Research Data Workflows
and LLMs

Session 1 · Making Estate Materials Digitally Accessible

Research Data Workflows in Stefan Zweig Digital

Learning objectives

  • Understand what research data is and why it matters.
  • Understand research data workflows and the role of metadata standards.
  • Understand the principles of the Semantic Web and Linked Open Data.

Describe the “Schulnachricht” in XML

From XML to IIIF with Python

Encode the Schulnachricht in TEI XML with LLM Assistance

Represent a Source Image in RDF with LLM Assistance

Session 2 · Large Language Models for Research Data Workflows

An Introduction

Learning objectives

  • Develop a basic understanding of large language models (LLMs).
  • Understand the fundamentals of prompt and context engineering.
  • Gain initial hands-on experience using LLMs in research data workflows.

Exploring Research Data with LLMs

From Facsimiles to TEI XML

Sessions 3 and 4 · Large Language Models for Research Data Workflows

Hands-On Practice

Learning objectives

  • Use LLMs for coding and build small research tools through Promptotyping.
  • Understand AI agents and the role of an AI harness.
  • Apply context and knowledge engineering to research tasks.
  • Practise agentic engineering by inspecting tool use, checking results and refining instructions.

AI Harness and M³GIM Data

Promptotyping Project: A Small Digital Edition or Research Dashboard

Tools

From XML to IIIF viewer

Built for the Session 1 hands-on with Claude Fable 5.1. Select your XML description or generated IIIF manifest with its page images and see them as a digital object in Mirador, entirely in your browser.