The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

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.

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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...

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

According to a survey carried out in August 2020 in the United Kingdom (UK), 72 percent of marketing companies collected customer data through their website. Half did so through social media, while a slightly smaller share said they recorded customer data at organized events. Collection via purchase lists and preference centres were the least used methods.

As part of "Online Data Collection and Management" (taught at Tilburg University, Spring 2022), students collected publicly available datasets for use in academic research projects. With this repository, I am sharing (a) the documentation of these data sets, and (b) the associated source code that led to the collection of the data. The repository also contains the collected datasets.

The data consists of the following projects:

Autoscout (electric cars vs gasoline cars in the Dutch market)

Mediamarkt (e-commerce)

Steam API

Twitch (chat capture)

Zalando (e-commerce)

Course website: https://odcm.hannesdatta.com. Archived at https://doi.org/10.5281/zenodo.6641811 (check for more recent versions if available).

No Code Web Scraper Tool Market Outlook

As of 2023, the global market size for No Code Web Scraper Tools is valued at approximately USD 850 million and is projected to reach nearly USD 2.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5%. This growth is primarily driven by the increasing demand for simplified data extraction solutions that do not require extensive coding knowledge, enabling businesses of all sizes to efficiently collect and utilize web data.

The burgeoning need for data-driven decision-making across industries is a significant growth factor for the No Code Web Scraper Tool market. Organizations are increasingly recognizing the value of web data in gaining competitive insights, making informed business decisions, and optimizing operations. The ability to scrape web data without needing advanced technical skills democratizes data access, allowing non-technical users to harness the power of web data extraction. This trend is further propelled by the rise of small and medium enterprises (SMEs) that require cost-effective and efficient tools to stay competitive.

The growth of e-commerce and digital marketing also plays a pivotal role in the expansion of the No Code Web Scraper Tool market. As online retail continues to flourish, businesses are keen to monitor competitor pricing, track customer reviews, and gather market intelligence. No code web scraping tools provide an accessible and scalable solution for e-commerce platforms to automate these data collection processes. Furthermore, digital marketers utilize these tools to gather and analyze data from various online sources, supporting more targeted and effective marketing campaigns.

Technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) in web scraping tools are additional drivers of market growth. These advancements enhance the capabilities of web scraping tools, making them more efficient, accurate, and user-friendly. AI and ML technologies facilitate the automatic adaptation to changes in website structures, reducing the need for manual intervention and ensuring continuous data extraction. This technological evolution not only improves the functionality of these tools but also broadens their applicability across different sectors.

In the realm of web scraping, a Hook Extractor is a crucial component that enhances the efficiency and accuracy of data extraction processes. This tool is designed to seamlessly integrate with existing web scraping frameworks, allowing users to capture specific data elements from complex web pages. By utilizing a Hook Extractor, businesses can streamline their data collection efforts, ensuring that they gather only the most relevant information for their needs. This is particularly beneficial in scenarios where web pages are dynamic and frequently updated, as the Hook Extractor can adapt to changes in the website structure without requiring manual reconfiguration. As a result, organizations can maintain a competitive edge by continuously accessing up-to-date data insights.

Regionally, North America holds a significant share of the No Code Web Scraper Tool market, driven by the high adoption rate of advanced technologies and the presence of numerous tech-savvy enterprises. The Asia-Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid digitalization and increasing internet penetration. Europe also presents lucrative opportunities, supported by the growing awareness and adoption of data-driven strategies among businesses. Latin America, the Middle East, and Africa are gradually catching up, with increasing investments in IT infrastructure and digital transformation initiatives.

Component Analysis

The No Code Web Scraper Tool market by component is segmented into software and services. The software segment comprises the actual web scraping tools that businesses use to extract data from websites. These tools are designed to be user-friendly, often featuring drag-and-drop interfaces and pre-built templates that simplify the data extraction process. The software segment is expected to dominate the market, driven by continuous innovations and the development of more sophisticated, AI-powered scraping solutions. Additionally, the subscription-based pricing model for these tools makes them accessible to a wide range of users, from individual entrepreneurs to large enterprises.

In contrast,

This statistic displays the behavior of French surveyed in 2019 about the message regarding data collection terms appearing when they visit a website. It appears that half of the respondents said accepting the data collection terms without reading them.

The no-code web scraping tool market is experiencing robust growth, driven by the increasing demand for automated data extraction across diverse sectors. The market's expansion is fueled by several key factors. Firstly, the rise of e-commerce and the need for competitive pricing intelligence necessitates efficient data collection. Secondly, the travel and hospitality industries leverage web scraping for dynamic pricing and competitor analysis. Thirdly, academic research, finance, and human resources departments utilize these tools for large-scale data analysis and trend identification. The ease of use offered by no-code platforms democratizes web scraping, eliminating the need for coding expertise, and significantly accelerating the data acquisition process. This accessibility attracts a wider user base, contributing to market expansion. The market is segmented by application (e-commerce, travel & hospitality, academic research, finance, human resources, and others) and type (text-based, cloud-based, and API-based web scrapers). While the market is competitive, with numerous players offering varying functionalities and pricing models, the continued growth in data-driven decision-making across industries assures continued expansion. Cloud-based solutions are expected to dominate due to scalability and ease of access. Future growth hinges on the development of more sophisticated no-code platforms offering enhanced features such as AI-powered data cleaning and intelligent data analysis capabilities. Geographic regions like North America and Europe currently hold significant market share, but Asia-Pacific is poised for substantial growth due to increasing digital adoption and expanding e-commerce markets. The historical period (2019-2024) likely witnessed a moderate growth rate, setting the stage for the accelerated expansion projected for the forecast period (2025-2033). Assuming a conservative CAGR of 15% for the historical period, resulting in a 2024 market size of approximately $500 million, and applying a slightly higher CAGR of 20% for the forecast period, reflects the increasing adoption and sophistication of these tools. Factors such as stringent data privacy regulations and the increasing sophistication of anti-scraping measures present potential restraints, but innovative solutions are emerging to address these challenges, including ethical data sourcing and advanced proxy management features. The ongoing integration of AI and machine learning capabilities into no-code platforms is also expected to propel market growth, enabling more sophisticated data extraction and analysis with minimal user input.

Web Scraping Services Market Outlook

The global web scraping services market size is expected to reach $2.5 billion by 2023 and is projected to grow at a CAGR of 25.5% from 2024 to 2032, reaching an estimated $23.8 billion by 2032. The significant growth of the web scraping services market can be attributed to the increasing demand for automated data collection and processing across various industries. Businesses are increasingly leveraging web scraping services to gather valuable insights, optimize operations, and enhance decision-making processes, driving the market forward.

Several factors are contributing to the robust growth of the web scraping services market. One primary driver is the exponential increase in the volume of data generated online. As the internet becomes more data-rich, businesses seek efficient ways to extract and utilize this information, making web scraping services indispensable. Additionally, the growing adoption of big data analytics and artificial intelligence technologies necessitates extensive data collection, further propelling the demand for web scraping solutions. Companies are looking to gain a competitive edge by utilizing these technologies to analyze customer behavior, market trends, and competitor activities.

Another key growth factor is the expanding application of web scraping services across diverse industries. For instance, in the retail and e-commerce sector, companies use web scraping to monitor competitor pricing, product availability, and customer reviews, enabling them to adjust their strategies accordingly. In the finance sector, web scraping is utilized to gather financial data, news, and market sentiment analysis, aiding in better investment decisions. The healthcare industry leverages web scraping to collect data on medical research, patient feedback, and drug pricing, driving advancements in medical research and patient care.

The increasing preference for cloud-based solutions is also fueling the growth of the web scraping services market. Cloud-based web scraping services offer scalable and cost-effective solutions, allowing businesses of all sizes to access and process large datasets without the need for significant infrastructure investment. This trend is particularly beneficial for small and medium enterprises (SMEs) that may have limited resources. Furthermore, the integration of web scraping services with advanced analytics and machine learning algorithms enhances the value derived from the collected data, making these services more attractive to businesses.

In the realm of data collection, the concept of Yard Scrapers has emerged as a novel approach to efficiently manage and organize large datasets. Yard Scrapers are specialized tools designed to sift through extensive volumes of data, much like their web scraping counterparts, but with a focus on structured environments such as databases or data warehouses. These tools are particularly beneficial for industries that require meticulous data management, ensuring that the data is not only collected but also categorized and stored in an accessible manner. By employing Yard Scrapers, businesses can streamline their data handling processes, reducing the time and resources needed to manage large datasets, and ultimately enhancing their ability to make informed decisions based on comprehensive data insights.

Regionally, North America holds the largest market share in the web scraping services market, driven by the presence of numerous tech-savvy businesses and advanced IT infrastructure. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rapid digital transformation and increasing adoption of data-driven decision-making processes in emerging economies such as China and India. Europe is also a significant market, with growing awareness of the benefits of web scraping services among businesses.

Type Analysis

Web scraping services can be categorized into several types, including data extraction, data integration, data analysis, and others. Data extraction services involve the automated collection of data from various web sources, such as websites, social media platforms, and online databases. This type of service is highly sought after by businesses looking to gather large volumes of data quickly and efficiently. Data extraction is crucial for applications such as competitive analysis, market research, and lead generation. The demand for data extraction services is expected to grow s

We offer comprehensive data collection services that cater to a wide range of industries and applications. Whether you require image, audio, or text data, we have the expertise and resources to collect and deliver high-quality data that meets your specific requirements. Our data collection methods include manual collection, web scraping, and other automated techniques that ensure accuracy and completeness of data.

Our team of experienced data collectors and quality assurance professionals ensure that the data is collected and processed according to the highest standards of quality. We also take great care to ensure that the data we collect is relevant and applicable to your use case. This means that you can rely on us to provide you with clean and useful data that can be used to train machine learning models, improve business processes, or conduct research.

We are committed to delivering data in the format that you require. Whether you need raw data or a processed dataset, we can deliver the data in your preferred format, including CSV, JSON, or XML. We understand that every project is unique, and we work closely with our clients to ensure that we deliver the data that meets their specific needs. So if you need reliable data collection services for your next project, look no further than us.

Web Scraping Tools Market Outlook

The global web scraping tools market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.5% during the forecast period. The growing demand for web scraping tools is primarily driven by the increasing need for data extraction and analysis across various industries. These tools have become essential in gathering competitive intelligence, monitoring prices, conducting market research, and generating leads, which are critical activities for businesses looking to maintain a competitive edge in a data-driven world.

One of the primary growth factors for the web scraping tools market is the exponential increase in data generation on the internet. With the proliferation of e-commerce, social media, and other online platforms, businesses need to collect vast amounts of data to analyze consumer behavior, market trends, and competitor strategies. Web scraping tools enable automated data extraction from various online sources, providing businesses with valuable insights that can inform decision-making and strategic planning. Moreover, advancements in machine learning and artificial intelligence have enhanced the capabilities of these tools, making them more efficient and accurate in extracting relevant data.

Another significant growth driver is the rising adoption of web scraping tools by small and medium enterprises (SMEs). These enterprises often lack the resources to conduct extensive market research or data analysis in-house. Web scraping tools offer a cost-effective solution for SMEs to gather critical business intelligence without substantial investment in manual data collection. Furthermore, the availability of cloud-based web scraping solutions has made these tools more accessible to SMEs, enabling them to leverage scalable and flexible data extraction capabilities without the need for significant infrastructure or technical expertise.

The increasing application of web scraping tools across various industry verticals is also contributing to market growth. Industries such as retail and e-commerce, banking, financial services, and insurance (BFSI), healthcare, media and entertainment, and information technology and telecommunications are leveraging these tools for various purposes. For instance, in the retail sector, web scraping tools are used for price monitoring and competitive analysis, while in the BFSI sector, they assist in fraud detection and risk management. The growing demand for these applications is expected to drive the adoption of web scraping tools across different industries.

Data Extraction Software plays a pivotal role in the web scraping ecosystem, providing the backbone for efficient data collection processes. These software solutions are designed to handle vast amounts of data from diverse online sources, ensuring that businesses can access the information they need for strategic decision-making. With the increasing complexity of data available on the internet, Data Extraction Software has evolved to include advanced features such as machine learning algorithms and artificial intelligence capabilities. These enhancements allow for more precise and accurate data extraction, enabling businesses to gain deeper insights into market trends and consumer behavior. As industries continue to rely on data-driven strategies, the demand for robust Data Extraction Software is expected to grow, further fueling the expansion of the web scraping tools market.

From a regional perspective, North America holds the largest market share for web scraping tools, driven by the high adoption of advanced technologies and a strong presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rapid digital transformation and increasing internet penetration in countries like China and India. The growing number of start-ups and SMEs in the region is also contributing to the rising demand for web scraping tools. Europe and Latin America are also experiencing steady growth, driven by the increasing focus on data-driven decision-making and business intelligence.

Type Analysis

The web scraping tools market can be segmented by type into browser extensions, standalone software, cloud-based so

Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought; habitat loss; coastal protection) to biogeochemical processes (e.g., carbon storage; water quality). In this research, our focus was on the impact of mangrove forest migration on coastal wetland soil processes and the consequent implications for coastal wetland responses to sea level rise, ecosystem resilience, and carbon storage. Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. We quantified relationships between plant community composition and structure, soil and porewater physicochemical properties, hydroperiod, and climatic conditions. The suite of measurements that we collected provide initial insights into how different geographic areas of an ecotone, with different environmental conditions, may be impacted by mangrove forest expansion and development, and how these changes may alter the supply of specific ecosystem goods and services. This file includes the site-level elevation data. This work was conducted via a collaborative effort between scientists at the U.S. Geological Survey National Wetland Research Center and the Department of Biology of the University of Louisiana at Lafayette.

This dataset contains information on the prices and fees charged by for-hire fishing operations in the Southeastern US.

Convert websites into useful data Fully managed enterprise-grade web scraping service Many of the world's largest companies trust ScrapeHero to transform billions of web pages into actionable data. Our Data as a Service provides high-quality structured data to improve business outcomes and enable intelligent decision making

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Large Scale Web Crawling for Price and Product Monitoring - eCommerce, Grocery, Home improvement, Shipping, Inventory, Realtime, Advertising, Sponsored Content - ANYTHING you see on ANY website.

Amazon, Walmart, Target, Home Depot, Lowes, Publix, Safeway, Albertsons, DoorDash, Grubhub, Yelp, Zillow, Trulia, Realtor, Twitter, McDonalds, Starbucks, Permits, Indeed, Glassdoor, Best Buy, Wayfair - any website.

Travel, Airline and Hotel Data Real Estate and Housing Data Brand Monitoring Human Capital Management Alternative Data Location Intelligence Training Data for Artificial Intelligence and Machine Learning Realtime and Custom APIs Distribution Channel Monitoring Sales Leads - Data Enrichment Job Monitoring Business Intelligence and so many more use cases

We provide data to almost EVERY industry and some of the BIGGEST GLOBAL COMPANIES

This repository contains Python code and data used in the Museums in the Pandemic (MIP) project, including aggregated social media datasets and analysis results. The input data cannot be disseminated for copyright reasons.

Project description: Museums have an important role in our economy, education and cultural life. They add to the texture and richness of villages, towns and cities, and can help build and maintain communities. During the pandemic, their continuing existence has been under threat, and while many museums have benefitted from emergency funding or government schemes, their position remains precarious. In order to better support the UK museum sector, the museum services need to identify which types of museums are at risk of closure, which remain resilient, and which close on a permanent basis. Doing so presents a considerable challenge. Data collection is selective and tends not to cover unaccredited museums, it is dispersed across multiple platforms, there are no mechanisms for documenting closure, and establishing risk of closure entirely relies on individual organisations self-reporting. The Museums in the Pandemic project investigates how ‘big data techniques’ can inform research into the UK museum sector. It combines qualitative and quantitative research, and has three inter-related strands: Developing new ways to collect data on museums. We will use web analytics, natural language processing, and sentiment analysis to digitally track trends as they emerge. The data will be analysed with respect to museum characteristics – such as governance, location and size – to provide a nuanced understanding of the sector at a given moment. Manually checking and validating the information generated by big data collection. Using interview-based research to better understand what constitutes risk during a pandemic, the triggers for permanent closure, and how museums have and continue to remain resilient.

URL: https://www.bbk.ac.uk/research/projects/museums-in-the-pandemic

PI: Fiona Candlin (Birkbeck, UoL) Co-I: Andrea Ballatore (King's College London) Co-I: Alex Poulovassilis (Birkbeck, UoL) Co-I: Peter Wood (Birkbeck, UoL)

Different science fair experiences of high school (HS) and post high school (PHS) students depending upon whether or not they received help from scientists.

This collection contains survey data collected at the end of October 2004 from the 49 state law enforcement agencies in the United States that had traffic patrol responsibility. Information was gathered about their policies for recording race and ethnicity data for persons in traffic stops, including the circumstances under which demographic data should be collected for traffic-related stops and whether such information should be stored in an electronically accessible format. The survey was not designed to obtain available agency databases containing traffic stop records.

According to a 2020 survey, internet users in the United Kingdom (UK) had the same level of distrust (74 percent) towards both social media companies and advertisers when it came to customer data collection. There was a slightly lower level of distrust when it came to search engines, at 71 percent.

1. Environmental volunteering can benefit participants and nature through improving physical and mental wellbeing while encouraging environmental stewardship. To enhance achievement of these outcomes, conservation organisations need to reach different groups of people to increase participation in environmental volunteering. This paper explores what engages communities searching online for environmental volunteering.
   

2. We conducted a literature review of 1032 papers to determine key factors fostering participation by existing volunteers in environmental projects. We found the most important factor was to tailor projects to the motivations of participants. Also important were: promoting projects to people with relevant interests; meeting the perceived benefits of volunteers and removing barriers to participation.
   

3. We then assessed the composition and factors fostering participation of the NatureVolunteers’s online community (n = 2216) of potential environmental volunteers and compared findings with those from the literature review. We asked whether projects advertised by conservation organisations meet motivations and interests of this online community.
   

4. Using Facebook insights and Google Analytics we found that the online community were on average younger than extant communities observed in studies of environmental volunteering. Their motivations were also different as they were more interested in physical activity and using skills and less in social factors. They also exhibited preference for projects which are outdoor based, and which offer close contact with wildlife. Finally, we found that the online community showed a stronger preference for habitat improvement projects over those involving species-survey based citizen science.
   

5. Our results demonstrate mis-matches between what our online community are looking for and what is advertised by conservation organisations. The online community are looking for projects which are more solitary, more physically active and more accessible by organised transport. We discuss how our results may be used by conservation organisations to better engage with more people searching for environmental volunteering opportunities online.
   

6. We conclude that there is a pool of young people attracted to environmental volunteering projects whose interests are different to those of current volunteers. If conservation organisations can develop projects that meet these interests, they can engage larger and more diverse communities in nature volunteering.
   

Methods The data set consists of separate sheets for each set of results presented in the paper. Each sheet contains the full data, summary descriptive statistics analysis and graphs presented in the paper. The method for collection and processing of the dataset in each sheet is as follows:

The data set for results presented in Figure 1 in the paper - Sheet: "Literature"

We conducted a review of literature on improving participation within nature conservation projects. This enabled us to determine what the most important factors were for participating in environmental projects, the composition of the populations sampled and the methods by which data were collected. The search terms used were (Environment* OR nature OR conservation) AND (Volunteer* OR “citizen science”) AND (Recruit* OR participat* OR retain* OR interest*). We reviewed all articles identified in the Web of Science database and the first 50 articles sorted for relevance in Google Scholar on the 22nd October 2019. Articles were first reviewed by title, secondly by abstract and thirdly by full text. They were retained or excluded according to criteria agreed by the authors of this paper. These criteria were as follows - that the paper topic was volunteering in the environment, including citizen science, community-based projects and conservation abroad, and included the study of factors which could improve participation in projects. Papers were excluded for topics irrelevant to this study, the most frequent being the outcomes of volunteering for participants (such as behavioural change and knowledge gain), improving citizen science data and the usefulness of citizen science data. The remaining final set of selected papers was then read to extract information on the factors influencing participation, the population sampled and the data collection methods. In total 1032 papers were reviewed of which 31 comprised the final selected set read in full. Four factors were identified in these papers which improve volunteer recruitment and retention. These were: tailoring projects to the motivations of participants, promoting projects to people with relevant hobbies and interests, meeting the perceived benefits of volunteers and removing barriers to participation.

The data set for results presented in Figure 2 and Figure 3 in the paper - Sheet "Demographics"

To determine if the motivations and interests expressed by volunteers in literature were representative of wider society, NatureVolunteers was exhibited at three UK public engagement events during May and June 2019; Hullabaloo Festival (Isle of Wight), The Great Wildlife Exploration (Bournemouth) and Festival of Nature (Bristol). This allowed us to engage with people who may not have ordinarily considered volunteering and encourage people to use the website. A combination of surveys and semi-structured interviews were used to collect information from the public regarding demographics and volunteering. In line with our ethics approval, no personal data were collected that could identify individuals and all participants gave informed consent for their anonymous information to be used for research purposes. The semi-structured interviews consisted of conducting the survey in a conversation with the respondent, rather than the respondent filling in the questionnaire privately and responses were recorded immediately by the interviewer. Hullabaloo Festival was a free discovery and exploration event where NatureVolunteers had a small display and surveys available. The Great Wildlife Exploration was a Bioblitz designed to highlight the importance of urban greenspaces where we had a stall with wildlife crafts promoting NatureVolunteers. The Festival of Nature was the UK’s largest nature-based festival in 2019 where we again had wildlife crafts available promoting NatureVolunteers. The surveys conducted at these events sampled a population of people who already expressed an interest in nature and the environment by attending the events and visiting the NatureVolunteers stand. In total 100 completed surveys were received from the events NatureVolunteers exhibited at; 21 from Hullabaloo Festival, 25 from the Great Wildlife Exploration and 54 from the Festival of Nature. At Hullabaloo Festival information on gender was not recorded for all responses and was consequently entered as “unrecorded”.

OVERALL DESCRIPTION OF METHOD DATA COLLECTION FOR ALL OTHER RESULTS (Figures 4-7 and Tables 1-2)

The remaining data were all collected from the NatureVolunteers website. The NatureVolunteers website https://www.naturevolunteers.uk/ was set up in 2018 with funding support from the Higher Education Innovation Fund to expand the range of people accessing nature volunteering opportunities in the UK. It is designed to particularly appeal to people who are new to nature volunteering including young adults wishing to expand their horizons, families looking for ways connect with nature to enhance well-being and older people wishing to share their time and life experiences to help nature. In addition, it was designed to be helpful to professionals working in the countryside & wildlife conservation sectors who wish to enhance their skills through volunteering. As part of the website’s development we created and used an online project database, www.naturevolunteers.uk (hereafter referred to as NatureVolunteers), to assess the needs and interests of our online community. Our research work was granted ethical approval by the Bournemouth University Ethics Committee. The website collects entirely anonymous data on our online community of website users that enables us to evaluate what sort of projects and project attributes most appeal to our online community. Visitors using the website to find projects are informed as part of the guidance on using the search function that this fully anonymous information is collected by the website to enhance and share research understanding of how conservation organisations can tailor their future projects to better match the interests of potential volunteers. Our online community was built up over the 2018-2019 through open advertising of the website nationally through the social media channels of our partner conservation organisations, through a range of public engagement in science events and nature-based festivals across southern England and through our extended network of friends and families, their own social media networks and the NatureVolunteers website’s own social network on Facebook and Twitter. There were 2216 searches for projects on NatureVolunteers from January 1st to October 25th, 2019.

The data set for results presented in Figure 2 and Figure 3 in the paper - Sheet "Demographics"

On the website, users searching for projects were firstly asked to specify their expectations of projects. These expectations encompass the benefits of volunteering by asking whether the project includes social interaction, whether particular skills are required or can be developed, and whether physical activity is involved. The barriers to participation are incorporated by asking whether the project is suitable for families, and whether organised transport is provided. Users were asked to rate the importance of the five project expectations on a Likert scale of 1 to 5 (Not at all = 1, Not really = 2, Neutral = 3, It

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.