In 2007 the International Mathematical Union (IMU), the Institute of Mathematics and Statistics (IMS) and the International Council for Industrial and Applied Mathematics (ICIAM), set up a Committee on "Quantitative Assessment of Research" that was asked to investigate various aspects of impact factors and similar numerical values.

The Committee consisted of:

- John Ewing (Providence, USA), appointed by IMU;
- Robert Adler (Haifa, Israeli), appointed by IMS;
- Peter Taylor (Melbourne, Australia), appointed by ICIAM.

(see http://www.iciam.org/setup.html)

The terms of reference of the committee can be found at:

`http://www.mathunion.org/Publications/2007/Charge-ComOnQuantAssessmRes070521.pdf`

.

The committee has addressed this charge, documented and discussed many of the problems arising with the use of such indicators in the report, and submitted it to the Executive Committees of IMU, the ICIAM board at its meeting in Vancouver on 12 April, and IMS. The two ECs and the ICIAM board have endorsed the report, titled *Citation Statistics*, and the three institutions are making it public on 11 June 2008.

The report is accompanied by the following Press Release (.doc, .pdf; in German):

#### Press release: *Citation Statistics* (Numbers with a number of problems)

Citation-based statistics, such as the impact factor, are often used to assess scientific research, but are they the best measures of research quality? Three international mathematics organizations have today released a report, *Citation Statistics*, on the use of citations in assessing research quality — a topic that is of increasing interest throughout the world's scientific community. The report is written from a mathematical perspective and strongly cautions against the over-reliance on citation statistics such as the impact factor and *h*-index. These are often promoted because of the belief in their accuracy, objectivity, and simplicity, but these beliefs are unfounded.

Among the report's key findings:

- Statistics are not more accurate when they are improperly used; statistics can mislead when they are misused or misunderstood.
- The objectivity of citations is illusory because the meaning of citations is not well-understood. A citation's meaning can be very far from "impact".
- While having a single number to judge quality is indeed simple, it can lead to a shallow understanding of something as complicated as research. Numbers are not inherently superior to sound judgments.

The report promotes the sensible use of citation statistics in evaluating research and points out several common misuses. It is written by mathematical scientists about a widespread application of mathematics. While the authors of the report recognize that assessment must be practical and that easily-derived citation statistics will be part of the process, they caution that citations provide only a limited and incomplete view of research quality. Research is too important, they say, to measure its value with only a single coarse tool.

The report was commissioned by the International Mathematical Union (IMU) in cooperation with the International Council on Industrial and Applied Mathematics (ICIAM), and the Institute of Mathematical Statistics (IMS). It draws upon a broad literature on the use of citation data to evaluate research, including articles on the impact factor (the most common citation-based statistic) and the *h*-index along with its many variants. The work was also based on practices as reported from mathematicians and other scientists from around the world.

IMU, ICIAM, IMS