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

This record is for Approval for Access product AfA501 for approximately 1000 automatic rainfall data from the Environment Agency rainfall API.

The data is available on an update cycle which varies across the country, typically updated daily but updated faster is rainfall is detected. This is update frequency is usually increased during times of flooding, etc.

Readings are transferred via telemetry to internal and external systems in or close to real-time.

Measurement of the rainfall is taken in millimetres (mm) accumulated over 15 minutes. Note that rainfall data is recorded in GMT, so during British Summer Time (BST) data may appear to be an hour old. Data comes from a network of over 1000 gauges across England. Data shown is raw data collected from the gauges and is subject to quality control procedures. As a result, values may change after publication on this website.

Continuous rainfall information is also stored on our hydrometric archive, Wiski, and can be provided in non real-time on request through our customer contact centre. This raw rainfall data is provided to the Met Office for quality control along with all the data from our registered daily storage gauges (c.1400). The quality controlled dataset is covered in AfA148 Quality Controlled Daily and Monthly Raingauge Data from Environment Agency Gauges.

Data from a small selection of Met Office raingauges are included in our open data feed. This data is also available from the Met Office as open data.

As part of the NERP TE project 2.3 temperature loggers were deployed at 15 sites across Torres Strait to measure the sea temperature. The loggers regularly measure the sea water temperature and record it in their memory. Every year or so the loggers are swapped with new loggers and the recorded data is extracted and recorded in the AIMS Real Time Data Systems database as part of the Australia wide Sea Temperature Observing System. While these loggers do not provide real time data, they provide a vital historical record of conditions across Torres Strait allowing researchers to better understand potential temperature thresholds that might cause coral bleaching and to calibrate satellite measurements of sea temperature. At each site loggers were placed at two depths and were located close to islands or cays to facilitate easy maintenance and replacement. This dataset contains the locations of loggers that may not have been recollected from the field yet. Logger data access: The data from the retrieved data loggers can be accessed by following the link to the "Logger metadata and data tool" associated with each island. These pages can also be accessed via the interactive map showing the retrieved data. Each site on this map contains a link to the associated metadata and data access for that site. Format: 1 shapefile, 2 interactive maps. A shapefile and its interactive map showing the Torres Strait temperature data logger deployed sites. The associated data contains links to pdf files showing the data loggers and their position information. An interactive map providing access to the retrieved data from the Torres Strait temperature data loggers.

PredictLeads News Events Data provides real-time market intelligence by capturing business-critical news events, categorizing them for sentiment analysis, company profiling, and competitive tracking. Our dataset leverages advanced web scraping and AI-driven classification, ensuring access to highly relevant insights that help businesses monitor competitors, assess risks, and refine growth strategies.

Use Cases: ✅ Sentiment Analysis – Gauge public perception and market sentiment to refine brand positioning. ✅ Account Profiling – Enrich CRM systems with real-time company event tracking. ✅ Competitive Intelligence – Monitor industry news, mergers, and expansions to anticipate market shifts. ✅ Market Research – Analyze business website updates and categorized news data for trend forecasting. ✅ Risk Assessment – Detect negative sentiment or financial distress indicators in key market players.

Key API Attributes: - id (string, UUID) – Unique identifier for the news event. - category (string) – Categorization of the event (e.g., funding, acquisition, leadership change). - summary (string) – A brief overview of the detected event. - sentiment_score (float, nullable) – Positive, neutral, or negative sentiment rating for the event. - found_at (ISO 8601 date-time) – Timestamp when the news event was detected. - article_sentence (string, nullable) – Extracted key sentence from the news article. - location (string, nullable) – Geographic relevance of the event (e.g., company HQ, expansion region). - company (object) – The company associated with the event, including: - domain (string) – Company’s website domain. - company_name (string) – Official company name. - ticker (string, nullable) – Stock ticker (if publicly traded). - source_url (string, URL) – Link to the original news article or company update.

📌 PredictLeads News Events Data is trusted by market leaders for real-time competitive intelligence, ensuring faster, data-driven decision-making in sales, finance, and strategic planning.

PredictLeads News Events Dataset Docs: https://docs.predictleads.com/v3/guide/news_events_dataset

PredictLeads Job Openings Data provides real-time hiring insights sourced directly from company websites, ensuring the highest level of accuracy and freshness. Unlike job boards that rely on aggregated listings, our dataset delivers unmatched granularity on job postings, salary trends, and workforce demand - making it a powerful tool for HR, talent acquisition, and market analysis.

Use Cases: ✅ Job Boards Enhancement – Improve job listings with, high-quality postings. ✅ HR Consulting – Analyze hiring trends to guide workforce planning strategies. ✅ Employment Analytics – Track job market shifts, salary benchmarks, and demand for skills. ✅ HR Operations – Optimize recruitment pipelines with direct employer-sourced data. ✅ Competitive Intelligence – Monitor hiring activities of competitors for strategic insights.

Key API Attributes:

• id (string, UUID) – Unique job posting identifier.
• title (string) – Job title as posted by the employer.
• description (string) – Full job description.
• url (string, URL) – Direct link to the job posting.
• first_seen_at (ISO 8601 date-time) – When the job was first detected.
• last_seen_at (ISO 8601 date-time) – When the job was last detected.
• contract_types (array of strings) – Employment type (e.g., full-time, contract).
• categories (array of strings) – Job categories (e.g., engineering, sales).
• seniority (string) – Job seniority level (e.g., manager, entry-level).
• salary_data (object) – Salary range, currency, and converted USD values.
• location_data (object) – City, country, and region details.
• tags (array of strings) – Extracted skills and keywords from job descriptions.

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

This record is for Approval for Access product AfA104 Real-time Flood Data River Levels via our API. This dataset covers monitoring data that is only updated on our systems during a routine daily update cycle, some sites will update more frequently as we update the field technology used over the next few years till 2025. During times of flooding, or other water level related incidents our update frequency can be increased if the technology at site allows. Readings are transferred via telemetry to internal and external systems in or close to real-time. This data is transferred to other systems, including the API. Data for sites in Wales is included in the Open Data feed, but is owned by Natural Resources Wales (NRW). NRW also class the data as Open Data, and you may use it under the same terms as the England data (the standard Open Government Licence, available on The National Archives website). Measurements of the height (m) of water in a river lake or coastal site are taken using automatic field instruments and typically log the value every 15 minutes. Information is available for 1400 river gauging stations (where flow is also measured) and 1800 river level only monitoring sites throughout England as well as some reservoirs and coastal sites. Attribution statement: © Environment Agency copyright and/or database right 2021. All rights reserved.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Between 1987 and 1988, twenty-four approximately 2-acre plots were established in Jones County, Georgia on the Hitchiti Experimental Forest which is also known as the Brender Demonstration Forest. These plots have not burned since prior to 1939. Treatments were applied to track site changes over time from five short return interval underburn treatments. These treatments, replicated 4 times, were comprised of: biennial dormant season headfires, triennial dormant season headfires, triennial dormant season backfires, triennial growing season headfires, growing season headfires every 6 years, and unburned controls. Triennial dormant season treatments were eventually combined. Variables tracked over time include the impact of fire on overstory pine growth, midstory (in this study midstory includes understory plants > 4.5 feet high) structure and composition, seedling (in this study including all woody plants < 4.5 feet high and all other plants regardless of height) species dominance, percent cover, pine seedling establishment and mortality, and forest floor consumption. Several thousand overstory and midstory trees were tagged, GPS coordinates recorded and their survival and growth followed over time. Vegetation was measured in nested circular plots and on line transects. Live and dead overstory trees on two 0.2 acre (1/5 ac) subplots per treatment plot were tallied annually by species with diameter at breast height and height, measured and pest damage/mortality by pathogen, lightning and wind damage recorded. Basal area was calculated periodically. Some overstory pines were bored to determine age (typically after death). Midstory live and dead trees were tallied annually on six 0.02 acre subplots per treatment plot. Seedlings were tallied on eighteen 0.001 acre (MA = milacre) subplots per treatment plot by species/species group, and percent of the subplot area in vines, herbs, moss, live woody material, dead plant material, and void of plant material (exposed mineral soil). Six 33 feet line transects per treatment plot were divided into 6 inch segments and dominant seedling species/species group tallied annually. Over 150 species/species groups were identified and tracked over time. Weights of likely available live fuel were determined by species/species group prior to each burn, as were weights of likely available dead fuel for various categories/size classes. Paired postburn samples were collected to determine consumption of various fuel categories. Overstory and midstory pine crown scorch, foliage consumption, and hardwood mortality were tallied within two weeks following each burn. Other vegetation datasets include pine seedling establishment and survival over time on the 18 MA subplots per treatment plot. Red cockaded woodpecker (RCW) related information was collected annually by Region 8 (Southern Region) of the USFS and is available from them. Live and dead fuel moisture data were sampled prior to every burn and can include preburn moisture content grab samples, 10-hour fuel stick readings, and random lumber probe readings. Fire behavior records of headfires and backfires can include rate of spread, flame length, flame angle, flame zone depth, short distance spotting, slopovers, burnout time, and percent of plot burned. The study plan called for observations of fire residence time as well, but such observations were rarely recorded. Weather data include on-plot hand-held instrument observations of surface wind velocity, ambient temperature and relative humidity (RH). On-site data collected can include precipitation, ambient temperature, RH and wind traces from recording gauges. Keetch Byram Drought Index (KBDI) calculation and National Fire danger Rating System NFDRS predictions and other weather observations taken at two nearby Georgia Forestry Commission weather stations were also included.

PredictLeads Technographic Data is a powerful tool for B2B organizations, providing detailed technographic and firmographic insights extracted through sophisticated web scraping techniques. Unlike traditional datasets, it identifies emerging technologies in job postings, revealing real-time technology adoption trends across industries. These insights fuel technical decision-making, B2B data cleansing, account profiling, and 360-degree customer analysis.

Use Cases:

✅ Technical Account Profiling – Analyze a company’s technology stack and hiring trends for better-targeted sales and marketing. ✅ B2B Data Cleansing – Enhance CRM and data enrichment efforts with up-to-date, verified technographic insights. ✅ Technology Trend Analysis – Identify high-growth industries and emerging tech adoption patterns. ✅ Competitive Intelligence – Assess competitor tech stacks and innovation roadmaps based on hiring activity. ✅ 360-Degree Customer View – Integrate firmographic and technographic data for a complete B2B customer profile.

Key API Attributes:

• id (string, UUID) – Unique identifier for the technology detection.
• first_seen_at (ISO 8601 date-time) – Date when the technology was first detected.
• last_seen_at (ISO 8601 date-time) – Last observed instance of the technology in use.
• technology (object) – Details about the detected technology:
• name (string) – Technology name (e.g., "AWS Lambda", "Kubernetes").
• company (object) – Data about the company using the technology:
• domain (string) – Company website domain.
• company_name (string) – Full company name.
• seen_on_job_openings (array, nullable) – List of job postings mentioning the technology, indicating hiring demand.
• seen_on_subpages (array) – URLs of web pages where the technology was detected, providing additional context.

📌 PredictLeads Technographic Data is the go-to solution for B2B professionals looking to optimize technical sales strategies, refine account targeting, and gain a competitive edge in technology-driven markets.

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

This project develop components of a polar cyberinfrastructure (CI) to support researchers and users for data discovery and access. The main goal is to provide tools that will enable a better access to polar data and information, hence allowing to spend more time on analysis and research, and significantly less time on discovery and searching. A large-scale web crawler, PolarHub, is developed to continuously mine the Internet to discover dispersed polar data. Beside identifying polar data in major data repositories, PolarHub is also able to bring individual hidden resources forward, hence increasing the discoverability of polar data. Quality and assessment of data resources are analyzed inside of PolarHub, providing a key tool for not only identifying issues but also to connect the research community with optimal data resources.

In the current PolarHub system, seven different types of geospatial data and processing services that are compliant with OGC (Open Geospatial Consortium) are supported in the system. They are: -- OGC Web Map Service (WMS): is a standard protocol for serving (over the Internet)georeferenced map images which a map server generates using data from a GIS database. -- OGC Web Feature Service (WFS): provides an interface allowing requests for geographical features across the web using platform-independent calls. -- OGC Web Coverage Service (WCS): Interface Standard defines Web-based retrieval of coverages; that is, digital geospatial information representing space/time-varying phenomena. -- OGC Web Map Tile Service (WMTS): is a standard protocol for serving pre-rendered georeferenced map tiles over the Internet. -- OGC Sensor Observation Service (SOS): is a web service to query real-time sensor data and sensor data time series and is part of theSensor Web. The offered sensor data comprises descriptions of sensors themselves, which are encoded in the Sensor Model Language (SensorML), and the measured values in the Observations and Measurements (O and M) encoding format. -- OGC Web Processing Service (WPS): Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for invoking geospatial processing services, such as polygon overlay, as a web service. -- OGC Catalog Service for the Web (CSW): is a standard for exposing a catalogue of geospatial records in XML on the Internet (over HTTP). The catalogue is made up of records that describe geospatial data (e.g. KML), geospatial services (e.g. WMS), and related resources.

PolarHub has three main functions: (1) visualization and metadata viewing of geospatial data services; (2) user-guided real-time data crawling; and (3) data filtering and search from PolarHub data repository.

PredictLeads Global Technographic Dataset delivers in-depth insights into technology adoption across millions of companies worldwide. Our dataset, sourced from HTML, JavaScript, and job postings, enables B2B sales, marketing, and data enrichment teams to refine targeting, enhance lead scoring, and optimize outreach strategies. By tracking 25,000+ technologies across 92M+ websites, businesses can uncover market trends, assess competitor technology stacks, and personalize their approach.

Use Cases:

✅ Enhance CRM Data – Enrich company records with detailed real-time technology insights. ✅ Targeted Sales Outreach – Identify prospects based on their tech stack and personalize outreach. ✅ Competitor & Market Analysis – Gain insights into competitor technology adoption and industry trends. ✅ Lead Scoring & Prioritization – Rank potential customers based on adopted technologies. ✅ Personalized Marketing – Craft highly relevant campaigns based on technology adoption trends.

API Attributes & Structure:

• id (string, UUID) – Unique identifier for each technology detection.
• first_seen_at (ISO 8601 date-time) – Timestamp when the technology was first detected on the company's website.
• last_seen_at (ISO 8601 date-time) – Most recent timestamp when the technology was last observed.
• behind_firewall (boolean) – Indicates whether the technology is protected behind a firewall.
• score (float, 0–1) – Confidence score for the detection accuracy.
• company (object) – The company using the detected technology, including:
• - id (UUID) – Unique company identifier.
• - domain (string) – Company website domain.
• - company_name (string) – Company's official name.
• - ticker (string, nullable) – Stock ticker (if publicly listed).
• technology (object) – Information on the detected technology, including:
• - id (UUID) – Unique technology identifier.
• - name (string) – Technology name (e.g., Salesforce, HubSpot, AWS).
• seen_on_job_openings (boolean) – True/False flag indicating if the technology is - mentioned in job postings.
• seen_on_subpages (array of objects) – List of company subpages where the technology was detected.

📌 PredictLeads Technographic Data is trusted by enterprises and B2B professionals for accurate, real-time technology intelligence, enabling smarter prospecting, data-driven marketing, and competitive analysis

PredictLeads Technology Detections Dataset https://docs.predictleads.com/v3/guide/technology_detections_dataset

Near real time wave and sea surface temperature data for selected sites along the Queensland coast.

For more information please refer to www.qld.gov.au/waves.

Field names are;
Hs - Significant wave height, an average of the highest third of the waves in a record (26.6 minute recording period).
Hmax - The maximum wave height in the record.
Tz - The zero upcrossing wave period.
Tp- The peak energy wave period.
Peak Direction- Direction (related to true north) from which the peak period waves are coming from.
SST - Approximation of sea surface temperature.

NEXRAD Level 3 products are used to remotely detect atmospheric features, such as precipitation, precipitation-type, storms, turbulence and wind, for operational forecasting and data research analysis. Level 3 data consists of over 40 products that are the output product data of the Radar Product Generator. The products assist forecasters and others in weather analysis, forecasts, warnings, and weather tracking. The offerings from the Google Cloud Public Datasets Program include: Real-time NEXRAD L3 data Historic NEXRAD L3 data back to 1992 The Level 3 data consists of reduced resolution, low-bandwidth, base products and many derived, post-processed products. Level 3 products are recorded at most U.S. sites, though non-US sites do not have Level 3 products. General products for Level 3 include the base and composite reflectivity, storm relative velocity, vertical integrated liquid, echo tops and VAD wind profile. Precipitation products for Level 3 include estimated ground accumulated rainfall amounts for one and three hour periods, storm totals, and digital arrays. Estimates are based on reflectivity to rainfall rate (Z-R) relationships. Overlay products for Level 3 are alphanumeric data that give detailed information on certain parameters for an identified storm cell. These include storm structure, hail index, mesocyclone identification, tornadic vortex signature, and storm tracking information. You can see the current status of each radar site on NOAA's status site . This public dataset is hosted in Google Cloud Storage and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

APISCRAPY offers comprehensive California legal data, including court data, litigation records, attorney information, and legal datasets for other states such as Texas, New York, Florida, Illinois, and more. Our AI-driven web scraping tool simplifies data extraction and integration, transforming complex legal data into ready-to-use APIs.

With APISCRAPY, you gain access to precise state-based legal data for lawyers, law data, and USA legal data, enabling seamless workflows and actionable insights. Our solution guarantees 50% cost savings compared to traditional methods, with flexible pricing tailored to your needs.

Key Benefits:

Extract and classify court data and litigation records for multiple states. Verified and accurate datasets for attorneys and legal professionals. Pre-built automation and real-time data delivery. Seamless integration with databases and BI tools, no coding required. Access free data samples to evaluate the quality of our services.

Whether you're focused on compliance, market research, or business intelligence, APISCRAPY provides reliable legal data solutions across the USA, ensuring accuracy, efficiency, and affordability. Contact us today for your California legal data and beyond!

Premium B2B Marketing Database - 18+ Million Company Records

Accelerate your B2B sales and marketing success with our comprehensive business database featuring over 18 million verified company records and 70 million employee profiles. Our 20+ year data expertise delivers superior quality and coverage compared to competitors.

Core Database Statistics

Company Records: 18,243,524 (verified businesses)

Employee Records: 70,420,010 (professional profiles)

Business Email Addresses: 38,731,006 (verified and deliverable)

Phone Numbers: 9,728,410 (direct business lines)

Geographic Coverage: Complete US business landscape

Industry Classification: Full SIC code taxonomy

Advanced Targeting Categories

Geographic Targeting: Target businesses by precise location parameters including nationwide campaigns, state-level focus, Metropolitan Service Areas (MSA), zip code radius, city and county targeting, and carrier route precision for local market penetration.

Business Profile Segmentation: Segment companies by annual revenue (sales volume), employee count (startup to enterprise), year founded (established vs. emerging), business type (small business, corporation, public company), facility ownership status, stock exchange listings (NYSE, NASDAQ, ASE), and franchise operations.

Industry Classification (SIC Codes): Leverage Standard Industrial Classification codes for precision targeting across 2-digit (broad categories), 4-digit (sub-industries), 6-digit (niche markets), and 8-digit (hyper-specific) classifications covering all major industries including Manufacturing, Healthcare, Technology, Financial Services, Professional Services, and more.

Employee & Decision Maker Targeting: Identify key decision makers by job title (C-level, VP, Director, Manager), department focus (IT, Marketing, Finance, Operations), purchasing authority levels, seniority positions, and functional roles across technical, administrative, and strategic positions.

Multi-Channel Campaign Applications

Deploy across all major B2B marketing channels:

Email Marketing: Direct outreach to verified business email addresses

LinkedIn Advertising: Professional network targeting with job title precision

Social Media: Facebook, Instagram, and Twitter/X B2B campaigns

Search Advertising: Google, BING and YouTube business targeting

Direct Mail: Physical address campaigns for high-value prospects

Telemarketing: Direct phone outreach to decision makers

Account-Based Marketing: Multi-touch ABM campaign coordination

Data Quality & Sources

Our business database aggregates from multiple verified sources:

Business registration and licensing records

Professional association memberships and directories

Industry publications and trade organizations

Conference and trade show participation data

Online business profiles and corporate websites

Financial reporting and SEC filing information

Employment databases and HR records

Technical Delivery & Integration

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download portals

Integration Options: CRM systems, marketing automation platforms, ad platforms

Custom Selections: 1,000+ selectable business and employee attributes

Update Frequency: Monthly data refreshes with real-time validation

Minimum Orders: Flexible based on targeting complexity and campaign size

Account-Based Marketing (ABM) Excellence

Specifically designed for sophisticated ABM strategies:

Target Account Identification: Find companies matching ideal customer profiles

Decision Maker Mapping: Multiple contacts within target accounts

Account Prioritization: Focus on high-revenue, high-employee companies

Personalized Outreach: Industry and company-specific messaging

Multi-Touch Coordination: Synchronized campaigns across channels

Unique Value Propositions

20+ Year Data Heritage: Established industry expertise and proven track record

Superior Data Coverage: More extensive and accurate than competitors

Real-Time Validation: Continuous data refreshing and quality assurance

Advanced Segmentation: Combine multiple targeting criteria for precision

Compliance Management: Built-in suppression lists and opt-out handling

Technical Flexibility: API access and custom integration support

Ideal Customer Profiles

Technology Companies: Software, SaaS, hardware, and IT services

Professional Services: Consulting, legal, accounting, and advisory firms

Financial Services: Banks, insurance, investment, and fintech companies

Healthcare Organizations: Medical devices, pharmaceuticals, and healthcare IT

Manufacturing Companies: Industrial equipment, automotive, and consumer goods

Marketing Agencies: Digital agencies serving B2B clients

Sales Organizations: Inside sales, field sales, and business development teams

Performance Optimization Features

Lookalike ...

The Temporary Program, is no longer accepting applications. *Visit Permanent Dining Out website for information: https://www.diningoutnyc.info/ The New York City Open Restaurant is an effort to implement a citywide multi-phase program to expand outdoor seating options for food establishments to promote open space, enhance social distancing, and help them rebound in these difficult economic times. For real time updates on restaurants registered in the program, please visit NYC Open Restaurants dashboard: https://bit.ly/2Z00kn8 ** Please note this Open Restaurant Applications dataset may contain multiple entries (e.g. restaurants submitting 2 or more applications). The Open Restaurants dashboard website containing real time update, noted above, will have fewer total records due to the removal of multiple applications and only list the newest entry.

This dataset contains the raw images as well as the analysis pipelines and scripts used in the paper "Multi-site assessment of reproducibility in high-content live cell imaging data".

The Original data-2D.rar file contains the raw timelapse images of HT1080 cell line stably expressing H2B-EGFP and Lifeact-mCherry seeded on collagen I coated glass surface. Migration behavior of the cells was recorded in 5 min intervals for 6 h with fluorescent light microscopes equipped with environmental chamber. The experiment was performed by 3 labs, 3 person in each lab, 3 independent experiments by each person, 3 technical replicates in each experiment, and two conditions (control and ROCK inhibition) for each technical repliates.

The Data processing and analysis-2D.rar file contains the Matlab, CellProfiler, ImageJ, and R pipelines and scripts used in this study to process, quantify, and analyze the images. Detailed procedure could be found in the "Image processing and analysis procedures.txt" file within this .rar file.

The 3D Image data from Lab 1.zip and 3D Image data from Lab 2.zip contain the raw images and the quantified results of the 3D migration assay from Lab 1 and Lab 2, respectively. The experiment was performed with HT1080 cell line stably expressing H2B-EGFP and Lifeact-mCherry embedded in 2.5mg/ml or 6mg/ml collagen I gels. The invasion of the cells from 3D spheroid was recorded with confocal microscopy 24 h after seeding. The experiment was performed by 2 labs, 3 independent experiments in each lab, 3 technical replicates in each experiment, and two conditions (2.5 mg/ml and 6 mg/ml of collagen I) for each technical repliates.

The Meta data of the 3D experiment.zip contains the meta data of the 3D image data from Lab 1 (Radboudumc) and Lab 2 (Crick) as well as the software to read the meta data. After unzipping, ISAcreator program should be used to read the ISAfiles of Lab 1 or Lab 2.

The Fiji Plugins and parameters for 3D image data analysis.rar contains the Fiji plugins and also the parameters used during the 3D image data analysis.

The 3D Data Analysis Scripts.rar contains the R scripts used in this study to analyze the 3D data set, as well as the quantified results needed by the R scripts.

The Supplementary Materials 2-8.rar contains 2D experimental protocol (supplementary materials 2-4), 2D experimental survey (supplementary materials 3), and 3D experimental and image analysis protocols (supplementary materials 5-8) that are used in this study.

We encourage reuse using the same CC BY 4.0 License.

The GNSS (Global Navigation Satellite System), or satellite positioning system, includes all satellite navigation systems. It allows you to know your location, anywhere in the country. Theoretical GNSS specifications estimate the accuracy of the position obtained from a receiver to be approximately 15 meters in planimetry and 25 meters in altimetry. By combining the data with that of another receiver placed on a known geodesic point, the accuracy of the obtained position can vary from a few centimeters to a few meters, depending on the type of receiver used. In order to increase accuracy, the Government of Quebec records data continuously through a network of 18 GNSS stations. These stations are located on geodetic points that are free of any obstacles and capture data from the GPS and GLONASS constellations. Some of these stations receive signals from the Galileo constellation. This data is available in the standard exchange format*Receiver Independent Exchange Format* (RINEX), version 2.11. This format is recognized by the majority of GNSS data processing software. The data is accessible on the _ ftp server_) of the MRNF or using the _ Interactive Map_) of the geodetic network. It should be noted that only data from the last 366 days is kept. The structure of the directories and files on the _ ftp server_) as well as the coordinates of the stations are presented in the document _ GNSS sensor stations_. # #État of GNSS stations## You can consult the status of the stations in the document _ Status of GNSS stations_. You will be notified if a station is in service, out of service, or if equipment maintenance is planned. # #GNSS in real time by cellular telephony The government also offers GNSS data by cellular telephony that allows centimeter positioning work to be carried out in real time. Users of georeferenced data can thus, with a single multi-frequency GNSS receiver equipped with a modem by cellular telephone, identify or implement any physical detail with an accuracy of a few centimeters in the NAD 83 reference system (SCRS) (period 1997.0). The signal that contains this data is available to everyone. The range depends on telephone coverage, ionospheric conditions and especially on the instruments used. For more information on using GNSS in real time, see document _ Guidelines for GNSS RTK/RTN Surveys in Canada_. # #Détails techniques The transmission of GNSS data as well as the station's NAD 83 (SCRS) coordinates (era 1997.0) is transmitted by cellular telephony from an IP address on the Internet. Each station transmits its data in one of the following two formats: CMR+ or RTCM V3.2. The document _ GNSS capture stations_) gives for each city the IP address of the CMR+ or RTCM V3.2 formats as well as the antenna model. It should be noted that the data is not broadcast according to the*Networked Transport of RTCM protocol via Internet Protocol* (NTRIP).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The new National Water Information System (NWISWeb) of the U.S. Geological Survey (USGS) provides access to data from over 1.5 million ground water well and surface water sites in the United States. NWISWeb system is a new system that was based on the effort/development from several other USGS water related web systems: the national system for historic streamflow data (NWIS-W); the real-time streamflow data system; and several development efforts that were established in USGS districts. Spatial coverage includes the conterminous United States, Alaska, Hawaii, Puerto Rico, and the Virgin Islands.

NWISWeb provides Internet access to real-time or historical streamflow and stage data, lake, reservoir and ground water data, water-quality data, and site information. The NWISWeb can be queried online to access the following data and information:

Real-Time Water Data for the Nation -- This link provides access to current-conditions data transmitted from selected surface-water, ground-water, and water-quality sites. Users can view maps of real-time streamflow and data tables of real-time streamflow by predefined displays. Users can also build a custom summary table for one or more stations, or build a custom sequence of graphical or tabular data for one or more stations. Data can be grouped by county, state, or hydrologic parameter.

Surface-Water Data for the Nation -- Nationally, USGS surface-water data includes more than 850,000 station years of time-series data that describe stream levels, streamflow (discharge), reservoir and lake levels, surface-water quality, and rainfall. The data are collected by automatic recorders and manual measurements at field installations across the Nation. Data access categories include: (1) Real Time Data (data for selected sites recorded at 5-60 minute intervals -- may include surface-water, ground-water, water-quality, and meteorological parameters); (2) Recent Data (provisional daily data for the previous 18 months -- includes published streamflow; (3) Daily Streamflow (data for the period of record at each site, including historical streamflow data); (4) Daily, Monthly, and Calendar Year Streamflow Statistics (computed from published daily data); (5) Peak Streamflow (annual maximum instantaneous peak streamflow and gage height); and (6) Measurements (field measurements of streamflow and gage height).

Ground-Water Data for the Nation -- The ground-water database contains ground-water site inventory, ground-water level data, and water-quality data. Data access categories include: (1) Real Time Data [data transmitted from selected ground-water sites for depth below land surface (water level, in feet) and elevation above ngvd (feet)]; (2) Ground-water Site Inventory (descriptive site information including latitude, longitude, well depth, and site use); and Ground-water Levels (depth to water or water-surface elevation in wells).

Water-Quality Data for the Nation -- The NWISWeb discrete sample database is a compilation of over 3.5 million historical water quality analyses of chemical, physical, and biological properties of water, sediment and tissue samples from streams, lakes, springs, and wells across the Nation. The data are current through September 1999. The discrete sample data is a large and complex set of data that has been collected by a variety of projects ranging from national programs to studies in small watersheds. Data include real-time water quality data (real-time water-quality data are returned directly from field instruments) and water quality samples (water-quality data from field and/or laboratory analysis of water, biological tissue, stream sediments, and other environmental samples).

Site Inventory System -- The inventory contains information about sites at stream reaches, wells, test holes, springs, tunnels, drains, lakes, reservoirs, ponds, excavations, and water-use facilities.

This record is for Approval for Access product AfA422 Realtime Flood Data Air Temperature. This dataset covers monitoring data that is only updated on our systems on a daily update cycle. This is usually increased during times of flooding etc. Readings are transferred via telemetry to internal and external systems in, or close to real time. This data may be transferred to these systems or users at different intervals varying, for example, from once per day during normal conditions to several times per day during a flood event. Data for sites in Wales is included in the Open Data feed, but is owned by Natural Resources Wales (NRW). NRW also class the data as Open Data, and you may use it under the same terms as the England data (the standard Open Government Licence, available on The National Archives website). This data is retrieved automatically and is unvalidated. At present there are only sites in the English Midlands. Measurements of air temperature at Environment Agency rain gauge sites in England, usually taken every hour but sometimes every 15 minutes. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.