datasets

KO | EN

gitlite — search

#python #hacktoberfest #ai #llm #go #golang #machine-learning #rust #deep-learning #nodejs #macos #security #mcp #open-source

(★ 15)

datasets for database research

datasets Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

This repository offers a wide range of *datasets and queries* from open data or our own practices (with necessary desensitization).

**Datasets** include *a large number* of typical domains, with diversified data characters (e.g., different column/tuple numbers).

**Queries** are *real*  SQL statements that support various functionalities, such as feature extraction (![](https://img.shields.io/badge/-fe-Informational)), transactions (![](https://img.shields.io/badge/-tp-informational)), and analytical queries (coming soon).

# energy
|name|description|table number|column number|SQL|source|
|---|---|---|---|---|---|
|GEF2012-wind-forecasting|Hourly power generation at 7 wind farms|10|61|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/GEF2012-wind-forecasting)|
|electric-power-consumption|Per capita energy consumption in Morocco|1|9|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/datasets/fedesoriano/electric-power-consumption)|
|energydata_complete||2|59|![](https://img.shields.io/badge/-fe-Informational)||
|ashrae-energy-prediction|Energy usage from over 1,000 buildings over a three-year timeframe|5|32|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/ashrae-energy-prediction)|

# finance
|name|description|table number|column number|SQL|source|
|---|---|---|---|---|---|
|recruit-restaurant-visitor-forecasting|The browsing statistics of two restaurant websites|8|28|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting)|
|santander-customer-satisfaction|Hundreds of anonymized features that could reflect whether  a customer is satisfied with their banking experience|1|372|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/santander-customer-satisfaction)|
|GiveMeSomeCredit|Credit features of 250,000 borrowers in banking scenario|1|13|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/GiveMeSomeCredit)|
|daily-financial-news|Daily financial news for over 6,000 stocks|2|12|![](https://img.shields.io/badge/-fe-Informational)|[tianchi](https://tianchi.aliyun.com/dataset/dataDetail?dataId=94123)|
|restaurant-revenue-prediction|Demographic, real estate, and commercial data for the investments of new restaurant sites|2|85|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/restaurant-revenue-prediction)|
|homesite-quote-conversion|An anonymized database of information on customer and sales activity|2|597|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/homesite-quote-conversion)|
|allstate-claims-severity|Insurance claims for worry-free customer experiences|3|265|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/allstate-claims-severity)|
|tiantian|The price-related features constructed using the fund market data downloaded from TianTian Fund website|1|332|![](https://img.shields.io/badge/-fe-Informational)|[tianchi](https://tianchi.aliyun.com/dataset/dataDetail?dataId=129763)|
|sberbank-russian-housing-market|Information about overall conditions in the country's economy and finance sector|4|685|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/sberbank-russian-housing-market)|
|dow_jones_index||1|16|![](https://img.shields.io/badge/-fe-Informational)||
|robinhood-stock-data|The historical stock price of Robinhood (ticker symbol HOOD)|1|6|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/datasets/evangower/robinhood-stock-data)|
|porto-seguro-safe-driver-prediction|The features that affect an auto insurance policy holder files a claim|1|60|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction)|
|amex-default-prediction||4|384|![](https://img.shields.io/badge/-fe-Informational)||
|house-rent-prediction-dataset|Information on almost 4700+ Houses/Apartments/Flats Available for Rent|1|12|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/datasets/iamsouravbanerjee/house-rent-prediction-dataset)|

# health
|name|description|table number|column number|SQL|source|
|---|---|---|---|---|---|
|big-data-derby-2022|A wealth of data is now collected, including measures for heart rate, EKG, longitudinal movement, et al|3|24|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/big-data-derby-2022)|
|predict-west-nile-virus|Weather, location, testing, and spraying data|5|51|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/predict-west-nile-virus)|
|covid19-global-forecasting-week-2|Statistics of COVID19 cases in various locations across the world|1|6|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/covid19-global-forecasting-week-2)|
|covid19-global-forecasting-week-5|Statistics of COVID19 cases in various locations across the world|1|9|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/covid19-global-forecasting-week-5)|
|covid19-global-forecasting-week-4|Statistics of COVID19 cases in various locations across the world|1|6|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/covid19-global-forecasting-week-4)|
|covid19-global-forecasting-week-1|Statistics of COVID19 cases in various locations across the world|1|8|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/covid19-global-forecasting-week-1)|
|covid19-global-forecasting-week-3|Statistics of COVID19 cases in various locations across the world|1|6|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/covid19-global-forecasting-week-3)|

# media
|name|description|table number|column number|SQL|source|
|---|---|---|---|---|---|
|facebook-v-predicting-check-ins||3|13|![](https://img.shields.io/badge/-fe-Informational)||
|telstra-recruiting-network||7|18|![](https://img.shields.io/badge/-fe-Informational)||
|twitter-threads|Thread functionality in Twitter|5|35|![](https://img.shields.io/badge/-fe-Informational)|[tianchi](https://tianchi.aliyun.com/dataset/dataDetail?dataId=94115)|
|spotify-app-reviews-2022|Spotify reviews on Google Play Store|1|6|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/datasets/mfaaris/spotify-app-reviews-2022)|

# meteorology
|name|description|table number|column number|SQL|source|
|---|---|---|---|---|---|
|PRSA2017_Data_20130301-20170228||12|216|![](https://img.shields.io/badge/-fe-Informational)||
|AirQualityUCI|The responses of a gas multisensor device deployed on the field in an Italian city|1|1|![](https://img.shields.io/badge/-fe-Informational)|[UCI_ML](https://archive.ics.uci.edu/ml/datasets/Air+Quality)|
|historicalweatherdataforindiancities|Temperature data (Minimum, Average, Maximum) in degrees Centigrade and Precipitation data|7|34|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/datasets/vanvalkenberg/historicalweatherdataforindiancities)|

# retails
|name|description|table number|column number|SQL|source|
|---|---|---|---|---|---|
|store-sales-time-series-forecasting|Dates, store and product information|5|22|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/store-sales-time-series-forecasting)|
|coupon-purchase-prediction|A year of transactional data for 22,873 users on the site ponpare.jp|9|80|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/coupon-purchase-prediction)|
|grupo-bimbo-inventory-demand|9 weeks of sales transactions in Mexico|6|28|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/grupo-bimbo-inventory-demand)|
|rossmann-store-sales|Historical sales data for 1,115 Rossmann stores|2|19|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/rossmann-store-sales)|
|favorita-grocery-sales-forecasting|Dates, store and item information, whether that item was being promoted, as well as the unit sales|6|26|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/favorita-grocery-sales-forecasting)|
|walmart-recruiting-store-sales-forecasting||5|26|![](https://img.shields.io/badge/-fe-Informational)||
|walmart-recruiting-sales-in-stormy-weather|Sales data for 111 products whose sales may be affected by the weather (such as milk, bread, umbrellas, etc.)|4|28|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/walmart-recruiting-sales-in-stormy-weather)|
|ecommerce-customerssales-record|Order Statistics|1|41|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/datasets/digantdixit/ecommerce-customerssales-record)|
|competitive-data-science-predict-future-sales|Daily historical sales data.|5|16|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/competitive-data-science-predict-future-sales)|
|m5-forecasting-accuracy|Item sales at stores in various locations for two 28-day time periods|3|1965|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/m5-forecasting-accuracy)|
|goods|Public production introduction information|41|807|![](https://img.shields.io/badge/-tp-informational)||
|material|Historical inventory statistics|79|1265|![](https://img.shields.io/badge/-tp-informational)||
|orders|Historical order details|35|809|![](https://img.shields.io/badge/-tp-informational)||
|shopmall|Comments and shelf status of goods|35|809|![](https://img.shields.io/badge/-tp-informational)||
|transaction|Order details (query only)|50|1069|![](https://img.shields.io/badge/-tp-informational)||

# transport
|name|description|table number|column number|SQL|source|
|---|---|---|---|---|---|
|pkdd-15-taxi-trip-time-prediction-ii||4|24|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i)|
|nyc-taxi-trip-duration|NYC Yellow Cab trip record data |3|22|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/nyc-taxi-trip-duration)|
|taxi-trajectory|A complete year (from 01/07/2013 to 30/06/2014) of the trajectories for all the 442 taxis running in the city of Porto|1|9|![](https://img.shields.io/badge/-fe-Informational)|[tianchi](https://tianchi.aliyun.com/dataset/94216)|
|pkdd-15-predict-taxi-service-trajectory-i| |4|25|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/pkdd-15-taxi-trip-time-prediction-ii)|

# others
|name|description|table number|column number|SQL|source|
|---|---|---|---|---|---|
|talkingdata-mobile-user-demographics|                                                              |8|34|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/talkingdata-mobile-user-demographics)|
|sf-crime|incidents derived from SFPD Crime Incident Reporting system|3|57|![](https://img.shields.io/badge/-fe-Informational)|[tianchi](https://tianchi.aliyun.com/dataset/42205)|
|detecting-insults-in-social-commentary|Detect social spam, account hacking, bot attacks, and more.|1|5|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/detecting-insults-in-social-commentary)|
|expedia-hotel-recommendations|Customer behavior|2|174|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/expedia-hotel-recommendations)|
|nfl-big-data-bowl-2022||7|113|![](https://img.shields.io/badge/-fe-Informational)||
|airbnb-recruiting-new-user-bookings|Users along with their demographics, web session records, and some summary statistics|6|51|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/airbnb-recruiting-new-user-bookings)|
|unimelb|Information on the investigators who are applying for the grant|1|251|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/unimelb)|
|Ipin2016Dataset||8|314|![](https://img.shields.io/badge/-fe-Informational)||
|dspp1|                                                              |4|19|![](https://img.shields.io/badge/-fe-Informational)||
|lish-moa||4|1488|![](https://img.shields.io/badge/-fe-Informational)||
|foursquare-location-matching||2|38|![](https://img.shields.io/badge/-fe-Informational)||
|bike-sharing-demand|The duration of travel, departure location, arrival location, and time elapsed|1|12|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/competitions/bike-sharing-demand)|
|web-traffic-time-series-forecasting||6|1363|![](https://img.shields.io/badge/-fe-Informational)||
|web-traffic-time-series-forecasting-1||2|553|![](https://img.shields.io/badge/-fe-Informational)||
|korean-baseball-pitching-data-1982-2021|Team pitching data from every season of KBO Baseball|1|34|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/datasets/mattop/korean-baseball-pitching-data-1982-2021)|
|RSSI_dataset|RSSIs obtained on smartphones|2|12|![](https://img.shields.io/badge/-fe-Informational)|[UCI_ML](https://archive.ics.uci.edu/dataset/586/ble+rssi+dataset+for+indoor+localization)|
|DontGetKicked|Car information|2|67|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/c/DontGetKicked)|
|cyclistic-bike-share-user-dataset-1-year|Cyclistic bikes|1|18|![](https://img.shields.io/badge/-fe-Informational)|[kaggle](https://www.kaggle.com/datasets/koustavghosh149/cyclistic-bike-share-user-dataset-1-year)|
|data-science-job-salaries||1|12|![](https://img.shields.io/badge/-fe-Informational)||
|Hybrid_Indoor_Positioning|                                                              | 1         |67|![](https://img.shields.io/badge/-fe-Informational)|[UCI_ML](https://archive.ics.uci.edu/dataset/402/hybrid+indoor+positioning+dataset+from+wifi+rssi+bluetooth+and+magnetometer)|

datasets

datasets Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

datasets

datasets Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README