Yahli Helmet No Helmet

## Overview

Yahli Helmet No Helmet is a dataset for object detection tasks - it contains Helmet annotations for 653 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Source Raw Data More Information ::

PlantVill dataset,PlantVillage dataset,https://www.kaggle.com/datasets/mohitsingh1804/plantvillage PlantVillage-AD (Augmented Dataset)

Shuvo Kumar Basak. (2025). Plant Village Augmented Dataset New and Update Tec [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/11077233

The Plant Village Augmented Dataset is an enhanced version of the original PlantVillage dataset, designed to provide a more diverse and comprehensive collection of images for plant disease detection. This augmented dataset includes a variety of image processing techniques, such as edge enhancement, noise addition, and transformations like rotation, flipping, and scaling. It also incorporates adjustments to brightness, contrast, and saturation, helping to simulate real-world conditions and improve model robustness. The dataset contains images of healthy and diseased plant leaves across multiple species, making it ideal for training and evaluating machine learning models for plant health monitoring and disease classification.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F9ad4aea3445f6e43b5a6f5e7981f8e06%2F_results_2_0.png?generation=1742438764723367&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F71166fe9ca1c6a2c33d9d4d1b5cbcac1%2F_results_7_0.png?generation=1742438779439472&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F5fbd37c9a81a927e72eba6b1bed1a315%2F_results_5_0.png?generation=1742438798463325&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F0f151b595e4a5e1d4d89f9a87c03ac63%2F_results_4_0.png?generation=1742438810704589&alt=media" alt="">

Source Raw Data More Information ::

https://www.kaggle.com/datasets/mohitsingh1804/plantvillage PlantVill dataset,PlantVillage dataset,https://www.kaggle.com/datasets/mohitsingh1804/plantvillage PlantVillage-AD (Augmented Dataset)

Shuvo Kumar Basak. (2025). Plant Village Augmented Dataset New and Update Tec [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/11077233

**More Dataset:: ** https://www.kaggle.com/shuvokumarbasak4004/datasets

…………………………………..Note for Researchers Using the dataset………………………………………………………………………

If you use this dataset for your research or academic purposes, please ensure to cite this dataset appropriately. If you have published your research using this dataset, please share a link to your paper. Good Luck.

About this dataset

Context

The dataset tabulates the population of Merced by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Merced across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.64% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Merced is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Merced total population. 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 Merced Population by Race & Ethnicity. You can refer the same here

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This dataset is a comprehensive collection of conversation models, offering insight and challenge for research on dialogue systems and conversation much further than what was ever thought possible. Split into three sets - training, validation, and test - every set contains conversations with corresponding speaker IDs to form a context, as well as columns for utterance index, prompt/topic of the conversation, self-evaluation of the utterance, and assigned tags. With this deluge of information compiled together in one place it is possible to explore the potentiality of conversation topics further past what we ever thought possible. This dataset has untold possibilities just waiting to be explored!

More Datasets For more datasets, click here.

Featured Notebooks 🚨 Your notebook can be here! 🚨! How to use the dataset Getting Started Begin by downloading the dataset from Kaggle at https://www.kaggle.com/rakshitshah/empathicconversationalmodelbenchmark The downloaded folder should contain three CSV files - train.csv, validation.csv, and test.csv . These contain conversations with corresponding speaker IDs, topics, self-evaluations, and tags that can be used to train conversation models or evaluate their performance Each row in each of the three CSV files has 8 columns: index of utterance (utterance_index), context (context), prompt (prompt), utterance (utterance), selfevaluation of utterance (selfevaluation) assigned tags for utterances (tags). Utterances are individual statements made by each speaker in the conversation - speakers are identified by ID’s or names included in respective rows under ‘participants’ column Making Use Of The Dataset Use train set to create Machine Learning models that can generate natural conversations based on context, assign empathetic scores to generated conversation responses based on sentiment analysis etc 2) Use validation set to run tests and make sure model is functioning correctly 3) Evaluate models using test set 4) Using ‘tags’ column label different conversations with appropriate tags such as ‘casual chat’ or ‘career advice', make comparison between standard & ML model etc Research Ideas To develop empathetic open-domain conversation models for use in virtual assistants or chatbots, such as sorting conversations by topics and training models to reply accordingly. Utilizing the self-evaluation from each utterance as a metric to observe changes in language atmospheres within conversations, such as mood shifts and tonality variations. Using the dataset for research purposes that focus on convolutional attention models, LSTMs, seq2seq architectures, Gated Recurrent Units (GRUs), and Transformer Networks to further improve conversation model performance and accuracy

Columns File: validation.csv

Column name Description context The context of the conversation. (String) prompt The prompt or topic for the conversation. (String) utterance The utterance or response from a speaker. (String) selfeval The self-evaluation score assigned to each utterance. (Integer) tags The associated tags that can be used to categorize or label dialogues. (String) File: train.csv

Column name Description context The context of the conversation. (String) prompt The prompt or topic for the conversation. (String) utterance The utterance or response from a speaker. (String) selfeval The self-evaluation score assigned to each utterance. (Integer) tags The associated tags that can be used to categorize or label dialogues. (String) File: test.csv

Column name Description context The context of the conversation. (String) prompt The prompt or topic for the conversation. (String) utterance The utterance or response from a speaker. (String) selfeval The self-evaluation score assigned to each utterance. (Integer) tags The associated tags that can be used to categorize or label dialogues. (String) Acknowledgements If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

License

CC0

Original Data Source: Empathetic Conversational Model Benchmark

Overview

This data contains salaries of University of Vermont (UVM) faculty from 2009 to 2021. We present two datasets. The second dataset is richer because it contains information on faculty departments/colleges; however, it contains less rows due to how we chose to join this data. 1. salaries_without_dept.csv contains all of the data we extracted from the PDFs. The four columns are: Year, Faculty Name, Primary Job Title, and Base Pay. There are 47,479 rows. 2. salaries_final.csv contains the same columns as [1], but also joins with data about the faculty's "Department" and "College" (for a total of six columns). There are only 14,470 rows in this dataset because we removed rows for which we could not identify the Department/College of the faculty.

Data collection

All data is publicly available on the University of Vermont website. I downloaded all PDFs from https://www.uvm.edu/oir/faculty-and-staff. Then I used a Python package (Camelot) to parse the tabular PDFs and used regex matching to ensure data was correctly parsed. I performed some initial cleaning (removed dollar signs from monetary values, etc.). At this stage, I saved the data to salaries_without_dept.csv.

I also wanted to know what department and college each faculty belonged to. I used http://catalogue.uvm.edu/undergraduate/faculty/fulltime (plus Python's lxml package to parse the HTML) to determine "Department" and then manually built an encoding to map "Department" to "College". Note that this link provides faculty information for 2020, thus after joining we end up only with faculty that are still employed as of 2020 (this should be taken into consideration). Secondly, this link does not include UVM administration (and possibly some other personnel) so they are not present in this dataset. Thirdly, there were several different ways names were reported (sometimes even the same person has their name reported differently in different years). We tried joining first on LastName+FirstName and then on LastName+FirstInitial but did not bother using middle name. To handle ambiguity, we removed duplicates (e.g. we removed Martin, Jacob and Martin, Jacob William as they were not distinguishable by our criteria). The joined data is available in salaries_final.csv.

Note: perhaps "College" was not the best naming, since faculty of UVM Libraries and other miscellaneous fields are included.

Data dictionary

The column definitions are self-explanatory, but the "College" abbreviation meanings are unclear to a non-UVM-affiliate. We've included data_dictionary.csv to explain what each "College" abbreviation means. You can use this dictionary to filter out miscellaneous "colleges" (e.g. UVM Libraries) and only include colleges within the undergraduate program (e.g. filter out College of Medicine).

Uses

Despite there only being a few (six) columns, I think this is quite a rich dataset and could also be paired with other UVM data or combined with data from other universities. This dataset is mainly for data analytics and exploratory data analysis (EDA), but perhaps it could also be used for forecasting (however, there's only 12 time values so you'd probably want to make use of "College" or "Primary Job Title"). Interesting EDA questions could be: 1. "Are the faculty in arts & humanities departments being paid less?" This news article -- UVM to eliminate 23 programs in the College of Arts and Sciences -- suggests so. Give a quantitative answer.
2. "Are lecturers declining in quantity and pay?" This news article -- ‘I’m going to miss this:’ Three cut lecturers reflect on time at UVM -- suggests so. Give a quantitative answer. 3. "How does the College of Medicine compare to the undergraduate colleges in terms of number of faculty and pay?" See data_dictionay.csv for which colleges are in the undergraduate program. 4. "How long does it take for a faculty member to become a full professor?" Yes, this is also answerable from the data because Primary Job Title updates when a faculty member is promoted.

Future updates

I do not plan to maintain this dataset. If I get the chance, I may update it with future year salaries.

This archive contains code and data for reproducing the analysis for “Replication Data for Revisiting ‘The Rise and Decline’ in a Population of Peer Production Projects”. Depending on what you hope to do with the data you probabbly do not want to download all of the files. Depending on your computation resources you may not be able to run all stages of the analysis. The code for all stages of the analysis, including typesetting the manuscript and running the analysis, is in code.tar. If you only want to run the final analysis or to play with datasets used in the analysis of the paper, you want intermediate_data.7z or the uncompressed tab and csv files. The data files are created in a four-stage process. The first stage uses the program “wikiq” to parse mediawiki xml dumps and create tsv files that have edit data for each wiki. The second stage generates all.edits.RDS file which combines these tsvs into a dataset of edits from all the wikis. This file is expensive to generate and at 1.5GB is pretty big. The third stage builds smaller intermediate files that contain the analytical variables from these tsv files. The fourth stage uses the intermediate files to generate smaller RDS files that contain the results. Finally, knitr and latex typeset the manuscript. A stage will only run if the outputs from the previous stages do not exist. So if the intermediate files exist they will not be regenerated. Only the final analysis will run. The exception is that stage 4, fitting models and generating plots, always runs. If you only want to replicate from the second stage onward, you want wikiq_tsvs.7z. If you want to replicate everything, you want wikia_mediawiki_xml_dumps.7z.001 wikia_mediawiki_xml_dumps.7z.002, and wikia_mediawiki_xml_dumps.7z.003. These instructions work backwards from building the manuscript using knitr, loading the datasets, running the analysis, to building the intermediate datasets. Building the manuscript using knitr This requires working latex, latexmk, and knitr installations. Depending on your operating system you might install these packages in different ways. On Debian Linux you can run apt install r-cran-knitr latexmk texlive-latex-extra. Alternatively, you can upload the necessary files to a project on Overleaf.com. Download code.tar. This has everything you need to typeset the manuscript. Unpack the tar archive. On a unix system this can be done by running tar xf code.tar. Navigate to code/paper_source. Install R dependencies. In R. run install.packages(c("data.table","scales","ggplot2","lubridate","texreg")) On a unix system you should be able to run make to build the manuscript generalizable_wiki.pdf. Otherwise you should try uploading all of the files (including the tables, figure, and knitr folders) to a new project on Overleaf.com. Loading intermediate datasets The intermediate datasets are found in the intermediate_data.7z archive. They can be extracted on a unix system using the command 7z x intermediate_data.7z. The files are 95MB uncompressed. These are RDS (R data set) files and can be loaded in R using the readRDS. For example newcomer.ds <- readRDS("newcomers.RDS"). If you wish to work with these datasets using a tool other than R, you might prefer to work with the .tab files. Running the analysis Fitting the models may not work on machines with less than 32GB of RAM. If you have trouble, you may find the functions in lib-01-sample-datasets.R useful to create stratified samples of data for fitting models. See line 89 of 02_model_newcomer_survival.R for an example. Download code.tar and intermediate_data.7z to your working folder and extract both archives. On a unix system this can be done with the command tar xf code.tar && 7z x intermediate_data.7z. Install R dependencies. install.packages(c("data.table","ggplot2","urltools","texreg","optimx","lme4","bootstrap","scales","effects","lubridate","devtools","roxygen2")). On a unix system you can simply run regen.all.sh to fit the models, build the plots and create the RDS files. Generating datasets Building the intermediate files The intermediate files are generated from all.edits.RDS. This process requires about 20GB of memory. Download all.edits.RDS, userroles_data.7z,selected.wikis.csv, and code.tar. Unpack code.tar and userroles_data.7z. On a unix system this can be done using tar xf code.tar && 7z x userroles_data.7z. Install R dependencies. In R run install.packages(c("data.table","ggplot2","urltools","texreg","optimx","lme4","bootstrap","scales","effects","lubridate","devtools","roxygen2")). Run 01_build_datasets.R. Building all.edits.RDS The intermediate RDS files used in the analysis are created from all.edits.RDS. To replicate building all.edits.RDS, you only need to run 01_build_datasets.R when the intermediate RDS files and all.edits.RDS files do not exist in the working directory. all.edits.RDS is generated from the tsv files generated by wikiq. This may take several hours. By default building the dataset will...

Not A Inv

## Overview

Not A Inv is a dataset for object detection tasks - it contains `Client`, `Company`, `Total`, `Invoice`, `Table` annotations for 622 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal Buffers (this dataset)Without Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal BuffersCity and County AbbreviationsUnincorporated Areas (Coming Soon)Census Designated PlacesCartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except OFFSHORE and AREA_SQMI to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.CENSUS_GEOID: numeric geographic identifiers from the US Census BureauCENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or countyCENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.Boundary AccuracyCounty boundaries were originally derived from a 1:24,000 accuracy dataset, with improvements made in some places to boundary alignments based on research into historical records and boundary changes as CDTFA learns of them. City boundary data are derived from pre-GIS tax maps, digitized at BOE and CDTFA, with adjustments made directly in GIS for new annexations, detachments, and corrections. Boundary accuracy within the dataset varies. While CDTFA strives to correctly include or exclude parcels from jurisdictions for accurate tax assessment, this dataset does not guarantee that a parcel is placed in the correct jurisdiction. When a parcel is in the correct jurisdiction, this dataset cannot guarantee accurate placement of boundary lines within or between parcels or rights of way. This dataset also provides no information on parcel boundaries. For exact jurisdictional or parcel boundary locations, please consult the county assessor's office and a licensed surveyor.CDTFA's data is used as the best available source because BOE and CDTFA receive information about changes in jurisdictions which otherwise need to be collected independently by an agency or company to compile into usable map boundaries. CDTFA maintains the best available statewide boundary information.CDTFA's source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include

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Defect Detection In Screws

## Overview

Defect Detection In Screws is a dataset for classification tasks - it contains Screw annotations for 480 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

About this dataset

Context

The dataset tabulates the population of Saltaire by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Saltaire across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 53.85% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Saltaire is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Saltaire total population. 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 Saltaire Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Levasy by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Levasy across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 57.81% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Levasy is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Levasy total population. 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 Levasy Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Hopkinsville by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Hopkinsville across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.8% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Hopkinsville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Hopkinsville total population. 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 Hopkinsville Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Kirbyville by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Kirbyville across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 55.6% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Kirbyville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Kirbyville total population. 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 Kirbyville Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Lake Mary by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Lake Mary across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 55.21% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Lake Mary is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Lake Mary total population. 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 Lake Mary Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Newville by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Newville across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 53.66% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Newville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Newville total population. 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 Newville Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Brown County by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Brown County across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.07% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Brown County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Brown County total population. 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 Brown County Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of National City by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for National City. The dataset can be utilized to understand the population distribution of National City by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in National City. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for National City.

Key observations

Largest age group (population): Male # 30-34 years (2,482) | Female # 10-14 years (2,264). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the National City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the National City is shown in the following column.
• Population (Female): The female population in the National City is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in National City for each age group.

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 National City Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Franklin County by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Franklin County across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.6% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Franklin County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Franklin County total population. 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 Franklin County Population by Race & Ethnicity. You can refer the same here