Public data set for NASA Agency Intellectual Property (IP). The distribution contains both Patent information as well as General Release of Open Source Software.

The US Consumer IP Address file contains information on the location and observation dates of IP addresses tied to individuals in the Consumer Database. It is updated monthly from a database containing billions of IP<>email linkages.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

This statistic offers a forecast for cloud data center IP traffic from 2015 to 2021. In 2018, the amount of cloud data center IP traffic is expected to reach 10.6 zettabytes. This is expected to grow to 19.5 zettabytes by 2021.

The data set contains a sample of Names and IP adresses.

With Versium REACH's IP to Domain you unlock the ability to de-anonymize your database of IP addresses. Receive firmographic data for an IP address that includes up to 3 likely businesses, including key attributes such as domain, company size, location, and many other valuable firmographic insights.

According to a survey conducted among U.S. enterprises in China in 2022, more than one-third of companies stated that the possibility of IP leakage and data security was higher in China. But looking at the long-term trend, the proportion of companies concerned about larger IP and data threats in China gradually decreased.

Observed linkages between consumer and B2B emails and IP addresses derived from website and device activity.

What is IPGOD?\r

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.\r \r \r

How do I use IPGOD?\r

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.\r \r \r

IP Data Platform\r

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform\r \r

References\r

\r The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.\r \r * Patents\r * Trade Marks\r * Designs\r * Plant Breeder’s Rights\r \r \r

Updates\r

\r

Tables and columns\r

\r Due to the changes in our systems, some tables have been affected.\r \r * We have added IPGOD 225 and IPGOD 325 to the dataset!\r * The IPGOD 206 table is not available this year.\r * Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.\r \r

Data quality improvements\r

\r Data quality has been improved across all tables.\r \r * Null values are simply empty rather than '31/12/9999'.\r * All date columns are now in ISO format 'yyyy-mm-dd'.\r * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.\r * All tables are encoded in UTF-8.\r * All tables use the backslash \ as the escape character.\r * The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

In September 2024, the value of exported charges for the use of intellectual property in the United States was around 11.84 billion U.S. dollars. This was a slight increase from the previous month, continuing a general upward trend since the end last year. The data are seasonally adjusted.

Patent data is aggregated across multiple Intellectual Property (IP) registries, including USPTO, CIPO, EUIPO and WIPO (USA, Canada, Europe). Our complete dataset of active patent records is updated weekly. Customized reports available based on company lists, or full dataset via raw feed or one-off reports. Full bibliographic data provided for each IP record; including filing date, grant date, expiry date, inventor(s), IPC, full text abstract, title, etc. Ownership/entity relationship mapping, ticker mapping, ISIN mapping, Crunchbase uuid mapping, Crunchbase domain mapping. We also provide our proprietary IP Activity Score for each owner, which can assist to compare recent innovation activity amongst owners, as reflected in their Intellectual Property filings.

Ipqwery's Patent data is also available as a combined dataset with our Trademark dataset, enabling full IP profiles for corporate entities.

The Consumer Mobile Device file contains MAIDs connected to an individual in the Consumer Database. The fields available include latitude and longitude, device type, hashed emails, and plain-text emails.

This is updated monthly from a database containing billions of MAID<>email linkages.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

Dynamic IP Proxy Service Market Outlook

The global Dynamic IP Proxy Service market size was estimated to be worth $1.5 billion in 2023, and is projected to reach approximately $5.2 billion by 2032, growing at a CAGR of 14.8% from 2024 to 2032. This substantial growth is driven by the increasing need for anonymized web browsing and heightened online security measures.

The demand for Dynamic IP Proxy Services is primarily fueled by the growing emphasis on cybersecurity and privacy. As the digital landscape expands, so too does the threat of cyberattacks, making anonymized browsing and secure internet access pivotal for both individuals and organizations. The increasing sophistication of cyber threats, including data breaches, phishing attacks, and online fraud, has necessitated the adoption of robust IP proxy services that can offer dynamic IP addresses to circumvent such risks effectively.

Moreover, the rise in remote work and the global shift towards digitalization have significantly bolstered the need for Dynamic IP Proxy Services. With more employees accessing company resources from remote locations, businesses are turning to dynamic IP solutions to ensure secure and encrypted connections. This trend has been accelerated by the COVID-19 pandemic, which has fundamentally altered the way businesses operate, pushing them towards more flexible and secure digital solutions. The market is also seeing growth due to the increased use of web scraping and data harvesting techniques by businesses aiming to gather intelligence and maintain a competitive edge.

Another critical factor driving the market is the growing regulatory scrutiny around data privacy and protection. Legislation such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the U.S. has placed stringent requirements on how companies manage and protect user data. Dynamic IP Proxy Services offer a way for companies to comply with these regulations by ensuring that data transmission is secure and that user identities remain anonymous.

Regionally, North America remains a dominant player in the Dynamic IP Proxy Service market, thanks to its advanced infrastructure and high adoption rates of cybersecurity solutions. However, Asia Pacific is expected to witness the fastest growth during the forecast period due to the rapid digital transformation across industries in countries like India and China. The rise of e-commerce, coupled with increasing internet penetration, is driving the demand for these services in the region.

Type Analysis

Dynamic IP Proxy Services can be broadly categorized into three types: Residential Proxy, Data Center Proxy, and Mobile Proxy. Residential Proxies are IP addresses assigned by Internet Service Providers (ISPs) to homeowners. These proxies are highly sought after due to their legitimacy and low likelihood of being flagged by websites, making them ideal for applications such as web scraping and ad verification. The demand for Residential Proxies is expected to grow significantly, driven by the need for reliable and trustable IP addresses that can bypass sophisticated detection mechanisms implemented by websites.

Data Center Proxies, on the other hand, are IP addresses provided by data centers and are not affiliated with ISPs. These proxies are typically used for tasks that require high-speed internet and large bandwidth, such as bulk data scraping and automated tasks. Their lower cost compared to Residential Proxies makes them an attractive option for businesses looking to perform large-scale data operations without incurring significant expenses. However, they are more susceptible to being blocked by websites due to their association with data centers.

Mobile Proxies represent a unique segment within the Dynamic IP Proxy Service market. These proxies use IP addresses assigned by mobile carriers, making them valuable for tasks that require mobile verification or browsing. The increasing use of mobile devices for internet access and the growing trend of mobile-first strategies among businesses are propelling the demand for Mobile Proxies. These proxies offer a high degree of anonymity and are less likely to be flagged or blocked, making them ideal for various applications, including market research and ad verification.

Each type of proxy service has its own set of advantages and disadvantages, and businesses often choose based on their specific needs and budgets. Residential Proxies are favored for their reliability, Data Center Proxies for their cos

This dataset offers a comprehensive collection of Telegram users' geolocation data, including IP addresses, with full user consent, covering 50,000 records. This data is specifically tailored for use in AI, ML, DL, and LLM models, as well as applications requiring Geographic Data and Social Media Data. The dataset provides critical geospatial information, making it a valuable resource for developing location-based services, targeted marketing strategies, and more.

What Makes This Data Unique? This dataset is unique due to its focus on geolocation data tied to Telegram users, a platform with a global user base. It includes IP to Geolocation Data, offering precise geospatial insights that are essential for accurate geographic analysis. The inclusion of user consent ensures that the data is ethically sourced and legally compliant. The dataset's broad coverage across various regions makes it particularly valuable for AI and machine learning models that require diverse, real-world data inputs.

Data Sourcing: The data is collected through a network of in-app tasks across different mini-apps within Telegram. Users participate in these tasks voluntarily, providing explicit consent to share their geolocation and IP information. The data is collected in real-time, capturing accurate geospatial details as users interact with various Telegram mini-apps. This method of data collection ensures that the information is both relevant and up-to-date, making it highly valuable for applications that require current location data.

Primary Use-Cases: This dataset is highly versatile and can be applied across multiple categories, including:

IP to Geolocation Data: The dataset provides precise mapping of IP addresses to geographical locations, making it ideal for applications that require accurate geolocation services. Geographic Data: The geospatial information contained in the dataset supports a wide range of geographic analysis, including regional behavior studies and location-based service optimization. Social Media Data: The dataset's integration with Telegram users' activities provides insights into social media behaviors across different regions, enhancing social media analytics and targeted marketing. Large Language Model (LLM) Data: The geolocation data can be used to train LLMs to better understand and generate content that is contextually relevant to specific regions. Deep Learning (DL) Data: The dataset is ideal for training deep learning models that require accurate and diverse geospatial inputs, such as those used in autonomous systems and advanced geographic analytics. Integration with Broader Data Offering: This geolocation dataset is a valuable addition to the broader data offerings from FileMarket. It can be combined with other datasets, such as web browsing behavior or social media activity data, to create comprehensive AI models that provide deep insights into user behaviors across different contexts. Whether used independently or as part of a larger data strategy, this dataset offers unique value for developers and data scientists focused on enhancing their models with precise, consented geospatial data.

What is IPGOD? The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD. # How do I use IPGOD? IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar. # IP Data Platform IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform # References The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset. * Patents * Trade Marks * Designs * Plant Breeder’s Rights # Updates ### Tables and columns Due to the changes in our systems, some tables have been affected. * We have added IPGOD 225 and IPGOD 325 to the dataset! * The IPGOD 206 table is not available this year. * Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use. ### Data quality improvements Data quality has been improved across all tables. * Null values are simply empty rather than '31/12/9999'. * All date columns are now in ISO format 'yyyy-mm-dd'. * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0. * All tables are encoded in UTF-8. * All tables use the backslash \ as the escape character. * The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

The data tables provide the statistical results from the Survey Intellectual Property Awareness and Use (IPAU) . The survey measures general awareness of intellectual property by businesses in Canada, as well as whether and how they protect their intellectual property in Canada and abroad. The tables are available by enterprise size, by sector, and by economic region, for the reference period from 2017 to 2019.

Survey of intellectual property commercialization, by higher education sector indicators, for Canada from 1998 to 2009.

This repository contains that data that we collected and used for the following manuscript (currently under review):RESIP Host Detection: Identification of Malicious Residential IP Proxy FlowsA. Tosun, M. De Donno, N. Dragoni and X. FafoutisThe data are in the standard pcap format, thus readable with network monitoring tools (e.g. Wireshark) or pcap libraries (e.g. libpcap, scapy). The exact conditions under which the data were collected are described in the aforementioned manuscript. The scripts to process the data are available in the git repository found in the references.

This statistic offers a forecast for data center IP traffic growth from 2013 to 2021, by segment. In 2016, the amount of data center traffic in the business segment was around 2,319 exabytes per year.

IP Address Data Software Market Outlook

The global market size for IP Address Data Software was valued at approximately $2.8 billion in 2023 and is projected to reach around $6.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.4%. The robust growth of this market can be attributed to the increasing demand for efficient network management solutions coupled with the rising need for data security across various industries. The proliferation of connected devices and the advent of advanced technologies such as the Internet of Things (IoT) and 5G are also fuelling the demand for IP address data software.

The primary growth factor driving the IP Address Data Software market is the exponential increase in the number of connected devices. As more devices become internet-enabled, the complexity of managing IP addresses grows, necessitating more sophisticated solutions. Businesses and organizations, regardless of their size, are increasingly adopting IP address data software to streamline their network management processes. The advent of IoT has further complicated network architectures, making the management of IP addresses critical for maintaining network efficiency and security. As a result, the adoption of IP address data software is becoming an indispensable component for modern enterprises.

Another significant growth factor is the escalating need for robust security management. With the increasing frequency and sophistication of cyber-attacks, organizations are prioritizing network security more than ever. IP address data software plays a crucial role in identifying and mitigating potential threats by providing real-time insights into network activities. This capability not only enhances security but also helps in compliance with regulatory standards. The heightened focus on cybersecurity across industries such as BFSI, healthcare, and IT and telecommunications is boosting the demand for IP address data software.

Data analytics is also a key growth driver for the IP Address Data Software market. Organizations are leveraging data analytics to gain actionable insights from their network data, which can help in optimizing performance and improving decision-making processes. IP address data software equipped with advanced analytics capabilities can provide detailed reports and analytics, which can be used for various business intelligence purposes. This trend is particularly prominent in large enterprises that operate extensive and complex networks, thereby creating a substantial market opportunity for IP address data software vendors.

From a regional perspective, North America holds a dominant position in the IP Address Data Software market, primarily due to the presence of numerous technology giants and a highly developed IT infrastructure. The region's early adoption of advanced technologies, coupled with stringent regulatory standards regarding data security, is driving the market growth. Europe is also a significant market, with countries like Germany, France, and the United Kingdom leading the adoption of IP address data software. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid digital transformation initiatives in countries like China, India, and Japan.

Component Analysis

The IP Address Data Software market is segmented into two primary components: software and services. The software segment encompasses the various IP address management solutions that are deployed by organizations to manage their network IPs effectively. This segment holds the largest market share due to the increasing complexity of networks and the need for automated solutions. Modern IP address management software comes with features like automated IP tracking, DHCP server management, and DNS configuration, which are essential for maintaining network efficiency and security. These advanced features are driving the adoption of IP address management software across different industries.

On the other hand, the services segment includes consulting, integration, and maintenance services that are essential for the successful deployment and functioning of IP address data software. Organizations often require expert consultation and integration services to ensure that the IP address management solutions are tailored to their specific needs. Maintenance services are also crucial for the ongoing performance and security of these systems. As networks grow in size and complexity, the demand for specialized services to support IP address management solutions is also increasing, contributing significantly to the overal