Mapping Memory Landscapes in nodegoat, the Indonesian killings of 1965-66

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In the summer of 2014, LAB1100 spent two weeks in Semarang, Indonesia working with a group of students to reveal an infrastructure of violence. These students interviewed survivors of state-sanctioned violence and entered the information they gathered directly into nodegoat.

The Dutch Institute for War, Holocaust and Genocide Studies (NIOD) has set up a cooperation with the Universitas Katolik Soegijapranata (UNIKA) in Semarang, Indonesia that aims to address the anti-communist/leftist violence of 1965-66 in Semarang and the following years. The project that has emerged from this cooperation, ‘Memory Landscapes and the Regime Change of 1965-66 in Semarang’, is led by dr. Martijn Eickhoff (NIOD) and has resulted in two workshops at the UNIKA University in Semarang organised by Donny Danardono. Read more about the second workshop, in which LAB1100 was involved, on the nodegoat blog.

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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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