image of lab notebook with graphs

Whether we like it or not, we, as scientists, are judged based on our communication skills. Tenure committees, academic hiring teams, award panels: they all pour over our papers, scoring us on both the number of papers and their impact. Often, most of the people on these committees will be outside our own areas and will be assessing the impact, not by the scientific advances that we made, but rather through unreliable proxies such as citations, h-index and other statistics. In turn, the number of times a paper is cited or the degree to which the discoveries reported therein are accepted, depends crucially on how well the paper or grant is written.

What makes a compelling, publishable article? A compelling, fundable proposal?

Over my academic career, I have had the opportunity to listen to several talks given by Nobel prize laureates. After watching a few of these, I started to notice a trend. These people were not  presenting data. They were neither just describing experiments, nor just showing results. They were all telling stories.

The best presenters, the ones that made the audience forget their phones and laptops, the ones who mesmerized the audience, were not even using slides that much! These extraordinary scientists started their presentation, not with some flashy PowerPoint slides, but just by talking. They shared how they became interested in something, and why that was so very important to them, and what they thought would happen, and why that was not quite as they thought it would be, and so on, and so on. Slides were only secondary, for color and support, or maybe to show you some graph from time to time, but only because that graph would be a turning point in their story (or sometimes the evil villain).

The bottom line is this: grant and manuscript reviewers tend to be forgiving on many technical details. What they are really looking for is a compelling, logical and convincing narrative that will get others reading their journal or following the news from their funding agency. What they are really looking for is a story!

How to Improve Your Science Storytelling

Randy Olson, in his book, Houston We Have a Narrative: Why Science Needs Story, argues that storytelling is the secret ingredient. He argues that the work of understanding any communication should be done by the communicator, not the reader or listener.

Olson offers some simple strategies for better communication. In the book, he describes many scientific papers and presentations as piles of facts, a narrative structure he calls, “And And And” or “AAA”.  The book also provides specific strategies to get around the “AAA” or pile-of-stuff narrative style of some presenters and scientific authors. He recommends an “And But Therefore” transformation of the narrative.

You can learn more about AAA and ABT in his 10 mins TED MED talk included below.

Note – the video features some profanity.

Bioinformatics Storytelling

As humans, we respond to stories, we remember stories. Using them– truthfully, accurately, and well– will help you as a scientist, achieve your goals.

Good life science research, including bioinformatics, is no exception. An extraordinary bioinformatics analysis will tell you the story behind your experiment. It will give you some facts that match your expectations and setup the framework of you story. But then, it must go beyond that. It should tell you in a coherent, logical sequence what is happening as far as the underlying biological phenomena are concerned. Like a good mystery novel, it should piece together small facts and events and take you to the sometimes unexpected, but ultimately logical, conclusion about what is happening.

Consider the following examples, which start out the same, and diverge from there:

We wanted to better understand [this phenomenon] so we did an experiment.

  • AND Our bioinformatics analysis found that [list of pathways] were important.
  • AND we found that [list of biological processes] are implicated.
  • AND we found that [list of genes is involved].

We wanted to better understand [this phenomenon] so we did an experiment.

  • AND our bioinformatics analysis found that [pathway Y/ gene Z] were important, which we expected.
  • BUT then we also found that [pathway P/ gene Q] are implicated, which points to a mechanism we had not predicted.
  • THEREFORE, we can now hypothesize that [process X is induced when genes R and Z act through pathways P and Y].

With the right tools to help you make sense of your bioinformatics results, telling a compelling story with a beginning, middle, and end will be almost automatic.

In the next post in this series, I write about how not to do science storytelling.


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About the Author: Sorin Draghici

Dr. Draghici is a Professor in the Department of Computer Science, and the head of the Intelligent Systems and Bioinformatics Laboratory at Wayne State University. He also holds a joint appointment in the Department Obstetrics and Gynecology and is an Associate Dean in Wayne State University's College of Engineering. Dr. Draghici is a senior member of IEEE, and an editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Protocols in Bioinformatics, Discoveries Journals, Journal of Biomedicine and Biotechnology, and International Journal of Functional Informatics and Personalized Medicine. His publications include two books (”Data Analysis Tools for DNA Microarrays and Statistics” and ”Data Analysis for Microarrays using R”), 8 book chapters, and over 150 peer-reviewed journal and conference publications which gathered over 12,000 citations to date.