About Dataset This dataset is designed for Formula 1 (F1) enthusiasts, researchers, and data scientists aiming to analyze performance metrics of F1 drivers and cars. It includes telemetry data collected and preprocessed from FastF1 and Ergast APIs, offering valuable insights into driver performance across various races and scenarios.

The dataset is structured to facilitate dynamic updates and high-quality visualizations, making it suitable for advanced analyses such as race strategy optimization, performance comparisons, and machine learning applications. Key metrics include lap times, sector splits, RPM, throttle, speed, and more, enabling users to identify critical performance bottlenecks and trends.

Features High-Resolution Telemetry Data: Every tenth of a second for temporal data and lap-based aggregates. Sector and Mini-Sector Analysis: Performance indices calculated for each driver in every circuit mini-sector. Driver and Race Comparisons: Facilitate multi-driver evaluations with ready-to-use datasets. Dynamic Database: Updates automatically with preprocessed data after each race. Use Cases Build advanced visualizations to understand F1 performance dynamics. Apply machine learning models to predict driver or team success. Study the impact of car setups and driving styles on race outcomes. Structure Driver Data: Includes detailed lap times, RPM, and braking data. Race Information: Metadata about circuits, weather, and track conditions. Preprocessed Tables: Optimized for quick access and analysis. Intended Audience F1 fans exploring race telemetry. Researchers in motorsports analytics. Developers building visualization or simulation tools.

The dataset is a comprehensive, structured collection of historical Formula 1 race data compiled from the Ergast API. It is organized in a relational format across multiple CSV files, each capturing a different aspect of the sport. The `races.csv` file includes metadata on each race such as date, circuit, and season. The `results.csv` file provides final race outcomes for every driver, while `qualifying.csv` contains qualifying session results. `lap_times.csv` and `pit_stops.csv` offer granular, session-level data for each driver’s performance throughout the race. Additional files such as `drivers.csv` and `constructors.csv` provide biographical and team-related information, while `constructor_standings.csv`, `driver_standings.csv`, and `constructor_results.csv` track season-long performance. Files like `circuits.csv`, `status.csv`, and `seasons.csv` provide supporting metadata that enhances the usability and relational structure of the dataset. This dataset is well-suited for time series analysis, predictive modeling, performance evaluation, and motorsport analytics.

Formula 1 Comprehensive Dataset (2020-2025)

Dataset Description This comprehensive Formula 1 dataset contains detailed racing data spanning from 2020 to 2025, including race results, qualifying sessions, championship standings, circuit information, and historical driver statistics.

Perfect for:

📊 F1 performance analysis

🤖 Machine learning projects

📈 Data visualization

🏆 Championship predictions

📋 Racing statistics research

📁 Files Included 1. f1_race_results_2020_2025.csv (53 entries) Race winners and results from Grand Prix weekends

Date, Grand Prix name, race winner

Constructor, nationality, grid position

Race time, fastest lap time, points scored

1. f1_qualifying_results_2020_2024.csv (820 entries) Qualifying session results with timing data

Q1, Q2, Q3 session times

Grid positions, laps completed

Driver and constructor information

1. f1_driver_standings_progressive.csv (600 entries) Championship standings progression throughout seasons

Points accumulation over race weekends

Wins, podiums, pole positions tracking

Season-long championship battle data

1. f1_constructor_standings_progressive.csv (360 entries) Team championship standings evolution

Constructor points and wins

Team performance metrics

Manufacturer rivalry data

1. f1_circuits_technical_data.csv (24 entries) Technical specifications for all F1 circuits

Track length, number of turns

Lap records and record holders

Circuit designers and first F1 usage

1. f1_historical_driver_statistics.csv (30 entries) All-time career statistics for F1 drivers

Career wins, poles, podiums

Racing entries and achievements

Active and retired driver records

1. f1_comprehensive_dataset_2020_2025.csv (432 entries) MAIN DATASET - Combined data from all sources

Multiple data types in one file

Ready for immediate analysis

Comprehensive F1 information hub

🔧 Data Features Clean & Structured: All data professionally format

Welcome to the Formula 1 dataset!

This page includes a Formula 1 dataset that has been scraped from two main sources. The first source is the Formula 1 website, and the second source is data.world. Both sources are highly reliable, and the data from these sites have been used as part of Kaggle competitions in the past. Four csv files named circuits, constructors, drivers and driverGrid are available on this page. The data can be used for a range of beneficial outcomes, such as exploratory data analysis, back-end database building or for creating an application.

I wish you all the best in your learning journey!

Explore how Google's F1 database technology evolved from NoSQL to power $100B+ in ad revenue, and what this means for modern data infrastructure startups.

Context

Formula One is the highest class of international racing for open-wheel single-seater racing cars sanctioned by the Fédération Internationale de l'Automobile (FIA). Ever since its inaugural season in 1950, Formula1 has been regarded as the pinnacle of motorsport.

Content

This dataset contains detailed information about qualifying and race results for all the tracks over the course of multiple seasons. There is a separate directory for each season. There are 2 sub-directories for each season, namely: Qualifying Results and Race Results. The Race Results directory contains an overall_race_results.csv file which summarizes the race results throughout the entire season. It also contains multiple .csv files for the results of each race in the season. The Qualifying Results directory contains multiple .csv files for the qualifying results before the start of each race.

Note

For the 1982 season and before the qualifying results contain only 1 entry in the file which is that of the polesitter. The lap times of the other drivers were not accounted for, and on the official website there is only 1 entry under the qualifying results.

Inspiration

F1 is one of my favorite sports and I almost never miss a race 😄

The motivation behind creating this dataset was to learn more about web scraping and try to perform a statistical analysis of the data. Some of the things you could do with the entire dataset are as follows: - Identify the driver with the most poles - Compare qualifying times of different drivers (championship contenders, team-mates, etc) - Determine how often a particular driver out-qualifies his team-mate - Compare qualifying lap times of a race from previous seasons - Identify the driver with the most number of wins at a particular track - Analyze how the championship battle unfolded based on the number of points scored by the drivers (specially interesting for the 2021 f1 season 👀) - Identify drivers with the highest number of wins, podiums, DNFs, etc - Compare the average lap times of different tracks to identify the slowest and fastest tracks on the calendar - Compare the number of laps for each race in the season (Belgium 2021 being the clear winner 😂) - Find out who won the Driver's Championship based on the total number of points - Find out who won the Constructor's Championship based on the total number of points for each team

Some Common F1 Terms You Might Come Across

• DNF: Did Not Finish. Commonly used nomenclature for drivers that crashed/failed to complete the entire race
• DNQ: Did Not Qualify. Eliminated missing values from the qualifying datasets by introducing this abbreviation for drivers who failed to qualify.
• NC: Not Confirmed. For drivers that DNF the term NC is used in the Position column
• DQ: Disqualified. Generally drivers are disqualified from races due to technical infringements or a breach of sporting regulations (Example: Sebastian Vettel was disqualified from the 2021 Hungarian Grand Prix due to fuel irregularites and stripped of all the points he earned from finishing the race in P2)

Future Work

As I collect more data for the previous seasons, I will create new versions for the dataset. The goal with this dataset is to create an archive of qualifying and race data from 1950-2021. The dataset will also be updated when the 2022 season commences.

227 Global export shipment records of F1 Mobile with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset is a result of extensive scraping, merging, and processing from multiple F1 data sources including: 1. FastF1 API – for lap data, stint & tire compound info 2. Open-Meteo – for historical weather per race location 3.Ergast API / Telemetry tools – for race schedules and positions

What the Dataset Contains: Each row represents a driver's stint in a particular race and includes: 2. Driver & Race Details: Driver, Race Name, Season, Team, Round 3. Behavior Metrics: Aggression score, Fast lap attempts, Tire usage aggression 4. Pit Stop Data: Number of pit stops, average stop time, stint length, pit lap, pit duration 5. Weather Metrics: Track temperature, air temperature, humidity, wind speed

wasmdashai/db-arabic-f1-nn dataset hosted on Hugging Face and contributed by the HF Datasets community

This data was recorded during Flight 1 of the Blue Origin Deorbit, Descent, and Landing Tipping Point (BODDL-TP) Game Changing Development (GCD) Program. The flight included IMU, cameras for terrain relative navigation, and range and velocity lidar sensors. The flight was completed under NASA contract 80LARC19C0005 in October 2020.

This dataset provides information about the number of properties, residents, and average property values for Road F1 cross streets in Schuyler, NE.

Comprehensive dataset containing 15 verified F1 locations in Indonesia with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 32 verified Formula 1 locations in Russia with complete contact information, ratings, reviews, and location data.

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Context

The Japanese Grand Prix is a motor racing event in the calendar of the Formula One World Championship.

The dataset was generated using the FastF1 API by loading the Japanese Grand Prix race session (denoted as "R") for the 2025 season. Instead of directly accessing a dedicated pit stop attribute (which might not exist in some versions of the API), the data is obtained by filtering the laps data to identify laps that include pit stop times. This approach leverages the 'PitOutTime' field to capture when pit stops occurred during the race.

The context of this data is rooted in understanding the in-race strategies related to pit stops, which can greatly influence race outcomes. Teams and analysts use such information to assess the efficiency of pit stop timings and their impact on overall performance.

Circuit length: 5.807 km (3.608 miles)

First held: 1963

Laps: 53

Most wins (constructors): McLaren (9)

Most wins (drivers): Michael Schumacher (6)

Number of times held: 50

Content

The dataset consists of all information on the Formula 1 Japanese Grand Prix 2025, drivers, constructors, qualifying, lap times, and pit stops.

Time period: 4 - 6 April 2025

Acknowledgements

The data is fetched using Fast F1 Package.

Inspiration to Use This Dataset

Unlock strategic insights and race trends from the Japanese Grand Prix to fuel your data analysis, visual storytelling, or predictive modeling in motorsport analytics.

"Racing is life. Anything before or after is just waiting." – Steve McQueen

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454 Global exporters importers export import shipment records of F1 mobile with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Max Verstappen's Full Race Car Data: 2024 Miami Grand Prix

  Data Source

Data obtained using the fastf1 API, ensuring reliability and accuracy in the collected data.

  Metrics

The dataset comprises a comprehensive range of metrics crucial for analyzing Max Verstappen's performance during the 2024 Miami Grand Prix, including:

Data: Timestamps for each recorded data point. RPM: Engine revolutions per minute, indicating engine performance and power delivery. Speed:… See the full description on the dataset page: https://huggingface.co/datasets/Draichi/Formula1-2024-Miami-Verstappen-telemetry.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Borehole. The data include parameters of borehole with a geographic location of China, Eastern Asia. The time period coverage is from 450 to -33 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

592 Global export shipment records of F1 Phone with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

11 Global import shipment records of F1 Phone with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field of pharmacovigilance. Although researchers have attempted to investigate DDIs from biomedical texts and predict unknown DDIs, the lack of accurate manual annotations significantly hinders the performance of machine learning algorithms. In this study, a new DDI prediction framework, Subgraph Enhance model, was developed for DDI (SubGE-DDI) to improve the performance of machine learning algorithms. This model uses drug pairs knowledge subgraph information to achieve large-scale plain text prediction without many annotations. This model treats DDI prediction as a multi-class classification problem and predicts the specific DDI type for each drug pair (e.g. Mechanism, Effect, Advise, Interact and Negative). The drug pairs knowledge subgraph was derived from a huge drug knowledge graph containing various public datasets, such as DrugBank, TwoSIDES, OffSIDES, DrugCentral, EntrezeGene, SMPDB (The Small Molecule Pathway Database), CTD (The Comparative Toxicogenomics Database) and SIDER. The SubGE-DDI was evaluated from the public dataset (SemEval-2013 Task 9 dataset) and then compared with other state-of-the-art baselines. SubGE-DDI achieves 83.91% micro F1 score and 84.75% macro F1 score in the test dataset, outperforming the other state-of-the-art baselines. These findings show that the proposed drug pairs knowledge subgraph-assisted model can effectively improve the prediction performance of DDIs from biomedical texts.