The Moment I Realized Science Data Wasn’t Speaking

During my Ph.D. research on microbiota, I vividly remember a moment. I had just finished analyzing data on respiratory microbiota and was ready to publish, but something wasn't right. I realized that the data we had generated was only partially analyzed, and I couldn't shake the feeling that we had missed something important. How many valuable insights were we missing?

As I continued my work, I realized that many researchers around the world were working on the same topic and generating similar data sets. But there was no centralized database to bring all our results together, no platform that could give us a global view and help us identify patterns or indicators that might explain differences in our findings. Everyone was working in silos.

I saw this problem firsthand when I embarked on my first systematic review. My goal was to build a repertoire of bacterial species isolated from the respiratory tract-a monumental task. It took years to complete, and by the time it was published, the database was outdated. The process wasn't automated, so it was nearly impossible to keep the information current. And with each update, we were forced to repeat the same time-consuming tasks.

While working on this overhaul, I also realized something else-many important papers were going unnoticed. We weren't using all the data we had. I began to ask myself: What if we could systematically analyze and update research in real time? How many decisions-whether for researchers, institutions, or policymakers-could be better informed by this data? What insights were we missing that could drive innovation?

If we could solve this problem—if we could merge and analyze data across institutions, disciplines, and even borders—think of the impact it could have. It would transform the way we approach Institutional Research Strategy, help us identify emerging research trends, measure the societal and policy impact of research more accurately, and optimize everything from collaborations to funding opportunities.

But to make this vision a reality, I knew I needed more than just research experience. I needed to understand data science, machine learning, and automation—fields that were far from my original training. I realized that solving these problems would require a team of experts: Data Scientists, Data Engineers, Web Developers, AI Developers, and, of course, Domain Experts like myself. This realization drove me to pursue another Master’s degree and countless online courses, building the skills I would need to help researchers and decision-makers unlock the full potential of their data.

And that’s how I arrived at my mission: to Let Science Data Speak. I’ve seen firsthand how much valuable data goes unnoticed, and my goal is to ensure that the hidden insights in scientific publications, patents, and social media aren’t lost. Over the next few weeks, I’ll be sharing how we can unlock these insights and use them to drive innovation and informed decision-making.

What challenges have you faced with research data? Have you ever come across an important insight that was almost missed? Share your story in the comments, and follow me to explore how we can Let Science Data Speak together.

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