What Data Science Taught Me About a 2,000-Year-Old Emperor's Diary

This dataset contains the data associated with the article "What Data Science Taught Me About a 2,000-Year-Old Emperor's Diary" published on Medium. The deposit includes Python scripts and processed data files designed to extract and analyze the vocabulary of Marcus Aurelius’s Meditations. By integrating the LAGT (Latin and Ancient Greek Treebank) corpus with the LSJ (Liddell-Scott-Jones) Greek-English Lexicon, the pipeline calculates lexical variety metrics (such as Hapax Legomena percentages) and tabulates word frequencies across the traditional Stoic tripartite division of Physics, Ethics, and Logic. Included files:- Processing scripts (.py) for data extraction, filtering, and analysis.- Requirement files for environment setup.- README.txt with execution instructions. This resource is intended to support the transparency of the article's findings and provide a framework for further computational linguistic study of Stoic texts.本数据集包含与刊载于Medium平台的《What Data Science Taught Me About a 2,000-Year-Old Emperor's Diary》一文相关的数据。 本存档包包含Python脚本与经处理的数据文件，用于提取并分析马可·奥勒留《沉思录》的词汇特征。本流程通过整合LAGT（拉丁语与古希腊语树库，Latin and Ancient Greek Treebank）语料库与LSJ（利德尔-斯科特-琼斯希腊语-英语词典，Liddell-Scott-Jones Greek-English Lexicon），计算词汇多样性指标（如单次出现词（Hapax Legomena）占比），并依据斯多葛学派传统的物理学、伦理学与逻辑学三分法，对词频进行制表统计。 包含文件如下： - 用于数据提取、过滤与分析的Python处理脚本（.py格式） - 用于环境配置的依赖需求文件 - 包含运行说明的README.txt文档 本资源旨在提升该文章研究结论的透明度，并为后续针对斯多葛学派文本的计算语言学研究提供基础框架。