Reflection on How to Be a Modern Scientist

After reading How to Be a Modern Scientist, I have developed a more well-rounded understanding of the modern research ecosystem.

First of all, I feel sympathetic toward those working in data generation. Many people have spent years cultivating deep clinical or theoretical expertise in their fields. However, due to a lack of systematic training in data analysis, they often struggle to design rigorous and scientifically sound research plans, and thus fail to generate reliable data. This not only leads to a waste of valuable resources but also hinders the production of high-quality scientific outcomes. The book emphasizes that “using modern tools and methods is a skill every scientist must master,” and I fully agree. Science relies on standardized processes, clear documentation, and verifiable data support.

When it comes to the balance between career advancement and scientific pursuit, I am reminded of Maslow’s hierarchy of needs. If basic survival is not yet secured, relying solely on lofty scientific ideals to drive one’s motivation can be overly idealistic. In reality, career promotion is often a short-term goal for researchers. I believe the academic system should establish a “credit” or “recognition” mechanism to encourage truly valuable scientific contributions, rather than simply evaluating researchers based on the number of papers published. If someone fabricates data or assembles papers just for promotion, such false information, once mixed into the scientific system, is like using inferior materials to build a structure. A flawed foundation can cause decades of research built on it to collapse, and may even mislead future directions. As the author puts it, “Don’t pursue perfect form—clarity, transparency, and reproducibility are the true marks of scientific work.”

On the topic of data sharing, I believe we are still lacking a mechanism to give proper credit to data generators. When other researchers use these datasets, they should respect the original contributors instead of treating the data as free resources. From the perspective of scientific progress, sharing can greatly increase the efficiency of data use and lead to new discoveries—even within the same field, different researchers may interpret the same data from different angles and raise new questions. Open data allows science to move forward through collective intelligence, which is one of the most exciting aspects of modern research.

At the same time, I recognize that not all data is suitable for open sharing. In particular, some clinical studies are based on specific designs and patient groups, and therefore have limited generalizability. If someone uses the data without fully understanding the context, it may result in findings that seem rigorous but are actually unscientific. Therefore, data sharing must be accompanied by clear annotations, complete background information, and proper usage guidelines.

In terms of scientific communication, I strongly agree with the author’s advice: “You should accept almost every opportunity to give a talk about your research.” When we’ve worked deeply in a field, each of us tends to develop our own cognitive frameworks and methodologies. But the beauty of science lies in constantly engaging with different perspectives and being inspired through intellectual exchange. Giving a talk is not only a way to present yourself, but also an opportunity to expand your thinking. At academic conferences, questions, challenges, and even “crazy” confrontations from peers often push us to rethink our assumptions. We shouldn’t try to impress others with flashy but meaningless graphs—scientific communication should be simple, sincere, accurate, and substantive.

Academic conferences serve as hubs for the exchange of ideas. This is especially true in fields like medicine and statistics, where individual differences, boundary conditions, and contextual complexity are so prominent that no single model can explain everything. We need case studies, viewpoints, and dialogue to expand our understanding of general principles.

In conclusion, How to Be a Modern Scientist is not just a practical guidebook—it is a call to adopt a new scientific philosophy. It made me realize that in the process of pursuing research outcomes, we should also reflect on how to do science more responsibly, and how to help build a more open, collaborative, and credit-aware research culture. These are principles I will continue to practice in my future studies and research.