RESUMO
BACKGROUND: During COVID-19, a Kaggle challenge was issued to data scientists to leverage text mining to provide high-level summaries of full-text articles in the COVID-19 Open Research Dataset (CORD-19) data set, a data set containing articles around COVID-19 and other epidemics. A question was asked: "What if nursing had something similar?" PURPOSE: Describe the development and function of the Nursing COVID and Historical Epidemic Literature and describe high-level summaries of abstracts within the repository. METHOD: Nurse-specific literature was abstracted from two data sets: CORD-19 and LitCOVID. LitCOVID is a data set containing the most up-to-date literature around COVID-19. Multiple text mining algorithms were utilized to provide summaries of the articles. DISCUSSION: As of July 2020, the repository contains 760 articles. Summaries indicate the importance of psychological support for nurses and of high-impact rapid education. CONCLUSION: To our knowledge, this repository is the only repository specific for nursing that utilizes text mining to provide summaries.