1. Writing a Science or Engineering Paper: It is just a story Frank Shipman Department of Computer Science Texas A&M University

2. Scientific Writing as Storytelling What is the goal of science / engineering? –To answer questions of what, where, when how, and who. –To convey these answers to others. But how do we convince others of our results?

3. Convincing Results Different fields use different (primary) methods for generating and evaluating the validity of results. –Proofs in mathematics –Statistics in psychology –Grounded observation in anthropology –Precise argument in the humanities

4. But it all comes down to … Why do we care about the proof? Why do we believe the interpretation of the statistics or observations? Why do we believe the humanities argument? Storytelling

5. Not a Derogatory Term Storytelling frequently is used as a derogatory term indicating the presentation of untruths. But in the end it is the story that you tell about the proof, the statistics, the observations, or the argument that will make your results convincing.

6. Telling a Good Scientific Story Have a protagonist –a user trying to accomplish something, something your audience cares about –in some cases the protagonist is implicit Examples –the person using the network or computer to make decisions (scheduling deliveries, deciding on investments) –the person performing a task with computer support (landing a broken airplane, teaching a class, etc.)

7. Telling a Good Scientific Story Have a villain –the problem that threatens to keep the protagonist from accomplishing their goals –the problem should be real in order to keep your reader’s attention Examples –an insurmountable amount of information –an unpredictable communication channel –a limited amount of human attention, etc.

8. Telling a Good Scientific Story Have a plot –an approach for the protagonist to win out over the villain (solving the problem) –this is the hypothesis and contribution –it can be very focused or very big Examples –an algorithm for dealing with more data –a new flight-control system for pilots

9. Telling a Good Scientific Story Have a full and rich backdrop –stories must happen in “believable” settings – consistency is a must –stories are rarely simple, there are other stories that interact with the main one Examples –Related work and prior results –Details of the setting –Interactions with other systems and solutions that the protagonist may be using

10. Telling a Good Scientific Story Have a strong finale –have an answer about the outcome of the story (is the protagonist’s problem solved?) –good stories do not always have happy endings Examples –The algorithm locates (or not) information that lets the decision be made –The system makes (or not) the person’s task more efficient, more accurate, or more satisfying.

11. The (Idealized) Outline Introduction and Problem Statement –The protagonist and antagonist Approach –The plot Related and prior work, design and implementation –The setting Evaluation results and interpretation –The finale

12. Common Mistakes: The Vision Statement Spends most of the time describing the goals of a project but lacks related work, instantiation details, on interpretation of results. Example: –Presentations that start with high-level problems that are only partially related to the work done.

13. Common Mistakes: The Activity Report Describes what was done but not why, what was learned, and does not differentiate between what is important and what is not. Examples: –Going into detail about the libraries used when they play no role in the results –Describing early versions in the iterative design process when not providing insight

14. Common Mistakes: The Data Dump Presents lots of results but leaves out which are important, what they mean, and the context of the data gathering Example: –When presenting statistical data, showing that the result is significant (e.g. p<.05 or whatever level of confidence is desired) but not relating this result back to the main problem.

15. Common Mistakes: The Sales Pitch Presents the work done as close to perfect, claiming to have achieved all goals set out in the vision. Examples: –Being overly critical of related work –Selective presentation of data/results –Interpretation that focuses exclusively on the positive

16. Finale When writing research papers, don’t just describe what you did. Describe why you did it. Describe how it compared to other options. Describe lessons learned grounded in what did and did not work.

17. My Finale Computer science is a new field, relative to other disciplines like physics, that answers a variety of questions: –What can be computed using what resources? –What problems can be solved using computers? To answer these questions, methods are borrowed from a number of disciplines. It needs researchers that can author and recognize good stories regardless of the particular methodology.