Executive Summary: Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016,the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federallyfunded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts. To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems. Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals. Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy. Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources. Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. . Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan. The AI R&D Strategic Plan closes with two recommendations: Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan. Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.

Artificial intelligence (AI) holds tremendous promise to benefit nearly all aspects of society, including the economy, healthcare, security, the law, transportation, even technology itself. On February 11, 2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial Intelligence. This order launched the American AI Initiative, a concerted effort to promote and protect AI technology and innovation in the United States. The Initiative implements a whole-of-government strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners. Among other actions, key directives in the Initiative call for Federal agencies to prioritize AI research and development (R&emp;D) investments, enhance access to high-quality cyberinfrastructure and data, ensure that the Nation leads in the development of technical standards for AI, and provide education and training opportunities to prepare the American workforce for the new era of AI. In support of the American AI Initiative, this National AI R&emp;D Strategic Plan: 2019 Update defines the priority areas for Federal investments in AI R&emp;D. This 2019 update builds upon the first National AI R&emp;D Strategic Plan released in 2016, accounting for new research, technical innovations, and other considerations that have emerged over the past three years. This update has been developed by leading AI researchers and research administrators from across the Federal Government, with input from the broader civil society, including from many of America’s leading academic research institutions, nonprofit organizations, and private sector technology companies. Feedback from these key stakeholders affirmed the continued relevance of each part of the 2016 Strategic Plan while also calling for greater attention to making AI trustworthy, to partnering with the private sector, and other imperatives.

This dataset is a list of Department of Transportation (DOT) Artificial Intelligence (AI) use cases. Artificial intelligence (AI) promises to drive the growth of the United States economy and improve the quality of life of all Americans. Pursuant to Section 5 of Executive Order (EO) 13960, "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government," Federal agencies are required to inventory their AI use cases and share their inventories with other government agencies and the public. In accordance with the requirements of EO 13960, this spreadsheet provides the mechanism for federal agencies to create their inaugural AI use case inventories. https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government

According to Cognitive Market Research, the global AI Training Dataset Market size will be USD 2962.4 million in 2025. It will expand at a compound annual growth rate (CAGR) of 28.60% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 1096.09 million in 2025 and will grow at a compound annual growth rate (CAGR) of 26.4% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue, with a market size of USD 859.10 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 710.98 million in 2025 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2025 to 2033.
South America has a market share of more than 3.8% of the global revenue, with a market size of USD 112.57 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.6% from 2025 to 2033.
Middle East had a market share of around 4% of the global revenue and was estimated at a market size of USD 118.50 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.9% from 2025 to 2033.
Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 65.17 million in 2025 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2025 to 2033.
Data Annotation category is the fastest growing segment of the AI Training Dataset Market

Market Dynamics of AI Training Dataset Market

Key Drivers for AI Training Dataset Market

Government-Led Open Data Initiatives Fueling AI Training Dataset Market Growth

In recent years, Government-initiated open data efforts have strongly driven the development of the AI Training Dataset Market through offering affordable, high-quality datasets that are vital in training sound AI models. For instance, the U.S. government's drive for openness and innovation can be seen through portals such as Data.gov, which provides an enormous collection of datasets from many industries, ranging from healthcare, finance, and transportation. Such datasets are basic building blocks in constructing AI applications and training models using real-world data. In the same way, the platform data.gov.uk, run by the U.K. government, offers ample datasets to aid AI research and development, creating an environment that is supportive of technological growth. By releasing such information into the public domain, governments not only enhance transparency but also encourage innovation in the AI industry, resulting in greater demand for training datasets and helping to drive the market's growth.

India's IndiaAI Datasets Platform Accelerates AI Training Dataset Market Growth

India's upcoming launch of the IndiaAI Datasets Platform in January 2025 is likely to greatly increase the AI Training Dataset Market. The project, which is part of the government's ?10,000 crore IndiaAI Mission, will establish an open-source repository similar to platforms such as HuggingFace to enable developers to create, train, and deploy AI models. The platform will collect datasets from central and state governments and private sector organizations to provide a wide and rich data pool. Through improved access to high-quality, non-personal data, the platform is filling an important requirement for high-quality datasets for training AI models, thus driving innovation and development in the AI industry. This public initiative reflects India's determination to become a global AI hub, offering the infrastructure required to facilitate startups, researchers, and businesses in creating cutting-edge AI solutions. The initiative not only simplifies data access but also creates a model for public-private partnerships in AI development.

Restraint Factor for the AI Training Dataset Market

Data Privacy Regulations Impeding AI Training Dataset Market Growth

Strict data privacy laws are coming up as a major constraint in the AI Training Dataset Market since governments across the globe are establishing legislation to safeguard personal data. In the European Union, explicit consent for using personal data is required under the General Data Protection Regulation (GDPR), reducing the availability of datasets for training AI. Likewise, the data protection regulator in Brazil ordered Meta and others to stop the use of Brazilian personal data in training AI models due to dangers to individuals' funda...

This document includes relevant text from the 2016 and 2019 national AI R&D strategic plans, along with updates prepared in 2023 based on Administration and interagency evaluation of the National AI R&D Strategic Plan: 2019 Update as well as community responses to a Request for Information on updating the Plan. The 2019 strategies were broadly determined to be valid going forward. The 2023 update adds a new Strategy 9, which establishes a principled and coordinated approach to international collaboration in AI research.

Government Open Data Management Platform Market Outlook

The global government open data management platform market size was valued at USD 2.5 billion in 2023 and is projected to reach USD 6.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% during the forecast period. The rising emphasis on transparency, accountability, and citizen engagement by governments worldwide is a significant driving factor for this market's growth.

The proliferation of digital governance initiatives is one of the primary growth factors for the government open data management platform market. Governments across the globe are increasingly adopting digital platforms to improve public service delivery, enhance citizen engagement, and increase operational efficiency. By providing open access to data, these platforms enable better decision-making and foster innovation among various stakeholders, including businesses, researchers, and the general public. This trend is further accelerated by the growing demand for data-driven governance and public policies that are more responsive and accountable.

Moreover, advancements in data analytics and artificial intelligence (AI) are significantly contributing to the growth of the government open data management platform market. Modern open data platforms are increasingly incorporating sophisticated analytics tools and AI capabilities to offer more insightful and actionable data. These technological advancements enable governments to leverage large datasets for predictive analytics, enhancing their ability to anticipate and respond to public needs effectively. Additionally, the integration of AI in data management platforms helps in automating data processing tasks, thereby improving efficiency and reducing operational costs.

The increasing focus on smart city initiatives is another critical factor driving the demand for government open data management platforms. Smart cities rely heavily on data to optimize urban planning, improve traffic management, enhance public safety, and provide efficient public services. Open data platforms play a crucial role in these initiatives by providing a centralized repository for diverse data sets collected from various sensors and systems across the city. This data can be accessed and analyzed by different stakeholders to develop innovative solutions that address urban challenges and improve the quality of life for citizens.

Government Software plays a pivotal role in the development and implementation of open data management platforms. These software solutions are designed to meet the specific needs of government agencies, providing robust tools for data collection, analysis, and dissemination. By leveraging government software, agencies can ensure data accuracy, enhance transparency, and improve public service delivery. The integration of advanced features such as data visualization, predictive analytics, and machine learning within government software allows for more informed decision-making and policy formulation. As governments continue to prioritize digital transformation, the demand for specialized government software solutions is expected to rise, driving further growth in the open data management platform market.

From a regional perspective, North America holds a significant share of the government open data management platform market, driven by the early adoption of digital governance solutions and the presence of major technology providers in the region. Europe is also a prominent market, with several countries implementing open data policies to promote transparency and citizen participation. The Asia Pacific region is expected to witness substantial growth during the forecast period, supported by increasing government initiatives to digitize public services and the rising adoption of smart city projects. Latin America, the Middle East, and Africa are also anticipated to show promising growth, although at a comparatively slower pace due to varying levels of technological infrastructure and government investment in these regions.

Component Analysis

The government open data management platform market is segmented by component into software and services. Software components include the core data management platforms, which facilitate the collection, storage, and dissemination of open data. These software solutions are designed to handle large volumes of data and provide various functionalities such as data analytics, visualization, and integration. The increasi

Analysis of ‘State Virtual Server Growth’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/487e390c-aced-4e39-8e74-e216d18cba7d on 26 January 2022.

--- Dataset description provided by original source is as follows ---

In effort to increase efficiencies and reduce hardware costs, the State of Missouri has adopted virtualized servers to service agency IT infrastructure and computing needs.

--- Original source retains full ownership of the source dataset ---

On June 4-6, 2019, the NSTC NITRD Program, in collaboration with the NSTC's MLAI Subcommittee, held a workshop to assess the research challenges and opportunities at the intersection of cybersecurity and artificial intelligence. The workshop brought together senior members of the government, academic, and industrial communities to discuss the current state of the art and future research needs, and to identify key research gaps. This report is a summary of those discussions, framed around research questions and possible topics for future research directions. More information is available at https://www.nitrd.gov/nitrdgroups/index.php?title=AI-CYBER-2019.

Summary: This Plan is an important milestone in the Administrations Big Data Research and Development (R&D) Initiative

Analysis of ‘FOIA Request Log - Community Development - Historical’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a456c58e-4172-46a1-9dd4-321048c9e4cb on 26 January 2022.

--- Dataset description provided by original source is as follows ---

FOIA requests received by Community Development May 1, 2010-December 31, 2010; For requests made after January 1, 2011, please see Housing & Economic Development FOIA Request Log at https://data.cityofchicago.org/Government/FOIA-Request-Log-Housing-And-Economic-Development/5ztz-espx

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Oregon Growth Account’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ff22ec1c-0dff-44b5-be9d-84f505e35e1b on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Within the Oregon Growth Account, investments are made in institutional investment vehicles and pre-institutional investment vehicles. Investments made in Fiscal Years 2016-2021. Visit www.oregongrowthboard.com for more information about the account.

--- Original source retains full ownership of the source dataset ---

The Survey of State Government Research and Development measures the extent of R&D activity performed and funded by the governments of each of the nation’s 50 states, the District of Columbia, and Puerto Rico (collectively, states). By employing consistent, uniform definitions and collection techniques, the survey allows collection of state R&D expenditures data that are comparable nationwide. The survey is a census of state government departments, agencies, commissions, public authorities, and dependent entities with R&D activities.

This report is a product of interagency collaboration, led by the National Science and Technology Council (NSTC) Select Committee on AI and Subcommittee on Machine Learning and AI (MLAI), that is one of several measures responding to the February 11, 2019, Executive Order on Maintaining American Leadership in Artificial Intelligence. The report details four key recommendations for leveraging cloud computing resources for federally funded artificial intelligence (AI) research and development (R&D). Cloud platforms provide robust, agile, reliable, and scalable computing capabilities, which can help accelerate advances in AI. Cloud computing can also democratize access to the powerful computing capabilities needed for many types of AI R&D. This report is another step in the overall Federal Government strategy to position the United States as the world leader in artificial intelligence.

The market for artificial intelligence grew beyond *** billion U.S. dollars in 2025, a considerable jump of nearly ** billion compared to 2023. This staggering growth is expected to continue, with the market racing past the trillion U.S. dollar mark in 2031. AI demands data Data management remains the most difficult task of AI-related infrastructure. This challenge takes many forms for AI companies. Some require more specific data, while others have difficulty maintaining and organizing the data their enterprise already possesses. Large international bodies like the EU, the US, and China all have limitations on how much data can be stored outside their borders. Together, these bodies pose significant challenges to data-hungry AI companies. AI could boost productivity growth Both in productivity and labor changes, the U.S. is likely to be heavily impacted by the adoption of AI. This impact need not be purely negative. Labor rotation, if handled correctly, can swiftly move workers to more productive and value-added industries rather than simple manual labor ones. In turn, these industry shifts will lead to a more productive economy. Indeed, AI could boost U.S. labor productivity growth over a 10-year period. This, of course, depends on various factors, such as how powerful the next generation of AI is, the difficulty of tasks it will be able to perform, and the number of workers displaced.

The global Big Data Analytics in Defense market is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) exceeding 13% from 2025 to 2033. This expansion is fueled by several key factors. The increasing reliance on advanced technologies for enhanced situational awareness and improved decision-making within military operations is a primary driver. The need to analyze vast quantities of data from diverse sources, including sensor networks, satellite imagery, and social media, is pushing the adoption of sophisticated big data analytics solutions. Furthermore, the growing demand for predictive intelligence and improved cybersecurity within defense organizations is further accelerating market growth. Technological advancements in artificial intelligence (AI), machine learning (ML), and cloud computing are continuously enhancing the capabilities of big data analytics platforms, making them more efficient and effective. Segmentation reveals a strong demand across all platforms (Army, Navy, Air Force), with hardware, software, and services all contributing significantly to the overall market value. While the market faces some restraints, such as data security concerns and the high cost of implementation, these are being mitigated by ongoing innovation and government investment in defense modernization initiatives. The North American market currently holds a substantial share, driven by significant defense spending and the presence of major technology players. However, the Asia-Pacific region is poised for rapid expansion due to increasing military modernization efforts in countries like China and India. The competitive landscape is dominated by established defense contractors and technology giants, indicating a robust ecosystem fostering further innovation and market penetration. The market's trajectory suggests continued high growth over the forecast period, driven by the increasing strategic importance of big data analytics in national security and defense operations. The market's future is characterized by a strong focus on developing AI-powered analytics solutions for real-time threat detection, predictive maintenance of defense equipment, and optimized resource allocation. Furthermore, the integration of big data analytics with other emerging technologies, such as the Internet of Things (IoT) and blockchain, will further expand its capabilities and applications. The increasing emphasis on cybersecurity and data privacy is likely to drive demand for robust and secure data analytics solutions. Collaborative partnerships between defense organizations and technology providers are crucial for developing and deploying effective big data analytics solutions. Government initiatives to encourage innovation and investment in the defense technology sector will play a significant role in shaping the market's future trajectory. The continued growth in defense budgets globally will further support the market's expansion, making it a highly attractive investment opportunity for both established players and emerging technology companies. Recent developments include: September 2022: The United States Air Force signed a contract worth USD 1.25 million with ZeroEyto procure an AI gun detection solution for the service's unmanned aerial vehicles (UAVs) at the Dover Air Force Base, Delaware. ZeroEyes' technology will enable drones to detect handheld weapons for base protection., July 2022: The Indian Ministry of Defense launched 75 newly developed artificial intelligence (AI) products and technologies during the first-ever 'AI in Defense symposium and exhibition in New Delhi. These include autonomous systems, AI platform automation, command, control, communication, computer (C4), blockchain-based automation, intelligence, surveillance & reconnaissance (ISR), intelligent monitoring systems, cyber security, and others.. Notable trends are: Software Segment Will Showcase Remarkable Growth During the Forecast Period.

In effort to increase efficiencies and reduce hardware costs, the State of Missouri has adopted virtualized servers to service agency IT infrastructure and computing needs.

The dataset consists of 375 extracted quotes from 31 community reports relevant to the development of a materials data strategy for the NIST Materials Measurement Laboratory (MML). The dataset is used in the NIST internal report "A Materials Data Strategy." In the past decade, numerous public and private sector documents have highlighted the need for materials data to facilitate advanced technologies in myriad industrial and economic sectors. These documents have been analyzed to identify prevalent gaps in the establishment of an interconnected materials data infrastructure akin to that envisioned in the federal agency-wide Materials Genome Initiative. The internal report uses a uniform schematic format to portray these gaps, illustrate progress in addressing the gaps, and propose an MML roadmap of action items to further address the gaps.

Washington, DC is a city that has a little something for everyone. In addition to the almost 700,000 Washingtonians who call DC home, we also welcome tens of millions of visitors from around the world every year.

Every day, more human activities are assisted by computer vision, a form of artificial intelligence (AI) that enables an automated understanding of the visual world. Computer vision research drives new applications in the public safety domain for both government and industry. The Networking and Information Technology Research and Development (NITRD) Program's Video and Image Analytics (VIA) Team was formed to collaborate and engage in this rapidly developing research area. VIA, which includes researchers from 30 government organizations spanning nearly all Federal agencies, developed these research and development (R&D) opportunities to foster a robust multisector ecosystem that supports the Nation's requirements in video and image analytics...

The Arlington Profile combines countywide data sources and provides a comprehensive outlook of the most current data on population, housing, employment, development, transportation, and community services. These datasets are used to obtain an understanding of community, plan future services/needs, guide policy decisions, and secure grant funding. A PDF Version of the Arlington Profile can be accessed on the Arlington County website.