In the months leading up to the 2024 eclipse, experts predicted that millions of people would migrate to the path of totality to witness this extraordinary event. Tiny towns across the continent braced themselves for tourists, advising residents to stock up on food and gasoline in case of shortages. Highway signs warned travelers to prepare for extended delays. Some people who lived on the edge of totality drove two or three hours from their hometown just to experience one extra minute of darkness.

Part of the beauty of a modern solar eclipse—indeed, the only thing that makes it possible to travel to the center line—is that we understand the science behind the phenomenon. Knowing exactly where and when the darkness will hit, we can anticipate it with excitement and pleasure.

Among ancient people, for whom the sudden disappearance of the Sun provoked fear and dread, those four minutes could not have passed more slowly.

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More than three centuries ago, in 1715, another solar eclipse hit the scene smack-dab in the middle of the Age of Enlightenment. Just two decades earlier, Isaac Newton had published his Principia, ushering in the eponymous era of Newtonian physics. The Sun, Moon, and stars—once seen as mystical celestial bodies—had been reduced to mere balls of rocks and gas, subject to the same laws of motion and gravity as the rest of us on Earth.

Newton set in motion a reshaping of the universe: from a mysterious, unknowable cosmos into one governed by data. With enough data points, early Enlightenment thinkers hypothesized they could anticipate the future movements of every object in the universe.

The 1715 solar eclipse was noteworthy in many respects. It was the first eclipse to pass over London, England in more than 500 years. It was the first time the path of totality could be mapped in advance thanks to the new laws of astronomy and physics. It was, therefore, the first eclipse to attract tourists. And the first to inspire scientific investigation. 

Astronomer Edmund Halley, most famous for his discovery of Halley’s Comet, was also a data visualization pioneer. He published the world’s first weather map, which depicted trade and monsoon wind patterns across the globe and was subsequently used by sailors as a navigational tool. He is also recognized as the first to plot two variables against each other on a Cartesian plane (as seen in his bivariate plot of barometric pressure and altitude) and the first to use contour lines on maps.

Halley saw the upcoming solar eclipse as a chance to test out Newton’s theories of gravity and motion. He published a pamphlet that claimed the darkness was neither an evil omen nor a divine event, but in fact the “necessary result of the Motions of the Sun and Moon.”

Halley’s pamphlet included a map that depicted the path of totality as seen from above—the first of its kind ever recorded and one which sparked a “golden age of eclipse maps.” 

Halley also kicked off the first citizen science project in modern history. In his pamphlet, he addressed the “Curious” people of England, urging them to watch the sky during the eclipse and record their observations: “The Curious are desired to Observe it, and especially the duration of Total Darkness, with all the care they can; for therby [sic] the Situation and dimensions of the Shadow will be nicely determin’d…”

In the end, about 25 people answered Halley’s call, sending him the times that totality began and ended in their specific location, along with a short description of what they saw in the sky. Halley himself wrote about his own experience in a mix of both scientific and poetic observations: “by Nine of the Clock . . . the Face and Colour of the Sky began to change from perfect serene azure blew [sic] to a more dusky living Colour having an eye of Purple intermixt, and grew darker and darker till the total Immersion of the Sun…”

Halley used the data he collected to correct the path of totality on his map, setting the stage for countless future scientists and eclipse chasers.

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Leading up to the 2024 total solar eclipse, I prepared myself as best I could. I booked a weekend cabin along the path of totality, bought eclipse glasses for my whole family, and stocked up on Moon Pies, Sun Chips, and Cosmic Brownies. I vowed not to take pictures during totality, desiring instead to stay fully present and “in the moment.” After all, the eclipse would likely be the most photographed astronomical event in human history; there would be plenty of opportunities to download iconic images later.

But nothing could have prepared me for the experience of totality: four minutes of darkness, of disorientation, of complete awe and wonder. Four minutes of walking a strange, fine line between science and mysticism. Four minutes of feeling connected to birds and squirrels, to everyone else who was watching the sky at the same moment, and even to the ancient Vikings, who believed eclipses resulted from a monster devouring the Sun.

I took pictures, of course: terrible, blurry, amateur shots from my iPhone. I couldn’t stop myself—I felt an overwhelming compulsion to capture the strange sights and sounds around me and to document that I was there. 

Afterward, I couldn’t help but wonder whether other people felt that same sense of connection… or that same compulsion to take pictures. These weren’t questions of physical science, of course; nonetheless, they were questions that could be answered with data. Following in the footsteps of Edmund Halley, I sent out a call on social media, asking people to share their own photos and stories from the eclipse. Naively, optimistically, I hoped to receive hundreds, if not thousands of responses. But I’m no great social media influencer, and after posting my Google Form link everywhere I could imagine, I ended up with 62 responses—a tiny fraction of the total population who watched the eclipse. But to my delighted surprise, they represented a broad swath of locations along the path of totality and contained all the depth and complexity of a strong qualitative dataset.

Initially, I created a Google map of the responses I received, a nod to Edmund Halley’s original visualization. But I couldn’t help but wonder if there might be a different way to present the data, one that might capture what the experience felt like.

So, I set out to analyze the rich mix of words and images that comprised my dataset. Using poetic inquiry, a qualitative process developed in the 1970s by multiculturalist and feminist researchers, I engaged in thematic analysis of respondents’ written submissions. A few themes that emerged in this process included feelings of transcendence (including connectedness to nature, humanity, and God), descriptions of the weather (especially the cool temperatures that accompanied the darkness), changes in animal behavior (dogs barking, birds roosting), and a communal feeling of celebration (gathering, cheering, public festivities). I highlighted certain “poetic turns of phrase” that appeared in participants’ responses; then I cut and pasted words and phrases to create 10 found poems that each represented a shared theme from participants’ experiences. (A condensed version of the poems, entitled “Six Ways to View an Eclipse,” appears in the online literary journal Unlost). 

I also coded the photos that I received. Most people submitted some version of the Moon covering the Sun during totality; these photos were coded based on the size of the Moon, whether it was in the foreground or background, and what other elements appeared in the photo (such as people, buildings, or trees). Some photos depicted a photo from before or after totality, featuring a “crescent sun,” and a few photos included people without the Sun or Moon appearing at all. In the end, I selected 20 photos that collectively showcased all the different visual elements that appeared in the dataset.

In thinking about how to visualize this data, I wanted to create an opportunity for viewers to interact with the photos and poems in a novel way. After brainstorming several installation ideas with the team at Fusiform Props and Exhibits, I finally settled on the idea of printing the photos and poems using a special technique called lenticular printing. Lenticular printing is a technology that uses plastic lenses with ridges on top to display multiple, interlaced images at one time. The different images float in and out of visibility, depending on the angle from which the print is viewed.

Each of the final lenticular prints consisted of two photos and one poem, thereby displaying the words and images from multiple participants at one time. From April to June of 2025, the 10  prints appeared as part of a larger exhibition, entitled “Data Is Poetry,” at Artspace in Shreveport, LA.

During the opening reception, I watched as people walked past the prints on the wall. At first, most people strolled past casually at first, then did a double-take after realizing that the prints contained “hidden” images and words. They proceeded to adjust their own position, moving forward, backward, and side to side as they tried to see (and read) all the layers in the image. 

I was reminded of my own experience from a year earlier and how earnestly I had watched the sky through my eclipse glasses, looking for the slightest changes in the Sun as the Moon passed in front of it. The data visualization, therefore, mirrored the eclipse itself—an astronomical phenomenon that shifted with mathematical precision based on angles and movement. 

But the visualization also effectively symbolized our shared experience of the eclipse. Though all of the participants in the project had shown up for the same event, their view was necessarily determined, and limited, by their specific location and context. Only by compiling multiple viewpoints could we see the composite: a collective phenomenon that was as human as it was cosmic.

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International collaboration

The International Geophysical Year (IGY) of 1957-1958 was a meticulously planned global campaign to understand the Earth through simultaneous, coordinated observations from pole to pole and from the ocean depths to the almost unexplored near-space environment.

The IGY brought Earth science into the modern era with international collaboration and precise coordination of data collection and processing. Thousands of researchers and volunteers from sixty-seven countries participated in the 20-month campaign to build a big picture view of how the Earth worked as a complex system, from atmosphere to oceans, ice flows to magnetic fields. The quality and scope of the dataset fueled years of analysis and discovery.

Space weather’s fingerprint: the aurora

A key goal of the IGY was to understand the effect of space weather, which is the continuous and constantly changing flow of solar wind plasma from the sun. This stream of particles interacts with Earth’s magnetic field, like a river flowing around a rock, but more intricate and powerful. The energy involved is on the scale of all simultaneous human electricity production. Solar storms can disrupt our technology from GPS to power grids. They have caused blackouts affecting millions and necessitated storm shelter areas on the International Space Station for astronauts. Even now there are many open questions about Earth’s interactions with space weather, and new discoveries are made by researchers in cooperation with amateur observers.

We can see the fingerprint of space weather as the northern and southern lights, or aurorae.  This glow of Earth’s thin upper atmosphere is similar to a neon sign glowing in the presence of electric energy flow. Aurorae are a continuously evolving three-dimensional structure extending upwards from about ten times the cruising altitude of an airliner to the orbital altitude of the International Space Station, from about 80km to 400km.

The Visual Auroral Program

Understanding space weather meant getting snapshots of its auroral fingerprint across the Earth, but the observation of global aurorae was not trivial.  There were not enough scientists and instruments to gather near-simultaneous data snapshots across the entire planet. Sydney Chapman, one of the architects of the IGY data collection program, suggested that volunteer observers could provide the extra eyes needed to fill in the gaps. An observer on the ground could see and record auroral activity in a 500km radius, but the IGY researchers needed a way to make these amateur observations simple, scientifically useful, consistent, and flexible.

Using data visualization as a method of data capture

The approach chosen was to supply observers in Canada and the United States with paper pads resembling postcards or speeding tickets. Each sheet of the pad could be completed with a single moment’s observation and later mailed to a collection centre. More than 30,000 would be sent in during the IGY.

These forms were designed around a space representing the sky in four compass directions, like a carefully flattened orange peel. The observer could sketch what they were seeing in 360 degrees around them, and from the horizon to overhead. But what should they sketch and how could this be standardized enough to aggregate and analyze, while still being flexible enough to capture unknown phenomena?

The answer was a consistent and flexible symbology.  Like a coach’s playbook, the symbols could be easily parsed by experts, while leaving room for the unexpected. Auroral types were divided into broad categories of shape and internal structure by sketches; their brightness, color, and behavior could be specified with abbreviations. 

These categories were meaningful – each correlated with different particle velocities, densities, and behaviours in Earth’s upper atmosphere. Green aurorae are the most common, varying from smooth featureless arcs to maelstroms and rays of high-speed motion. More energy results in more intricate structure and motion. Red aurora above the green indicates a denser rain of lower energy particles; dancing stabs of magenta below the green indicate fast-moving, energetic particles penetrating deeper into the atmosphere. Pulsing patches of aurora are a signature of the ‘auroral chorus,’ natural radio waves that energize particles.

The shapes can also hint at the observer’s relative position under the aurora, as seen in the ray examples – rays emerging from overhead in all directions indicate aurora directly above the observer, key to determining the latitude of the auroral activity.

Choosing categories and symbols that separated these phenomena, while allowing the observer to add nuance and the unexpected, was key to gathering a rich dataset that was still easily aggregated.

Aggregation into synoptic maps

Thousands of observer forms were filled out by volunteers, many of whom were astronomers, pilots, meteorologists, and other disciplines trained in observation and reporting. This ensured that observers had a background likely to produce high-quality observations.

Forms were encoded into a standard format using paper cards with punched patterns of holes defining data such as date, longitude, and latitude. Punch cards trace their ancestry back to the first automated programmable machines in the 19th century and were still used at the time as an input to computers. This was enough to plot each sighting location and type on a map. The sketched data and annotations were used to connect these dots of observer positions with auroral arcs, glows, and other forms, creating a more continuous, big picture view of the aurora.

Closing the loop with observers was an important part of the campaign. A newsletter and synoptic maps were published regularly so observers could see the results of their work – each mark was one of their reports. This practice kept the observers engaged over the course of the 20-month observation campaign.

Experiment: sketching aurora sightings using the 1957 reporting forms

Today, digital photos submitted to data collection websites like Aurorasaurus.org have replaced paper forms. I wanted to try filling in a few observation forms using my aurora photos to better understand the challenges and advantages of the original IGY approach.

In this photo, the auroral arc stretches away to the horizon like a curving highway. The first challenge I found was that I was tending to draw all the detail in a single quadrant of the reporting form since it splits the compass directions into four columns. I had to consciously turn to each direction to make a more accurate sketch of true sky coverage, for example, aurora that spanned the South-East.

This photo has two unique features – strongly contrasting dark bands separating the bands of light, and fast-moving, magenta detail at the bottom of the auroral curtain. The camera could not capture this rapid motion, so I annotated it in the form along with the strong dark bands separating the light, which had no label convention. Without the ability to add my own notes to the sketch, that extra detail and data may have been lost when many forms are aggregated later on.

This photo shows that the auroral curtains can break up into ragged pieces as midnight moves into the early morning. Each stab of auroral light was pulsating in rhythm a few times a second. The Auroral reporting notation standard includes the “P” label for pulsing or flickering forms, an example of having an effective set of standard notations for expected categories of data.

This photo shows the view of the aurora when directly overhead – a giveaway is the central vanishing point at which all light rays converge. The form includes a checkbox for overhead aurora, which is key to determining where they would be located on a synoptic map. Although I did not know the exact longitude and latitude, I did know the lake’s name –  having multiple ways to specify key data can prevent observations from being spoiled by missing information.

Understanding where human observation can be inaccurate

I often misjudged the angle where aurorae were seen, which is key to estimating their distance from the observer.  The creators of the forms understood the importance of this measurement and designed the back of each form to act as a simple instrument to measure the altitude angle where the feature was seen.

Challenges of using human observation data

This method of citizen science data collection requires that observers have two skills at once:

Reliable pattern recognition and transcription – This is the same skill that data visualization leverages for presenting data visually through shape, form, and pattern. But drawing is a skill subject to many errors of perspective and memory. A telling example is an experiment that asked subjects to draw a bicycle as best they could from memory.  Many people could get the key parts right – two wheels, a seat, and handlebars – but failed to accurately draw the frame, pedals, and chain.

Consistent data encoding – Humans are adept at adapting, and what may be recorded as novel and new may later be ignored as obvious or even subconsciously filtered. An observer must overcome this to reliably and repeatedly encode data in some set of categories or some numeric scale, “Rate your pain on a scale of 1-10”, “How satisfied were you with your experience today?”, “How likely are you to get a vaccine if there was a risk of side effects?”, “How bright was that aurora?”. Encoding is hard – it assumes that the designer of the encoding system understands what every observer will experience, and that they will encode it the same way. As anyone who has taken a personality test has seen, your results may vary from day to day or even before and after lunch.

Lessons for data collection

This was a mobilization of volunteer skywatchers, amateurs, students, professionals, and hobbyists who were provided with effective tools to contribute their individual piece to the data puzzle. An intuitive and robust pipeline had to be built from the observers’ eyes to the global maps of auroral activity. This may seem commonplace now, with data aggregation from smartphones enabling real time maps of traffic flow, or sentiment analysis on social media networks. Even though the IGY effort is almost  70 years old, data visualization practitioners can learn critical lessons from their solutions:

1. Effective data visualization techniques are not just an effective output from a data source, but can also be an effective input. it is worth letting your users input data as sketches or other freeform methods, especially if your data is difficult to precisely encode numerically or categorically. Visual data encoding directly taps our ability to see patterns, trends, correlations, especially for observed data.
2. By allowing your data to be captured visually, you do not inadvertently filter out unexpected information. The aurora observing community has benefitted several times from this serendipity and open-mindedness, when entirely new types of visual reports turned out not to be aurora at all, but a different phenomena which led to a new understanding of Earth’s interaction with space weather.
3. Plan for the expected: provide a set of standards to allow consistent encoding. Provide standard colours, shapes, and convenient notations along with examples of good encoding against which observers can calibrate their own transcriptions.
4. Plan for the unexpected: allow flexibility in the encoding.  The most interesting findings may be the unexpected ones, so allow flexibility in the encoding so that data collection does not turn into accidental data filtering.
5. Provide feedback to your observers on how the data is being used – in a long campaign with expectation that data collectors will be submitting multiple reports, their enthusiasm and data quality may lessen over time. Provide feedback on how their data is being used and aggregated, showing them they are contributing in a meaningful way.

Aurora citizen science today

Aurora observing, chasing, and photography has grown dramatically with technology and travel. Millions of photos and timelapse videos are captured annually. An entirely new aurora-like phenomena was recently discovered and named by amateur photographers, who were confident this ‘STEVE’ phenomena (as they called it) was something novel. The research community combined these reports with careful measurement of the photos relative to starfields to build a three-dimensional picture of the light structures, much like a medical CAT scan. 

Citizen scientists can follow in the footsteps of the original IGY auroral observers and upload their photos and sighting reports to Aurorasaurus.org, a web application that aggregates and displays a map of photos and reports.

If you want to explore nights of auroral activity yourself, you can learn more about a data visualization called keograms and see aurorae from a first-person view at Keogramist.com. You can print a re-creation of the observer form at jufaintermedia.com/observerform.pdf and try sketching what you see in Keogramist’s first-person view- there are hundreds of aurora-filled nights to explore. 

References

Millman, Peter M. A Visual Auroral Programme for the I.G.Y., Journal of the Royal Astronomical Society of Canada, Vol. 51, p.186, June 1957, https://ui.adsabs.harvard.edu/abs/1957JRASC..51..186M

Gartlein, C. W.; Millman, P. M. Visual Auroral Observing for North America in the I.Q.S.Y., Journal of the Royal Astronomical Society of Canada, Vol. 58, p.1, February 1964, https://ui.adsabs.harvard.edu/abs/1964JRASC..58….1G

Schröder, W. (2007). “Amateur observations of atmospheric phenomena during the IGY”, Eos Trans. AGU, 88( 12), 141– 143, doi:10.1029/2007EO120002.

Material from Geoff Gaherty, IGY Visual Aurora Program – observations and correspondence. , RASC Montreal Centre. Volume PB1. https://www.rasc.ca/programs-binder-aurora-igy 

National Research Council. (1965). Report on the U.S. Program for the International Geophysical Year: July 1, 1957 – December 31, 1958, https://doi.org/10.17226/26118

Rebecca Lawson, The Science of Cycology, accessed July 1, 2021. https://www.liverpool.ac.uk/~rlawson/cycleweb.html

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