Science Stories: The Art of Scientific Storytelling
Series Introduction by Alison Hawthorne Deming
Science Stories showcases the impressive literary work done by graduate students who participated in the first run of “The Art of Scientific Storytelling,” a new course I taught in Spring 2020 at the University of Arizona. The course, developed in collaboration with my Creative Writing program colleague Christopher Cokinos, was eligible for credit in the new Graduate Certificate in Science Communications offered by the College of Science. Its aim was to inspire creative works that were science-smart, works that might enhance science literacy among readers. The class read contemporary writers who covet the perspectives of science and the personal stories of scientists who write for non-technical audiences. We read memoirs, essays, op-eds, and poetry. We read works inspired by chemistry, astronomy, paleobiology, traditional indigenous knowledge: Primo Levi, Hope Jahren, Robin Wall Kimmerer, Kathleen Jamie, Alan Lightman, Gary Paul Nabhan, and Maggie Nelson, among others. The students came from a range of disciplines including optical science, astronomy, geography, climate adaptation, hydrology, mathematics, speech pathology, and creative writing. The conversations were rich and the talent abundant. They surprised me each week with their inventive and insightful takes on writing assignments. We offer this showcase of our experiment in the meeting of art and science.
I. The Initial State
There was only one thing I enjoyed at the Udvar-Hazy National Air and Space Museum: the world-class dehydrated ice cream sandwiches. At least once a year, my parents would drag me to a collection of planes and rockets where I obediently trudged behind as decades of aviation history washed over me. Walking through the 100-foot-ceilinged airplane hangar, past the rows of historic flight gear and massive aircrafts, my mind would go into zombie-mode and my eyes would glaze over as I shuffled behind tour groups led by middle-aged men explaining the long and complex history of each aircraft in a monotonous, never-ending drone. The placards were no more helpful, only explaining the dry statistics or mechanics of the vehicles in complex hieroglyphics only rocket scientists could understand, with no mention of when or how important activities took place, like how astronauts brushed their teeth or digested food without gravity.
My eyes would briefly come into focus as we passed the space shuttle Discovery, a behemoth structure that seemed to have its own part of the hangar built just for it. The massive nose of the shuttle stood at least 20 feet high and the belly of the spacecraft was twice as big. The size of the shuttle had a notable impact on me, yet all the technical details were lost. Somehow, simply exposing me to these sights was supposed to stir up an appreciation and passion that my brother had no problem identifying.
As we exit the museum my brother is drawn by a middle-school-aged-boy-magnetism towards the toy model rockets and planes, as an equal and opposite force pushes me towards the plushies, which I methodically touch one by one before continuing onwards toward the snacks and treats placed conveniently at my eye level. I wave the space ice cream at my parents, which I already know I’ll have to share with my brother. Bargaining with him over such things was hopeless, as he lived in a pre-inflationary state, often priming me to do things for a penny that were easily worth $10.
As I grew old enough to choose to explore subjects of my own volition, the disinterest I felt towards spacecrafts, space science, and spaces like the Udvar-Hazy museum was slowly replaced by excitement and that innate human fascination for worlds beyond our own. I realized that the joy of learning came from excitement and discovery, rather than dictated plans of learning. In this way, I rediscovered the planets and stars, the mechanisms that get people closer to understanding them, and the computation in the background of it all.
I would simply scribble insanely, since that is what research feels like.
II. The Formation
I pursued computer science as an undergraduate degree as it was a subject I was utterly clueless about, yet I reveled at the thought of being able to write software in any industry and to feel there are infinite avenues for me to pursue. Unfortunately, the incredible lack of background in the subject matter did present some challenges. I remember a figure displayed in one of my first courses, meant to visualize the learning curve for programming. The start of the curve was exceptionally steep and tall, representing the enormous difficulty at the start. This was true for me. Nothing made sense and my “code” was mostly comments, meaning that it didn’t compile, didn’t run, and I didn’t know why. The top of this almost vertical line was basic comprehension, which took me at least a year and several courses—but then something clicked. Suddenly, you get it, and everything else follows naturally. Then, the line goes down slightly and the learning curve shows a relatively consistent struggle until the end. This encapsulates my remaining coursework: projects, low-level programming, increasingly more time spent in office hours until my classmates and I were routinely the first students in and the last students out of our study lounge. Now, as a graduate student, no such curves exists anymore. I would simply scribble insanely, since that is what research feels like.
I was hopeful computer science would enable me to pursue tangential interests, allowing me to somehow combine them eventually. Early in my undergraduate education I identified the opportunity for an astronomy minor and decided to pursue it without a plan of how the two subjects might collide. As with my discipline, I had no background in astronomy and had no memories of ever looking through a telescope before my first astronomy lab. In that class, I would trudge about half a mile from my dorm room each Monday night. This was a gorgeous walk across Virginia Tech’s campus, through the Drillfield and endless array of Hokie Stone buildings that felt like a mixture of castle-meets-college in southwest Virginia. The sun sat brilliantly, sometimes to a sky full of paralyzingly pink clouds overhead. By late fall, it was a cold, dark walk, with early stars and planets twinkling overhead.
The first few classes were mostly in the lab, doing worksheets and simple experiments. Our first night outside we spent near the Duck Pond, a scenic pool of water on campus, home to unruly ducks in the daytime and silence at night. We brought constellation charts to try and map what we saw overhead. Our drawings and charts all would look fantastic in the dark, and it was a surprise to find entirely different and far less accurate figures once they were examined in the light of the lab. Another class we built a Galileoscope, a small refractor telescope based off the original refracting telescope Galileo first used to observe stars, planets, and moons. Since our eyes are limited in how much light they can collect (and thus limit our ability to see celestial objects in greater detail), a refracting telescope uses a lens much larger than the human eye, known as the objective lens, to gather light. The light from far away objects is concentrated into an eye-sized lens, the eyepiece, which we look through to see something where there is seemingly nothing above. However, due to some undiagnosed complication in my telescope’s construction, my lens was obstinately black no matter where I pointed it.
By the time we got to pull out the school’s Dobsonian telescopes we were well into winter and bundled excessively for each frigid night of observing. Unlike the spyglass used by Galileo, which you easily hold up to the eye, the Dobsonian telescope is a massive instrument, strongly resembling its nickname: light bucket. Light enters one side of a large cylinder and hits a mirror on the opposite end, bouncing back up to a mirror located roughly halfway and situated at a slight angle so the light goes sideways, exiting out of an eyepiece. The Dobsonian itself is a type of reflecting telescope first invented by Newton to solve the problem of color distortion in refracting telescopes, like the Galileoscope invented 60 years earlier. Whereas light from a large part of the sky is bent (refracted) and concentrated into a single point in the eyepiece of the refracting telescope, in a reflecting telescope, light bounces off a series of mirrors (reflected) into the eyepiece, avoiding the negative side effects of refraction that stem from different colors of light having different wavelengths and thus refracting at different rates.
While observing, we were meant to take notes and draw what we saw from the eyepiece. I didn’t have any gloves so I often found myself drawing messy pictures with shaking hands as I felt my fingers grow numb from cold and wind on the rooftop from which we observed. Despite the weather, though, I spent as much time as it took to get a perfect photo of the moon through the tiny eyepiece with my cellphone’s camera. The friendly graduate student who was our instructor observed what I was doing and wished me luck, saying it was very tricky to get a good photo in this manner. Although there are plenty of pictures of this beautiful object, with infinitely better resolution than this camera can ever achieve, this was my picture of the moon. I had focused the telescope, I had been manually tracking the moon all night, and I now had this gorgeous photo of countless bright white craters and massive swaths of dark lunar maria, giving contrast to a celestial body that was once thought perfect and flawless but is easily so much more beautiful with these perceived defects than it could ever be otherwise.
It is the very tiny things, like simply meeting the right advisor at the right time, that can permanently change an infinitesimally tiny life trajectory a fraction of an angle and result in a radically different future that is entirely unnoticed by the universe but of profound importance to me.
III. The Collision
Although I had a notion that computer science could be applied to other subjects, I couldn’t imagine exactly how until I realized these ambitions in the concrete application of scientific programming in astronomy. In my second year, I signed up for a graduate-level cosmology course with no idea of what I was getting myself into. Due to concerns that the class might be cancelled because of insufficient enrollment, I was generously invited to do undergraduate research with the professor of the course and pursued it alongside the course. Without understanding what research is, that undergraduate research is something one can even do, and without a clear notion of why I was even doing it, I found myself meeting weekly with my first research advisor.
There were countless conversations that stood out in my memory, but each one left me feeling supported, encouraged, and motivated, both in our project and in long-term goals. Duncan was the first and only one to plant the notion of graduate school in my mind. From him I learned the success of research is measured by work, by working hard, and asking questions.
Astronomy lets us open our minds to unimaginable sizes and lengths of time, to the bewildering mass of the cosmos and the unbelievability that life even exists, and despite the grandness of all the universe, it is the very tiny things, like simply meeting the right advisor at the right time, that can permanently change an infinitesimally tiny life trajectory a fraction of an angle and result in a radically different future that is entirely unnoticed by the universe but of profound importance to me.
IV. The Expansion
Remember the space shuttle Discovery at the Udvar Hazy Museum? On 39 distinct occasions, that enormous spacecraft burned tons of rocket fuel, went up into space, dropped something off, and then came back to make a gentle touchdown. It was responsible for sending the Hubble Space Telescope into space, the high resolution images of which I look at routinely, and you likely look at too if you find any astounding imagery of the depths of space. The ongoing successes of Discovery and NASA’s other space shuttles buoyed the public in a time of endless spaceflight optimism. An engineer from Great Britain, Robert Parkinson, had his article about a manned mission to Mars published in the international magazine of space, Spaceflight, in 1981 with the optimistic title “Mars in 1995!”
Although a few years off, and without humans, we are indeed on Mars. The Curiosity rover is still wandering around, taking measurements, and sending them back. It will soon be joined by the Mars 2020 Perseverance rover, set to launch July 30 off an Atlas V rocket which travels twice as fast as the space shuttle Discovery. The rover is designed to collect samples of the surface of Mars which will eventually be brought back to Earth. If humans can’t yet get to Mars, we will at least bring back a bit of Mars to us. But what bits of Mars should we scrape off and bring back? What scientists want to know, what it seems like everyone wants to know, is the potential for life on Mars, which can be informed by the samples with the most relevant mineralogy.
After my undergraduate research in astronomy, I never stopped thinking of how I could do more. Four years later, I started my Ph.D. researching computer science applications in astronomical and planetary sciences, at a university that is home to the most exciting astronomical endeavors. As a result, one of my research projects focuses on this very problem of estimating the mineralogy on the surface of Mars, in order to aid the Mars 2020 rover in its sample collection. My collaborators and I use hyperspectral images taken by instruments orbiting Mars in order to infer the mineral makeup along the surface. Unlike the telescopes of my astronomy lab which capture only visible light, the instrument that takes these images of Mars does so across a range of wavelengths beyond our sight. In fact, they measure light at 544 distinct wavelengths, from visible light to infrared. Similar to snakes, who can see by the heat given off by prey, instruments that capture infrared light can “see” more than we can, by examining the light reflected in wavelengths invisible to us. The resulting measurements are known as a spectra, which are unique to different materials and let us infer the material makeup of the surface.
But back on Earth, true investment in the exploration of Mars by the tax-paying public is simply not here. According to a survey by the Pew Research Center in 2018, only 18 percent of Americans think sending astronauts to Mars should be a top priority for NASA. As we realize the impacts of climate change, the necessities of asteroid deterrent programs, and the myriad of other problems that plague our home world, we grow more protective of our domain and feel compelled to fix what problems we started here. Yet, as we can see from the space exploration of the Cold War era and beyond, pushing the scientific boundaries beyond our world often has profound impact for our home planet. Investment in space travel has paved the way for countless inventions and applications in our everyday world, such as cellphone cameras, CAT scans, LEDs, water purification, baby formula, and artificial limbs, just to name a few reported by the NASA Jet Propulsion Laboratory. And these material advancements only constitute the physical portion of progress, excluding the immense societal advancements made by the space industry.
Women and men in the 1940s and 1950s were far from equal, yet working through NASA as mathematicians opened countless doors for women, including African American women who were still battling for desegregation in Virginia, the state home to NASA’s Langley Research Center. As Laura Sydell discusses in a 2014 NPR article, these women were called “computers,” as they calculated by hand the trajectories of rockets, the amount of fuel, or whatever other space calculations were required. Once physical computers were adopted, the women computers transitioned into the operators of these contraptions. As the majority of early programmers, they were paramount in setting standards for software, and in even defining the term “software engineering,” as Margaret Hamilton did when developing the software for the Apollo program that allowed humans to finally set foot on the moon.
Yet, history takes a turn, as it often does. As outlined in a Timeline article by Josh O’Connor, and easily in line with countless ventures first started by women and overtaken by men when found as profitable, once software development was exposed as an intellectually challenging pursuit with high monetary rewards, men began pursuing it fervently, while actively discouraging women once they were in positions to do so. And so, the tide reversed, and women were outed of the field they began to be almost entirely replaced by men. Men dominated software. And still do.
A few select men of notable rank in software have publicly denied this history of software. In 2017, Google engineer James Damore was fired for proliferating an internal memo which claimed that the gender gap in computer science can be attributed to biological differences between men and women. In one of the countless articles written about the new celebrity, an article by Paul Lewis, associate editor for The Guardian, explains the attention Damore got after being fired: he was the receiver of many interviews, free photo shoots, and a complementary t-shirt in which Google’s logo was rearranged to form the word Goolag. Most in the United States are aware of a divided culture, a growing gap in societal values, and a widening chasm in which one can fall. Damore can be disproved by logic, by the past, by the truth. And yet, an ideological war takes place based on projected values rather that innate facts.
As the pendulum of progressive change in society swings backwards, support continues for Damore’s viewpoint. Stuart Reges, a computer science professor at the University of Washington(and self-proclaimed advocate for women in the field, genuinely agrees with Damore’s ideas, claiming, “Women can code, but often they don’t want to. We will never reach gender parity… My honest view is that having 20 percent women in tech is probably the best we are likely to achieve.” Yet these claims are disproved by the very women whose shoulders they stand on. Margaret Hamilton and Grace Hopper and Katherine Johnson and Dorothy Vaughan and countless others shake underneath.
When characters like these instill thoughts of innate inequity, and are furthermore supported in significant numbers, I fear their arguments may convince those who already doubt their own capabilities. Each agreement, each well-stated opinion on gender stereotype, pulls at the corners of a traditional societal fabric, tightening it, whether we like it or not. This is compounded by the fact that gender disparity in STEM fields appears to start much younger than we realize. No doubt, my unpleasant experience at space museums as a young child could be due to any number of factors (not least of which is a lack of sugar) and yet I am compelled to wonder how I’d feel if the tour guide was someone more relatable and engaging. What if half of the astronauts they spoke of were women, if I was told that women did math to help get us to the moon, or if I was simply looked in the eyes and asked what I thought of space.
Science supports the notion that gender stereotypes may be partially responsible for gender disparity in STEM, subconsciously altering the careers children believe they can pursue. A study last year by Elena Makarova and her colleagues in Switzerland found that female students rated chemistry, math, and physics all as strongly masculine. Makarova’s findings support the undesirable fact that gender-science stereotypes exist and influence young people’s aspirations to enroll in STEM degrees at university. If stereotypes can impact a person’s choice in life, then what can happen if we convince the youth that some people are biologically predisposed for certain fields of work, as Reges and Damore would like us to do?
I want these gender stereotypes and biases to vanish. I hope my work, research, and outreach contribute in some way. And if you’d like to help, it’s as simple as asking any young person—especially your daughter, niece, or sister: What do you think of space?
Marina Kisley is a graduate student in the Department of Computer Science at the University of Arizona. Her research focuses on machine learning applications in the astronomical and planetary sciences. She is passionate about diversity, doing good science, and her pet dog named Kitty.