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.

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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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  • Large-scale study of attention change trends using ngrams

    Töövoog võimaldab analüüsida eri kategooriate mainimissagedusi pika ajavahemiku jooksul või tihedate andmetega, et teha nähtavaks sarnasused ja tõstatuvad mustrid. Andmed koondatakse maatriksiteks, mille põhjal luuakse kuumkaarte (heatmap’e), mis võimaldavad esitada suuri andmehulkasid visuaalselt selgelt ja võrreldavalt. Neid visuaale saab lugeda nii ajas kui ka kategooriate lõikes, mis teeb võimalikuks eri perioodide ja teemade kõrvutamise ning

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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

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  • Making (Meta)Data of Musical Works Accessible as a Thematic–Bibliographic Catalogue

    Currently, information on Estonian musical works is fragmented across several platforms: general information on works is available on the website of the Estonian Music Information Centre, data on manuscripts in the Museums Public Portal, and bibliographic records of printed scores as well as audio and video recordings in the shared catalogue of ESTERFurthermore, data in these systems is structured according to quite differing principles. For example, the MARC21

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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

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  • Comparative Topic Analysis of Ukrainian and Estonian Folk Songs Using AI Translation and Computational Methods

    Uurimuse eesmärk oli tuvastada ukraina ja eesti rahvalaulude temaatilist kattuvust, kultuurilisi sarnasusi ja ainuomaseid teemasid arvutuslike meetodite abil, kasutades eelkõige tehisintellektipõhist tõlget ja LDA-teemamodelleerimist. Kuigi ukraina ja eesti rahvalaulud esindavad erinevaid keele- ja kultuuritraditsioone (läänemeresoome ja idaslaavi), on mõlemat kultuuri ühendanud ajaloolised kontaktiperioodid, mis võivad kajastuda ka rahvapärimuses. Juba varakeskajal ühendasid mõlemat piirkonda põhja–lõunasuunalised kaubateed,

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