Roundtable: challenges in repeatable experiments and reproducible research in data science

This article provides a summary of the results of a roundtable discussion on experimental design and research reproducibility in data science. A distinction is made between scientific and applied research, the issues of determining the quality for both types of research are considered, which is the essence of the reproducibility of the results in each case. In addition, an attempt is made to determine the directions for further development of infrastructure and methodology for the development of predictive models, algorithms and experiments. The recommendations formulated can be useful for the development of machine learning courses curricula.