The International Google Trends dataset will provide critical signals that individual users and businesses alike can leverage to make better data-driven decisions. This dataset simplifies the manual interaction with the existing Google Trends UI by automating and exposing anonymized, aggregated, and indexed search data in BigQuery. This dataset includes the Top 25 stories and Top 25 Rising queries from Google Trends. It will be made available as two separate BigQuery tables, with a set of new top terms appended daily. Each set of Top 25 and Top 25 rising expires after 30 days, and will be accompanied by a rolling five-year window of historical data for each country and region across the globe, where data is available. This Google dataset is hosted in Google BigQuery as part of Google Cloud's Datasets solution 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

In an effort to help combat COVID-19, we created a COVID-19 Public Datasets program to make data more accessible to researchers, data scientists and analysts. The program will host a repository of public datasets that relate to the COVID-19 crisis and make them free to access and analyze. These include datasets from the New York Times, European Centre for Disease Prevention and Control, Google, Global Health Data from the World Bank, and OpenStreetMap. Free hosting and queries of COVID datasets As with all data in the Google Cloud Public Datasets Program , Google pays for storage of datasets in the program. BigQuery also provides free queries over certain COVID-related datasets to support the response to COVID-19. Queries on COVID datasets will not count against the BigQuery sandbox free tier , where you can query up to 1TB free each month. Limitations and duration Queries of COVID data are free. If, during your analysis, you join COVID datasets with non-COVID datasets, the bytes processed in the non-COVID datasets will be counted against the free tier, then charged accordingly, to prevent abuse. Queries of COVID datasets will remain free until Sept 15, 2021. The contents of these datasets are provided to the public strictly for educational and research purposes only. We are not onboarding or managing PHI or PII data as part of the COVID-19 Public Dataset Program. Google has practices & policies in place to ensure that data is handled in accordance with widely recognized patient privacy and data security policies. See the list of all datasets included in the program

Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC) dataset that includes class probabilities and label information for nine classes. Dynamic World predictions are available for the Sentinel-2 L1C collection from 2015-06-27 to present. The revisit frequency of Sentinel-2 is between 2-5 days depending on latitude. Dynamic World predictions are generated for Sentinel-2 L1C images with CLOUDY_PIXEL_PERCENTAGE <= 35%. Predictions are masked to remove clouds and cloud shadows using a combination of S2 Cloud Probability, Cloud Displacement Index, and Directional Distance Transform. Images in the Dynamic World collection have names matching the individual Sentinel-2 L1C asset names from which they were derived, e.g: ee.Image('COPERNICUS/S2/20160711T084022_20160711T084751_T35PKT') has a matching Dynamic World image named: ee.Image('GOOGLE/DYNAMICWORLD/V1/20160711T084022_20160711T084751_T35PKT'). All probability bands except the "label" band collectively sum to 1. To learn more about the Dynamic World dataset and see examples for generating composites, calculating regional statistics, and working with the time series, see the Introduction to Dynamic World tutorial series. Given Dynamic World class estimations are derived from single images using a spatial context from a small moving window, top-1 "probabilities" for predicted land covers that are in-part defined by cover over time, like crops, can be comparatively low in the absence of obvious distinguishing features. High-return surfaces in arid climates, sand, sunglint, etc may also exhibit this phenomenon. To select only pixels that confidently belong to a Dynamic World class, it is recommended to mask Dynamic World outputs by thresholding the estimated "probability" of the top-1 prediction.

UPDATE: The Community Mobility Reports are no longer being updated as of October 15, 2022. All historical data will remain publicly available for research purposes. This dataset aims to provide insights into what has changed in response to policies aimed at combating COVID-19. It reports movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. This dataset is intended to help remediate the impact of COVID-19. It shouldn’t be used for medical diagnostic, prognostic, or treatment purposes. It also isn’t intended to be used for guidance on personal travel plans. To learn more about the dataset, the place categories and how we calculate these trends and preserve privacy, visit our help center or read the data documentation 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 .

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

San Francisco Ford GoBike , managed by Motivate, provides the Bay Area’s bike share system. Bike share is a convenient, healthy, affordable, and fun form of transportation. It involves a fleet of specially designed bikes that are locked into a network of docking stations. Bikes can be unlocked from one station and returned to any other station in the system. People use bike share to commute to work or school, run errands, get to appointments, and more. The dataset contains trip data from 2013-2018, including start time, end time, start station, end station, and latitude/longitude for each station. See detailed metadata for historical and real-time data . 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 .

Theta pioneered the next generation esports entertainment platform powered by blockchain technology. Theta rewards anyone to restream video and enables existing video platforms to deepen viewer engagement, drive incremental revenues, and reduce CDN costs. Theta network enables users to earn rewards by relaying video on a peer-to-peer basis, utilizing excess bandwidth on any PC, mobile, Smart TV or IoT device. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? Learn more

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km². For each building in this dataset we include the polygon describing its footprint on the ground, a confidence score indicating how sure we are that this is a building, and a Plus Code corresponding to the center of the building. There is no information about the type of building, its street address, or any details other than its geometry. Building footprints are useful for a range of important applications: from population estimation, urban planning and humanitarian response to environmental and climate science. The project is based in Ghana, with an initial focus on the continent of Africa and new updates on South Asia, South-East Asia, Latin America and the Caribbean. Inference was carried out during May 2023. For more details see the official website of the Open Buildings dataset.

WeatherNext 3 (0.1°) dataset is an experimental dataset of global medium-range ensemble weather forecasts by Google DeepMind and Google Research, produced by an operational version of the model described in: WeatherNext 3: Increasing resolution and performance of global weather models with raw observations. Data Access To request access to the experimental dataset, please submit the WeatherNext Data Request form. Model Highlights High resolution & rapid refresh: 0.1° (~11 km) global spatial resolution, native 1-hour forecast timesteps, hourly initialization, and lead times extending up to 15 days. Real-time observational grounding: Integrates global geostationary satellite mosaics directly into model inputs, enabling rapid assimilation of current weather patterns. Calibrated surface variables: Calibrated against ground weather station networks for surface temperature/dewpoint and satellite observations (IMERG) for precipitation. Solar & energy-focused fields: Predicts multi-layer cloud cover and downward, direct, and clear-sky solar irradiance to support renewable energy and environmental modeling. This Earth Engine collection provides ensemble summary statistics (mean and spread) derived from the operational WeatherNext 3 ensemble, optimized for geospatial analysis. It includes 19 0.1° gridded surface variables across six precomputed statistical metrics -- specifically mean, p10, p25, p50, p75, and p90 (total of 114 bands). Forecast Horizon: 6-hour inits (00, 06, 12, 18 UTC): with a 360-hour (15-day) horizon. Interim 1-hour inits (01-05, 07-11, 13-17, 19-23 UTC): with a 48-hour horizon. Learn more about WeatherNext here. For access to the full ensemble dataset and atmospheric data, go to our Google Cloud Storage bucket. Data Availability The experimental dataset includes both real-time and historic data: Real-Time Experimental Data: Any data relating to a time more recent than 1 hour ago. Historic Experimental Data: Any data relating to a time 1 hour ago or older. More information Model guide and specifications: For more details on WeatherNext, including technical details and specifications, visit the WeatherNext Models Guide and the WeatherNext Developer Guide. Research paper: More information on the underlying research model is available here. Please note that the accuracy of this operational model may not directly correspond to the accuracy reported for the research model, and that additional variables may be included in this forecast dataset. Contact: For questions or usage outside permitted terms, contact weathernext@google.com. Any information collected via email will be used in accordance with Google's Privacy Policy. Dissemination Schedule For forecast release timing, expected delivery windows, and latency across platforms, see the WeatherNext Dissemination Schedule. Acknowledgements This experimental dataset was generated by a model using data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as additional third-party providers. For a full list of attribution and acknowledgements, please see our acknowledgements page.

This table contains release notes for the majority of generally available Google Cloud products found on cloud.google.com . You can use this BigQuery public dataset to consume release notes programmatically across all products. HTML versions of release notes are available within each product's documentation and also in a filterable format at https://console.cloud.google.com/release-notes . 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 High-Resolution Rapid Refresh (HRRR) is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh. For more information, see the HRRR Info Page from NOAA ESRL. In addition to the real-time data that is continuously updated, archived data is now available for HRRR forecasts. This data dates back as far as 2014, and is one of the most complete publicly-available archives of HRRR data. This dataset includes a Pub/Sub topic you can subscribe to in order to be notified of updates. Subscribe to the topic 'projects/gcp-public-data-weather/topics/gcp-public-data-hrrr'. Use the Pub/Sub Quickstarts guide to learn more. This public dataset is hosted in Google Cloud Storage and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

Cloud Score+ is a quality assessment (QA) processor for medium-to-high resolution optical satellite imagery. The Cloud Score+ S2_HARMONIZED dataset is being operationally produced from the harmonized Sentinel-2 L1C collection, and Cloud Score+ outputs can be used to identify relatively clear pixels and effectively remove clouds and cloud shadows from L1C (Top-of-Atmosphere) or L2A (Surface Reflectance) imagery. The Cloud Score+ S2_HARMONIZED dataset includes two QA bands, cs and cs_cdf, that both grade the usability of individual pixels with respect to surface visibility on a continuous scale between 0 and 1, where 0 represents "not clear" (occluded), while 1 represents "clear" (unoccluded) observations. The cs band scores QA based on a spectral distance between the observed pixel and a (theoretical) clear reference observation, while the cs_cdf band represents the likelihood an observed pixel is clear based on an estimated cumulative distribution of scores for a given location through time. In other words, cs can be thought of as a more instantaneous atmospheric similarity score (i.e., how similar is this pixel to what we'd expect to see in a perfectly a clear reference), while cs_cdf captures an expectation of the estimated score through time (i.e., if we had all the scores for this pixel through time, how would this score rank?). Images in the Cloud Score+ S2_HARMONIZED collection have the same id and system:index properties as the individual Sentinel-2 L1C assets from which they were produced such that Cloud Score+ bands can be linked to source images based on their shared system:index. Cloud Score+ backfill for the entire Sentinel-2 archive is currently in progress and Dataset Availability dates will be updated periodically as new results are added to the Cloud Score+ collection. For more information about the Cloud Score+ dataset and modelling approach, see this Medium post.

The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6, see Thrasher et al. 2022) and across two of the four "Tier 1" greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. This collection contains 34 different models. One model, "GFDL-CM4," has data for two different configurations that can be differentiated by further filtering on the grid_label property. Bands are replaced as new versions become available and the version property is updated with them. See also the provider tech note. You can submit data questions about CMIP6 to the provider and see their answers.

TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces. It uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser spatial resolution, but time-varying data from CRU Ts4.0 and the Japanese 55-year Reanalysis (JRA55). Conceptually, the procedure applies interpolated time-varying anomalies from CRU Ts4.0/JRA55 to the high-spatial resolution climatology of WorldClim to create a high-spatial resolution dataset that covers a broader temporal record. Temporal information is inherited from CRU Ts4.0 for most global land surfaces for temperature, precipitation, and vapor pressure. However, JRA55 data is used for regions where CRU data had zero climate stations contributing (including all of Antarctica, and parts of Africa, South America, and scattered islands). For primary climate variables of temperature, vapor pressure, and precipitation, the University of Idaho provides additional data on the number of stations (between 0 and 8) that contributed to the CRU Ts4.0 data used by TerraClimate. JRA55 was used exclusively for solar radiation and wind speeds. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. A modified Thornthwaite-Mather climatic water-balance model and extractable soil water storage capacity data was used at a 0.5° grid from Wang-Erlandsson et al. (2016). Data Limitations: Long-term trends in data are inherited from parent datasets. TerraClimate should not be used directly for independent assessments of trends. TerraClimate will not capture temporal variability at finer scales than parent datasets and thus is not able to capture variability in orographic precipitation ratios and inversions. The water balance model is very simple and does not account for heterogeneity in vegetation types or their physiological response to changing environmental conditions. Limited validation in data-sparse regions (e.g., Antarctica).

This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2021 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its long-term changes (Nature, 2016) and the online Data Users Guide. These data were generated using 4,716,475 scenes from Landsat 5, 7, and 8 acquired between 16 March 1984 and 31 December 2021. Each pixel was individually classified into water / non-water using an expert system and the results were collated into a monthly history for the entire time period and two epochs (1984-1999, 2000-2021) for change detection. This Monthly History collection holds the entire history of water detection on a month-by-month basis. The collection contains 454 images, one for each month between March 1984 and December 2021.

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.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

WeatherNext 2 is an experimental dataset of global medium-range ensemble weather forecasts produced by an operational version of Google DeepMind's Functional Network Generative weather model. The experimental dataset includes real-time and historic data. Real-time data is any data that relates to a time that is no more than 48 hours …

This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2021 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its long-term changes (Nature, 2016) and the online Data Users Guide. These data were generated using 4,716,475 scenes from Landsat 5, 7, and 8 acquired between 16 March 1984 and 31 December 2021. Each pixel was individually classified into water / non-water using an expert system and the results were collated into a monthly history for the entire time period and two epochs (1984-1999, 2000-2021) for change detection. This mapping layers product consists of 1 image containing 7 bands. It maps different facets of the spatial and temporal distribution of surface water over the last 38 years. Areas where water has never been detected are masked. Note:: the mask value for the occurrence band is equal to the band value. That is, if the occurrence band is 0.5, the mask is also 0.5, so the dataset is double-counting the partial occurrence. This is probably not the intuitively expected behavior.

This dataset contains two tables: creative_stats and removed_creative_stats. The creative_stats table contains information about advertisers that served ads in the European Economic Area or Turkey: their legal name, verification status, disclosed name, and location. It also includes ad specific information: impression ranges per region (including aggregate impressions for the European Economic Area), first shown and last shown dates, which criteria were used in audience selection, the format of the ad, the ad topic and whether the ad is funded by Google Ad Grants program. A link to the ad in the Google Ads Transparency Center is also provided. The removed_creative_stats table contains information about ads that served in the European Economic Area that Google removed: where and why they were removed and per-region information on when they served. The removed_creative_stats table also contains a link to the Google Ads Transparency Center for the removed ad. Data for both tables updates periodically and may be delayed from what appears on the Google Ads Transparency Center website. About BigQuery This data is hosted in Google BigQuery for users to easily query using SQL. Note that to use BigQuery, users must have a Google account and create a GCP project. This public dataset is included in BigQuery's 1TB/mo of free tier processing. 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 . Download Dataset This public dataset is also hosted in Google Cloud Storage here and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. We provide the raw data in JSON format, sharded across multiple files to support easier download of the large dataset. A README file which describes the data structure and our Terms of Service (also listed below) is included with the dataset. You can also download the results from a custom query. See here for options and instructions. Signed out users can download the full dataset by using the gCloud CLI. Follow the instructions here to download and install the gCloud CLI. To remove the login requirement, run "$ gcloud config set auth/disable_credentials True" To download the dataset, run "$ gcloud storage cp gs://ads-transparency-center/* . -R" 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 .

GitHub is how people build software and is home to the largest community of open source developers in the world, with over 12 million people contributing to 31 million projects on GitHub since 2008. This 3TB+ dataset comprises the largest released source of GitHub activity to date. It contains a full snapshot of the content of more than 2.8 million open source GitHub repositories including more than 145 million unique commits, over 2 billion different file paths, and the contents of the latest revision for 163 million files, all of which are searchable with regular expressions. 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 .