In recent years, JSTOR Labs launched Constellate Lab, a modern successor of JSTOR Data for Research, to enable computational analysis of historical newspapers, scholarly journals, and other documents within the JSTOR corpora. With tutorials including analysis with Python notebooks and datasets builders, Constellate focuses on data pedagogy to sharpen and enhance ones data skills.
Dr. Natalie M. Susmann, digital scholarship librarian at Brandeis University and Mediterranean Landscape Archaeologist, shares her experience with Constellate in a recent post from JSTOR Daily, “Modeling Cultural Networks in the Classroom with Constellate: Using JSTOR’s Constellate lab to teach students how to do digital text analysis and data visualization for historical subjects.” She explores conducting text analysis on peer-reviewed articles related to the Argive Heraion sanctuary in Argos, Greece.
Her step-by-step process offers a use-case and template for exploring Constellate’s tools and data for your own research and especially for pedagogical purposes in courses and workshops.
dh+lib Review
This post was produced through a cooperation between Kelly Hammond, Tugba Korhan, Divya Mathur, Mimosa Shah, Michelle Speed, Ashlyn Stewart, April Urban, Erica Leslie Weidner, Chelsea Wells, and Vera Zoricic (Editors-at-large for the week), Nickoal Eichmann-Kalwara and Pamella Lach (Editors for the week), Claudia Berger, Linsey Ford, Hillary Richardson, John Russell, and Rachel Starry (dh+lib Review Editors).