It's really up to you, as long as you don't do something that looks, well, "weird." My only questions would be: (1) how central is age to your research? and (2) what kind of statistical analyses do you intend?

The simplest thing is not to break them into categories at all: just report descriptive statistics (mean, SD, range) and, if you're running correlational or regression analyses, use age as a continuous variable.

But if you want to report results for age groups, and maybe run ANOVAs with age as an IV, then you do want to break age into categories. One easy approach is to go by decades (i.e., 20-29, 30-39, 40-49, 50-59). Another would be to use just two groups ("younger" workers = those under 40, "more mature" workers = those 40 and up). Which is better? Well, more groups gives you a finer-grained analysis. But if your sample is small, then you might end up with groups comprised of just a few people, and that would not be good. In general, you'd like to have at least 8-10 people in each group for a one-way ANOVA, perhaps several times that many if you envision factorial ANOVAs with age as only one IV.

Adequate and effective assessment of the efficiency, effectiveness and the quality of scientific activities of specific scientists and research teams is crucial for any information society and a society based on knowledge. The solution to this problem is the subject of scientometrics and its purpose. The current stage of development scientometrics differs greatly from his previous appearance in the open as well as paid on-line access to huge amount of detailed data on a large number of indicators on individual authors and on scientific organizations and universities. The world has well-known bibliographic databases: Web of Science, Scopus, Astrophysics Data System, PubMed, MathSciNet, zbMATH, Chemical Abstracts, Springer, Agris, or GeoRef. In Russia, it is primarily the Russian scientific citing index (RSCI). RSCI is a national information-analytical system, accumulating more than 9 million publications of Russian scientists, as well as the information about citation of these publications from more than 6,000 Russian journals. There is too much information; it is so-called "Big data". But the problem is how to make sense of these large data, more precisely, to identify the meaning of scientometric indicators) and thus to convert them into great information ("great information"), and then apply this information to achieve the objective of scientometrics, i.e. to transform it into a lot of knowledge ("great knowledge") about the specific scientists and research teams. The solution to this problem is creating a "Scientific smart metering system" based on the use of the automated systemcognitive analysis and its software tools – an intellectual system called "Eidos". The article provides a numerical example of the creation and application of Scientometric intelligent measurement system based on a small amount of real scientific data that are publicly available using free on-line access to the RSCI:

Lutsenko E. V. Scientometric intelligent measuring system according to the RISC-based ask analysis and system "Eidos" / E. V. Lutsenko, A. I. Orlov, V. A. Glukhov // Polythematic network electronic scientific journal of the Kuban state agrarian University (the Scientific magazine of Kubsau) [an Electronic resource]. - Krasnodar: Kubgau, 2016. – No. 08(122). P. 157 – 212. – IDA [article ID]: 1221608014. - Access mode: http://ej.kubagro.ru/2016/08/pdf/14.pdf, 3.5 C. p. l.

I would not categorize. Use interval-scale mi mean, median and SD. Use the calendary age and the subjective age (effects are interesting!) and perhaps activity and social group.