Workflows
Workflows in the HUM Data Lab
Here you can find workflow descriptions by researchers in the HUM Data Lab that help make the research process more understandable, transparent, and reproducible. The workflows show step by step how a research question develops into data selection, analysis, and interpretation. In the future, these workflow descriptions, together with the code and tools needed to carry them out, will be available in a separate environment.
Publish your workflow
It is also possible to publish your own workflow description in the HUM Data Lab. To do so, please use the workflow description form. All submitted workflows receive feedback from reviewers before publication.
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Pattern Analysis of Striped Skirts
This workflow describes how image-based patterns are compared using machine learning methods. The aim is to investigate the pattern logic that characterised Estonian striped skirts of the 18th–19th centuries and to examine how these patterns relate to striped, glazed-surface woollen callimanco fabrics produced at the Norwich factory in England. The workflow combines digital humanities approaches with machine learning. For analysing large image datasets, we use machine learning models designed for visual pattern recognition, specifically convolutional neural networks (CNNs).
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Comparative Topic Analysis of Ukrainian and Estonian Folk Songs Using AI Translation and Computational Methods
The aim of the research was to identify thematic overlap, cultural similarities, and unique topics in Ukrainian and Estonian folk songs using computational methods, particularly LLM translation and LDA topic modelling. Although the two nations belong to different linguistic and cultural traditions (Finnic and East Slavic), they shared periods of historical contact that may be reflected in their folklore. From the early Middle Ages, both regions were connected through northern–southern trade networks...