The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). The GFS dataset consists of selected model outputs (described below) as gridded forecast variables. The 384-hour forecasts, with 3-hour forecast interval, are made at 6-hour temporal resolution (i.e. updated four times daily). Use the 'creation_time' and 'forecast_time' properties to select data of interest. The GFS is a coupled model, composed of an atmosphere model, an ocean model, a land/soil model, and a sea ice model which work together to provide an accurate picture of weather conditions. See history of recent modifications to the global forecast/analysis system , the model performance statistical web page , and the documentation homepage for more information.Learn more

Weather Source, a leading provider of weather and climate technologies for business intelligence, is offering complimentary data for those researching the potential connections between weather and COVID-19 viability and transmission. This share includes: Global historical weather data dating back to October 2019 Present data Forecast data out to 15 days The data supports temperature and humidity, both specific and relative, at the daily level. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . This dataset is created and owned by Weather Source and made available for educational and academic research purposes. This dataset has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate.

This dataset is a cleaned-up extract from the following public BigQuery dataset: https://console.cloud.google.com/marketplace/details/noaa-public/ghcn-d

The dataset contains daily min/max temperatures from a selection of 1666 weather stations. The data spans exactly 50 years. Missing values have been interpolated and are marked as such.

This dataset is in TFRecord format.

About the original dataset: NOAA’s Global Historical Climatology Network (GHCN) is an integrated database of climate summaries from land surface stations across the globe that have been subjected to a common suite of quality assurance reviews. The data are obtained from more than 20 sources. The GHCN-Daily is an integrated database of daily climate summaries from land surface stations across the globe, and is comprised of daily climate records from over 100,000 stations in 180 countries and territories, and includes some data from every year since 1763.

Overview

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries.

Content

Over 9000 stations' data are typically available.

The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches)

Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

This public dataset was created by the National Oceanic and Atmospheric Administration (NOAA) and includes global data obtained from the USAF Climatology Center. This dataset covers GSOD data between 1929 and present, collected from over 9000 stations. Dataset Source: NOAA

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Photo by Allan Nygren on Unsplash

OnPoint Weather is a global weather dataset for business available for any lat/lon point and geographic area such as ZIP codes. OnPoint Weather provides a continuum of hourly and daily weather from the year 2000 to current time and a forward forecast of 45 days. OnPoint Climatology provides hourly and daily weather statistics which can be used to determine ‘departures from normal’ and to provide climatological guidance of expected weather for any location at any point in time. The OnPoint Climatology provides weather statistics such as means, standard deviations and frequency of occurrence. Weather has a significant impact on businesses and accounts for hundreds of billions in lost revenue annually. OnPoint Weather allows businesses to quantify weather impacts and develop strategies to optimize for weather to improve business performance. Examples of Usage Quantify the impact of weather on sales across diverse locations and times of the year Understand how supply chains are impacted by weather Understand how employee’s attendance and performance are impacted by weather Understand how weather influences foot traffic at malls, stores and restaurants OnPoint Weather is available through Google Cloud Platform’s Commercial Dataset Program and can be easily integrated with other Google Cloud Platform Services to quickly reveal and quantify weather impacts on business. Weather Source provides a full range of support services from answering quick questions to consulting and building custom solutions. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery 瞭解詳情

NOAA’s Storm Prediction Center (SPC) maintains a database of daily US storm data as reported by local National Weather Service offices from trained weather spotters. The types of storm data recorded by SPC include reports of Tornados, Wind, and Hail. This dataset has been subjected to a common suite of quality assurance reviews to avoid duplication of the reported weather events in the data set. The respective report type datasets are available in BigQuery. The dataset is updated daily and provides initial details from a storm event. For complete details for each storm event, see NOAA's severe storm events page. This dataset includes detailed information about property damage assessment, storm severity, and more. It is published within 120 days of the storm event, with detailed information verified by the National Weather Service as early as 1950. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Overview

The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) is a global ocean marine meteorological and surface ocean dataset. It is formed by merging many national and international data sources that contain measurements and visual observations from ships (merchant, navy, research), moored and drifting buoys, coastal stations, and other marine and near-surface ocean platforms. Each marine report contains individual observations of meteorological and oceanographic variables, such as sea surface and air temperatures, wind, pressure, humidity, and cloudiness. The coverage is global and sampling density varies depending on date and geographic position relative to shipping routes and ocean observing systems.

Content

The ICOADS dataset contains global marine data from ships (merchant, navy, research) and buoys, each capturing details according to the current weather or ocean conditions (wave height, sea temperature, wind speed, and so on). Each record contains the exact location of the observation which is great for visualizations. The historical depth of the data is quite comprehensive — There are records going back to 1662!

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

Dataset Source: NOAA Category: Meteorological, Climate, Transportation

Citation: National Centers for Environmental Information/NESDIS/NOAA/U.S. Department of Commerce, Research Data Archive/Computational and Information Systems Laboratory/National Center for Atmospheric Research/University Corporation for Atmospheric Research, Earth System Research Laboratory/NOAA/U.S. Department of Commerce, Cooperative Institute for Research in Environmental Sciences/University of Colorado, National Oceanography Centre/Natural Environment Research Council/United Kingdom, Met Office/Ministry of Defence/United Kingdom, Deutscher Wetterdienst (German Meteorological Service)/Germany, Department of Atmospheric Science/University of Washington, and Center for Ocean-Atmospheric Prediction Studies/Florida State University. 2016, updated monthly. International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Release 3, Individual Observations. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory: https://doi.org/10.5065/D6ZS2TR3. Accessed 01 04 2017.

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Photo by Gleb Kozenko on Unsplash

This dataset contains cloud-to-ground lightning strike information collected by Vaisala's National Lightning Detection Network and aggregated into 0.1 x 0.1 degree tiles by the experts at the National Centers for Environmental Information (NCEI) as part of their Severe Weather Data Inventory. This data provides historical cloud-to-ground data aggregated into tiles that around roughly 11 KMs for redistribution. This provides users with the number of lightning strikes each day, as well as the center point for each tile. The sample queries below will help you get started using BigQuery's GIS capabilities to analyze the data. For more on BigQuery GIS, see the documentation available here. The data begins in 1987 and runs through current day, with a delay of a few days for processing. For near real-time lightning information, see the Cloud Public Data's metadata listing of GOES-16 data for cloud-to-cloud and cloud-to-ground strikes over the eastern half of the western hemisphere. GOES-17 data covering the western half of the western hemisphere will be available soon. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The Storm Events Database is an integrated database of severe weather events across the United States from 1950 to this year, with information about a storm event's location, azimuth, distance, impact, and severity, including the cost of damages to property and crops. It contains data documenting: The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce Rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the San Diego coastal area Other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event. Data about a specific event is added to the dataset within 120 days to allow time for damage assessments and other analysis. For preliminary data about storms within the last 120 days, see the preliminary storm reports dataset from the Storm Prediction Center. You can find more detailed information about the dataset, including the list of the 40 possible storm event types, by looking at the documentation published by the National Weather Service. Though every effort is made to ensure the dataset is as complete as possible, some storm events are missing. Use of the data should cite NOAA and NESDIS/NCEI as the dataset creator and the Severe Weather Data Inventory as the dataset. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset.

Context:

Anticipating public nuisances and allocating proper resources is a critical part of public duties.

Content:

This dataset contains 5 years (2008-2011, 2016) worth of public incidents, both criminal and non-criminal. Data includes time, location, description, and unique key.

Acknowledgements:

This dataset was compiled by the City of Austin and published on Google Cloud Public Data.

Dataset Description

Use this dataset with BigQuery You can use Kernels to analyze, share, and discuss this data on Kaggle, but if you’re looking for real-time updates and bigger data, check out the data on BigQuery, too.

Inspiration:

• Are there notable time variations in incidences?
• Can you predict incidence patterns for 2016 based on the 2008-2011 training data?
• Do weather patterns or other changes related to incidence changes?

ERA5 is the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF ) Atmospheric Reanalysis, providing hourly estimates of a large number of atmospheric, land, and oceanic climate variables. This data spans from 1979 to the present, covering the Earth on a 30 km grid and resolves the atmosphere using 137 levels from the surface up to a height of 80 km. A reanalysis is the “most complete picture currently possible of past weather and climate.” Reanalyses are created from assimilation of a wide range of data sources via numerical weather prediction (NWP) models. Meteorologically valuable variables for land and atmosphere were ingested and converted from grib data to Zarr (with no other modifications) to surface a cloud-optimized version of ERA5. In addition, an open-sourced code base is provided to show the providence of the data as well as demonstrate common research workflows. This dataset includes both raw (grib) and cloud-optimized (zarr) files. Use cases. ERA5 data can be used in many different applications, including: Training ML models that predict the impact of weather on different phenomena Training and evaluating ML models that forecast the weather Computing climatologies, the average weather for a region over a given period of time Visualizing and studying historical weather events, such as Hurricane Sandy Thanks to the open data policy of the Copernicus Climate Change and Atmosphere Monitoring Services and ECMWF, this dataset is available free as part of the Google Cloud Public Dataset Program. Please see below for license information.

Dataset Description

This dataset contains yearly averages of PM2.5 µg/m3 measurements for all of the cities disclosing to the CDP.

### How was this dataset created? 1. Yearly average averages of PM2.5 µg/m3 measurement for all weather stations was obtained from Historic Air Quality (https://www.epa.gov) data dataset hosted in Public Datasets hosted on Google Cloud BigQuery platform (https://cloud.google.com/public-datasets).

Query

WITH BASE_DATA AS
  (
 SELECT
  site_num,
  year,
  parameter_name,
  arithmetic_mean,
  arithmetic_standard_dev, ten_percentile, fifty_percentile, seventy_five_percentile, ninety_percentile, ninety_five_percentile,
  units_of_measure,
  ROW_NUMBER() OVER (PARTITION BY site_num, year, parameter_name, units_of_measure ORDER BY date_of_last_change DESC ) AS row_number

 FROM
  `bigquery-public-data.epa_historical_air_quality.air_quality_annual_summary` AS epa
 )

SELECT
 * EXCEPT(row_number)
FROM
 BASE_DATA
WHERE
 row_number = 1
#WHERE
 #epa.units_of_measure = "Micrograms/cubic meter (LC)"
 #AND epa.parameter_name = "Acceptable PM2.5 AQI & Speciation Mass"

1. For each city in the CDP dataset where location information was available, the measurements were obtained by cross-referencing with the closest weather station.

Query - Get nearest monitoring station to CDP City

WITH
 BASE_DATA AS (
 SELECT
  AS VALUE ARRAY_AGG(STRUCT< site_num STRING, City_CDE STRING, account_number INT64, Dist FLOAT64>( site_num ,
    City_CDE,
    account_number,
    ST_DISTANCE( address_spatial ,
     City_CDE_LOC))
  ORDER BY
   ST_DISTANCE( address_spatial ,
    City_CDE_LOC)
  LIMIT 1)[OFFSET(0)]
 FROM (
  SELECT
   site_num ,
   address_spatial
  FROM
   `stunning-object-277601.cdp_unlocking_climate_solutions_questions_analysis.air_quality_monitoring_sites`),
  (
  SELECT
   organization AS City_CDE,
   city_location AS City_CDE_LOC,
   account_number
  FROM
   `stunning-object-277601.cdp_unlocking_climate_solutions.cities_disclosing_to_cdp_processed`)
 WHERE
  City_CDE_LOC IS NOT NULL
  AND address_spatial IS NOT NULL
 GROUP BY
  account_number )
SELECT
 site_num, account_number, SAFE_DIVIDE(Dist, 1000) AS distance
FROM
 BASE_DATA
ORDER BY
 distance DESC

Query - Join to CDP City to station and get yearly measurements

WITH BASE_DATA AS
 (
  SELECT
   account_number,
   organization, city, country,
   year,
   MAX(arithmetic_mean) AS arithmetic_mean,
   MAX(ninety_five_percentile)AS ninety_five_percentile,

  FROM
   `stunning-object-277601.cdp_unlocking_climate_solutions_questions_analysis.nearest_monitoring_station_to_city`
  JOIN
   `stunning-object-277601.cdp_unlocking_climate_solutions_questions_analysis.yearly_air_quality_data` USING ( site_num )
  JOIN
   (SELECT DISTINCT CAST(account_number AS INT64) account_number, organization, city, country FROM `stunning-object-277601.cdp_unlocking_climate_solutions.cities_disclosing_to_cdp_processed`) USING ( account_number )
  WHERE
   units_of_measure = "Micrograms/cubic meter (LC)"
   AND parameter_name = "Acceptable PM2.5 AQI & Speciation Mass"
  GROUP BY
   account_number,
   organization, city, country,
   year
  ORDER BY
   organization
 )

SELECT
 *
FROM
 BASE_DATA
WHERE
 year >=2000

This public dataset was created by the National Oceanic and Atmospheric Administration (NOAA) and includes global data obtained from the USAF Climatology Center. This dataset covers GSOD data between 1929 and present (updated daily), collected from over 9000 stations. Global summary of the day is comprised of a dozen daily averages computed from global hourly station data. Daily weather elements include mean values of: temperature, dew point temperature, sea level pressure, station pressure, visibility, and wind speed plus maximum and minimum temperature, maximum sustained wind speed and maximum gust, precipitation amount, snow depth, and weather indicators. With the exception of U.S. stations, 24-hour periods are based upon UTC times. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The ECMWF open data are real-time meteorological and oceanographic products from the ECMWF forecasting system. They are a subset of the full Catalogue of ECMWF Real-time Products and are based on the medium-range (high-resolution and ensemble ) and seasonal forecast models.The data are released 1 hour after the real-time dissemination schedule . Products are produced at 0.4 degrees resolution in GRIB2 format unless stated otherwise. ( With IFS Cycle 48r1, targeted for implementation in June 2023, the encoding of products in GRIB2 will change to use CCSDS compression . )Additional data, including higher resolutions and more parameters, can be obtained from the ECMWF's Product Requirements Catalogue and are subject to the ECMWF Standard Licence Agreement.

The dataset is about the Share-Bike usage from April 2023 - March 2024 in the Chicago Area. Data were consolidated from 12 months and cleaned up by removing unwanted columns, null, and duplicated data.

Bike data were obtained from here. The data has been made available by Motivate International Inc. under this license.

The weather data were pulled from GSOD from NOAA in BigQuery. Press here to view the dataset.

The Storm Events Database is an integrated database of severe weather events across the United States from 1950 to this year, with information about a storm event's location, azimuth, distance, impact, and severity, including the cost of damages to property and crops. It contains data documenting: • The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce • Rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the San Diego coastal area • Other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event.

Content

I have collected the data from the National Oceanic And Atmospheric Administration's Storm Events database hosted on Google BigQuery. Each case represents a tornado that hit the U.S. after 1950

Acknowledgements

Google Cloud: Severe Storm Event Details**

This repository contains the largest COVID-19 epidemiological database available in addition to a powerful set of expansive covariates. It includes open sourced data with a permissive license (enabling commercial use) relating to vaccinations, epidemiology, hospitalizations, demographics, economy, geography, health, mobility, government response, weather, and more. Moreover, the data merges daily time-series from hundreds of data sources at a fine spatial resolution, containing over 20,000 locations and using a consistent set of region keys. This dataset is available in both the US and EU regions of BigQuery at the following links: COVID-19 Open Data: US Region COVID-19 Open Data: EU Region All data in this dataset is retrieved automatically. When possible, data is retrieved directly from the relevant authorities, like a country's ministry of health. This dataset has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

NOTICE: NEW GOES-19 Data!!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete. NOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer be available. Operational GOES-West products can be found in the GOES-18 bucket. The Geostationary Operational Environmental Satellite-R Series (GOES-R) is the next generation of geostationary weather satellites. The GOES-R series will significantly improve the detection and observation of environmental phenomena that directly affect public safety, protection of property and our nation’s economic health and prosperity. GOES satellites (GOES-16, GOES-17, GOES-18, and GOES-19) provide continuous weather imagery and monitoring of meteorological and space environment data across North America. GOES satellites provide the kind of continuous monitoring necessary for intensive data analysis. They hover continuously over one position on the surface. The satellites orbit high enough to allow for a full-disc view of the Earth. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric "triggers" for severe weather conditions such as tornadoes, flash floods, hailstorms, and hurricanes. When these conditions develop, the GOES satellites are able to monitor storm development and track their movements. SUVI products available in both NetCDF and FITS. GOES data can be found in the GCS buckets: gs://gcp-public-data-goes-16 gs://gcp-public-data-goes-18 gs://gcp-public-data-goes-19 Pub/Sub topics you can subscribe to for updates: projects/gcp-public-data---goes-16/topics/gcp-public-data-goes-16 projects/gcs-public-datasets/topics/gcp-public-data-goes-18 projects/noaa-public/topics/gcp-public-data-goes-19 This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Context

The Storm Events Database is an integrated database of severe weather events across the United States , with information about a storm event's location, azimuth, distance, impact, and severity, including the cost of damages to property and crops. It contains data documenting: • The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce • Rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the San Diego coastal area • Other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event.

Content

I have collected the datasets from the National Oceanic And Atmospheric Administration's Storm Events database hosted on Google BigQuery. In the storms datasets, each case represents a storm that hit the U.S. after 2013 and the tornado path dataset represents each tornado that hit the U.S. (and affiliated damages) from 1950 onwards.

More information about the datasets and the data dictionary can be found here:

Data Source: Google Cloud: Severe Storm Event Details

Inspiration

The dataset can be combined with other datasets to measure correlations between increased severity of storms in the U.S. and rising carbon emissions, etc.

The ICOADS dataset contains global marine data from ships (merchant, navy, research) and buoys, each capturing details according to the current weather or ocean conditions (wave height, sea temperature, wind speed, and so on). Each record contains the exact location of the observation which is great for visualizations. The historical depth of the data is quite comprehensive — there are records going back to 1662. For a complete description of data variables available in this dataset, see NOAA's ICOADS documentation . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .