When Prof. Ruggeri and I met at one of the major conferences on cognitive development (BCCCD) in January 2020, we realized the unique research opportunity ahead of us: merging innovative eye-tracking methods (my expertise) with strategic exploration paradigms (hers) to identify the early emergence of human exploration. This is a prime example of how sharing is innovating: without conferences, the latest advances and ideas struggle to spread, and cutting-edge research—especially at the intersection of disciplines—cannot materialize. This reality became evident shortly after the conference, when, as COVID-19 began to spread, ideas dwindled.
Once the collaborative project kicked off, it was all about sharing: while ideas travel as fast as a video call, technological innovations in academia can be slower. Every research institute and every lab has specific regulations, pipelines, and software. I started developing a simple Python environment on an external drive, which I could easily activate in any lab, bypassing eye-tracking proprietary software, which often lacks flexibility, transparency, and is also quite expensive. After successfully implementing this in my labs at the Donders Institute, I managed to run it smoothly at the Max Planck Institute, where we ultimately collected the data. The sharing, from ideas to lines of code, was completed, and the project could finally take off.
A few months later, I was in London, where I tried out my Python drive on a local computer and happily realized this sharing could extend beyond a single project to anyone interested in running experimental studies openly and effectively. In the lab with me that day was Giulia Serino, a researcher with a similar interest in open science, who encouraged me to think bigger: why keep the code on a single drive when it could become an open-access tutorial that anyone can not only use but also improve? Here, over coffee breaks, DevStart began to take shape. Fortunately, Tommaso Ghilardi soon joined us for coffee and quickly embraced the open-access cause. He had the technical skills to make this happen: an online guidebook that teaches how to implement experimental paradigms in Python from scratch to completion, including data collection and analysis.
This is the stuff that is still hard to share. Data is now often (if not always) available online, but how was it collected and preprocessed? These steps are as much a part of open science as any other, and DevStart is now filling that gap.
I want to conclude with this: open science is inclusive science. These tools are freely available online, and everyone can use them. Additionally, they allow researchers to conduct studies with lower costs, including centers and universities from disadvantaged backgrounds. But this is not enough. All of this wouldn’t have happened if I hadn’t attended a conference in person, and this is still too hard for most people. It is not only the costs of traveling but also the unjust barriers posed by visas. I know too many researchers who missed conferences because their visas did not come in time, and this has to stop. Organizing conferences in countries that process visas more swiftly, as well as communicating talk and poster acceptances ahead of time, are two huge imperatives for truly open and inclusive science.
I wish fun, inclusive, and open science to everyone, and do get in touch if you want to know more about DevStart (https://tommasoghilardi.github.io/DevStart/) .
Sharing is Innovating: Transparent Data Collection and Processing with DevStart
