Example Data and Narrative

Last updated on 2024-09-09 | Edit this page

Overview

Questions

  • Why should a lesson tell a story?
  • What considerations are there when choosing an example dataset for a lesson?
  • Where can I find openly-licensed, published data to use in a lesson?

Objectives

After completing this episode, participants should be able to…

  • Find candidate datasets to use in a lesson.
  • Evaluate the suitability of a dataset to be used in a lesson.
  • Choose examples that will prepare learners for formative assessments in the lesson.
  • Develop a story

With your high-level lesson objectives set, now is a good time to consider any additional resources you may need to effectively communicate your message to learners before you dive deeper into writing the lesson content.

Creating a Narrative

Writing your lesson as a story helps learners stay motivated and engaged. The story you create can also help learners more easily connect how the skills they are learning now could be useful after the workshop. You can enable learners to make connections between what they learn in your lesson and their own work by creating a narrative that resembles a situation the learners might encounter there.

Here are some example narratives from lessons:

  • Software Carpentry Git lesson uses the story of Wolfman and Dracula who have been hired by Universal Missions (a space services spinoff from Euphoric State University) to investigate if it is possible to send their next planetary lander to Mars. They want to be able to work on the plans at the same time, but they have run into problems doing this in the past. They now need a system that will enable them to work collaboratibely and in parallel and track each change.
  • Building Websites With Jekyll and GitHub lesson uses the narrative of creating a website for a research project to teach authoring webpages with Markdown and compiling them into a website served on GitHub.
  • FAIR Research Software lesson uses the narrative of a poorly designed software project (which analyses NASA’s open data on spacewalks undertaken by astronauts from 1965 to 2013) that over the course of the lesson gets improved in terms of its accessibility, readability, interoperability and reusability to become more FAIR

Sometimes, a single and continuous narrative threads through the entire lesson, while other times, you use a mini-narrative to teach a specific point in an episode. For example, within the overall narrative of creating a research project website in Building Websites With Jekyll and GitHub lesson, there is a mini-narrative of creating a series of short blog posts to teach how to extract a class of structurally similar pages into a common layout template and filter out common structural components of pages.

You may find that images and figures enhance your narrative. Images are powerful communicators, conveying a lot of information with few or no words. Images can also be distracting for learners. If you choose to include images as part of the narrative of your lesson, be sure that they are consistent with the situation you are describing so that they do not increase learners’ cognitive load.

Depending on the tool you are teaching, you might also include a particular dataset as a part of the story you are weaving into your lesson. It is common for lessons to include a dataset that is used in examples and discussed throughout. This can help you maintain a narrative flow and make the lesson feel more authentic. Even if your topic doesn’t require a dataset, deciding on a consistent narrative will help create a flow between episodes and reduce cognitive load for learners.

Use varied examples if you want to teach abstract concepts.

– Neil Brown

From How Your Mind Learns to Program, a lightning talk in the Never Work In Theory series

The focus of this episode is on finding one central example to be used throughout a lesson. However, where you wish for learners to develop understanding of more abstract concepts, you should try to provide a variety of examples with a common theme. This could be achieved by providing multiple opportunities to recognise and apply the concept in taught examples, activities, and exercises.

Finding Images

Copying an image from a website is technologically simple but can be legally and ethically complex. Images are intellectual property and are subject to intellectual property laws including, but not limited to, copyright and trademark laws. These laws differ by country but are consistent in theme: do not take intellectual property that does not belong to you without permission.

When looking for images that illustrate the narrative of your lesson, avoid copying images that do not include a reuse license. Assume that you cannot reuse these images unless you seek written permission from the image creator or owner. Instead, look for images that indicate that they are in the public domain or carry a permissive reuse license such as CC0 or CC-BY. Public domain images can be freely reused and adapted. Images carrying a reuse license can be used and adapted in accordance with their license terms.

If you cannot find reusable images that match your narrative, you can create your own images or seek help from others in the Carpentries community. When incorporating original images into your lesson, be sure to license these images to be compatible with the license on the rest of your lesson materials.

The guidance in this section is not a substitute for legal advice.

Compatible Licenses

Creative Commons offers a chart that identifies which CC licenses are compatible with each other for adaptation (“remix”) purposes: Creative Commons license comparison chart.

GNU offers commentary about a variety of licenses for free software; this resource may be valuable when considering a license for code: GNU: Various Licenses and Comments about Them.

Finding a Dataset

Testimonial

It was very distracting to me in a workshop, when we were given a dataset to work with but no-one explained what that data was and what it meant. It prevented me from following what the instructors were actually trying to teach me, while I tried to figure out the meaning of the numbers, columns names etc on my own.

When searching for a dataset to use in your lesson, there are a number of factors to consider. First, if possible you want the dataset to be an authentic use case for researchers in your target audience. This means balancing the size and complexity of a dataset while avoiding additional cognative load for learners. The dataset may need a certain number of observations to demonstrate some of the skills you are teaching or have a number of variables that is sufficiently similar to what learners may encounter in their work to feel authentic. At the same time, you want your learners to be able to interpret the data fairly easily during the lesson so they can focus on the skills you are teaching. This may mean you will need to subset your dataset by removing some observations (rows) or variables observed (columns).

You may also want to include and review a data dictionary in your lesson, explicitly taking the time to review the information included in the dataset. For inspiration, see the Social Sciences Data Carpentry data dictionary. An additional factor to consider when choosing a dataset to include is the license. You want to find a dataset where the data provider allows for you to freely use it. The best option is a dataset with a CC0 (Public Domain Dedication) license, as other licenses may have more ambiguity around data reuse. Lessons included in The Carpentries Incubator are encouraged to use CC0 licensed data, and may be required to do so to qualify for peer review in The Carpentries Lab. Even with a CC0 license, you will still want to follow best practice in giving attribution to the data provider or collecting agency.

If you’d like to read more about CC0 and CC-BY, Katie Fortney wrote an excellent blogpost on why CC-BY is not always a good fit.

Here are some example datasets from lessons:

  • The Ecology Data Carpentry lessons’ dataset comes from the Portal Project Teaching Database. This dataset is an actual ecological research project’s data that was simplified for teaching. The reuse of this dataset throughout the Data Carpentry Ecology lessons helps stitch together the process of data analysis throughout the workshop, from data entry and cleaning to analysis and visualization.
  • The Social Sciences Data Carpentry lessons’ dataset is the teaching version of the full Studying African Farmer-Led Irrigation (SAFI) dataset. The SAFI dataset represents interviews of farmers in Mozambique and Tanzania, conducted between November 2016 and June 2017. The interviews surveyed household features (e.g. construction materials used for dwellings, number of household members), agricultural practices (e.g. water usage) and assets (e.g. number and types of livestock). The teaching version of the SAFI dataset has been simplified and intentionally “messed up”” to enable demonstrating common data cleaning issues often found in real-life data.
  • Patient inflammation dataset - from the Software Carpentry Python novice and the incubating intermediate lessons - is used to study the effect of a new treatment for arthritis by analysing the inflammation levels in patients who have been given this treatment.
  • A river catchment dataset from the Lowland Catchment Research (LOCAR) Datasets is used in the Earth and Environmental Sciences Intermediate Python lesson to analyse hydrological, hydrogeological, geomorphological and ecological interactions within permeable catchment systems.
  • Data Carpentry’s Astronomical Data Science with Python lesson uses two astronomical datasets, from the Gaia satellite and the Pan-STARRS photometric survey, to reproduce part of an analysis described in a published article.

When deciding on a dataset, it is also important to consider the ethical use of each dataset. Does the data contain personally identifiable information? Was the data collected without permission from the groups or individuals included? Will the data be upsetting to learners in the workshop? If the answer to any of these questions might be yes, you will need to do more research on it and continue to look for other options. Commonly data is misused from historically excluded and exploited groups. The CARE Principles for Indigenous Data Governance1 (Collective Benefit, Authority to Control, Responsibility, and Ethics) are good starting point for thinking about data sovereignty and considering the ethics of data collected about an individual or groups of people.

Examples of Public Repositories

When looking for data to reuse, consider public repositories in the subject area for your lesson or general data repositories such as:

Exercise: Choosing a Dataset or Narrative (30 minutes)

Referring to the advice given above, find an appropriate dataset or a narrative for your lesson. Identify one or more potential candidates and note down the advantages and disadvantages of each one. As a reminder, here are some aspects we suggest that you consider:

  • For datasets:
    • size
    • complexity
    • “messiness”/noise
    • relevance to target audience
    • availability
    • license
    • ethics
  • For narratives:
    • authenticity
    • relevance to target audience
    • complexity
    • possibility to teach useful things first/early

Takes notes in your Lesson Design Notes document about your discussion and the decisions made. It may be particularly helpful to record:

  • Which datasets and narratives did you consider?
  • How and why did you choose between them?
  • What implications do you think your choice of dataset and/or narrative will have for the design and further implementation of your lesson?

People learn faster if they are motivated2, and learners will be motivated if you teach most useful things first. As you think about what story your lesson will tell, it is important to put the pieces that are most interesting to learners up front. If they are able to quickly learn tools or skills that they see as useful from your lesson, they will be more interested in continuing to learn other concepts that are needed. You may notice this trend in many of the Software Carpentry lessons. In particular, many of the coding language lessons (R and Python) have the learners create a plot very early in the lesson and then go back and teach coding fundamentals such as loops and conditionals. Visualization of data is often a very motivating and much needed skill by learners. Getting to visualization early, keeps learners interested in learning the additional and vital skills where the application might be less clear.

Testimonial

“Let them eat cake (first).”

- Dr. Mine Çetinkaya-Rundel

Summary

Remember, even if you do not need a dataset for your lesson, you should decide on a narrative. Centering your lesson around a central example reduces the cognitive load of switching between examples throughout the lesson. Using an authentic, yet simple, dataset will also help reduce cognitive load and help learners to see how they might apply what they learned to their own projects. It is also important to consider licensing and ethical considerations when looking for a lesson dataset.

Key Points

  • Using a narrative throughout a lesson helps reduce learner cognitive load
  • Choosing a lesson includes considering data license and ethical considerations.
  • Openly-licensed datasets can be found in subject area repositories or general data repositories.

  1. Carroll et al. (2020)↩︎

  2. The evidence for this is summarised well in chapter 3, What Factors Motivate Students to Learn?, of Ambrose et al. 2010. The Carpentries Instructor Training curriculum also includes a helpful summary of how lesson content can influence Learner motivation.↩︎