DASWOW Notebooks

DASWOW Notebook Subset This dataset is a curated subset of 470 Jupyter notebooks extracted from the original collection of ~1M Jupyter Notebooks published by: Rule, Adam; Tabard, Aurélien; Hollan, James D. (2018): Data from: Exploration and Explanation in Computational Notebooks, UC San Diego Library Digital Collections. Link: https://doi.org/10.6075/J0JW8C39 The subset specifically corresponds to the Jupyter notebooks used in the DASWOW dataset (Data Science Notebooks with Workflow Information) and includes 470 curated Jupyter notebooks, which are located under the `DASWOW/DASWOW_Notebooks/` directory. For completeness, the DASWOW dataset itself is provided in:1) .csv format: f_DASWOW.csv and 2) .pkl format: f_DASWOW.pkl Both are available in the `DASWOW/` directory. Original DASWOW dataset and analyses:The DASWOW dataset was originally published on Zenodo: https://doi.org/10.5281/zenodo.5635475The dataset is also accessible in its original repository under the `data-science-code-analysis/features/` directory on Zenodo, where all related analyses of the DASWOW dataset are also published. Further details and findings are discussed in the original scientific publication: https://link.springer.com/article/10.1007/s10664-022-10229-z Citation guidelines:To ensure proper attribution, please cite the relevant works depending on your use: 1. Original ~1M notebooks collectionIf you use the raw Jupyter notebooks in this subset, cite:Rule, Adam; Tabard, Aurélien; Hollan, James D. (2018): Data from: Exploration and Explanation in Computational Notebooks, UC San Diego Library Digital Collections. https://doi.org/10.6075/J0JW8C392. Curated DASWOW Jupyter Notebook SubsetIf you use this curated subset of notebooks (e.g., for analysis, experiments, or feature extraction), cite the DOI of this curated release (https://doi.org/10.5281/zenodo.16611317) along with the citation to the original notebooks collection by Rule et.al. 3. DASWOW dataset and analysisIf your work relies on the original full DASWOW dataset itself or its analyses, we request that you also cite the original dataset (https://doi.org/10.5281/zenodo.5635475) and the associated publication (https://link.springer.com/article/10.1007/s10664-022-10229-z) to acknowledge the foundational work..