In 2020, the average height of males aged between 18 and 44 years in China figured at 169.7 centimeters, up 1.2 centimeters compared to that in 2015. On the other side, obesity and overweight conditions have seen a gradual increase across the country mainly related to an unhealthy diet and a less active urban lifestyle.

Descriptive statistics for height components by quintiles in women: Height (m): Quintile 1: mean = 1.50, range = 1.19–1.54, std = 0.03; Quintile 2: mean = 1.55, range = 1.54–1.57, std = 0.008; Quintile 3: mean = 1.58, range = 1.57–1.60, std = 0.008; Quintile 4: mean = 1.61, range = 1.60–1.63, std = 0.009; Quintile 5: mean = 1.66, range = 1.63–1.86, std = 0.02. Leg Length (cm): Quintile 1: mean = 68.3, range = 0.40–0.70, std = 1.86; Quintile 2: mean = 71.4, range = 70–72.3, std = 0.59; Quintile 3: mean = 73.4, range = 72.3–74.0, std = 0.54; Quintile 4: mean = 75.4, range = 74.05–76.5, std = 0.60; Quintile 5: mean = 78.9, range = 76.5–105.0, std = 2.18. Sitting height (cm): Quintile 1: mean = 80.3, range = 52.0–82.0, std = 2.13; Quintile 2: mean = 83.5, range = 82.1–84.0, std = 0.53; Quintile 3: mean = 85.0, range = 84.1–85.9, std = 0.30; Quintile 4: mean = 86.5, range = 86.0–87.0, std = 0.47; Quintile 5: mean = 89.2, range = 87.1–105.0, std = 1.48.*Expressed as per standard deviation change. Model 1: univariate analyses. Model 2 controlled for birth cohort, education, and income. Model 3 controlled for birth cohort, education, income, and BMI at baseline. Model 4 controlled for birth cohort, education, income, smoking before age 20, BMI at age 20, BMI at baseline, and participation in team sports during adolescence.

Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, R = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. Macau has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 10⁸ m³). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.

Evaporation duct height in China offshore based on meteorological data，Including daily maximum height and average height，the time range is from May 1st, 2023 to April 30th, 2024.

In 2022, the average height of South Korean men in their thirties lay at 174.71 centimeters. Men in older age groups tended to be shorter. On average, South Korean men were 171.49 centimeters tall that year.

 Diet and healthcare in South Korea

It has been observed that improvements in nutrition and healthcare lead to increased average height over time. With the rapid industrialization in South Korea came improvements in healthcare and nutritional intake. South Korea ranks among the leading countries in the health index, which measures a population’s health and a country’s healthcare system. Even with an excellent healthcare system, South Koreans have increasingly been concerned about their diet and nutrition, exemplified by the share of people trying to consume certain nutrients every day.

 Height preferences in South Korea 

According to a 2019 survey, for most respondents the preferred height for South Korean men was higher than the current average. This discrepancy was similar for the preferred height for women, showing how preferences for taller people stretched across genders. Not only are South Koreans preferring taller partners, but they are also getting taller over time. Another survey found that the ideal height for a spouse in the country came closer to the average height of younger generations.

China Steel: Export: Medium & Small Section: L Section: Height <80mm data was reported at 27.272 USD mn in Nov 2024. This records an increase from the previous number of 24.793 USD mn for Oct 2024. China Steel: Export: Medium & Small Section: L Section: Height <80mm data is updated monthly, averaging 9.882 USD mn from Jan 2010 (Median) to Nov 2024, with 179 observations. The data reached an all-time high of 50.014 USD mn in Mar 2023 and a record low of 1.292 USD mn in Feb 2020. China Steel: Export: Medium & Small Section: L Section: Height <80mm data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s Metal and Steel Sector – Table CN.WAG: Steel Export: Monthly.

ObjectiveTo examine the trends in the prevalence of overweight and obesity among preschool children from 2006 to 2014.MethodsA total of 145,078 children aged 3–6 years from 46 kindergartens finished the annual health examination in Tianjin, China. Height, weight and other information were obtained using standardized methods. Z-scores for weight, height, and BMI were calculated based on the standards for the World Health Organization (WHO) child growth standards.ResultsFrom 2006 to 2014, mean values of height z-scores significantly increased from 0.34 to 0.54, mean values of weight z-scores kept constant, and mean values of BMI z-scores significantly decreased from 0.40 to 0.23. Mean values of height z-scores, weight z-scores, and BMI z-scores slightly decreased among children from 3 to 4 years old, and then increased among children from 4 to 6 years old. Between 2006 and 2014, there were no significant changes in prevalence of overweight (BMI z-scores >2 SD) and obesity (BMI z-scores >3 SD) among 3–4 years children. However, prevalence of obesity (BMI z-scores >2 SD) increased from 8.8% in 2006 to 10.1% in 2010, and then kept stable until 2014 among 5–6 years children. Boys had higher prevalence of obesity than girls.ConclusionsMean values of BMI z-scores decreased from 2006 to 2014 among Chinese children aged 3–6 years old due to the significant increase of height z-scores. Prevalence of obesity increased from 2006 to 2010, and then kept stable until 2014 among children aged 5–6 years. The prevalence of obesity was higher in boys than in girls.

Thanks to the substantial economic development in the country, obesity is replacing malnutrition and growth delay in becoming a new prominent health issue among China's youth. In December 2020, China's National Health Commission reported that while the average height of youngsters between six and 17 years old increased between 2015 and 2020, the obesity rate also rose continuously, with almost one in five children and adolescents aged between six and 17 years being obese or overweight.

Objective: The objective of this study is to further explore the difference between elevated blood pressure (EBP), elevated pulse pressure (EPP), and elevated mean arterial pressure (EMAP) and obesity in Chinese school-age children by sex.Methods: We performed a cross-sectional study of 935 children between 7 and 12 years old. Overweight and obesity were defined by body mass index and body composition. The multivariate logistic regression and the adjusted population attributable risk were used to assess the effects of obesity on pre-EBP/EBP, EPP, and EMAP. The interactions were used to identify the modification of obese on the relationship between related indicators of blood pressure and height or age.Results: The average age of the children included in the study was 10. Boys with overweight and obesity had higher pre-EBP/EBP, EPP, and EMAP (p < 0.05). The multivariate logistic regression analysis showed that overweight and obesity had a greater impact on BP and MAP than PP, especially in boys [odds ratio (OR) > 1]. Pre-EBP/EBP in 79% of boys and 76% of girls could be attributable to the visceral fat level. The interaction between BP, PP, MAP, and height or age was modestly increased in children with overweight and obesity, especially in boys.Conclusions: Independent of age and height, obesity not only increases blood pressure, it also increases mean arterial pressure and pulse pressure, and this effect is more pronounced in boys.

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Due to Zenodo's file size limitations, we are releasing different parts of CBF and GBD in different versions. See the below for specific information:

1. China's first sub-meter building footprints (CBF) derived by deep learning:

• part1: version12 (v12), City 1 to 210, https://doi.org/10.5281/zenodo.10473278
• part2: version13 (v13), City 211 to 356, https://doi.org/10.5281/zenodo.10475803 (current version)

Building attributes:

• id: Index number of the current building.
• year: Year of construction retrieved from GISA.
• height_mean: The average height of the building (computed from the pixels within the building footprint) obtained from CNBH (meters).
• height_max: Maximum height of the building (based on the highest pixel value within the building footprint) obtained from CNBH (meters).
• height_min: Minimum height of the building (based on the lowest pixel value within the building footprint) obtained from CNBH (meters).
• miniDist: Shortest straight-line distance to another building.
• dist_id: Index number of the building with the shortest straight-line distance to the current building.
• area: Area of the current building (square meters).
• perimeter: Perimeter of the current building (meters).
• inurban_19: A value of 1 indicates that the building was situated in an urban area in 1990, while a value of 0 signifies that it was located in a rural area in 1990. This determination is made using GUB data.
• inurban_1: A value of 1 indicates that the building was situated in an urban area in 1995, while a value of 0 signifies that it was located in a rural area in 1995. This determination is made using GUB data.
• inurban_20: A value of 1 indicates that the building was situated in an urban area in 2000, while a value of 0 signifies that it was located in a rural area in 2000. This determination is made using GUB data.
• inurban_2: A value of 1 indicates that the building was situated in an urban area in 2005, while a value of 0 signifies that it was located in a rural area in 2005. This determination is made using GUB data.
• inurban_3: A value of 1 indicates that the building was situated in an urban area in 2010, while a value of 0 signifies that it was located in a rural area in 2010. This determination is made using GUB data.
• inurban_4: A value of 1 indicates that the building was situated in an urban area in 2015, while a value of 0 signifies that it was located in a rural area in 2015. This determination is made using GUB data.
• inurban_5: A value of 1 indicates that the building was situated in an urban area in 2020, while a value of 0 signifies that it was located in a rural area in 2020. This determination is made using GUB data.

2. Global Building Dataset (GBD):

This dataset comprises approximately 800,000 images(512*512) with diverse architectural styles worldwide. It can be served as training and test samples for building extraction in different regions globally. In order to enhance usability, we did not break the continuity of the image and published it in 1024*1024 size.

Data: "data_updated_0618-empty rows removed-3-2.xlsx" contains 968 records of total annual litterfall production (Mg/ha/yr) collected at 314 forest sites covering the full geographical range of Chinese forests. The sites were distributed across various climatic zones, spanning latitudes from 18.26° to 51.50° N, longitudes from 82.25° to 129.53° E, altitudes from 0 to 4115 m above sea level, mean annual temperatures (MAT) from −5.4 to 25.4 °C, and mean annual precipitation (MAP) levels from 370 to 2800 mm. In addition to the geographical location and climate conditions, associated stand information is also included, such as forest type, stand origin, stand age, mean diameter at breast height (DBH), mean tree height, and stand density. Trap size (i.e., the surface area of the litter traps) is also included as it is a potentially important factor affecting litter collection. Code: "Litterfall v1.2_[total]_public release.ipynb" contains a complete pipeline of data parsing, cleaning, preprocessing, model training, and prediction.

Dahurian larch (Larix gmelinii Rupr.) is the dominant species in northeast China, which situated in the southernmost part of the global boreal forest biome and undergoing the greatest climatically induced changes. Published studies (1965-2015) on tree aboveground growth of Larix gmelinii forests in northeast China were collected in this study, critically reviewed, and a comprehensive growth dataset was developed from 122 sites, which distributed between 40.85° N and 53.47° N in latitude, between 118.20° E and 133.70° E in longitude, between 130 m and 1260 m in altitude. The dataset was composed of 743 entries, including growth data (mean tree height, mean DBH, mean tree volume and/or stand volume) and the associated information, i.e., geographical location (latitude, longitude, altitude, aspect and slope), climate (mean annual temperature (MAT) and mean annual precipitation (MAP)), stand description (origin, stand age, stand density and canopy density), and sample regime (observing year, plot area and number). It would provide quantitative references for plantation management practices and boreal forest growth prediction under future climate change.

Objective: The objective of this study is to further explore the difference between elevated blood pressure (EBP), elevated pulse pressure (EPP), and elevated mean arterial pressure (EMAP) and obesity in Chinese school-age children by sex.Methods: We performed a cross-sectional study of 935 children between 7 and 12 years old. Overweight and obesity were defined by body mass index and body composition. The multivariate logistic regression and the adjusted population attributable risk were used to assess the effects of obesity on pre-EBP/EBP, EPP, and EMAP. The interactions were used to identify the modification of obese on the relationship between related indicators of blood pressure and height or age.Results: The average age of the children included in the study was 10. Boys with overweight and obesity had higher pre-EBP/EBP, EPP, and EMAP (p < 0.05). The multivariate logistic regression analysis showed that overweight and obesity had a greater impact on BP and MAP than PP, especially in boys [odds ratio (OR) > 1]. Pre-EBP/EBP in 79% of boys and 76% of girls could be attributable to the visceral fat level. The interaction between BP, PP, MAP, and height or age was modestly increased in children with overweight and obesity, especially in boys.Conclusions: Independent of age and height, obesity not only increases blood pressure, it also increases mean arterial pressure and pulse pressure, and this effect is more pronounced in boys.

China EQI: MoM: HS4: Tall Oil, Whether or not Refined. data was reported at 3.796 Average 12 Mths PY=100 in Dec 2024. This records a decrease from the previous number of 81.900 Average 12 Mths PY=100 for Nov 2024. China EQI: MoM: HS4: Tall Oil, Whether or not Refined. data is updated monthly, averaging 101.800 Average 12 Mths PY=100 from Feb 2018 (Median) to Dec 2024, with 53 observations. The data reached an all-time high of 4,421.100 Average 12 Mths PY=100 in Nov 2020 and a record low of 0.842 Average 12 Mths PY=100 in Nov 2018. China EQI: MoM: HS4: Tall Oil, Whether or not Refined. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: MoM: HS4 Classification.

China Trade Index: MoM: Unit Value: Export HS4: Residual Lyes From the Manufacture of Wood Pulp, Whether or not Concentrated, Desugared or Chemically Treated, Including Lignin Sulphonates, but Excluding Tall Oil of Heading 38.03. data was reported at 98.339 Average 12 Mths PY=100 in Dec 2024. This records a decrease from the previous number of 119.400 Average 12 Mths PY=100 for Nov 2024. China Trade Index: MoM: Unit Value: Export HS4: Residual Lyes From the Manufacture of Wood Pulp, Whether or not Concentrated, Desugared or Chemically Treated, Including Lignin Sulphonates, but Excluding Tall Oil of Heading 38.03. data is updated monthly, averaging 109.500 Average 12 Mths PY=100 from Feb 2018 (Median) to Dec 2024, with 61 observations. The data reached an all-time high of 174.934 Average 12 Mths PY=100 in Mar 2018 and a record low of 69.700 Average 12 Mths PY=100 in Mar 2020. China Trade Index: MoM: Unit Value: Export HS4: Residual Lyes From the Manufacture of Wood Pulp, Whether or not Concentrated, Desugared or Chemically Treated, Including Lignin Sulphonates, but Excluding Tall Oil of Heading 38.03. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: MoM: HS4 Classification.

China EQI: MoM: HS4: Residual Lyes From the Manufacture of Wood Pulp, Whether or not Concentrated, Desugared or Chemically Treated, Including Lignin Sulphonates, but Excluding Tall Oil of Heading 38.03. data was reported at 130.056 Average 12 Mths PY=100 in Dec 2024. This records an increase from the previous number of 70.100 Average 12 Mths PY=100 for Nov 2024. China EQI: MoM: HS4: Residual Lyes From the Manufacture of Wood Pulp, Whether or not Concentrated, Desugared or Chemically Treated, Including Lignin Sulphonates, but Excluding Tall Oil of Heading 38.03. data is updated monthly, averaging 90.400 Average 12 Mths PY=100 from Feb 2018 (Median) to Dec 2024, with 61 observations. The data reached an all-time high of 292.100 Average 12 Mths PY=100 in Mar 2020 and a record low of 56.300 Average 12 Mths PY=100 in Apr 2020. China EQI: MoM: HS4: Residual Lyes From the Manufacture of Wood Pulp, Whether or not Concentrated, Desugared or Chemically Treated, Including Lignin Sulphonates, but Excluding Tall Oil of Heading 38.03. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: MoM: HS4 Classification.