Join us for a multi-day event where we will dive into bibliographic metadata. This is a multi-disciplinary event for researchers and students to come together to explore a dataset together, sharing their expertise and building resuable tools and workflows. No prior experience is needed - just bring a laptop! Consultants from DKZ.2R will be on hand to help implement ideas and provide support with data retrieval, cleaning, and transformation.

Event Details

Date: June 30 (14:00) – July 3, 2026 (12:00) Location: Aachen, Seffenterweg 23 / Kopernikusstr. 6 (IT Center) Catering provided | Bring your own laptop

Registration

To help us plan the event please sign up using this form

Event Poster

When Metadata Becomes Data

What can you really do with bibliographic metadata — and how much knowledge is hiding in plain sight? This hackathon brings together researchers, developers, and students from all disciplines to explore the boundary between metadata and research data.

Our shared data source is iDAI.bibliography, a curated bibliographic database maintained by the German Archaeological Institute, comprising around 1.4 million entries. Working with data from outside your own domain is the best way to appreciate just how rich and powerful metadata can be.

All tools and technologies are welcome — the one requirement is that every step is documented and reproducible.

Tracks

(still under development)

Data Retrieval and Cleaning

Retrieve data from iDAI.bibliography, clean it, and transform it into a consistent, reusable format — the foundation for all other tracks. Tasks include normalizing author names, standardizing dates, and extracting a minimal schema across entries.

Data Analysis and Visualization

Take the cleaned metadata further and turn it into insight. Explore co-authorship networks, trace how research topics rise and fall over decades, or uncover publication patterns by language, place, or document type. Produce interactive dashboards, network graphs, or timelines that make the data speak for itself.

Data Transformation (Wikidata & Semantic Linking)

Map bibliographic entries to Wikidata and contribute to an open, global knowledge graph. Link authors, institutions, journals, and topics to existing Wikidata items — or create new ones where they are missing. Tools like OpenRefine, QuickStatements, or custom scripts are all fair game.

Hackathon Focus

Projects will be evaluated on:

  • Data quality – cleanliness, correctness, and robustness
  • Reproducibility – can others re-run your workflow?
  • Novelty and insight – how much new understanding does your work create?
  • Reuse potential – can the community build on it after the hackathon?
  • Communication – clarity of documentation and quality of visualizations

Satellite Event

On June 30, 4:30–6:30 PM, join us for the Battle of the Editors — a playful warm-up competition kicking off the hackathon weekend. → See separate announcement

Organized in collaboration with DKZ.2R, DKZ WiNoDa and NFDI4Objects.

Related Posts

Call for participation

Call for participation

Call for participation!

The Data Literacy Center Rhine-Ruhr (DKZ.2R) issues a call for participation in its “rent-an-expert” project! We offer support for ambitious research projects of PhD students and early postdocs dealing with Data Science and Artificial Intelligence, High Performance Computing and Simulation, and Research Data Management. As the DKZ.2R is funded by the German Federal Ministry of Education and Research (BMBF) as well as the EU, this offer is free of charge!

Read More

Intro to Programming with Python

As part of our “Trainings” work package, the DKZ.2R creates, curates and presents a variety of free trainings, seminars and courses. Our next offering will be a two-day Python Intro course in collaboration with hpc.nrw. The Lecture part of the workshop will be streamed live from Bonn to different locations where participants are supported by on-site instructors in working on the exercises and in the case of questions. The DKZ.2R will support participants on-site in Aachen for the two-day event.

Read More

Seminar - Statistical and Machine Learning Methods

Course Description and Goals:

Statistical learning is a field that teaches students how to analyze and interpret data by applying statistical methods and machine learning algorithms to uncover patterns, make predictions, and gain insights from data. The syllabus includes: • Statistical and machine learning methods, including linear and polynomial regression, logistic regression, and linear discriminant analysis. • Model validation techniques such as cross-validation and bootstrap, model selection, and regularization methods (ridge and lasso). • Nonlinear models, splines, and generalized additive models. • Tree-based methods, including random forests and boosting. • Support-vector machines and an introduction to causal inference. • Unsupervised learning methods such as principal components analysis and clustering (k-means and hierarchical). Examination Format: Report and Presentation. Further information, including locations, and Zoom links, can be found on our homepage: https://oek.wiwi.uni-due.de/studium-lehre/lehrveranstaltungen/sommersemester-26/statistical-learning-vorlesung-17350/

Read More