Research Day 2023

SmallData had the pleasure of participating in the University of Freiburg’s Faculty of Medicine’s Research Day on Turning Data-Driven Research into Health Benefits on Friday 24th November 2023. The CRC speaker Harald Binder gave a talk on The Promise of Machine Learning with SmallData and presented the vision and aims of the CRC in general. PI Joschka Bödecker contributed a […]

Successful Welcome Days Workshop for Doctoral Researchers in the SMART Integrated Research Training Group

On November 9th and 10th, 2023, we hosted the inaugural ‘Welcome Days’ workshop for the newly established SmallData Integrated Research Training Group SMART for all doctoral researchers within our CRC. The two-day workshop was designed to familiarize doctoral researchers with SMART’s mission, stimulate group activities, and to initiate networking. Key highlights of the Welcome Days […]

SmallData goes Science Days

Our CRC Small Data brought both principal investigators and doctoral researchers to the forefront at Science Days from October 19-21, 2023, in Europa-Park Rust. Engaging students of all ages from Germany, France and Switzerland with interactive exhibits like building cubes from puzzle pieces, they illuminated the field of developing small data methods by combining pieces […]

CRC 1597 Small Data has officially started

October 1st, 2023 marks the official start of our CRC 1597 Small Data. It has been quite a journey to get to this point. Starting with a brainstorming meeting in 2019, where we Freiburg researchers from different areas of data analysis and artificial intelligence discussed ideas for collaboration and converged on the topic of “small […]


New Methodology: Testing similarity of parametric competing risks models for identifying potentially similar pathways in healthcare

Our PIs Nadine Binder and Holger Dette have produced a flexible new tool to address the identification of similar patient pathways in clinical healthcare with funding from the CRC 1597 Small Data. Specifically, they considered parametric competing risk models, where transition intensities may be specified by a variety of parametric distributions. They assessed the similarity […]

SmallData is now on LinkedIn™! 

SmallData is now on LinkedIn™ under “Collaborative Research Center 1597 Small Data”. Follow us there for regular updates on our events, publications and relevant information in the field of small data. We can’t wait to see you there! 

Josif Grabocka is a new W3 professor at the Technical University of Nuremberg

Our PI Josif Grabocka is now a W3 professor for Machine Learning at the Technical University of Nuremberg (UTN). Automated machine learning is the main focus of the Machine Learning Lab, who are currently developing cutting edge methods to optimise the hyper parameters of deep learning models. Congratulations Josif! Click here to view Josif’s profile […]

Carsten Dormann among eight most cited researchers at University of Freiburg

Our PI Carsten Dormann is among the “Highly Cited Researchers” for 2023 published by the company Clarivate Analytics. Together with seven other researchers from the University of Freiburg he is among the most-cited authors in his research field.  Congratulations!  Click here to view the full press release of the University of Freiburg.

Our Mission

For tackling small data challenges, we focus on three key areas — similarity, transfer, and uncertainty — and establish a shared language for fusing methods across disciplines.

The recent progress in artificial intelligence has been facilitated by big data volumes and data-driven modeling approaches. However, there is a much larger number of applications where data analysis has to be performed with a relatively small number of observations. This can be addressed with knowledge-driven approaches, such as differential equations that incorporate biomedical domain knowledge, or by adapting data-driven approaches, e.g., neural networks, or ideally by combining the two types of approaches. When developing such methods for small data settings, three key areas need to be addressed: similarity, transfer, and uncertainty. This enables combination of similar observations, e.g., from different hospitals, and more generally transfer of information, e.g., between genome screening and mechanistic models, while quantifying and reducing uncertainty. For creating comprehensive solutions that fuse such exciting emerging ideas, our Collaborative Research Center (CRC) 1597 'Small Data' (SmallData), which started on October 1st, 2023, integrates contributions from computer science, mathematics, and statistics/systems modeling, with input from biomedicine. Our doctoral program SMART is key for establishing a shared language across those disciplines. We are located in Freiburg, Germany, with external partners in Bochum and Greifswald. For more details see our scientific framework and interdisciplinary approach, or have a look at the individual research projects.

Administrative Manager

Marc Schumacher

Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center –
University of Freiburg