Learn how to visualise historical data on a historical map

CORE Admin

The nodegoat guides have recently been expanded with sections on working with spatial data and on visualising data on any historical map.

The best way to learn how to use nodegoat is by following the nodegoat guides: nodegoat.net/guides. New sections of this guide teach you how to store various kinds of spatial data and how to visualise your data on historical maps: 'Storing Locations', 'Create your own Gazetteer', 'Import an existing Gazetteer', 'Use a Historical Map'

You are able to use historical maps from existing collections of historical maps, like the David Rumsey Map Collection. You can also use your own images of historical maps as a background for your spatial data. In both cases you can use the Georeferencer service, which publishes map tiles.

You can find an overview of institutes that publish their maps as tiles via the Georeferencer service on this page. See this page for an overview of georeferenced maps from the David Rumsey Map Collection. To upload your own maps, go to the Georeferencer service and create an account to get started.

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Data and Dialogue: Retrieval-Augmented Generation in nodegoat

CORE Admin

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

CORE Admin

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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