TRAINING

We are committed to helping researchers orient themselves and learn new methods, that's why we have developed even more Training Pathways.

This year's additions cover the topics of Statistical Analysis and Text Analysis. Two new pathways on Managing Digitised Documents and Analysing Geographical Data are currently under development.

We kicked off the second semester with a new type of training: the workshop series. These are interconnected training events that focus on different aspects of the same topic. Attendees can book all of the associated workshops or just individual workshops.

The first four series covered: Copyright, Recording 3D data, Digitised Documents and Statistics. 

The Data Surgeries meetings are a new initiative this year. These one-to-one meetings with our training fellows can be booked by CAHSS researchers looking for help with the application of data and digital-driven techniques. After attending training, researchers can sometimes struggle to apply their learning to real-world data: this format provides a chance to get advice on how to process data, how to troubleshoot issues and how to find further information and training options.

James is a PhD student in Science, Technology and Innovation Studies. His research concerns the implementation of e-ID systems, specifically looking at the EU Settlement Scheme. He uses text mining alongside social surveys and interviews to understand user experience with the EUSS.

Stefano has recently completed a PhD in Architectural Archaeology at the School of History, Classics and Archaeology. In his thesis, he turned the building materials used in medieval Umbria into quantitative data, in order to identify patterns. Through this, he is knowledgeable in photogrammetric surveying, AutoCAD vector drawing, QGIS geoprocessing, RStudio data management and visualisation.

Lucy is a PhD student based in the School of Informatics, and she researches bias in cultural heritage metadata. Using a collection of case studies within cultural heritage collections (such as the Archives at the University of Edinburgh), she combines natural language processing and data visualisation technologies to identify and classify the bias present in the language of cultural heritage catalogues.

Fang is a PhD student at the School of Philosophy, Psychology, and Language Sciences. Her project investigates how speakers encode prominent information in simulated conversations and how listeners predict upcoming utterances in comprehension. She works with both laboratory and corpus data. She conducts data analyses in R using multivariate statistical tools such as mixed-effects models.

Andrew is finishing his PhD at the School of History, Classics and Archaeology. As an archaeologist with research interests currently focused on the economy of the Roman Adriatic, his methodological approaches include GIS and statistical analysis, particularly expanding on traditional Least Cost Path (LCP) analysis by using circuit theory to model maritime movement.

Enric is a PhD researcher at the School of Economics, focusing on macroeconomics and inequality. His research is based on quantitative heterogeneous agent models estimated using cross-sectional and longitudinal household-level data, which he uses in order to identify the effect of different macroeconomic policies on consumption, wealth, and income inequality.

Daniel holds a PhD degree in musicology. He has worked with museums and private collections in the UK, Germany, and USA, using traditional and digital technologies to extract information from original art objects. As a Chester Dale fellow at the Metropolitan Museum of Art (NY), he also took part in a project investigating the efficacy of photogrammetry in studying museum objects.