Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We develop a methodology for converting card index archival records into usable data frames for statistical and textual analyses. Leveraging machine learning and natural-language processing tools from Amazon Web Services (AWS), we overcome hurdles associated with character recognition, inconsistent data reporting, column misalignment, and irregular naming. In this article, we detail the step-by-step conversion process and discuss remedies for common problems and edge cases, using historical records from the Reconstruction Finance Corporation.