The National Geologic Map Database (NGMDB) is a Congressionally mandated national archive of geoscience maps, reports, and stratigraphic information. The Geologic Mapping Act of 1992 and its Reauthorizations calls for the U.S. Geological Survey and the Association of American State Geologists (AASG) to cooperatively build this national archive, according to technical and scientific standards whose development is coordinated by the NGMDB. The NGMDB consists of a comprehensive set of publication citations, stratigraphic nomenclature, downloadable content in raster and vector formats, unpublished source information, and guidance on standards development. The NGMDB contains information on more than 110,000 maps and related geoscience reports published from the early 1800s to the present day, by more than 630 agencies, universities, associations, and private companies.

👨‍💻 Author: Azhar Saleem

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Dataset Overview

Welcome to the Google Places Comprehensive Business Dataset! This dataset has been meticulously scraped from Google Maps and presents extensive information about businesses across several countries. Each entry in the dataset provides detailed insights into business operations, location specifics, customer interactions, and much more, making it an invaluable resource for data analysts and scientists looking to explore business trends, geographic data analysis, or consumer behaviour patterns.

Key Features

• Business Details: Includes unique identifiers, names, and contact information.
• Geolocation Data: Precise latitude and longitude for pinpointing business locations on a map.
• Operational Timings: Detailed opening and closing hours for each day of the week, allowing analysis of business activity patterns.
• Customer Engagement: Data on review counts and ratings, offering insights into customer satisfaction and business popularity.
• Additional Attributes: Links to business websites, time zone information, and country-specific details enrich the dataset for comprehensive analysis.

Potential Use Cases

This dataset is ideal for a variety of analytical projects, including: - Market Analysis: Understand business distribution and popularity across different regions. - Customer Sentiment Analysis: Explore relationships between customer ratings and business characteristics. - Temporal Trend Analysis: Analyze patterns of business activity throughout the week. - Geospatial Analysis: Integrate with mapping software to visualise business distribution or cluster businesses based on location.

Dataset Structure

The dataset contains 46 columns, providing a thorough profile for each listed business. Key columns include:

• business_id: A unique Google Places identifier for each business, ensuring distinct entries.
• phone_number: The contact number associated with the business. It provides a direct means of communication.
• name: The official name of the business as listed on Google Maps.
• full_address: The complete postal address of the business, including locality and geographic details.
• latitude: The geographic latitude coordinate of the business location, useful for mapping and spatial analysis.
• longitude: The geographic longitude coordinate of the business location.
• review_count: The total number of reviews the business has received on Google Maps.
• rating: The average user rating out of 5 for the business, reflecting customer satisfaction.
• timezone: The world timezone the business is located in, important for temporal analysis.
• website: The official website URL of the business, providing further information and contact options.
• category: The category or type of service the business provides, such as restaurant, museum, etc.
• claim_status: Indicates whether the business listing has been claimed by the owner on Google Maps.
• plus_code: A sho...

Distributed archive of standardized geoscience information.

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

This dataset is intended to provide seamless, integrated, surficial geologic mapping of the U.S. Intermountain West region and is supported by the National Cooperative Geologic Mapping Program of the U.S. Geological Survey. Surficial geology included as part of this data release as independent of bedrock geologic mapping and is compiled at a variable resolution from 1:50,000 to 1:250,000 scale. No original interpretations are presented in this dataset; rather, all interpretive data are assimilated from referenceable publications. Initial contributions to this data release are along an east-west transect that parallels 37-degrees north latitude extending from the Rio Grande Rift and Great Plains in the east to the Basin and Range and Sierra Nevada to the west. Other areas of the Intermountain West region will be incorporated over time. Data are presented as a downloadable file geodatabase (*.gdb) and as features services that can be directly ingested into GIS software for analysis. This dataset is intended to be versioned regularly as new geologic map data is integrated. The data structure follows the Seamless Integrated Geologic Mapping extension (SIGMa) (Turner and others, 2022) to the Geologic Map Schema (GeMS) (USGS, 2020). U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020, GeMS (Geologic Map Schema)—A standard format for the digital publication of geologic maps: U.S. Geological Survey Techniques and Methods, book 11, chap. B10, 74 p., https://doi.org/10.3133/tm11B10. Turner, K.J., Workman, J.B., Colgan, J.P., Gilmer, A.K., Berry, M.E., Johnstone, S.A., Warrell, K.F., Dechesne, M., VanSistine, D.P., Thompson, R.A., Hudson, A.M., Zellman, K.L., Sweetkind, D., and Ruleman, C.A., 2022, The Seamless Integrated Geologic Mapping (SIGMa) extension to the Geologic Map Schema (GeMS): U.S. Geological Survey Scientific Investigations Report 2022–5115, 33 p., https://doi.org/10.3133/ sir20225115.

The dataset contains metadata records for 50,600 maps from the Sanborn Fire Insurance Maps collection and their corresponding 440,048 JPEG images. The Sanborn collection at Library of Congress includes over fifty thousand editions of fire insurance maps comprising almost seven hundred thousand individual sheets. The Library of Congress holdings represent the largest extant collection of maps produced by the Sanborn Map Company.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. A database of over 700 retinal topography maps of a wide variety of species published in a diversity of journals. It has been assembled to assist vision and neuroscience researchers to locate and compare the distribution of retinal neurons within and across species. The maps can be searched by taxonomic or common name classification, cell type sampled, type of retinal specialization and staining/visualization method. Maps can be compared by selecting multiple maps and clicking the Compare Selected button. An interactive spreadsheet can be also downloaded.

This digital database is the product of collaboration between the U.S. Geological Survey, the Alfred Wegener Institute for Polar and Marine Research Potsdam, Foothill College GeoSpatial Technology Certificate Program, and the Geophysical Institute at the University of Alaska. The primary goal for creating this digital database is to enhance current estimates of organic carbon stored in deep permafrost, in particular Late Pleistocene syngenetic ice-rich loess permafrost deposits, called Yedoma. This deposit is vulnerable to thermokarst and erosion due to natural and anthropogenic disturbances. The original paper maps were issued by the Department of Natural Resources of the Russian Federation or its predecessor the Department of Geology of the Soviet Union and have their foundation in decades of geological field and remote sensing work and mapping at scales 1:50,000 to 1:500,000 by Russian geologists and cartographers in the respective regions. Eleven paper maps were scanned and digitized to record the geology unit boundaries, genetic type and clast size of each geologic unit, and borehole and outcrop locations. We also calculated area in km2, perimeter in km for each polygon. These attributes were used in support of (Grosse and others, 2013) which focused on extracting geologic units interpreted as Yedoma, based on lithology, ground ice conditions, geochronology, geomorphologic, and spatial association.

Uses of this digital geologic map should not violate the spatial resolution of the data. Although the digital form of the data removes the constraint imposed by the scale of a paper map, the detail and accuracy inherent in map scale are also present in the digital data. The data was edited at a scale of 1:1,000,000 and higher resolution information is not present in the dataset. Plotting at scales larger than 1:1,000,000 will not yield greater real detail, although it may reveal fine-scale irregularities below the intended resolution of the database. Similarly, where this database is used in combination with other data of higher resolution, the resolution of the combined output will be limited by the lower resolution of these data. Acknowledgment of the U.S. Geological Survey would be appreciated in products derived from these data.

Citation

In order to use these data, you must cite this data set with the following citation:

Bryant, R.N., Robinson, J.E., Taylor, M.D., Harper, William, DeMasi, Amy, Kyker-Snowman, Emily, Veremeeva, Alexandra, Schirrmeister, Lutz, Harden, Jennifer and Grosse, Guido, 2017, Digital Database and Maps of Quaternary Deposits in East and Central Siberia: U.S. Geological Survey data release, https://doi.org/10.5066/F7VT1Q89.

This data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content includes maximum soil depth and eight soil attributes including sand, silt, and clay content, gravel content, organic carbon content, pH, cation exchange capacity, and bulk density for the topsoil layer (0-30 cm) and the subsoil layer (30-100 cm). The spatial resolution is 0.25 degree. The Unified North American Soil Map (UNASM) combined information from the state-of-the-art US General Soil Map (STATSGO2) and Soil Landscape of Canada (SLCs) databases. The area not covered by these data sets was filled by using the Harmonized World Soil Database version 1.21 (HWSD1.21). The Northern Circumpolar Soil Carbon (NCSCD) database was used to provide more accurate and up-to-date soil organic carbon information for the high-latitude permafrost region and was combined with soil organic carbon content derived from the UNASM (Liu et al., 2013). The UNASM data were utilized in the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC). MAST-DC was a component of the NACP (www.nacarbon.org) designed to support NACP by providing data products and data management services needed for modeling and synthesis activities.

This digital map shows the areal extent of surficial deposits and rock stratigraphic units (formations) as compiled by Trimble and Machette from 1973 to 1977 and published in 1979 under the Front Range Urban Corridor Geology Program. Trimble and Machette compiled their geologic map from published geologic maps and unpublished geologic mapping having varied map unit schemes. A convenient feature of the compiled map is its uniform classification of geologic units that mostly matches those of companion maps to the north (USGS I-855-G) and to the south (USGS I-857-F). Published as a color paper map, the Trimble and Machette map was intended for land-use planning in the Front Range Urban Corridor. This map recently (1997-1999) was digitized under the USGS Front Range Infrastructure Resources Project. In general, the mountainous areas in the western part of the map exhibit various igneous and metamorphic bedrock units of Precambrian age, major faults, and fault brecciation zones at the east margin (5-20 km wide) of the Front Range. The eastern and central parts of the map (Colorado Piedmont) depict a mantle of unconsolidated deposits of Quaternary age and interspersed outcroppings of Cretaceous or Tertiary-Cretaceous sedimentary bedrock. The Quaternary mantle comprises eolian deposits (quartz sand and silt), alluvium (gravel, sand, and silt of variable composition), colluvium, and a few landslides. At the mountain front, north-trending, dipping Paleozoic and Mesozoic sandstone, shale, and limestone bedrock formations form hogbacks and intervening valleys.

This geospatial database maps the distribution of landforms along the Umatilla River in northeastern Oregon and covers a corridor 127 kilometers long from the confluence of the Umatilla River with the Columbia River upstream to Meacham Creek. The database encompasses the valley bottom and extends about 1 kilometer up the adjoining hillslopes. Map data are intended to support water quality and fisheries enhancement efforts pursuant to the First Foods, a resource-management approach that focuses on traditionally gathered foods including water, fish, big game, roots, and berries and calls attention to the reciprocity between people and the foods upon which humans depend. The Umatilla River drains about 6,300 square kilometers on the northwest slope of the Blue Mountains in northeast Oregon. Most of the drainage basin is underlain by Miocene basalt flows of the Columbia River Basalt Group. Younger, weakly lithified, late Miocene and early Pliocene gravel deposits of local origin (for ...

The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps(FIRMs). The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The specifications for the horizontal control of DFIRM data are consistent with those required for mapping at a scale of 1:12,000. The NFHL data contain layers in the Standard DFIRM datasets except for S_Label_Pt and S_Label_Ld. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all DFIRMs and corresponding LOMRs available on the publication date of the data set.

This digital data release contains spatial datasets of bedrock geology, volcanic ash bed locations, test hole locations, bedrock outcrops, and structure contours of the top of bedrock and the base of the Ogallala Group from a previously published map (Souders, 2000). The GeologicMap feature dataset contains separate feature classes for the Ogallala Group map unit (ContactsAndFaults and MapUnitPolys) and the underlying pre-Ogallala bedrock map units (ContactsAndFaults_Bedrock and MapUnitPolys_Bedrock). The VolcanicAshBedPoints feature class contains the locations of volcanic ash beds within the Ogallala Group. The contours depicting the elevation of the top of bedrock (top of Ogallala Group where present and top of pre-Ogallala bedrock where Ogallala is absent) are contained in the IsoValueLines_TopBedrock feature class. The contours depicting the elevation of the base of the Ogallala Group are contained in the IsoValueLines_BaseOgallala feature class. Contoured values are given in both feet and meters. Feature classes containing the _location of test holes (TestHolePoints) and bedrock outcrops (OverlayPolys) that were used in generating the structure contour surfaces are included. Nonspatial tables define the data sources used, define terms used in the dataset, and describe the geologic units. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables. Surficial geologic units that are only represented as cross-sections on the original map publication, and the cross-sections themselves, are not included in this digital data release.

This page provides access to NOAA FIsheries' data and map page. The page hosts databases, maps and GIS datasets, metadata catalog among many other information and products.

NOAA Fisheries' science-based conservation and management of sustainable fisheries, marine mammals, endangered species, and their habitats have become global models for marine stewardship and sustainability.

This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (cdbegeo.txt or cdbegeo.ps), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller.

This dataset contains old maps of Hong Kong, including “Plan of the City of Victoria Hong Kong (1889)”, “Victoria Hong Kong (1897)”, “Kowloon Peninsula (1892, 1947, 1963 & 1970)”, “Hong Kong (1927 & 1957)”, “Sha Tin (1904)”, “Tsuen Wan (1958)”, “Central (1938)” and “Wan Chai (1947)”. The map images scanned from paper maps are geo-referenced to the Hong Kong 1980 Grid coordinate system.

This submission offers a link to a web mapping application hosted instance of the Global Oil & Gas Features Database (GOGI), via EDX Spatial. This offers users with the ability to visualize, interact, and create maps with data of their choice, as well as download specific attributes or fields of view from the database. This data can also be downloaded as a File Geodatabse from EDX at https://edx.netl.doe.gov/dataset/global-oil-gas-features-database. Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase” This data was developed using a combination of big data computing, custom search and data integration algorithms, and expert driven search to collect open oil and gas data resources worldwide. This approach identified over 380 data sets and integrated more than 4.8 million features into the GOGI database. Acknowledgements: This work was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.

This dataset contains geospatial data, code, and documentation relevant to the Maryland Food System Map, a web mapping application maintained by the Johns Hopkins Center for a Livable Future between 2012 and 2023. Approximately 500 geospatial data layers that were featured on the application have been preserved here for use in future analyses of the food system in Maryland. The code behind the application has also been preserved in this dataset and can be used to better understand how the application worked and to develop similar applications in the future. The documentation provides more information about the Maryland Food System Map, including both the history of the application and how it was used. There is also metadata about when and where the data for data layers were obtained.

Summary:

A sub-set of the Gaia Data Release 3 data centered on the Sun for use in mapping the local Galaxy. The data includes three columns for each star: parallax, heliocentric longitude, and heliocentric latitude. Data can be converted to Galactocentric Rectangular Coordinate (X, Y, Z) or Galactocentric Cylindrical Coordinate (R, Phi, Z). PLEASE NOTE: There are many incorrectly measured parallaxes -- all negative parallaxes must be removed.

Columns:

• parallax (units: mas) - Parallactic angle (used for determining the distance to a star).
• longitude (units: degrees) - The heliocentric longitude, with zero degrees corresponding to the direction of the Galactic Center.
• latitude (units: degrees) - The heliocentric latitude, with +90 degrees corresponding to the North Galactic Pole and 0 degrees corresponding to the Galaxy's mid-plane.

Query from Gaia Database:

SELECT gaia_source.parallax, gaia_source.l, gaia_source.b

FROM gaiadr3.gaia_source 

WHERE 

gaia_source.random_index < 5000000 AND

gaia_source.phot_g_mean_mag BETWEEN 14 AND 18 AND

gaia_source.bp_rp BETWEEN 0.5 AND 2.5 AND

(1.0 / gaia_source.parallax) * COS(RADIANS(gaia_source.b)) < 0.250

Note the final condition in the query limits the selection of stars to those within 250 parsecs (in-plane distance) of the Sun. In other words, we are examining the stars in a cylinder of radius 250 parsecs centered on the Sun, punching perpendicularly through the Milky Way disk.

License:

The Gaia Data is under the following license: Open Source With Attribution to ESA/Gaia/DPAC, reproduced here:

"The Gaia data are open and free to use, provided credit is given to 'ESA/Gaia/DPAC'. In general, access to, and use of, ESA's Gaia Archive (hereafter called 'the website') constitutes acceptance of the following general terms and conditions. Neither ESA nor any other party involved in creating, producing, or delivering the website shall be liable for any direct, incidental, consequential, indirect, or punitive damages arising out of user access to, or use of, the website. The website does not guarantee the accuracy of information provided by external sources and accepts no responsibility or liability for any consequences arising from the use of such data."

All of my course materials are free to use with attribution as well.

Multi-Agency Ground Plot (MAGPlot) database (DB) is a pan-Canadian forest ground-plot data repository. The database synthesize forest ground plot data from various agencies, including the National Forest Inventory (NFI) and 12 Canadian jurisdictions: Alberta (AB), British Columbia (BC), Manitoba (MB), New Brunswick (NB), Newfoundland and Labrador (NL), Nova Scotia (NS), Northwest Territories (NT), Ontario (ON), Prince Edward Island (PE), Quebec (QC), Saskatchewan (SK), and Yukon Territory (YT), contributed in their original format. These datasets underwent data cleaning and quality assessment using the set of rules and standards set by the contributors and associated documentations, and were standardized, harmonized, and integrated into a single, centralized, and analysis-ready database. The primary objective of the MAGPlot project is to collate and harmonize forest ground plot data and to present the data in a findable, accessible, interoperable, and reusable (FAIR) format for pan-Canadian forest research. The current version includes both historical and contemporary forest ground plot data provided by data contributors. The standardized and harmonized dataset includes eight data tables (five site related and three tree measurement tables) in a relational database schema. Site-related tables contain information on geographical locations, treatments (e.g. stand tending, regeneration, and cutting), and disturbances caused by abiotic factors (e.g., weather, wildfires) or biotic factors (e.g., disease, insects, animals). Tree-related tables, on the other hand, focus on measured tree attributes, including biophysical and growth parameters (e.g., DBH, height, crown class), species, status, stem conditions (e.g., broken or dead tops), and health conditions. While most contributors provided large and small tree plot measurements, only NFI, AB, MB, and SK contributed datasets reported at regeneration plot level (e.g., stem count, regeneration species). Future versions are expected to include updated and/or new measurement records as well as additional tables and measured and compiled (e.g., tree volume and biomass) attributes. MAGPlot is hosted through Canada’s National Forest Information System (https://nfi.nfis.org/en/maps). --------------------------------------------------- LATEST SITE TREATMENTS LAYER: --------------------------------------------------- Shows the most recently applied treatment class for each MAGPlot site. These treatment classes are broad categories, with more specific treatment details available in the full dataset. ----------- NOTES: ----------- The MAGPlot release (v1.0 and v1.1) does not include NL and SK datasets due to pending Data Sharing Agreements, ongoing data processing, or restrictions on third-party sharing. These datasets will be included in future releases. While certain jurisdictions permit open or public data sharing, given that requestor signs and adheres the Data Use agreement, there are some jurisdictions that require a jurisdiction-specific request form to be signed in addition to the Data Use Agreement form. For the MAGPlot Data Dictionary, other metadata, datasets available for open sharing (with approximate locations), data requests (for other datasets or exact coordinates), and available data visualization products, please check all the folders in the “Data and Resources” section below. Coordinates in web services have been randomized within 5km of true location to preserve site integrity Access the WMS (Web Map Service) layers from the “Data and Resources” section below. A data request must be submitted to access historical datasets, datasets restricted by data-use agreements, or exact plot coordinates using the link below. NFI Data Request Form: https://nfi.nfis.org/en/datarequestform --------------------------------- ACKNOWLEDGEMENT: --------------------------------- We acknowledge and recognize the following agencies that have contributed data to the MAGPlot database: Government of Alberta - Ministry of Agriculture, Forestry, and Rural Economic Development - Forest Stewardship and Trade Branch Government of British Columbia - Ministry of Forests - Forest Analysis and Inventory Branch Government of Manitoba - Ministry of Economic, Development, Investment, Trade, and Natural Resources - Forestry and Peatlands Branch Government of New Brunswick - Ministry of Natural Resources and Energy Development - Forestry Division, Forest Planning and Stewardship Branch Government of Newfoundland & Labrador - Department of Fisheries, Forestry and Agriculture - Forestry Branch Government of Nova Scotia - Ministry of Natural Resources and Renewables - Department of Natural Resources and Renewables Government of Northwest Territories - Department of Environment & Climate Change - Forest Management Division Government of Ontario - Ministry of Natural Resources and Forestry - Science and Research Branch, Forest Resources Inventory Unit Government of Prince Edward Island - Department of Environment, Energy, and Climate Action - Forests, Fish, and Wildlife Division Government of Quebec - Ministry of Natural Resources and Forests - Forestry Sector Government of Saskatchewan - Ministry of Environment - Forest Service Branch Government of Yukon - Ministry of Energy, Mines, and Resources - Forest Management Branch Government of Canada - Natural Resources Canada - Canadian Forest Service - National Forest Inventory Projects Office