Over the course of two years working on Always Already Computational: Collections as Data, the team has heard repeated requests from people who work in libraries, archives, and museums for help understanding what it is people want to do with their collections. Generally, of course, we all want to see our collections get used, and we want to help make them available for new uses, but beyond reading or looking at them, what do people want to do with them “as data”?
In order to help answer that question, we are launching a new series aimed at profiling common methods used in the computational analysis of collections. The goal is to have a group of “Methods Profiles” that people can begin with as they have conversations with their colleagues and communities. These profiles will be short, and aim to help people who work in libraries, archives, and museums get a sense of methods people might like to use to engage their collections computationally. Each profile gives a brief overview of a method. They are designed to be used in combination with the principles in the Santa Barbara Statement on Collections as Data.
Laurie Allen will serve as editor for the first round of profiles, and we are looking for volunteers to create draft profiles. If you’re willing to volunteer to create a profile, or you’d like to recommend that Laurie try to recruit someone to volunteer to create a methods profile, please email Laurie Allen (laallen@upenn.edu).
Completed Profiles
- Laurie Allen and Scott Enderle, University of Pennsylvania
Network Analysis (pending review)
- Scott Weingart, Carnegie Mellon, Thomas Padilla, UNLV
Needed Profiles
Mapping (assigned)
Image Analysis (needs volunteers)
Audio Analysis (needs volunteers)
Collation (needs volunteers)
Visualization (needs volunteers)
Your idea here (needs volunteers!)
A note about drafting these profiles - The first profile was created by Laurie Allen (a librarian with a deep understanding of libraries and a broad understanding of text mining) with massive help from Scott Enderle (a scholar with a deep understanding of text mining and a broad understanding of libraries). It is hard for someone with deep knowledge of the methods to generalize about it (though Scott is particularly awesome at that) and it is hard for someone without deep knowledge to know what the sticking points are that libraries should look out for.
We encourage profiles to be co-authored in this way, so that they reflect the combined expertise of disciplinary and library colleagues.