As my research has been focused exclusively in neuroscience, this entry will assume that every relationship with science, classes, lectures, laboratory and other things are embeded in the neuroscience field.
Soft Skills for the not-so-soft Scientific World
Considering the incredible ammount of abbilities and techniques that a scientist must adquire and master in order to get its degree, the time spent in the experiments, meetings, paper reading and writting, struggling with data,
crying desperately for your mom and having a life, the chances that you get time to do all of the above is nearly zero.
Of course, you try to prioritize your activities. One of the first items to be left behind is learning new things outside your main research topics. Ok, we all know some genious guy or gal who does master ALL. Just ALL of the things you wanted to do, learn, study, hack, but for us, mortals trying to cope with life, dropping one or two (ok, make it 10) things is normal, and even desirable, but maybe not very convenient for your further development.
Additionaly, it has become a trend that after the Ph.D., the scientists start to look jobs outside the academia as well (check for example this article on Nature).
So, the question rises from the obvious: What can I do to improve myself and learn new tools and techniques, but without wasting one semester and/or leave my work aside?
Summer School FTW!
The Summer Schools try, at some point, to fill that gap. They are a quite intensive experience, usually 1 month, where you not only will learn new techniques, but also have the opportunity to get along with your classmates and create strong bonds with them, develop current or future collaboration partnerships and develop your soft skills. But, how does the school accomplish this?
Most of the schools try to mix a lot of hands-on experience with theoretical lectures, where the difference among them is their main topic. So for example we have Experimental Summer Schools, Summer program in Excelence, Ethics and Survival, like the one from Marine Biological Laboratory, Summer Schools in computational techniques or Summer school in specific topics.
For example, the Transylvanian Experimental Neuroscience Summer School (TENSS) is an experimental school. Their teaching style is to put you and your classmates in an empty lab, where you have to build the equipments that you need from the scratch, with the help of your classmates and teachers. (I mean, they provide you microscopes and the big gear, but you must connect, calibrate and everything). This building process is followed by a massive ammount of theoretical lectures.
On the other hand, the Summer program in Excelence, Ethics and Survival focuses on teaching not only the science, but also gives you a lot (really, A LOT!) of techniques in developing your career as a scientist. To do that, they put a lot of talks and lectures in topics like research ethics, life/works balance, reviewing papers, funding and other not-so-scientific-but-freaking-helpful topics.
Computational tools for the XXI Century Scientist
You may think that, because I am a computational neuroscientist I would like everyone to join my research (which is, half true) and that I am biased towards this kind of scientific field. Well, to be honest, it is not only my point of view. In fact, this kind of discussion has been rising the last few years, and it is becoming one of the key skills that a scientist must master, or at least, know its existance and be able to do some basic stuffs. Big Data, data science and computational tools are common words in the scientific community and we are just getting started in this data-driven world.
One of the best examples that shows the relevancy of this topic is the creation of Software-Carpentry. As the state in their webpage “Since 1998, Software Carpentry has been teaching researchers in science, engineering, medicine, and related disciplines the computing skills they need to get more done in less time and with less pain.” You can see more of their work and related readings here. Also, there is a nice paper from Greg Wilson that is worth reading.
Computational Summer Schools: your best(?) option
This particular kind of school, like LACONEU - Latin American Summer School in Computational Neuroscience) or LASCON - Latin American School in Computational Neuroscience focus on the teaching of techniques, tools and research topics in Computational Neuroscience.
Their style is to mix hands-on classes that put in practice the lessons taught in the theoretical lectures.
For me, this kind of schools should be compulsory for every neuroscientist. Why? Because you will not only learn some fancy math, and go through coding hell, but also it will teach you really good and usefull tools, like keeping notes and lab’s notebook (check out the Lab Notebook created by Carl Boettiger), Control Version System (CVS) or fast prototyping your data analysis using python.
I guess that the need for mastering computational tools will be the next differentiating step in the scientific career, so it is better to get ready. As Jeffrey Leek says in his book How to be a Modern Scientist “The face of academia is changing. It is no longer sufficient to just publish or perish. We are now in an era where Twitter, Github, Figshare, and Alt Metrics are regular parts of the scientific workflow.”
Song of the day: Dream Theater - The Great Debate
- Callaway, E. (2014). Life outside the lab: The ones who got away. Nature, 513(7516), 20.
- Haddock, S. H. D., & Dunn, C. W. (2011). Practical computing for biologists (No. 57: 004 HAD). Sunderland, MA: Sinauer Associates.
- Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., … & Pollmann, S. (2015). PyMVPA: a unifying approach to the analysis of neuroscientific data. Python in Neuroscience, 157.
- Muller, E., Bednar, J. A., Diesmann, M., Gewaltig, M. O., Hines, M., & Davison, A. P. (2015). Python in neuroscience. Frontiers in neuroinformatics, 9.
- Sejnowski, T. J., Churchland, P. S., & Movshon, J. A. (2014). Putting big data to good use in neuroscience. Nature neuroscience, 17(11), 1440-1441.
- Voit, E. O. (2000). Computational analysis of biochemical systems: a practical guide for biochemists and molecular biologists. Cambridge University Press.
- Wilson, G., Aruliah, D. A., Brown, C. T., Hong, N. P. C., Davis, M., Guy, R. T., … & Waugh, B. (2014). Best practices for scientific computing. PLoS Biol, 12(1), e1001745.