Chemistry has always held a certain allure. It is a subject steeped in complexity yet rooted in the simplicity of atoms. Among the particles composing this vast universe of matter, neutrons stand out, not for their charge but for their capacity to spark curiosity. Neutrons are not just subatomic particles; they are the silent epicenters of universal chaos, the enigmatic glue holding the cosmos together.
My fascination with neutrons began in ninth grade. The teacher that day explained how to calculate their number by subtracting the number of protons from the mass number. It seemed straightforward at the start: Hydrogen had one neutron, helium had two. But then came lithium, and with it, the first fissure in the logic of our textbook data. “Why does lithium have four neutrons instead of three?” we asked. The answer was, “Because science says so.”
This pattern was consistently observed with more elements, including beryllium, sodium, and beyond. We were repeatedly told to accept inconsistencies in the name of “research” or “science.” Chemistry is a mystery; we were told laughingly. These small cracks in our understanding were my first glimpse into the fallibility. The smooth logic of science faltered in the face of isotopes, the elements that stubbornly refused to fit the neat mold of predictability.
One day, during an experiment, I tried to create a chart to map the neutron counts of various elements. My data, gathered from multiple sources, did not align. For carbon, I found not one isotope but two prominent ones: carbon-12 and carbon-14. Which was the true carbon? The realization struck me like a chemical reaction gone awry data was not always the immutable truth I had imagined. It could be fragmented, context-dependent, and misleading if interpreted incorrectly.
This growing disillusionment culminated in a science fair where I presented a project titled “The Chaos of Neutrons.” My poster detailed the isotopes of various elements and questioned the reliability of data we take for granted. I argued that the failure was not in the neutrons themselves but in the way we represented them—as static, predictable entities rather than as dynamic players in a complex dance of probabilities.
Although I never became a scientific scholar, my journey as an imaginative scientist taught me this: data can fail us, not because it is inherently flawed, but because we often fail to question its assumptions. Neutrons became a metaphor for this chaotic beauty, a reminder that understanding the universe requires more than just calculations; it demands curiosity, critical thinking, and a willingness to confront the unknown.
Today, when I hear discussions about big data, algorithms, or artificial intelligence, I think back to those neutrons. They remind me that every dataset, no matter how precise, carries the potential for failure if not handled with care. It is not enough to accept numbers at face value; we must dive deeper, asking questions and seeking truths that lie beneath the surface. After all, even the most stable atom is a story of complexity, a balance of forces teetering on the edge of chaos.
Data, like water, is the essence of modern life. It surrounds us, flows through every system we create, and touches every decision we make. It is omnipresent, speaking in whispers and shouts about the states of profits, poverty, peace, progress, pollution, power, and population, which I refer to as 7Ps. It nourishes innovation and sustains progress, much like water sustains life. Yet, just as water can be polluted, so too can data.
There are moments when data brings clarity and celebration, like when the streams of information align to reveal truths and drive positive change. At other times, it feels tainted, muddied by bias, manipulation, or misuse, leaving us parched for meaningful insights amid a flood of numbers and charts.
We marvel at its abundance and curse its elusiveness. For all its potential to illuminate, data often mirrors the challenges of water: access and purity. Clean, actionable data free from distortion, error, or prejudice can transform societies. But polluted data, like contaminated water, can lead to harmful consequences, deepen inequalities, and stifle development.
Chemistry class was the starting point of my obsession with neutrons and, unknowingly, with data itself. Neutrons don’t sing. Or perhaps they do, in a frequency too quiet for human ears. If they could, I imagine their song would be one of chaos and harmony, of being the bridge between opposites. A neutron is a paradox—uncharged, yet the cornerstone of stability. Without them, atoms collapse into impossibilities. But here’s the twist: like all things in the universe, neutrons are imperfect. They decay, they vary, they defy expectations. Much like the data we rely on, that whisper truths that are only half-heard, half-understood.
Years later, I began to see the same cracks in the data systems that governed our world. We live in an era of unprecedented information—a deluge so vast it drowns us in its abundance. Yet with all this information, meaning has never been more elusive. Take healthcare, for instance. Electronic medical records promise precision, yet they are riddled with noise. Data fields are left blank, medication records are unreliable, and crucial variables—like patient frailty—go undocumented. In one case, a missing piece of information might be flagged as “null.” In another, that same absence might be misinterpreted as evidence of something negative. Or consider predictive policing. Algorithms, heralded as impartial arbiters of justice, are often trained on biased historical data. Instead of eliminating prejudice, they amplify it, sending officers back to the same neighborhoods over and over again. The result is a self-fulfilling prophecy—a feedback loop of inequity.
Imagine a map so detailed it shows every grain of sand, every blade of grass. Such a map would be useless. To navigate the world, we don’t need more data; we need the right data. We need meaning, not just numbers. But the meaning is slippery. It hides in the spaces between data points, in the context that raw numbers can never fully capture. A valve closed—does it signal safety or danger? A 10% decrease in emissions—progress or manipulation? The answers depend on who you ask, and what story they choose to tell.
Technology was supposed to save us from this chaos. Instead, it has become part of the cacophony. Smartphones, fitness trackers, health apps—they bombard us with notifications, metrics, and charts. Each promises clarity, but together they create noise. Even traditional approaches to managing this overload have failed. Filtering data sounds sensible, but what if the filter blocks something critical? Prioritizing data seems logical, but priorities shift with context. Automation offers a glimmer of hope, yet even the smartest algorithms can’t account for every nuance.
Humans, for all our flaws, are remarkably good at navigating complexity. We excel at finding patterns, focusing on anomalies, and making sense of chaos. But even our cognitive strengths are no match for systems that obscure meaning.
If I’ve learned anything from my love of neutrons, it’s this: truth is rarely simple. It’s messy, contextual, and ever-changing. A neutron’s mass can vary, its stability can waver, its role in the atom can shift depending on its neighbors. Data is no different. It’s not inherently failing us—we are failing it. We collect the wrong data, strip it of context, and present it in ways that confuse rather than clarify. But there’s hope. Just as neutrons bring balance to the chaos of the atom, we can bring meaning to the chaos of data. We can design systems that respect the complexity of human cognition, that present information in ways that reveal relationships, contrasts, and events. So here I am, still chasing neutrons, still searching for the hidden stories in the chaos. Because if there’s one thing I know, it’s that meaning doesn’t come easily. It must be constructed, nurtured, and sometimes fought for. And that’s a story worth telling.
JK Studio 