New and Original BACHMANN BS207 BIO459 BIO459/01 In Stock

Overview

Notebook tools like Jupyter have emerged as a popular programming abstraction for data exploration and curation, model-building, and rapid prototyping. Notebooks promise re-use and reproducibility as well as interactive refinement of code with immediate feedback to support iterative construction of pipelines for data curation and analysis. However, a recent study by Pimentel et. al. [56] found only 4% of notebooks sampled from GitHub to be reproducible, and only 24% to be directly re-usable. These unfortunate statistics stem from Jupyter’s heritage as a thin facade over a read-evaluate-print-loop (REPL). Many existing notebooks, like Jupyter, are not designed as a historical log — as one would want for reproducibility and iterative exploration — but rather as library managers for code snippets (i.e., cells). Reproducibility, re-usability, and iterative pipeline construction require active effort from users to organize cells, keep cells up to date, and manage inter-cell dependencies and cell versions (e.g., using tools like git).

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