About this dataset

Context

The dataset tabulates the population of Main township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Main township. The dataset can be utilized to understand the population distribution of Main township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Main township. 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 Main township.

Key observations

Largest age group (population): Male # 10-14 years (92) | Female # 15-19 years (122). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Main township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Main township is shown in the following column.
• Population (Female): The female population in the Main township 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 Main township 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 Main township Population by Gender. You can refer the same here

The Mega Star Distribution Centre Dataset is a comprehensive simulation dataset designed to provide valuable insights and hands-on experience in the field of warehousing and logistics. Created by leveraging extensive knowledge in inventory management, this dataset aims to assist individuals and organizations in understanding the fundamental tasks involved in efficiently managing a large-scale distribution centre.

Overview: The Mega Star Distribution Centre is a fictitious facility, encompassing an impressive area of over 50,000 square meters and containing more than 60,000 distinct locations for storing various products. The dataset comprises four primary components, each serving a unique purpose in offering a holistic view of the distribution centre's operations:

Product List: The "Product List" contains essential information about all the products housed within the distribution centre. This dataset provides crucial details, such as product names, descriptions, unique identifiers, and other relevant attributes necessary for managing inventory effectively.

Warehouse Stocks: The "Warehouse Stocks" section of the dataset offers a snapshot of the current stock levels within the Mega Star Distribution Centre. It provides a comprehensive inventory report, listing the available quantities of each product and their respective locations within the facility. Understanding the stock levels and their distribution across various locations is essential for optimizing storage space and managing inventory replenishment efficiently.

Receiving Records: The "Receiving" records track the activities related to receiving new stock into the distribution centre over five days. This dataset captures information about incoming shipments, including the products received, their quantities, origins, and the personnel responsible for handling the receiving tasks. Analyzing this data can provide valuable insights into the efficiency of the receiving process and identify any potential bottlenecks.

Picking Records: The "Picking" records document the tasks involved in fulfilling customer orders from the distribution centre during the same five-day period. This dataset includes details on the products picked, the quantities involved, the locations from which the items were retrieved, and the personnel responsible for executing the picking tasks. Analyzing this data can help optimize the order fulfilment process, minimize picking errors, and improve overall customer satisfaction.

Benefits and Applications: The Mega Star Distribution Centre Dataset is a valuable resource for anyone seeking to gain practical experience and insights into the complex world of inventory management. It offers a safe and controlled environment for honing skills, testing strategies, and understanding the challenges faced in real-world distribution centres. Some key benefits and applications include:

Training and Education: The dataset can serve as an educational tool for students, professionals, and researchers in logistics, supply chain management, and related fields, allowing them to explore and experiment with inventory management concepts.

Process Optimization: By analysing the dataset, supply chain managers and warehouse operators can identify areas for process improvement, streamline operations, and enhance overall efficiency.

Decision-making Support: The data can aid in making informed decisions regarding inventory levels, replenishment strategies, and resource allocation, leading to better inventory control and cost savings.

Performance Evaluation: The dataset enables the evaluation of key performance indicators (KPIs) related to warehousing and logistics, facilitating a data-driven approach to assessing the distribution centre's effectiveness.

Conclusion: The Mega Star Distribution Centre Dataset offers a rich and diverse collection of data, providing an immersive experience in managing inventory in a large-scale distribution centre. Whether you are a student, a logistics professional, or a researcher, this dataset presents a unique opportunity to gain practical insights and refine your skills in inventory management. With its simulated yet realistic scenarios, the dataset aims to contribute to the continuous improvement and advancement of warehousing and logistics practices.

Wikipedia Movie Data

Exploring Production and Distribution Trends Across Four Decades

By Michael Tauberg [source]

About this dataset

This comprehensive dataset spans a substantial sampling of movies from the last five decades, giving insight into the financial and creative successes of Hollywood film productions. Containing various production details such as director, actors, editing team, budget, and overall gross revenue, it can be used to understand how different elements come together to make a movie successful. With information covering all aspects of movie-making – from country of origin to soundtrack composer – this collection offers an unparalleled opportunity for a data-driven dive into the world of cinematic storytelling

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The columns are important factors to analyze the data in depth – they range from general information such as year, name and language of movie to more specific info such as directors and editors of movie production teams. A good first step is to get an understanding of what kind of data exists and getting familiar with different columns.

Good luck exploring!

Research Ideas

• Analyzing the correlations between budget, gross revenue, and number of awards or nominations won by a movie. Movie-makers and studios can use this data to understand what factors have an impact on the success of a movie and make better creative decisions accordingly.
• Studying the trend of movies from different countries over time to understand how popular genres are changing over time across regions and countries; this data could be used by international film producers to identify potential opportunities for co-productions with other countries or regions.
• Identifying unique topics for films (based on writers, directors, music etc) that hadn’t been explored in previous decades - studios can use this data to find unique stories or ideas for new films that often succeed commercially due to its novelty factor with audiences

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: movies_1970_2018.csv | Column name | Description | |:-------------------|:----------------------------------------------------------| | year | Year the movie was released. (Integer) | | wiki_ref | Reference to the Wikipedia page for the movie. (String) | | wiki_query | Query used to search for the movie on Wikipedia. (String) | | producer | Name of the producer of the movie. (String) | | distributor | Name of the distributor of the movie. (String) | | name | Name of the movie. (String) | | country | Country of origin of the movie. (String) | | director | Name of the director of the movie. (String) | | cinematography | Name of the cinematographer of the movie. (String) | | editing | Name of the editor of the movie. (String) | | studio | Name of the studio that produced the movie. (String) | | budget | Budget of the movie. (Integer) | | gross | Gross box office receipts of the movie. (Integer) | | runtime | Length of the movie in minutes. (Integer) | | music | Name of the composer of the movie's soundtrack. (String) | | writer | Name of the writer of the movie. (String) | | starring | Names of the actors in the movie. (String) | | language | Language of the movie. (String) |

Acknowledgements

If you use this dataset in your research, p...

Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend’s big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species’ distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species’ responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions. Methods Study area and survey data The study area covers the U.S. state of California, which has steep environmental gradients that support an array of species (Dobrowski et al. 2011). Because California is ecologically diverse, with regions ranging from forested mountain ranges to deserts, we examined local environmental needs by modeling at both the state-wide and ecoregion scale, using U.S. Environmental Protection Agency (EPA) Level III ecoregion designations and there are thirteen Level III ecoregions in California (Table S1.1) (Griffith et al. 2016). Species occurrence data used in this study were from a statewide survey of C. townsendii in California conducted by Harris et al. (2019). Briefly, methods included field surveys from 2014-2017 following a modified bat survey protocol to create a stratified random sampling scheme. Corynorhinus townsendii presence at roost sites was based on visual bat sightings. From these survey efforts, we have visual occurrence data for 65 maternity roosts, 82 hibernation roosts (hibernacula), and 91 active-season non-maternity roosts (transition roosts) for a total of 238 occurrence records (Figure 1, Table S1.1). Ecogeographical factors We downloaded climatic variables from WorldClim 2.0 bioclimatic variables (Fick & Hijmans, 2017) at a resolution of 5 arcmin for broad-scale analysis and 30 arcsec for our ecoregion-specific analyses. To calculate elevation and slope, we used a digital elevation model (USGS 2022) in ArcGIS 10.8.1 (ESRI, 2006). The chosen set of environmental variables reflects knowledge on climatic conditions and habitat relevant to bat physiology, phenology, and life history (Rebelo et al. 2010, Razgour et al. 2011, Loeb and Winters 2013, Razgour 2015, Ancillotto et al. 2016). To trim the global environmental variables to the same extent (the state of California), we used the R package “raster” (Hijmans et al. 2022). We performed a correlation analysis on the raster layers using the “layerStats” function and removed variables with a Pearson’s coefficient > 0.7 (see Table 1 for final model variables). For future climate conditions, we selected three general circulation models (GCMs) based on previous species distribution models of temperate bat species (Razgour et al. 2019) [Hadley Centre Global Environment Model version 2 Earth Systems model (HadGEM3-GC31_LL; Webb, 2019), Institut Pierre-Simon Laplace Coupled Model 6th Assessment Low Resolution (IPSL-CM6A-LR; Boucher et al., 2018), and Max Planck Institute for Meteorology Earth System Model Low Resolution (MPI-ESM1-2-LR; Brovkin et al., 2019)] and two contrasting greenhouse concentration trajectories (Shared Socio-economic Pathways (SSPs): a steady decline pathway with CO2 concentrations of 360 ppmv (SSP1-2.6) and an increasing pathway with CO2 reaching around 2,000 ppmv (SSP5-8.5) (IPCC6). We modeled distribution for present conditions future (2061-2080) time periods. Because one aim of our study was to determine the consequences of changing climate, we changed only the climatic data when projecting future distributions, while keeping the other variables constant over time (elevation, slope). Species distribution modeling We generated distribution maps for total occurrences (maternity + hibernacula + transition, hereafter defined as “combined models”), maternity colonies , hibernacula, and transition roosts. To estimate the present and future habitat suitability for C. townsendii in California, we used the maximum entropy (MaxEnt) algorithm in the “dismo” R package (Hijmans et al. 2021) through the advanced computing resources provided by Texas A&M High Performance Research Computing. We chose MaxEnt to aid in the comparisons of state-wide and ecoregion-specific models as MaxEnt outperforms other approaches when using small datasets (as is the case in our ecoregion-specific models). We created 1,000 background points from random points in the environmental layers and performed a 5-fold cross validation approach, which divided the occurrence records into training (80%) and testing (20%) datasets. We assessed the performance of our models by measuring the area under the receiver operating characteristic curve (AUC; Hanley & McNeil, 1982), where values >0.5 indicate that the model is performing better than random, values 0.5-0.7 indicating poor performance, 0.7-0.9 moderate performance and values of 0.9-1 excellent performance (BCCVL, Hallgren et al., 2016). We also measured the maximum true skill statistic (TSS; Allouche, Tsoar, & Kadmon, 2006) to assess model performance. The maxTSS ranges from -1 to +1:values <0.4 indicate a model that performs no better than random, 0.4-0.55 indicates poor performance, (0.55-0.7) moderate performance, (0.7-0.85) good performance, and values >0.80 indicate excellent performance (Samadi et al. 2022). Final distribution maps were generated using all occurrence records for each region (rather than the training/testing subset), and the models were projected onto present and future climate conditions. Additionally, because the climatic conditions of the different ecoregions of California vary widely, we generated separate models for each ecoregion in an attempt to capture potential local effects of climate change. A general rule in species distribution modeling is that the occurrence points should be 10 times the number of predictors included in the model, meaning that we would need 50 occurrences in each ecoregion. One common way to overcome this limitation is through the ensemble of small models (ESMs) (Breiner et al. 2015., 2018; Virtanen et al. 2018; Scherrer et al. 2019; Song et al. 2019) included in ecospat R package (references). For our ESMs we implemented MaxEnt modeling, and the final ensemble model was created by averaging individual bivariate models by weighted performance (AUC > 0.5). We also used null model significance testing with to evaluate the performance of our ESMs (Raes and Ter Steege 2007). To perform null model testing we compared AUC scores from 100 null models using randomly generated presence locations equal to the number used in the developed distribution model. All ecoregion models outperformed the null expectation (p<0.002). Estimating range shifts For each of the three GCMs and each RCP scenario, we converted the probability distribution map into a binary map (0=unsuitable, 1=suitable) using the threshold that maximizes sensitivity and specificity (Liu et al. 2016). To create the final maps for each SSP scenario, we summed the three binary GCM layers and took a consensus approach, meaning climatically suitable areas were pixels where at least two of the three models predicted species presence (Araújo and New 2007, Piccioli Cappelli et al. 2021). We combined the future binary maps (fmap) and the present binary maps (pmap) following the formula fmap x 2 + pmap (from Huang et al., 2017) to produce maps with values of 0 (areas not suitable), 1 (areas that are suitable in the present but not the future), 2 (areas that are not suitable in the present but suitable in the future), and 3 (areas currently suitable that will remain suitable) using the raster calculator function in QGIS. We then calculated the total area of suitability, area of maintenance, area of expansion, and area of contraction for each binary model using the “BIOMOD_RangeSize” function in R package “biomod2” (Thuiller et al. 2021).

About this dataset

Context

The dataset tabulates the Cedar Key population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Cedar Key. The dataset can be utilized to understand the population distribution of Cedar Key by age. For example, using this dataset, we can identify the largest age group in Cedar Key.

Key observations

The largest age group in Cedar Key, FL was for the group of age 20-24 years with a population of 155 (17.28%), according to the 2021 American Community Survey. At the same time, the smallest age group in Cedar Key, FL was the 5-9 years with a population of 0 (0.00%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Cedar Key is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Cedar Key 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 Cedar Key Population by Age. You can refer the same here

A County Geologic Atlas (CGA) project is a study of a county's geology, and its mineral and ground-water resources. The information collected during the project is used to develop maps, data-base files, and reports. This same information is also produced as digital files. The map information is formatted as geographic information system (GIS) files with associated data bases. The maps and reports are also reproduced as portable document files (PDFs) that can be opened on virtually any computer using the free Acrobat Reader from Adobe.com. All of the digital files for the CGA's can be downloaded from the University of Minnesota Digital Conservancy. The majority of the files can also be viewed and queried through the use of this Story Map.Atlas information is commonly used in planning and environmental protection programs, as an educational resource, and by industries involved in water and mineral resources. It represents a comprehensive, detailed compilation of geologic data and interpretations within a county. The distribution and character of geologic materials determine how and where water enters the earth, and where it is stored in aquifers that can supply our needs. Geologic maps are a key element in delineating those flow paths and in relating land use to water quality. The atlas also provides a framework and terminology to support more detailed, site-specific studies. The records of water wells drilled in the area are an important source of data for constructing the maps and for understanding the distribution and use of ground water in the county. A data base of the information from those wells is one of the atlas products, and it can be queried with the GIS files to yield valuable insights for managing the ground-water resource.The atlas is also useful to non-professionals who simply wish to learn more about the geology of the county. It is a one-stop, comprehensive collection of information in a variety of forms and styles that should be useful to anyone with an interest in earth science or the county.The geologic data and maps are produced and distributed by the Minnesota Geological Survey (MGS) as Part A of an Atlas. The Minnesota Department of Natural Resources follows with an investigation of the quantity, quality, and pollution sensitivity of ground water. Their products are distributed as Part B of the atlas, at a later date. If necessary, a report with additional information that was not possible to include on the limited space of the printed maps is produced by MGS as Part C of, or included as a supplement to, an atlas. The Atlas CD or DVD, which is available online at the Digital Conservancy, includes all the atlas products developed by the Minnesota Geological Survey.

Constituting the bulk of rare-earth elements, lanthanides need to be separated to fully realize their potential as critical materials in many important technologies. The discovery of new ligands for improving rare-earth separations by solvent extraction, the most practical rare-earth separation process, is still largely based on trial and error, a low-throughput and inefficient approach. A predictive model that allows high-throughput screening of ligands is needed to identify suitable ligands to achieve enhanced separation performance. Here, we show that deep neural networks, trained on the available experimental data, can be used to predict accurate distribution coefficients for solvent extraction of lanthanide ions, thereby opening the door to high-throughput screening of ligands for rare-earth separations. One innovative approach that we employed is a combined representation of ligands with both molecular physicochemical descriptors and atomic extended-connectivity fingerprints, which greatly boosts the accuracy of the trained model. More importantly, we synthesized four new ligands and found that the predicted distribution coefficients from our trained machine-learning model match well with the measured values. Therefore, our machine-learning approach paves the way for accelerating the discovery of new ligands for rare-earth separations.

About this dataset

Context

The dataset tabulates the Longboat Key population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Longboat Key. The dataset can be utilized to understand the population distribution of Longboat Key by age. For example, using this dataset, we can identify the largest age group in Longboat Key.

Key observations

The largest age group in Longboat Key, FL was for the group of age 70-74 years with a population of 1,173 (15.75%), according to the 2021 American Community Survey. At the same time, the smallest age group in Longboat Key, FL was the 0-4 years with a population of 8 (0.11%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Longboat Key is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Longboat Key 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 Longboat Key Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Key Colony Beach population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Key Colony Beach. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 65 years and above with a poulation of 424 (56.99% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Key Colony Beach population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Key Colony Beach is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Key Colony Beach is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Key Colony Beach Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Main township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Main township. The dataset can be utilized to understand the population distribution of Main township by age. For example, using this dataset, we can identify the largest age group in Main township.

Key observations

The largest age group in Main Township, Pennsylvania was for the group of age 10 to 14 years years with a population of 167 (11.16%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Main Township, Pennsylvania was the 80 to 84 years years with a population of 14 (0.94%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Main township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Main township 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 Main township Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Longboat Key population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Longboat Key.

Content

The dataset constitues the following two datasets across these two themes

• Longboat Key, FL Population Breakdown by Gender
• Longboat Key, FL Population Breakdown by Gender and Age

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/.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Key Colony Beach by race. It includes the distribution of the Non-Hispanic population of Key Colony Beach across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Key Colony Beach across relevant racial categories.

Key observations

Of the Non-Hispanic population in Key Colony Beach, the largest racial group is White alone with a population of 489 (100% of the total Non-Hispanic population).

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Key Colony Beach
• Population: The population of the racial category (for Non-Hispanic) in the Key Colony Beach is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Key Colony Beach total Non-Hispanic 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 Key Colony Beach Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Cedar Key by race. It includes the population of Cedar Key across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Cedar Key across relevant racial categories.

Key observations

The percent distribution of Cedar Key population by race (across all racial categories recognized by the U.S. Census Bureau): 88.19% are white, 9.15% are Black or African American and 2.66% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Cedar Key
• Population: The population of the racial category (excluding ethnicity) in the Cedar Key is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cedar Key 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 Cedar Key Population by Race & Ethnicity. You can refer the same here