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> 本数据集工件包含流水线执行过程中生成的中间数据集，可用于复现本论文的研究成果。我们共享此工件，以期为其他研究者提供研究起点，以拓展针对笔记本（Notebook）的分析、深入探究其可访问性（accessibility），并为提升数据科学的可访问性提供解决方案。用于生成和分析这些数据集的脚本已共享至本研究的GitHub仓库中。<br> <strong>本数据集包含约60 GB的大文件，从压缩包中解压数据时请谨慎操作。</strong> <br> <strong>本数据集包含的文件，其生成/复现所需的脚本运行耗时较长。</strong> <strong>数据集内容</strong> 我们将简要汇总本数据集包含的文件。如需了解这些文件内的数据结构、生成脚本以及本研究数据处理流水线各环节的运行时长，请参阅配套文档。 <code>epoch_9_loss_0.04706_testAcc_0.96867_X_resnext101_docSeg.pth</code>: 我们共享此由<em>Jobin等人</em>最初提供的模型文件，用于对本数据集中的图表进行分类。请将该文件放置于`model/`目录下。 <code>model-results.csv</code>: 本文件包含针对本数据集中笔记本内图表所执行的分类任务结果。完成该分类任务可能耗时长达一天。 a11y-scan-dataset.zip: 该压缩包包含两个文件，解压后总数据集大小约为60 GB。请确保拥有足够的磁盘空间以解压此zip压缩包。该压缩包包含： <code>a11y/a11y-detailed-result.csv</code>: 本数据集包含针对10万个不同主题的笔记本执行可访问性扫描所得的结果。该详细结果文件体积庞大（超过60 GB），构建耗时较长。 <code>a11y/a11y-aggregate-scan.csv</code>: 本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。该文件同样被共享在压缩包目录之外。 <code>errors-different-counts-a11y-analyze-errors-summary.csv</code>: 本文件包含不同主题笔记本中各类错误的出现次数统计。 <code>nb_processed_cell_html.csv</code>: 本文件包含从我们的笔记本HTML导出文件中提取的每个单元格对应的元数据。 <code>nb_first_interactive_cell.csv</code>: 本文件包含必要的元数据，用于计算每本笔记本中本文所定义的首个交互元素。 <code>nb_processed.csv</code>: 本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、语言类型以及单元格层级的相关信息。 <code>processed_function_calls.csv</code>: 本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和函数调用信息。