Scientific impact

Making a difference in research

Digitalization is fundamentally changing academic research and is happening in all domains. We value all academic disciplines equally and we’re interested in the content of research above the politics. Most of all, we believe in the power of an incisive research question. To that end, our research projects are chosen for the pioneering challenge, exceptional innovation and research promise they represent.

In 2020 we again invited submissions from interested researchers. We began work on a range of projects touching on fascinating topics from dark matter and spoken language analysis, to glacier change and the cancer genome – to name but a few. And in the course of the year, we celebrated a number of important milestones, elevating the scientific importance of our work.


Papers published


Project proposals received


Jointly funded projects


Completed projects


Blog posts


Life Sciences and eHealth


APRIL 28TH, 2020

Royal Honour awarded to Patrick Aerts


Chris Broekema and Michèle Nuijten winners of the Young eScientist Award 2020

When it comes to ambitions, one of our strategic pillars in the coming years is to build digital expertise. And one way we’re setting out to do this is by sharing our own expertise. We're an organization of passionate and highly engaged professionals; enthusiasts who continually occupy ourselves both at work and at play with diverse thinking in the world of eScience and research. We’ve created a small team of Community Managers now to further evolve past work engaging our audiences, whether it’s through conferences, hosting skills and technology workshops, publishing in the public domain or the many other forms of thought leadership we undertake. Below is a sample of some of our thinking from 3 blog posts this past year:

JUNE 18, 2020

Machine learning: when it is easy & when it is difficult

Sonja Georgievska

Blog posts

AUG 17, 2020

Parallel R in a nutshell

Pablo Rodríguez-Sánchez

APR 15, 2020

Mcfly: An easy-to-use tool for deep learning for time series classification

Florian Huber