LAB1100 celebrates its 10 year anniversary

CORE Admin

This year we celebrate our 10 year anniversary as LAB1100. We are grateful for all the exciting projects that we have been involved in, and for being able to collaborate with so many inspiring people. We look forward to continuing to work together with scholars from all over the world to deliver trailblazing research software.

LAB1100 was established as a company in March 2012. Our first tweet happened even a couple of months before that, while we (Pim van Bree & Geert Kessels) were finishing our studies (Media Studies and History, respectively).

We started to work on our first diachronic geographic visualisation of correspondence networks in October 2011, commissioned by Joep Leerssen in the framework of his Study Platform on Interlocking Nationalisms. The interactive visualisation that we launched in January 2012 still runs, and is described in "SpinTime: Dynamically Visualizing. How Diffusion Patterns Evolve over Space and Time".

Later in 2012 we started the development of a web-based application to host the data of the initial diachronic geographic visualisation. We gave this application the name ‘Chrono Spatial Research Platform’ (CSRP). In 2013 we extended CSRP to become a research environment for the humanities and we renamed the application to ‘nodegoat’.

First sketch of the nodegoat logo made in October 2013.

In 2013 we also gave our first presentations on CSRP/nodegoat in Jasna (SK), Hamburg (DE), Saint Petersburg (RU), and Luxembourg (LU). In 2014 we started to provide nodegoat services to Huygens ING (Mapping Notes & Nodes), Universiteit Gent and Universiteit Maastricht (TIC), Universiteit Leiden (Mapping Visions of Rome), and Arsip Nasional Republik Indonesia (Diplomatic Letters).

The Encyclopedia of Romantic Nationalism that runs on the nodegoat installation at the University of Amsterdam has been using nodegoat to create and visualise networks of over 40.000 letters.

Since 2014 LAB1100 has been providing nodegoat services to Universiteit Utrecht, Universitat Oberta de Catalunya, ADVN | archief voor nationale bewegingen, GRIMMWELT, Université du Luxembourg, Universität Bern, Colorado College, University of Western Australia, Ludwig-Maximilians-Universität München, Universidade NOVA de Lisboa, Università degli Studi di Padova, NIOD Instituut voor Oorlogs-, Holocaust- en Genocidestudies, Znanstvenoraziskovalni center Slovenske akademije znanosti in umetnosti, Freie Universität Berlin, Norsk institutt for kulturminneforskning, The Australian National University, University of New Hampshire, Università degli Studi di Trieste, Universität Basel, Johann Wolfgang Goethe-Universität Frankfurt am Main, Forschungsstelle für die Geschichte der Hanse und des Ostseeraums, Universität Wien, Bölcsészettudományi Kutatóközpont, Uniwersytet Warszawski, Universiteit Antwerpen, Università degli Studi di Firenze, Centre national de la recherche scientifique, Wesleyan University, Joods Cultureel Kwartier, Leibniz Institut für Zeitgeschichte, Uppsala Universitet.

Click here to explore an interactive visualisation of our work in the past 10 years.

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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

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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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