- The paper introduces and analyzes the new Excellence Indicator used in the SCImago Institutions Rankings, explaining its field-normalized, non-parametric approach to measuring high-citation performance.
- The indicator calculates the percentage of an institution's papers in the top 10% most cited globally, enabling statistically significant comparisons between institutions based on excellence rate.
- Its implementation offers institutions, policymakers, and funding bodies a robust, data-driven method for evaluating research impact and informing strategic decisions beyond simple publication counts.
The Excellence Indicator in SCImago Institutions Rankings: An Analytical Overview
The paper examines the introduction and implications of the new Excellence Indicator within the SCImago Institutions Rankings (SIR) World Reports. These reports annually assess over 2,000 research institutions using indicators derived from Scopus data, focusing on publication and citation outputs. The introduction of the Excellence Indicator marks a significant extension of the SIR’s analytical framework, as it adds a measure of how an institution’s publications stand in terms of citations relative to global benchmarks.
The Excellence Indicator is designed to provide an item-oriented citation score normalized for fields and publication years. It calculates the percentage of an institution’s papers that rank in the top-10% of the most-cited papers in the same subject area and year, thereby capturing high-citation performance. This approach deviates from traditional normalization based on average values and employs non-parametric statistics, addressing the inherent skew in citation distributions.
A key advantage of the Excellence Indicator is its ability to compare an institution's performance against a global standard and other institutions directly. The reference standard posits that 10% of papers fall into the top-10% category; institutions exceeding this benchmark are deemed to perform above expectations. This allows researchers to determine the statistical significance of performance disparities among institutions using the z-test for proportions.
Using the Excellence Indicator, institutions like UCLA and Stanford can be compared not only on their sheer output but based on whether their excellence rate significantly exceeds or falls short of the 10% basis. Evaluations using this method have shown that differences like the z-score between UCLA's and Stanford's excellence rates (-0.607) are not statistically significant, demonstrating a quantitative approach to institutional assessment beyond publication counts.
The implications of using the Excellence Indicator are profound, both theoretically and practically. Theoretically, it challenges conventional citation paradigms by applying a clearer, field-normalized benchmark to measure academic impact. Practically, it enables institutions, policymakers, and funding bodies to make more informed decisions based on robust, interpretable data comparisons.
Future developments in AI and bibliometrics might further refine these techniques, potentially incorporating algorithmic evaluations and expanding denominator sets for normalized impact assessments. As these methodologies evolve, they may also include enhanced data visualizations or integrate with other analytical frameworks, increasing their accessibility to non-specialist audiences while maintaining rigor.
In conclusion, the Excellence Indicator represents a strategic advancement in how research impact is quantified and utilized, offering new analytic possibilities in the quest for scientific excellence measurement. Its adoption paves the way for more nuanced, statistically robust interpretations of institutional research contributions, informing strategic development and scholarly discourse.