For Open Science to Work, It Must Become a Natural Part of Everyday Research

Research in time series and data streams is very close to real-world needs – typically in predicting electricity, gas, or other commodity consumption. Predictive models have wide applications in industry, for instance, in predictive quality control or maintenance, where they help detect potential problems before they occur.

The quality of data is crucial. You cannot achieve accurate results if data are not managed or stored. Transparent data management allows methods to be compared, their benefits evaluated, and experiments replicated – which is the foundation of trustworthy science.

One of the cornerstones of proper data management is the FAIR principle – data should be Findable, Accessible, Interoperable, and Reusable. Where do you see the main challenges in implementing these principles?

The biggest challenge is awareness and user-friendliness. Researchers need to understand why these principles exist, how to apply them, and – most importantly – how they can benefit their research. To ensure widespread adoption, tools for storing and sharing data must be as user-friendly and straightforward as possible, which unfortunately is still not the case.

At the university, we are gradually building a comprehensive open science support system – we organise training, educate, and newly employ data stewards who assist researchers in managing their data. We aim to make the principles of open science accessible not only to experienced researchers but also to doctoral students through our Ph.D. Academy. We are also preparing a summer school for data stewards and other training activities to expand this area further.

What is your vision for research data management at the university in the next five years?

The ideal scenario is that open science principles and proper data management become integral to every researcher’s daily routine. I don’t think we are lagging in this regard. Thanks to the activities we are implementing and those we are preparing, we are keeping pace with developments across Europe. We aim for the next generation of researchers to regard these principles as a natural part of scientific practice.

These meetings are extremely valuable. They help share experiences, establish collaborations, and spread examples of good practice. In the future, I would like to see an even wider range of participants involved so that the principles of open science and responsible data management can spread even faster throughout the academic community.

And now, on a lighter note, do you have a favourite tool or algorithm that has impressed you recently?

The rise of artificial intelligence and generative models is definitely the reason. They represent a giant leap forward – the availability of data and tools has opened up opportunities for new applications and more precise algorithms that were not possible just a few years ago.