Gain a competitive edge with Crunchbase Dataset: Worldwide firmographic and statups insights, ready for action.

1) Data Introduction • The Crunchbase company Data includes a variety of startup-related data, including startup enterprise information, year of establishment, investment history, and industrial sector released by Crunchbase.

2) Data Utilization (1) Crunchbase company Data has characteristics that: • This dataset provides a variety of attributes that can impact the growth and success of a start-up, including company name, year of establishment, location, industry, investment stage, investment amount, and investors. (2) Crunchbase company Data can be used to: • Development of Startup Success Prediction Model: By utilizing key attributes such as the year of establishment, investment history, and industrial sector, we can build a model that predicts a startup's chances of success. • Venture investment and market analysis: It can be used to identify venture investment strategies and market trends by analyzing startup distribution and growth patterns by investment stage and industry.

I was thinking about whether I can make a startup prediction model to basically predict the success and failure of the startup in order to save lots of energy and resources.

This database is open-sourced by Crunchbase.

I am sure that startup enthusiasts will love this dataset and try to build working models.

Startups Valued at $1 Billion or More

A Comprehensive Dataset of Successful Startups around the World

About this dataset

This dataset contains a list of startup companies around the world that have been valued at $1 billion or more. This dataset can be used to research the most innovative and successful startups in the world, and to compare and contrast companies in different industries, locations, etc

How to use the dataset

This dataset can be used to research the most innovative and successful startups in the world, and to compare and contrast companies in different industries, locations, etc.

Some possible questions that could be answered using this data include: - Which startups have been valued at $1 billion or more? - Where are these startups located? - What industries do these startups belong to? - Who are the investors in these startups? - What is the last valuation of each startup? - When did each startup join Crunchbase?

Research Ideas

• Identifying the most innovative and successful startups in the world
• Analyzing and comparing startups in different industries, locations, etc.
• Predicting which startups are most likely to achieve unicorn status

Acknowledgements

This dataset was scraped from Crunchbase, a website that provides information on startups and tech companies.

Columns

File: unicorns.csv | Column name | Description | |:-------------------------------|:------------------------------------------------------| | Updated at | The date when the dataset was last updated. (Date) | | Company | The name of the startup company. (Text) | | Crunchbase Url | The URL of the company's Crunchbase profile. (URL) | | Last Valuation (Billion $) | The company's last valuation, in US dollars. (Number) | | Date Joined | The date when the company joined Crunchbase. (Date) | | Year Joined | The year when the company joined Crunchbase. (Number) | | City | The city where the company is located. (Text) | | Country | The country where the company is located. (Text) | | Industry | The industry in which the company operates. (Text) | | Investors | The investors in the company. (Text) | | Company Website | The URL of the company's website. (URL) |

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• 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:

• Company name;
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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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Those who said, "yes, we have an open government mandate" were then asked if it was funded. Responses are tabulated by type of government showing the % of respondents in each group who selected, Yes, No, or Unsure.

These data were generated as part of a two-and-a-half-year ESRC-funded research project examining the digitalisation of higher education (HE) and the educational technology (Edtech) industry in HE. Building on a theoretical lens of assetisation, it focused on forms of value in the sector, and governance challenges of digital data. It followed three groups of actors: UK universities, Edtech companies, and investors in Edtech. The researchers first sought to develop an overview of the Edtech industry in HE by building three databases on Edtech companies, investors in Edtech, and investment deals, using data downloaded from Crunchbase, a proprietary platform. Due to Crunchbase’s Terms of Service, only parts of one database are allowed to be submitted to this repository, i.e. a list of companies with the project’s classification. A report offering descriptive analysis of all three databases was produced and is submitted as well. A qualitative discursive analysis was conducted by analysing seven documents in depth. In the second phase, researchers conducted interviews with participants representing three groups of actors (n=43) and collected documents on their organisations. Moreover, a list of documents collected from Big Tech (Microsoft, Amazon, and Salesforce) were collected to contextualise the role of global digital infrastructure in HE. Due to commercial sensitivity, only lists of documents collected about investors and Big Tech are submitted to the repository. Researchers then conducted focus groups (n=6) with representatives of universities (n=19). The dataset includes transcripts of focus groups and outputs of writing by participants during the focus group. Finally, a public consultation was held via a survey, and 15 participants offered qualitative answers.

The higher education (HE) sector has been marketised for decades; but the speed, scope, and extent of marketisation has led key education scholars to conceptualise it as a global industry (Verger, Lubienski, & Steiner-Khamsi, 2016). Further, the use of technology to transform teaching and learning, as well as the profound digitalisation of universities more broadly, has led universities to collect and process an unprecedented amount of digital data. Education technology (EdTech) companies have become one of the key players in the HE industry and the UK has made EdTech one of its key pillars in its recent international education strategy (HM Government, 2019). EdTech companies are reporting unprecedented growth. In 2019, Coursera became a 'unicorn' (i.e. a company worth over $1 billion), while British-based FutureLearn secured £50 million investment by selling 50% shares of the company. Investment in EdTech is growing at an impressive rate and reached $16.3bn in 2018 (ET, 2019). While EdTech start-up companies strive to become 'unicorns' and profit from HE, so too might universities increasingly look for new ways of profiting from the wealth of digital data they produce.

The study of HE markets has so far focused on service-commodities. However, data and data products do not act like commodities. Commodities are consumed once used, but data is reproducible at almost zero marginal cost. New products and services can be created from data and monetised through subscription fees, an app, or a platform that does not transfer ownership, control, or reproduction rights to the user. Furthermore, data use creates yet more data, and the network effects increase the value of these platforms. Therefore, there is a new quality at play in the monetisation and marketisation of these digital HE products and services: 'assetization'. We are witnessing a widespread change from creating value via market exchange towards extracting value via the ownership and control of assets.

This research project aims to investigate these new processes of value creation and extraction in an HE sector that is digitalising its operations and introducing new digital solutions premised on the expansion of service fees. By introducing a focus on assets, and economic rents, this project offers a theoretically and empirically transformative approach to understand emerging HE markets and their implications for the HE sector. The assetization of HE is consequential because of the legal and technical implications for its regulation. It is also crucial to examine in any discussion about the legitimate and socially just arrangement and distribution of assets, their ownership, and their uses. The project employs an innovative, comparative, and participatory mixed-methods research design. It combines digital methods, interviews, observation, document analysis, deliberative focus groups, knowledge exchange and co-production with stakeholders, and public consultation. Data analysis will include quantitative and qualitative analysis of investment trends, comparative case studies of investors, EdTech companies and universities, and social network analysis.

The application of this research project is fourfold. First, it will help universities understand the emerging processes of assetization so they can develop policies and practices for protecting their rights. Second, it will assist entrepreneurs in finding ways to incorporate ethical and sustainable considerations in their innovation processes. Third, it will mediate between the financial interests of investors and the social function of universities. Here, it will provide evidence for policymakers on how to include assets in HE sector regulation. Finally, it will unpack potential forms of inequality that assetization might bring into the HE sector.

This dataset contains information on private companies valued at over $1 billion (“unicorns”) as of March 2022. Each record includes the company’s valuation, funding, country of origin, industry, key investors, and the years the company was founded and became a unicorn.

Key Features

• Company Name – The unicorn company’s name.
• Valuation – Current company valuation in USD.
• Funding – Total funding raised to date in USD.
• Country – Country where the company is headquartered.
• City – City of headquarters, useful for identifying industry hubs.
• Industry – Primary industry of the company (e.g., Fintech, E-commerce, AI).
• Investors – Select investors backing the company.
• Year Founded – Year the company was established.
• Year Became Unicorn – Year the company reached a $1B valuation.

Potential Analyses

• ROI Leaders – Which companies have achieved the largest return on investment?
• Speed to Unicorn – How long it takes for startups to reach unicorn status and trends over time.
• Geographic Distribution – Countries and cities with the highest concentration of unicorns.
• Investor Impact – Which investors have funded the most unicorns?

Source

Data compiled from CB Insights and Crunchbase. Licensed under Public Domain.

Crypto, Web3 and Blockchain Jobs

Scraped active crypto jobs listed on cryptojobslist.com

About this dataset

Our crypto job market dataset contains data on job postings in the blockchain/cryptocurrency industry from 75 different websites. The data spans from January 1st, 2018 to December 31st, 2019.

This dataset provides a unique opportunity to understand the trends and dynamics of the burgeoningcrypto job market. It includes information on job postings from a wide range of companies, spanning startups to established enterprises. The data includes job titles, salary ranges, tags, and the date the job was posted.

This dataset can help answer important questions about the crypto job market, such as: - What types of jobs are most popular in the industry? - What skills are most in demand? - What are typical salaries for different positions?

How to use the dataset

The data in this dataset can be used to analyze the trends in the blockchain/cryptocurrency job market. The data includes information on job postings from 75 different websites, spanning from January 1st, 2018 to December 31st, 2019.

The data can be used to track the number of job postings over time, as well as the average salary for each position. Additionally, the tags column can be used to identify which skills are most in demand by employers

Research Ideas

• Identify trends in the types of jobs being posted in the blockchain/cryptocurrency industry.
• Study which companies are hiring the most in the blockchain/cryptocurrency industry.

Acknowledgements

The dataset was scraped from here, and here. And was originally posted here

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: companies.csv | Column name | Description | |:------------------------|:-------------------------------------------------------------| | Crunchbase Rank | The rank of the company on Crunchbase. (Integer) | | Company Name | The name of the company. (String) | | Total Funding | The total amount of funding the company has raised. (String) | | Number of Employees | The number of employees the company has. (Integer) |

File: all_jobs.csv | Column name | Description | |:------------------|:-------------------------------------------| | Company Name | The name of the company. (String) | | Job Link | A link to the job posting. (String) | | Job Location | The location of the job. (String) | | Job Title | The title of the job. (String) | | Salary Range | The salary range for the job. (String) | | Tags | The tags associated with the job. (String) | | Posted Before | The date the job was posted. (Date) |

File: Aave.csv | Column name | Description | |:-----------------|:-------------------------------------------| | Company Name | The name of the company. (String) | | Job Title | The title of the job. (String) | | Salary Range | The salary range for the job. (String) | | Tags | The tags associated with the job. (String) |

File: Alchemy.csv | Column name | Description | |:-----------------|:-------------------------------------------| | Company Name | The name of the company. (String) | | Job Title | The title of the job. (String) | | Salary Range | The salary range for the job. (String) | | Tags | The tags associated with the job. (String) |

File: Amun 21 Shares.csv | Column name | Description | |:-----------------|:-------------------------------------------| | Company Name | The name of the company. (String) | | Job Title | The title of the job. (String) | | Salary Range | The salary range for the job. (String) | | Tags | The tags associated with the job. (String) |

File: Anchorage Digital.csv | Column name | Description | |:-----------------|:-------------------------------------------| | **Company N...

Y Combinator Directory Scraper

I put together a dataset of all the companies in the Y Combinator directory as of July, 13th 2023. You can search for companies by industry, region, company size, and more in this dataset.

This dataset was scraped using YC Scraper.

About Y Combinator

Y Combinator is a startup accelerator that has invested in over 4,000 companies that have a combined valuation of over $600B. The overall goal of Y Combinator is to help startups really take off.

Attributes

Meta

Author: Miguel Corral Jr.
Email: corraljrmiguel@gmail.com
LinkedIn: https://www.linkedin.com/in/imiguel
GitHub: https://github.com/corralm

Tech Companies - Merger and Acquisitions Dataset - Software Companies

This dataset contains the list of acquisitions made by the following companies:

Microsoft, Google, IBM, Hp, Apple, Amazon, Facebook, Twitter, eBay, Adobe, Citrix, Redhat, Blackberry, Disney

The attributes include the date, year, month of the acquisition, name of the company acquired, value or the cost of acquisition, business use-case of the acquisition, and the country from which the acquisition was made. The source of the dataset is Wikipedia, TechCrunch, and CrunchBase.

Interesting Tasks and Analysis Ideas

• Which company makes the acquisitions quickly
• What is the trend of business use-cases among the acquired companies throughout the years
• What can be forecasted for upcoming years in terms of acquisitions
• Predict who is likely to make next acquisitions and when

The data contains information on almost 700 startup companies backed by Y Combinator between 2005 and 2014. The data is collected and aggregated from SeedDB, CrunchBase and AngelList. The it has been cleaned and made consistent.

The variables included are:

Startups.csv

Company
Satus
Year Founded
Mapping Location
Description
Categories
Founders
Y Combinator Year
Y Combinator Session
Investors
Amounts raised in different funding rounds
Office Address
Headquarters (City)
Headquarters (US State)
Headquarters (Country)
Logo
Seed-DB Profile
Crunchbase / Angel List Profile Website

Founders.csv

Founder
Company
Gender