Week 2.5: Data Analysis Theory

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Week 2.5: Data Analysis Theory#

One core piece of science is collecting data (see last week) and then analysing it. This week we’ll focus on considerations in analysing. It’s outside the scope of this minor to teach statistics and bioinformatics. This week will focus on some of the big picture—what considerations there are in deciding how to analyse data, and what to include when analysing your data.

The workshop will start the writing process for your paper.

Components this week are:

Monday: - Workshop: Data Analysis Theory - Science Spotlights

Wednesday: - Workshop: Writing an Abstract

Friday: - Friday Symposium

Workshop: Data Analysis Theory#

A core piece of science is collecting data (see last week) and then analysing it. This session we’ll focus on considerations in presenting those results. It’s outside the scope of this minor to teach statistics and bioinformatics. This week will focus on some of the big picture—what considerations there are in deciding how to analyse data, and what to include when analysing your data.

What to consider when analysing data to make visualizations. Different kinds of analysis and what they can show or hide. Also useful to think about this when reading scientific papers:, do you know why the writers chose the analysis they did?, and the visualization they did?.

Understanding the role analysis plays in our understanding of data

After careful data analysis, it is often as important to effectively communicate the results of the analysis. However, there can be many ways to visualize the results of your research, so what representation do you choose to best tell your story? This workshop provides the guidelines and tools for effective data visualization in an academic setting. We will discuss the most important principles of academic data visualization and get some hands-on practice with choosing different data representations, styling the figure to best tell your story, and recognizing misleading elements.

Key Concepts#

  • Write an analysis plan before you start analysing your data.

  • Think about alternate methods of analysing data

  • Cleaning data-what and when and keeping original data.

  • Guidelines for academic data visualization

  • How to pick the right representation for my data to tell my story

  • How to use style and color to highlight important features in data

Relevant Learning Goals#

  • Communicating data clearly

  • Choosing the most effective data representation

  • Using effective style for their visualizations

  • Recognize and avoid bad or misleading practices in representing data

Workshop: Writing an Abstract#

You’ve now read many abstracts and it’s time to write one. In this workshop you’ll be guided through writing one. The abstract is an important part of a research paper for both readers and writers. Readers use abstracts to be informed about the aim and main (expected) conclusions of the study, and to decide to continue reading the paper. Writers can use abstracts at an earlier stage of the writing process to determine the key elements of the paper that can guide the writing process, and often return to the abstract at the end of the writing process to make an informative summary for readers. This workshop focuses on the key elements of abstracts and on strategies for writing abstracts. We will analyse and evaluate examples of abstracts. In the workshop, individuals will each write an abstract, and later merge them into a group abstract. Note that the abstract may still change after this workshop – it is a living part of the paper that is subject to change until the final version of the paper is handed in.

Key Concepts#

Structure and Content: Learn how to structure an abstract, including key sections such as objectives, methods, results, and conclusions.

Clarity and Brevity: Master the art of concise writing to capture the essence of your research while maintaining clarity.

Impact: Understand how an engaging abstract can draw readers into your work and enhance the visibility of your research

Relevant Learning Goals#

  • Recognize the importance of a well-crafted abstract in scientific research.

  • Structure an abstract effectively, focusing on essential components.

  • Write an engaging abstract that concisely conveys the significance and findings of your research.

Group Activity of the Week#

Continue with research. Write your data analysis methods section which is the same as writing your plan. Look at how they have been written in the various papers you’ve read.

Discussion Questions#

  • What are ethical considerations in data analysis?

  • How can data be misrepresented by the type of visual used?

  • Why does making an analysis plan before you start collecting data matter?

  • At this stage, to what extent can you determine key elements in your research project that should be included in an abstract?

  • As you progress in your project, do you find it’s changing?

  • How are you planning on analyzing your data? What other ways could you do it?

  • What questions do you have about the other projects?

  • Have you figured out something that might help other groups?

  • Do you have a question that other groups could help you with?

Weekly Submitted Assignments#

Group#

Write an outline of the data analysis method section of your paper. How will you analyse your data?

Individual#

Submit your individual abstract from the workshop (you’ll merge your abstracts later with your group).

References#

Silyn-Roberts, H. (2013). Chapter 3: An Abstract, a Summary, an Executive Summary. In Writing for Science and Engineering. 2nd ed. Elsevier, pp. 53-61. Available here (if required, login using your TU Delft NetID).

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