This article reviews the digital technologies that support folklore research by tracing the full data cycle. The aim is to inspire beginners in digital humanities and computational folklore studies by presenting key technologies that support digital research. The article describes the process of data collection, pre-processing, analysis and storage. Data collection involves traditional fieldwork, collecting existing data from the web or via specialised repositories. The steps of data collection are presented and explained in the data management plan. The data are then pre-processed by digitising the analogue content through optical character recognition and speech-to-text conversion. The TEI XML and CoNNL-U formats for storing linguistic data are presented and key natural language processing techniques are listed, many of which are available for Slo-vene. Computational methods organise the digitised data, using statistical analysis and machine learning for labelling, segmentation and semantic analysis. We present freely available online tools for data analysis, ranging from less technical concordancers to more advanced machine learning tools. Finally, we discuss data storage and list key reposi-tories for linguistic data. Despite advanced tools, human understanding remains key to contextualising findings and determining meaning in folklore research.
