Category Archives: Mentoring

Emotion and story telling in a scientific talk?

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During a recent group meeting, one of the student was making a comment regarding the document really bad powerpoint by Seth Godin. Her point was how can one impart emotion to a scientific talk. Clearly, when you have 7 or 8 minutes to get to the point it could indeed be difficult to make time for humor…

Yet emotion can still be generated in term of response of the audience to your data, figure or conclusion: raising eyebrows, smiles, figures looking at you making yes (or no) motions. Of course in longer presentation, these could be much more involving.

Here is an example of a great presentation of data by Hans Rosling. He used this technique numerous times but you will get the idea: Hans Rosling’s new insights on poverty | Video on TED.com.

Now, story telling is of course at the heart of what you should be doing. It is sometimes easier said than done (sometimes it works and others it don’t, unfortunately). Again, here is a great link to a talk on story telling and getting the message across on TED: Nancy Duarte: The secret structure of great talks | Video on TED.com.

Hope these inspire you.

Don’t forget the basic stuff!

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This semester I thought an undergraduate laboratory to last year engineering students. These will be entering the job market (or pursue graduate studies) in a few days. I was really surprised going over the lab reports how very basic things are omitted. Things like error analysis or displaying error bars on figures. How can one make any valid statement on the results or general behavior of data without them?

While the final format is not unique a few elements are quite common

  • Your report will likely contain an introduction, review of the theory, method and materials with measurement set-up, results presented and discussed and a conclusion.
  • If a calibration of an instrument is involved, shows the graph, give the mathematical relationship with the fit parameters and the quality of the fit (R2 at the very least). Do not forget the error bars.
  • Make your figures readable! The experimental measurements should be represented by symbols while any fits should be represented by lines. Please avoid Excel. It can make acceptable figures if you work hard on them but specialized software (even GNUplot) are way better.
  • Do not underestimate the power of well design tables to present a resume of multiple measurements. Going over 10 or more pages of lab report to be able to sum your results is not a good way to get extra points. Furthermore, it is likely that this exercise will lead you to make a better discussion of (or find obvious mistakes in) your data and analyses.
  • To present (or unearth) trend in data, nothing beat graphical representations: do figures as often as you deem necessary and decide afterward to include them or not in the report. Again you might uncover interesting things in your data, which you have not thought of.
  • If data are expected to be compared to a theoretical model, display the results of that model along with the data (see previous comment!). If the model can be fitted to the data, again give the fit parameters and the quality of the fit. If the fit parameters are related to physical values, they also need to be discussed.
  • If data are to be compared to expected values or a theoretical model, your discussion should be quantitative ( not qualitative). Is the match perfect (within the error of x%)? If deviation are important, gives the deviation either in the form of absolute or relative differences or even replotting the data relative to the expected reference values or theoretical model. In any case, you are expected to make your discussion quantitative be default.
  • Any deviation or unexpected behavior in the data must be discussed (even if you do not know the reason for a deviation)

While the above might seems a lot of work, it will in fact save you work when it is time to write your discussion, not mentioning that you should see an immediate improvement of your grades.

Which type is your thesis advisor?

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In a discussion with a postdoc of mine, she was telling me that she observed that advisors are of three types: opener, middle cruncher and closer… I am not sure this is the whole story but there is something to it

Opener corresponds to those individuals that the exceptionally good in defining projects in details, breaking it in smaller parts, imparting a vision to it and setting an achievable target for success. They are also especially good in getting all that is needed to get the project going.

Middle “crunchers” are highly efficient to step in resolving issues, pointing out important elements during the realization and getting new ideas along the way. They can turn around a project that appears to be failing and make them winner. At the same time, a number of individuals in that category tends to jump from project to project without always finishing the previous one: once they understand what’s going on, they get bored and move to a new “problem”.

Closers are particularly good in identifying key moments in project where enough have been done and, for example, a pause should be taken to write a paper and your thesis. They will guide you to destination and make sure everything is perfect.

I found out that getting ideas for projects is usually an easy part for most peoples. However, it does take more to be successful as a researcher. You have to be able to funnel those ideas to actual projects that are executed and in the end published in a form that is accepted in your field. A good mix of the above categories is essential. We all know or have encountered peoples for which one or more of the above is lacking… they usually experience difficulties in being independent researchers.

Have a critical look at how you are conducting your research activities and try to find out which one of the three types need your attention right now. Repeat once in a while. See how improving your weakest “side” help you get better overall!

Which types are you? What type is your thesis advisor?

Recommended book: Organizing Creativity by Daniel Wessel

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The majority of graduate students that I had the chance to supervised (yes I consider student supervision as a core mission of being a researcher and university professor) have this spark in their eyes. Creative thinking is usually not the biggest problem they will face. In fact, for most of you getting tons of ideas is rarely the issue. Selecting, focusing and successfully bring one or a group of ideas to completion (meaning scientific publication most of the time) tends to be critical issue. As Vince Lombardi once said: “if you don’t keep score its just practice”.

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