As of January 2025, around 13.7 percent of paid iOS apps admitted collecting data from users engaging with their mobile products. In comparison, approximately 53 percent of free-to-download iOS apps reported they collect private data from users worldwide, while approximately 86 percent of paid apps have not declared whether they collect users' privacy data.

➡️ DOCS With just the company's LinkedIn profile URL, you can get the structured data to the person profiles of all employees of a company, including their name, accomplishments, experiences, profile URL and more. Check out our API Docs at ➡ nubela.co/proxycurl/docs

➡️ PRICING MODEL Get the data using our API at just $0.01/credit, with each successful request using up only 1 credit. If you need more advanced data points, use more credits for each API request.

➡️ COVERAGE Our Employee Listing API covers profiles globally.

➡️ FRESHNESS 88% of our data is fetched in real time, and the API takes 2-3 seconds to complete. If freshness is not a priority, you can choose cached results, which returns immediately.

➡️ LEGAL COMPLIANCE All our data and procedures are in place that meet major legal compliance requirements such as GDPR, CCPA. We help you be compliant too.

Data Make False

## Overview

Data Make False is a dataset for object detection tasks - it contains Forklift Person annotations for 765 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Altosight | AI Custom Web Scraping Data

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

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

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

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

― Key Use Cases ―

➤ Price Monitoring & Repricing Solutions

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

➤ E-commerce Optimization

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

➤ Product Assortment Analysis

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

➤ Marketplaces & Aggregators

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

➤ Business Website Data

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

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

➤ Domain Name Data

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

➤ Real Estate Data

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

― Data Collection & Quality ―

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

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

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

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

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

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

― Why Choose Altosight? ―

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

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

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

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

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

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

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

― Custom Projects & Real-Time Data ―

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

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

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey.
Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values.

Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Data standardization is an important part of effective management. However, sometimes people have data that doesn't match. This dataset includes different ways that counties might get written by different people. It can be used as a lookup table when you need County to be your unique identifier. For example, it allows you to match St. Mary's, St Marys, and Saint Mary's so that you can use it with disparate data from other data sets.

China Internet Service: Place to Get Online: Net bar data was reported at 19.000 % in Dec 2018. This records a decrease from the previous number of 21.200 % for Jun 2018. China Internet Service: Place to Get Online: Net bar data is updated semiannually, averaging 20.890 % from Jun 1999 (Median) to Dec 2018, with 39 observations. The data reached an all-time high of 42.400 % in Dec 2008 and a record low of 4.000 % in Jun 1999. China Internet Service: Place to Get Online: Net bar data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Device and Place for Internet Access.

As of February 2022, 71 percent of healthcare leaders surveyed globally said they have confidence in the actionable insights their hospital/healthcare facility is able to extract from available data. Overall, healthcare leaders had high confidence in the data utilization process of their organization and the value that data can bring to their work.

About this webinar We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there’s too much of it. Frequently, it has errors. This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool, Microsoft Excel, to sort, filter, copy, protect, transform, aggregate, summarise, and visualise research data. Webinar Topics Introduction to Microsoft Excel user interface Interpret data using sorting, filtering, and conditional formatting Summarise data using functions Analyse data using pivot tables Manipulate and visualise data Handy tips to speed up your work Licence Copyright © 2021 Intersect Australia Ltd. All rights reserved.

This data set contains the ISLSCP II fixed land/water masks and percentages of land or water in each cell. There are seven zip data files: four produced from a 1-km land/water mask compiled at the Jet Propulsion Laboratory (JPL) in support of NASA's Earth Observing System; two files of a land outline overlay created from the land/water mask files created at NASA's Goddard Space Flight Center; and one file which is a latitude grid coordinate file and longitude grid coordinate file produced by the ISLSCP II staff. All of these data are provided at three spatial resolutions of .25, 0.5 and 1-degree in latitude and longitude and on a common Earth grid.

15424 Global import shipment records of Ginseng Extract with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset provides detailed information on 1007 Pokémon from the popular mobile game Pokémon GO. It includes a wide range of attributes such as base stats, move sets, rarity, and acquisition methods. The data was collected using the RapidAPI Pokémon GO API, offering researchers and data enthusiasts a rich resource for analysis, machine learning projects, and game strategy development.

Key features of this dataset include:

• Comprehensive coverage of 1007 Pokémon
• 24 attributes for each Pokémon, including battle stats, type, and rarity
• Information on acquisition methods (wild, egg, raid, etc.)
• Move set details for both fast and charged moves
• Game mechanics data such as capture and flee rates

This dataset is ideal for:

• Analyzing Pokémon strengths and weaknesses
• Developing machine learning models for Pokémon classification or prediction
• Studying game balance and design in Pokémon GO
• Creating tools for players to optimize their gameplay strategies

Whether you're a data scientist, game developer, or Pokémon enthusiast, this dataset offers a wealth of information to explore and analyze the world of Pokémon GO.

This layer file consists of three related datasets:
- Statutory boundary polygons of State Forests
- Lands managed by the Division of Forestry within the statutory boundaries, known as Management Units
- Lands managed by the Division of Forestry outside of the statutory boundaries, known as Other Forestry Lands

State Forests - Statutory Boundaries:
This theme shows the boundaries of those areas of Minnesota that have been legislatively designated as State Forests ( http://www.dnr.state.mn.us/state_forests/index.html )

Minnesota's 58 state forests were established to produce timber and other forest crops, provide outdoor recreation, protect watersheds, and perpetuate rare and distinctive species of native flora and fauna. The mapped boundaries are based on legislative/statutory language and are described in broad terms based on legal descriptions. Private or other ownerships included inside a State Forest boundary are typically NOT identified in legislative language and subsequently are NOT mapped in this layer. It is important to note that these data do not represent public ownership. State Forest boundaries often include private land and should not be used to determine ownership. Ownership information can be found in State Surface Interests Administered by MNDNR or by Counties ( https://gisdata.mn.gov/dataset/plan-stateland-dnrcounty ) and the GAP Stewardship 2008 layer ( http://gisdata.mn.gov/dataset/plan-gap-stewardship-2008 ).

Data has been updated during 2009 by the MNDNR Forest Resource Assessment office.

State Forests - Management Units
This theme shows the land owned and managed by the Division of Forestry within the Statutory Boundaries. The shapes were derived mostly from county parcel data, where available, and from plat maps and other ownership resources. This data presents an approximate location of the land ownership and is intended for cartographic purposes only. It is not survey quality and should never be used to resolve land ownership disputes.

State Forests - Other Forest Lands
This theme shows State Forest lands outside of the State Forest Statutory Boundaries. It was derived from MNDNR's Land Records System PLS40 data layer. Sub-40 shapes are not represented. Partial PLS40 ownership is represented as a whole PLS40. This data is not survey quality and should never be used to resolve land ownership disputes.

A zipped set of CSV files containing a snapshot of personal property assessment data.

File generated from running the Extract Data solution.

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

Sample data for exercises in Further Adventures in Data Cleaning.

Context

Bitcoin is the longest running and most well known cryptocurrency, first released as open source in 2009 by the anonymous Satoshi Nakamoto. Bitcoin serves as a decentralized medium of digital exchange, with transactions verified and recorded in a public distributed ledger (the blockchain) without the need for a trusted record keeping authority or central intermediary. Transaction blocks contain a SHA-256 cryptographic hash of previous transaction blocks, and are thus "chained" together, serving as an immutable record of all transactions that have ever occurred. As with any currency/commodity on the market, bitcoin trading and financial instruments soon followed public adoption of bitcoin and continue to grow. Included here is historical bitcoin market data for select bitcoin exchanges where trading takes place. Happy (data) mining!

CSV files for select bitcoin exchanges for the time period of September 2011 to June 2024, with updates of OHLC (Open, High, Low, Close), Volume in BTC and indicated currency, and weighted bitcoin price. Timestamps are in Unix time. Timestamps without any trades or activity have their data fields filled with NaNs. If a timestamp is missing, or if there are jumps, this may be because the exchange (or its API) was down, the exchange (or its API) did not exist, or some other unforeseen technical error in data reporting or gathering. All effort has been made to deduplicate entries and verify the contents are correct and complete to the best of my ability, but obviously trust at your own risk.

Acknowledgements and Inspiration

Bitcoin charts for the data. The various exchange APIs, for making it difficult or unintuitive enough to get OHLC and volume data that I set out on this data scraping project. Satoshi Nakamoto and the novel core concept of the blockchain, as well as its first execution via the bitcoin protocol. I'd also like to thank viewers like you! Can't wait to see what code or insights you all have to share.

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.