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

This dataset is a cleaned and preprocessed version of the original Netflix Movies and TV Shows dataset available on Kaggle. All cleaning was done using Microsoft Excel — no programming involved.

🎯 What’s Included: - Cleaned Excel file (standardized columns, proper date format, removed duplicates/missing values) - A separate "formulas_used.txt" file listing all Excel formulas used during cleaning (e.g., TRIM, CLEAN, DATE, SUBSTITUTE, TEXTJOIN, etc.) - Columns like 'date_added' have been properly formatted into DMY structure - Multi-valued columns like 'listed_in' are split for better analysis - Null values replaced with “Unknown” for clarity - Duration field broken into numeric + unit components

🔍 Dataset Purpose: Ideal for beginners and analysts who want to: - Practice data cleaning in Excel - Explore Netflix content trends - Analyze content by type, country, genre, or date added

📁 Original Dataset Credit: The base version was originally published by Shivam Bansal on Kaggle: https://www.kaggle.com/shivamb/netflix-shows

📌 Bonus: You can find a step-by-step cleaning guide and the same dataset on GitHub as well — along with screenshots and formulas documentation.

This dataset contains the valuation template the researcher can use to retrieve real-time Excel stock price and stock price in Google Sheets. The dataset is provided by Finsheet, the leading financial data provider for spreadsheet users. To get more financial data, visit the website and explore their function. For instance, if a researcher would like to get the last 30 years of income statement for Meta Platform Inc, the syntax would be =FS_EquityFullFinancials("FB", "ic", "FY", 30) In addition, this syntax will return the latest stock price for Caterpillar Inc right in your spreadsheet. =FS_Latest("CAT") If you need assistance with any of the function, feel free to reach out to their customer support team. To get starter, install their Excel and Google Sheets add-on.

The first row of the Excel spreadsheet describes the data - ID number, Metamorphic Relation Topic, Title of Comment, Type of Comment, Content of the Comment. Our original dataset contained names but these were removed from the dataset.

Available on website, has all the reports published since 2009. Also provides bibliography and list in Excel format https://www.dol.gov/agencies/ilab/reports/child-labor/list-of-goods

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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 is the dataset for the paper: Understanding the Issues, Their Causes and Solutions in Microservices Systems: An Empirical Study. The dataset is recorded in an MS Excel file which contains the following Excel sheets, and the description of each sheet is briefly presented below.

(1) Selected Systems

contains the 15 selected open source microservices systems with the color code and URL of each system.

(2) Raw Data

contains the information of initially retrieved 10,222 issues, including issue titles, issue links, issue open date, issue closed date, and the number of participants in each issue discussion.

(3) Screened Issues

contains the issues that meet the initial selection criteria (i.e., 5,115 issues) and the issues that do not meet the initial selection criteria (i.e., 5,107 issues).

(4) Selected Issues (Round 1)

contains the list of 5,115 issues that meet the initial selection criteria.

(5) Selected Issues (Round 2)

contains the issues related to RQs (i.e., 2,641 issues) and the issues not related to RQs (i.e., 2,474 issues).

(6) Selected Issues

contains the list of selected 2,641 issues, which were used to answer the RQs.

(7) Initial Codes

contains the initial codes for identifying the types of issues, causes, and solutions. We used these codes to further generate the subcategories and categories of issues, causes, and solutions.

(8) Interview Questionnaire

contains the interview questions we asked microservices practitioners to identify any missing issues, causes, and solutions, as well as to improve the proposed taxonomies.

(9) Interview Results

contains the results of interviews that we conducted to confirm and improve the developed taxonomies of issues, causes, and solutions.

(10) Survey Questionnaire

contains the survey questions we asked microservices practitioners through a Web-based survey to validate our taxonomies of issues, causes, and solutions.

(11) Issue Taxonomy

contains the detailed issue taxonomy consisting of 19 categories, 54 subcategories, and 402 types of issues.

(12) Cause Taxonomy

contains the detailed cause taxonomy consisting of 8 categories, 26 subcategories, and 228 types of causes.

(13) Solution Taxonomy

contains the detailed solution taxonomy consisting of 8 categories, 32 subcategories, and 177 types of solutions.

Last Version: 4

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/12/15

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v4.xlsx: full list of 140 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v4.csv: full list of 140 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 4th version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR), Scopus and Web of Science (WOS), Journal Master List.

Version: 3

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/10/28

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v3.xlsx: full list of 124 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_3.csv: full list of 124 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 3rd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR).

Erratum - Data articles in journals Version 3:

Botanical Studies -- ISSN 1999-3110 -- JCR (JIF) Q2
Data -- ISSN 2306-5729 -- JCR (JIF) n/a
Data in Brief -- ISSN 2352-3409 -- JCR (JIF) n/a

Version: 2

Author: Francisco Rubio, Universitat Politècnia de València.

Date of data collection: 2020/06/23

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v2.xlsx: full list of 56 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v2.csv: full list of 56 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 2nd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Scimago Journal and Country Rank (SJR)

Total size: 32 KB

Version 1: Description

This dataset contains a list of journals that publish data articles, code, software articles and database articles.

The search strategy in DOAJ and Ulrichsweb was the search for the word data in the title of the journals.
Acknowledgements:
Xaquín Lores Torres for his invaluable help in preparing this dataset.

🛍️ 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.

IntroductionFollowing the identification of Local Area Energy Planning (LAEP) use cases, this dataset lists the data sources and/or information that could help facilitate this research. View our dedicated page to find out how we derived this list: Local Area Energy Plan — UK Power Networks (opendatasoft.com)

Methodological Approach Data upload: a list of datasets and ancillary details are uploaded into a static Excel file before uploaded onto the Open Data Portal.

Quality Control Statement

Quality Control Measures include: Manual review and correct of data inconsistencies Use of additional verification steps to ensure accuracy in the methodology

Assurance Statement The Open Data Team and Local Net Zero Team worked together to ensure data accuracy and consistency.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

Please note that "number of records" in the top left corner is higher than the number of datasets available as many datasets are indexed against multiple use cases leading to them being counted as multiple records.

Shark Tank India Data set.

Shark Tank India - Season 1 to season 4 information, with 80 fields/columns and 630+ records.

All seasons/episodes of 🦈 SHARKTANK INDIA 🇮🇳 were broadcasted on SonyLiv OTT/Sony TV.

Here is the data dictionary for (Indian) Shark Tank season's dataset.

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Episode Title - Episode title in SonyLiv
• Anchor - Name of the episode presenter/host
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Started in - Year in which startup was started/incorporated
• Number of Presenters - Number of presenters
• Male Presenters - Number of male presenters
• Female Presenters - Number of female presenters
• Transgender Presenters - Number of transgender/LGBTQ presenters
• Couple Presenters - Are presenters wife/husband ? 1-yes, 0-no
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Pitchers City - Presenter's town/city or place where company head office exists
• Pitchers State - Indian state pitcher hails from or state where company head office exists
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• EBITDA - Earnings Before Interest, Taxes, Depreciation, and Amortization
• Cash Burn - In loss in current year; burning/paying money from their pocket (yes/no)
• SKUs - Stock Keeping Units or number of varieties, at the time of pitch
• Has Patents - Pitcher has Patents/Intellectual property (filed/granted), at the time of pitch
• Bootstrapped - Startup is bootstrapped or not (yes/no)
• Part of Match off - Competition between two similar brands, pitched at same time
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal debt/loan amount, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of sharks in deal - Number of sharks involved in deal
• Deal has conditions - Deal has conditions or not? (yes or no)
• Royalty Percentage - Royalty percentage, if it's royalty deal
• Royalty Recouped Amount - Royalty recouped amount, if it's royalty deal, in lakhs
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Namita Investment Amount - Namita Investment Amount, in lakhs INR
• Namita Investment Equity - Namita Investment Equity, in percentages
• Namita Debt Amount - Namita Debt Amount, in lakhs INR
• Vineeta Investment Amount - Vineeta Investment Amount, in lakhs INR
• Vineeta Investment Equity - Vineeta Investment Equity, in percentages
• Vineeta Debt Amount - Vineeta Debt Amount, in lakhs INR
• Anupam Investment Amount - Anupam Investment Amount, in lakhs INR
• Anupam Investment Equity - Anupam Investment Equity, in percentages
• Anupam Debt Amount - Anupam Debt Amount, in lakhs INR
• Aman Investment Amount - Aman Investment Amount, in lakhs INR
• Aman Investment Equity - Aman Investment Equity, in percentages
• Aman Debt Amount - Aman Debt Amount, in lakhs INR
• Peyush Investment Amount - Peyush Investment Amount, in lakhs INR
• Peyush Investment Equity - Peyush Investment Equity, in percentages
• Peyush Debt Amount - Peyush Debt Amount, in lakhs INR
• Ritesh Investment Amount - Ritesh Investment Amount, in lakhs INR
• Ritesh Investment Equity - Ritesh Investment Equity, in percentages
• Ritesh Debt Amount - Ritesh Debt Amount, in lakhs INR
• Amit Investment Amount - Amit Investment Amount, in lakhs INR
• Amit Investment Equity - Amit Investment Equity, in percentages
• Amit Debt Amount - Amit Debt Amount, in lakhs INR
• Guest Investment Amount - Guest Investment Amount, in lakhs INR
• Guest Investment Equity - Guest Investment Equity, in percentages
• Guest Debt Amount - Guest Debt Amount, in lakhs INR
• Invested Guest Name - Name of the guest(s) who invested in deal
• All Guest Names - Name of all guests, who are present in episode
• Namita Present - Whether Namita present in episode or not
• Vineeta Present - Whether Vineeta present in episode or not
• Anupam ...

BOLD5000: Brains, Objects, Landscapes Dataset

For details please refer to BOLD5000.org and our paper on arXiv (http://arxiv.org/abs/1809.01281)

Participant Directories Content 1) Four participants: CSI1, CSI2, CSI3, & CSI4 2) Functional task data acquisition sessions: sessions #1-15 Each functional session includes: -3 sets of fieldmaps (EPI opposite phase encoding; spin-echo opposite phase encoding pairs with partial & non-partial Fourier) -9 or 10 functional scans of slow event-related 5000 scene data (5000scenes) -1 or 0 functional localizer scans used to define scene selective regions (localizer) -each event.json file lists each stimulus, the onset time, and the participant’s response (participants performed a simple valence task) 3) Anatomical data acquisition session: #16 Anatomical Data: T1 weighted MPRAGE scan, a T2 weighted SPACE, diffusion spectrum imaging

Notes: -All MRI and fMRI data provided is with Siemens pre-scan normalization filter.
-CSI4 only participated in 10 MRI sessions: 1-9 were functional acquisition sessions, and 10 was the anatomical data acquisition session.

Derivatives Directory Content 1) fMRIprep: -Preprocessed data for all functional data of CSI1 through CSI4 (listed in folders for each participant: derivatives/fmriprep/sub-CSIX). Data was preprocessed both in T1w image space and on surface space. Functional data was motion corrected, susceptibility distortion corrected, and aligned to the anatomical data using bbregister. Please refer to the paper for the details on preprocessing. -Reports resulting from fMRI prep, which include the success of anatomical alignment and distortion correction, among other measures of preprocessing success are all listed in the sub-CSIX.html files.
2) Freesurfer: Freesurfer reconstructions as a result of fMRIprep preprocessing stream. 3) MRIQC: Image quality metrics (IQMs) of the dataset using MRIQC. -CSIX-func.csv files are text files with a list of all IQMs for each session, for each run. -CSIX-anat.csv files are text files with a list of all IQMs for the scans acquired in the anatomical session (e.g., MPRAGE). -CSIX_IQM.xls an excel workbook, each sheet of workbook lists the IQMs for a single run. This is the same data as CSIX-func.csv, except formatted differently. -sub-CSIX/derivatives: contain .json with the MRIQC/IQM results for each run. -sub-CSIX/reports: contains .html file with MRIQC/IQM results for each run along with mean signal and standard deviation maps. 4)spm: A directory that contains the masks used to define each region of interest (ROI) in each participant. There were 10 ROIs: early visual (EarlyVis), lateral occipital cortex (LOC), occipital place area (OPA), parahippocampal place area (PPA), retrosplenial complex (RSC) for the left hemisphere (LH) and right hemisphere (RH).

This dataset is associated with the forthcoming publication entitled, "Microbial volatile organic compounds mediate attraction by a primary but not secondary stored product insect pest in wheat", and includes data on grain damage from near infrared spectroscopy, behavioral data from wind tunnel and release-recapture experiments, as well as volatile characterization of headspace from moldy grain. For all files, incubation intervals 9, 18, and 27 d represent how long grain was incubated after being tempered to a grain moisture of 12, 15, or 19% or left untempered (ctrl; 10.8% grain moisture). TSO = Trece storgard oil; empty = negative control (no stimulus), LGB = lesser grain borer (Rhzyopertha dominica), and RFB = red flour beetle (Tribolium castaneum). Note: The resource 'GC/MS Grain MVOC Headspace Data' was added 2021-08-04 with the deletion of some compounds as unlikely natural compounds and potential contaminants. This is the dataset that undergirds the non-metric multidimensional scaling analysis. See the included file list for more information about methods and results of each file in this dataset. Resources in this dataset:Resource Title: GC-MS/Headspace Data. File Name: tvw_final_gc_ms_data.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Microbial damage on wheat evaluated with near-infrared spectroscopy. File Name: tvw_nearinfrared_sorting_damaged_grain_fungal_exp.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Release-Recapture Datasets with LGB & RFB. File Name: tvw_rr_lgb_rfb_microbial_cues.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Wind tunnel response by RGB & LGB. File Name: tvw_wt_lgb_rfb_data_microbial_cues.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: GC/MS Grain MVOC Headspace Data. File Name: taylor_headspace_final_data_peer_reviewed_ag_commons.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: README file list. File Name: file_list_MVOCwheat.txt

Yearwise .csv's with [Date,HomeTeam,AwayTeam,FTR] as columns from 1993 to 2024

although the all columns can be downloaded by removing the argument usecols=['Date','HomeTeam','AwayTeam','FTR']) from the code in extraction.ipynb

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17313823%2Fc364dd6637e754a9705980049af1fc50%2FBundesliga.png?generation=1709996972725452&alt=media" alt="">

Data Files: Germany Last updated: 03/03/24

the below dataset is extracted from the football-data.co.uk by me

Registering with any of the advertised bookmakers on Football-Data will help keep access to the historical results & betting odds data files FREE.

Below you will find download links to all available CSV data files to use for quantitative testing of betting systems in spreadsheet applications like Excel. League tables, head2head statistics and information on goalscrores, first scorers and top scorers can now be accessed through the Livescore service. Latest betting odds are available through the Odds Comparison.

You are free experiment with the data yourselves, but if you are looking for a bespoke Excel application that has been desinged specifically to work with Football-Data's files, visit BetGPS for an exceptional data analysis workbook. Like all of Football-Data's files, it free to download. Notes.txt (text file key to the data files and data source acknowledgements)

Contact Football-Data.co.uk if you believe there are any errors in the data files.

Notes for Football Data

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use (in particular odds from specific bookmakers no longer used) and refer to data collected in earlier seasons. For a current list of what bookmakers are included in the dataset please visit http://www.football-data.co.uk/matches.php

Key to results data:

Div = League Division Date = Match Date (dd/mm/yy) Time = Time of match kick off HomeTeam = Home Team AwayTeam = Away Team FTHG and HG = Full Time Home Team Goals FTAG and AG = Full Time Away Team Goals FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win) HTHG = Half Time Home Team Goals HTAG = Half Time Away Team Goals HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available) Attendance = Crowd Attendance Referee = Match Referee HS = Home Team Shots AS = Away Team Shots HST = Home Team Shots on Target AST = Away Team Shots on Target HHW = Home Team Hit Woodwork AHW = Away Team Hit Woodwork HC = Home Team Corners AC = Away Team Corners HF = Home Team Fouls Committed AF = Away Team Fouls Committed HFKC = Home Team Free Kicks Conceded AFKC = Away Team Free Kicks Conceded HO = Home Team Offsides AO = Away Team Offsides HY = Home Team Yellow Cards AY = Away Team Yellow Cards HR = Home Team Red Cards AR = Away Team Red Cards HBP = Home Team Bookings Points (10 = yellow, 25 = red) ABP = Away Team Bookings Points (10 = yellow, 25 = red)

Note that Free Kicks Conceeded includes fouls, offsides and any other offense commmitted and will always be equal to or higher than the number of fouls. Fouls make up the vast majority of Free Kicks Conceded. Free Kicks Conceded are shown when specific data on Fouls are not available (France 2nd, Belgium 1st and Greece 1st divisions).

Note also that English and Scottish yellow cards do not include the initial yellow card when a second is shown to a player converting it into a red, but this is included as a yellow (plus red) for European games.

Key to 1X2 (match) betting odds data:

B365H = Bet365 home win odds B365D = Bet365 draw odds B365A = Bet365 away win odds BSH = Blue Square home win odds BSD = Blue Square draw odds BSA = Blue Square away win odds BWH = Bet&Win home win odds BWD = Bet&Win draw odds BWA = Bet&Win away win odds GBH = Gamebookers home win odds GBD = Gamebookers draw odds GBA = Gamebookers away win odds IWH = Interwetten home win odds IWD = Interwetten draw odds IWA = Interwetten away win odds LBH = Ladbrokes home win odds LBD = Ladbrokes draw odds LBA = Ladbrokes away win odds PSH and PH = Pinnacle home win odds PSD and PD = Pinnacle draw odds PSA and PA = Pinnacle away win odds SOH = Sporting Odds home win odds SOD = Sporting Odds draw odds SOA = Sporting Odds away win odds SBH = Sportingbet home win odds SBD = Sportingbet draw odds SBA = Sportingbet away win odds SJH = Stan James home win odds SJD = Stan James draw odds SJA = Stan James away win odds SYH = Stanleybet home win odds SYD = St...

This dataset contains species lists and cover values for the Barrow, Atqasuk, Oumalik, and Ivotuk grids on the Arctic Slope, Alaska. The data were collected from marked study plots in 1998 and 1999 for the Arctic Transitions in the Land-Atmosphere System (ATLAS) project and are in Excel format. See the README for additional information.

The dataset contains data from four Norwegian Red Lists. Data included are the Red List Categories, reasons for change, and threats. These data were used to evaluate metrics for quantifying the contributions of different threats to Red Lists, described by Sandvik & Pedersen (2023).

The dataset contains six files:

1. species.csv (semicolon-delimited plain-text file with Red Lists for species)
2. Species.pdf (explanations of species.csv)
3. Species.xlsx (microsoft excel spreadsheet workbook with Red Lists for species)
4. ecosyst.csv (semicolon-delimited plain-text file with the Red List for ecosystems)
5. Ecosyst.pdf (explanations of ecosyst.csv)
6. Ecosyst.xlsx (microsoft excel spreadsheet workbook with the Red List for ecosystems)

The excel workbooks contain the same information as the respective csv and pdf files combined.

Columns, abbreviations etc. are explained in the excel and pdf files.

Data were derived from the following sources, all published by the Norwegian Biodiversity Information Centre:

• 2010 Norwegian Red List for species
• 2015 Norwegian Red List for species
• 2018 Norwegian Red List for ecosystems and habitat types
• 2021 Norwegian Red List for species

R code to analyse the dataset and reproduce the results of the paper is available on Zenodo via doi:10.5281/zenodo.7843806.

This is a dataset downloaded off excelbianalytics.com created off of random VBA logic. I recently performed an extensive exploratory data analysis on it and I included new columns to it, namely: Unit margin, Order year, Order month, Order weekday and Order_Ship_Days which I think can help with analysis on the data. I shared it because I thought it was a great dataset to practice analytical processes on for newbies like myself.

These are the most recent Data Dictionary (pop-ups) and Panarctic Species List (PASL) zip files for all the vegetation plot data entered into Turboveg for the Alaska AVA. These files are necessary to correctly use the Turboveg data with regards to coded data. The Data Dictionary file will be updated when new datasets are entered into Turboveg which result in additions to coded data such as references, author code, habitat type, surficial geology, etc. Updates to the PASL will occur less frequently. Check the dates in the file names to be certain that you are using the most current files. Our data model is a set of tables that comprise our relational database. The Excel spreadsheet included in the resources below provides information about each field in our database, such as data type, description, if it is a required field, whether the information within the field is selected from a pop-up list, and whether the field is a standard within Turboveg or is specific to the AVA. Using Turboveg: 1) Download the installation file available through the link at Alaska Arctic Geoecological Atlas portal from the official Turboveg webpage (general installation file for worldwide users, however, some adjustments will be needed when using data from AAVA after installation of this program). 2) Open the Turboveg program and restore the most recent Data Dictionary and PASL zipped files into the Turboveg program by using the function 'Database-Backup/Restore-Restore.' All the previous versions of data dictionary files and PASL that are already in program will be overwritten. 3) Use the Alaska-AVA following the manual for Turboveg for Windows which is available at http://www.synbiosys.alterra.nl/turboveg/tvwin.pdf