Current and emerging practices for making large-scale scientific data FAIR
Sonja Crncevic
Undergraduate Researcher
Epidemiology Major (School of Public Health)
Angela Murillo
Faculty Mentor
Angela Murillo (IUPUI School of Informatics and Computing)
Project Description
The student(s) will join a team of researchers working on examining current and emerging practices in making large-scale scientific data "FAIR." FAIR data are data that meet the FAIR principles (findability, accessibility, interoperability, and reusability), making data more machine-actionable. The student(s) will analyze information on current and emerging FAIR practices by (1) reviewing data from a 2019 survey of FAIR data implementation in scientific data repositories, (2) assisting with developing a follow-up survey based on those findings, (3) an extensive survey of scientific data managers, and (4) data analysis of the survey.
Technology or Computational Component
The student(s) will gain exposure to the full research cycle, including literature reviews, data gathering, data quality control, data analysis, and writing and disseminating research results. The student(s) will gain experience using statistical software (R or SPSS) and working with R or Excel to create and modify graphics. The student will also gain experience in mixed-methods data analysis in software such as Dedoose or ATLAS.ti and Qualtrics for survey creation.