Title: Computational Reproducibility vs Transparency: Are we barking up the wrong tree?
Session Lead: Bertram Ludaescher
Time: 11 am – noon, Wednesday, 2021-02-03
Location: Zoom

Details:
The “reproducibility crisis” has resulted in much interest in methods and tools to improve computational reproducibility. FAIR data principles (data should be findable, accessible, interoperable, and reusable) are also being adapted and evolved to apply to other artifacts, notably computational analyses (scientific workflows, Jupyter notebooks, etc.)

The current focus on computational reproducibility of scripts and other computational workflows sometimes overshadows a somewhat neglected and arguably more important issue: transparency of data analysis (including data wrangling and cleaning).

To sort out things, I suggest we ask the question: What information is gained by conducting a reproducibility experiment?

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