Distribution of doses of a volatile organic compound from inhalation of one consumer product, other near -field sources, far-field sources, and aggregate (total) exposure. In this instance, far-field scenarios account for several orders of magnitude of less of the predicted dose compared to near-field scenarios. This dataset is associated with the following publication: Vallero, D. Air Pollution Monitoring Changes to Accompany the Transition from a Control to a Systems Focus. Sustainability. MDPI AG, Basel, SWITZERLAND, 8(12): 1216, (2016).

About this dataset

Context

The dataset tabulates the Excel 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 Excel. The dataset can be utilized to understand the population distribution of Excel by age. For example, using this dataset, we can identify the largest age group in Excel.

Key observations

The largest age group in Excel, AL was for the group of age 5 to 9 years years with a population of 77 (15.28%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Excel, AL was the 85 years and over years with a population of 2 (0.40%). 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

Variables / Data Columns

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

his dataset contains transactional grocery data from BlinkIT, a grocery delivery platform. It includes product names, categories, prices, units sold, and potentially order or date-based features (depending on the columns in the file

About this dataset

Context

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

Key observations

In 2023, the population of Excel township was 300, a 0.99% decrease year-by-year from 2022. Previously, in 2022, Excel township population was 303, a decline of 0.98% compared to a population of 306 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Excel township increased by 17. In this period, the peak population was 308 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

🛍️ Product Sales Dataset (2023–2024)

📌 Overview

This dataset contains 200,000 synthetic sales records simulating real-world product transactions across different U.S. regions. It is designed for data analysis, business intelligence, and machine learning projects, especially in the areas of sales forecasting, customer segmentation, profitability analysis, and regional trend evaluation.

The dataset provides detailed transactional data including customer names, product categories, pricing, and revenue details, making it highly versatile for both beginners and advanced analysts.

📂 Dataset Structure

• Rows: 200,000
• Columns: 14

Features

1. Order_ID – Unique identifier for each order
2. Order_Date – Date of transaction
3. Customer_Name – Name of the customer
4. City – City of the customer
5. State – State of the customer
6. Region – Region (East, West, South, Centre)
7. Country – Country (United States)
8. Category – Broad product category (e.g., Accessories, Clothing & Apparel)
9. Sub_Category – Subdivision of category (e.g., Sportswear, Bags)
10. Product_Name – Product description
11. Quantity – Units purchased
12. Unit_Price – Price per unit (USD)
13. Revenue – Total sales amount (Quantity × Unit Price)
14. Profit – Net profit earned from the transaction

🎯 Potential Use Cases

• Sales Analysis: Track revenue, profit, and performance by product, category, or region.
• Customer Analytics: Identify top customers, purchasing frequency, and loyalty patterns.
• Profitability Insights: Compare profit margins across categories and sub-categories.
• Time-Series Analysis: Study seasonal demand and forecast future sales.
• Visualization Projects: Build dashboards in Power BI, Tableau, or Excel.
• Machine Learning: Train models for demand prediction, price optimization, or segmentation.

📊 Example Insights

• Which region generates the highest revenue?
• What are the top 10 most profitable products?
• Are some product categories more popular in certain regions?
• Which customers contribute the most to total revenue?

🏷️ Tags

business · sales · profitability · forecasting · customer analysis · retail

📜 License

This dataset is synthetic and created for educational and analytical purposes. You are free to use, modify, and share it under the CC BY 4.0 License.

🙌 Acknowledgments

This dataset was generated to provide a realistic foundation for learning and practicing Data Analytics, Power BI, Tableau, Python, and Excel projects.

The dataset includes customer id,Martial Status,Gender,Income,Children,Education,Occupation,Home Owner,Cars,Commute Distance,Region,Age,Purchased Bike. Blog

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Data organization for the figures in the document: Figure 3A LineOutWithSun_SSAzi_135to225_green_Correct_ROI5_INFO.xls Figure 3b LineOutWithSun_SSAzi_m45to45_green_Correct_ROI5_INFO.xls Figure 4 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Sim_Correct_ROI5_INFO.xls Figure 5a LineOut_Camera_Elevation_SqAzi_m180to0_green_Sim_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls Figure 5b LineOut_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_0to180_green_Sim_Correct_ROI5_INFO.xls Figure 6a LineOutColor_SqAzi_m180to0_CP_20to50_Correct_ROI5_INFO.xls Figure 6b LineOutROI_SqAzi_m180to0_CP_20to50_green_Correct_INFO.xls Figure 7 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls

About Datasets:

Domain : Finance Project: Variance Analysis Datasets: Budget vs Actuals Dataset Type: Excel Data Dataset Size: 482 records

KPI's: 1. Total Income 2. Total Expenses 3. Total Savings 4. Budget vs Actual Income 5. Actual Expenses Breakdown

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains dynamic dashboard, data validation, index match, SUMIFS, conditional formatting, if conditions, column chart, pie chart.

This dataset contains PAPP Excel Community data from the Latnjajaure site, Sweden in 1995, 1996, 1997 & 1998. The Press and Pulse Program (PAPP) experiment is comprised of four replicate blocks, each of which has four plots. This dataset is in excel format and includes data from all plots. For more information, please see the readme file.

Spreadsheets targeted at the analysis of GHS safety fingerprints.AbstractOver a 20-year period, the UN developed the Globally Harmonized System (GHS) to address international variation in chemical safety information standards. By 2014, the GHS became widely accepted internationally and has become the cornerstone of OSHA’s Hazard Communication Standard. Despite this progress, today we observe that there are inconsistent results when different sources apply the GHS to specific chemicals, in terms of the GHS pictograms, hazard statements, precautionary statements, and signal words assigned to those chemicals. In order to assess the magnitude of this problem, this research uses an extension of the “chemical fingerprints” used in 2D chemical structure similarity analysis to GHS classifications. By generating a chemical safety fingerprint, the consistency of the GHS information for specific chemicals can be assessed. The problem is the sources for GHS information can differ. For example, the SDS for sodium hydroxide pellets found on Fisher Scientific’s website displays two pictograms, while the GHS information for sodium hydroxide pellets on Sigma Aldrich’s website has only one pictogram. A chemical information tool, which identifies such discrepancies within a specific chemical inventory, can assist in maintaining the quality of the safety information needed to support safe work in the laboratory. The tools for this analysis will be scaled to the size of a moderate large research lab or small chemistry department as a whole (between 1000 and 3000 chemical entities) so that labelling expectations within these universes can be established as consistently as possible.Most chemists are familiar with programs such as excel and google sheets which are spreadsheet programs that are used by many chemists daily. Though a monadal programming approach with these tools, the analysis of GHS information can be made possible for non-programmers. This monadal approach employs single spreadsheet functions to analyze the data collected rather than long programs, which can be difficult to debug and maintain. Another advantage of this approach is that the single monadal functions can be mixed and matched to meet new goals as information needs about the chemical inventory evolve over time. These monadal functions will be used to converts GHS information into binary strings of data called “bitstrings”. This approach is also used when comparing chemical structures. The binary approach make data analysis more manageable, as GHS information comes in a variety of formats such as pictures or alphanumeric strings which are difficult to compare on their face. Bitstrings generated using the GHS information can be compared using an operator such as the tanimoto coefficent to yield values from 0 for strings that have no similarity to 1 for strings that are the same. Once a particular set of information is analyzed the hope is the same techniques could be extended to more information. For example, if GHS hazard statements are analyzed through a spreadsheet approach the same techniques with minor modifications could be used to tackle more GHS information such as pictograms.Intellectual Merit. This research indicates that the use of the cheminformatic technique of structural fingerprints can be used to create safety fingerprints. Structural fingerprints are binary bit strings that are obtained from the non-numeric entity of 2D structure. This structural fingerprint allows comparison of 2D structure through the use of the tanimoto coefficient. The use of this structural fingerprint can be extended to safety fingerprints, which can be created by converting a non-numeric entity such as GHS information into a binary bit string and comparing data through the use of the tanimoto coefficient.Broader Impact. Extension of this research can be applied to many aspects of GHS information. This research focused on comparing GHS hazard statements, but could be further applied to other bits of GHS information such as pictograms and GHS precautionary statements. Another facet of this research is allowing the chemist who uses the data to be able to compare large dataset using spreadsheet programs such as excel and not need a large programming background. Development of this technique will also benefit the Chemical Health and Safety community and Chemical Information communities by better defining the quality of GHS information available and providing a scalable and transferable tool to manipulate this information to meet a variety of other organizational needs.

Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

Analytical and field sampling data for each 2018-2019 NRSA Fish Tissue Study chemical contaminant are provided, along with a data dictionary that describes the contents of each data file. All results for the fillet tissue concentrations are reported on a wet weight basis. All the fish fillet samples analyzed contained detectable levels of mercury and PCBs, and PFAS were detected in 95% of the fillet samples. This dataset is associated with the following publication: Stahl, L., B.D. Snyder, H.B. McCarty, T. Kincaid, A. Olsen, T.R. Cohen, and J. Healey. Contaminants in Fish from U.S. Rivers: Probability-Based National Assessments. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 861(25): 160557, (2023).

About this dataset

Context

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

Key observations

In 2022, the population of Excel was 539, a 1.46% decrease year-by-year from 2021. Previously, in 2021, Excel population was 547, a decline of 1.08% compared to a population of 553 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Excel decreased by 36. In this period, the peak population was 713 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Excel is shown in this column.
• Year on Year Change: This column displays the change in Excel population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Example of how I use MS Excel's VLOOKUP() function to filter my data.

These data are coded for warming treatments, snow augmentation, and location from the Niwot Ridge (Colorado) site of ITEX. Included are data from 1993 (warming treatments started), 1995 (control plots added), and 1996. This dataset is in excel format and includes the species file. For more information, please see the readme file.

The Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset:Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

This dataset contains Normalized Difference Vegetation Index (NDVI) images of the 1999 growing season of the Toolik Lake Field station to document differences in on study site in control and treatment plots. For more information, please see the readme file. NOTE: This dataset contains the data in EXCEL format.

About this dataset

Context

The dataset tabulates the data for the Excel Township, Minnesota population pyramid, which represents the Excel township population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Excel Township, Minnesota, is 25.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Excel Township, Minnesota, is 37.7.
• Total dependency ratio for Excel Township, Minnesota is 63.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Excel Township, Minnesota is 2.7.

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Excel sheet with data of the original research 'Evaluation of simple and cost-effective hematological inflammatory biomarkers in type 2 diabetes and their correlation with glycemic control'