The centerpiece of the Religious Toleration in Enlightenment Europe project is a PowerBI dashboard that can be used to produce visualizations of the data compiled by the project’s researchers. This page will provide an introduction to the data behind the dashboard, the process of analysis that produced that data, and how one can use the dashboard to explore the results of the project.
The Data
The data for Religious Toleration in Enlightenment Europe was generated by a number of researchers who each approached the main research questions at the center of the project in different ways:
- Bailey Bettencourt (BKE) compiled a host of newspaper articles that all report on events tied to various European revolutions. Bailey was particularly interested in seeing how news coverage of revolutions corresponded with larger debates about religious toleration.
- David Criscione (DDC) studied religious toleration by looking at fifty-six English-language novels published from 1750 until 1850.
- Katie Heatherly (KH) assembled a collection of women’s writings from the eighteenth century, including letters, published books, and a women’s magazine titled The Christian Lady’s Magazine.
- Jacob Huneycutt (JTH) found English-language Protestant sermons published during the period of 1750 to 1850.
- Joe Wilson (EJW) focused on a conservative newspaper named The Anti-Jacobin Review, published from 1798 until 1820.
These datasets present different vantage points into the debate about religious toleration.
The Analysis
Upon identifying their datasets, the project’s researchers did two different analyses of their data. The first produced keywords within the datasets. The output included lists of nouns, TF-IDF keywords, and named entities (e.g. locations, organizations, and events). The second analysis produced a sentiment score based on the language used to describe certain keywords. Both analyses were completed using a text data mining tool built by Joshua Been, Associate Librarian and Director of Data and Digital Scholarship at Baylor University. The tool is hosted on Google CoLab and is available for public use.
The resulting data was then inserted into a spreadsheet that populates the PowerBI dashboard. On the dashboard, you can see the results of the analyses of the various content sets listed above.
How to Use the Dashboard
There are many ways to use the dashboard for your own scholarly purposes. To start, you can filter the results of the dashboard based on the datasets described above by selecting one of the researchers using her/his initials. This will change the visualizations to the right. You can also drill down on their research by selecting one or many topics that the researcher identified as a sub-set of their data. For example, you could focus on one year of The Anti-Jacobin Review, or you could look only at women’s letters.
The visualizations present you with information about that dataset. The word cloudvisualization shows which keywords are most often mentioned. The sentiment chart reveals the sentiment scores for certain keywords —green corresponding to positive sentiment (+1.0 = the highest score) and red corresponding to negative sentiment (-1.0 = the lowest score). In the bottom left corner, there is a heatmap visualization that shows the relative frequency of the names of geographic places within the corpus. Finally, in the bottom right corner, the top ten TF-IDF terms associated with important keywords are listed when you click on the term of your choice
Before using the dashboard, it is recommended that you first read the interpretive essays written by the researchers. They will detail some of the main ways that these researchers used this data and interpreted the results of their analyses.