INTRODUCTION
The new digital humanities tool that I decided to examine was Palladio, a browser-based platform created by Stanford University. This resource is very valuable to the expansion of digital humanities, allowing researchers and students to create interactive maps, timelines, and other visualizations from spreadsheet data such as CSV files. This is super valuable in the field of digital humanities because it allows researchers and students to analyze historical datasets through visualizations. This doesn’t require r coding skills or other technical platforms, which widens the accessibility for meaningful use. It is as simple as uploading data sets and choosing proper pathways of graphs, maps, or images to make meaningful assessments that aren’t obvious from the raw data.
5 STEPS NECESSARY FOR USING PALLADIO
Step 1: Prepare your data in a spreadsheet – Before using Palladio, it is important to make sure that the data is exactly to the specifications of the user to simplify the process. The file should also be in csv format to simplify the importing process for the website.
Step 2: Upload the csv file to Palladio – To start on the Palladio browser, you need to click start and then proceed to drag your downloaded csv file to upload the data so we can use it within the application. Once you load the data in, it is automatically set up in Palladio and ready to use within a graph or table.
Step 3: Choose metrics: You then need to pick the metrics of interest from the data set, which will allow you to analyze key features and present them clearly to viewers to portray a story better. This is in the form of a chart which isolates important data and makes it clearer overall.
Step 4: Show the data visually: Use the Graph feature to make a visual of the key metrics you wish to focus on.
Step 5: Play with size and highlights: This allows you to specialize the graphs better so it is more tailored to the intentions of the project
Two other resources for tutorial and software documentation
The visualization looks so interesting. I wonder how analysis of this visualization works! And I love how this tool doesn’t require R coding skills or other technical platforms. I could understand how this tool works through your clean, logical steps. Nice work!