Free, daily updated MAC prefix and vendor CSV database. Download now for accurate device identification.

A csv file containing the tidal frequencies used for statistical analyses in the paper "Estimating Freshwater Flows From Tidally-Affected Hydrographic Data" by Dan Pagendam and Don Percival.

Survival after open versus endovascular repair of abdominal aortic aneurysm. Polish population analysis. (in press)

Sample Graph Datasets in CSV Format

Note: none of the data sets published here contain actual data, they are for testing purposes only.

Description

This data repository contains graph datasets, where each graph is represented by two CSV files: one for node information and another for edge details. To link the files to the same graph, their names include a common identifier based on the number of nodes. For example:

• dataset_30_nodes_interactions.csv:contains 30 rows (nodes).
• dataset_30_edges_interactions.csv: contains 47 rows (edges).
• the common identifier dataset_30 refers to the same graph.

CSV nodes

Each dataset contains the following columns:

CSV edges

Each dataset contains the following columns:

Metadata

This repository include two (2) additional tiny graph datasets to experiment before dealing with larger datasets.

CSV nodes (tiny graphs)

Each dataset contains the following columns:

CSV edges (tiny graphs)

Each dataset contains the following columns:

Metadata (tiny graphs)

Raw Data in .csv format for use with the R data wrangling scripts.

The Dog Food Data Extracted from Chewy (USA) dataset contains 4,500 detailed records of dog food products sourced from one of the leading pet supply platforms in the United States, Chewy. This dataset is ideal for businesses, researchers, and data analysts who want to explore and analyze the dog food market, including product offerings, pricing strategies, brand diversity, and customer preferences within the USA.

The dataset includes essential information such as product names, brands, prices, ingredient details, product descriptions, weight options, and availability. Organized in a CSV format for easy integration into analytics tools, this dataset provides valuable insights for those looking to study the pet food market, develop marketing strategies, or train machine learning models.

Key Features:

• Record Count: 4,500 dog food product records.
• Data Fields: Product names, brands, prices, descriptions, ingredients .. etc. Find more fields under data points section.
• Format: CSV, easy to import into databases and data analysis tools.
• Source: Extracted from Chewy’s official USA platform.
• Geography: Focused on the USA dog food market.

Use Cases:

• Market Research: Analyze trends and preferences in the USA dog food market, including popular brands, price ranges, and product availability.
• E-commerce Analysis: Understand how Chewy presents and prices dog food products, helping businesses compare their own product offerings.
• Competitor Analysis: Compare different brands and products to develop competitive strategies for dog food businesses.
• Machine Learning Models: Use the dataset for machine learning tasks such as product recommendation systems, demand forecasting, and price optimization.

A subset of Tenders Electronic Daily (TED) data covering public procurement for the European Union and beyond from 2006-01-01 to 2023-12-31 in comma separated value (CSV) format. This data includes the most important fields from the contract notice and contract award notice standard forms, such as who bought what from whom, for how much, and which procedure and award criteria were used.

Generally, the data consists of tenders above the procurement thresholds. However, publishing below threshold tenders in TED is considered good practice, and thus a non-negligible number of below threshold tenders is present as well.

Please see the documentation below for important information on the data and its usage, including a version history of the export.

The European Commission is interested in the results of research on public procurement coming from the re-use of this data. Thus, we will be grateful to receive links to any papers, reports, or applications at GROW-G4@ec.europa.eu.

TED with broader coverage is also available in XML format at https://data.europa.eu/euodp/en/data/dataset/ted-1.

eForms

On 14 November 2022, the format of notices published in TED changed: the Publications Office displays both the current standard forms and eForms and makes them available for reuse. If you reuse TED data, your systems must be ready to process both types of notices. To help adapt your systems, you can find resources, models and schemas in the eForms Software Development Kit on GitHub (https://github.com/OP-TED/eForms-SDK/https://github.com/OP-TED/eForms-SDK/). Documentation is available on the Ted Developers Documentation site (https://docs.ted.europa.eu/), including eForms FAQs (https://docs.ted.europa.eu/home/FAQ/eforms.html).

This dataset contains the provenance information (in CSV format) of all the citation data included in the OpenCitations Index, released on March 24, 2025. In particular, each line of the CSV file defines a citation, and includes the following information:[field "oci"] the Open Citation Identifier (OCI) for the citation;[field "snapshot"] the identifier of the snapshot;[field "agent"] the name of the agent that have created the citation data;[field "source"] the URL of the source dataset from where the citation data have been extracted;[field "created"] the creation time of the citation data.[field "invalidated"] the start of the destruction, cessation, or expiry of an existing entity by an activity;[field "description"] a textual description of the activity made;[field "update"] the UPDATE SPARQL query that keeps track of which metadata have been modified.The size of the zipped archive is 18 GB, while the size of the unzipped CSV files is 410 GB.

A diverse selection of 1000 empirical time series, along with results of an hctsa feature extraction, using v1.06 of hctsa and Matlab 2019b, computed on a server at The University of Sydney.

A beginner-friendly version of the MIT-BIH Arrhythmia Database, which contains 48 electrocardiograms (EKGs) from 47 patients that were at Beth Israel Deaconess Medical Center in Boston, MA in 1975-1979.

There are 48 CSVs, each of which is a 30-minute echocardiogram (EKG) from a single patient (record 201 and 202 are from the same patient). Data was collected at 360 Hz, meaning that 360 data points is equal to 1 second of time.

Banner photo by Joshua Chehov on Unsplash.

How to Analyze the Heart with Python

1. How to Analyze Heartbeats in 15 Minutes with Python
2. How the Heart Works (and What is a "QRS" Complex?)
3. How to Identify and Label the Waves of an EKG
4. How to Flatten a Wandering EKG
5. How to Calculate the Heart Rate

What is a 12-lead EKG?

EKGs, or electrocardiograms, measure the heart's function by looking at its electrical activity. The electrical activity in each part of the heart is supposed to happen in a particular order and intensity, creating that classic "heartbeat" line (or "QRS complex") you see on monitors in medical TV shows.

There are a few types of EKGs (4-lead, 5-lead, 12-lead, etc.), which give us varying detail about the heart. A 12-lead is one of the most detailed types of EKGs, as it allows us to get 12 different outputs or graphs, all looking at different, specific parts of the heart muscles.

This dataset only publishes two leads from each patient's 12-lead EKG, since that is all that the original MIT-BIH database provided.

What does each part of the QRS complex mean?

Check out Ninja Nerd's EKG Basics tutorial on YouTube to understand what each part of the QRS complex (or heartbeat) means from an electrical standpoint.

Filenames

Each file's name is the ID of the patient (except for 201 and 202, which are the same person).

Columns

• index
• calculated elapsed milliseconds (index / 360 * 1000)
• the first lead
• the second lead

The two leads are often lead MLII and another lead such as V1, V2, or V5, though some datasets do not use MLII at all. MLII is the lead most often associated with the classic QRS Complex (the medical name for a single heartbeat).

Milliseconds were calculated and added as a secondary index to each dataset. Calculations were made by dividing the index by 360 Hz then multiplying by 1000. The original index was preserved, since the calculation of milliseconds as digital signals processing (e.g. filtering) occurs may cause issues with the correlation and merging of data. You are encouraged to try whichever index is most suitable for your analysis and/or recalculate a time index with Pandas' to_timedelta().

Patient information

Info about each of the 47 patients is available here, including age, gender, medications, diagnoses, etc.

Getting Started

Physionet has some online tutorials and tips for analyzing EKGs and other time series / digital signals.

Check out our notebook for opening and visualizing the data.

How the CSVs were obtained

A write-up on how the data was converted from .dat to .csv files is available on Medium.com. Data was downloaded from the MIT-BIH Arrhythmia Database then converted to CSV.

Citations

Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID: 11446209)

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

Dataset

This dataset was created by Dilip Srivastava

Contents

The dataset has been collected in the frame of the Prac1 of the subject Tipology and Data Life Cycle of the Master's Degree in Data Science of the Universitat Oberta de Catalunya (UOC).

The dataset contains 25 variables and 52478 records corresponding to books on the GoodReads Best Books Ever list (the larges list on the site).

Original code used to retrieve the dataset can be found on github repository: github.com/scostap/goodreads_bbe_dataset

The data was retrieved in two sets, the first 30000 books and then the remainig 22478. Dates were not parsed and reformated on the second chunk so publishDate and firstPublishDate are representet in a mm/dd/yyyy format for the first 30000 records and Month Day Year for the rest.

Book cover images can be optionally downloaded from the url in the 'coverImg' field. Python code for doing so and an example can be found on the github repo.

The 25 fields of the dataset are:

| Attributes | Definition | Completeness |
| ------------- | ------------- | ------------- | 
| bookId | Book Identifier as in goodreads.com | 100 |
| title | Book title | 100 |
| series | Series Name | 45 |
| author | Book's Author | 100 |
| rating | Global goodreads rating | 100 |
| description | Book's description | 97 |
| language | Book's language | 93 |
| isbn | Book's ISBN | 92 |
| genres | Book's genres | 91 |
| characters | Main characters | 26 |
| bookFormat | Type of binding | 97 |
| edition | Type of edition (ex. Anniversary Edition) | 9 |
| pages | Number of pages | 96 |
| publisher | Editorial | 93 |
| publishDate | publication date | 98 |
| firstPublishDate | Publication date of first edition | 59 |
| awards | List of awards | 20 |
| numRatings | Number of total ratings | 100 |
| ratingsByStars | Number of ratings by stars | 97 |
| likedPercent | Derived field, percent of ratings over 2 starts (as in GoodReads) | 99 |
| setting | Story setting | 22 |
| coverImg | URL to cover image | 99 |
| bbeScore | Score in Best Books Ever list | 100 |
| bbeVotes | Number of votes in Best Books Ever list | 100 |
| price | Book's price (extracted from Iberlibro) | 73 |

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

Drug consumption database with original values of attributes. DescriptionDB.pdf contains detailed description of database.

Access our Trustpilot Reviews Data in CSV Format, offering a comprehensive collection of customer reviews from Trustpilot.

This dataset includes detailed reviews, ratings, and feedback across various industries and businesses. Available in a convenient CSV format, it is ideal for market research, sentiment analysis, and competitive benchmarking.

Leverage this data to gain insights into customer satisfaction, identify trends, and enhance your business strategies. Whether you're analyzing consumer sentiment or conducting competitive analysis, this dataset provides valuable information to support your needs.

The NHTSA Product Information Catalog and Vehicle Listing (vPIC) is a consolidated platform that presents data collected within the manufacturer reported data from CFR 49 Parts 551 - 574 for use in a variety of modern tools. NHTSA's vPIC platform is intended to serve as a centralized source for basic Vehicle Identification Number (VIN) decoding, Manufacturer Information Database (MID), Manufacturer Equipment Plant Identification and associated data. vPIC is intended to support the Open Data and Transparency initiatives of the agency by allowing the data to be freely used by the public without the burden of manual retrieval from a library of electronic documents (PDFs). While these documents will still be available online for viewing within the Manufacturer Information Database (MID) module of vPIC one can view and use the actual data through the VIN Decoder and Application Programming Interface (API) modules.

https://i.imgur.com/PcSDv8A.png" alt="Imgur">

Overview

The dataset provided here is a rich compilation of various data files gathered to support diverse analytical challenges and education in data science. It is especially curated to provide researchers, data enthusiasts, and students with real-world data across different domains, including biostatistics, travel, real estate, sports, media viewership, and more.

Files

Below is a brief overview of what each CSV file contains: - Addresses: Practical examples of string manipulation and address data formatting in CSV. - Air Travel: Historical dataset suitable for analyzing trends in air travel over a period of three years. - Biostats: A dataset of office workers' biometrics, ideal for introductory statistics and biology. - Cities: Geographic and administrative data for urban analysis or socio-demographic studies. - Car Crashes in Catalonia: Weekly traffic accident data from Catalonia, providing a base for public policy research. - De Niro's Film Ratings: Analyze trends in film ratings over time with this entertainment-focused dataset. - Ford Escort Sales: Pre-owned vehicle sales data, perfect for regression analysis or price prediction models. - Old Faithful Geyser: Geological data for pattern recognition and prediction in natural phenomena. - Freshman Year Weights and BMIs: Dataset depicting weight and BMI changes for health and lifestyle studies. - Grades: Education performance data which can be correlated with demographics or study patterns. - Home Sales: A dataset reflecting the housing market dynamics, useful for economic analysis or real estate appraisal. - Hooke's Law Demonstration: Physics data illustrating the classic principle of elasticity in springs. - Hurricanes and Storm Data: Climate data on hurricane and storm frequency for environmental risk assessments. - Height and Weight Measurements: Public health research dataset on anthropometric data. - Lead Shot Specs: Detailed engineering data for material sciences and manufacturing studies. - Alphabet Letter Frequency: Text analysis dataset for frequency distribution studies in large text samples. - MLB Player Statistics: Comprehensive athletic data set for analysis of performance metrics in sports. - MLB Teams' Seasonal Performance: A dataset combining financial and sports performance data from the 2012 MLB season. - TV News Viewership: Media consumption data which can be used to analyze viewing patterns and trends. - Historical Nile Flood Data: A unique environmental dataset for historical trend analysis in flood levels. - Oscar Winner Ages: A dataset to explore age trends among Oscar-winning actors and actresses. - Snakes and Ladders Statistics: Data from the game outcomes useful in studying probability and game theory. - Tallahassee Cab Fares: Price modeling data from the real-world pricing of taxi services. - Taxable Goods Data: A snapshot of economic data concerning taxation impact on prices. - Tree Measurements: Ecological and environmental science data related to tree growth and forest management. - Real Estate Prices from Zillow: Market analysis dataset for those interested in housing price determinants.

Format

The enclosed data respect the comma-separated values (CSV) file format standards, ensuring compatibility with most data processing libraries in Python, R, and other languages. The datasets are ready for import into Jupyter notebooks, RStudio, or any other integrated development environment (IDE) used for data science.

Quality Assurance

The data is pre-checked for common issues such as missing values, duplicate records, and inconsistent entries, offering a clean and reliable dataset for various analytical exercises. With initial header lines in some CSV files, users can easily identify dataset fields and start their analysis without additional data cleaning for headers.

Acknowledgements

The dataset adheres to the GNU LGPL license, making it freely available for modification and distribution, provided that the original source is cited. This opens up possibilities for educators to integrate real-world data into curricula, researchers to validate models against diverse datasets, and practitioners to refine their analytical skills with hands-on data.

This dataset has been compiled from https://people.sc.fsu.edu/~jburkardt/data/csv/csv.html, with gratitude to the authors and maintainers for their dedication to providing open data resources for educational and research purposes. https://i.imgur.com/HOtyghv.png" alt="Imgur">

This dataset contains all the citation data (in CSV format) included in the OpenCitation Index (https://opencitations.net/index), released on March 24, 2025. In particular, each line of the CSV file defines a citation, and includes the following information:[field "oci"] the Open Citation Identifier (OCI) for the citation;[field "citing"] the OMID of the citing entity;[field "cited"] the OMID of the cited entity;[field "creation"] the creation date of the citation (i.e. the publication date of the citing entity);[field "timespan"] the time span of the citation (i.e. the interval between the publication date of the cited entity and the publication date of the citing entity);[field "journal_sc"] it records whether the citation is a journal self-citations (i.e. the citing and the cited entities are published in the same journal);[field "author_sc"] it records whether the citation is an author self-citation (i.e. the citing and the cited entities have at least one author in common).Note: the information for each citation is sourced from OpenCitations Meta (https://opencitations.net/meta), a database that stores and delivers bibliographic metadata for all bibliographic resources included in the OpenCitations Index. The data provided in this dump is therefore based on the state of OpenCitations Meta at the time this collection was generated.This version of the dataset contains:2,155,497,918 citationsThe size of the zipped archive is 34.4 GB, while the size of the unzipped CSV file is 220 GB.

Gain access to a structured dataset featuring thousands of products listed on Amazon India. This dataset is ideal for e-commerce analytics, competitor research, pricing strategies, and market trend analysis.

Dataset Features:

• Product Details: Name, Brand, Category, and Unique ID

• Pricing Information: Current Price, Discounted Price, and Currency

• Availability & Ratings: Stock Status, Customer Ratings, and Reviews

• Seller Information: Seller Name and Fulfillment Details

• Additional Attributes: Product Description, Specifications, and Images

Dataset Specifications:

• Format: CSV

• Number of Records: 50,000+

• Delivery Time: 3 Days

• Price: $149.00

• Availability: Immediate

This dataset provides structured and actionable insights to support e-commerce businesses, pricing strategies, and product optimization. If you're looking for more datasets for e-commerce analysis, explore our E-commerce datasets for a broader selection.

This dataset is comprised of a collection of example DMPs from a wide array of fields; obtained from a number of different sources outlined below. Data included/extracted from the examples include the discipline and field of study, author, institutional affiliation and funding information, location, date created, title, research and data-type, description of project, link to the DMP, and where possible external links to related publications or grant pages. This CSV document serves as the content for a McMaster Data Management Plan (DMP) Database as part of the Research Data Management (RDM) Services website, located at https://u.mcmaster.ca/dmps. Other universities and organizations are encouraged to link to the DMP Database or use this dataset as the content for their own DMP Database. This dataset will be updated regularly to include new additions and will be versioned as such. We are gathering submissions at https://u.mcmaster.ca/submit-a-dmp to continue to expand the collection.