HUMAL researchers
Workflow origin
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Large-scale study of attention change trends using ngrams
The workflow enables the analysis of mention frequencies across different categories over long time periods or with dense data, in order to reveal similarities and emerging patterns. Data is aggregated into matrices, which are used to createheatmapsthat allow large datasets to be presented visually in a clear and comparable manner. These visuals can be read both across time and across categories, enabling the comparison of different periods and topics, and combining intuitive qualitative visual analysis with quantitative methods.
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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...