Scientific data pouring on us like heavy rain
- Rebecca Savioz
- Mar 6
- 4 min read
Rebecca from SaviozSciences. The other day, I was driving under pouring rain. Millions of drops were falling rapidly, hitting and soaking the windshield, to finally end up in an undefined pond on the wet ground. I was driving on my way to work, and my work, on that day, involved screening an insane amount of scientific literature search hits. The parallel between these rain drops and literature hits was evident. It brought up a feeling I’ve had for years: we generate too much data, and we publish too many articles, that we struggle dealing with, once they’re cascading on our computer screens. Years ago, I remember sitting in a meeting, after a huge amount of data was generated using super-powered technology, the team leader literally saying: “And now, let’s think of a scientific question can we come up with, with all that data.” Isn’t it crazy?
Introduction
The exponential growth of scientific data and publications has led to an unprecedented challenge in research: information overload. Every day, thousands of new studies are published, each contributing to an ever-expanding pool of knowledge. However, this rapid accumulation often outpaces our ability to effectively analyze, interpret, and integrate findings into meaningful scientific progress. The scientific community now faces a paradox: while technological advancements have enabled us to generate vast datasets at an extraordinary rate, the process of deriving valuable insights from this information has not evolved at the same pace.
The exponential growth of scientific data and why it is a problem
A simple search in PubMed, run with “studies” as a keyword, brings up over 16 million publications. Their distribution over time, and hence the distribution of their supporting data, is beautifully exponential, with the dramatic increase starting approximately four decades ago.
Why has this happened?
New technologies have significantly enhanced our ability to generate, store and process vast amounts of data. For example, high-throughput systems have invaded laboratories; user-friendly and connected data capture devices have proliferated; computer-aided systems have deployed, boosting productivity. In parallel, institutions have gone into a crazy rush towards more publications, driven by the pressure to secure funding, achieve tenure, and gain recognition, often leading to a quantity-over-quality approach.
But I’m not a technology expert; I beg your pardon if I used high-tech terms in wrong ways, sounding more like we’re being invaded by extra-terrestrials than aided by friendly machines. I don’t think we should be afraid (too much). We should apprehend how different this world is, compared to what my old university professor explained to me. Back then, research was conducted with meticulous planning, as data collection was laborious, and resources limited. Results graphs were to be manually drawn on paper and publications thoughtfully crafted rather than rushed on an excel sheet.
Why is it a problem?
There is of course a positive side to the increasing amount of scientific data, that I don’t want to overlook. More scientific data means an increasing engagement in science from a broader world-wide community of researchers. Our overall knowledge is increasing in all fields to inform our innate curiosity, to help science-based decision making, to derive more accurate predictions, or to create opportunities for innovation. So, why is it a problem?
The problem lays, in my opinion, mainly in the interpretation of the data. The data overload is overwhelming; it is impossible to grasp all of it, with all its implications, and considering all factors simultaneously. Unable to articulate the relevance of the findings, we’re left with a “so what” question. My brain, just like yours, is a human brain, which can only consciously process an estimated 120 bits per second. This is roughly 5.3 billion times less than a computer.
How can we deal with it?
Slowing down?
Going backwards and slowing down is an option, but history tells us that it’s rather unlikely at the society level. However, at an individual level, practicing “slow science”, which, just like slow food, advocates for mindful practices that prioritize quality over expediency, and a deep understanding over the rush to publish, could be desirable. Don’t be afraid to challenge the fast-paced, quantity-driven norms! Back in the 16th century, Rabelais was already warning us that "science without conscience is ruin of the soul".
Using Artificial Intelligence?
AI can help identifying patterns and insights that might be missed by humans and it can do that much faster. We scientist should however make sure we use the right tools, that help us derive correct interpretations. If not, we’re back with the “so what” question.
Conclusion
In the end, it’s up to each one of us scientists to decide how to best run our research. Rushing into generating tons of data and being overwhelmed; being cautious and thoughtful but too timid; or going for a clever blend of mindful science and modern technologies, using them just as the wonderful tools that they are.
At WordifyScience, we’re scientific experts used to dealing with data – literature search sorting, data curation, data analysis, literature reviews, etc. Give us a call if you feel soaked and overwhelmed with too much data to deal with. We are here to help transforming that heavy rain into a positive experience and taking the best advantage of it!
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