Welcome to the Danube Digital Accelerator! For readers meeting you for the first time, could you introduce Codevence Solutions and explain what FAIR Wizard does and who it helps?
Hi, thanks for having us. Codevence Solutions is a small Czech company founded by the core DSW team (Jan Slifka, Marek Suchánek, Vojtěch Knaisl, and Robert Pergl) to offer professional, custom-tailored services for commercial companies. Initially, it was mainly focused on custom development and DSW support, and consultancy. In 2021, I (Jakub Jirka) was approached by Vojtěch, my university friend, to join them as the “consultant” responsible for leading the business side. I accepted the challenge, and we started working together.
This is when the idea for a SaaS platform as an extension of our already existing DSW product started to take shape, and FAIR Wizard was born. In the beginning, it was just cloud hosting of the open-source DSW solution, but over time, we evolved it into a platform that helps institutions improve their research through efficient research data management, especially data management plans.

‍In simple terms – what does FAIR Wizard make easier for researchers and for institutional teams like data stewards, libraries, or research offices?
As mentioned above, we focus on helping institutions do better research by improving their research metadata. Research and its data are complex and often require a lot of effort to navigate. FAIR Wizard enables institutions to efficiently gather information about research and research data and work with it in a machine-actionable manner.
We do this using easy-to-use smart questionnaires that can serve many different use cases. The most common one is currently a Data Management Plan. Through this plan, institutions can retrieve important information from researchers about what they plan to do in their research project, ranging from data-related topics such as reuse, storage, and licensing, through administrative and legal information like ethical reviews and GDPR compliance, to specific research-related needs such as special equipment.
Usually, this knowledge exists only in the heads of researchers and other staff working on the project. We help data stewards and research officers collect it in a simple and efficient way, so researchers spend less time on administrative tasks and can focus more on actual research. When an institution has information about planned research, it can prepare more easily for what is needed, making the whole research process smoother for all interested parties.

‍You also embed education and best practices directly into the workflow. How do you help researchers “learn by doing” without slowing them down?
We use the concept of guiding and educational questionnaires combined with smart metrics and best-practice recommendations. We recommend that users rely primarily on closed-answer questions rather than open text fields. This makes it much easier to guide the person filling in the questionnaire toward providing only the information needed at a given time.
We combine this with metrics and recommendations. In practice, it works like this: if a researcher answers a question about the existence of previous data they will use in their research with “no,” but we know that in their institution, pre-existing data is usually available, the questionnaire indicates that this might not be the best approach. It informs the researcher that pre-existing data often exists and recommends repositories where they can look for it. If the researcher does find a dataset to reuse, they can update their answer accordingly.
For every answer, we can define how it affects a specific metric. This gives researchers direct feedback on how well aligned their plan is with the best practices of a given organization or grant specification. Together with collaborative features such as comments, to-dos, recommendations, and extended descriptions, this makes the process easier for researchers to complete and easier for institutions to review.

‍How did the team come together, and what’s the story behind FAIR Wizard’s evolution from Data Stewardship Wizard to a product for institutions?
In the beginning, as a company, we provided custom support for clients who had their own setups of the open-source Data Stewardship Wizard. With different client environments and custom deployments, most of the time was spent debugging issues caused outside of DSW itself, such as deployments or network setups, rather than actually improving the tool.
This led us to realize that if we wanted to deliver a better user experience, we needed to run the solution on infrastructure we fully understood and controlled. That is how FAIR Wizard started. Initially, it was simply cloud hosting of DSW, with additional services, B2B partnership, and guarantees like SLAs. Over time, we added many features on top that focus on helping large institutions work more efficiently with FAIR Wizard.
We did not forget where we started. DSW is still actively developed, and all core DMP features are contributed back to the open-source project, which continues to have many happy users and use cases.
For institutions that want an all-in-one solution with guarantees, support, and institutional-level features, FAIR Wizard aims to cover all of this.

‍When you joined the Danube Digital Accelerator, what were your main goals?
Our main goal was to gain insights into trends in research-related fields. We had previously been part of the Czech Crunch accelerator, which we really liked, and we wanted to see how people from academia approach topics such as funding, scaling, and leading a startup.
Secondly, I am looking forward to the events where real networking takes place. From my previous experience, the connections you make in the startup community are often the most valuable assets.

‍Has anything in the DDAccelerator already influenced your strategy?
I think that being part of the DDA has confirmed our existing strategy. We believe we are moving in the right direction, and now it is more a matter of time and resources.

Looking ahead, what are your next milestones over the next 6–12 months – new markets, clients, product upgrades, or new use cases?
Our next milestone is to grow the number of current customers. We are aiming for at least 50 percent growth over the next year. We will be opening our service in the Asia-Pacific region, and we are also exploring a few new use cases related to research and cybersecurity.

Thank you to the Codevence Solutions team for being part of the Danube Digital Accelerator. We appreciate the insights you shared and the work you are doing to make research data management easier for institutions and researchers alike. Wishing you the best of luck as you grow, expand into new regions, and reach your next milestones.