Extended collaboration with the University of Bern

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LAB1100 has extended its collaboration with the University of Bern in 2020 with two new projects.

The Department of Medieval History of the University of Bern started its first project with LAB1100 in 2017 when they migrated their database of medieval scholars to a nodegoat installation. This project, the Repertorium Academicum Germanicum, has since used nodegoat as their primary data storage application and research environment. nodegoat is also used to create and publish diachronic geographical and social visualisations.

From the beginning of this year to 2021, LAB1100 participates in the SNSF SPARK project 'Dynamic Data Ingestion (DDI): Server-side data harmonization in historical research. A centralized approach to networking and providing interoperable research data to answer specific scientific questions', led by Kaspar Gubler, head of Digital Development at the Repertorium Academicum Germanicum.

A third collaboration started in March of this year when the Faculty of Humanities of the University of Bern set up a nodegoat Go service for its researchers and students. A set of new tutorials has been created to serve this user base by Kaspar Gubler.

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Latest Blog Posts

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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