In this episode I explain how to read a scientific paper for beginners! It gives some basic information on the mindset you should have when approaching research papers. Going through the different parts of the typical “IMRaD” article, I provide questions the readers should ask themselves. I then give a brief intro in literature search, and how an expert in the field reads more efficient by jumping directly to the crucial parts of the paper that provide novelty.
One of my pet peeves with academia is the treatment of PhD students and postdocs which I feel borders(?) on exploitation. I talked with Maria Pinto, who is from Portugal and is currently PhD student in Austria in marine microbiology for my podcast “Science for Societal Progress”. Looking forward to her final year as a PhD student she is beginning to think more and more seriously about what a career in academia would mean to her.
I am running a podcast for Science for Progress, and there I am talking about how to improve academia, and how science/academia interacts with society. I interview people who are, for example, working on better metrics for research(er) evaluation, people who work on topics relevant to society (such as GMO food, and animal welfare), or someone who can tell us a bit about Science Communication, or the PhD experience.
Interested? Well, you should subscribe to the podcast, then!
When you are going to be interviewed for a podcast, you should remember that the episode may be downloaded hundreds or thousands of times. And you don’t want your listeners to turn it off after a few minutes because your sound is awful.
Sure, the podcaster can do a couple of things in post-production to rescue a bad recording. But you want to make sure that you did everything you could (in that moment) to help improve the quality of your sound.
Currently, every scientist with a salary above ca. $24k/yr is exempt from over time pay in the USA – and over time here means everything over 40hrs in a week. Obama now thinks about changing this threshold to ca $50k/yr. This is big, because postdocs make only about $43k/yr on average. So if this comes, and if postdocs become eligible for over time pay, PIs will need deep pockets, because postdocs often routinely work 60-80 hour weeks. In this scenario they probably would increase the minimum salary for postdocs above that $51k level, which would be an enormous jump for most postdocs. Now, while I am confident funding agencies and leading academics will lobby against it, and not all postdocs are officially employed, and I’m rather on the pessimistic side of things, I still think one should at least have spoken up. Otherwise I wouldn’t be eligible for wining, later, right? 😉
So, today I wrote the following comment and made it official by posting it on http://www.regulations.gov/#!docketDetail;D=WHD-2015-0001 – which I think is something every US grad student and postdoc should do, too.
There was a discussion about postdocs and if and how they would benefit from Obamas plans to increase the salary threshold below which overtime must be payed to a number considerably higher than the NIH average postdoc salary… or… fellowship. I won’t get into details why, but there were different types of postdoctoral positions thrown around for different reasons. I think I made things a bit chaotic because some assumptions about what would be a postdoc position really annoyed me.
I have often been curious about where neuroscientists come from in an academic career / interests sense. Meaning: what kind of interests did they first start with, when they were young and innocent ;). So, after someone on Twitter brought this topic up again, I made a quick survey using SurveyMonkey and posted it on Twitter and Facebook. Here I want to share the results.
So far so good. It appears that basically there is little discussion about whether neurons compute things. However, a new conversation between me and @mnxmnkmnd came up as I realized that for him it appears to be close to irrelevant whether you call a neuron a computer or not and you don’t learn more from emulating the computational function of a neuron than from simulating the thing the same way you would from simulating fluid dynamics or a pendulum.
So, here is my new attempt to explain, why I think it is important and meaningful to think of neurons as computing agents rather than simply physical phenomena.