Toward training and assessing reproducible data analysis in data science education

Bei Yu, Xiao Hu

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Reproducibility is a cornerstone of scientific research. Data science is not an exception. In recent years scientists were concerned about a large number of irreproducible studies. Such reproducibility crisis in science could severely undermine public trust in science and science-based public policy. Recent efforts to promote reproducible research mainly focused on matured scientists and much less on student training. In this study, we conducted action research on students in data science to evaluate to what extent students are ready for communicating reproducible data analysis. The results show that although two-thirds of the students claimed they were able to reproduce results in peer reports, only one-third of reports provided all necessary information for replication. The actual replication results also include conflicting claims; some lacked comparisons of original and replication results, indicating that some students did not share a consistent understanding of what reproducibility means and how to report replication results. The findings suggest that more training is needed to help data science students communicating reproducible data analysis.

Original languageEnglish (US)
Pages (from-to)381-392
Number of pages12
JournalData Intelligence
Issue number4
StatePublished - Sep 1 2019


  • Action research
  • Communication
  • Data science education
  • Reproducibility
  • Reproducible data analysis

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences
  • Artificial Intelligence


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