On why skills give the most value of all the elements in a scientist’s repertoire.

 

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This is the fourth and final post in a four-part series.  In an older post I defined “skills” as “procedures you use to channel caring into doing.”  You can view that post here.

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After I finished my first college degree I thought I wanted to be a screenwriter.

Action movies and sci-fi were just the thing to house my imagination and love of reading.  Or so I thought.

I had just moved to a new state and had no idea where to start.  And I had just finished a degree in psychology, where I studied the interplay of children’s acculturation with how they cope with school stress.

Writing Likert-type surveys taught me nothing about how to write a three-part story or how to hit the emotional plot points in a “hero’s arc.”  And I couldn’t find a screenwriting class.  (I’m old enough that back then the internet didn’t provide these things ready made in a MOOC).

So I signed up for the cheapest writing class I could find at a local university, while working three part-time jobs to make ends meet.

The class was called “Introduction to Writing Microfiction.”

Microfiction is where you write a short story in a minimal number of words, usually a few hundred words or less.  I don’t remember a darn thing about how to write good microfiction after taking that class.  But I do remember the notebook the instructor taught us to keep.

She advised us to buy a notebook we loved and a pen or pencil to match.  On the cover we wrote the number of that notebook.  For me that was “No. 1”.  We earmarked the first 2 pages for a table of contents, to fill in as the notebook filled up.  And after that we wrote anything and everything in it.

Our writing instructor had us do timed assignments in class, written in our notebook.  I took class notes, in my notebook.  Then I started scribbling physics ideas in my notebook too. (I had started out college in physics, got discouraged, ran out of money for college, and finally switched to psychology to graduate early.  But physics still echoed in my heart.)

In the early years, and through my writing degree, I called my notebooks “No. #” and numbered them in a continuous chain.

I still keep notebooks to this day.  Though they’ve gone through name changes (for a while in my Ph.D. I called them “Research Notebook #” to fit in) they are still filled up using the same basic skill set I was taught in my microfiction writing class.

When I graduated my Ph.D. program and moved on to a postdoc, I felt a little more constrained by the academic paradigm and took to calling my notebooks “Spark #”.

Then I found out about Bullet Journaling (I use a loose hybrid version of it now) and took to calling them “BuJo #”.  But I always kept the same continuous numbering.  I’m on Bujo-38 right now.

This art of writing an assorted collection of things in a notebook is a skill, which I learned that day in my first microfiction lecture.  And it was probably the wisest investment I made of anything I spent my hard earned part-time job money on during that decade.

Keeping a notebook is skill, not just an activity.  And it illustrates my point for this last part of my series on necessary components of a scientist’s repertoire (especially when you passionately want to make a discovery):

Adding skills to your repertoire gives the best return on investment of any single element of your scientist’s repertoire.

 

From a first principles perspective:

Skills are invaluable in your scientist’s repertoire because they can be put to many uses.

 

In principle, my reasoning is simple: skills are generalizable elements of one’s scientific repertoire, plus they are often low tech, easy to acquire, and they have a broad application.

I may have learned the skill of keeping an “ideas notebook” in a writing class, but I went on to use it when working in physics and in nonproliferation policy.

(I’ve also used the skill of keeping a notebook for personal projects like moving, changing jobs, and adapting my lifestyle routines.)

It’s important to distinguish between activities and skills.

Activities are things like writing certain types of content in your research notebook (maybe free writing, keeping a log of experimental trials, or keeping a list of concepts or sources to look up).

But a skill is knowing how to keep a research notebook so that it achieves a certain purpose (such as filled with mindmaps, free writes, and sketchnotes to engender creative fusion of received ideas or filled with dates, steps, prototype sketches, and outcomes for patent applications).

Skills are a nice hybrid between activities and mindset: they are where thinking meets doing, but at a higher level than just activities.  As such, skills are invaluable in a scientist’s repertoire and skills ensure that the activities you undertake are focused and well-executed.

Practice one skill (like keeping a notebook), combined with the proper resources (e.g.,  paper, pen or pencil), and you can eventually put it to a hundred uses.

 

From an applied perspective:

Adding skills to your scientist’s repertoire requires practice and feedback.

 

So, being able to acquire skills, which can then be applied to broad range of problems, is a tremendous value added to your repertoire.

I think the best way to acquire skills is through a combination of received instruction (i.e., training and feedback from a more skilled individual) and practice to gain mastery over the small elements of a skill.

Of course, that’s not always possible.  Just look at scientific discovery.  Nearly all science training programs train you in how to conduct experiments, utilize math, and run analyses.  But none actually teaches a module specifically on how to combine all those ingredients (and some other things) to actually make a scientific discovery!

In cases where you know a skill is needed, but you can’t receive instruction then I think you have to do the best you can to instruct yourself.  The most important part of being self-taught is providing yourself ways and set times to give yourself feedback.  And you always, ALWAYS, need practice or you won’t gain a skill.

For example, let’s go back to the case of the research, or ideas, notebook I’ve been talking about.  If you don’t have someone to instruct you in how to create or use one, then you might try teaching yourself.

Set up a notebook (i.e., put in a spot for a table of contents or an index to track the content you add to the notebook, add page numbers, and add a notebook title), think about a few ways to use it, pick one way to try, and then test it out (for a minimum of 7 days and a maximum of 30 days, assuming daily use).

Most importantly, provide yourself regular feedback sessions on whether or not your notebook is generating the kinds of results you want.  Ask yourself questions and assess outcomes:

Are you coming up with ideas more frequently (e.g., daily instead of monthly or weekly)?

Are more of your ideas getting tested (e.g., are you generating and running quick tables of numbers, small experiments, or bits of code  to try the ideas out)?

Are you able to remember to perform certain tasks more regularly and more diligently after tracking them in a notebook (e.g., do you systematically track changes you make to every iteration of an experiment, piece of code, website, etc. when you are fine-tuning or checking the response)?

Check in with yourself to see if things are working or if they need to be adapted or practiced more.  I think the key part is to be aware of the process and to consciously monitor your process as you practice the skill.

Don’t just focus on the outcome of a skill, process is crucial too.  Process is the piece that is generalizable, even more so than the outcome.  You want to learn how to add numbers, not just learn how to add 2+3 and 6+8.

Skills can be hard work to acquire.  As I said, skills are gained through practice—practice, practice, practice, and more practice eventually adds them to your repertoire.

Practice takes skills from “things you have to think about” to “things you do automatically” and ensures that you perform consistently, and with precision, every time you execute the skill.

But skills, once the basics are learned, can be fine-tuned, and used frequently to improve them.  Plus, you can always gain more finesse in a skill by seeking feedback from a more practiced expert.

Lastly, skills are not a one-off that you acquire when you are young, or in school, or a training program, and then don’t have to worry about anymore.  We’re striving to be  insightful scientists, not lazy scientists.  Whether we are early-, mid-, or late-career, we should be adding skills to our repertoire.  Discoveries won’t come looking for you, you go looking for them.  And you better care enough to have the skills to do it at any age and stage.

 

By analogy:

Skills are like language; you can do more with it than you can without it.

 

The analogy I think of for the role of skills in the scientist’s repertoire is the following:

Language skills let you say, orchestrate, or participate in events, beyond just grammar and vocabulary.

Learning vocabulary and grammar are language activities.  But speaking, reading, and writing are language skills that pay you back a thousand fold.  They can be put to almost any use.

Skills in the scientist’s repertoire are just the same.  The value of skills in your scientist’s repertoire cannot be overstated, only underestimated.

 

From a holistic perspective:

You can synchronize or learn from others with companion skills.

 

I’ve talked a lot in the previous three posts about making use of teams or communities, a body of people with diverse repertoires working in harmony, when I mention the holistic perspective relative to the scientist’s repertoire.

Again, it’s all true here, but with a nice added dimension.  I call them “companion skills.”

Maybe you are excellent at generating and manipulating mental ideas quickly.  But you struggle to articulate them into testable form.  Someone else might have the perfect companion skills: they are able to build mock-ups, generate quick-and-dirty code, or do back-of-the-envelope calculations in their sleep.

In fact, some discoveries have relied on this kind of companion skills team (Einstein’s work with his mathematician friend Marcel Grossman to produce general relativity is a famous example).

Working with someone who has a companion skill is a chance to move discovery along at a faster pace.

But it is also a chance to watch the skill in action and to learn by observation.

Interestingly enough, we learn better from watching the mistakes of others than from making our own mistakes.  So even if your skills companion gets it wrong, it will be a valuable opportunity to become a more skilled discoverer yourself.

Usually when we think about “skills” in science we think about math.  Or maybe statistics, or writing code, or using special high-tech hardware.

But more subtle skills are key too.  The ability to use conceptual or structural analogies is a good example.  And the ability to generate and manipulate mental models is another subtle skill.

We should take every opportunity we get to strengthen our repertoires, across all four themes (activities, mindset, knowledge, and skills).

But if there “aren’t enough hours in the day” then I think skills are the most fruitful place to start.

The more skilled you become, the more conversant you will be in the art of discovery.

 

Interesting Stuff Related to This Post

 

1. com. “Micr-O Fiction: 8 Provocative Writers Tell Us a Story in 300 Words or Less.” O, The Oprah Magazine, July 2006 issue, http://www.oprah.com/omagazine/micro-fiction-short-stories-from-famous-writers.
2. “Einstein’s Zurich Notebook.” https://www.pitt.edu/~jdnorton/Goodies/Zurich_Notebook/.
3. “Darwin Online: Darwin’s Notebooks on Geology, Transmutation, Metaphysical Enquiries and Reading Lists.” http://darwin-online.org.uk/EditorialIntroductions/vanWyhe_notebooks.html.
4. Noa Kageyama. “How Do Fear, Anxiety, and Other Negative Emotions Affect the Learning Process?,” The Bulletproof Musician Blog.  https://bulletproofmusician.com/how-do-fear-anxiety-and-other-negative-emotions-affect-the-learning-process/.

 

 

Related Content on The Insightful Scientist:

 

Blog Posts

• Elements of the Scientist’s Repertoire, Part 1 of 4: Activities – On why activities should be the largest part of a scientist’s repertoire
• Elements of the Scientist’s Repertoire, Part 2 of 4: Mindset – On why mindset is the most effortful part of a scientist’s repertoire
• Elements of the Scientist’s Repertoire, Part 3 of 4: Knowledge – On why knowledge is the easiest ingredient to add to a scientist’s repertoire
• Spring and Well: How to divide an individual’s discovery ability into four major themes

 

How To Posts

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Research Spotlight Posts

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How to cite this post in a reference list:

 

Bernadette K. Cogswell, “Elements of the Scientist’s Repertoire, Part 4 of 4: Skills – On why skills give the most value of all the elements in a scientist’s repertoire”, The Insightful Scientist Blog, December 27, 2019, https://insightfulscientist.com/blog/2019/elements-of-the-scientists-repertoire-part-4-of-4-skills.

 

[Page feature photo:  A student artfully displays books to photograph during her roadtrip. Photo by Clarissa Watson on Unsplash.]