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.

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.

Gravity anomaly data show variations in the gravity field caused by lateral variations in the density of the Earth's crust and upper mantle that reflect variations in composition and thickness. Systematic gravity mapping began in Canada in 1944 and is ongoing. All data are tied to the International Gravity Standardization Network 1971. Local gravity anomalies result from the juxtaposition of relatively high- and low-density rock types. Longer wavelength anomalies such as the gravity low over the Cordillera and the relative gravity high over oceanic crust largely reflect variations in the thickness of the crust.

Dataset

This dataset was created by Dilip Srivastava

Contents

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

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

towardsai-tutors/buster-ai-tutor-data-csv dataset hosted on Hugging Face and contributed by the HF Datasets community

You can also access an API version of this dataset.

TMS

(traffic monitoring system) daily-updated traffic counts API

Important note: due to the size of this dataset, you won't be able to open it fully in Excel. Use notepad / R / any software package which can open more than a million rows.

Data reuse caveats: as per license.

Data quality

statement: please read the accompanying user manual, explaining:

how

 this data is collected identification 

 of count stations traffic 

 monitoring technology monitoring 

 hierarchy and conventions typical 

 survey specification data 

 calculation TMS 

 operation. 

Traffic

monitoring for state highways: user manual

[PDF 465 KB]

The data is at daily granularity. However, the actual update

frequency of the data depends on the contract the site falls within. For telemetry

sites it's once a week on a Wednesday. Some regional sites are fortnightly, and

some monthly or quarterly. Some are only 4 weeks a year, with timing depending

on contractors’ programme of work.

Data quality caveats: you must use this data in

conjunction with the user manual and the following caveats.

The

 road sensors used in data collection are subject to both technical errors and 

 environmental interference.Data 

 is compiled from a variety of sources. Accuracy may vary and the data 

 should only be used as a guide.As 

 not all road sections are monitored, a direct calculation of Vehicle 

 Kilometres Travelled (VKT) for a region is not possible.Data 

 is sourced from Waka Kotahi New Zealand Transport Agency TMS data.For 

 sites that use dual loops classification is by length. Vehicles with a length of less than 5.5m are 

 classed as light vehicles. Vehicles over 11m long are classed as heavy 

 vehicles. Vehicles between 5.5 and 11m are split 50:50 into light and 

 heavy.In September 2022, the National Telemetry contract was handed to a new contractor. During the handover process, due to some missing documents and aged technology, 40 of the 96 national telemetry traffic count sites went offline. Current contractor has continued to upload data from all active sites and have gradually worked to bring most offline sites back online. Please note and account for possible gaps in data from National Telemetry Sites. 

The NZTA Vehicle

Classification Relationships diagram below shows the length classification (typically dual loops) and axle classification (typically pneumatic tube counts),

and how these map to the Monetised benefits and costs manual, table A37,

page 254.

Monetised benefits and costs manual [PDF 9 MB]

For the full TMS

classification schema see Appendix A of the traffic counting manual vehicle

classification scheme (NZTA 2011), below.

Traffic monitoring for state highways: user manual [PDF 465 KB]

State highway traffic monitoring (map)

State highway traffic monitoring sites

Student engagement and learning data (as an anonymised CSV file).

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">

When you need to analyze crypto market history, batch processing often beats streaming APIs. That's why we built the Flat Files S3 API - giving analysts and researchers direct access to structured historical cryptocurrency data without the integration complexity of traditional APIs.

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CoinAPI serves hundreds of institutions worldwide, from trading firms and hedge funds to research organizations and technology providers. Our S3 delivery method easily integrates with your existing workflows, offering familiar access patterns, reliable downloads, and straightforward automation for your data team. Our commitment to data quality and technical excellence, combined with accessible delivery options, makes us the trusted choice for institutions that demand both comprehensive historical data and real-time market intelligence

Data in the Classroom is an online curriculum to foster data literacy. This Ocean Acidification module is geared towards grades 8-12. Visit Data in the Classroom for more information.This application is the Ocean Acidification module.This module was developed to engage students in increasingly sophisticated modes of understanding and manipulation of data. It was completed prior to the release of the Next Generation Science Standards (NGSS)* and has recently been adapted to incorporate some of the innovations described in the NGSS.Each level of the module provides learning experiences that engage students in the three dimensions of the NGSS Framework while building towards competency in targeted performance expectations. Note: this document identifies the specific practice, core idea and concept directly associated with a performance expectation (shown in parentheses in the tables) but also includes additional practices and concepts that can help students build toward a standard.*NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press. Next Generation Science Standards is a registered trademark of Achieve. Neither Achieve nor the lead states and partners that developed the Next Generation Science Standards was involved in the production of, and does not endorse, this product.

In this research, we proposed the SNR-PPFS feature selection algorithms to identify key gene signatures for distinguishing COAD tumor samples from normal colon tissues. Using machine learning-based feature selection approaches to select key gene signatures from high-dimensional datasets can be an effective way for studying cancer genomic characteristics.

This dataset contains all the citation data (in CSV format) included in COCI, released on 23 January 2023. 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 DOI of the citing entity; [field "cited"] the DOI 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).

This version of the dataset contains:

1,463,920,523 citations; 77,045,952 bibliographic resources.

The size of the zipped archive is 37.5 GB, while the size of the unzipped CSV file is 238.5 GB.

Additional information about COCI can be found at the official webpage.

This dataset contains data source collection (e.g., COCI, DOCI, POCI, etc) information about all the citation data (in CSV format) included in the OpenCitations Index, released on March 27, 2025. In particular, any citation in the dataset, defined with its corresponding OCI (first column) has a corresponding value that defines the source (second column), e.g. "coci", "doci", "poci", etc.This version of the dataset contains:2,631,302,118 citationsThe size of the zipped archive is 21 GB, while the size of the unzipped CSV files is 97 GB.