3-D Anthropometry Measurements of Human Body Surface

A Novel Tool for Computer-Aided Design

By Andy R. Terrel [source]

About this dataset

This survey utilizes the cutting-edge three-dimensional (3-D) surface anthropometry technology, which measures the outermost surface of the human body. These technologies are a breakthrough in measuring capabilities, as they can accurately record hundreds of thousands of points in three dimensions in only a few seconds. With this data, designers and engineers are able to use computer-aided design tools and rapid prototyping in conjunction with more realistic postures to create better designs for their target audience more effectively.

Surface anthropometry has many advantages over traditional measuring methods like rulers and tape measures: it helps reduce guesswork through its accuracy; it allows measurements to be taken long after a subject has left; it provides an efficient way to capture individuals while wearing clothing, equipment or any other accessories; each measurement is comparable with those collected by other groups regardless of who took them; and lastly, the system is non-contact so there’s no risk for discrepancies between different measurers.

Our survey will look at 3 dimensional body measurements such demographics like age, gender, reported height and weight as well as individual body parts such waist circumference preferred braid size cup size ankle circumference scye circumference chest circumferences hip height spine elbow length arm part lengths should get out seams sleeveinseam biacromial breadth bicristal breadth bustbusters cervical height chest – els interscye distance acromion Hight acromion radial length axilla heights elbow heights knee heights radial mation length hand late neck circumstance based these 3 dimes entails taken from our dataset Caesarz dot csv make sure you provide us with all the necessary information thank you

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How to use the dataset

This dataset is provided to help researchers, designers, engineers and other professionals in related fields use 3-D surface anthropometry technology to effectively measure the outer surface of the human body.

Using this dataset can enable you to capture hundreds of thousands of points in three-dimensions on the human body surface. This data provides insights into sizing, fitting and proportions of a range of different body shapes and sizes which can be incredibly useful for many purposes like fashion design or biomedical research.

To get started with this dataset it is helpful to become familiar with some basic terminology such as biacromial breadth (the distance between furthest points on left and right shoulder), bicristal breadth (waist width measurement) , kneem height (the vertical distance from hip joint center to kneecap), ankle circumference (measurement taken at ankle joint) etc. Knowing these measurements can help you better interpret and utilize the data provided in this survey.

Next up, you’ll want familiarise yourself with the various measurements given for each column in this dataset including: age (Integer) , num_children (Integer) , gender (String) , reported_height (Float) , reported_weight (Float) . & more Once ready dive into the data by downloading it into your chosen analysis tool - popular options including KNIME or R Studio! You’ll be able to explore correlations between size & shape metrics as well as discovering patterns between participants based on gender/age etc. Spend some time getting comfortable playing around with your chosen system & just keep exploring interesting connections! Finally if there's a specific use case you have don't forget that user-defined variables are also possible - so create variables when needed! Thanks so much for taking part in our survey & we wish you all best luck analyzing the data - we hope it's useful!

Research Ideas

• Developing web-based applications or online platforms for measuring body dimensions using 3D technology for custom clothing and equipment.
• Establishing anthropometric databases, allowing user to easily find measurements of all kinds of body shapes and sizes;
• Analyzing patterns between anthropometric measurements and clinical data such as BMI (body mass index) to benefit the understanding of human health status and nutrition needs

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [Dataset copyright by authors](http...

State capacity is a core concept in political science research, and it is widely recognized that state institutions exert considerable influence on outcomes such as economic development, civil conflict, democratic consolidation, and international security. Yet, researchers across these fields of inquiry face common problems involved in conceptualizing and measuring state capacity. In this article, we examine these conceptual issues, identify three core dimensions of state capacity, and develop the expectation that they are mutually supporting and interlinked. We then use Bayesian latent variable analysis to estimate state capacity at the conjunction of indicators related to these dimensions. We find strong interrelationships between the three dimensions and produce a new, general-purpose measure of state capacity with demonstrated validity for use in a wide range of empirical inquiries. It is hoped that this project will provide effective guidance and tools for researchers studying the causes and consequences of state capacity.

The 2020 Census Demographic and Housing Characteristics Noisy Measurement File is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022], and implemented in DAS_2020_DHC_Production_Code/das_decennial/programs/engine/primitives.py at main uscensusbureau/DAS_2020_DHC_Production_Code (github.com) The 2020 Census Demographic and Housing Characteristics Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] ), which added positive or negative integer-valued noise to each of the resulting counts. These are estimated counts of individuals and housing units included in the 2020 Census Edited File (CEF), which includes confidential data collected in the 2020 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the Census Demographic and Housing Characteristics Summary File. In addition to the noisy measurements, constraints based on invariant calculations --- counts computed without noise --- are also included (with the exception of the state-level total populations, which can be sourced separately from data.census.gov). The Noisy Measurement File was produced using the official "production settings," the final set of algorithmic parameters and privacy-loss budget allocations that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File. The noisy measurements are produced in an early stage of the TDA. Afterward, these noisy measurements are post-processed to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these noisy measurements to enable data users to evaluate the impact of disclosure avoidance variability on 2020 Census data. The 2020 Census Demographic and Housing Characteristics (DHC) Noisy Measurement File has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004). These data are available for download (i.e. not restricted access). Due to their size, they must be downloaded through the link on this metadata page and not through the standard ICPSR download. The link will take you to the Globus site where these data are housed. A README file is located in the Globus repository. Please refer to that for pertinent information. The Globus holding site requires users to create an account to access these data. Accounts can be created through existing institutional access and by personal access. Please see the Globus "How to get Started" page for more information.

The experiment here was to demonstrate that we can reliably measure the Reference Waveforms designed in the IEEE P1765 proposed standard and calculate EVM along with the associated uncertainties. The measurements were performed using NIST's calibrated sampling oscilloscope and were traceable to the primary standards.We have uploaded the following two datasets. (1) Table 3 contains the EVM values (in %) for the Reference Waveforms 1--7 after performing the uncertainty analyses. The Monte Carlo means are also compared with the ideal values from the calculations in the IEEE P1765 standard.(2) Figure 3 shows the complete EVM distribution upon performing uncertainty analysis for Reference Waveform 3 as an example. Each of the entries in Table 3 is associated with an EVM distribution similar to that shown in Fig. 3.

Behavioral data associated with the IBL paper: A standardized and reproducible method to measure decision-making in mice.This data set contains contains 3 million choices 101 mice across seven laboratories at six different research institutions in three countries obtained during a perceptual decision making task.When citing this data, please also cite the associated paper: https://doi.org/10.1101/2020.01.17.909838This data can also be accessed using DataJoint and web browser tools at data.internationalbrainlab.orgAdditionally, we provide a Binder hosted interactive Jupyter notebook showing how to access the data via the Open Neurophysiology Environment (ONE) interface in Python : https://mybinder.org/v2/gh/int-brain-lab/paper-behavior-binder/master?filepath=one_example.ipynbFor more information about the International Brain Laboratory please see our website: www.internationalbrainlab.comBeta Disclaimer. Please note that this is a beta version of the IBL dataset, which is still undergoing final quality checks. If you find any issues or inconsistencies in the data, please contact us at info+behavior@internationalbrainlab.org .

American Community Survey Public Use Micro Sample, augmented by NYC Opportunity.

Data/code files for the following project: I study the viability of Twitter-based measures for measuring public attitudes about the police. I find that Twitter-based measures track Gallup's measure of public attitudes starting around 2014, when Twitter user base stabilized, but not before 2014. Increases in Black Lives Matter protests are also associated with increases in negative sentiment measures from Twitter. The findings suggest that Twitter-based measures can be used to acquire granular evaluations of police performance, but they can be more useful in analyzing panel data of multiple agencies over time than in tracking a single geographical area over time.

October 31, 2024 (Final DWR Data)

The 2018 Legislation required DWR to provide or otherwise identify data regarding the unique local conditions to support the calculation of an urban water use objective (CWC 10609. (b)(2) (C)). The urban water use objective (UWUO) is an estimate of aggregate efficient water use for the previous year based on adopted water use efficiency standards and local service area characteristics for that year.

UWUO is calculated as the sum of efficient indoor residential water use, efficient outdoor residential water use, efficient outdoor irrigation of landscape areas with dedicated irrigation meter for Commercial, Industrial, and Institutional (CII) water use, efficient water losses, and an estimated water use in accordance with variances, as appropriate. Details of urban water use objective calculations can be obtained from DWR’s Recommendations for Guidelines and Methodologies document (Recommendations for Guidelines and Methodologies for Calculating Urban Water Use Objective - https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/Performance-Measures/UWUO_GM_WUES-DWR-2021-01B_COMPLETE.pdf).

The datasets provided in the links below enable urban retail water suppliers calculate efficient outdoor water uses (both residential and CII), agricultural variances, variances for significant uses of water for dust control for horse corals, and temporary provisions for water use for existing pools (as stated in Water Boards’ draft regulation). DWR will provide technical assistance for estimating the remaining UWUO components, as needed. Data for calculating outdoor water uses include:

• Reference evapotranspiration (ETo) – ETo is evaporation plant and soil surface plus transpiration through the leaves of standardized grass surfaces over which weather stations stand. Standardization of the surfaces is required because evapotranspiration (ET) depends on combinations of several factors, making it impractical to take measurements under all sets of conditions. Plant factors, known as crop coefficients (Kc) or landscape coefficients (KL), are used to convert ETo to actual water use by specific crop/plant. The ETo data that DWR provides to urban retail water suppliers for urban water use objective calculation purposes is derived from the California Irrigation Management Information System (CIMIS) program (https://cimis.water.ca.gov/). CIMIS is a network of over 150 automated weather stations throughout the state that measure weather data that are used to estimate ETo. CIMIS also provides daily maps of ETo at 2-km grid using the Spatial CIMIS modeling approach that couples satellite data with point measurements. The ETo data provided below for each urban retail water supplier is an area weighted average value from the Spatial CIMIS ETo.

• Effective precipitation (Peff) - Peff is the portion of total precipitation which becomes available for plant growth. Peff is affected by soil type, slope, land cover type, and intensity and duration of rainfall. DWR is using a soil water balance model, known as Cal-SIMETAW, to estimate daily Peff at 4-km grid and an area weighted average value is calculated at the service area level. Cal-SIMETAW is a model that was developed by UC Davis and DWR and it is widely used to quantify agricultural, and to some extent urban, water uses for the publication of DWR’s Water Plan Update. Peff from Cal-SIMETAW is capped at 25% of total precipitation to account for potential uncertainties in its estimation. Daily Peff at each grid point is aggregated to produce weighted average annual or seasonal Peff at the service area level. The total precipitation that Cal-SIMETAW uses to estimate Peff comes from the Parameter-elevation Relationships on Independent Slopes Model (PRISM), which is a climate mapping model developed by the PRISM Climate Group at Oregon State University.

• Residential Landscape Area Measurement (LAM) – The 2018 Legislation required DWR to provide each urban retail water supplier with data regarding the area of residential irrigable lands in a manner that can reasonably be applied to the standards (CWC 10609.6.(b)). DWR delivered the LAM data to all retail water suppliers, and a tabular summary of selected data types will be provided here. The data summary that is provided in this file contains irrigable-irrigated (II), irrigable-not-irrigated (INI), and not irrigable (NI) irrigation status classes, as well as horse corral areas (HCL_area), agricultural areas (Ag_area), and pool areas (Pool_area) for all retail suppliers.

This CODMAC level 3 data set contains the key parameters of the Inertial Measurement Package. In particular, it provides information on the gyroscope attitude measurements on a global scale and individual. It covers the period from launch in 2004, through the 3 Earth and 1 Mars flyby, plus the hibernation phases, plus the asteroid flybys and finally covers the Prelanding, comet escort & Extension phases of the prime target of the mission. The prime target is comet 67P/Churyumov-Gerasimenko 1 (1969 R1). This version V1.0 is the first version of this dataset.

This dataset includes soil wet aggregate stability measurements from the Upper Mississippi River Basin LTAR site in Ames, Iowa. Samples were collected in 2021 from this long-term tillage and cover crop trial in a corn-based agroecosystem. We measured wet aggregate stability using digital photography to quantify disintegration (slaking) of submerged aggregates over time, similar to the technique described by Fajardo et al. (2016) and Rieke et al. (2021). However, we adapted the technique to larger sample numbers by using a multi-well tray to submerge 20-36 aggregates simultaneously. We used this approach to measure slaking index of 160 soil samples (2120 aggregates). This dataset includes slaking index calculated for each aggregates, and also summarized by samples. There were usually 10-12 aggregates measured per sample. We focused primarily on methodological issues, assessing the statistical power of slaking index, needed replication, sensitivity to cultural practices, and sensitivity to sample collection date. We found that small numbers of highly unstable aggregates lead to skewed distributions for slaking index. We concluded at least 20 aggregates per sample were preferred to provide confidence in measurement precision. However, the experiment had high statistical power with only 10-12 replicates per sample. Slaking index was not sensitive to the initial size of dry aggregates (3 to 10 mm diameter); therefore, pre-sieving soils was not necessary. The field trial showed greater aggregate stability under no-till than chisel plow practice, and changing stability over a growing season. These results will be useful to researchers and agricultural practitioners who want a simple, fast, low-cost method for measuring wet aggregate stability on many samples.

MEASURE Evaluation is the USAID Global Health Bureau's primary vehicle for supporting improvements in monitoring and evaluation in population, health and nutrition worldwide. They help to identify data needs, collect and analyze technically sound data, and use that data for health decision making. Some MEASURE Evaluation activities involve the collection of innovative evaluation data sets in order to increase the evidence-base on program impact and evaluate the strengths and weaknesses of recent evaluation methodological developments. Many of these data sets may be available to other researchers to answer questions of particular importance to global health and evaluation research. Some of these data sets are being added to the Dataverse on a rolling basis, as they become available. This collection on the Dataverse platform contains a growing variety and number of global health evaluation datasets.

Data repository for solar measurements from 4 WB funded stations in Armenia. The four solar measuring stations and the associated measurement campaign have been financed by the Scaling-Up Renewable Energy Program (SREP) as part of the preparation activities for the Armenia Utility-Scale Solar Project. This project, which is being jointly supported by SREP and the World Bank, will deliver the first utility-scale solar plant in the country. The locations for the measuring stations were selected by the Renewable Resources and Energy Efficiency Fund, the project’s implementing entity, following the recommendations from Effergy, the expert consultant firm. For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/

A dataset that includes the index of subnational electoral democracy (ISED) as well as the replication material for Measuring and Assessing Subnational Electoral Democracy: A New Dataset for the Americas and India.

Many enduring questions in international relations theory focus on power relations, so it is important that scholars have a good measure of relative power. The standard measure of relative military power, the capability ratio, is barely better than random guessing at pre- dicting militarized dispute outcomes. We use machine learning to build a superior proxy, the Dispute Outcome Expectations score, from the same underlying data. Our measure is an order of magnitude better than the capability ratio at predicting dispute outcomes. We replicate Reed et al. (2008) and find, contrary to the original conclusions, that the probability of conflict is always highest when the state with the least benefits has a preponderance of power. In replications of 18 other dyadic analyses that use power as a control, we find that replacing the standard measure with DOE scores usually improves both in-sample and out-of-sample goodness of fit. Note:This analysis involves many layers of computation: multiple imputation of the underlying data, creation of an ensemble of machine learning models on the imputed datasets, predictions from that ensemble, and replications of previous studies using those predictions. Our replication code sets seeds in any script where random numbers are drawn, and runs in a Docker environment to ensure identical package versions across machines. Nevertheless, because of differences in machine precision and floating point computations across CPUs, the replication code may not produce results identical to those in the paper. Any differences should be small in magnitude and should not affect any substantive conclusions of the analysis.

A standing issue is how to measure bias in Large Language Models (LLMs) like ChatGPT. We devise a novel method of sampling, bootstrapping, and impersonation that addresses concerns about the inherent randomness of LLMs and test if it can capture political bias in ChatGPT. Our results indicate that, by default, ChatGPT is aligned with Democrats in the US. Placebo tests indicate that our results are due to bias, not noise or spurious relationships. Robustness tests show that our findings are valid also for Brazil and the UK, different professions, and different numerical scales and questionnaires.

Replication datasets and R script for the forthcoming publication, entitled "Measuring public opinion about autonomous vehicles using data from Reddit, Public Deliberation, and Surveys", in Public Opinion Quarterly, Special Issue on New Data in Social and Behavioral Research.

We collected grapevine shoot growth over a growing season of 2024 (April to June) in a vineyard of the horticultural unit 2 farm of the Ohio State University (40.73866822022149, -81.90273359323078). The measurements were made with a measuring tape.

This data collection measures the feelings and attitudes of 558 adults in the Detroit metropolitan area about other nations and what should be done in the world in 1964. Respondents were questioned extensively about their sources of information, the media, their political activities, and their organizational memberships. They were asked about the quality of information they received from the news media, their knowledge of foreign affairs, their feelings about developing nations, the United Nations and its role in aiding political, social, and economic development in developing nations, United States' assistance to developing nations, the admission of communist China to the United Nations, effects of atomic weapons build-up on world peace, the United States' military-industrial complex, and disarmament agreements between the United States and Russia. Respondents were also asked to assess the goals that the United States should have in dealing with other countries, and the domestic sources of influence on United States' foreign policy. Information was also elicited on respondents' political activism, such as demonstrations, petition-signing, support of political action groups, voting behavior, and political party affiliation, and memberships and participation in clubs and organizations. Demographic variables specify age, sex, race, place of birth, nationality, education, marital status, religion, length of residence in the Detroit area, family income, occupation, place and length of military service, and foreign contacts.

We apply recent advances in machine learning to measure Congressmember personality traits using floor speeches from 1996--2014. We also demonstrate the superiority of text-based measurement over survey-based measurement by showing that personality traits are correlated with survey response rates for members of Congress. Finally, we provide one empirical application showcasing the importance of personality on Congressional behavior.