There are a collection of generally accepted parameters that are considered when grading the quality of an academic biomedical researcher.

Upon closer inspection, these can be divided into two groups, the 'hard' and the 'soft' factors.

The hard factors are readily measurable, and easily lend themselves to quantification and digitalization. These include how many articles have been published, how much grant money that was aquired, or the impact factors of journals. Sometimes referred to as 'quantitative', they also allow for the measurement of quality.

The soft factors are propensities we all want a good resarcher should exhibit. Factors in this category include for example innovative capacity, quality of teaching, or quality of reasearch.

The problem is that the soft, qualitative items are next to impossible to measure. Someone appointed to help select the best scientist among a large group of scientists face numerous practical problems if he/she wants to be fair and balanced. For example, does a given publication really represent the first discovery of a particular scientific fact? Who performed most of the work behind a given publication? Who really had the original, brilliant idea that led to this publication? Who in a lab was the graduate student of whom? Who is now the creative leader? Is it the old professor, or one of his/her younger co-workers?

Clearly, to know all these things for sure, one would have to be a member of the same group for several years, or perform in-depth interviews with every single person in that laboratory. Regarding novelty of discoveries, one would have to have knowledge of that individual field of science going back several years. This means that in most cases, when the reviewer of a collection of applicants face the task of sorting them, he/she will resort to using the hard factors. In addition, manual decisions are often gender biased.