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Google data search exercises can be used to practice finding data or statistics on a topic of interest, including using Google's own internal tools and by using advanced operators.
This dataset was created by Vipul Acholia
It contains the following files:
OpenWeb Ninja's Google Images Data (Google SERP Data) API provides real-time image search capabilities for images sourced from all public sources on the web.
The API enables you to search and access more than 100 billion images from across the web including advanced filtering capabilities as supported by Google Advanced Image Search. The API provides Google Images Data (Google SERP Data) including details such as image URL, title, size information, thumbnail, source information, and more data points. The API supports advanced filtering and options such as file type, image color, usage rights, creation time, and more. In addition, any Advanced Google Search operators can be used with the API.
OpenWeb Ninja's Google Images Data & Google SERP Data API common use cases:
Creative Media Production: Enhance digital content with a vast array of real-time images, ensuring engaging and brand-aligned visuals for blogs, social media, and advertising.
AI Model Enhancement: Train and refine AI models with diverse, annotated images, improving object recognition and image classification accuracy.
Trend Analysis: Identify emerging market trends and consumer preferences through real-time visual data, enabling proactive business decisions.
Innovative Product Design: Inspire product innovation by exploring current design trends and competitor products, ensuring market-relevant offerings.
Advanced Search Optimization: Improve search engines and applications with enriched image datasets, providing users with accurate, relevant, and visually appealing search results.
OpenWeb Ninja's Annotated Imagery Data & Google SERP Data Stats & Capabilities:
100B+ Images: Access an extensive database of over 100 billion images.
Images Data from all Public Sources (Google SERP Data): Benefit from a comprehensive aggregation of image data from various public websites, ensuring a wide range of sources and perspectives.
Extensive Search and Filtering Capabilities: Utilize advanced search operators and filters to refine image searches by file type, color, usage rights, creation time, and more, making it easy to find exactly what you need.
Rich Data Points: Each image comes with more than 10 data points, including URL, title (annotation), size information, thumbnail, and source information, providing a detailed context for each image.
This tutorial will teach you how to take time-series data from many field sites and create a shareable online map, where clicking on a field location brings you to a page with interactive graph(s).
The tutorial can be completed with a sample dataset (provided via a Google Drive link within the document) or with your own time-series data from multiple field sites.
Part 1 covers how to make interactive graphs in Google Data Studio and Part 2 covers how to link data pages to an interactive map with ArcGIS Online. The tutorial will take 1-2 hours to complete.
An example interactive map and data portal can be found at: https://temple.maps.arcgis.com/apps/View/index.html?appid=a259e4ec88c94ddfbf3528dc8a5d77e8
This dataset was created by Husain Unwala
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Google Data Studio report for water quality data collected on Stillwater Run.
In the first half of 2024, Google received over 82,000 requests for disclosure of user information from the U.S. federal agencies and other government entities. The Indian government ranked second by the number of requests about user information disclosure sent to Google, followed by Germany.
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Raw Data of manuscript: "Social isolation intensified the interests in toothache-related digital information during the COVID-19 pandemic"
This dataset was created by Deepe98
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
For a country to efficiently monitor international migration, quick access to information on migration flows is helpful. However, traditional data sources fail to provide immediate information on migration flows and do not facilitate the correct anticipation of these flows in the short term. To tackle this issue, this paper evaluates the predictive capacity of big data to estimate the current level or to predict short-term flows. The results show that Google Trends can provide information that reflects the attractiveness of Switzerland for to immigrants from different countries and predict, to some extent, current and future (short-term) migration flows of adults arriving from Spain or Italy. However, the predictions appear not to be satisfactory for other flows (from France and Germany). Additional studies based on alternative approaches are needed to validate or overturn our study results.
Data and codebook along DDI standard
In 2023, Google’s data centers accounted for over 7.6 billion gallons of water withdrawals. Council Bluffs, in Iowa, accounted for the largest share, with around 1.3 billions gallons. Mayes County, Oklahoma, ranked second, also with more than one billion gallons' worth of withdrawals. Water in data centers is mainly used for cooling.
This dataset was created by Raí Santos
This dataset was created by Eloy
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Comparisons of different xHier-Hybrid architectures with flyweel or CAES as secondary ESD at the data center-level, based on the optimal Hier-Homo architecture.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Nvidia's dominance in the AI chip market promises financial success but raises environmental concerns due to soaring electricity demands.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Summary of key characteristics of ESDs.
This dataset was created by Phil Poccia
This dataset was created by Riley Sackett
It contains the following files:
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Comparisons of Hier-Homo architectures with different ESD type combinations.
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically
Google data search exercises can be used to practice finding data or statistics on a topic of interest, including using Google's own internal tools and by using advanced operators.