Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the eighth in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the seventh in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the sixth in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the third in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the second in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

This data brief explores how international collaboration relates to the impact and output of research publications. Focusing on the top 10 countries with the highest publication output from 2010 to 2019, the authors provide a comprehensive analysis across the major fields of science and technology.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

CSET is excited to present the Map of Science user interface. Using a merged corpus of more than 260 million scientific documents, CSET clustered research based on citation links and mapped the clusters spatially using those linkages. The resulting Map provides a visual representation of the landscape of science, with detailed information on more than 123,000 clusters of research including 130 million scientific papers.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

Using CSET’s new Map of Science to examine clusters of research publications, this data brief presents a comparative analysis of U.S. and Chinese research publication outputs. The authors find that global competition outcomes differ depending on the level of granularity when comparing research publication data. In a granular view of global scientific research, the United States and China together dominate almost two-thirds of the research publication output, with the rest of the world leading in more than one-third of publication output. In a general view of global scientific research, only China and the United States appear as leaders in research output.

The global map of research has shifted dramatically over the last 20 years. Annual global investment in research and development has tripled, and the United States’ share of both global R&D funding and total research output is diminishing. The open research system, with its expanding rates of investment and interconnectedness, has delivered tremendous benefits to many nations but also created new challenges for research integrity and security.

Artificial intelligence is said to be transforming the global economy and society in what some dub the “fourth industrial revolution.” This data brief analyzes media representations of AI and the alignments, or misalignments, with job postings that include the AI-related skills needed to make AI a practical reality. This potential distortion is important as the U.S. Congress places an increasing emphasis on AI. If government funds are shifted away from other areas of science and technology, based partly on the representations that leaders and the public are exposed to in the media, it is important to understand how those representations align with real jobs across the country.

The advantages of nations depend in part on their access to new inventions—and modern applications of artificial intelligence can help accelerate the creation of new inventions in the years ahead. This data brief is a first step toward understanding how modern AI and machine learning have begun accelerating growth across a wide array of science and engineering disciplines in recent years.

China’s surge in artificial intelligence development has been fueled, in large part, by advances in computer vision, the AI subdomain that makes powerful facial recognition technologies possible. This data brief compares U.S. and Chinese computer vision patent data to illustrate the different approaches each country takes to AI development.

Future U.S. competitiveness in artificial intelligence will require a robust AI workforce. This data brief analyzes market demand for AI-related jobs to determine the skills necessary in the field. It concerns jobs considered both “core AI” and “AI-adjacent.”

The U.S. government and industry both see artificial intelligence as a pivotal technology for future growth and competitiveness. What skills will be needed to create, integrate, and deploy AI applications? This data brief analyzes market demand for AI-related jobs to determine their educational requirements, dominant sectors, and geographic distribution.