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Recognising the increasing accessibility and importance of patent data, the article underscores the need for standardised and transparent data analysis methods. We illustrate the construction and relevance of commonly used patent indicators derived from Google Patents Public Datasets. The indicators range from citation counts to more advanced metrics like patent text similarity. The BigQuery code is available in an open Kaggle notebook, explaining operational intricacies and potential data issues. By providing clear, adaptable queries and emphasising transparent methods, this article hopes to contribute to the standardisation and accessibility of patent analysis, offering a valuable resource for researchers and practitioners alike. [ABSTRACT FROM AUTHOR]

National and international rankings of universities are now an accepted part of the higher education landscape. Rankings aggregate different performance measures into a single scale and therefore depend on the methods and weights used to aggregate. The most common method is to scale each variable relative to the highest performing entity prior to aggregating. Other approaches involve transforming the data to allow for the different spread of the variables. This paper evaluates alternative methods and the sensitivity to weights with illustrations from the "Times Higher Education" and Shanghai Jiao Tong rankings of universities and the U21 rankings of national systems of higher education. The authors conclude that transforming the data clouds interpretation; the choice of included variables is more important than the weights attached to them; and there are limitations in extending ranking to a large number of universities/countries.

With the advent of mass higher education and the consequent absorption of significant national resources, both public and private, it is inevitable that universities are increasingly expected to meet a range of societal needs. They are expected to "connect" with society at large. In this paper, we argue that connectivity is best integrated with research, teaching and scholarship and should not be relegated to a "third stream". We compare degrees of connectivity of 50 national systems of higher education using ten indicators, making a distinction between domestic and international connectivity. The strongest finding is that smaller countries exhibit the highest level of international connectivity. The higher education systems in countries with large absolute numbers of researchers such as the USA, China and Japan are relatively self-contained compared with countries such as Ireland, Switzerland and Singapore. Another finding is the relative insularity of the education sector in Eastern Europe, including the Russian Federation. When differences in levels of economic development are allowed for, among lower-income countries South Africa stands out as having a well-connected higher education sector.

This symposium Article discusses issues raised for patent processes and policy created by inventions generated by artificial intelligence ("AI"). The Article begins by examining the normative desirability of allowing patents on AI-generated inventions. While it is unclear whether patent protection is needed to incentivize the creation of AI-generated inventions, a stronger case can be made that AI-generated inventions should be patent eligible to encourage the commercialization and technology transfer of AI-generated inventions. Next, the Article examines how the emergence of AI inventions will alter patentability standards, and whether a differentiated patent system that treats AI-generated inventions differently from human-generated inventions is normatively desirable. This Article concludes by considering the larger implications of allowing patents on AI-generated inventions, including changes to the patent examination process, a possible increase in the concentration of patent ownership and patent thickets, and potentially unlimited inventions. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of national higher education systems in 48 countries as measured with 20 variables grouped under the four headings of Resources, Environment, Connectivity and Output. Rankings within each module are then combined into an overall ranking that is topped by the United States followed by Sweden, Canada, Finland and Denmark. Relationships between different attributes are explored. Countries ranked highest on output tend to be ranked highly on resources. Research output is correlated with government funding, especially expenditure on research and development (R&D). The impact of the policy and regulatory environment is also examined. The weakest national systems are those with low government funding but high government control.