Visual analysis

keyword

• Analysis of Regional and Temporal Patterns in Estonian Instrumental Folk Music

  This workflow enables the analysis of the regional and temporal distribution of instruments and instrumental tunes in a dataset of Estonian instrumental folk music. During the workflow, the metadata of the archival dataset are cleaned, instrument and place names are standardised, and broader analytical instrument categories are created as needed. The frequencies of instruments and folk tunes are then calculated by county and time period. The results are presented as frequency tables, diagrams, and maps, which help make visible patterns that would be difficult to detect by looking at individual records.

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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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• Use cases for working photos of material culture researchers

  This project investigates how working photographs collected by material culture researchers can be used to automatically identify objects and assess their condition, and how such a large collection can be published under FAIR principles in a user-friendly way. Researchers of material culture may take thousands of photos of objects within the scope of each research topic, but in most cases, the use of photos is limited to typological identification, visual comparison of finds, and...

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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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