What I have learned about science, scientists & other such things

Before I transitioned to science, I had some widely shared (bad) assumptions about what academic science and scientists were like. Having now spent just over a year working in the space, the below is my more informed view on the subject, specifically within the world of academia and biomedical research. Emphasis on academia and biomedical research.

TL;DR:

1. There is such a thing as good, great and bad scientists.

2. All text in a paper is opinion.

3. You can not just “read” a paper.

4. Some scientists are in the business of simply selling to other scientists.

5. Science has market risk too.

6. Scientists can get trapped by the dogma that technical novelty is all that matters.

7. Scientists can be extremely welcoming.

8. Most scientists do not just “sit in an ivory tower”.

9. Scientists can be influenced by hype cycles too.

10. Scientists are not “truth purists”

11. Most scientists do not intentionally publish things that are not reproducible.

12. Not all scientists are curious.

13. Questions can be weaponised.

14. Most scientists don’t think about distribution.

15. Most scientists are great self learners.

• There is such a thing as good, great and bad scientists. I used to think that science was purely driven by luck and that people had no control over what they “discovered”. This is categorically false. The direction chosen by a scientist, coupled with their process of “science” has a lot more influence than luck in my opinion, and is often the driver of said luck. Take for example the task of pipetting small volumes of antibody across a 96 well plate. A bad scientist might try to pipette this small volume individually across all 96 wells, and in doing so will most likely fall into the trap of forgetting which well they have pipetted, having an uneven amount of antibody in each well and now unable to reproduce their results. A great scientist will probably make a master mix of the antibody and use a multichannel pipette to ensure even distribution of the antibody, and easy reproduction of the results. Tiny decisions like these add up and is what makes the difference between a bad scientist and a great one.

• All text in a paper is opinion. This blew my mind! Before transitioning to science, like other people, I was under the illusion that if something was written up in a paper it was near enough fact. In actuality, the text is simply the authors interpretation of their data that is also not fact, but most times just a suggestion with some quantitative backing. For example, an author could say “When we added X reagent, there was a great improvement in Y, which leads us to believe X is essential for Y”. But if you look deeper into the figures you’ll notice that the change between X and Y was actually less than 1% and they never even tested if there is a change in Y regardless of what was added. Very odd.

• You can not just “read” a paper. A continuation of the above point, knowing that all text in a paper is opinion, including to a larger extent the data provided, the idea of just “reading” a paper is kind of impossible. It often takes hours to really go through a paper, dissect the figures, understand if the methodology supports the outcome, figure out what information is missing etc. After realising this, I came to the conclusion that the “paper” format is not really suited for the purpose of sharing scientific information, or at least the way we write it up.

• Some scientists are in the business of simply selling to other scientists. This was an interesting observation. As a bit of background, I transitioned to science because I intend on creating a therapeutic that prevents kidney failure. Naturally I became aware of a number of scientists working in this space. What I did not expect to find is that a lot of scientists, specifically in the biomedical field, are not actually serious about bringing things to the clinic. What they are very serious about is getting the respect of their peers in the field, selling the importance of their work and having other scientists build on top of their work. But this does not translate to advancement in the clinics, some know this and some just don’t care. In a way, it is a very weird sort of Ponzi scheme. Researcher does work > sells it as an advancement to clinics > raises money >  researchers build on top of their work > selsl it as an advancement to clinics > raises money > researchers build on top of their work etc . What you end up with is a bunch of research papers and no real life work done. 

• Science has market risk too. There is this really false perception that science has no market risk, particularly biomedical science. This just isn’t true. Take my scientific interest - preventing kidney failure - for example. Someone creates a therapeutic that permanently halts minimal scarring from becoming severe scarring in the kidney. Naturally they target people with early stage kidney disease, a very large group of people with minimal scarring. The problem is that early stage kidney disease has no symptoms. Most people present with kidney disease when they are moderate to severe, meaning that doctors will never even have a chance to prescribe the therapeutic, no matter how effective it may be. Putting this particular kind of market risk aside, there is also the very real market risk of what doctors are even willing to prescribe. A lot of factors go into this decision such as cost, likely adherence to medication, risk of adverse events, hospital protocols etc And this can all change from country to country. All this is to say that market risk is very real, it just looks different in science. 

• Scientists can get trapped by the dogma that technical novelty is all that matters. This is something that has been slowly beaten out of software engineers, but has not yet spread to scientists (at least in academia). There is an unspoken belief that the novelty of idea is what dictates its importance. I don’t think this is inherently bad, I just think it depends on ones goal here. If the goal is science for the sake of curiosity then maybe it’s not so bad, however, if the goal is solve or understand a specific problem then yes, it can be.

• Scientists can be extremely welcoming. One of the best things I discovered about scientists is just how welcoming they can be. When I was trying to break into science I was warned of the elitism and push back I would face from scientists given my lack of credentials, but I can honestly say I have not experienced this from anyone (and I have reached out to a lot of scientists). This isn’t to say there aren’t elements of elitism, but I find that scientists are more meritocratic and open than people give them credit for.

• Most scientists do not just “sit in an ivory tower”. Perhaps my interpretation of this statement is wrong, but I have always understood it to mean that scientists don’t do much but “write papers” and “talk”. Not true. The amount of work, often repetitive and laborious, that goes into producing papers is really something. The value of the work may be debated, but the effort and work itself shouldn’t be.

• Scientists can be influenced by hype cycles too. We are probably all aware of what a tech hype cycle is, what I wasn’t aware of is that this can be found in science too. You can find scientists pivoting their work to ride the wave of what’s trendy just like you do in software. In the software world, this is often done to raise money, but I am still not sure why this is done in the science world. Perhaps to get published? I don’t know, but I know these hype cycles exist and express themselves in the same format e.g lots of buzzwords.

• Scientists are not “truth purists”. This was a very disappointing observation. I used to think that all scientists pursued truth and only really spoke truth. But like all things, the fault was on my part in forgetting that scientists are still human. It is not odd or rare to find a scientist pursuing ideas in support of their own, regardless of truth. This is particularly true for scientists who have invested a lot of their time and credibility into a particular school of thought that is currently being challenged. The problem seems to be that their prestige/credibility is tied to the discovery and maintenance of the field they are invested in. If that goes away due to new truths discovered, then so do they to a lesser extent. So they choose self-preservation and defend their position, rather than pursue truth. 

• Most scientists do not intentionally publish things that are not reproducible. Everyone talks about the reproducibility crisis and often frames it in a way that makes scientists look incompetent at best and villainous at worst. Again, not true. There are a lot of little things when doing experiments that contribute to the likely reproducibility of an experiment, that are more than likely just overlooked. For example, I once did a particular experiment where I could not replicate the results of another scientist. It turned out that the issue was in the mixing. Although she had stated “mix well”, it was not clear the importance of mixing well, nor was it stated how to mix. It turned out that different styles of mixing led to very different results. It’s the sum of tiny details like this that make it very hard to reproduce things, not bad intentions.

• Not all scientists are curious. Some scientists have extreme tunnel vision in achieving a specific outcome. They are not curious about why things are the way they are, if they could do things better, or if all that they’re doing is even wrong. They just care about the outcome. Not necessarily a bad thing, I just didn’t know that they existed. 

• Questions can be weaponised. Prior to working in science, I loved asking questions and being asked questions. It was a sign of curiosity. Now I have learned that questions can be a tool scientists use to make themselves seem smarter, and sometimes at the expense of others. Take for example scientist A presenting results on a drug that consistently reduces blood pressure by 20% with no adverse side effects. Scientist B might rightly ask “How long is this new blood pressure maintained?” a thoughtful and relevant question, given the goal of reducing blood pressure in people. Scientist C however may ask “Does it target cell X in the Y pathway, as this is a pretty well-established target in the literature?” Scientist A may not know, to which scientist C responds “Pathway Y is pretty critical and well established, so how can we be sure it is really doing what you say it’s doing? I suggest you find this out”. This is an example of a weaponised question that props up scientist C at the expense of scientist A, whilst missing the goal completely. 

• Most scientists don’t think about distribution. Like a lot of engineers, most scientists think that all the work rests in the doing, not the selling. Specifically they think their work ends at publication. What ends up happening is a lot of work is published, with very little being translated into the real world or being picked up by other scientists. 

• Most scientists are great self learners. In the software world most people are specialists. Someone who does marketing probably only does marketing, and someone who does data science usually only does data science. For scientists they do not have the luxury of skill specialisation. Often times they are working as a team of one, so they have to do everything e.g code, write, flow cytometry, cell culture, molecular biology etc. To top it off they often have to teach themselves these skills via reading papers or just observing other people do it. It’s quite impressive really. They are like a learning machine of 10 people compressed into one person.