Dataset

This dataset was created by Pawan Kumar

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Company Datasets for valuable business insights!

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• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

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• Competitors.

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Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

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Dataset

This dataset was created by Mohammed Azarudheen

Released under Other (specified in description)

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What is IPGOD?

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.

How do I use IPGOD?

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.

IP Data Platform

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform

References

The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.

• Patents
• Trade Marks
• Designs
• Plant Breeder’s Rights

Updates

Tables and columns

Due to the changes in our systems, some tables have been affected.

• We have added IPGOD 225 and IPGOD 325 to the dataset!
• The IPGOD 206 table is not available this year.
• Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.

Data quality improvements

Data quality has been improved across all tables.

• Null values are simply empty rather than '31/12/9999'.
• All date columns are now in ISO format 'yyyy-mm-dd'.
• All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.
• All tables are encoded in UTF-8.
• All tables use the backslash \ as the escape character.
• The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

From 2016 to 2018, we surveyed the world’s largest natural history museum collections to begin mapping this globally distributed scientific infrastructure. The resulting dataset includes 73 institutions across the globe. It has:

Basic institution data for the 73 contributing institutions, including estimated total collection sizes, geographic locations (to the city) and latitude/longitude, and Research Organization Registry (ROR) identifiers where available.

Resourcing information, covering the numbers of research, collections and volunteer staff in each institution.

Indicators of the presence and size of collections within each institution broken down into a grid of 19 collection disciplines and 16 geographic regions.

Measures of the depth and breadth of individual researcher experience across the same disciplines and geographic regions.

This dataset contains the data (raw and processed) collected for the survey, and specifications for the schema used to store the data. It includes:

A diagram of the MySQL database schema.

A SQL dump of the MySQL database schema, excluding the data.

A SQL dump of the MySQL database schema with all data. This may be imported into an instance of MySQL Server to create a complete reconstruction of the database.

Raw data from each database table in CSV format.

A set of more human-readable views of the data in CSV format. These correspond to the database tables, but foreign keys are substituted for values from the linked tables to make the data easier to read and analyse.

A text file containing the definitions of the size categories used in the collection_unit table.

The global collections data may also be accessed at https://rebrand.ly/global-collections. This is a preliminary dashboard, constructed and published using Microsoft Power BI, that enables the exploration of the data through a set of visualisations and filters. The dashboard consists of three pages:

Institutional profile: Enables the selection of a specific institution and provides summary information on the institution and its location, staffing, total collection size, collection breakdown and researcher expertise.

Overall heatmap: Supports an interactive exploration of the global picture, including a heatmap of collection distribution across the discipline and geographic categories, and visualisations that demonstrate the relative breadth of collections across institutions and correlations between collection size and breadth. Various filters allow the focus to be refined to specific regions and collection sizes.

Browse: Provides some alternative methods of filtering and visualising the global dataset to look at patterns in the distribution and size of different types of collections across the global view.

Dataset

This dataset was created by ParthAdlakha16

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The October 2017 dataset of the IMMAP-UNHCR Protection Movement Initiative for South Syria. Explore the PMI Power BI dashboard: https://app.powerbi.com/view?r=eyJrIjoiMjYyYjE5NWUtZTdmYi00ZDZhLTg2N2UtMDg1MzUxMWIxZDA2IiwidCI6ImY2ZjcwZjFiLTJhMmQtNGYzMC04NTJhLTY0YjhjZTBjMTlkNyIsImMiOjF9

This dataset is about book subjects and is filtered where the books is Beginning big data with Power BI and Excel 2013 : big data processing and analysis using Power BI in Excel 2013, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

Dataset

This dataset was created by Shivani Siripuram 6

Released under CC0: Public Domain

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This Power BI dashboard shows the COVID-19 vaccination rate by key demographics including age groups, race and ethnicity, and sex for Tempe zip codes.Data Source: Maricopa County GIS Open Data weekly count of COVID-19 vaccinations. The data were reformatted from the source data to accommodate dashboard configuration. The Maricopa County Department of Public Health (MCDPH) releases the COVID-19 vaccination data for each zip code and city in Maricopa County at ~12:00 PM weekly on Wednesdays via the Maricopa County GIS Open Data website (https://data-maricopa.opendata.arcgis.com/). More information about the data is available on the Maricopa County COVID-19 Vaccine Data page (https://www.maricopa.gov/5671/Public-Vaccine-Data#dashboard). The dashboard’s values are refreshed at 3:00 PM weekly on Wednesdays. The most recent date included on the dashboard is available by hovering over the last point on the right-hand side of each chart. Please note that the times when the Maricopa County Department of Public Health (MCDPH) releases weekly data for COVID-19 vaccines may vary. If data are not released by the time of the scheduled dashboard refresh, the values may appear on the dashboard with the next data release, which may be one or more days after the last scheduled release.Dates: Updated data shows publishing dates which represents values from the previous calendar week (Sunday through Saturday). For more details on data reporting, please see the Maricopa County COVID-19 data reporting notes at https://www.maricopa.gov/5460/Coronavirus-Disease-2019.

Dataset

This dataset was created by Muhammad Faheem Naeem

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Warning: Large file size (over 1GB). Each monthly data set is large (over 4 million rows), but can be viewed in standard software such as Microsoft WordPad (save by right-clicking on the file name and selecting 'Save Target As', or equivalent on Mac OSX). It is then possible to select the required rows of data and copy and paste the information into another software application, such as a spreadsheet. Alternatively, add-ons to existing software, such as the Microsoft PowerPivot add-on for Excel, to handle larger data sets, can be used. The Microsoft PowerPivot add-on for Excel is available from Microsoft http://office.microsoft.com/en-gb/excel/download-power-pivot-HA101959985.aspx Once PowerPivot has been installed, to load the large files, please follow the instructions below. Note that it may take at least 20 to 30 minutes to load one monthly file. 1. Start Excel as normal 2. Click on the PowerPivot tab 3. Click on the PowerPivot Window icon (top left) 4. In the PowerPivot Window, click on the "From Other Sources" icon 5. In the Table Import Wizard e.g. scroll to the bottom and select Text File 6. Browse to the file you want to open and choose the file extension you require e.g. CSV Once the data has been imported you can view it in a spreadsheet. What does the data cover? General practice prescribing data is a list of all medicines, dressings and appliances that are prescribed and dispensed each month. A record will only be produced when this has occurred and there is no record for a zero total. For each practice in England, the following information is presented at presentation level for each medicine, dressing and appliance, (by presentation name): - the total number of items prescribed and dispensed - the total net ingredient cost - the total actual cost - the total quantity The data covers NHS prescriptions written in England and dispensed in the community in the UK. Prescriptions written in England but dispensed outside England are included. The data includes prescriptions written by GPs and other non-medical prescribers (such as nurses and pharmacists) who are attached to GP practices. GP practices are identified only by their national code, so an additional data file - linked to the first by the practice code - provides further detail in relation to the practice. Presentations are identified only by their BNF code, so an additional data file - linked to the first by the BNF code - provides the chemical name for that presentation.

Dataset

This dataset was created by AmitRaghav007

Contents

E commerce website data to make reports.

This dataset is of 2017 properties. It is used for the City's Power BI training.

The GMP Dashboard report shows key quarterly statistics for Class 4 gambling (in pubs and clubs) in New Zealand, including gaming machine profits (GMP) and numbers of approved venues and gaming machines licensed to operate, as at the last day of each quarter. The data is provided by district (Territorial Authority) and at quarterly intervals from March 2015. As of the December 2024 quarter (published 28/02/2025), we are publishing the new Power BI version of the dashboard only. The static datasets are still available in Excel and CSV format.

Dataset

This dataset was created by Aasim Parwez

Contents

【東京都政策企画局】「未来の東京」戦略 政策ダッシュボード（PowerBI版）(https://app.powerbi.com/view?r=eyJrIjoiYjRjMjRlOTctMjI4Mi00NTExLWI1MTMtMmVhYmQ0MWIyYjdkIiwidCI6ImQwMzAyZmNjLTNlODEtNDljMy04MjM1LWQzMTFhMzY4NGNmYyJ9&pageName=ReportSectionea851bef35060b5c0d6e)で公表しているデータ一覧です。

Election Facebook’s Ad Metrics 2024: Trump vs. Harris

A key event of 2024 is the U.S. presidential election. This project focuses on analyzing how Donald Trump and Kamala Harris use advertising to win votes, exploring their strategies, actions, and effectiveness.

Here is the Dataset i have used in the analytic:

File name: trump.zip and harris.zip (Original Data)

The files were downloaded from the Facebook Ad Library. The data focuses on two primary accounts: Trump and Harris, which had the highest number of advertisements and the largest ad spend. These accounts promoted two types of campaigns: presidential campaigns and victory funds. However, I will concentrate solely on the presidential campaigns. Date Range: Based on my research, presidential campaigns typically begin about a year before the election. Therefore, I collected data starting from February 25, 2023, the date Harris announced her candidacy to compete with Trump, up to the current date, December 7, 2024.

File name: Trump-Harris add-id.csv (Processed Data)

This is the main data of the "Election Facebook’s Ad Metrics 2024: Trump vs. Harris"

File name: AD-Tech-Analytic-Project-DashBoard.pbix

Power BI chart imported data from Trump-Harris add-id.csv (Processed Data) and some others

File name: 6state trump data.csv, datamichigan.csv, data nevada.csv

Data that filters from Trump-Harris add-id.csv (Processed Data) have been used in AD-Tech-Analytic-Project-DashBoard.pbix

Dataset

This dataset was created by Muhammad Affaf

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Dataset

This dataset was created by ADITYA ANIL PHATAK

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