Historical dataset of population level and growth rate for the Beijing, China metro area from 1950 to 2025.

This point shapefile represents the locations of townships with 2000 Population Census Data, 9.95% Long Form data, table L1-L6) for the Beijing province of China for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.

This polygon dataset represents county boundaries and population data in China from the 2000 Census. This dataset also includes detailed demographic data such as: sex and age statistics, litteracy, employment, and professions, and birth and death rates. These data were primarily based on the "The Administrative Maps of the People's Republic of China, published by China Map Press.This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.Read More

This shapefile represents the rivers for the Beijing province of China for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.

This line shapefile represents the highways for the Beijing province of China for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.

This point shapefile represents the district locations, with 2000 population census data, for the Fujian Sheng province of China for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.The 2000 China township maps are developed for matching 2000 China population Census data, which should only be used as references for research or education instead of used as official maps.

This point shapefile represents the district locations, with 2000 population census data, for the Guangdong Sheng province of China for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.The 2000 China township maps are developed for matching 2000 China population Census data, which should only be used as references for research or education instead of used as official maps.

This line shapefile represents the provincial trunk roads for the Beijing province of China for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.

Based on time series of Landsat images, this study uses the Google Earth Engine cloud platform to extract built-up land in the Beijing-Tianjin-Hebei region, and integrates the results with GlobeLand30, GHS-Built, GAIA and GLC_FCS-2020 land cover products to derive the built-up land data set during the period 2000-2020 in the region. An overall accuracy higher than 90% was obtained. Based on this data set, the SDG 11.3.1 indicators-land consumption rate(LCR), population growth rate(PGR) and ratio of land consumption rate to population growth rate(LCRPGR) were calculated for each city.

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."

BackgroundOral health literacy (OHL) has been recognized as a component of oral health disparities; however, the precise relationship between literacy and oral health outcomes has not been established. To explore the role of parents' OHL for their own subjective oral health, related behavior, and for the proxy assessment of their child's oral health, oral health-related behavior.MethodsSurvey data were collected from 406 parents of 4- to 7-year-old children in Beijing, China. The background characteristics, oral health assessment, oral health-related behavior, knowledge and attitudes, and diet-related questions of parents and their children were surveyed by a questionnaire. OHL was assessed with the Hong Kong Rapid Estimate of Adult Literacy in Dentistry (HKREAL-30) Scale and a revised version that asked the respondents to indicate if they understood the words (HKREALD-30-Understand).ResultsThe HKREALD-30 responses correlated with the HKREALD-30-Understand responses. The higher the parents' HKREALD-30-Understand scores, the better they described the health of their own teeth and gums, the greater their child's diet was influenced by the protein, sugar and calories of the food, and the more positive their oral health-related attitudes were. The higher the parent's HKREALD-30 scores, the healthier they described their child's teeth and gums.ConclusionsBoth the HKREALD-30 and HKREALD-30-Understand Scores correlate with parents' self and proxy oral health-related responses. Chinese parents could understand that the word would add predictive value to the prediction of how parents' oral health literacy affects their own oral health care, children's oral health and other related aspects.

This point shapefile represents the district locations, with 2000 population census data, for the Anhui Sheng province of China for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.The 2000 China township maps are developed for matching 2000 China population Census data, which should only be used as references for research or education instead of used as official maps.

This point shapefile represents the district locations, with 2000 population census data, in the Autonomous region of Tibet (Xizang) for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.The 2000 China township maps are developed for matching 2000 China population Census data, which should only be used as references for research or education instead of used as official maps.

The below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

BackgroundTo date, there is a lack of sufficient evidence on the type of clusters in which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is most likely to spread. Notably, the differences between cluster-level and population-level outbreaks in epidemiological characteristics and transmissibility remain unclear. Identifying the characteristics of these two levels, including epidemiology and transmission dynamics, allows us to develop better surveillance and control strategies following the current removal of suppression measures in China.MethodsWe described the epidemiological characteristics of SARS-CoV-2 and calculated its transmissibility by taking a Chinese city as an example. We used descriptive analysis to characterize epidemiological features for coronavirus disease 2019 (COVID-19) incidence database from 1 Jan 2020 to 2 March 2020 in Chaoyang District, Beijing City, China. The susceptible-exposed-infected-asymptomatic-recovered (SEIAR) model was fitted with the dataset, and the effective reproduction number (Reff) was calculated as the transmissibility of a single population. Also, the basic reproduction number (R0) was calculated by definition for three clusters, such as household, factory and community, as the transmissibility of subgroups.ResultsThe epidemic curve in Chaoyang District was divided into three stages. We included nine clusters (subgroups), which comprised of seven household-level and one factory-level and one community-level cluster, with sizes ranging from 2 to 17 cases. For the nine clusters, the median incubation period was 17.0 days [Interquartile range (IQR): 8.4–24.0 days (d)], and the average interval between date of onset (report date) and diagnosis date was 1.9 d (IQR: 1.7 to 6.4 d). At the population level, the transmissibility of the virus was high in the early stage of the epidemic (Reff = 4.81). The transmissibility was higher in factory-level clusters (R0 = 16) than in community-level clusters (R0 = 3), and household-level clusters (R0 = 1).ConclusionsIn Chaoyang District, the epidemiological features of SARS-CoV-2 showed multi-stage pattern. Many clusters were reported to occur indoors, mostly from households and factories, and few from the community. The risk of transmission varies by setting, with indoor settings being more severe than outdoor settings. Reported household clusters were the predominant type, but the population size of the different types of clusters limited transmission. The transmissibility of SARS-CoV-2 was different between a single population and its subgroups, with cluster-level transmissibility higher than population-level transmissibility.

This dataset is used for urban informal settlements classification. The dataset contains a total of 1833 sets of samples, each of which includes a remote sensing image of size 3×224×224 and four street view images of size 3×512×1024. The remote sensing images come from Google Earth 18-level high-resolution remote sensing image with a resolution of 1.19m. The street view images come from Baidu Street View API. The samples in this dataset are divided into two categories, namely urban informal settlements (UIS) and non-urban informal settlements (Not-UIS). The dataset contains a total of 643 UIS samples and 1190 Not-UIS samples. All samples come from China’s eight megacities with a permanent urban population of more than 10 million (Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin, Chengdu, Wuhan, and Chongqing).

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• It is perhaps unsurprising that the majority of the most populous cities in the world are in the two most populated countries in the world, China and India. Among these are Shanghai and Beijing, with populations of 25 and 22 million respectively, Delhi (27 million), and Mumbai (over 21.5 million).

• Tokyo is the largest city in the world if the entire Tokyo metro area is included, with a total of more than 38 million residents. Another Japanese city, Osaka, also has a very large population of almost 20.5 million. There are also a number of non-Asian cities with high populations, including Mexico City (over 21 million), Cairo (almost 19.5 million), and Buenos Aires (almost 15.5 million).

• European cities, Istanbul is the most populous, with more than 14.5 million residents. This is followed by Moscow (over 12 million) and Paris (11 million including the Paris metro area). These cities are of course also culturally significant and between them welcome millions of tourists each year.

• There are quite a number of popular and culturally rich cities that have smaller populations, often making for higher living standards for their residents. Barcelona, Sydney, Berlin and Vancouver all have fewer than five million residents, but are very popular choices for city living. There are also some comparatively very small cities with big cultural, historical or political reputations, such as Sarajevo (314,000), Edinburgh (502,000), and Venice (631,000), demonstrating that small cities can be highly significant regardless of the size of their population.

This point shapefile represents the prefecture city locations, with 2000 population census data, for the Jiangsu Sheng province of China for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.The 2000 China township maps are developed for matching 2000 China population Census data, which should only be used as references for research or education instead of used as official maps.

This point shapefile represents the locations of townships with 2000 Population Census Data, 9.95% Long Form data, table L1-L6) for the Inner Mongolia province of China for 2000. These data are represented at 1:1,000,000 scale. This layer is part of the China 2000 township population census dataset.The 2000 China township maps are developed for matching 2000 China population Census data, which should only be used as references for research or education instead of used as official maps.

Endometrial cancer patients diagnosed and treated in Peking University People’s Hospital (PKUPH, Beijing, China) from January 2020 to December 2021 were consecutively included in this study. With our center (PKUPH) being a major referral center, the patients included were from different provinces of China, including those initially diagnosed and treated in our center and those diagnosed in local hospitals and then referred to our center. Of the 240 patients diagnosed with newly diagnosed ECs at PKUPH from January 2020 to December 2021, 229 patients whose pathology was confirmed as EEC or SEC by histology were retrospectively analyzed. Formalin-fixed paraffin-embedded (FFPE) tissue was collected from the 229 EC patients. Clinical information obtained from hospital records included age at diagnosis, surgical stage, and disease status. The patients were staged according to the International Federation of Gynecology and Obstetrics criteria. Tumor grade, histologic subtype, depth of myometrial invasion, and lymphovascular invasion were reviewed by a gynecologic pathologist based on the 2020 World Health Organization criteria. All ECs in this dataset were classified in one of the four molecular subgroups according a modified TCGA classification (TransPROTECT).