• Update Frequency: Weekly

Data from this dataset can be downloaded/accessed through this dataset page and Kaggle's API.

Context

Severe weather is defined as a destructive storm or weather. It is usually applied to local, intense, often damaging storms such as thunderstorms, hail storms, and tornadoes, but it can also describe more widespread events such as tropical systems, blizzards, nor'easters, and derechos.

The Severe Weather Data Inventory (SWDI) is an integrated database of severe weather records for the United States. The records in SWDI come from a variety of sources in the NCDC archive. SWDI provides the ability to search through all of these data to find records covering a particular time period and geographic region, and to download the results of your search in a variety of formats. The formats currently supported are Shapefile (for GIS), KMZ (for Google Earth), CSV (comma-separated), and XML.

Content

The current data layers in SWDI are:
- Filtered Storm Cells (Max Reflectivity >= 45 dBZ) from NEXRAD (Level-III Storm Structure Product)
- All Storm Cells from NEXRAD (Level-III Storm Structure Product)
- Filtered Hail Signatures (Max Size > 0 and Probability = 100%) from NEXRAD (Level-III Hail Product)
- All Hail Signatures from NEXRAD (Level-III Hail Product)
- Mesocyclone Signatures from NEXRAD (Level-III Meso Product)
- Digital Mesocyclone Detection Algorithm from NEXRAD (Level-III MDA Product)
- Tornado Signatures from NEXRAD (Level-III TVS Product)
- Preliminary Local Storm Reports from the NOAA National Weather Service
- Lightning Strikes from Vaisala NLDN

Disclaimer:
SWDI provides a uniform way to access data from a variety of sources, but it does not provide any additional quality control beyond the processing which took place when the data were archived. The data sources in SWDI will not provide complete severe weather coverage of a geographic region or time period, due to a number of factors (eg, reports for a location or time period not provided to NOAA). The absence of SWDI data for a particular location and time should not be interpreted as an indication that no severe weather occurred at that time and location. Furthermore, much of the data in SWDI is automatically derived from radar data and represents probable conditions for an event, rather than a confirmed occurrence.

Acknowledgements

Dataset Source: NOAA. 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.

Cover photo by NASA on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

• GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).
• GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

• fid - Unique identifier
• bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.
• bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.
• gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).
• bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).
• prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).
• cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.
• doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.
• heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.
• feat_code - Code describing the type of building, pulled from the Building Footprint Data.
• groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.
• qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'
• notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.
• classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)
• digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)
• newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)
• original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.
• address - Address based on MapPLUTO, joined to the dataset based on bbl.
• borough - Borough abbreviation pulled from MapPLUTO.
• ownertype - Owner type field pulled from MapPLUTO.
• zonedist1 - Zoning District 1 type pulled from MapPLUTO.
• spdist1 - Special District 1 pulled from MapPLUTO.
• bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

• xcoord - Longitude in decimal degrees.
• ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

In the most recently reported fiscal year, Google's revenue amounted to 348.16 billion U.S. dollars. Google's revenue is largely made up by advertising revenue, which amounted to 264.59 billion U.S. dollars in 2024. As of October 2024, parent company Alphabet ranked first among worldwide internet companies, with a market capitalization of 2,02 billion U.S. dollars. Google’s revenue Founded in 1998, Google is a multinational internet service corporation headquartered in California, United States. Initially conceptualized as a web search engine based on a PageRank algorithm, Google now offers a multitude of desktop, mobile and online products. Google Search remains the company’s core web-based product along with advertising services, communication and publishing tools, development and statistical tools as well as map-related products. Google is also the producer of the mobile operating system Android, Chrome OS, Google TV as well as desktop and mobile applications such as the internet browser Google Chrome or mobile web applications based on pre-existing Google products. Recently, Google has also been developing selected pieces of hardware which ranges from the Nexus series of mobile devices to smart home devices and driverless cars. Due to its immense scale, Google also offers a crisis response service covering disasters, turmoil and emergencies, as well as an open source missing person finder in times of disaster. Despite the vast scope of Google products, the company still collects the majority of its revenue through online advertising on Google Site and Google network websites. Other revenues are generated via product licensing and most recently, digital content and mobile apps via the Google Play Store, a distribution platform for digital content. As of September 2020, some of the highest-grossing Android apps worldwide included mobile games such as Candy Crush Saga, Pokemon Go, and Coin Master.

TheLook is a fictitious eCommerce clothing site developed by the Looker team. The dataset contains information about customers, products, orders, logistics, web events and digital marketing campaigns. The contents of this dataset are synthetic, and are provided to industry practitioners for the purpose of product discovery, testing, and evaluation. 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 Healthcare Common Procedure Coding System (HCPCS, often pronounced by its acronym as "hick picks") is a set of health care procedure codes based on the American Medical Association's Current Procedural Terminology (CPT).

HCPCS includes three levels of codes: Level I consists of the American Medical Association's Current Procedural Terminology (CPT) and is numeric. Level II codes are alphanumeric and primarily include non-physician services such as ambulance services and prosthetic devices, and represent items and supplies and non-physician services, not covered by CPT-4 codes (Level I). Level III codes, also called local codes, were developed by state Medicaid agencies, Medicare contractors, and private insurers for use in specific programs and jurisdictions. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) instructed CMS to adopt a standard coding systems for reporting medical transactions. The use of Level III codes was discontinued on December 31, 2003, in order to adhere to consistent coding standards.

Content

Classification of procedures performed for patients is important for billing and reimbursement in healthcare. The primary classification system used in the United States is Healthcare Common Procedure Coding System (HCPCS), maintained by Centers for Medicare and Medicaid Services (CMS). This system is divided into two levels: level I and level II.

Level I HCPCS codes classify services rendered by physicians. This system is based on Common Procedure Terminology (CPT), a coding system maintained by the American Medical Association (AMA). Level II codes, which are the focus of this public dataset, are used to identify products, supplies, and services not included in level I codes. The level II codes include items such as ambulance services, durable medical goods, prosthetics, orthotics and supplies used outside a physician’s office.

Given the ubiquity of administrative data in healthcare, HCPCS coding systems are also commonly used in areas of clinical research such as outcomes based research.

Update Frequency: Yearly

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/table/bigquery-public-data:cms_codes.hcpcs

https://cloud.google.com/bigquery/public-data/hcpcs-level2

Dataset Source: Center for Medicare and Medicaid Services. 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.

Banner Photo by @rawpixel from Unplash.

Inspiration

What are the descriptions for a set of HCPCS level II codes?

In 2023, Google's ad revenue amounted to 264.59 billion U.S. dollars. The company generates advertising revenue through its Google Ads platform, which enables advertisers to display ads, product listings and service offerings across Google’s extensive ad network (properties, partner sites, and apps) to web users. Google advertising Advertising accounts for the majority of Google’s revenue, which amounted to a total of 305.63 billion U.S. dollars in 2023. The majority of Google's advertising revenue comes from search advertising. Google market share These revenue figures come as no surprise, as Google accounts for the majority of the online and mobile search market worldwide. As of September 2023, Google was responsible for more than 84 percent of global desktop search traffic. The company holds a market share of more than 80 percent in a wide range of digital markets, having little to no domestic competition in many of them. China, Russia, and to a certain extent, Japan, are some of the few notable exceptions, where local products are more preferred.

In the first quarter of 2025, Google's revenue amounted to over 89.52 billion U.S. dollars, up from the 79.97 billion U.S. dollars registered in the same quarter a year prior. The company amounted to an annual revenue of 348.16 billion U.S. dollars throughout 2024, its highest value to date, with most of its earnings being powered by advertising through Google sites and its network. Google advertising The foundations of Google's earnings are its advertising revenues, generated through its Google Ads platform, which enables advertisers to display ads, product listings, and service offerings across its extensive network (properties, partner sites, and apps) to web users via programs like AdSense or AdSearch. In 2024, Google accounted for most of its parent company Alphabet's annual revenues with 234.2 billion U.S. dollars in Google website ad revenues alone. Other sources of revenue Google's multitude of income sources also includes digital content products and apps sold through the digital content distribution platform Google Play, as well as hardware including Chromecast devices and smartphones. Geographically, the biggest single country share of Alphabet’s revenue comes from the United States, and close to 30 percent of revenues originate from the EMEA region.

The Digital Geologic-GIS Map of San Miguel Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (smis_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (smis_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (smis_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (smis_geology_metadata_faq.pdf). Please read the chis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (smis_geology_metadata.txt or smis_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The Digital Geologic-GIS Map of Isle Royale National Park and Vicinity, Michigan is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (isro_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (isro_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (isro_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (isro_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (isro_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (isro_geology_metadata_faq.pdf). Please read the isro_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (isro_geology_metadata.txt or isro_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.