Data sharing provides many benefits to science, researchers, and trainees. However, qualitative data sharing (QDS) is uncommon, and the often sensitive nature of qualitative data poses significant ethical and practical challenges that must be overcome prior to sharing qualitative data. This presentation will cover the outcomes of a 5-year National Institutes of Health funded project aimed at identifying barriers and facilitating QDS, including; a toolkit with guidelines to help research teams incorporate QDS, a web-based de-identification support tool called the QuaDS Software, and guidance from data repositories to help researchers share qualitative data in a responsible, ethical manner.
In this session, Meredith Parsons will describe the overall QDS project, QDS Toolkit features, and data sharing process. Aditi Gupta will discuss the development and features of the de-identification software.
Meredith Parsons, MS, CHES, is a Senior Public Health Research Technician in the Bioethics Research Center at Washington University School of Medicine. Meredith has served as research staff in the Bioethics Research Center (BRC) since the Qualitative Data Sharing Project (QDS) began in 2017. During that time, Meredith has had the privilege of connecting with other qualitative researchers, IRB professionals, repository curators, and research participants to understand the ethical and practical considerations that are unique to qualitative data sharing. She has supported the de-identification and sharing of 30 qualitative datasets from research teams across the US, and has first-hand experience completing this process with datasets collected by the BRC. Today, Meredith provides training and guidance to qualitative research faculty and staff on how to de-identify and share qualitative research data in a responsible manner.
Aditi Gupta, PhD, is an Instructor in the Division of Biostatistics and the Institute for Informatics at the Washington University in St. Louis. Her primary areas of research are biomedical data science and clinical research informatics. Dr. Gupta's research projects focus on analyzing and implementing informatics-based methodologies, such as artificial intelligence, predictive modeling, and natural language processing for the identification of prognostic clinical phenotypes in multiple disease conditions including NF1, sepsis, and Alzheimer's disease.