Number Of News Articles About Names Of Minor Planets Reflect Their Degree Differences From The Sun

Unveiling the Three Stories of Science

In the realm of scientific exploration, there lies a captivating truth - doing science is akin to any cultural act; it is a matter of telling stories. Within each scientific project, we can identify at least three distinct narratives: data analysis, data cultivation, and data presentation. These narratives intertwine to create a symphony of knowledge and discovery. So, let us embark on this exhilarating journey and unravel the secrets hidden within each story.

The Dance of Data Analysis

To truly understand the essence of scientific inquiry, we must first delve into the enchanting world of data analysis. This crucial step sets the stage for the entire study, guiding researchers towards their desired outcomes. By conceiving and coding this part first, I gained clarity on the data I needed from the very beginning. This approach also embraces a relatively new trend in science - pre-registration.

Pre-registration involves declaring upfront what and how a study will be conducted. It's like aiming to hit a pre-drawn bullseye rather than drawing one around the data after the fact and calling it a win. This practice brings transparency and integrity to scientific research. In fact, it is often recorded publicly, just like in this very presentation.

However, amidst this pursuit of truth, we encounter an unfortunate practice known as "p-hacking." This dark cloud looms over the scientific community, tarnishing its reputation. P-hacking involves manipulating methods and results to fit predetermined hypotheses, leading to biased outcomes. We must strive to eradicate this malpractice and uphold the principles of genuine scientific exploration.

Unveiling Patterns: The Quest for Data Cultivation

As we venture further into our scientific odyssey, we find ourselves immersed in the art of data cultivation. Like skilled gardeners tending to their plants, scientists meticulously nurture their datasets to uncover hidden patterns and insights. This process, however, is not without its challenges.

The ideal code, much like a moving target, constantly evolves. In the ever-changing landscape of scientific inquiry, platforms and methodologies shift, demanding adaptability and resilience. As I embarked on this journey, I encountered numerous obstacles - changes in operating systems, modifications in coding platforms such as Python and Mathematica, and even alterations in Google News scraping rules.

In a traditional laboratory setting, a dedicated grad student would assume the role of a data wrangler, tending to the code's needs. However, in my