As linked data technology has developed over the last several years, the Linked Jazz project has continued to experiment — most recently interlinking our core jazz name entity list, derived from oral histories, with other jazz archival materials and their related metadata. Our research benefits from many ongoing collaborations, including that with Jeff Rubin and The Hogan Jazz Archive at Tulane University (our work identifying jazz relationships through historical photographs from Tulane University archives has been described here by William Levay), and with Gino Francesconi and Rob Hudson at the Carnegie Hall Archives. This post details a pilot we conducted to identify jazz musicians in both the Linked Jazz network and a subset of the Carnegie Hall Performance History Database focusing on jazz events from 1912-1955. From these entity matches, we created a visualization of the shared relationships between the two datasets. This first step in data interlinking allowed us to explore the possibilities as well as the limitations of the data integration process, and to identify common problems and best practices when reviewed alongside related use cases.
Inspired by Judy Chaikin’s “The Girls in the Band”, a documentary spotlighting the lesser-known history of women in jazz, Linked Jazz set out in 2014 to amplify the stories of jazz women by processing more interviews with female jazz musicians. A result of this activity was that the percentage of women in our list of people mentioned in interviews seemed to grow at a more rapid pace than previously. The list until then had been overwhelmingly men. We wondered: Could we preliminarily assume that jazz women mention other women in the context of their lives and careers more often than men in jazz mention women? This was more a tangential observation for us than a formal research area to pursue. But we realized adding such attributes to our list of names could enable new discoveries for users. Enriching our dataset of 2000+ names with gender information became Linked Jazz’s first attempt to create a data mash-up with other open sets of data that provide semantic definition.