About this dataset

Context

The dataset presents median household incomes for various household sizes in Level Plains, AL, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Level Plains did not include 7-person households. Across the different household sizes in Level Plains the mean income is $89,713, and the standard deviation is $39,637. The coefficient of variation (CV) is 44.18%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $31,076. It then further increased to $90,006 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/level-plains-al-median-household-income-by-household-size.jpeg" alt="Level Plains, AL median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 Level Plains median household income. You can refer the same here

The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software. Currently datasets and certified values are provided for assessing the accuracy of software for univariate statistics, linear regression, nonlinear regression, and analysis of variance. The collection includes both generated and 'real-world' data of varying levels of difficulty. Generated datasets are designed to challenge specific computations. These include the classic Wampler datasets for testing linear regression algorithms and the Simon & Lesage datasets for testing analysis of variance algorithms. Real-world data include challenging datasets such as the Longley data for linear regression, and more benign datasets such as the Daniel & Wood data for nonlinear regression. Certified values are 'best-available' solutions. The certification procedure is described in the web pages for each statistical method. Datasets are ordered by level of difficulty (lower, average, and higher). Strictly speaking the level of difficulty of a dataset depends on the algorithm. These levels are merely provided as rough guidance for the user. Producing correct results on all datasets of higher difficulty does not imply that your software will pass all datasets of average or even lower difficulty. Similarly, producing correct results for all datasets in this collection does not imply that your software will do the same for your particular dataset. It will, however, provide some degree of assurance, in the sense that your package provides correct results for datasets known to yield incorrect results for some software. The Statistical Reference Datasets is also supported by the Standard Reference Data Program.

These test data files were used to debug the code used in the following study: "Is the Gini Coefficient Enough? A Microeconomic Data Decomposition Study."

List of test data: 1. it14ih.dta - household-level dataset for Italy. 2. it14ip.dta - person-level dataset for Italy. 3. mx16ih.dta - household-level dataset for Mexico. 4. mx16ip.dta - person-level dataset for Mexico. 5. us18ih.dta - household-level dataset for the USA. 6. us18ip.dta - person-level dataset for the USA.

All files can be used for testing/debugging of the following scripts: lis_theil.R, lis_scv.R, lis_theil_functions.R, lis_scv_functions.R.

These datasets were donloaded from the following website. https://www.lisdatacenter.org/resources/self-teaching/.

Data sets consists of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different engine i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data is contaminated with sensor noise. The engine is operating normally at the start of each time series, and develops a fault at some point during the series. In the training set, the fault grows in magnitude until system failure. In the test set, the time series ends some time prior to system failure. The objective of the competition is to predict the number of remaining operational cycles before failure in the test set, i.e., the number of operational cycles after the last cycle that the engine will continue to operate. Also provided a vector of true Remaining Useful Life (RUL) values for the test data. The data are provided as a zip-compressed text file with 26 columns of numbers, separated by spaces. Each row is a snapshot of data taken during a single operational cycle, each column is a different variable. The columns correspond to: 1) unit number 2) time, in cycles 3) operational setting 1 4) operational setting 2 5) operational setting 3 6) sensor measurement 1 7) sensor measurement 2 ... 26) sensor measurement 26 Data Set: FD001 Train trjectories: 100 Test trajectories: 100 Conditions: ONE (Sea Level) Fault Modes: ONE (HPC Degradation) Data Set: FD002 Train trjectories: 260 Test trajectories: 259 Conditions: SIX Fault Modes: ONE (HPC Degradation) Data Set: FD003 Train trjectories: 100 Test trajectories: 100 Conditions: ONE (Sea Level) Fault Modes: TWO (HPC Degradation, Fan Degradation) Data Set: FD004 Train trjectories: 248 Test trajectories: 249 Conditions: SIX Fault Modes: TWO (HPC Degradation, Fan Degradation) Reference: A. Saxena, K. Goebel, D. Simon, and N. Eklund, ‘Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation’, in the Proceedings of the 1st International Conference on Prognostics and Health Management (PHM08), Denver CO, Oct 2008.

RNA sequencing (RNA-seq) is widely used for RNA quantification in the environmental, biological and medical sciences. It enables the description of genome-wide patterns of expression and the identification of regulatory interactions and networks. The aim of RNA-seq data analyses is to achieve rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite variation in levels of noise and inherent biases in sequencing data. This can be especially challenging for datasets in which gene expression differences are subtle, as in the behavioural transcriptomics test dataset from D. melanogaster that we used here. We investigated the power of existing approaches for quality checking mRNA-seq data and explored additional, quantitative quality checks. To accommodate nested, multi-level experimental designs, we incorporated sample layout into our analyses. We employed a subsampling without replacement-based normalization and an identification of DE that accounted for the hierarchy and amplitude of effect sizes within samples, then evaluated the resulting differential expression call in comparison to existing approaches. In a final step to test for broader applicability, we applied our approaches to a published set of H. sapiens mRNA-seq samples, The dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. The proposed approaches have the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments.

Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student’s t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.

BackgroundRecent studies suggest a role for hyperlipidemia in the development of diabetes. The aim of this study is to analyze the relationship between variations of total cholesterol (TC) levels and the risk for type 2 diabetes development from a Korean nationwide population-based database.Materials and methodsWe examined the General Health Check-up sub-dataset of the Korean National Health Insurance Service (NHIS) of 2,827,950 participants who had at least three health check-ups between 2002 and 2007, and were not reported to have diabetes during that time. The variations of TC levels between the examinations were calculated as follows: . The examinees were divided into 10 groups according to TC variation, and the hazard ratio for diabetes development from 2007 to 2013, were analyzed.ResultsDuring the follow-up period, 3.4% of the participants had developed diabetes. The hazard ratio (HR) for diabetes development relative to the overall risk in the whole study population started to be higher than 1.0 from eighth decile of TC variation. The highest decile group showed an increased HR for diabetes development after adjustment for confounding variables (1.139; 95% confidence interval 1.116~1.163). These results were similar regardless of the use of anti-hyperlipidemic medication and baseline TC levels.ConclusionsThe participants with a large variation in TC levels showed an increased risk for diabetes development, independent of the use of anti-hyperlipidemic medications. These results suggest a relationship between fluctuations in lipid levels and the development of type 2 diabetes.

Scenario description:

Platoon formation, starting from different locations, with live traffic light data included in planner. - Enabled live traffic light data included in planner - Not using the Android app - Starting at different locations

Session description:

Platoon formation variation testing

Datasets descriptions:

AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction: Data extracted from the CAN of the vehicle

This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle

AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors

This dataset contains information about detected object, with absolute coordinates

AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors

This dataset contains information about detected object, with relative coordinates

AUTOPILOT_BrainPort_Platooning_IotVehicleMessage: Data sent between all devices, vehicles and services

Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.

AUTOPILOT_BrainPort_Platooning_PlatoonFormation: Data sent from PlatoonService to vehicle

This dataset contains information about the route and speed for a specific vehicle for forming a platoon

AUTOPILOT_BrainPort_Platooning_PlatooningAction: Data logged by vehicle

This dataset contains information about the current status of the platooning

AUTOPILOT_BrainPort_Platooning_PlatooningEvent: Data logged by vehicle

This dataset contains information about the identifiers used for each specific platooning event

AUTOPILOT_BrainPort_Platooning_PlatoonStatus: Data sent by vehicle to PlatoonService

This dataset contains information about the current status of the platooning

AUTOPILOT_BrainPort_Platooning_PositioningSystem: Data from GPS on the vehicle

This dataset contains speed, longitude, latitude, heading from the GPS

AUTOPILOT_BrainPort_Platooning_PositioningSystemResample: Data from GPS on the vehicle

This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds

AUTOPILOT_BrainPort_Platooning_PSInfo: Data sent by PlatoonService to the vehicle

This dataset contains speed and route information for the vehicle to create a platoon

AUTOPILOT_BrainPort_Platooning_Target: Data from sensors on the vehicle

Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor

AUTOPILOT_BrainPort_Platooning_Vehicle: Data from the CAN and sensors about the state of the vehicle

This dataset contains a.o temperature and battery state of the vehicles

AUTOPILOT_BrainPort_Platooning_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle

This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

AUTOPILOT_BrainPort_Platooning_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle

This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

About this dataset

Context

The dataset presents median household incomes for various household sizes in Pine Level, NC, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Pine Level did not include 7-person households. Across the different household sizes in Pine Level the mean income is $73,752, and the standard deviation is $26,410. The coefficient of variation (CV) is 35.81%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $31,076. It then further increased to $88,500 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/pine-level-nc-median-household-income-by-household-size.jpeg" alt="Pine Level, NC median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 Pine Level median household income. You can refer the same here

To determine inundation patterns and calculate site-specific tidal datums, we deployed water level data loggers (Model 3001, Solinst Canada Ltd., Georgetown, Ontario, Canada and Model U-20-001-01-Ti, Onset Computer Corp., Bourne, MA, USA) at all sites over the study period. Each site had one or two loggers (n = 16). We placed loggers at the mouth and upper reaches of second-order tidal channels to capture high tides and determine seasonal inundation patterns. Water loggers collected water level readings every six minutes starting on the date of deployment and continuing to the present. We used data from the lowest elevation logger at each site to develop local hydrographs and inundation rates. We surveyed loggers with RTK GPS at the time of deployment and at each data download that occurred quarterly, to correct for any vertical movement. We corrected all raw water level data with local time series of barometric pressure. For Solinst loggers, we deployed independent barometric loggers (Model 3001, Solinst Canada Ltd., Georgetown, Ontario, Canada); for Hobo water level loggers, we used barometric pressure from local airports (distance less than 10 miles). To determine tidal channel salinities, we deployed one conductivity logger at each site next to the lower elevation water level logger (Odyssey conductivity/temperature logger, Dataflow Systems Pty Limited, Christchurch, New Zealand). We converted specific conductance values obtained with the Odyssey loggers to practical salinity units using the equation from UNESCO (1983). We used water level data to estimate local tidal datums for all sites using procedures outlined in the NOAA Tidal Datums Handbook (NOAA 2003). We only calculated local MHW and MHHW because the loggers were positioned in the intertidal, which is relatively high in the tidal frame, and therefore did not capture MLW or MLLW and could not be used to compute these lower datums. We estimated mean tide level (MTL) for each site by using NOAA’s VDATUM 3.4 software (vdatum.noaa.gov), except at Bandon where we used MTL directly from historic NOAA data. Many results in this report are reported relative to local MHHW calculated from local water data. Water level loggers deployed within marsh channels recorded variation in water levels and salinity throughout the study duration. Loggers often did not capture lower portions of the tidal curve because of their location in tidal marsh channels which frequently drain at lower tides. From peak water levels, we calculated site-specific tidal datums (MHW and MHHW), and information on the highest observed water level (HOWL) during the time series. Our site specific tidal datum calculations generally closely matched tidal datums computed at nearby NOAA stations (tidesandcurrents.noaa.gov). Differences likely reflect site-specific tidal and bathymetric conditions in local estuarine hydrology. We collected salinity data at all sites, however, due to equipment recalls and failure we do not have salinity data for the duration of the study. We report weekly maximum salinities since many of our salinity loggers were not submerged during the entire tidal cycle at all sites, except for Grays Harbor due to recalled loggers and loggers being washed away during storm events. We observed a high level of variation in salinity between and within sites. Siletz experienced the greatest variation in salinity during the study period, ranging from 0.8 to 32 ppt. Willapa was the freshest system, ranging from 12-15 ppt and had very little temporal variation. The largest variation in salinity at most sites occurred from September through December. All sites had salinity below 35 ppt throughout most of the year; however the highest salinities were measured in August. See appendices for detailed site specific results.

Stable isotope ratios and Hg concentrations for various fish and invertebrates that are common prey of Thick-billed murres from Coats Island, Canada.

Additional file 1. QTL detected using the whole dataset to determine the genetic architecture of egg production and egg quality traits. This file gives the position of all the QTL detected using the whole dataset, with the top SNP corresponding to the SNP with the highest effect in the QTL region. The QTL is defined by the first (left) and last (right) SNPs that are 1 % significant at the chromosome level, respectively. Var (%) is the percentage of variance explained by the top SNP in the analysis with the whole dataset. Var LE(%) is the percentage of variance explained by the top SNP in the analysis with data for the low-energy diet only. Var HE(%) is the percentage of variance explained by the top SNP in the analysis with data for the high-energy diet only. Var 50(%) is the percentage of variance explained by the top SNP in the analysis with data for egg collection at 50 weeks only. Var 70(%) is the percentage of variance explained by the top SNP in the analysis with data for egg collection at 70 weeks only. Z Diet is the Z test statistics used to compare the two estimates calculated from the data for LE and HE diets. Z Age is the Z test statistics used to compare the two estimates calculated from the data for egg collection at 50 and 70 weeks of age. The difference was significant when P

Breitburg et al field and laboratory data - effects of hypoxia on P marinus infections in oystersdata used for analysis of field and laboratory experimentsS1 Data file.xls

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demographic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor characteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

Harmonized Data: - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets. - The harmonization process started with cleaning all raw data files received from the Statistical Office. - Cleaned data files were then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process was run on the data. - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format.

Key Table Information.Table Title.Selected Sectors: Industry by Products for the U.S. and States: 2022.Table ID.ECNNAPCSIND2022.EC2200NAPCSINDPRD.Survey/Program.Economic Census.Year.2022.Dataset.ECN Multi-Sector Statistics Product Statistics.Source.U.S. Census Bureau, 2022 Economic Census.Release Date.2025-05-29.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of establishmentsQuantity produced for the NAPCS collection code (sectors 21 and 31-33 only, units defined by Unit of Measurement column)Quantity shipped for the NAPCS collection code (sectors 21 and 31-33 only, units defined by Unit of Measurement column)Sales, value of shipments, or revenue of NAPCS collection code ($1,000)NAPCS collection code sales, value of shipments, or revenue as % of industry sales, value of shipments, or revenue (%)NAPCS collection code sales, value of shipments, or revenue as % of total sales, value of shipments, or revenue of establishments with the NAPCS collection code (%)Number of establishments with NAPCS collection code as % of industry establishments (%)Coefficient of variation for number of establishments (%)Coefficient of variation for quantity produced for the NAPCS collection code (%)Coefficient of variation for quantity shipped for the NAPCS collection code (%)Coefficient of variation for NAPCS collection code sales, value of shipments, or revenue (%)Standard error of NAPCS collection code sales, value of shipments, or revenue as % of industry sales, value of shipments, or revenue (%)Standard error of NAPCS collection code sales, value of shipments, or revenue as % of total sales, value of shipments, or revenue of establishments with the NAPCS collection code (%)Standard error of number of establishments with NAPCS collection code as % of industry establishments (%)Range indicating imputed percentage of total NAPCS collection code sales, value of shipments, or revenueDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. level for all sectors and at the U.S. and state levels for sectors 44-45, 61, 62, 71, 72, and 81. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels for all sectors except Agriculture and for selected 7- and 8-digit 2022 NAICS-based code levels for various sectors. For information about NAICS, see Economic Census Code Lists..Business Characteristics.For Wholesale Trade (42), data are presented by Type of Operation (All establishments; Merchant Wholesalers, except Manufacturers’ Sales Branches and Offices; and Manufacturers’ Sales Branches and Offices).For selected Services sectors, data are presented by Tax Status (All establishments, Establishments subject to federal income tax, and Establishments exempt from federal income tax)..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For some data on this table, estimates come only from the establishments selected into the sample. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review ...

Boosted top tagging is an essential binary classification task for experiments at the Large Hadron Collider (LHC) to measure the properties of the top quark. The ATLAS Top Tagging Open Data Set is a publicly available dataset for the development of Machine Learning (ML) based boosted top tagging algorithms. The dataset consists of a nominal piece used for the training and evaluation of algorithms, and a systematic piece used for estimating the size of systematic uncertainties produced by an algorithm. The nominal data are is split into two orthogonal sets, named train and test. The systematic varied data is split into many more pieces that should only be used for evaluation in most cases. Both nominal sets are composed of equal parts signal (jets initiated by a boosted top quark) and background (jets initiated by light quarks or gluons).

A brief overview of these datasets is as follows. For more detailed information see arxiv:2047.20127.

• train_nominal - 92,820,427 jets, equal parts signal and background
• test_nominal - 10,306,813 jets, equal parts signal and background
• esup - 10,032,472 jets with the cluster energy scale up systematic variation active, equal parts signal and background
• esdown - 10,032,472 jets with the cluster energy scale down systematic variation active, equal parts signal and background
• cer - 10,040,653 jets with the cluster energy resolution systematic variation active, equal parts signal and background
• cpos - 10,032,472 jets with the cluster energy position systematic variation active, equal parts signal and background
• teg - 7,421,204 jets with the track efficiency global systematic variation active, 30% signal jets
• tej - 7,017,046 jets with the track efficiency in jets systematic variation active, 32% signal jets
• tfl - 5,907,310 jets with the track fake rate loose systematic variation active, 18% signal jets
• tfj - 6,977,371 jets with the track fake rate in jets systematic variation active, 32% signal jets
• bias - 10,011,330 jets with the track bias systematic variation active, 52% signal jets
• ttbar_pythia - 193,792 jets from Pythia simulated events containing Standard Model top-anti top quark pair production, all signal jets
• ttbar_herwig - 180,811 jets from Herwig simulated events containing Standard Model top-anti top quark pair production, all signal jets
• cluster - 5,000,004 jets simulated using the Sherpa cluster based hadronization model, all background jets
• string - 5,000,001 jets simulated using the Lund string based hadronization model, all background jets
• angular - 4,900,000 jets simulated using the Herwig angular ordered parton shower model, all background jets
• dipole - 4,900,000 jets simulated using the Herwig dipole parton shower model, all background jets

For each jet, the datasets contain:

• The four vectors of constituent particles
• 15 high level summary quantities evaluated on the jet
• The four vector of the whole jet
• A training weight (nominal only)
• PYTHIA shower weights (nominal only)
• A signal (1) vs background (0) label

There are two rules for using this data set: the contribution to a loss function from any jet should always be weighted by the training weight, and any performance claim is incomplete without an estimate of the systematic uncertainties via the method illustrated in this repository. The ideal model shows high performance but also small systematic uncertainties.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Lake View, AR, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 6,394, while the mean income for the highest quintile (20% of households with the highest income) is 84,535. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 105,681, which is 125.01% higher compared to the highest quintile, and 1652.82% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Lake View median household income. You can refer the same here

Scripts for analyses and plotsContent: 'admixtureplot.R' to plot admixture analyses, 'maices_estimatingpopgenstats_wrapall.R' R code for estimating He, Ho, and Fst, 'maices_PCAs_plots.html' and 'maices_PCAs_plots.Rmd' show a Rmarkdown document used to plot the PCA, NJ trees and maps. 'runadmixture.sh' code used to run admixture. These scripts need the data and metada available at the DataandMetadata.zip section of this repositorybin.zipAdmixture outputContains the log, .Q, .P and se files of the admixture analyses. For command line used and code for plotting see the Scripts section of this repo.admixture.zipData and Meta'data/SNPs' contains SNP data provided in plink and gds format along with a README of how each file was produced. 'data/spatial' contains the shapefiles used for the maps. Please uncompress each one before use. 'meta/maizteocintle_SNP50k_meta_extended.txt' is a text file containing sampling locality, lat, long, and other metadata of each of the samples. Columns correspon...

While there is strong evidence of diversity effects on temporal variability of productivity, whether this mechanism extends to variability across space remains elusive. Here, we present data from Nutrien Network (www.nutnet.org) that were used to determine the relationship between diferent scales of plant diversity and spatial variability of productivity in 83 grasslands worldwide, and to quantify the effect of experimentally increased spatial heterogeneity in environmental conditions on this relationship. There are two data sets, one for the pre-treatment (observational_data.csv) data, and other for the experimentally increased heterogeneity (increased heterogeneity.csv). In these data sets, study sites contained at least three replicates that originated from blocks each composed of ten 5 m × 5 m plots. In addition, pre-treatment data has a subset of sites in where soil conditions where measured (observational_data_soil.csv) and a version in which data for each site are sumarized and site-level climatic variables obtained from WorldClim (www.worldclim.org) are added. (observational_data_site_climate.csv). If you need any clarification or further information, please contact us.

During microsatellite marker development, researchers must choose from a pool of possible primer pairs to further test in their species of interest. In many cases, the goal is maximizing detectable levels of genetic variation. To guide researchers and determine which markers are associated with higher levels of genetic variation, we conducted a literature review based on 6782 genomic microsatellite markers published from 1997–2012. We examined relationships between heterozygosity (He or Ho) or allele number (A) with the following marker characteristics: repeat type, motif length, motif region, repeat frequency, and microsatellite size. Variation across taxonomic groups was also analyzed. There were significant differences between imperfect and perfect repeat types in A and He. Dinucleotide motifs exhibited significantly higher A, He, and Ho than most other motifs. Repeat frequency and motif region were positively correlated with A, He, and Ho, but correlations with microsatellite size were minimal. Higher taxonomic groups were disproportionately represented in the literature and showed little consistency. In conclusion, researchers should carefully consider marker characteristics so they can be tailored to the desired application. If researchers aim to target high genetic variation, dinucleotide motif lengths with large repeat frequencies may be best. Plant Microsatellite DatabaseThis database contains plant microsatellite markers (Simple Sequence Repeats; SSRs) for the larger part of the last two decades. The dataset was compiled in an Excel 2007 spreadsheet from the Molecular Ecology Resources online database (where authors are required to submit primer information as a condition of publication) at http://tomato.bio.trinity.edu or were manually compiled from published articles in Molecular Ecology, Molecular Ecology Notes, Molecular Ecology Resources, and American Journal of Botany (AJB), spanning in total from 1997 to 2012. The articles were found by screening the Scopus (Elsevier) search engine. Please see the publication for more specifics on how the dataset was compiled and analyzed. Null values are represented with a period (‘.’). We have included a ReadMe file to describe column headings.Microsatellite_Database.csvExcluded entriesThis file contains entries that were purposefully removed from our analyzed dataset. Please see the publication for more details. The ReadMe file is the same as that associated with the Plant Microsatellite Database file and contains column heading descriptions.Excluded_entries.csv