Writing a Science or Engineering Paper: It is just a story

  • Published on

  • View

  • Download

Embed Size (px)


Writing a Science or Engineering Paper: It is just a story. Frank Shipman Department 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. - PowerPoint PPT Presentation


  • Writing a Science or Engineering Paper:It is just a storyFrank ShipmanDepartment of Computer ScienceTexas A&M University

  • Scientific Writing as StorytellingWhat 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 ResultsDifferent fields use different (primary) methods for generating and evaluating the validity of results.Proofs in mathematicsStatistics in psychologyGrounded observation in anthropologyPrecise 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 TermStorytelling 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 StoryHave a protagonista user trying to accomplish something, something your audience cares aboutin some cases the protagonist is implicitExamplesthe 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 StoryHave a villainthe problem that threatens to keep the protagonist from accomplishing their goalsthe problem should be real in order to keep your readers attentionExamplesan insurmountable amount of informationan unpredictable communication channela limited amount of human attention, etc.

  • Telling a Good Scientific StoryHave a plotan approach for the protagonist to win out over the villain (solving the problem)this is the hypothesis and contributionit can be very focused or very bigExamplesan algorithm for dealing with more dataa new flight-control system for pilots

  • Telling a Good Scientific StoryHave a full and rich backdropstories must happen in believable settings consistency is a muststories are rarely simple, there are other stories that interact with the main oneExamplesRelated work and prior resultsDetails of the settingInteractions with other systems and solutions that the protagonist may be using

  • Telling a Good Scientific StoryHave a strong finalehave an answer about the outcome of the story (is the protagonists problem solved?)good stories do not always have happy endingsExamplesThe algorithm locates (or not) information that lets the decision be madeThe system makes (or not) the persons task more efficient, more accurate, or more satisfying.

  • The (Idealized) OutlineIntroduction and Problem StatementThe protagonist and antagonistApproachThe plotRelated and prior work, design and implementationThe settingEvaluation results and interpretationThe finale

  • Common Mistakes:The Vision StatementSpends 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 ReportDescribes 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 resultsDescribing early versions in the iterative design process when not providing insight

  • Common Mistakes:The Data DumpPresents lots of results but leaves out which are important, what they mean, and the context of the data gatheringExample:When presenting statistical data, showing that the result is significant (e.g. p
  • Common Mistakes:The Sales PitchPresents the work done as close to perfect, claiming to have achieved all goals set out in the vision.Examples:Being overly critical of related workSelective presentation of data/resultsInterpretation that focuses exclusively on the positive

  • FinaleWhen writing research papers, dont 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 FinaleComputer 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.



View more >