| By Sydney Fairman |
Gale is excited to announce several enhancements to the Digital Scholar Lab. We have made notable improvements to the overall workflow, text cleaning capabilities, and topic modeling tool. For instance, the topic modeling tool offers a new topic overview and “topic proportion by document” results.
Along with tabular data, these enhancements better align with what users expect from the Machine Learning for Language Toolkit (MALLET). Typically, MALLET requires an advanced understanding of programming. This can be difficult to interpret and assign meaning to the results if you’re a Digital Humanities scholar that lacks a background in computer science and statistics. The topic modeling tool improvements allow users to form clear connections between MALLET data and their content set.
Read below to see what American Reference Books Annual (ARBA) has to say about the overall value of Digital Scholar Lab:
“The appeal to graduate students and scholars is obvious, but the Digital Scholar Lab also has the potential to make in-depth research accessible to undergraduates. Highly recommended for academic libraries.”
—ARBA Staff Reviewer