Notably Inaccessible – Data Driven Understanding of Data Science Notebook (In)Accessibility

<strong>Overview</strong> This dataset artifact contains the intermediate datasets from pipeline executions necessary to reproduce the results of the paper.<br> We share this artifact in hopes of providing a starting point for other researchers to extend the analysis on notebooks, discover more about their accessibility, and offer solutions to make data science more accessible. The scripts needed to generate these datasets and analyse them are shared in the GitHub repository for this work. <strong>The dataset contains large files of approximately 60 GB so please exercise caution when extracting the data from compressed files.</strong> <br> <strong>The dataset contains files which could take a significant amount of run time of the scripts to generate/reproduce.</strong> <strong>Dataset Contents</strong> We briefly summarize the included files in our dataset. Please refer to the documentation for specific information about the structure of the data in these files, the scripts to generate them, and runtimes for various parts of our data processing pipeline. <code>epoch_9_loss_0.04706_testAcc_0.96867_X_resnext101_docSeg.pth</code>: We share this model file, originally provided by Jobin <em>et al.</em>, to enable the classification of figures found in our dataset. Please place this into the `model/` directory. <code>model-results.csv</code>: This file contains results from the classification performed on the figures found in the notebooks in our dataset. Performing this classification may take upto a day. a11y-scan-dataset.zip: This archive contains two files and results in datasets of approximately 60GB when extracted. Please ensure that you have sufficient disk space to uncompress this zip archive. The archive contains: <code>a11y/a11y-detailed-result.csv</code>: This dataset contains the accessibility scan results from the scans run on the 100k notebooks across themes. <strong>The detailed result file can be really large (&gt; 60 GB) and can be time-consuming to construct.</strong> <code>a11y/a11y-aggregate-scan.csv</code>: This file is an aggregate of the detailed result that contains the number of each type of error found in each notebook. <strong>This file is also shared outside the compressed directory.</strong> <code>errors-different-counts-a11y-analyze-errors-summary.csv</code>: This file contains the counts of errors that occur in notebooks across different themes. <code>nb_processed_cell_html.csv</code>: This file contains metadata corresponding to each cell extracted from the html exports of our notebooks. <code>nb_first_interactive_cell.csv</code>: This file contains the necessary metadata to compute the first interactive element, as defined in our paper, in each notebook. <code>nb_processed.csv</code>: This file contains the necessary data after processing the notebooks extracting the number of images, imports, languages, and cell level information. <code>processed_function_calls.csv</code>: This file contains the information about the notebooks, the various imports and function calls used within the notebooks.<strong>概述</strong> 本数据集工件包含复现该论文成果所需的流水线执行中间数据集。我们公开此工件，以期为其他研究者提供研究起点，以拓展针对笔记本的分析工作、深入探究其可访问性，并为提升数据科学领域的可访问性提供可行方案。本研究所需的用于生成与分析此类数据集的脚本，已公开在本工作对应的GitHub仓库中。<strong>本数据集包含约60 GB的大型文件，从压缩包中解压数据时请务必谨慎操作。</strong><br><strong>生成或复现本数据集所需的脚本运行时长可观。</strong><br><strong>数据集内容</strong> 我们将简要概述本数据集包含的文件。如需了解各文件内的数据结构、生成脚本以及各数据处理流水线环节的运行时长，请参阅配套文档。<code>epoch_9_loss_0.04706_testAcc_0.96867_X_resnext101_docSeg.pth</code>：我们公开此模型文件（最初由Jobin等人提供），以支持对本数据集内的图表进行分类。请将此文件放置于`model/`目录下。<code>model-results.csv</code>：该文件包含针对本数据集内笔记本中的图表所执行的分类任务结果。完成此分类任务可能耗时长达一天。<code>a11y-scan-dataset.zip</code>：此压缩包内含两个文件，解压后总数据量约为60 GB。请确保拥有足够的磁盘空间以完成解压操作。该压缩包包含：<code>a11y/a11y-detailed-result.csv</code>：此数据集包含针对10万个不同主题的笔记本执行可访问性扫描所得的结果。<strong>该详细结果文件体积庞大（超过60 GB），构建过程耗时较长。</strong><code>a11y/a11y-aggregate-scan.csv</code>：该文件为详细结果的聚合版本，包含每本笔记本中各类错误的统计数量。<strong>该文件同样未置于压缩目录内公开。</strong><code>errors-different-counts-a11y-analyze-errors-summary.csv</code>：此文件包含不同主题笔记本中的错误出现次数统计信息。<code>nb_processed_cell_html.csv</code>：此文件包含从笔记本的HTML导出内容中提取的每个单元格对应的元数据。<code>nb_first_interactive_cell.csv</code>：此文件包含所需元数据，用于计算每本笔记本中本文所定义的首个交互式元素。<code>nb_processed.csv</code>：此文件包含处理笔记本后得到的必要数据，其中提取了图像数量、导入项、编程语言以及单元格级别的相关信息。<code>processed_function_calls.csv</code>：此文件包含有关笔记本的相关信息，以及笔记本中使用的各类导入项与函数调用信息。