New nodegoat Data Publication Module

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Publication of the 'Imagology' project of Joep Leerssen, see this page for more info and a public user interface

Publish your project with the new data publication module. nodegoat users can now select any project to generate a data publication that is web-accessible and downloadable as a ZIP-file. By generating a new publication a Project's data model and all of its data are published and archived. The publication remains accessible also when new publications are generated at a later stage.

Publications are stand-alone self-containing archives which include both the HTML-interface to the data model as well as all of its data in both JSON and CSV.

This new publish feature extends the existing data extraction and data publication options, i.e.: the export functionality, the API, and the public user interface.

All these functionalities ensure that nodegoat users are able to maintain a clear separation between their data and software as their data can always be extracted from the software. By means of this new nodegoat publication module it is possible to upload or update a data publication in a repository like zenodo.org by simply uploading a newly generated zip-file of your nodegoat project.

This module has become possible thanks to a commission of Joep Leerssen’s 2008 NWO Spinoza Prize fund.

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Latest Blog Posts

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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