Understanding machine learning dataset search behaviors: A survey

These files represent the data and accompanying documents of an independent research study by a student researcher examining the searchability and usability of machine learning dataset metadata. The purpose of this exploratory study was to understand how machine learning (ML) practitioners are searching for and evaluating datasets for use in their work. This research will help inform development of the ML dataset metadata standard Croissant, which is actively being developed by the Croissant MLCommons working group, so it can aid ML practitioners' workflows and promote best practices like Responsible Artificial Intelligence (RAI). The study consisted of a pre-interview Qualtrics survey ("Survey_questions_pre_interview.pdf") that focused on ranking various metadata elements on a Likert importance scale. The interview consisted of open questions ("Interview_script_and_questions.pdf") on a range of topics from search of datasets to interoperability to AI used in dataset search. Additionally, participants were asked to share their screen at one point and recall a recent dataset search they had performed. The resulting survey dataset ("Survey_p1.csv") and interview ("Interview_p1.txt") of participants are presented in open standard formats for accessibility. Identifying data has been removed from the files so there will be missing columns and rows potentially referenced in the files.