nodegoat Workshop at the 2022 FGHO Summer School

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From August 22 until August 24 2022 LAB1100 ran a nodegoat workshop at the Summer School organised by the Forschungsstelle für die Geschichte der Hanse und des Ostseeraums (FGHO) in Lübeck.

This year's Summer School was focused on working with databases and analysing source material and was titled 'Versammeln und Entscheiden: Willensbildungsprozesse auf Tagfahrten der Vormoderne'. The Summer School was co-organised by the University of Kiel and the University of Freiburg and took place at the Europäisches Hansemuseum Lübeck.

Since multiple participants were interested in creating their own dataset of spatial data, we discussed various approaches on how to setup your own historical gazetteer. We used the dataset created by the Viabundus project as an example of an existing gazetteer that can be imported into nodegoat and used to geocode historical data.

During the nodegoat workshop, Angela Huang and Vivien Popken of the FGHO presented their nodegoat environment to the participants and showed their data entry workflows. Participants could then login and enter data on Hanseatic Diets into the nodegoat environment of the FGHO.

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Data and Dialogue: Retrieval-Augmented Generation in nodegoat

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We have extended nodegoat in order to be able to communicate with large language models (LLMs). Conceptually this allows users of nodegoat to prompt their structured data. Technically this means nodegoat users are able to create vector embeddings for their objects and use these embeddings to perform retrieval-augmented generation (RAG) processes in nodegoat.

This development connects three of nodegoat’s main functionalities into a dynamic workflow:  Linked Data Resources, the new vector store (nodegoat documentation: Object Descriptions, see ‘vector’), and Filtering. The steps to take are as follows:

Vector Embedding

The first step is to use one or multiple Reversed Collection templates to determine the textual content for each Object. This step transforms any dataset stored as structured data into a textual representation that can be used as input value for the generation of a vector embedding. This allows the user to select only those elements that are relevant for the process.

A Reversed Collection using a template (left) to collect structured data into full text (right).

Next, the textual representation of each Object is sent to an LLM in order to create an embedding for each Object. The communication between nodegoat and an LLM is achieved by making use of Linked Data Resources and Ingestion Processes.[....]

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Upcoming nodegoat workshops

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In the next couple of months we will be running these events at various locations throughout Europe. Find the latest information about this here: https://nodegoat.net/workshop

  • 05-02-2026: nodegoat Workshop at the University of Basel organised by the Research and Infrastructure Support team and the Swiss National Data and Service Center for the Humanities.
  • 19-02-2026: nodegoat Workshop at the University of Jena.
  • 25-03-2026: Workshop: Einführung in nodegoat at the University of Bonn.
  • 16-04-2026: nodegoat Workshop at the Research Centre of the Slovenian Academy of Sciences and Arts in Ljubljana.
  • 24-04-2026: nodegoat Workshop at KU Leuven, organised by CLARIAH-VL.
  • 10-07-2026: nodegoat Curious: Building a Custom Relational Database for Your Research at the Digital Medieval Studies Institute, IMC Leeds.
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