When chatting with our colleagues, we mentioned a misunderstanding:
scientists may tend to write very complete papers. Some outside this circle may question:
Why do we have to tell a full “story”?
Why not publish an exciting observation with some experimental evidence first, and follow up with more data later?
Do journals and reviewers really prefer articles that are long, data-heavy, and packed with charts?
Some lesser-known entomology journals publish cool things like: “We saw a weird bug doing something interesting—great!” That’s fun, and in some cases, valuable.
But the “story” in scientific research doesn’t have to be overly complicated. The key is that storytelling helps us convince experts in the field to care. It helps explain to our peers: Why am I doing this research? What’s the broader context? Without this narrative, we risk doing experiments for their own sake, disconnected from purpose.
Papers that can “tell a story” often appear more valuable—not because they are fancier, but because they guide the reader through the logic and significance of the findings. Novel results are easy to obtain; convincing others they are meaningful is much harder. We can’t just publish a paper that says, “I wanted to see when this gene is expressed, and here are some odd qPCR results.” As scientists, we have a responsibility—not just to our peers, but to the public—to communicate research progress clearly and constructively. After all, casually “playing” in the lab on research funds isn't a responsible way to do science.
Dear Lucian,
I'll start with one of your last remarks: "As scientists, we have a responsibility, not just to our peers, but to the public, to communicate research progress clearly and constructively." I agree with you. Science, as we've been pursuing it for several centuries (after Galileo), is founded on experiments, the numerical representation of what we observe, and the analysis of the results. It is the basis of human knowledge. Communicating science is equally important because it represents how to transmit it to other humans so they can critically evaluate it. Through "communication," we transfer information and data. There is a significant difference between scientific information and data.
Data is an objective, experimental, and repeatable representation of reality through quantitative symbols. While discussing information, an author (Capurro R. 2009, https://doi.org/10.31269/triplec.v7i2.113) wrote: "(Information) connects the Latin etymology of the term informatio with the Greek concepts of eidos/idea and morphé and shows how the objective meaning of information ('giving form to something') becomes obsolete in modernity, where only the communicative meaning ('telling something (new) to someone') remains. Information theories in the 20th century were primarily focused on developing technical message transmission systems."
Therefore, many systems used in science, such as text mining and databases, primarily transmit information, often accompanied by data (metadata), but very little of it. While numerical data is inherently unquestionable and always accurate, information is governed by the Boolean logic principle of true or false. This provides the theoretical basis for binary logic used in digital systems, where values like "true" and "false" (or 1 and 0) are represented and processed to handle information. Boolean algebra reliably produces precise and accurate data when applied correctly to problems where variables have only two possible states (true or false), and the logical operations are clearly defined. Thus, it is unsuitable for representing highly complex problems; by definition, an organism is a complex, chaotic system. It cannot fully capture the richness and variability of highly heterogeneous biological data. Alternatively, a Boolean expression can verify whether data entered into a database meets specific constraints, such as whether a value falls within a particular range. Still, it does not confirm whether the range is correct, as the database curator determines that.
This makes the information different from the data and makes it not always reliable. Information is a gift of digitalization and its algorithms, which allow us to access a dimension previously forbidden to the human mind by analyzing enormous amounts of scientific results (Big Data). These digitalized systems produce indirect scientific data because they have never been experimentally validated and remain hypothetical. In practice, almost no one conducts experiments to validate these hypotheses. Over the last twenty to thirty years, the result of all this has been a significant contamination of human knowledge in many fields.
Many fields of biomedical science progress through areas of knowledge where indirect, hypothetical data, often exceeding 70%, and rarely experimentally validated, are used by researchers to support their findings. This significantly expands a confusing and uncertain body of knowledge, creating a vast bubble of scientific speculation where every argument and its opposite seem justified. That’s why the "story" must always be complete, including experimental validation. Only then will it be "clear and constructive."
I'll explain the origin of all this another time.
Best regards,
Giovanni Colonna
University of Campania
Computational Biochemist.
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