Writing a Science or Engineering Paper:

It is just a story

Frank ShipmanDepartment of Computer Science

Texas A&M University

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?

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

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

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.

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.)

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.

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

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 oneExamples

– Related work and prior results– Details of the setting– Interactions with other systems and solutions that

the protagonist may be using

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.

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

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.

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

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.

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

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.

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.