Xverum’s Company Data delivers comprehensive insights into over 50 million global businesses, from fast-growing startups to established private companies. This dataset is a trusted source for investors, analysts, and B2B teams seeking reliable firmographic data, company registry attributes, and organizational details across industries and geographies.

Whether you’re researching potential clients, running B2B campaigns, or building smarter go-to-market strategies, this company dataset gives you the full picture—updated every 30 days.

What’s Included: ✅ 50M+ Verified Company Records across 249 countries ✅ 40+ Firmographic Attributes, including: ✔️ Company Name, Industry ✔️ Employee Count, HQ Location, Founding Year ✔️ Company Domain, Company Profile URL, Registry Type ✅ Private, Public & Startup Coverage with a focus on any business size. ✅ Custom Region Delivery – segment by country, region or worldwide. ✅ 30-Day Refresh Cycle to keep your data fresh and investment-ready ✅ Available in CSV, JSON, or via API & S3

Use Cases: ➡️ Company Research & Competitive Benchmarking Analyze growth metrics and benchmarks across industries and private company peers.

➡️ B2B Lead Generation & Outreach Fuel CRM and outbound sales platforms with firmographic-enriched startup and SMB records.

➡️ Investor Intelligence & Deal Sourcing Spot high-growth startups by tracking employee expansion, market entry, and location-based clusters.

➡️ Market Mapping & Go-To-Market Planning Build total addressable market (TAM) maps using verified business registry records and firmographics.

Why Choose Xverum’s Company Dataset? ✅ Global Reach: 50M companies, with data on startups, SMEs, and private firms in emerging and developed markets ✅ Flexible Formats: Delivered via API, bulk export, or cloud delivery ✅ GDPR & CCPA Compliant: Ethically sourced and privacy-focused

Ready to enrich your CRM or power your next B2B campaign? Request a free sample today or contact us to dive deeper into your data needs.

Techsalerator's Corporate Actions Dataset in Israel offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 473 companies traded on the Tel-Aviv Stock Exchange (XTAE).

‍

Top 5 used data fields in the Corporate Actions Dataset for Israel:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

‍

Top 5 corporate actions in Israel:

Technology and Startups: Corporate actions in Israel's renowned technology sector, including mergers, acquisitions, and initial public offerings (IPOs), are crucial for the country's innovation ecosystem and its reputation as the "Startup Nation."

Healthcare and Life Sciences: Corporate actions related to pharmaceuticals, medical research, and healthcare startups contribute to Israel's reputation as a hub for medical innovation and cutting-edge research.

Cybersecurity and Defense Technology: Corporate actions in the cybersecurity and defense technology sectors reflect Israel's expertise in developing advanced cybersecurity solutions and defense systems.

Renewable Energy and Cleantech: Corporate actions related to renewable energy projects and cleantech initiatives align with Israel's efforts to develop sustainable energy sources and address environmental challenges.

Financial Services and Fintech: Corporate actions involving financial technology (fintech) startups, digital payment solutions, and blockchain technology contribute to Israel's financial services sector's modernization.

‍

Top 5 financial instruments with corporate action Data in Israel

Israel Stock Exchange (ISE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Israel Stock Exchange. This index would provide insights into the performance of the Israeli stock market.

Israel Stock Exchange (ISE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Israel Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in Israel.

SuperMart Israel: An Israel-based supermarket chain with operations in multiple regions. SuperMart focuses on providing essential products to local communities and contributing to the retail sector's growth.

FinanceIsrael: A financial services provider in Israel with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.

AgriTech Israel: A company dedicated to advancing agricultural technology in Israel, focusing on optimizing crop yields and improving food security to support the country's agricultural sector.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Israel, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Israel?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

How complete is the Corporate Actions Dataset coverage in Israel ?

Techsalerator provides comprehensive coverage of Corporate Actions Data for various companies and...

Character Pool Dataset: 10 Characters - Generated by Conversation Dataset Generator

This dataset was generated using the Conversation Dataset Generator script available at https://cahlen.github.io/conversation-dataset-generator/.

  Generation Parameters

Number of Conversations Requested: 1000 Number of Conversations Successfully Generated: 999 Total Turns: 10755 Model ID: meta-llama/Meta-Llama-3-8B-Instruct Generation Mode:

  Topic & Scenario

Initial Topic:… See the full description on the dataset page: https://huggingface.co/datasets/cahlen/cdg-southpark-tech-startup-1k.

Business-critical Data Types We offer access to robust datasets sourced from over 13M job ads daily. Track companies’ growth, market focus, technological shifts, planned geographic expansion, and more: - Identify new business opportunities - Identify and forecast industry & technological trends - Help identify the jobs, teams, and business units that have the highest impact on corporate goals - Identify most in-demand skills and qualifications for key positions.

Fresh Datasets We regularly update our datasets, assuring you access to the latest data and allowing for timely analysis of rapidly evolving markets & dynamic businesses.

Historical Datasets We maintain at your disposal historical datasets, allowing for comprehensive, reliable, and statistically sound historical analysis, trend identification, and forecasting.

Easy Access and Retrieval Our job listing datasets are available in industry-standard, convenient JSON and CSV formats. These structured formats make our datasets compatible with machine learning, artificial intelligence training, and similar applications. The historical data retrieval process is quick and reliable thanks to our robust, easy-to-implement API integration.

Datasets for investors Investment firms and hedge funds use our datasets to better inform their investment decisions by gaining up-to-date, reliable insights into workforce growth, geographic expansion, market focus, technology shifts, and other factors of start-ups and established companies.

Datasets for businesses Our datasets are used by retailers, manufacturers, real estate agents, and many other types of B2B & B2C businesses to stay ahead of the curve. They can gain insights into the competitive landscape, technology, and product adoption trends as well as power their lead generation processes with data-driven decision-making.

➡️ You can choose from multiple data formats, delivery frequency options, and delivery methods;

➡️ You can select raw or clean and AI-enriched datasets;

➡️ Multiple APIs designed for effortless search and enrichment (accessible using a user-friendly self-service tool);

➡️ Fresh data: daily updates, easy change tracking with dedicated data fields, and a constant flow of new data;

➡️ You get all necessary resources for evaluating our data: a free consultation, a data sample, or free credits for testing our APIs.

Coresignal's employee and company data enables you to create and improve innovative data-driven solutions and extract actionable business insights. These datasets are popular among companies from different industries, including investment, sales, and HR technology.

✅ For investors

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal's global Employee Data and Company Data.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies hungry for the next investment
3. Check if a startup is about to reach the next maturity phase

✅ For HR tech

Coresignal's global Employee Data and Company Data enable you to build and improve AI-based talent-sourcing and other HR technology solutions.

Use cases

1. Build AI-based tools
2. Find qualified candidates
3. Enrich existing hiring data

✅ For sales tech

Companies use our large-scale datasets to improve their lead generation engines and power sales technology platforms.

Use cases

1. Extract targeted lead lists
2. Fill in the gaps in your lead data
3. Enable data-driven sales strategies

➡️ Why 400+ data-powered businesses choose Coresignal:

1. Experienced data provider (in the market since 2016);
2. Exceptional client service;
3. Responsible and secure data collection.

Our Web Scraping dataset includes such data points as company name, location, headcount, industry, and size, among others. It offers extensive fresh and historical data, including even companies that operate in stealth mode.

For lead generation

With millions of companies from around the globe, this scraped data enables you to filter potential clients based on specific criteria and hasten the conversion process.

Use cases

1. Filter potential clients according to location, size, and other criteria
2. Enrich your existing database
3. Improve conversion rates
4. Use predictive models to identify potential leads
5. Group your leads in segments for more accurate targeting

For market and business analysis

Our Web Scraping Data on companies gives information about millions of businesses, allowing you to evaluate your competitors.

Use cases

1. Know your competitors
2. See your competitors' size, headcount, and revenue
3. Come up with a data-driven strategy for the next quarter

For Investors

We recommend Web Scraping Data for investors to discover and evaluate businesses with the highest potential.

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal’s global Web Scraping Data.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies looking for the next investment
3. Check if a startup is about to reach its maturity
4. Predict a startup's potential at the founding moment
5. Choose companies that fit you in terms of size and headcount

For sales prospecting

Web Scraping Data saves time your employees would otherwise use it to find potential clients and choose the best prospects manually.

Use cases

1. Make a short list of the top prospects
2. Define which companies are large or small enough to buy your product
3. Based on the revenue, determine which companies are ready to convert
4. Sort the companies by their distance from your warehouse to draw a line where selling won't result in satisfactory profit

Analysis of ‘UBER Stock Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/varpit94/uber-stock-data on 21 November 2021.

--- Dataset description provided by original source is as follows ---

What is UBER?

Uber Technologies, Inc., commonly known as Uber, is an American technology company. Its services include ride-hailing, food delivery (Uber Eats and Postmates), package delivery, couriers, freight transportation, and, through a partnership with Lime, electric bicycle and motorized scooter rental. The company is based in San Francisco and has operations in over 900 metropolitan areas worldwide. It is one of the largest firms in the gig economy. Uber is estimated to have over 93 million monthly active users worldwide. In the United States, Uber has a 71% market share for ride-sharing and a 22% market share for food delivery. Uber has been so prominent in the sharing economy that changes in various industries as a result of Uber have been referred to as uberisation, and many startups have described their offerings as "Uber for X".

Information about this dataset

This dataset provides historical data of Uber Technologies, Inc. (UBER). The data is available at a daily level. Currency is USD.

--- Original source retains full ownership of the source dataset ---

Our Web Data dataset includes such data points as company name, location, headcount, industry, and size, among others. It offers extensive fresh and historical data, including even companies that operate in stealth mode.

For lead generation

With millions of companies worldwide, Web Company Database helps you filter potential clients based on custom criteria and speed up the conversion process.

Use cases

1. Filter potential clients according to location, size, and other criteria
2. Enrich your existing database
3. Improve conversion rates
4. Use predictive models to identify potential leads
5. Group your leads in segments for more accurate targeting

For market and business analysis

Our Web Company Data provides information about millions of companies, allowing you to find your competitors and see their weaknesses and strengths.

Use cases

1. Pinpoint your competitors
2. Learn about your competitors' size, headcount, and revenue
3. Prepare a data-driven plan for the next quarter

For Investors

We recommend B2B Web Data for investors to discover and evaluate businesses with the highest potential.

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal’s global B2B Web Dataset.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies hungry for the next investment
3. Check if a startup is about to reach the next maturity phase
4. Identify and predict a startup's potential at the founding moment
5. Choose companies that fit you in terms of size and headcount

For sales prospecting

B2B Web Database saves time your employees would otherwise use to search for potential clients manually.

Use cases

1. Make a short list of the top prospects
2. Define which companies are large or small enough to buy your product
3. Based on the revenue, determine which companies are ready to convert
4. Sort the companies by their distance from your warehouse to draw a line where selling won't result in satisfactory profit

Obtain unmatched insights into the Indian market using our database of over 2.5 million company profiles. Our Xverum team is client-oriented and we focus on delivering structured and fresh B2B and brand data, enabling you to make informed decisions and drive your business forward.

      Where this Indian B2B data can benefit you?

Competitor Analysis: Assess potential risks associated with specific real-time brand data, enabling you to mitigate financial and reputational risks in your business dealings.

Direct Marketing: Utilize our business data to target your marketing efforts with precision, reaching the right audience for your products and services.

B2B List Validation: Ensure the accuracy and legitimacy of your B2B contact lists, optimizing your lead generation and sales efforts.

With our comprehensive database of over 2.5 million Indian company profiles, you have the power to make informed decisions, drive your business strategy, and achieve success in the dynamic Indian market.

4 key features of our Indian data: - 40+ Data Attributes per Company Profile - Structured and Raw Data - Easily Integrated into Your Solutions - 100% Safe Source Promise

Contact our Xverum team and we'll be glad to find the best option due to your data requirements.

Please Note: Our dataset does not include PII and/or phone numbers.

Our Business Listings Data dataset includes such data points as company name, location, headcount, industry, and size, among others. It offers extensive fresh and historical data, including even companies that operate in stealth mode.

For market and business analysis

Our Business Listings Data gives information about millions of companies, allowing you to find your competitors and see their weak and strong points.

Use cases

1. Pinpoint your competitors
2. Learn about your competitors' size, headcount, and revenue
3. Prepare a data-driven plan for the next quarter

For Investors

We recommend Business Listings Data for investors to discover and evaluate businesses with the highest potential.

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal’s global Business Listings Data.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies hungry for the next investment
3. Check if a startup is about to reach the next maturity phase
4. Identify and predict a startup's potential at the founding moment
5. Choose companies that fit you in terms of size and headcount

For sales prospecting

Business Listings Data saves time your employees would otherwise use it to manually find potential clients and choose the best prospects.

Use cases

1. Make a short list of the top prospects
2. Define which companies are large or small enough to buy your product
3. Based on the revenue, determine which companies are ready to convert
4. Sort the companies by their distance from your warehouse to draw a line where selling won't result in satisfactory profit

Context --> Interview task

Hey Kaggle, I am currently involved in the recruitment process of a big startup in the banking industry in London (raised over 300m) and they asked me to do this data analysis test. I would like your opinion on my solution because I've submitted it and they just said that my analysis wasn't correct or incomplete yet.

I have a few days to fixe it and I am counting on your

Content --> KYC problem investigation

As a financial institution regulated by the FCA, the startup "XYZ" has the obligation to verify the identity of all customers who want to open a XYZ account. Each prospective customer has to go through a Know Your Customer (KYC) process by submitting a government-issued photo ID and a facial picture of themself to our partner, Veritas. Veritas then would perform 2 checks: • Document check: To verify that the photo ID is valid and authentic; • Facial Similarity check: To verify that the face in the picture is the same with that on the submitted ID.

The customer will ‘pass’ the KYC process and get aboard if the results of both Document and Facial Similarity checks are ‘clear’. If the result of any check is not ‘clear’, the customer has to submit all the photos again.

The pass rate is defined as the number of customers who pass both the KYC process divided by the number of customers who attempt the process. Each customer has up to 2 attempts. The pass rate has decreased substantially in the recent period. Please write a report that outlines the root causes and solutions.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Please help me ! I've put my solution in the kernels so you can have a look, I might have fusion the files in a wrong way.

What is UBER?

Uber Technologies, Inc., commonly known as Uber, is an American technology company. Its services include ride-hailing, food delivery (Uber Eats and Postmates), package delivery, couriers, freight transportation, and, through a partnership with Lime, electric bicycle and motorized scooter rental. The company is based in San Francisco and has operations in over 900 metropolitan areas worldwide. It is one of the largest firms in the gig economy. Uber is estimated to have over 93 million monthly active users worldwide. In the United States, Uber has a 71% market share for ride-sharing and a 22% market share for food delivery. Uber has been so prominent in the sharing economy that changes in various industries as a result of Uber have been referred to as uberisation, and many startups have described their offerings as "Uber for X".

Information about this dataset

This dataset provides historical data of Uber Technologies, Inc. (UBER). The data is available at a daily level. Currency is USD.

DATAtourisme is an R & D project winner of the Future Investment Program (PIA). It aims to gather in a national platform, tourist information data produced by the Tourist Offices, Departmental Agencies and Regional Committees of Tourism, in order to disseminate them in open-data and thus facilitate the creation of innovative tourism services by start-ups, digital agencies, media and other public or private actors.

This dataset is the daily export of tourist data present on the platform DATAtourism.

It contains: — description of tourist sites — description of the events taking place on these sites

The content is described on https://framagit.org/datatourisme/ontology/tree/master

as well as https://framagit.org/datatourisme/ontology/raw/master/Documentation/Ontologie%20DATAtourisme%20v1.0_Documentation.pdf

A simplified version in CSV format is also available, with the following fields: — ID: event ID (URI) — label: title of the event — type: type of event (separated by/) — theme: theme of the event (separated by/) — StartDate: start date — EndDate: end date — street: address — PostalCode: postal code — city: city — INSEE: INSEE code of the municipality — latitude,longitude: geographical Position (WGS84) — email — website — phone — lastupdate: last update of the data — how: detailed text describing the event

The CSV file generation script is available at https://github.com/cquest/datatourisme

Analysis of ‘Ethereum Cryptocurrency Historical Dataset ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kaushiksuresh147/ethereum-cryptocurrency-historical-dataset on 30 September 2021.

--- Dataset description provided by original source is as follows ---

https://www.bernardmarr.com/img/What%20Is%20The%20Difference%20Between%20Bitcoin%20and%20Ethereum.png">

Context

Ethereum a decentralized, open-source blockchain featuring smart contract functionality was proposed in 2013 by programmer Vitalik Buterin. Development was crowdfunded in 2014, and the network went live on 30 July 2015, with 72 million coins premined.

Some interesting facts about Ethereum(ETH): - Ether (ETH) is the native cryptocurrency of the platform. It is the second-largest cryptocurrency by market capitalization, after Bitcoin. Ethereum is the most actively used blockchain. - Some of the world’s leading corporations joined the EEA(Ethereum Alliance, is a collaboration of many block start-ups) and supported “further development.” Some of the most famous companies are Samsung SDS, Toyota Research Institute, Banco Santander, Microsoft, J.P.Morgan, Merck GaA, Intel, Deloitte, DTCC, ING, Accenture, Consensys, Bank of Canada, and BNY Mellon.

Content

The dataset consists of ETH prices from March-2016 to the current date(1830days) and the dataset will be updated on a weekly basis.

Information regarding the data

The data totally consists of 1813 records(1813 days) with 7 columns. The description of the features is given below

| No |Columns | Descriptions | | -- | -- | -- | | 1 | Date | Date of the ETH prices | | 2 | Price | Prices of ETH(dollars) | | 3 | Open | Opening price of ETH on the respective date(Dollars) | | 4 | High | Highest price of ETH on the respective date(Dollars) | | 5 | Low | Lowest price of ETH on the respective date(Dollars) | | 6 | Vol. | Volume of ETH on the respective date(Dollars). | | 7 | Change % | Percentage of Change in ETH prices on the respective date | |

Acknowledgements

The dataset was extracted from investing.com

Inspiration

Experts say that ethereum has a huge potential in the future. Do you believe it? Well, let's find it by building our own creative models to predict if the statement is true.

--- Original source retains full ownership of the source dataset ---

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Mullen's Ride: Is the Auto Startup Poised for a Comeback? (MULN)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

About San Francisco San Francisco is a vibrant and dynamic city located on the west coast of the United States, in the state of California. Known for its hilly terrain, diverse neighborhoods, and iconic landmarks such as the Golden Gate Bridge and Alcatraz Island, San Francisco is a hub of culture, creativity, and innovation. The city is renowned for its world-class restaurants, thriving arts scene, and historic architecture, and is home to many tech companies and startups. With its mild climate, stunning views, and rich history, San Francisco is a must-visit destination for travelers from around the world.

About Dataset This dataset contains daily weather observations for San Francisco, USA from January 1, 1993 to January 1, 2023. The data is collected from Meteostat. The dataset contains 10 columns with 10958 rows.

This dataset provides daily aggregations of 3 cycling variables of base-load power stations in the GB power system for the period 2009--2017. It describes the number of startups, severe ramping events and average load factor of base-load generators (coal and gas-fired power stations with capacity > 100MW). It is the first of its kind, and may help improve our understanding of how cycling interacts with other factors in the power system such as renewables generation.

The cycling data has been derived from operational data for the GB power system from Elexon's P114 dataset. We also include data on IRES (intermittent renewable energy sources; wind + solar) generation, total generation, and the number of active base-load generators in that year.

Definitions of variables:

• metered_wind_MWh: total generation of wind generators in the Elexon dataset.

• unmetered_wind_MWh: estimates for total generation of unmetered wind generators from National Grid Demand data.

• solar_MWh: estimates for total PV generation from National Grid Demand data.

• total_IRES_generation: sum of metered_wind_MWh, unmetered_wind_MWh, solar_MWh (in MWh)

• total_generation_MWh: calculated as the sum of all metered generation (from Elexon data), interconnection imports, unmetered wind generation and solar generation.

• IRES_penetration: calculated as total_IRES_generation/total_generation_MWh

• annual_active_baseload_gens: the number of base-load generators* which started up at least once in that calendar year.

• startups_baseload: count of base-load *startups (count of events where production changed from < 1 MWh to >= 1 MWh)

• load_factor_baseload: average instantenous output of base-load* generators divided by capacity during online hours only (hence measuring part-loading during operational hours)

• severe_ramping_events: defined as a change in production of >= 25% of a base-load generator's capacity in consecutive settlement periods.

• base-load generators defined as coal- and gas-fired generators of at least 100 MW capacity.

This dataset provides daily aggregations of 3 cycling variables of base-load power stations in the GB power system for the period 2009--2017. It describes the number of startups, severe ramping events and average load factor of base-load generators (coal and gas-fired power stations with capacity > 100MW). It is the first of its kind, and may help improve our understanding of how cycling interacts with other factors in the power system such as renewables generation.

The cycling data has been derived from operational data for the GB power system from Elexon's P114 dataset. We also include data on IRES (intermittent renewable energy sources; wind + solar) generation, total generation, and the number of active base-load generators in that year.

Definitions of variables:

• metered_wind_MWh: total generation of wind generators in the Elexon dataset.

• unmetered_wind_MWh: estimates for total generation of unmetered wind generators from National Grid Demand data.

• solar_MWh: estimates for total PV generation from National Grid Demand data.

• total_IRES_generation: sum of metered_wind_MWh, unmetered_wind_MWh, solar_MWh (in MWh)

• total_generation_MWh: calculated as the sum of all metered generation (from Elexon data), interconnection imports, unmetered wind generation and solar generation.

• IRES_penetration: calculated as total_IRES_generation/total_generation_MWh

• annual_active_baseload_gens: the number of base-load generators* which started up at least once in that calendar year.

• startups_baseload: count of base-load *startups (count of events where production changed from < 1 MWh to >= 1 MWh)

• load_factor_baseload: average instantenous output of base-load* generators divided by capacity during online hours only (hence measuring part-loading during operational hours)

• severe_ramping_events: defined as a change in production of >= 25% of a base-load generator's capacity in consecutive settlement periods.

• base-load generators defined as coal- and gas-fired generators of at least 100 MW capacity.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8127972%2F63017a82c729165f1634ecdf9bace29e%2Fbase%20load%20and%20peak%20load.png?generation=1714619217756134&alt=media" alt="">https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8127972%2F655d785320a7b1fcd7d70c94971e1227%2FCaptu1re.JPG?generation=1714619225747612&alt=media" alt="">

Techsalerator's Corporate Actions Dataset in Latvia offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 34 companies traded on the Nasdaq Baltic Riga (XRIS).

‍

Top 5 used data fields in the Corporate Actions Dataset for Latvia:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

‍

Top 5 corporate actions in Latvia:

Mergers and Acquisitions (M&A): Mergers, acquisitions, and corporate restructurings are significant in Latvia, impacting various industries and contributing to market changes.

Dividend Declarations: Latvian companies often declare dividends to distribute profits to shareholders. Dividend announcements can influence stock prices and investor sentiment.

Technology and IT Industry Developments: Latvia has a growing technology and IT sector. Corporate actions related to startups, innovation, and digital transformation initiatives can be notable.

Real Estate and Construction Projects: Real estate and construction activities are essential for Latvia's economic growth. Corporate actions related to real estate developments, property sales, and infrastructure projects are prominent.

Energy and Environment Initiatives: Like many European countries, Latvia is focused on sustainable energy and environmental protection. Corporate actions in renewable energy, green technologies, and environmental policies are significant.

‍

Top 5 financial instruments with corporate action Data in Latvia

Riga Stock Exchange Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Riga Stock Exchange. This index would provide insights into the performance of the Latvian stock market.

Riga Stock Exchange Foreign Company Index: The index that tracks the performance of foreign companies listed on the Riga Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in the Latvian market.

BalticGrocers: A Latvia-based supermarket chain with operations in multiple regions. BalticGrocers focuses on providing high-quality products and convenience to consumers across Latvia.

BalticFinance Group: A financial services provider in Latvia with a focus on inclusive finance, offering banking and financial solutions to individuals and businesses across the country.

BalticSeed Co: A leading producer and distributor of certified crop seeds in various regions of Latvia, contributing to the country's agriculture and food production.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Latvia, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.