The rise of Large Language Models in academic writing. Postdoctoral researcher Dr Verena Haage was reviewing a manuscript for a reputable neuroscience journal when she began to notice unusual inconsistencies. Haage noticed that some figures had strange proportions, or illogical experimental timepoints, and that figures were arranged in a senseless order.

By the end of my PhD, I was somewhat disillusioned by the way scientific research worked and decided to pick a field that could open up a (small) door to industry, if I ever decided to take that route one day. (I was very naive about that, but that is a story for another time.) After some digging, I could find two labs focusing on the relatively narrow question I wanted to pursue for my postdoc.

Meet Larry, the Scientific Cat Larry — a fluffy, slightly overweight cat — has achieved what many postdocs or even professors only dream of: becoming the most cited “author” in his field. Obviously, Larry has never written a word or attended a conference. His main activities? Napping, demanding food, and scratching furnitures. Yet, there he is — the most cited author without lifting a paw.How Did We Get Here?

If doing your job requires constant applications to keep doing it, is that really a “job”?  Why do we need to keep “auditioning” for work we’re already performing? Let’s first agree that there isn’t a typical career trajectory in academia. But there are some recurring steps one usually has to go through. Getting the job There are definitely some unwritten laws about getting jobs in academia.

We all know this person. Or we are this person. You troubleshoot broken connections in the lab, fix the department printer, teach undergrads how to use the Nanodrop and pH-meter, and share all your best Excel hacks. Congratulations: you're the Swiss Army knife of your lab. But guess what?

Six years ago, during a science communication session at a conference, we were given the task to explain “why we do what we do” to people unfamiliar with our research. While having this chat with a fellow postdoc, I told him I’m curious about how the human body works, and that is why I became a scientist.

So far we have dived into the historical origins of the peer review concept and the earliest attempts at an external referee system used by the Royal Society. Even in this early exploration, we have encountered issues with peer review that persist today.  The first part of this series concluded with the beginnings of peer review, but we still hadn’t arrived at peer review as we know it in the 21st century.

Peer review is a staple of science, a seemingly inescapable barrier that every researcher must cross in order to share their work with the world. The internet is awash with complaints about Reviewer #2 and long, drawn out review processes. Despite this, most academics believe peer review to be a gold standard process that protects the scientific record from poor quality studies.

Being a researcher implies a long stretch of studies at the university to specialize in a certain field. To me personally, it felt like I just kept studying and studying, year after year, and… here I am.  One day, when I was 18 I went to the university and I am still there. I have come to realize that I never really mentally switched from being a student to being a professional or having a job.

Before starting - what is Omics Data? Omics data represent massive volumes of information on genomes, epigenomes, transcriptomes — you name it. They’ve become a favorite among researchers, reviewers, and publishers. But at what cost? I’m not even talking about the huge financial investments it takes to generate good quality data with proper replicates.

The “publish or perish” culture pushes researchers to publish papers (regardless of the state of their research) for career survival. Academic jobs are tied to people’s publication records; and although change is happening, it is at a glacial pace. Since the system is gamified in this way, people are tempted to play. And they play to “win”.  This leads to many side effects that distort the way science is done.