We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy. By closing this message, you are consenting to our use of cookies.

Submit a Manuscript to the Journal
Journal of Statistics and Data Science Education

For a Special Issue on
Teaching Reproducibility and Responsible Workflow

Manuscript deadline
15 September 2021

Cover image - Journal of Statistics and Data Science Education

Special Issue Editor(s)

Nicholas Horton, Amherst College
[email protected]

Submit an ArticleVisit JournalArticles

Teaching Reproducibility and Responsible Workflow

Modern statistics and data science utilizes an iterative data analysis process to solve problems and extract meaning from data in a reproducible manner. Models such as the PPDAC (Problem, Plan, Data, Analysis, Conclusion (https://dataschools.education/about-data-literacy/ppdac-the-data-problem-solving-cycle)) Cycle have been widely adopted in many pre-secondary classrooms. The importance of the data analysis cycle has also been described in guidelines for statistics majors (https://www.amstat.org/asa/education/CurriculumGuidelines-for-Undergraduate-Programs-in-Statistical-Science.aspx), undergraduate data science curricula (http://dstf.acm.org), and data science courses (https://r4ds.had.co.nz/introduction.html).

The National Academies of Science, Engineering, and Medicine’s 2018 “Data Science for Undergraduates” consensus study (https://nas.org/envisioningds) identified the importance of workflow and reproducibility as a component of data acumen needed in our graduates. The NASEM report reiterated that “documenting, incrementally improving, sharing, and generalizing such workflows are an important part of data science practice owing to the team nature of data science and broader significance of scientific reproducibility and replicability.” They also noted that reproducibility and workflow raised important questions about the ethical conduct of science. These reports identify the need for students to have multiple experiences with the entire data analysis cycle.

However, many challenges exist:
1. technologies are rapidly evolving
2. few faculty were trained in the use of these methods
3. best practices have not been clearly identified
4. insufficient vetted and inclusive curricular materials are available
5. accounting for student heterogeneity and broadening participation
6. many aspects of student understandings in this area are unknown

To highlight work in this important and developing area, the Journal of Statistics and Data Science Education is inviting submission of papers related to “Teaching reproducibility and responsible workflow” to appear in a forthcoming issue.

Sample Topics (non-exhaustive):
- Teaching workflows and workflow systems
- Fostering reproducible analysis
- Promoting reproducibility as a component of replicability and scientific conduct
- Developing and implementing documentation and code standards
- Incorporating source code (version) control systems
- Supporting collaboration
- Integrating ethics
- Conducting effective formative and summative assessment

Submissions at all levels of education (primary through graduate programs and continuing education) and
disciplines (social sciences, digital humanities, and STEM) are encouraged.

Submission Instructions

Timetable:
- May 2021 (call for submissions)
- September 1, 2021 (call for reviewers)
- September 15, 2021 (deadline for submissions via the Journal of Statistics and Data Science Education submission site (https://mc.manuscriptcentral.com/ujse), please select the “Teaching reproducibility and workflow” option)
- July 2022 (proposed publication date)

Papers received after September are in scope and will be considered as regular submissions.

Instructions for AuthorsSubmit an Article