Our group worked with a data set about people accused of witchcraft in Scotland between 1563-1736. The data set was obtained from The Survey of Scottish Witchcraft at the University of Edinburgh, a project that was spearheaded by Julian Goodare, Lauren Martin, Joyce Miller and Louise Yeoman. The data set gave us a lot of details about the accused witches, their cases, and their trials, but in order to garner historical context, we looked to various literary sources. We came across sources that address important topics  such as the history of witch hunting in Scotland, torture in Scotland, and Scottish governmental structure. It should be noted that Julian Goodare is a prominent figurehead in Scottish witchcraft literature, and he has many publications about the topic, which we found useful.


We quickly realized that the data set is extensive and not necessarily navigable. The data is grouped across different sheets, and there are various types of reference numbers. The lack of a unifying type of reference number proved challenging when we tried to link different sheets together in Tableau. Chapter 8 of “Data + Design” by Chiasson and Gregory notes that no data set is ever 100% clean, but how much you choose to clean is up to you and what you want to accomplish.

To solve this, we identified inconsistencies throughout the whole data set and noted that each variable is minimally dirty, since there are not many misspelled values or inconsistent labels. With OpenRefine, we extracted and only used the year from the full date given. In Tableau, we also unionized and combined cell values to match the Case Reference ID with the Accusation Reference ID in order to combine different sheets. With the cleaned data, we formed a subset data sheet with pertinent columns for our own use. 

We then decided to focus our efforts on variables relevant to our research questions. From there, the group members working on data visualization and mapping had their own ways of working with the data in both Tableau and Excel. We used Tableau to create our data visualizations that would allow us to present our data and help us answer our research questions. We chose visualizations that are appropriate for the type of data that we have. Our narrative is woven around our visualizations, bringing cohesion to our website.  

In cleaning our data, we found that the information across our sheets was not always connected.  This left holes in our data for many cases, often reducing our starting number of cases from 3,500 to numbers in the hundreds. This lack of continuous data for a case leaves some gaps in what happened to many people tried for witchcraft in Scotland, which we have to take into account. It is possible that this information was never recorded or simply lost over time. Thus, we want to address the silences in our data set to remind our users that there are still many stories that are unable to be represented.


We use WordPress to configure our website that is hosted on UCLA’s Humspace portal. In Chapter 6 of Data Points: Visualization That Means Something by Nathan Yau, we are reminded to put ourselves in our readers’ shoes and to consider what our audience knows or doesn’t know. We chose a theme we felt is navigable and opted for black text on white background with an appropriately sized font so that the text is legible. We also implemented color blind palette in our visualizations where we felt it was applicable.  Many of our embedded visualizations from Tableau are interactive, so readers can filter by categories and see how other categories change as a result. We explain how to utilize this feature because there may be users who aren’t familiar with Tableau. Readers can also hover over different marks in the visualization to obtain information about that specific data point. Guided by lecture presentations and “Which chart or graph is right for you?” by Hardin et al., we took into account what type of data we had (qualitative or quantitative) and the questions we wanted to answer so that we would choose appropriate visualization types. For example, since we are interested in geographical location, we took the counties we were interested in and matched coordinates to them so that they could be visualized in a map format. The visualizations are embedded within our narrative so that we can both give context to the visualizations and also have the visualizations support our narrative.

Meet the Witches

We are a group of 5 undergraduate students with a passion in learning Digital Humanities at UCLA. Due to our mutual interest in this particular dataset, our team collaborated in learning and honing the relevant skills required to make this project a success. It’s time for you to meet the witches behind this project!

Anthropology / Class of 2020 / Data Mapping Specialist
As the data mapping specialist, Lilly converted qualitative data about locations in the dataset into geographical coordinates and generated the maps for our website using Tableau. This is a crucial element of our project since our research explores disparities within geographical locations.

Economics / Class of 2022 / Project Manager, Web Specialist
As the web specialist, Edryna administered the design and structure of the website in order to present the team’s findings in an ordered manner for the audience to explore. She also organized group meetings and tracked the team’s progress as project manager.

Anthropology / Class of 2020 / Data Visualization Specialist
As the data visualization specialist, Jessica teamed up with Vicki and Lilly to create various visualizations on Tableau to help us view our data in different ways. Jessica also guided the discussion in finding our research questions.

History / Class of 2021 / Content Specialist
As the content specialist, Emily contributed by assembling the narrative to answer our research questions. She also ensured that the data visualizations were woven together smoothly with the data narrative and worked on the timeline with Lilly.

Statistics / Class of 2021 / Data Specialist
As the data cleaning specialist, Vicki teamed up with Lilly to clean almost 20 sheets of data. Vicki also created some of our data visualizations on Tableau and drew important conclusions to answer our research questions.


This project would not have been possible without the input and guidance of Professor Ashley Sanders Garcia from the UCLA Digital Humanities department and our Teaching Assistant Deanna Gao. We are also extremely grateful to Yoh Kawano from the Institute for Digital Research and Education for his advice on data cleaning, processing, and visualizations.


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