Publication of dataset created in nodegoat by the MapModern project

CORE Admin

The MapModern project at the Universitat Oberta de Catalunya (UOC) that uses nodegoat to analyse cross-border literary networks and cultural mediators in the hispanic world between 1908 and 1939, has recently published a dataset on translations and reviews in hispanic modernist journals.

This dataset has been created in their nodegoat database by Laura Fólica and Ventsislav Ikoff, under the leadership of Diana Roig Sanz. With the help of the staff of the UOC library, the dataset has been made Dublin Core compliant. The dataset can be downloaded from the data repository of the UOC: http://hdl.handle.net/10609/86485, or from the EUDAT Collaborative Data Infrastructure which has assigned the dataset with a DOI: 10.23728/b2share.eb5c468d3dc3401c8b2fb4605d868a00.

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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

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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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