100+ datasets found
  1. d

    Web Scraping Data | Key Customers Domain Name Data | Scanning Logos found on...

    • datarade.ai
    .json
    Updated Jun 27, 2024
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    PredictLeads (2024). Web Scraping Data | Key Customers Domain Name Data | Scanning Logos found on Websites | 248M+ Records [Dataset]. https://datarade.ai/data-products/predictleads-web-scraping-data-domain-name-data-business-predictleads
    Explore at:
    .jsonAvailable download formats
    Dataset updated
    Jun 27, 2024
    Dataset authored and provided by
    PredictLeads
    Area covered
    Benin, Burkina Faso, Malaysia, Curaçao, Turkmenistan, Colombia, Nigeria, Svalbard and Jan Mayen, Oman, Northern Mariana Islands
    Description

    PredictLeads Key Customers Data provides essential business intelligence by analyzing company relationships, uncovering vendor partnerships, client connections, and strategic affiliations through advanced web scraping and logo recognition. This dataset captures business interactions directly from company websites, offering valuable insights into market positioning, competitive landscapes, and growth opportunities.

    Use Cases:

    ✅ Account Profiling – Gain a 360-degree customer view by mapping company relationships and partnerships. ✅ Competitive Intelligence – Track vendor-client connections and business affiliations to identify key industry players. ✅ B2B Lead Targeting – Prioritize leads based on their business relationships, improving sales and marketing efficiency. ✅ CRM Data Enrichment – Enhance company records with detailed key customer data, ensuring data accuracy. ✅ Market Research – Identify emerging trends and industry networks to optimize strategic planning.

    Key API Attributes:

    • id (string, UUID) – Unique identifier for the company connection.
    • category (string) – Type of relationship (e.g., vendor, client, partner).
    • source_category (string) – Where the connection was detected (e.g., partner page, case study).
    • source_url (string, URL) – Website where the relationship was found.
    • individual_source_url (string, URL) – Specific page confirming the connection.
    • context (string) – Extracted description of the business relationship (e.g., "Company X - partners with Company Y to enhance payment processing").
    • first_seen_at (ISO 8601 date-time) – Date the connection was first detected.
    • last_seen_at (ISO 8601 date-time) – Most recent confirmation of the relationship.
    • company1 & company2 (objects) – Details of the two connected companies, including:
    • - domain (string) – Company website domain.
    • - company_name (string) – Official company name.
    • - ticker (string, nullable) – Stock ticker, if available.

    📌 PredictLeads Key Customers Data is an indispensable tool for B2B sales, marketing, and market intelligence teams, providing actionable relationship insights to drive targeted outreach, competitor tracking, and strategic decision-making.

    PredictLeads Docs: https://docs.predictleads.com/v3/guide/connections_dataset

  2. d

    Altosight | AI Custom Web Scraping Data | 100% Global | Free Unlimited Data...

    • datarade.ai
    .json, .csv, .xls
    Updated Sep 7, 2024
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    Altosight (2024). Altosight | AI Custom Web Scraping Data | 100% Global | Free Unlimited Data Points | Bypassing All CAPTCHAs & Blocking Mechanisms | GDPR Compliant [Dataset]. https://datarade.ai/data-products/altosight-ai-custom-web-scraping-data-100-global-free-altosight
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    .json, .csv, .xlsAvailable download formats
    Dataset updated
    Sep 7, 2024
    Dataset authored and provided by
    Altosight
    Area covered
    Paraguay, Wallis and Futuna, Singapore, Côte d'Ivoire, Greenland, Chile, Tajikistan, Guatemala, Svalbard and Jan Mayen, Czech Republic
    Description

    Altosight | AI Custom Web Scraping Data

    ✦ Altosight provides global web scraping data services with AI-powered technology that bypasses CAPTCHAs, blocking mechanisms, and handles dynamic content.

    We extract data from marketplaces like Amazon, aggregators, e-commerce, and real estate websites, ensuring comprehensive and accurate results.

    ✦ Our solution offers free unlimited data points across any project, with no additional setup costs.

    We deliver data through flexible methods such as API, CSV, JSON, and FTP, all at no extra charge.

    ― Key Use Cases ―

    ➤ Price Monitoring & Repricing Solutions

    🔹 Automatic repricing, AI-driven repricing, and custom repricing rules 🔹 Receive price suggestions via API or CSV to stay competitive 🔹 Track competitors in real-time or at scheduled intervals

    ➤ E-commerce Optimization

    🔹 Extract product prices, reviews, ratings, images, and trends 🔹 Identify trending products and enhance your e-commerce strategy 🔹 Build dropshipping tools or marketplace optimization platforms with our data

    ➤ Product Assortment Analysis

    🔹 Extract the entire product catalog from competitor websites 🔹 Analyze product assortment to refine your own offerings and identify gaps 🔹 Understand competitor strategies and optimize your product lineup

    ➤ Marketplaces & Aggregators

    🔹 Crawl entire product categories and track best-sellers 🔹 Monitor position changes across categories 🔹 Identify which eRetailers sell specific brands and which SKUs for better market analysis

    ➤ Business Website Data

    🔹 Extract detailed company profiles, including financial statements, key personnel, industry reports, and market trends, enabling in-depth competitor and market analysis

    🔹 Collect customer reviews and ratings from business websites to analyze brand sentiment and product performance, helping businesses refine their strategies

    ➤ Domain Name Data

    🔹 Access comprehensive data, including domain registration details, ownership information, expiration dates, and contact information. Ideal for market research, brand monitoring, lead generation, and cybersecurity efforts

    ➤ Real Estate Data

    🔹 Access property listings, prices, and availability 🔹 Analyze trends and opportunities for investment or sales strategies

    ― Data Collection & Quality ―

    ► Publicly Sourced Data: Altosight collects web scraping data from publicly available websites, online platforms, and industry-specific aggregators

    ► AI-Powered Scraping: Our technology handles dynamic content, JavaScript-heavy sites, and pagination, ensuring complete data extraction

    ► High Data Quality: We clean and structure unstructured data, ensuring it is reliable, accurate, and delivered in formats such as API, CSV, JSON, and more

    ► Industry Coverage: We serve industries including e-commerce, real estate, travel, finance, and more. Our solution supports use cases like market research, competitive analysis, and business intelligence

    ► Bulk Data Extraction: We support large-scale data extraction from multiple websites, allowing you to gather millions of data points across industries in a single project

    ► Scalable Infrastructure: Our platform is built to scale with your needs, allowing seamless extraction for projects of any size, from small pilot projects to ongoing, large-scale data extraction

    ― Why Choose Altosight? ―

    ✔ Unlimited Data Points: Altosight offers unlimited free attributes, meaning you can extract as many data points from a page as you need without extra charges

    ✔ Proprietary Anti-Blocking Technology: Altosight utilizes proprietary techniques to bypass blocking mechanisms, including CAPTCHAs, Cloudflare, and other obstacles. This ensures uninterrupted access to data, no matter how complex the target websites are

    ✔ Flexible Across Industries: Our crawlers easily adapt across industries, including e-commerce, real estate, finance, and more. We offer customized data solutions tailored to specific needs

    ✔ GDPR & CCPA Compliance: Your data is handled securely and ethically, ensuring compliance with GDPR, CCPA and other regulations

    ✔ No Setup or Infrastructure Costs: Start scraping without worrying about additional costs. We provide a hassle-free experience with fast project deployment

    ✔ Free Data Delivery Methods: Receive your data via API, CSV, JSON, or FTP at no extra charge. We ensure seamless integration with your systems

    ✔ Fast Support: Our team is always available via phone and email, resolving over 90% of support tickets within the same day

    ― Custom Projects & Real-Time Data ―

    ✦ Tailored Solutions: Every business has unique needs, which is why Altosight offers custom data projects. Contact us for a feasibility analysis, and we’ll design a solution that fits your goals

    ✦ Real-Time Data: Whether you need real-time data delivery or scheduled updates, we provide the flexibility to receive data when you need it. Track price changes, monitor product trends, or gather...

  3. t

    The Surprising Impact of Data-backed Website Design and User Experience...

    • thegood.com
    html
    Updated May 14, 2025
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    The Good (2025). The Surprising Impact of Data-backed Website Design and User Experience Testing [Dataset]. https://thegood.com/insights/data-backed-website-design/
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    htmlAvailable download formats
    Dataset updated
    May 14, 2025
    Dataset authored and provided by
    The Good
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    When a prospect enters a brick and mortar business, staff will likely get several opportunities to engage that person and encourage a purchase. Not so online. You have seconds, not minutes to give visitors a reason to stay long enough to engage. There’s a common thread that helped companies like Facebook, Amazon, and Apple rise […]

  4. D

    Website Analytics

    • data.nola.gov
    • gimi9.com
    • +4more
    application/rdfxml +5
    Updated Feb 2, 2017
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    Information Technology and Innovation Web Team (2017). Website Analytics [Dataset]. https://data.nola.gov/City-Administration/Website-Analytics/62d3-pst8
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    csv, tsv, xml, application/rssxml, application/rdfxml, jsonAvailable download formats
    Dataset updated
    Feb 2, 2017
    Dataset authored and provided by
    Information Technology and Innovation Web Team
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

  5. NYC Open Data Plan: Website Data

    • data.cityofnewyork.us
    • catalog.data.gov
    application/rdfxml +5
    Updated Oct 28, 2024
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    Office of Technology and Innovation (OTI) (2024). NYC Open Data Plan: Website Data [Dataset]. https://data.cityofnewyork.us/City-Government/NYC-Open-Data-Plan-Website-Data/duz4-2gn9
    Explore at:
    application/rdfxml, csv, application/rssxml, tsv, xml, jsonAvailable download formats
    Dataset updated
    Oct 28, 2024
    Dataset provided by
    New York City Office of Technology and Innovationhttps://www.nyc.gov/content/oti/pages/
    Authors
    Office of Technology and Innovation (OTI)
    Description

    NOTE: To review the latest plan, make sure to filter the "Report Year" column to the latest year.

    Data on public websites maintained by or on behalf of the city agencies.

  6. DISCOVER-AQ Maryland Deployment Edgewood Ground Site Data - Dataset - NASA...

    • data.nasa.gov
    • data.staging.idas-ds1.appdat.jsc.nasa.gov
    Updated Apr 1, 2025
    + more versions
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    nasa.gov (2025). DISCOVER-AQ Maryland Deployment Edgewood Ground Site Data - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/discover-aq-maryland-deployment-edgewood-ground-site-data-02d32
    Explore at:
    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Area covered
    Maryland
    Description

    DISCOVERAQ_Maryland_Ground_Edgewood_Data contains data collected at the Edgewood ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

  7. d

    Facility Site Data Dictionary

    • catalog.data.gov
    • data-test-lakecountyil.opendata.arcgis.com
    Updated Mar 17, 2023
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    Lake County Illinois GIS (2023). Facility Site Data Dictionary [Dataset]. https://catalog.data.gov/dataset/facility-site-data-dictionary-8654d
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    Dataset updated
    Mar 17, 2023
    Dataset provided by
    Lake County Illinois GIS
    Description

    Data Dictionary for Facility Site & Landmark data.

  8. DISCOVER-AQ Maryland Deployment Essex Ground Site Data - Dataset - NASA Open...

    • data.nasa.gov
    Updated Apr 1, 2025
    + more versions
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    nasa.gov (2025). DISCOVER-AQ Maryland Deployment Essex Ground Site Data - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/discover-aq-maryland-deployment-essex-ground-site-data-84f66
    Explore at:
    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Area covered
    Maryland
    Description

    DISCOVERAQ_Maryland_Ground_Essex_Data contains data collected at the Essex ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

  9. Superfund Site Information - Site Sampling Data

    • catalog.data.gov
    • datasets.ai
    • +1more
    Updated Feb 25, 2025
    + more versions
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    U.S. EPA Office of Land and Emergency Management (OLEM) - Office of Superfund Remediation and Technology Innovation (OSRTI) (Owner) (2025). Superfund Site Information - Site Sampling Data [Dataset]. https://catalog.data.gov/dataset/superfund-site-information-site-sampling-data10
    Explore at:
    Dataset updated
    Feb 25, 2025
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    This asset includes Superfund site-specific sampling information including location of samples, types of samples, and analytical chemistry characteristics of samples. Information is associated with a particular contaminated sate as there is no national database of this information.

  10. Data Processing & Hosting & Website Operating in Europe - Market Research...

    • ibisworld.com
    Updated Jun 15, 2025
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    IBISWorld (2025). Data Processing & Hosting & Website Operating in Europe - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/europe/industry/data-processing-hosting-website-operating/200269/
    Explore at:
    Dataset updated
    Jun 15, 2025
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2015 - 2030
    Area covered
    Europe
    Description

    This group includes the provision of infrastructure for hosting, data processing services and related activities, as well as search facilities and other portals for the Internet.

  11. d

    Water Data for Nisqually River at Site NR0

    • catalog.data.gov
    • data.usgs.gov
    • +1more
    Updated Jul 6, 2024
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    U.S. Geological Survey (2024). Water Data for Nisqually River at Site NR0 [Dataset]. https://catalog.data.gov/dataset/water-data-for-nisqually-river-at-site-nr0
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Nisqually River
    Description

    Discharge and suspended sediment data were collected from October 2016 to Febuary 2017 at the NR0 site. Data was collected immediately down stream of Old Pacific Hwy SE bridge during a bridge measurement and approximately 100 meters below bridge for a boat measurement. Data collection from the bridge has been ongoing since 1968 but data collection from a boat was first attempted October 21, 2016 during this data collection series. Suspended sediment sample and discrete discharge data at this site are available at: https://waterdata.usgs.gov/wa/nwis/inventory/?site_no=12090240&agency_cd=USGS&. A summary of suspended-sediment sample data are provided with this data release in the file NR0_SSC_summary.csv.

  12. d

    Global Web Data | Web Scraping Data | Job Postings Data | Source: Company...

    • datarade.ai
    .json
    + more versions
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    PredictLeads, Global Web Data | Web Scraping Data | Job Postings Data | Source: Company Website | 214M+ Records [Dataset]. https://datarade.ai/data-products/predictleads-web-data-web-scraping-data-job-postings-dat-predictleads
    Explore at:
    .jsonAvailable download formats
    Dataset authored and provided by
    PredictLeads
    Area covered
    El Salvador, Kuwait, Virgin Islands (British), Northern Mariana Islands, Guadeloupe, Bosnia and Herzegovina, Bonaire, French Guiana, Kosovo, Comoros
    Description

    PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

    Key Features:

    ✅214M+ Job Postings Tracked – Data sourced from 92 Million company websites worldwide. ✅7,1M+ Active Job Openings – Updated in real-time to reflect hiring demand. ✅Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

    Primary Attributes:

    • id (string, UUID) – Unique identifier for the job posting.
    • type (string, constant: "job_opening") – Object type.
    • title (string) – Job title.
    • description (string) – Full job description, extracted from the job listing.
    • url (string, URL) – Direct link to the job posting.
    • first_seen_at – Timestamp when the job was first detected.
    • last_seen_at – Timestamp when the job was last detected.
    • last_processed_at – Timestamp when the job data was last processed.

    Job Metadata:

    • contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
    • categories (array of strings) – Job categories (e.g., "engineering", "marketing").
    • seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
    • status (string) – Job status (e.g., "open", "closed").
    • language (string) – Language of the job posting.
    • location (string) – Full location details as listed in the job description.
    • Location Data (location_data) (array of objects)
    • city (string, nullable) – City where the job is located.
    • state (string, nullable) – State or region of the job location.
    • zip_code (string, nullable) – Postal/ZIP code.
    • country (string, nullable) – Country where the job is located.
    • region (string, nullable) – Broader geographical region.
    • continent (string, nullable) – Continent name.
    • fuzzy_match (boolean) – Indicates whether the location was inferred.

    Salary Data (salary_data)

    • salary (string) – Salary range extracted from the job listing.
    • salary_low (float, nullable) – Minimum salary in original currency.
    • salary_high (float, nullable) – Maximum salary in original currency.
    • salary_currency (string, nullable) – Currency of the salary (e.g., "USD", "EUR").
    • salary_low_usd (float, nullable) – Converted minimum salary in USD.
    • salary_high_usd (float, nullable) – Converted maximum salary in USD.
    • salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

    Occupational Data (onet_data) (object, nullable)

    • code (string, nullable) – ONET occupation code.
    • family (string, nullable) – Broad occupational family (e.g., "Computer and Mathematical").
    • occupation_name (string, nullable) – Official ONET occupation title.

    Additional Attributes:

    • tags (array of strings, nullable) – Extracted skills and keywords (e.g., "Python", "JavaScript").

    📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

    PredictLeads Dataset: https://docs.predictleads.com/v3/guide/job_openings_dataset

  13. Data from: LMOS Milwaukee Ground Site Data

    • data.nasa.gov
    • gimi9.com
    • +4more
    Updated Apr 1, 2025
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    nasa.gov (2025). LMOS Milwaukee Ground Site Data [Dataset]. https://data.nasa.gov/dataset/lmos-milwaukee-ground-site-data-0c3d8
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    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Area covered
    Milwaukee
    Description

    LMOS_Ground_Milwaukee_Data_1 is the Lake Michigan Ozone Study (LMOS) Milwaukee ground site data collected during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.Elevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime “lake breeze” airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.

  14. O

    Access to public assets on data.ct.gov by access type and month

    • data.ct.gov
    application/rdfxml +5
    Updated Jul 13, 2025
    + more versions
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    (2025). Access to public assets on data.ct.gov by access type and month [Dataset]. https://data.ct.gov/Government/Access-to-public-assets-on-data-ct-gov-by-access-t/d8rk-jqha
    Explore at:
    csv, json, xml, application/rdfxml, tsv, application/rssxmlAvailable download formats
    Dataset updated
    Jul 13, 2025
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    Connecticut
    Description

    This dataset provides information about access to public assets on the CT Open Data Portal by day. Types of access include:

    -Grid view -Primer page view -Download -API read -Story page view -Visualization page view

    It includes assets that meet the following criteria:

    -Published on the data.ct.gov domain -Public -Official (ie published by a registered user) -Not a derived view

  15. Data from: POLARIS Ground Site Data

    • s.cnmilf.com
    • gimi9.com
    • +4more
    Updated Jun 28, 2025
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    NASA/LARC/SD/ASDC (2025). POLARIS Ground Site Data [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/polaris-ground-site-data-8d6c2
    Explore at:
    Dataset updated
    Jun 28, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    POLARIS_Ground_Data is the ground site data collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data from the Composition and Photo-Dissociative Flux Measurement (CPFM) are featured in this collection. Data collection for this product is complete.The POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.

  16. c

    Website Classification Dataset

    • cubig.ai
    Updated Feb 25, 2025
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    CUBIG (2025). Website Classification Dataset [Dataset]. https://cubig.ai/store/products/138/website-classification-dataset
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    Dataset updated
    Feb 25, 2025
    Dataset authored and provided by
    CUBIG
    License

    https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service

    Measurement technique
    Privacy-preserving data transformation via differential privacy, Synthetic data generation using AI techniques for model training
    Description

    1) Data Introduction • The Website dataset designed to facilitate the development of models for URL-based website classification.

    2) Data Utilization (1) Website data has characteristics that: • This dataset is crucial for training models that can automatically classify websites based on their URL structures. (2) Website data can be used to: • Enhancing cybersecurity measures by detecting malicious websites. • Improving content filtering systems for safer browsing experiences.

  17. e

    Earnest Analytics Home Builder Web Data

    • earnestanalytics.com
    Updated Apr 18, 2023
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    Earnest Analytics (2023). Earnest Analytics Home Builder Web Data [Dataset]. https://www.earnestanalytics.com/datasets/homebuilders
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    Dataset updated
    Apr 18, 2023
    Dataset authored and provided by
    Earnest Analytics
    Area covered
    US
    Description

    Predict revenue surprises, monitor selling price, track net order flow, and quantify market share by geography and community. Web Homebuilders data is sourced from housing sales, pricing, and availability detail for US homebuilders.

  18. d

    Google Address Data, Google Address API, Google location API, Google Map...

    • datarade.ai
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    APISCRAPY, Google Address Data, Google Address API, Google location API, Google Map API, Business Location Data- 100 M Google Address Data Available [Dataset]. https://datarade.ai/data-products/google-address-data-google-address-api-google-location-api-apiscrapy
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset authored and provided by
    APISCRAPY
    Area covered
    Luxembourg, Liechtenstein, United Kingdom, China, Spain, Estonia, Moldova (Republic of), Monaco, Åland Islands, Andorra
    Description

    Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

    Key Features:

    Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

    Integration with Google Address API: Seamlessly integrate our datasets with the powerful Google Address API. This collaboration ensures not just accessibility but a robust combination that amplifies the precision of your location-based insights.

    Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

    Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

    Use Cases:

    Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

    Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

    E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

    Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

    Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

    Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

    Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

    Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

    Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

    Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

  19. g

    DISCOVER-AQ Colorado Deployment Platteville Ground Site Data | gimi9.com

    • gimi9.com
    Updated Jun 24, 2014
    + more versions
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    (2014). DISCOVER-AQ Colorado Deployment Platteville Ground Site Data | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_discover-aq-colorado-deployment-platteville-ground-site-data-789d6
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    Dataset updated
    Jun 24, 2014
    Area covered
    Colorado
    Description

    DISCOVERAQ_Colorado_Ground_Platteville_Data contains data collected at the Platteville ground site during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete. Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality. DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS). The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

  20. Data from: LISTOS Bronx Pfizer Ground Site Data

    • catalog.data.gov
    • datasets.ai
    • +1more
    Updated Jul 10, 2025
    + more versions
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    NASA/LARC/SD/ASDC (2025). LISTOS Bronx Pfizer Ground Site Data [Dataset]. https://catalog.data.gov/dataset/listos-bronx-pfizer-ground-site-data-ac70e
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    Dataset updated
    Jul 10, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Area covered
    The Bronx
    Description

    LISTOS_Ground_BronxPfizer_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the Bronx Pfizer ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.The New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.

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PredictLeads (2024). Web Scraping Data | Key Customers Domain Name Data | Scanning Logos found on Websites | 248M+ Records [Dataset]. https://datarade.ai/data-products/predictleads-web-scraping-data-domain-name-data-business-predictleads

Web Scraping Data | Key Customers Domain Name Data | Scanning Logos found on Websites | 248M+ Records

Explore at:
.jsonAvailable download formats
Dataset updated
Jun 27, 2024
Dataset authored and provided by
PredictLeads
Area covered
Benin, Burkina Faso, Malaysia, Curaçao, Turkmenistan, Colombia, Nigeria, Svalbard and Jan Mayen, Oman, Northern Mariana Islands
Description

PredictLeads Key Customers Data provides essential business intelligence by analyzing company relationships, uncovering vendor partnerships, client connections, and strategic affiliations through advanced web scraping and logo recognition. This dataset captures business interactions directly from company websites, offering valuable insights into market positioning, competitive landscapes, and growth opportunities.

Use Cases:

✅ Account Profiling – Gain a 360-degree customer view by mapping company relationships and partnerships. ✅ Competitive Intelligence – Track vendor-client connections and business affiliations to identify key industry players. ✅ B2B Lead Targeting – Prioritize leads based on their business relationships, improving sales and marketing efficiency. ✅ CRM Data Enrichment – Enhance company records with detailed key customer data, ensuring data accuracy. ✅ Market Research – Identify emerging trends and industry networks to optimize strategic planning.

Key API Attributes:

  • id (string, UUID) – Unique identifier for the company connection.
  • category (string) – Type of relationship (e.g., vendor, client, partner).
  • source_category (string) – Where the connection was detected (e.g., partner page, case study).
  • source_url (string, URL) – Website where the relationship was found.
  • individual_source_url (string, URL) – Specific page confirming the connection.
  • context (string) – Extracted description of the business relationship (e.g., "Company X - partners with Company Y to enhance payment processing").
  • first_seen_at (ISO 8601 date-time) – Date the connection was first detected.
  • last_seen_at (ISO 8601 date-time) – Most recent confirmation of the relationship.
  • company1 & company2 (objects) – Details of the two connected companies, including:
  • - domain (string) – Company website domain.
  • - company_name (string) – Official company name.
  • - ticker (string, nullable) – Stock ticker, if available.

📌 PredictLeads Key Customers Data is an indispensable tool for B2B sales, marketing, and market intelligence teams, providing actionable relationship insights to drive targeted outreach, competitor tracking, and strategic decision-making.

PredictLeads Docs: https://docs.predictleads.com/v3/guide/connections_dataset

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