This dataset contains detailed information about players participating in the Indian Premier League (IPL) 2025 season. It includes player names, their auction prices, player type (capped/uncapped, Indian/Overseas), acquisition method (retained, auction, RTM), role (batter, bowler, all-rounder, wicketkeeper), and the team they belong to. This dataset is ideal for analyzing player valuations, team compositions, and trends in IPL auctions.

Columns/Features:

Player: Name of the player (including nationality for overseas players).

Price_in_cr: Price of the player in Indian Rupees (in crores).

Type: Player type (e.g., Indian capped, Indian uncapped, Overseas capped).

Acquisition: Method of acquisition (Retained, Auction, RTM).

Role: Player's role in the team (Batter, Bowler, All-rounder, Wicketkeeper).

Team: IPL team the player belongs to (e.g., Chennai Super Kings, Mumbai Indians).

Use Cases:

Player Valuation Analysis: Analyze how player prices vary based on their role, type, and acquisition method.

Team Composition Analysis: Study how teams are structured in terms of batters, bowlers, and all-rounders.

Auction Trends: Identify trends in player retention, auction prices, and RTM usage.

Machine Learning: Predict player prices or team performance based on player roles and types.

Visualizations: Create visualizations like bar charts, pie charts, and heatmaps to explore the data.

Bitcoin Historical Data (2018-2024) - 15M, 1H, 4H, and 1D Timeframes

Dataset Overview

This dataset contains historical price data for Bitcoin (BTC/USDT) from January 1, 2018, to the present. The data is sourced using the Binance API, providing granular candlestick data in four timeframes: - 15-minute (15M) - 1-hour (1H) - 4-hour (4H) - 1-day (1D)

This dataset includes the following fields for each timeframe: - Open time: The timestamp for when the interval began. - Open: The price of Bitcoin at the beginning of the interval. - High: The highest price during the interval. - Low: The lowest price during the interval. - Close: The price of Bitcoin at the end of the interval. - Volume: The trading volume during the interval. - Close time: The timestamp for when the interval closed. - Quote asset volume: The total quote asset volume traded during the interval. - Number of trades: The number of trades executed within the interval. - Taker buy base asset volume: The volume of the base asset bought by takers. - Taker buy quote asset volume: The volume of the quote asset spent by takers. - Ignore: A placeholder column from Binance API, not used in analysis.

Data Sources

Binance API: Used for retrieving 15-minute, 1-hour, 4-hour, and 1-day candlestick data from 2018 to the present.

File Contents

1. btc_15m_data_2018_to_present.csv: 15-minute interval data from 2018 to the present.
2. btc_1h_data_2018_to_present.csv: 1-hour interval data from 2018 to the present.
3. btc_4h_data_2018_to_present.csv: 4-hour interval data from 2018 to the present.
4. btc_1d_data_2018_to_present.csv: 1-day interval data from 2018 to the present.

Automated Daily Updates

This dataset is automatically updated every day using a custom Python program.
The source code for the update script is available on GitHub:
🔗 Bitcoin Dataset Kaggle Auto Updater

Licensing

This dataset is provided under the CC0 Public Domain Dedication. It is free to use for any purpose, with no restrictions on usage or redistribution.

Testbed as a Service (TaaS): A Scalable Ecosystem for Smart Manufacturing and Industry 4.0 Collaboration defines a method for the acquisition, distribution, and real-time utilization of manufacturing data. Preconfigured tooling is intended to jump-start the utilization of this dataset can be downloaded from the Rocket Assembly Line Release in the TaaS GitHub Repository.

Academic Citations

If utilizing this dataset to support an academic publication, please cite the Rocket Assembly Line Testbed as a Service (TaaS): A Comparison of Data Acquisition Strategies paper, the TaaS Method paper, the Source Dataset 1 paper, and the Source Dataset 2 paper. Likewise, if distributing a dataset which utilizes this method, please include and request that anyone utilizing your dataset also cites the TaaS Method paper. Furthermore, if distributing a dataset derived from datasets in this release, please cite all associated papers and request that anyone utilizing your dataset also cites all associated upstream papers.

@article{mccormick-2025-rocket-assembly-line,
  author = {McCormick, M. R. and El Kalach, Fadi and Harik, Ramy and Wuest, Thorsten},
  title = {Rocket Assembly Line Testbed as a Service (TaaS): A Comparison of Data Acquisition Strategies},
  year = {2025},
  doi = {10.13140/RG.2.2.20357.77285},
  url = {http://dx.doi.org/10.13140/RG.2.2.20357.77285},
}
@article{mccormick-2025-testbed-as-a,
  author = {McCormick, M. R. and Wuest, Thorsten},
  title = {Testbed as a Service (TaaS): A Scalable Ecosystem for Smart Manufacturing and Industry 4.0 Collaboration},
  year = {2025},
  doi = {10.13140/RG.2.2.25803.60967},
  url = {http://dx.doi.org/10.13140/RG.2.2.25803.60967},
}
@article{harik-2024-analog-and-multi,
   title={Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform}, 
   author={Ramy Harik and Fadi El Kalach and Jad Samaha and Devon Clark and Drew Sander and Philip Samaha and Liam Burns and Ibrahim Yousif and Victor Gadow and Theodros Tarekegne and Nitol Saha},
   year={2024},
   eprint={2401.15544},
   archivePrefix={arXiv},
   primaryClass={cs.LG},
   url={https://arxiv.org/abs/2401.15544}, 
}
@article{harik-2025-analog-and-multi,
   title={Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform V2}, 
   author={Ramy Harik and Fadi El Kalach and Jad Samaha and Philip Samaha and Devon Clark and Drew Sander and Liam Burns and Ibrahim Yousif and Victor Gadow and Ahmed Mahmoud and Thorsten Wuest},
   year={2025},
   eprint={2502.05020},
   archivePrefix={arXiv},
   primaryClass={cs.LG},
   url={https://arxiv.org/abs/2502.05020}, 
}

Source Datasets

This dataset was derived from the following datasets:

• FF | 2023 12 12 | Multi-Modal Dataset [1/6]
• FF | 2023 12 12 | Multi-Modal Dataset [2/6]
• FF | 2023 12 12 | Multi-Modal Dataset [3/6]
• FF | 2023 12 12 | Multi-Modal Dataset [4/6]
• FF | 2023 12 12 | Multi-Modal Dataset [5/6]
• FF | 2023 12 12 | Multi-Modal Dataset [6/6]
• FF | 2023 12 12 | Analog Dataset

The AI training data market is experiencing robust growth, driven by the escalating demand for advanced AI applications across diverse sectors. The market's expansion is fueled by the increasing adoption of machine learning (ML) and deep learning (DL) algorithms, which require vast quantities of high-quality data for effective training. Key application areas like autonomous vehicles, healthcare diagnostics, and personalized recommendations are significantly contributing to market expansion. The market is segmented by application (IT, Automotive, Government, Healthcare, BFSI, Retail & E-commerce, Others) and data type (Text, Image/Video, Audio). While North America currently holds a dominant market share due to the presence of major technology companies and robust research & development activities, the Asia-Pacific region is projected to witness the fastest growth rate in the coming years, propelled by rapid digitalization and increasing investments in AI infrastructure across countries like China and India. The competitive landscape is characterized by a mix of established technology giants and specialized data annotation companies, each vying for market dominance through innovative data solutions and strategic partnerships. Significant restraints include the high cost of data acquisition and annotation, concerns about data privacy and security, and the need for specialized expertise in data management and labeling. However, advancements in automated data annotation tools and the emergence of synthetic data generation techniques are expected to mitigate some of these challenges. The forecast period of 2025-2033 suggests a continued upward trajectory for the market, driven by factors such as increasing investment in AI research, expanding adoption of cloud-based AI platforms, and the growing need for personalized and intelligent services across numerous industries. While precise figures for market size and CAGR are unavailable, a conservative estimate, considering industry trends and recent reports on similar markets, would project a substantial compound annual growth rate (CAGR) of around 20% from 2025, resulting in a market value exceeding $50 billion by 2033.

TechCorner Mobile Purchase & Engagement Data (2024-2025)

Context

TechCorner Mobile Sales & Customer Insights is a real-world dataset capturing 10 months of mobile phone sales transactions from a retail shop in Bangladesh. This dataset was designed to analyze customer location, buying behavior, and the impact of Facebook marketing efforts.

The primary goal was to identify whether customers are from the local area (Rangamati Sadar, Inside Rangamati) or completely outside Rangamati. Since TechCorner operates a Facebook page, the dataset also includes insights into whether Facebook marketing is effectively reaching potential buyers.

Additionally, the dataset helps in determining: ✔ How many customers are new vs. returning buyers ✔ If customers are followers of the shop’s Facebook page ✔ Whether a customer was recommended by an existing buyer

This dataset is valuable for:

Retail sales analysis to understand product demand fluctuations.

Marketing impact measurement (Facebook engagement vs. actual purchase behavior).

Customer segmentation (local vs. non-local buyers, social media influence, word-of-mouth impact).

Sales trend analysis based on preferred phone models and price ranges.

With a realistic, non-uniform distribution of daily sales and some intentional missing values, this dataset reflects actual retail business conditions rather than artificially smooth AI-generated data.

Marketing & Customer Queries

Does he/she Come from Facebook Page? → Whether the customer came from a Facebook page (Yes/No). Used to analyze Facebook marketing reach.

Does he/she Followed Our Page? → Whether the customer is already a follower of the shop’s Facebook page (Yes/No). Helps measure brand loyalty and organic engagement.

Did he/she buy any mobile before? → Whether the customer is a repeat buyer (Yes/No). Determines the percentage of returning customers.

Did he/she hear of our shop before? → Whether the customer knew about the shop before purchasing (Yes/No). Identifies the impact of referrals or previous marketing efforts.

Was this customer recommended by an old customer? → Whether an existing customer referred them to the shop (Yes/No). Helps evaluate the effectiveness of word-of-mouth marketing.

Acknowledgements

This dataset is derived from real-world mobile sales transactions recorded at TechCorner, a retail shop in Bangladesh. It accurately reflects customer purchasing behavior, pricing trends, and the effectiveness of Facebook marketing in driving sales. Special appreciation to TechCorner for providing comprehensive insights into daily sales patterns, customer demographics, and market dynamics.

This dataset can be used for:

📊 Predictive modeling of sales trends based on customer demographics and marketing channels. 📈 Marketing effectiveness analysis (impact of Facebook promotions vs. organic sales). 🔍 Clustering customers based on purchasing habits (new vs. returning buyers, Facebook users vs. walk-ins). 📌 Understanding demand for different smartphone brands in a local retail market. 🚀 Analyzing how word-of-mouth recommendations influence new customer acquisition.

💡 Can you build a model to predict if a customer is likely to return? 💬 How effective is Facebook in driving actual sales compared to walk-ins? 🔍 Can we cluster customers based on behavior and brand preferences?