I used Streamlit to create an app for exploring collections assessment data* exported from ArchivesSpace, an open-source collections management system for archives.
It’s not related to an ML project, but I wanted to share with the community to show how the Streamlit framework can be a valuable tool for people doing data-centric work in the cultural heritage sector.
The functionality and layout are very basic, and I had to use fake data due to the variability of the ArchivesSpace assessment module. (This is also why there’s no file upload option: every archive that uses ArchivesSpace adds its own custom variables to the survey tool.)
I can think of several possible improvements, like the capability to detect different categories of variables to support file uploads, or adding a text search box to the search results table. This is my foray into Streamlit, so I tried to keep it simple for now.
If you’re curious about why I think there’s a need for an app like this one, you may want to check out a blog post I wrote here.
Thanks for reading. Feedback is welcome!
*Collections assessments are a type of survey tool that libraries, archives, and museums use to collect data about the stuff on their shelves (and drives). They use this data to inform a variety of operations, from prioritizing materials for preservation to developing fundraising strategies.