Example Data and Narrative
Last updated on 2023-07-10 | Edit this page
- 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?
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
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.
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 lessons and reduce cognitive load for learners.
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.
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
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 or remove variables observed.
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.
An example dataset used in the Data Carpentry Ecology lessons is the Portal Project Teaching Database. This dataset is an actual ecological research project 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.
When deciding on a dataset, it is also important to consider the ethical use of each dataset considered. 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.
When looking for data to reuse, consider public repositories in the subject area for your lesson or general data repositories such as:
- The Data Curation Network’s datasets
- The Offical Portal for European Data
- The Official Portal for Argentina Data (in Spanish)
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.
- Dr. Mine Çetinkaya-Rundel
Looking back at one of the exercises you designed before: what examples could you include in your narrative to teach learners the skills they will need to apply to complete the formative assessments you have designed?
Examples: In the Software Carpentry Plotting and Programming with Python Lesson: Exercise to load and inspect CSV file for Americas -> In the lesson, the Instructor demonstrates how to load the data table for another continent (Oceania) and explores the values with a few different functions. This shows learners how to call the function to load the CSV into a data frame, and demonstrates what success looks like for this task. In the Python Interactive Data Visualization Lesson in the Incubator: Exercise to find the correct widget (a slider) for an action and modify the script to use it -> In the lesson the instructor introduces a cheatsheet and documentation for interactive widgets and then creates a dropdown widget for the application. The slider widget required in the exercise has not been demonstrated but the preceding example shows all of the necessary steps to add a widget, and provides the supporting information that learners can consult to discover how to implement the specific tool.
Outline one of these examples in your episode file.
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.
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.↩︎