NBA data ranging from 1996 to 2024 contains physical attributes, bio information, (advanced) stats, and positions of players.

No missing values, certain data preprocessing will be needed depending on the task.

Data was gathered from the nba.com and Basketball Reference - starting with the season 1996/97 and up until the latest season 2023/24.

A lot of options for EDA & ML present - analyzing the change of physical attributes by position, how the number of 3-point shots changed throughout years, how the number of foreign players increased; using Machine Learning to predict player's points, rebounds and assists, predicting player's position, player clustering, etc.

The issue with the data was that the data about player height and weight was in Imperial system, so the scatterplot of heights and weights was not looking good (around only 20 distinct values for height and around 150 for weight, which is quite bad for the dataset of 13.000 players). I created a script in which I assign a random height to the player between 2 heights (let's say between 200.66 cm and 203.2 cm, which would be 6-7 and 6-8 in Imperial system), but I did it in a way that 80% of values fall in the range of 5 to 35% increase, which still keeps the integrity of the data (average height of the whole dataset increased for less than 1 cm). I did the same thing for the weight: since difference between 2 pounds is around 0.44 kg, I would assign a random value for weight for each player that is either +/- 0.22 from his original weight. Here I observed a change in the average weight of the whole dataset of around 0.09 kg, which is insignificant.

Unfortunately the NBA doesn't provide the data in cm and kg, and although this is not the perfect approach regarding accuracy, it is still much better than assigning only 20 heights to the dataset of 13.000 players.

About this dataset

Context

The dataset tabulates the Height of Land township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Height of Land township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Height of Land township was 683, a 0.15% decrease year-by-year from 2022. Previously, in 2022, Height of Land township population was 684, an increase of 0.29% compared to a population of 682 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Height of Land township increased by 54. In this period, the peak population was 718 in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Height of Land township is shown in this column.
• Year on Year Change: This column displays the change in Height of Land township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Height of Land township Population by Year. You can refer the same here

Summary statistics on growth.

These are height measurement data for female, individually-known African elephants in the Samburu and Buffalo Springs National Reserves. These data were used in the manuscript "Orphaning stunts growth in African Elephants", currently under review. The first Excel worksheet is titled "GW.growth.curve". It shows the median of the height measurements taken from an elephant on a single date by author George Wittemyer. These medians were used to create a von Bertalanffy growth curve upon which we structured the Bayesian analysis that addressed our main hypotheses. The second worksheet titled "All.data" shows all measurements taken by either author. The third worksheet shows a summary of author Jenna Parker's measurements, including which individuals were not included in the main analysis because we are unsure of their exact birthdate.

Objectives: With the accumulation of genetic basis for familial (genetic) short stature (FSS), the genetic association of FSS with health-related outcomes remains to be elucidated. In this study, we aimed to investigate the FSS genetic architecture and its causal effect on three quantitative traits in Taiwan. Methods: We conducted an FSS genome-wide association study (GWAS) analysis (1,640 FSS cases and 22,372 controls). We performed a GWAS meta-analysis for the Taiwanese meta-height from the Taiwan Biobank (TWB)_height (N = 67,452) and the China Medical University Hospital (CMUH)_height GWAS summary statistics (N = 88,854). We calculated three polygenic risk scores (PRSs) of SNPs (P < 5 x 10-8) for FSS and Taiwanese meta-height with/without FSS, respectively. We explored the associations between three PRSs and the measured height, respectively. We also performed Mendelian randomization (MR) analysis for the causal effect of FSS and Taiwanese meta-height with/without FSS, on anthropometric, bone mineral density (BMD), and female reproductive traits. Results: FSS GWAS identified 172 SNPs in 4 genomic regions, reported in height (P < 5 x 10-8). Higher FSS genetic scores correlate with an increased risk of short stature and height reduction tendency (p < 0.001). The causal effect showed that a higher risk of FSS was associated with decreased body height, but increased body mass index, and body fat (p < 0.001). However, higher genetic scores of Taiwanese meta-height with/without FSS correspond with increased body height, body weight, hip circumference, and age at menarche, but decreased BMD_T-score, BMD_Z-score, and stiffness index (p < 0.001). Conclusion: This study contributes to the FSS and height genetic features and their causal effects on three quantitative traits in individuals of Han Chinese ancestry in Taiwan. Methods Study design: Figure 1 shows an overview of the study design. For a single GWAS discovery, a GWAS was performed to identify FSS susceptible loci, with 1,640 cases and 22,372 controls of Han Chinese ancestry in Taiwan. For a multi-GWAS discovery, two-stage GWAS meta-analyses were conducted. In stage 1, the effect of each SNP was combined from the Taiwan Biobank (TWB)_height GWAS summary statistics (N = 67,452) and the CMUH_height GWAS summary statistics (N = 88,854) (not shown) using a fixed-effect meta-analysis of beta values. A Taiwanese meta-height GWAS summary statistics (N = 156,306) was then obtained. In stage 2, the effect of each SNP was also combined from the Taiwanese meta-height and our FSS GWAS summary statistics (Case: 1,640; control: 22,372) as previously described. A GWAS summary statistics of Taiwanese meta-height + FSS (N = 180,318) was also obtained. GWAS summary statistics of FSS and Taiwanese meta-height with/without FSS were then applied in the evaluation cohorts. The evaluation cohorts included the FSS testing group (Case: 410; control: 5,594) and the TWB_height testing + validation groups (N = 28,909). These three GWAS summary statistics were applied to optimize polygenic risk score (PRS), to investigate the PRS associations with the risk of short stature and/or height tendency, and to perform the MR analysis in the anthropometric, BMD, and female reproductive traits.
FSS GWAS study population: In this study, we selected 142,935 participants from CMUH, Taichung, Taiwan (Appendix Figure 1). Individuals who with the following criteria were excluded: (1) individuals who were included in our previous FSS genetic study (2) (N = 62), (2) individuals with skeletal dysplasia (ICD-9-CM code: 756.9) (N = 100), (3) individuals with dysmorphic feature (ICD-9-CM code: 738.19) (N = 88), (4) individuals with constitutional delay of growth and puberty (ICD-9-CM code: 259.0) (N = 3), (5) individuals with abnormal IGF-1 serum level within 1 year after FSS diagnosed (N = 0), (6) individuals with abnormal thyroid function within 1 year after FSS diagnosed (N = 375), and (7) individuals with abnormal puberty onset within 1 year after FSS diagnosed (N = 303). These resulted in 142,004 study subjects. The study subjects included 2,050 FSS cases and 139,954 controls. The 2,050 FSS cases (ICD-9-CM code: 783.43 or ICD-10-CM code: R62.52) diagnosed by pediatric endocrinologists were individuals of Han Chinese ancestry in Taiwan. The 139,954 controls were further processed according to the exclusion criteria: (1) individuals with their age less than 18 years old (N = 10,644), (2) individuals without height information (N = 18,486), and (3) individuals with a height less than 75th percentile (N = 82,858). Finally, these resulted in 2,050 cases and 27,966 controls. Then we used a simple random sampling method to assign the training and testing groups at an 8:2 ratio. The training group (Case: 1,640; control: 22,372) comprised 80% of the total study population and was applied for the GWAS analysis (case-control study) under an additive genetic model, adjusted with gender and the first 10 principal component analyses (PCAs) (Appendix Figure 1, Appendix Figure 2, and Figure 2A), using the PLINK software (version 1.9, 2.0) (28). A significant P-value threshold of genome-wide association P < 5.00E-8 was used for the additive test. The testing group (Case: 410; control: 5,594) comprised 20% of the total study population, served as one of the evaluation cohorts, was applied to optimize PRS and to investigate the PRS associations with risk of short stature using logistic regression analysis (Appendix Figure 3). The Human Studies Committee of CMUH in Taichung, Taiwan approved this study (approval number: CMUH107-REC3-074 and CMUH110-REC3-005). Quality control of the genetic data: In this study, imputed GWAS data were extracted from CMUH, Taichung, Taiwan. The SNP quality control (QC) and individual QC procedures were performed before FSS GWAS analysis. The following SNPs were excluded in the SNP QC procedure: (1) SNPs with MAF < 0.001, (2) SNPs with HWE p-value of < 1 × 10-6, and (3) SNP with a missing call rate of > 5%. The resulting SNPs were used to perform ancestry PCA for the population structure analysis after SNP QC. Furthermore, the following individuals were excluded from the individual QC procedure: (1) individuals who did not fit the sex check: male: 0.75 and female: 0.25, (2) heterozygosity rate > ±3 standard deviation (SD), (3) individual with a missing call rate > 5%, (4) individuals with their divergent ancestry of PCA >±5 SD, (5) individual with kinship > 0.0884. DNA contamination, evidence of relatedness, or participants of non-Chinese ancestry, were excluded. PRS calculation using the Bayesian polygenic prediction approach (PRS-CS): GWAS summary statistics of FSS and Taiwanese meta-height with/without FSS were used as the training data sets, respectively, for PRS-CS together with Asians in the 1000 Genomes Phase 3 project as the linkage disequilibrium reference panel. The PRSs were then calculated using Asian-specific posterior weights and PLINK software (versions 1.9 and 2.0) (28) in the evaluation cohorts (validation data set). The evaluation cohorts included the FSS testing group (Case: 410; control: 5,594) and the TWB_height testing + validation groups (N = 28,909).
For the evaluation cohort- the FSS testing group (Case: 410; control: 5,594), FSS PRSs were generated according to the all SNPs and the SNPs of FSS GWAS summary statistics-associated P-value thresholds (P < 5 x 10-6, P < 5 x 10-7, and P < 5 x 10-8) using Asian-specific posterior weights and PLINK software (versions 1.9 and 2.0). For the evaluation cohort- the TWB_height testing + validation groups (N = 28,909), PRSs of FSS, and Taiwanese meta-height with/without FSS were generated according to the SNPs of their GWAS summary statistics using Asian-specific posterior weights and PLINK software (versions 1.9 and 2.0). Data centering and standardization were performed for the PRS data. PRS calculation using the clumping and P-value threshold approach: Similarity, FSS GWAS summary statistics were also used as the training data set for the clumping and P-value threshold approach. The SNPs of FSS GWAS summary statistics-associated P-value thresholds (P < 5 x 10-6, P < 5 x 10-7, and P < 5 x 10-8) were then subjected to the clumping procedure (within the range of 250,000 base pairs of the index SNP, where SNPs were removed when r2 > 0.1), according to the estimated linkage disequilibrium (LD) among the SNPs in the FSS testing group (Case: 410; control: 5,594). After clumping, FSS PRSs were generated according to the P-value thresholds (P < 5 x 10-6, P < 5 x 10-7, and P < 5 x 10-8 ) using PRSice and PLINK software (versions 1.9 and 2.0). Data centering and standardization were performed for the PRS data. TWB phenotypic and genetic data: Database of TWB_height testing + validation groups (N = 28,909) including phenotypic and genetic data was also applied as one of the evaluation cohorts for the linear regression association between measured height (cm) and PRS and for MR analysis. In this study, the phenotypic data included anthropometric, BMD, and female reproductive quantitative traits. The anthropometric trait included body height (cm), body weight (kilogram), body mass index (BMI), waist circumference (WC) (cm), hip circumference (HC) (cm), waist-hip ratio (WHR), and body fat (%). Body mass index (BMI) was calculated as BMI = body weight/body height2. The WHR was calculated as WHR = WC/ HC. The BMD trait included BMD_T-score, BMD_Z-score, and stiffness index. The female reproductive trait included age at menarche (years old), age at menopause (years old), and reproductive life span (age at menopause minus age at menarche; years). The genetic data included the imputed GWAS data obtained from the TWB_height testing + validation groups (N = 28,909). The SNP QC and individual QC procedures were described as previously reported. Statistical analyses: Imputed genotype data was applied for the GWAS analysis

BackgroundAdult height reflects childhood circumstances and is associated with health, longevity, and maternal–fetal outcomes. Mean height is an important population metric, and declines in height have occurred in several low- and middle-income countries, especially in Africa, over the last several decades. This study examines changes at the population level in the distribution of height over time across a broad range of low- and middle-income countries during the past half century.Methods and findingsThe study population comprised 1,122,845 women aged 25–49 years from 59 countries with women’s height measures available from four 10-year birth cohorts from 1950 to 1989 using data from the Demographic and Health Surveys (DHS) collected between 1993 and 2013. Multilevel regression models were used to examine the association between (1) mean height and standard deviation (SD) of height (a population-level measure of inequality) and (2) median height and the 5th and 95th percentiles of height. Mean-difference plots were used to conduct a graphical analysis of shifts in the distribution within countries over time. Overall, 26 countries experienced a significant increase, 26 experienced no significant change, and 7 experienced a significant decline in mean height between the first and last birth cohorts. Rwanda experienced the greatest loss in height (−1.4 cm, 95% CI: −1.84 cm, −0.96 cm) while Colombia experienced the greatest gain in height (2.6 cm, 95% CI: 2.36 cm, 2.84 cm). Between 1950 and 1989, 24 out of 59 countries experienced a significant change in the SD of women’s height, with increased SD in 7 countries—all of which are located in sub-Saharan Africa. The distribution of women’s height has not stayed constant across successive birth cohorts, and regression models suggest there is no evidence of a significant relationship between mean height and the SD of height (β = 0.015 cm, 95% CI: −0.032 cm, 0.061 cm), while there is evidence for a positive association between median height and the 5th percentile (β = 0.915 cm, 95% CI: 0.820 cm, 1.002 cm) and 95th percentile (β = 0.995 cm, 95% CI: 0.925 cm, 1.066 cm) of height. Benin experienced the largest relative expansion in the distribution of height. In Benin, the ratio of variance between the latest and earliest cohort is estimated as 1.5 (95% CI: 1.4, 1.6), while Lesotho and Uganda experienced the greatest relative contraction of the distribution, with the ratio of variance between the latest and earliest cohort estimated as 0.8 (95% CI: 0.7, 0.9) in both countries. Limitations of the study include the representativeness of DHS surveys over time, age-related height loss, and consistency in the measurement of height between surveys.ConclusionsThe findings of this study indicate that the population-level distribution of women’s height does not stay constant in relation to mean changes. Because using mean height as a summary population measure does not capture broader distributional changes, overreliance on the mean may lead investigators to underestimate disparities in the distribution of environmental and nutritional determinants of health.

Summary statistics of variables in regression analysis.

Genetic data plays an increasingly important role in modern medicine. Decrease in the cost of sequencing with subsequent increase in imputation accuracy, and the accumulation of large amounts of high-quality genetic data enable the creation of polygenic risk scores (PRSs) to perform genotype–phenotype associations. The accuracy of phenotype prediction primarily depends on the overall trait heritability, Genome-wide association studies cohort size, and the similarity of genetic background between the base and the target cohort. Here we utilized 8,664 high coverage genomic samples collected across Russia by “Evogen”, a Russian biomedical company, to evaluate the predictive power of PRSs based on summary statistics established on cohorts of European ancestry for basic phenotypic traits, namely height and BMI. We have demonstrated that the PRSs calculated for selected traits in three distinct Russian populations, recapitulate the predictive power from the original studies. This is evidence that GWAS summary statistics calculated on cohorts of European ancestry are transferable onto at least some ethnic groups in Russia.

About this dataset

Context

The dataset tabulates the data for the Height of Land Township, Minnesota population pyramid, which represents the Height of Land township population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Height of Land Township, Minnesota, is 24.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Height of Land Township, Minnesota, is 39.7.
• Total dependency ratio for Height of Land Township, Minnesota is 63.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Height of Land Township, Minnesota is 2.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Height of Land township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Height of Land township for the selected age group is shown in the following column.
• Population (Female): The female population in the Height of Land township for the selected age group is shown in the following column.
• Total Population: The total population of the Height of Land township for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Height of Land township Population by Age. You can refer the same here

Background: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life. Methods: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) then in a large, outpatient, pediatric sample (N=14,695). Results: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby ..., Sources of data: Information on infants was ascertained from two sources: the STORK birth cohort and the STARR research registry. (1) Detailed methods for the STORK birth cohort have been described previously. In brief, a multiethnic cohort of mothers and babies was followed from the second trimester of pregnancy to the babies’ third birthday. Healthy women aged 18–42 years with a single-fetus pregnancy were enrolled. Households were visited every four months until the baby’s third birthday (nine baby visits), with the weight of the baby at each visit recorded in pounds. Medical charts were abstracted for birth weight and length. (2) STARR (starr.stanford.edu) contains electronic medical record information from all pediatric and adult patients seen at Stanford Health Care (Stanford, CA). STARR staff provided anonymized information (weight, height and age in days for each visit through age three years; sex; race/ethnicity) for all babies during the period 03/2013–01/2022 followed from bi..., The R code, as written in RStudio, are saved as MME_weights.RMD, MME_heights.RMD, MME_predictions_weights.RMD, and MME_predictions_heights.RMD. The tab-delimited and anonymized source data for weights and heights (both jittered) are posted. These can be used with the R code-but the user will need to correct input and output filepaths used in the script. The HTML version of these files is available as well, in case viewing the scripts without opening them in R is desired. R_sessionInfo.txt contains the R software version, as well as the versions of the packages included in the code. See the methods section for the description of the starting parameters for the nls() function., # Data for: A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the US

https://doi.org/10.5061/dryad.4j0zpc8jf

Description of the data and file structure

Data for this study include, per baby: sex, age in days, and, over time, weight in Kg and height in cm. Each baby had at least 5 visits. Our goal was to fit each baby’s data to a curve as described by a modified Michaelis-Menten equation, allowing interpolation of missing weight or height values. Among the subset of all infants who had 7 well-baby visits in the first year of life, and 12 visits over 3 years, we further explored the minimum number of, and which, data points were necessary for good fit. Finally, among babies with 5 time points in year 1, and 2 in both year 2 and year 3, we examined whether weight or height data early in life could predict growth in later months.

To meet anonymization guidelines, we are providing only STARR dat...

I offer here both dataset and computing code related to a stem analysis algorithm to reconstruct height growth of trees. First, the dataset has time series records of tree height for Nothofagus alpina ("rauli"), N. dombeyi ("coigue"), N. obliqua ("roble"), and Pseudotsuga menziesii ("Douglas-fir"). The data come from stem analysis sample trees in both southern Chile and the Inland Northwest, USA.  These trees are part of the ones used in an article about a new algorithm for reconstructing tree height growth. The article is published in Methods in Ecology and Evolution (https://doi.org/10.1111/2041-210x.13616). Second, I provide an R code implementing the proposed algorithm for a given dataset as example.

The Standing and Height Adjustable Desk market has witnessed remarkable growth over the past several years, driven by an increasing awareness of health and wellness in the workplace. These innovative desks are designed to accommodate various workstyles, allowing users to alternate between sitting and standing throug

This is an R script that reproduces the analysis of ice hockey player's height dynamics based on the collected open data from IIHF world championships 2001-2016.

Dataset Overview: This dataset provides a comprehensive list of the world's tallest women and men, meticulously gathered from Wikipedia. It includes detailed information about each individual, such as their country of origin, height in both metric and imperial units, name, and notable facts. Additionally, the dataset includes lifespan information, highlighting the age at death where applicable. This collection offers valuable insights into the extraordinary heights achieved by individuals across different regions and times.

Colour Code.

Green- living White- deceased Pink - height disputed Blue -No growth related pathological disorder (gigantism, acromegaly)

Data Science Applications: With less than 200 entries, this dataset is ideal for a variety of data science applications. Researchers and enthusiasts can use it to:

• Perform statistical analysis and visualization of height distribution among the tallest individuals.
• Explore correlations between height and geographical regions.
• Conduct studies on the impact of extreme height on lifespan and health.
• Develop machine learning models to predict potential factors contributing to extraordinary height.

Data Columns:

• Country: The country of origin of the individual.
• Metric: The height of the individual in centimeters.
• Imperial: The height of the individual in feet and inches.
• Name: The name of the individual.
• Note: Notable facts,whether verified or disputed etc.
• Lifespan (age at death): The lifespan of the individual, including the age at death.

Ethically Obtained: This dataset has been ethically sourced from Wikipedia, then saved into 2 csv files for Men and Women (No web scraping was performed).

Acknowledgements: I would like to acknowledge Wikipedia for the primary source of information and Adobe Firefly for the dataset thumbnail image.

1. Data Collection LocationThe Changbai Mountain deciduous Korean pine forest comprehensive observation site (central geographic coordinates: 128.0956E, 42.4030N, elevation 784 m) is located in Erdaobaihe Town, Antu County, Yanbian Korean Autonomous Prefecture, Jilin Province.2. Data Collection MethodsThe Changbai Mountain Forest Ecosystem Research Station, following the "Observation Indicators and Standards for Terrestrial Ecosystem Biology," further divides the long-term monitoring plot of 40 m × 40 m into secondary plots of 5 m × 5 m, totaling 64. For convenience and research needs, the monitoring plot is referred to as the Level I plot for tree layer observation, and the secondary plot is referred to as the Level II plot for shrub layer and herbaceous layer observation. - Tree layer observation: Investigate the diameter at breast height, height, and cover of each tree in the Level I plot. - Shrub layer observation: Mechanically sample and conduct long-term observation on 17 fixed Level II plots. Set up a 2 m × 2 m small plot in each selected Level II plot to investigate the height and cover of each shrub (clump). - Herbaceous layer observation: Conducted within the Level II plots selected for shrub layer investigation. Set up a 1 m × 1 m small plot in each selected Level II plot for inter-annual observation of the herbaceous layer. If necessary, all herbaceous plants can be removed for observation to investigate the height and cover of each herbaceous plant (clump) within the small plot.- Epiphyte observation: Investigate the category of epiphytes on each tree in the Level I plot. - Liana observation: Investigate the base diameter and length of lianas within the Level I plot.3. Data ProcessingData processing includes checking and completing original record information, data entry and verification, and data statistical analysis.The specific statistical analysis methods are as follows: - Tree layer: Based on individual tree surveys, statistics are calculated by Level II plot and species: number of individuals, average diameter, average height, and biomass calculated using models (including stem dry weight, branch dry weight, leaf dry weight, fruit (flower) dry weight, bark dry weight, aerial root dry weight, aboveground total dry weight, and underground total dry weight). Based on the results of individual tree surveys by species, statistics are calculated by Level II plot: species number, dominant species, average height of dominant species, density, aboveground total dry weight, and underground total dry weight. - Shrub layer: Based on species surveys by Level II plot, statistics are calculated by plot: number of individual plants (clumps), average height, biomass calculated using models (including branch dry weight, leaf dry weight, aboveground total dry weight, and underground total dry weight), species number, dominant species, average height of dominant species, density, aboveground total dry weight, and underground total dry weight. - Herbaceous layer: Based on species surveys by Level II plot, statistics are calculated by plot: number of individual plants (clumps), average height, aboveground total dry weight, species number, dominant species, average height of dominant species, density, aboveground total dry weight, and underground total dry weight (underground sampling plot 1 m × 1 m × 0.25 m). - Epiphytes: Based on the survey of epiphytes on each tree, statistics are calculated by Level II plot and species: number of individual plants (clumps). - Liana: Based on the survey within the Level I plot, statistics are calculated by Level II plot and species: number of individual plants (clumps), average base diameter, and average height.4. Database CompositionThe data set is stored in Excel format, including eight sheets. Sheet1 is for the composition of tree species in the Changbai Mountain deciduous Korean pine forest, with a total of 269 records, including indicators as shown in Table 2; Sheet2 is for the composition of shrub species, with a total of 66 records, including indicators as shown in Table 3; Sheet3 is for the composition of herbaceous species, with a total of 193 records, including indicators as shown in Table 4; Sheet4 is for the community characteristics of the tree layer, with a total of 118 records, including indicators as shown in Table 5; Sheet5 is for the community characteristics of the shrub layer, with a total of 32 records, including indicators as shown in Table 6; Sheet6 is for the community characteristics of the herbaceous layer, with a total of 32 records, including indicators as shown in Table 7; Sheet7 is for the species composition of epiphytes, with a total of 130 records, including indicators as shown in Table 8; Sheet8 is for the composition of liana species, with a total of 65 records, including indicators as shown in Table 9.5. Data Quality Control and AssessmentThe quality control of this data set follows the relevant monitoring specifications of the "Observation Indicators and Standards for Terrestrial Ecosystem Biology," with field surveys conducted by technicians with rich experience and professional skills, and the survey data is reviewed and verified by scientific researchers to ensure the scientific and accurate nature of the data.Specific measures are as follows: - During field surveys: The observation time for the species composition and community characteristics of the Changbai Mountain deciduous Korean pine forest is mid-August (the peak of plant growth). Standardized measurement tools and methods are used for data collection, such as using the same model of measuring instruments to measure tree diameter, plant height, and liana base diameter to reduce measurement errors. Plant species identification, common names, and scientific names are based on the Plant Smart database. For plant species that cannot be determined on-site, photos should be taken and specimens collected for indoor analysis and identification. Field survey data records are checked by both the investigator and the recorder to ensure the accuracy of the data. - Data entry: Paper data is transformed into electronic data, with one person entering and another verifying to ensure the accuracy of the data entry. - Quality control and assessment: Quality control methods include threshold checks (comparing monitoring data with historical data over the years, verifying data that exceeds the historical data threshold range, deleting outliers or marking explanations), consistency checks (such as different order of magnitude compared to other measurement values), etc. Quality assessment is carried out by plotting dynamic graphs based on annual or seasonal units and comparing data from the same period.

Sweden Data Center Rack Market is Segmented by Rack Size (Quarter Rack, Half Rack, Full Rack), by Rack Height (42U, 45U, 48U, Other Heights (≥52U and Custom), Rack Type (Cabinet (Closed) Racks, Open-Frame Racks, Wall-Mount Rack), Data Center Type (Colocation Facilities and More), Material (Steel and More). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Segments.

The Height-Adjustable Monitor market has seen substantial growth in recent years, driven by the increasing awareness of ergonomics and the rising demand for flexible work environments. These monitors, designed to accommodate varying user heights and preferences, allow employees to switch between sitting and standing

The Height Adjustable Drafting Table market has witnessed significant growth as professionals in design, architecture, and art continue to prioritize ergonomics and versatility in their workspaces. These tables offer adjustable heights, enabling users to transition seamlessly between sitting and standing positions,

The global height measurement devices market is experiencing robust growth, driven by increasing healthcare awareness, technological advancements, and rising demand for accurate anthropometric data in various sectors. The market, estimated at $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 5% from 2025 to 2033, reaching an estimated value of $2.3 billion by 2033. This growth is fueled by several factors, including the expanding adoption of stadiometers in hospitals and clinics for routine check-ups and diagnosis, the increasing use of portable and digital height measurement devices in personal health management, and the growing integration of height measurement systems in ergonomic assessments for workplace safety. Furthermore, advancements in sensor technology and the development of smart height measurement devices with data connectivity features contribute to market expansion. However, the market faces some constraints. The relatively high cost of advanced height measurement systems, especially those with integrated data analysis capabilities, might limit adoption in resource-constrained settings. Additionally, the market's growth is also influenced by factors such as variations in healthcare infrastructure across different regions and the regulatory landscape surrounding medical devices. Despite these challenges, the long-term outlook for the height measurement devices market remains positive, driven by continued technological innovations, increasing healthcare spending, and the growing need for accurate and efficient height measurement solutions across diverse applications. Key players like Befour, Sunbeam Products, and others are actively engaged in developing innovative products and expanding their market reach to capitalize on this growth opportunity.

Summary statistics of the growth markers by cohorts and by sexes: aPHV, PHV, estimated height at 8 years and estimated height at 18 years.