This document presents the research artifacts for the paper “How to Support ML End-User Programmers through a Conversational Agent” accepted in ICSE 2024. In our study, we designed a conversational agent named “Newton” that can act as a 24/7 expert to support Machine Learning End-User Programmers (ML-EUPs). We evaluated Newton’s design by conducting a Wizard of Oz within-subjects study with 12 ML-EUPs to understand how it was able to help participants overcome challenges described in the literature. Based on the results, we proposed six design guidelines for future conversational agents in this domain. The research artifacts described in this document include (i) the forms used to collect data for the experiment; (ii) the participant’s experimental task workflow with Newton; (iii) the responses collected from participants during the experiments; (iv) the data analysis scripts and results of the experiments; (v) the tool we used for the WoZ experiment; and (vi) the WoZ scripts we used in the experiment to provide consistent answers.