods from 2000 to 2014 were extracted by artificial visual interpretation. According to the principle of artificial visual interpretation of the lake area determined by the “Investigation on water quality, water quantity and biological resources of lakes in China” of the special of basic work of science and technology in the Ministry of science and technology, the accuracy of interpretation is controlled within one pixel.The data set includes three parts: (1) boundary vector data of northwest China, and (2) lake boundary vector data from 2000 to 2014, and (3) lake location and area statistics vector file. The data set basically reflects the change in lake’s boundary in northwest China from 2000 to 2014, which can be used as basic data for research on temporal and spatial changes of lakes in the region, climate change, and manual intervention in regional water resources.

Soil samples recorded the history of the evolution of soils, and is extremely valuable to scientific researches.The CERN soil sub-center has established a dataset of the soil sample archives of Chinabyregulating data standards of tens of thousands of soil samples resource in China. The dataset can support the researches of soil and environmental changes and the effects of agricultural activities. It is alsoimportant for promoting the sharing and use of soil samples.

Background

In the VR-enabled driving simulator environment, this dataset is collected from 21 drivers from Beihang university in China, who have different demographic information (age, gender, driving years) and prior driving-simulator and VR experience for usage. And in our study about to be submitted to a journal, it is used to study the difference of behaviors of drivers towards autonomous vehicle(AV) and human-driven vehicle(HV) in the mixed traffic flows.

Data collection environment: CARLA + DReyeVR(https://github.com/HARPLab/DReyeVR, which offers the support for VR) + SUMO

Experiment scenario: a non-signalized crossing in the built-in map of Town 03 of CARLA

Dataset structure

There are two zip files in this dataset. The one zip file "All_Frames_of_All_drivers.zip" is consist of each frame picture for every driver/participant during the experiment, which shows the real-time scenario in the first-person perspective of driver/participant. And the other zip file "All_excelData_of_All_drivers.zip" contains all data in excel format of all drivers/participants, which is corresponding to that in "All_Frames_of_All_drivers.zip".

The names of all files in these two zip files obey the following rules: name of driver/participant + date of his/her experiment + type of vehicle(AV or HV) + experiment times(1, 2 or 3). Taking "cmy_20240104_AV_1" for an example, it means it is "cmy" who finished the first AV experiment on 4th, January, 2024.

Humanities and Social Sciences (HSS) increasingly absorb knowledge from Hard Sciences, i.e., Science, Technology, Agriculture and Medicine (STAM), as testified by a growing number of citations. However, whether citing more Hard Sciences brings more citations to HSS remains to be investigated. Based on China’s HSS articles indexed by the Web of Science during 1998–2014, this paper estimated two-way fixed effects negative binomial models, with journal effects and year effects. Findings include: (1) An inverse U-shaped curve was observed between the percentage of STAM references to the HSS articles and the number of citations they received; (2) STAM contributed increasing knowledge to China’s HSS, while Science and Technology knowledge contributed more citations to HSS articles. It is recommended that research policy should be adjusted to encourage HSS researchers to adequately integrate STAM knowledge when conducting interdisciplinary research, as over-cited STAM knowledge may jeopardize the readability of HSS articles.

The Agricultural Management Dataset contains two variables which help describe management regimes within agricultural lands of China. The first variable, Irrigation Index, reports the fraction of cropland in a county which is under irrigation, excluding rice paddies. The Second variable, Nitrogen Fertilizer, reports the tonnes of nitrogen fertilizer applied in the county per year.

See the references for the sources of these data.

China County Data collection contains seven datasets which were compiled in the early 1990s for use as inputs to the DNDC (Denitrification-Decomposition) model at UNH. DNDC is a computer simulation model for predicting carbon (C) and nitrogen (N) biogeochemistry in agricultural ecosystems. The datasets were compiled from multiple Chinese sources and all are at the county scale for 1990. The datasets which comprise this collection are listed below.

1) Agricultural Management 2) Crops 3) N-Deposition 4) Geography and Population 5) Land Use 6) Livestock 7) Soil Properties

This dataset is about books. It has 1 row and is filtered where the book is Science and technology in the development of modern China : an annotated bibliography. It features 7 columns including author, publication date, language, and book publisher.

File List CForBioData_v1.0.txt (MD5: ab568043ef106964809368b1efad63e1)

  Description
    Forest biomass and its allocation have long been considered important in forest ecosystem structure and function. However, discrete forest biomass data and its allocation to various forest components must be standardized to explore many ecological questions, e.g., plant allometric scaling laws, biomass allocation theory, and terrestrial carbon cycling. Currently, available data sets focus either on regions other than China, or studies in China are spatially and/or temporally limited, e.g., specific habitats and/or time periods. Therefore, the applicability of conclusions to world or China forests might be problematic. Consequently, in the present study, published studies (during the 1978–2008 period) on biomass and its allocations in China’s forests (excluding Hong Kong, Macao, and Taiwan) were collected, critically reviewed, and a comprehensive forest biomass data set of China was developed. The data set included the following biomass data: tree overstory components (stems, branches, leaves, and roots, among all other plant material), the understory vegetation (saplings, shrubs, herbs, and mosses), woody liana vegetation, and the necromass components of dead organic matter (litterfall, suspended branches, and dead trees). Moreover, associated information was also included, i.e., geographical location, climate, soil fertility, stand description, sampling regime (the dimension and sample size of plots), and biomass measurement methods. The data set included 1607 entries for 348 study sites, which exhibited a broad spatial distribution, and covered broad climatic gradients (-5.1–23.8°C in mean annual temperature and 223–2515 mm in mean annual precipitation). Our data set can be used to verify the accuracy of models used to budget China’s forest carbon dynamics, and also provides an opportunity to further elucidate and confirm general principles and patterns in ecology. Finally, the data set is freely available for noncommercial scientific applications.


      Key words: allocation; biomass; China; climate; forest type; necromass; stand structure.