Narrative structure

The following is a narrative structure commonly used in academic publications (Title, abstract, keywords, introduction, methods, results, discussion, conclusion).

The bullets below are GUIDELINES, not rules. Different audiences require different narrative structures. For example, a data driven “New York Times” article will have a different format than a scientific publication found in “Nature”. However, some form of these components are common to most narratives. Therefore, they can be useful for adding structure and order to your work.

Title section:

• Title, group members, institution affiliations

Abstract:

• Brief “teaser” to draw the reader in and summary of what was done
• An abstract is a 150- to 250-word paragraph that provides readers with a quick overview of your essay or report and its organization. It should express your thesis (or central idea) and your key points; it should also suggest any implications or applications of the research you discuss in the paper. The function of an abstract is to describe, not to evaluate or defend, the paper. The abstract should begin with a brief but precise statement of the problem or issue, followed by a description of the research method and design, the major findings, and the conclusions reached. [source Links to an external site.]

Keywords:

• Provides a list of keywords relevant to your work.
• Think of these as “search terms” that other researchers might google to find your paper.

Introduction:

• This section includes a summary of the topic, why it is important, why the reader should continue, what work has been done in the past by other research groups, what are the “different points of views”/interpretations in the literature, what are you exploring, what questions are you trying to address, what are your goals and hypothesis, etc
• For a data driven study, the Introduction is about the data science question and the topics you plan to explore. It helps the reader to understand what the data science question is, what the supporting topics and issues are, and what the overall research area is all about. An introduction allows the reader to “get to know” the data science question and related areas of interest. Ideally, an introduction should make the reader care about the topics and read more. The Introduction is NOT about the datasets, variables, methods or models. The Intro should not contain any information about the dataset or the data cleaning, prep, processing, etc. These things should go into the methods section. Introductions can and should include basis, background, history, the state-of-the-art, images, references, etc. An introduction will also help the reader to understand who the topics affect and why the topics matter.
• The following bullets show common components of an introduction section, they come from the following source Links to an external site..
  • Introduce your topic
    • The first job of the introduction is to tell the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening hook.The hook is a striking opening sentence that clearly conveys the relevance of your topic. Think of an interesting fact or statistic, a strong statement, a question, or a brief anecdote that will get the reader wondering about your topic.
  • Describe the background
    • Part of the introduction is a concise literature review to demonstrate that the writer is familiar with the relevant research. You can provide an overview of the most relevant research that has already been conducted. This is a sort of miniature literature review, a sketch of the current state of research into your topic, boiled down to a few paragraphs.
    • This should be informed by genuine engagement with the literature. Your search can be less extensive than in a full literature review, but a clear sense of the relevant research is crucial to inform your own work.
  • Establish your research problem
    • In an empirical research paper, try to lead into the problem on the basis of your discussion of the literature. Think in terms of these questions:
      • What research gap is your work intended to fill?
      • What limitations in previous work does it address?
      • What contribution to knowledge does it make?
  • Specify your objective(s)
    • The research question is the question you want to answer in an empirical research paper. Present your research question clearly and directly, with a minimum of discussion at this point. The rest of the paper will be taken up with discussing and investigating this question; here you just need to express it.
  • Map out your paper
    • The final part of the introduction is often dedicated to a brief overview of the rest of the paper.
    • In a paper structured using the standard scientific “introduction, methods, results, discussion” format, this isn’t always necessary. But if your paper is structured in a less predictable way, it’s important to describe the shape of it for the reader. If included, the overview should be concise, direct, and written in the present tense.

Methods:

• This section describes what you did, how you did it, and why you chose to do what you did.
• The methods section of a research paper provides the information by which a study’s validity is judged. Therefore, it requires a clear and precise description of how a study was done, and the rationale for why specific procedures were chosen. The methods section should describe what was done to answer the research question, describe how it was done, justify the study design, and explain how the results were analyzed. Scientific writing is direct and orderly. Therefore, the methods section structure should: describe the materials used in the study, explain how the materials were prepared for the study, describe the research protocol, explain how measurements were made and what calculations were performed, and state which statistical tests were done to analyze the data. Once all elements of the methods section are written, subsequent drafts should focus on how to present those elements as clearly and logically as possibly. The description of preparations, measurements, and the protocol should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. Material in each section should be organized by topic from most to least important [source Links to an external site.]

Results and discussion:

• The results section should state the findings of the research without bias or interpretation.
• What is your interpretation of the results, why is it relevant, what does it mean, why were the discoveries important or useful, what effects do the results have on the world

Conclusions:

• The goal is to summarize & wrap-up the report or paper. It explains what was found, in a way that would make sense to a general readership.
• This area is non-technical. Technical descriptions of what you did belong in the methods sections, while technical results belong in the results sections, not conclusions.
• The Conclusions should focus on key and important findings and how these findings affect real-life and real people.
• Some say that the Conclusions are the most difficult to write. If you do not understand what you really did, how can you explain it to others? Being able to make technical results and complex models use-able to normal humans (like managers, CEOs, Deans, clients, etc.) is critical in data science. The Conclusions area is important and if it is not good, many points can be lost.
• A conclusion is an important part of the paper; it provides closure for the reader while reminding the reader of the contents and importance of the paper. It accomplishes this by stepping back from the specifics in order to view the bigger picture of the document. In other words, it is reminding the reader of the main argument [source Links to an external site.]
• For most papers, it is usually a few paragraphs that simply and succinctly restates the main ideas and arguments, pulling everything together to help clarify the thesis of the paper. A conclusion does not introduce new ideas; instead, it should clarify the intent and importance of the paper. It can also suggest possible future research on the topic [source Links to an external site.]

References:

• Graduate level work should typically include linked and numbered internal citations. These references should be included at the end as a numbered citation list pointing to all textbooks and peer-reviewed articles mentioned in the work.

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