Lessons from the Classroom: MEI for Data Scientists
Many data science and computer science students today are familiar with JSON, and may even have worked with APIs to extract data from the web. Ask about XML,1 however, let alone TEI or MEI, and you are often met with quizzical looks. Yet XML files contain much information that can be productively an...
Gardado en:
| Publicado en: | URI:https://journals.openedition.org/jtei, |
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| Auteurs principaux: | , |
| Formato: | Article ou chapitre numérique |
| Idioma: | Anglais |
| Publicado: |
Journal of the Text Encoding Initiative
2025
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| Sujets: | |
| Acceso en liña: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Résumé: | Many data science and computer science students today are familiar with JSON, and may even have worked with APIs to extract data from the web. Ask about XML,1 however, let alone TEI or MEI, and you are often met with quizzical looks. Yet XML files contain much information that can be productively analyzed with modern data science tools, so training students to leverage these materials is a worthwhile endeavor. The article shows some of the methods we use to help students understand XML as a hierarchical network of elements, how to traverse this network in search of relevant data, and how to harvest XML elements and attributes as tabular data for further analysis. It also reflects on some of the larger lessons learned through all of this work, as students were encouraged to consider the implications of representing the same knowledge in different ways, or what is gained or lost in the transformation of that knowledge from one representation to another. |
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