In ‘Men, women and ghosts in science’ (PLoS Biology 2006; 4, 13-15) you tackle the notion of men and women in science from a biological viewpoint. You say there are men and women, male brains and female brains, but that the actual characteristics underlying what we would identify as masculine qualities and feminine qualities can be fused in men and women in different proportions. Then you argue that the scientific system has been pushed over towards a very masculine, aggressive stance, where we’re encouraging people who are insensitive to others and aggressive. In fact, they’re nasty! Not only has this led to fewer women higher up the system but it’s actually making life very unpleasant for people lower down the system – students and postdocs – especially if they’re gentle people.
Lawrence: Yes, you put it very well. Essentially, it could be argued that you should encourage competitiveness if you have the view that creativity goes handin-hand with it. But there doesn’t seem to be much evidence of that. Look at people in the Arts or musicians. I don’t get the impression that many of the best need to be very aggressive. Creativity is not confined to science. My hypothesis is that creativity is fairly well distributed among individuals in a very unpredictable and variable way. I think that we should have a system where we select for what we want. And what we want is people who make discoveries. In my opinion, science is not like some kind of an army, with a large number of people who make the main steps forward together. You need to have individually creative people who are making breakthroughs – who make things different. But how do you find those people? I don’t think you want to have a situation in which only those who are competitive and tough can get to the top, and those who are reflective and retiring would be cast aside. I’ve been in research for so long now. I’ve talked to so many young people. I get to know them personally because I work on the bench myself. And I hear all the time that people get put off from continuing in science. Not because they’re unable but because they just don’t like it. Those people are often women but there are also many ‘gentle’ men who don’t like it.
What we’re doing is telling people to be tough, to be pushy, to give self-congratulatory talks, to be confident. While those characteristics may be of value in certain walks of life, for example, if you want to be a soldier, they may not be what we want in scientists. I’m not saying it should be forbidden in science but I think there should be more room for people who have more gentle aspirations, who are more social, who understand other people better. In that article I went over some thorny ground, which is constantly being debated, but it seems obvious to me that men and women are, ON AVERAGE (he emphasises), fundamentally, genetically and psychologically, distinct. Of course, there is a tremendous overlap between the sexes and stereotyping of individuals by their gender is neither objective nor correct. So, I think we need to think again about how we select people. This brings us back to the same old problem – people who get their names on other people’s papers, who annex credit from their students and get rewarded. These people are very often men, although there can be very tough, competitive women scientists as well. But the idea that politically correct people have, that all professions will one day have equal numbers of men and women is not only wrong, it’s silly. There’s no reason to aspire to that aim. Individuals should do the kind of work they enjoy doing, that they’re good at. And this can lead to different proportions of men and women in the arts and sciences. How the gender numbers work out doesn’t really matter if we can have a society organised in such a way as to take advantage of “the qualities of people”.
Well, there are a lot of cases even if you use the same parameters, but here is one I faced.
Here I am, trying to plot the heatmap of my data using the standart heatmap function:
and then, with exactly the same parameters I run the gplots function:
scalecharacter indicating if the values should be centered and scaled in either the row direction or the column direction, or none. The default is
I’ve noticed that it actually defaults to “row” anyways. And wrong scaling can change your heatmap completely! So beware to scale by the unique experiments(on the rows in my case) to make the result more appearing. and don’t forget to add the scale parameter to the gplots function:
*I’ve skipped the dendrograms and rows and columns names since in this example it’s only the image that matters
**The heatmap.2 plot is going to look a bit different after all - because of the different dimensions of the plot and modifications of the color range.
Short video series explaining why and how we are not free and how the modern economical system works
Writing the paper with the 1st year PhD student :)