Some good tips on writing scientific papers are given in the post: http://greatresearch.org/2013/10/11/storytelling-101-writing-tips-for-academics/
The blog at http://greatresearch.org/ has several other posts of interest for PhD students. Recommended!
Source: Twenty rules for good graphics
A set of rules, worth reading or re-reading at the start of the new academic year.
The NIH (USA, National Institutes of Health) has opened a new web site on the subject, which although focused on Biomedical research, provides a good account of current trends and problems, how to overcome them and guidelines that could be easily adapted for the rest of the Biosciences including Plant Science.
Yesterday after our Biophilosophy Society session, I had an interesting chat with Matan about whether switching research subjects is good or bad.
Today, just by chance I ended watching this video from 2008. I think it nicely answers what we discussed. NOW WATCH THE VIDEO… (19 minutes long, but really worth your time)
Only after watching the video, you will understand what follows:
If you you feel that the field your are working on, does not provide enough of a challenge to get you into ‘flow’ at least now and then, then you have two options: find new challenges that you find exciting within your current discipline, or shift your interests to another discipline. Which of these routes you take, is quite irrelevant as long as you can reuse enough of your current skills in the new subject to be within your safe zone of comfort. Reusing the skills does not require the skills to be used in the same way as in the previous discipline, just that you find a way of making use of your skills even if by analogy when analysing a new problem.
I am currently participating in the “Leadership training” organized by the university, and in one recent meetings of my research group when discussing how to better work as a group, I emphasized that for every member of the group their work should be fun. This idea is formalized, and backed with data in the talk in the video I embedded above. So, if you skipped it, scroll up the page and watch it!
Link to the original Q&A thread at ResearchGate
This is another topic worthwhile looking at, and especially thinking about. I copy here, my answer, that is to some extent off-topic (you will need to follow the link above to read the original post and other answers):
Frequently students that I have supervised, seem to think that statistical tests come first, rather than being a source of guidance on how far we can stretch the inferences that we can make by “looking at the data” and derived summaries. They just describe effects as statistically significant or not. This results in very boring “results” sections lacking the information that the reader wants to know. When I read a paper I want to know the direction and size of an effect, what patterns are present in the data, and if there is a test, then statistical tests should help us decide what amount of precaution we need to use until additional evidence becomes available. Many students and experienced researchers which “worship” p-values and the use of strict risk levels ignore how powerful and important is the careful design of experiments, and how the frequently seen use of “approximate” randomization procedures or the approach of repeating an experiment until the results become significant invalidate the p-values they report.
[edited 5 min later] As I read again what I wrote it feels off-topic, but what I am trying to say is that not only the proliferation of p-values and especially the use fixed risk levels, but also many times how results are presented, is the reflection of a much bigger problem: statistics being taught as a mechanical and exact science based on clear and fixed rules. Oversimplifying the subtleties and degree of subjectivity involved in any data analysis, especially in relation to what assumptions are reasonable or not, and how any experimental protocol relates to which assumptions are tenable or not, is simply not teaching what would be the most useful training for anybody doing experimental research. So, in my opinion, yes we need to understand much more than basic statistics in terms of principles, but this does not mean that we need to know advanced statistical procedures unless we use them or assess work that uses them.
Daniel Kahneman (2012) Thinking, Fast and Slow. Penguin Books, London.ISBN 978-0-141-03357-0.
I am currently reading this book. I am finding it extremely interesting. Understanding how and why we make choices, is important for everybody. If you are a scientist or aspire to be one in the future, understanding why we accept more readily some experimental results than others, why we are more comfortable with some hypotheses than others, is of fundamental importance, both to guard against bias, and to be able to present our new ideas in a way that will make them more acceptable. Continue reading “Thinking, Fast and Slow”