Workshop 1: Into the mind of Friedrich Nietzsche

Basic protoype is live here: https://nietzsche-app-git-main-vivipazos.vercel.app/

Students: Rebecca, Rocío, and Sara ; Mentors: Carlo and Karma ; Expert: Joaquin Campodonico

• Slack channel
• Airtable Calendar
• Github: This one!
• Figma
• Svelte app

Timeline

• Project kickoff: Sunday, March 7
• Static prototype: Friday, March 19
• Interactive prototype: Friday, March 26
• Hackathon: March 27 - 80% of the project ready + 7 hours to complete
• Deadline: Monday, April 5

Final objectives

• Provide a search & display function to explore his Posthumous fragments
• Selected topics grouped by four or five main themes that focus on his swaying attitudes from his youth (before c. 1878) and in his more mature years, selected in collaboration with our Nietzsche expert.
• Provide a voting function to determine whether the selected paragraph was positive or negatively with regards to its subject in order to collectively crowdsource and spark philosophical conversation. Our expert will also provide a few sentences of analysis. Possible links to further exploration or maybe related terms from the opposing thoughts on the same subject?
• Furthermore, an additional search field for users to input their own terms for exploration.

Themes and keywords

Link to CSV

Original idea

We would like to apply data visualisation to philosophical texts so that we can create alternative ways to engage with philosophical concepts. The focus is on a known narrative in the philosophical sphere, that of the philosopher Nietzsche and his relationship with Schopenhauer. Much of Nietzsche’s concepts are influenced by his admiration, and later disapproval of Schopenhauer. It remains a mystery how this happened so we want to visualize which negative terms associated with Shopenhauer’s name. We think there is great potential in parsing philosophical texts into a visual format for greater understanding and learning that could only contribute to advancing our collective human knowledge.

Audience

Anyone who would benefit from this alternative way of visually parsing philosophical texts, i.e., researchers, students, general population.

Goals

• Build a scraper to pull all the Posthumous Fragments from this website.
• Find an appropriate sentiment analysis model to train with the scraped data. Ensure all ethical considerations are applied. //We ended up choosing to include a crowdsourced sentiment analysis in order to spark philosophical questions and thoughts rather than impose them.
• Visualise the data in a format that is first narrative, and second exploratory.

About the data

The data is taken from Nietzsche Source (http://www.nietzschesource.org/#eKGWB/NF-1869,1). More details about the particular edition we are using can be found here. Nietzsche Source supports a fully open access policy. All content is published under Creative Commons General Public License "Attribution, Non-Commercial, NoDerivatives"

Inspiration / Examples

We have seen some works done on philosophical texts that drove our motivation to do more with this: The most interesting was done in 2013 by the University of Milan and DensityDesign Research Lab called ‘Minerva’ (https://densitydesign.org/2013/08/minerva-data-visualization-to-support-the-interpretation-of-kants-work/) They looked at Immanuel Kant's most commonly used words across his published work. There were plans to develop an interactive tool but it was never published. Another group used Wikipedia to find influences between philosophers and visualise this as a network graph (http://www.coppelia.io/wp-content/uploads/2015/08/philprettyv4.png). In this version, Nietzsche is shown as a large circle showing his heavy influence on other philosophers. Another one (https://s4n0i.github.io/schoolofathens/) looked at the same relationships of influence as a network graph but placed on a timeline which better shows the historical influences. Github repo here (https://github.com/S4N0I/theschoolofathens). Team and contacts

Hackathon winners

Also, we won the hackathon for most progress made during. So, thanks!