Dihydrofolate reductase and membrane translocation: evolution of a classic experiment: Classic landmark papers, irrespective of their age, can teach students how best science is practiced and inspire new experiments.

Abstract

Any PhD student or postdoc, who scrambles to write and submit a publication before getting scooped, can testify that science is a fast‐moving endeavor. Given our limited time and the ever‐increasing pace with which scientific studies are published, few students and postdocs—and PIs as a matter of fact—have time to keep up with the literature. “Reading” a manuscript often means just skimming through the abstract, having a quick glimpse at the figures and searching the PDF file for keywords of immediate interest. Notwithstanding these constraints, I would argue that the scientific literature is a treasure trove of information and ideas that go beyond contemporary papers to include classical publications. However, it can be difficult to motivate students and postdocs to read old landmark publications that reported groundbreaking discoveries and opened up new avenues of research. Many think that these papers are of merely historic interest and have little to contribute to scientists expected to use cutting‐edge methods to produce high‐impact publications. > Reading a manuscript often means just skimming through the abstract, having a quick glimpse at the figures and searching the PDF file for keywords of immediate interest. However, classic papers still have a lot to offer. For once, they withstood the test of the time, as the reported results have shown to be correct and reproducible. This is unfortunately not the case for many high‐impact publications today, which, owing to the hype of selling it to the highest impact factor journal, just tell a cool story that in reality may be much less clear‐cut than reported. Furthermore, classic scientific publications often impress by a conceptual clarity and in many cases simplicity and thereby offer valuable lessons of how best science should be practiced. These are qualities I miss in many of today's papers that contain exceedingly large data sets …