The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period.
An R script (create_boxplot.R) is provided which generates boxplots of change factors by NOAA Atlas 14 station, or for all NOAA Atlas 14 stations in a Florida HUC-8 basin or county. In addition, the R script basin_boxplot.R is provided as an example on how to create a wrapper function that will automate the generation of boxplots of change factors for all Florida HUC-8 basins. This Microsoft Word file (Documentation_R_script_create_boxplot.docx) serves as documentation on the code usage and available options for running the scripts. As described in the documentation, the R scripts rely on some of the Microsoft Excel spreadsheets published as part of this data release. The script uses basins defined in the "Florida Hydrologic Unit Code (HUC) Basins (areas)" from the Florida Department of Environmental Protection (FDEP; https://geodata.dep.state.fl.us/datasets/FDEP::florida-hydrologic-unit-code-huc-basins-areas/explore) and their names are listed in the file basins_list.txt provided with the script. County names are listed in the file counties_list.txt provided with the script. NOAA Atlas 14 stations located in each Florida HUC-8 basin or county are defined in the Microsoft Excel spreadsheet Datasets_station_information.xlsx which is part of this data release. Instructions are provided in code documentation (see highlighted text on page 7 of Documentation_R_script_create_boxplot.docx) so that users can modify the script to generate boxplots for basins different from the FDEP "Florida Hydrologic Unit Code (HUC) Basins (areas)."

The excel file contains data about 10-18 years old students' affect/ well-being during lock-down in Pakistan. The cells in the top row contain the items. The numbers in the columns 'C' to 'J' represent following. 1 = Very Often; 2 = Often; 3 = Sometimes; 2 = Rarely; 1 = Never The numbers in the column 'K' have been coded in reverse order. Column 'L' contains response of the question "Have you started fighting more with your siblings?" Column 'M' and 'N' include open-ended, short responses.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided that tabulates best models for each downscaled climate dataset and for all downscaled climate datasets considered together. Best models were identified based on how well the models capture the climatology and interannual variability of four climate extreme indices using the Model Climatology Index (MCI) and the Model Variability Index (MVI) of Srivastava and others (2020). The four indices consist of annual maxima consecutive precipitation for durations of 1, 3, 5, and 7 days compared against the same indices computed based on the PRISM and SFWMD gridded precipitation datasets for two climate regions: climate region 4 in South Central Florida, and climate region 5 in South Florida. The PRISM dataset is based on the Parameter-elevation Relationships on Independent Slopes Model interpolation method of Daly and others (2008). The South Florida Water Management District’s (SFWMD) precipitation super-grid is a gridded precipitation dataset developed by modelers at the agency for use in hydrologic modeling (SFWMD, 2005). This dataset is considered by the SFWMD as the best available gridded rainfall dataset for south Florida. Best models were selected based on MCI and MVI evaluated within each individual downscaled dataset. In addition, best models were selected by comparison across datasets and referred to as "ALL DATASETS" hereafter. Due to the small sample size, all models in the using the Weather Research and Forecasting Model (JupiterWRF) dataset were considered as best models.

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'

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

This excel contains data for Chapter 4 “Land Use” of the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org). It includes the raw data behind Figure 4, “Historical changes in percentage of Narragansett Bay Watershed classified as forest or urban,” (page 121). For more information, please reference the Technical Report or contact info@nbep.org. Original figures are available at http://nbep.org/the-state-of-our-watershed/figures/.

This project contains the Stata code as well as additional information used for the following paper:Randell, H & C Gray (Forthcoming). Climate Change and Educational Attainment in the Global Tropics. Proceedings of the National Academy of Sciences.The data are publicly available and can be accessed freely. The census data were obtained from IPUMS-International (https://international.ipums.org/international/) and the climate data were obtained from the CRU-Time Series Version 4.00 (http://data.ceda.ac.uk//badc/cru/data/cru_ts/cru_ts_4.00/).We include three do-files in this project:"Climate_-1_to_5.do" -- this file was used to convert the climate data into z-scores of climatic conditions experienced during ages -1 to 5 years among children in the sample. "ClimEducation_PNAS_FINAL.do" -- this file was used to process the census data downloaded from IPUMS-International, link it to the climate data, and perform all of the analyses in the study."Climate_6-10_and_11-current.do" -- this file was used to convert the climate data into z-scores of climatic conditions experienced during ages 6-10 and 11-current age among children in the sample.In addition, we include a shapefile (as well as related GIS files) for the final sample of analysis countries. The attribute "birthplace" is used to link the climate data to the census data. We include Python scripts for extracting monthly climate data for each 10-year temperature and precipitation file downloaded from CRU. "py0_60" extracts data for years one through five, and "py61_120" extracts data for years six through ten.Lastly, we include an excel file with inclusion/exclusion criteria for the countries and censuses available from IPUMS.

The Climate Change Vulnerability Index (CCVI) uses a scoring system that integrates a species’ exposure to projected climate change within an assessment area, including sea level rise, and three sets of factors associated with climate change sensitivity, each supported by published studies: 1) species-specific sensitivity and adaptive capacity factors, 2) threat multipliers such as barriers to dispersal and anthropogenic threats, and 3) documented and modeled responses to climate change. Assessing species with the CCVI facilitates grouping unrelated taxa by their relative risk to climate change as well as identifying patterns of climate stressors that affect multiple taxa.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates change factors derived from the Localized Constructed Analogues (LOCA) dataset at grid cells closest to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical precipitation depths fitted to extreme precipitation data using a constrained maximum likelihood approach. The change factors are tabulated by duration (1, 3, and 7 days) and return period (5, 10, 25, 50, 100, and 200 years).

Supplementary Material: Dataset S1: Excel spreadsheet with bathymetric analysis of Lake Victoria and model for present Lake Victoria using Sutcliffe and Parks (1999) and Howell et al. (1988) datasets. Dataset S2: Excel spreadsheet with water budget model to drain paleo-Lake Victoria. Dataset S3: Excel spreadsheet used to develop model to fill Lake Victoria during the late Pleistocene. Dataset S4: Excel spreadsheet used to develop model to predict future of Lake Victoria.

These are a collection of XLSX sheets containing some of my favorite Excel tricks to reformat data to make analysis easier. I often use these to reformat column formatted data into plate layout or vice versa to better visualize and understand my data.

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. Each file contains annual total releases for the past ten years by media (air, water or land), broken-down by province, industry or substance. Files are in .CSV format. The results can be further broken down using the pre-defined search available at the bottom of the NPRI Data Search webpage. The results returned by the NPRI search engine may differ from the numbers contained in the downloadable files. The online search engine’s results will display releases, disposals and transfers reported by facilities, but does not distinguish between media type (i.e. air, water, land). It also displays facilities reporting only under Ontario Regulation 127/01 and facilities submitting “did not meet criteria” reports. Please consult the following resources to enhance your analysis: - Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html - Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html Supplemental Information More NPRI datasets and mapping products are available here: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/access.html Supporting Projects: National Pollutant Release Inventory (NPRI)

This excel contains results from the 2020 indicator update of the 2017 State of Narragansett Bay and Its Watershed Technical Report (nbep.org), Chapter 5: "Land Use." Land use in 2001, 2004, 2006, 2008, 2011, 2013, and 2016 in the Narragansett Bay, Little Narragansett Bay, and the Southwest Coastal Ponds watersheds was analyzed using the 30-meter 2016 edition National Land Cover Database (NLCD). Seven overarching land use categories were reclassified from the NLCD based on the Anderson Level I classification scheme (Forest - 41, 42, 43; Developed - 21, 22, 23, 24; Agricultural Land - 71, 72, 81, 82; Shrubland - 52; Wetland - 90, 95; Barren Land - 31; Water - 11). The gross change (in acres) and percent change of developed land uses are summarized at a variety of watershed scales across 2001, 2006, 2011, and 2016 in the Narragansett Bay region (note that developed land uses are only summarized for four of the seven available years based on the source NLCD data). The methods for analyzing land use as an indicator of environmental conditions in the Narragansett Bay region were developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners.

This dataset contains all current and active business licenses issued by the Department of Business Affairs and Consumer Protection. This dataset contains a large number of records /rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: 'ISSUE' is the record associated with the initial license application. 'RENEW' is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. 'C_LOC' is a change of location record. It means the business moved. 'C_CAPA' is a change of capacity record. Only a few license types my file this type of application. 'C_EXPA' only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: 'AAI' means the license was issued.

Business license owners may be accessed at: http://data.cityofchicago.org/Community-Economic-Development/Business-Owners/ezma-pppn To identify the owner of a business, you will need the account number or legal name.

Data Owner: Business Affairs and Consumer Protection

Time Period: Current

Frequency: Data is updated daily

We provide the data used for this research in both Excel (one file with one matrix per sheet, 'Allmatrices.xlsx'), and CSV (one file per matrix).

Patent applications (Patent_applications.csv) Patent applications from residents and no residents per million inhabitants. Data obtained from the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

High-tech exports (High-tech_exports.csv) The proportion of exports of high-level technology manufactures from total exports by technology intensity, obtained from the Trade Structure by Partner, Product or Service-Category database (Lall, 2000; UNCTAD, 2019)

Expenditure on education (Expenditure_on_education.csv) Per capita government expenditure on education, total (2010 US$). The data was obtained from the government expenditure on education (total % of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Scientific publications (Scientific_publications.csv) Scientific and technical journal articles per million inhabitants. The data were obtained from the scientific and technical journal articles and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Expenditure on R&D (Expenditure_on_R&D.csv) Expenditure on research and development. Data obtained from the research and development expenditure (% of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Two centuries of GDP (GDP_two_centuries.csv) GDP per capita that accounts for inflation. Data obtained from the Maddison Project Database, version 2018 (Inklaar et al. 2018), and available from the Open Numbers community (open-numbers.github.io).

Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing “Maddison”: new income comparisons and the shape of long-run economic development (GD-174; GGDC Research Memorandum). https://www.rug.nl/research/portal/files/53088705/gd174.pdf

Lall, S. (2000). The Technological Structure and Performance of Developing Country Manufactured Exports, 1985‐98. Oxford Development Studies, 28(3), 337–369. https://doi.org/10.1080/713688318

Unctad. 2019. “Trade Structure by Partner, Product or Service-Category.” 2019. https://unctadstat.unctad.org/EN/.

World Bank. (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

Research Blogging Reports raw data. Excel spreadsheet with Research Blogging data from November 1, 2007 until December 31, 2011. Sheet S1-A: RB Blog Report with blog name, blog URL, status, Research Blogging topic, number of posts and blog language. Sheet S1-B: RB Citations Report with publication date, post title, number of views, blog name, DOI, journal title and Research Blogging topic. (XLSX)

This dataset contains all current and active business licenses issued by the Department of Business Affairs and Consumer Protection. This dataset contains a large number of records /rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: 'ISSUE' is the record associated with the initial license application. 'RENEW' is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. 'C_LOC' is a change of location record. It means the business moved. 'C_CAPA' is a change of capacity record. Only a few license types my file this type of application. 'C_EXPA' only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: 'AAI' means the license was issued.

Business license owners may be accessed at: http://data.cityofchicago.org/Community-Economic-Development/Business-Owners/ezma-pppn To identify the owner of a business, you will need the account number or legal name.

Data Owner: Business Affairs and Consumer Protection

Time Period: Current

Frequency: Data is updated daily

This dataset contains all current and active business licenses issued by the Department of Business Affairs and Consumer Protection. This dataset contains a large number of records /rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: 'ISSUE' is the record associated with the initial license application. 'RENEW' is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. 'C_LOC' is a change of location record. It means the business moved. 'C_CAPA' is a change of capacity record. Only a few license types my file this type of application. 'C_EXPA' only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: 'AAI' means the license was issued.

Business license owners may be accessed at: http://data.cityofchicago.org/Community-Economic-Development/Business-Owners/ezma-pppn To identify the owner of a business, you will need the account number or legal name.

Data Owner: Business Affairs and Consumer Protection

Time Period: Current

Frequency: Data is updated daily

This file is an Excel 2010 companion and support file to a research manuscript entitled "Computational methodology for quantifying regional and global temperature changes caused by multiple concurrent processes". This file contains multiple tables for the collection of personal expert data and physical process data.