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You are here: Home / Additional Features / Journal Highlights / Journal of Statistics and Data Science Education Highlights / JSDSE Calls for Papers About Teaching Reproducibility and Responsible Workflow

JSDSE Calls for Papers About Teaching Reproducibility and Responsible Workflow

July 1, 2021 Leave a Comment

Nicholas Horton, Amherst College, JSDSE Incoming Editor
    About JSDSE
    The Journal of Statistics and Data Science Education is an open-access, peer-reviewed journal with no author fees published by Taylor & Francis and the American Statistical Association. Articles accepted for publication are promptly made available online and featured on the journal’s website.

    Modern statistics and data science use 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;) have been introduced in pre-secondary classrooms. The importance of the data analysis cycle has also been described in guidelines for statistics majors, undergraduate data science curricula, and data science courses.

    The National Academies of Science, Engineering, and Medicine’s 2018 “Data Science for Undergraduates” consensus study identified the importance of workflow and reproducibility as a component of data acumen needed in graduates. The 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.” Also noted was 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, including the following:

    • Technologies are rapidly evolving 
    • Few faculty members were trained in the use of these methods
    • Best practices have not been clearly identified
    • Insufficient vetted and inclusive curricular materials are available
    • Accounting for student heterogeneity and broadening participation
    • Many aspects of student understandings in this area are unknown

    To highlight work in this developing area, the Journal of Statistics and Data Science Education is inviting submissions of papers related to teaching reproducibility and responsible workflow. Sample topics include the following:

    • Teaching workflows and workflow systems
    • Fostering reproducible analysis
    • Developing and implementing documentation and code standards
    • Incorporating source code (version) control systems
    • Supporting collaboration
    • Integrating ethics
    • Formative and summative assessment

    Submissions at all levels of education (primary through graduate programs and continuing education) are welcome.

    Submissions must be uploaded by September 15. Select the “teaching reproducibility and workflow” option. The proposed publication date is July 2022.

    Questions about submissions can be sent to Nicholas Horton, JSDSE incoming editor.

    Filed Under: Journal of Statistics and Data Science Education Highlights, Previous Features Tagged With: Analysis, and Medicine, ASA, big data, code, Conclusion, data, data science, Data Science for Undergraduates, documentation, Education, Engineering, graduate education, JSDSE, JSDSE Call for papers, k-12 edu, National Academies of Science, plan, PPDAC, Problem, reproducibility, Responsible Workflow, statistician, statistics, statistics curriculum, statistics education, stats ed, students, teachers, undergraduate education

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