Okay, maybe space whales aren’t behind every spreadsheet, but they’re behind this one, dated 1662, notable for the gigantic nail it hammered into the coffin of our belief that heaven above is perfect and unchanging. The following post is the first in my new series full-stack dev (f-s d), where I explore the secret life of data. 1
The Princess Bride teaches us a good story involves “fencing, fighting, torture, revenge, giants, monsters, chases, escapes, true love, miracles”. In this story, Cetus, three of those play a prominent role: (red) giants, (sea) monsters, and (cosmic) miracles. Also Greek myths, interstellar explosions, beer-brewing astronomers, meticulous archivists, and top-secret digitization facilities. All together, they reveal how technologies, people, and stars aligned to stick this 350-year-old spreadsheet in your browser today.
When Aethiopian queen Cassiopeia claimed herself more beautiful than all the sea nymphs, Poseidon was, let’s say, less than pleased. Mildly miffed. He maybe sent a sea monster named Cetus to destroy Aethiopia.
Because obviously the best way to stop a flood is to drown a princess, Queen Cassiopeia chained her daughter to the rocks as a sacrifice to Cetus. Thankfully the hero Perseus just happened to be passing through Aethiopia, returning home after beheading Medusa, that snake-haired woman whose eyes turned living creatures to stone. Perseus (depicted below as the world’s most boring 2-ball juggler) revealed Medusa’s severed head to Cetus, turning the sea monster to stone and saving the princess. And then they got married because traditional gender roles I guess?
Cetaceans, you may recall from grade school, are those giant carnivorous sea-mammals that Captain Ahab warned you about. Cetaceans, from Cetus. You may also remember we have a thing for naming star constellations and dividing the sky up into sections (see the Zodiac), and that we have a long history of comparing the sky to the ocean (see Carl Sagan or Star Trek IV).
It should come as no surprise, then, that we’ve designated a whole section of space as ‘The Sea‘, home of Cetus (the whale), Aquarius (the God) and Eridanus (the water pouring from Aquarius’ vase, source of river floods), Pisces (two fish tied together by a rope, which makes total sense I promise), Delphinus (the dolphin), and Capricornus (the goat-fish. Listen, I didn’t make these up, okay?).
Ptolemy listed most of these constellations in his Almagest (ca. 150 A.D.), including Cetus, along with descriptions of over a thousand stars. Ptolemy’s model, with Earth at the center and the constellations just past Saturn, set the course of cosmology for over a thousand years.
In this cosmos, reigning in Western Europe for centuries past Copernicus’ death in 1543, the stars were fixed and motionless. There was no vacuum of space; every planet was embedded in a shell made of aether or quintessence (quint-essence, the fifth element), and each shell sat atop the next until reaching the celestial sphere. This last sphere held the stars, each one fixed to it as with a pushpin. Of course, all of it revolved around the earth.
The domain of heavenly spheres was assumed perfect in all sorts of ways. They slid across each other without friction, and the planets and stars were perfect spheres which could not change and were unmarred by inconsistencies. One reason it was so difficult for even “great thinkers” to believe the earth orbited the sun, rather than vice-versa, was because such a system would be at complete odds with how people knew physics to work. It would break gravity, break motion, and break the outer perfection of the cosmos, which was essential (…heh) 2 to our notions of, well, everything.
Which is why, when astronomers with their telescopes and their spreadsheets started systematically observing imperfections in planets and stars, lots of people didn’t believe them—even other astronomers. Over the course of centuries, though, these imperfections became impossible to ignore, and helped launch the earth in rotation ’round the sun.
This is the story of one such imperfection.
A Star is Born (and then dies)
Around 1296 A.D., over the course of half a year, a red dwarf star some 2 quadrillion miles away grew from 300 to 400 times the size of our sun. Over the next half year, the star shrunk back down to its previous size. Light from the star took 300 years to reach earth, eventually striking the retina of German pastor David Fabricius. It was very early Tuesday morning on August 13, 1596, and Pastor Fabricius was looking for Jupiter. 3
At that time of year, Jupiter would have been near the constellation Cetus (remember our sea monster?), but Fabricius noticed a nearby bright star (labeled ‘Mira’ in the below figure) which he did not remember from Ptolemy or Tycho Brahe’s star charts.
Spotting an unrecognized star wasn’t unusual, but one so bright in so common a constellation was certainly worthy of note. He wrote down some observations of the star throughout September and October, after which it seemed to have disappeared as suddenly as it appeared. The disappearance prompted Fabricius to write a letter about it to famed astronomer Tycho Brahe, who had described a similar appearing-then-disappearing star between 1572 and 1574. Brahe jotted Fabricius’ observations down in his journal. This sort of behavior, after all, was a bit shocking for a supposedly fixed and unchanging celestial sphere.
More shocking, however, was what happened 13 years later, on February 15, 1609. Once again searching for Jupiter, pastor Fabricius spotted another new star in the same spot as the last one. Tycho Brahe having recently died, Fabricius wrote a letter to his astronomical successor, Johannes Kepler, describing the miracle. This was unprecedented. No star had ever vanished and returned, and nobody knew what to make of it.
Unfortunately for Fabricius, nobody did make anything of it. His observations were either ignored or, occasionally, dismissed as an error. To add injury to insult, a local goose thief killed Fabricius with a shovel blow, thus ending his place in this star’s story, among other stories.
Three decades passed. On the winter solstice, 1638, Johannes Phocylides Holwarda prepared to view a lunar eclipse. He reported with excitement the star’s appearance and, by August 1639, its disappearance. The new star, Holwarda claimed, should be considered of the same class as Brahe, Kepler, and Fabricius’ new stars. As much a surprise to him as Fabricius, Holwarda saw the star again on November 7, 1639. Although he was not aware of it, his new star was the same as the one Fabricius spotted 30 years prior.
Two more decades passed before the new star in the neck of Cetus would be systematically sought and observed, this time by Johannes Hevelius: local politician, astronomer, and brewer of fine beers. By that time many had seen the star, but it was difficult to know whether it was the same celestial body, or even what was going on.
Hevelius brought everything together. He found recorded observations from Holwarda, Fabricius, and others, from today’s Netherlands to Germany to Poland, and realized these disparate observations were of the same star. Befitting its puzzling and seemingly miraculous nature, Hevelius dubbed the star Mira (miraculous) Ceti. The image below, from Hevelius’ Firmamentum Sobiescianum sive Uranographia (1687), depicts Mira Ceti as the bright star in the sea monster’s neck.
Going further, from 1659 to 1683, Hevelius observed Mira Ceti in a more consistent fashion than any before. There were eleven recorded observations in the 65 years between Fabricius’ first sighting of the star and Hevelius’ undertaking; in the following three, he had recorded 75 more such observations. Oddly, while Hevelius was a remarkably meticulous observer, he insisted the star was inherently unpredictable, with no regularity in its reappearances or variable brightness.
Beginning shortly after Hevelius, the astronomer Ismaël Boulliau also undertook a thirty year search for Mira Ceti. He even published a prediction, that the star would go through its vanishing cycle every 332 days, which turned out to be incredibly accurate. As today’s astronomers note, Mira Ceti‘s brightness increases and decreases by several orders of magnitude every 331 days, caused by an interplay between radiation pressure and gravity in the star’s gaseous exterior.
While of course Boulliau didn’t arrive at today’s explanation for Mira‘s variability, his solution did require a rethinking of the fixity of stars, and eventually contributed to the notion that maybe the same physical laws that apply on Earth also rule the sun and stars.
But we’re not here to talk about Boulliau, or Mira Ceti. We’re here to talk about this spreadsheet:
This snippet represents Hevelius’ attempt to systematically collected prior observations of Mira Ceti. Unreasonably meticulous readers of this post may note an inconsistency: I wrote that Johannes Phocylides Holwarda observed Mira Ceti on November 7th, 1639, yet Hevelius here shows Holwarda observing the star on December 7th, 1639, an entire month later. The little notes on the side are basically the observers saying: “wtf this star keeps reappearing???”
This mistake was not a simple printer’s error. It reappeared in Hevelius’ printed books three times: 1662, 1668, and 1685. This is an early example of what Raymond Panko and others call a spreadsheet error, which appear in nearly 90% of 21st century spreadsheets. Hand-entry is difficult, and mistakes are bound to happen. In this case, a game of telephone also played a part: Hevelius may have pulled some observations not directly from the original astronomers, but from the notes of Tycho Brahe and Johannes Kepler, to which he had access.
Unfortunately, with so few observations, and many of the early ones so sloppy, mistakes compound themselves. It’s difficult to predict a variable star’s periodicity when you don’t have the right dates of observation, which may have contributed to Hevelius’ continued insistence that Mira Ceti kept no regular schedule. The other contributing factor, of course, is that Hevelius worked without a telescope and under cloudy skies, and stars are hard to measure under even the best circumstances.
To Be Continued
Here ends the first half of Cetus. The second half will cover how Hevelius’ book was preserved, the labor behind its digitization, and a bit about the technologies involved in creating the image you see.
Early modern astronomy is a particularly good pre-digital subject for full-stack dev (f-s d), since it required vast international correspondence networks and distributed labor in order to succeed. Hevelius could not have created this table, compiled from the observations of several others, without access to cutting-edge astronomical instruments and the contemporary scholarly network.
You may ask why I included that whole section on Greek myths and Ptolemy’s constellations. Would as many early modern astronomers have noticed Mira Ceti had it not sat in the center of a familiar constellation, I wonder?
I promised this series will be about the secret life of data, answering the question of what’s behind a spreadsheet. Cetus is only the first story (well, second, I guess), but the idea is to upturn the iceberg underlying seemingly mundane datasets to reveal the complicated stories of their creation and usage. Stay-tuned for future installments.
This is the incredibly belated transcript of my HASTAC 2015 keynote. Many thanks to the organizers for inviting me, and to my fellow participants for all the wonderful discussions. The video and slides are also online. You can find citations to some of the historical illustrations and many of my intellectual inspirations here. What I said and what I wrote probably don’t align perfectly.
If you take a second to expand and disentangle “HASTAC”, you get a name of an organization that doubles as a fairly strong claim about the world: that Humanities, Arts, Science, and Technology are separate things, that they probably aren’t currently in alliance with one another, and that they ought to form an alliance.
This intention is reinforced in the theme of this year’s conference: “The Art and Science of Digital Humanities.” Here again we get the four pillars: humanities, arts, science, and technology. In fact, bear with me as I read from the CFP:
We welcome sessions that address, exemplify, and interrogate the interdisciplinary nature of DH work. HASTAC 2015 challenges participants to consider how the interplay of science, technology, social sciences, humanities, and arts are producing new forms of knowledge, disrupting older forms, challenging or reifying power relationships, among other possibilities.
Here again is that implicit message: disciplines are isolated, and their interplay can somehow influence power structures. As with a lot of digital humanities and cultural studies, there’s also a hint of activism: that building intentional bridges is a beneficial activity, and we’re organizing the community of HASTAC around this goal.
This is what I’ll be commenting on today. First, what does disciplinary isolation mean? I put this historically, and argue that we must frame disciplinary isolation in a rhetorical context.
This brings me to my second point about ontology. It turns out the way we talk about isolation is deeply related to the way we think about knowledge, the way we illustrate it, and ultimately the shape of knowledge itself. That’s ontology.
My third point brings us back to HASTAC: that we represent an intentional community, and this intent is to build bridges which positively affect the academy and the world.
I’ll connect these three strands by arguing that we need a map to build bridges, and we need to carefully think about the ontology of knowledge to draw that map. And once we have a map, we can use it to design a better territory.
In short, this plenary is a call-to-action. It’s my vocal support for an intentionally improved academy, my exploration of its historical and rhetorical underpinnings, and my suggestions for affecting positive change in the future.
Let’s begin at the beginning. With isolation.
Stop me if you’ve heard this one before:
Within this circle is the sum of all human knowledge. It’s nice, it’s enclosed, it’s bounded. It’s a comforting thought, that everything we’ve ever learned or created sits comfortably inside these boundaries.
This blue dot is you, when you’re born. It’s a beautiful baby picture. You’ve got the whole world ahead of you, an entire universe to learn, just waiting. You’re at the center because you have yet to reach your proverbial hand out in any direction and begin to learn.
But time passes and you grow. You go to highschool, you take your liberal arts and sciences, and you slowly expand your circle into the great known. Rounding out your knowledge, as it were.
Then college happens! Oh, those heady days of youth. We all remember it, when the shape of our knowledge started leaning tumorously to one side. The ill-effects of specialization and declaring a major, I suspect.
As you complete a master’s degree, your specialty pulls your knowledge inexorably towards the edge of the circle of the known. You’re not a jack of all trades anymore. You’re an expert.
Then your PhD advisor yells at you to focus and get even smaller. So you complete your qualifying exams and reach the edge of what’s known. What lies beyond the circle? Let’s zoom in and see!
You’ve reached the edge. The end of the line. The sum of all human knowledge stops here. If you want to go further, you’ll need to come up with something new. So you start writing your dissertation.
That’s your PhD. Right there, at the end of the little arrow.
You did it. Congratulations!
You now know more about less than anybody else in the world. You made a dent in the circle, you pushed human knowledge out just a tiny bit further, and all it cost you was your mental health, thirty years of your life, and the promise of a certain future. …Yay?
So here’s the new world that you helped build, the new circle of knowledge. With everyone in this room, I bet we’ve managed to make a lot of dents. Maybe we’ve even managed to increase the circle’s radius a bit!
And, though I’m being snarky about it, it’s a pretty uplifting narrative. It provides that same dual feeling of insignificance and importance that you get when you stare at the Hubble Ultra Deep Field. You know the picture, right?
There are 10,000 galaxies on display here, each with a hundred billion stars. To think that we, humans, from our tiny vantage point on Earth, could see so far and so much because of the clever way we shape glass lenses? That’s really cool.
And saying that every pinprick of light we see is someone else’s PhD? Well, that’s a pretty fantastic metaphor. Makes getting the PhD seem worth it, right?
It kinda reminds me of the cosmological theories of some of our philosophical ancestors.
The cosmos (Greek for “Order”), consisted of concentric, perfectly layered spheres, with us at the very center.
The cosmos was bordered by celestial fire, the light from heaven, and stars were simply pin-pricks in a dark curtain which let the heavenly light shine through.
So, if we beat Matt Might’s PhD metaphor to death, each of our dissertations are poking holes in the cosmic curtain, letting the light of heaven shine through. And that’s a beautiful thought, right? Enough pinpricks, and we’ll all be bathed in light.
But I promised we’d talk about isolation, and even if we have to destroy this metaphor to get there, we’ll get there.
The universe is expanding. That circle of knowledge we’re pushing the boundaries of? It’s getting bigger too. And as it gets larger, things that were once close get further and further apart. You and I and Alpha Centauri were all neighbors for the big bang, but things have changed since then, and the star that was once our neighbor is now 5 light years away.
In short, if we’re to take Matt Might’s PhD model as accurate, then the result of specialization is inexorable isolation. Let’s play this out.
Let’s say two thousand years ago, a white dude from Greece invented science. He wore a beard.
[Note for readers: the following narrative is intentionally awful. Read on and you’ll see why.]
He and his bearded friends created pretty much every discipline we’re familiar with at Western universities: biology, cosmology, linguistics, philosophy, administration, NCAA football, you name it.
Over time, as Ancient Greek beards finished their dissertations, the boundaries of science expanded in every direction. But the sum of human knowledge was still pretty small back then, so one beard could write many dissertations, and didn’t have to specialize in only one direction. Polymaths still roamed the earth.
Fast forward a thousand years or so. Human knowledge had expanded in the interim, and the first European universities branched into faculties: theology, law, medicine, arts.
Another few hundred years, and we’ve reached the first age of information overload. It’s barely possible to be a master of all things, and though we remember scholars and artists known for their amazing breadth, this breadth is becoming increasingly difficult to manage.
We begin to see the first published library catalogs, since the multitude of books required increasingly clever and systematic cataloging schemes. If you were to walk through Oxford in 1620, you’d see a set of newly-constructed doors with signs above them denoting their disciplinary uses: music, metaphysics, history, moral philosophy, and so on.
Time goes on a bit further, the circle of knowledge expands, and specialization eventually leads to fracturing.
We’ve reached the age of these massive hierarchical disciplinary schemes, with learning branching in every direction. Our little circle has become unmanageable.
A few more centuries pass. Some German universities perfect the art of specialization, and they pass it along to everyone else, including the American university system.
Within another 50 years, CP Snow famously invoked the “Two Cultures” of humanities and sciences.
And suddenly here we are
On the edge of our circle, pushing outward, with every new dissertation expanding our radius, and increasing the distance to our neighbors.
Basically, the inevitable growth of knowledge results in an equally inevitable isolation. This is the culmination of super-specialization: a world where the gulf between disciplines is impossible to traverse, filled with language barriers, value differences, and intellectual incommensurabilities. You name it.
By this point, 99% of the room is probably horrified. Maybe it’s by the prospect of an increasingly isolated academy. More likely the horror’s at my racist, sexist, whiggish, Eurocentric account of the history of science, or at my absurdly reductivist and genealogical account of the growth of knowledge.
This was intentional, and I hope you’ll forgive me, because I did it to prove a point: the power of visual rhetoric in shaping our thoughts. We use the word “imagine” to describe every act of internal creation, whether or not it conforms to the root word of “image”. In classical and medieval philosophy, thought itself was a visual process, and complex concepts were often illustrated visually in order to help students understand and remember. Ars memoriae, it was called.
And in ars memoriae, concepts were not only given visual form, they were given order. This order wasn’t merely a clever memorization technique, it was a reflection on underlying truths about the relationship between concepts. In a sense, visual representations helped bridge human thought with divine structure.
This is our entrance into ontology. We’ve essentially been talking about interdisciplinarity for two thousand years, and always alongside a visual rhetoric about the shape, or ontology, of knowledge. Over the next 10 minutes, I’ll trace the interwoven histories of ontology, illustrations, and rhetoric of interdisciplinarity. This will help contextualize our current moment, and the intention behind meeting at a conference like this one. It should, I hope, also inform how we design our community going forward.
Let’s take a look some alternatives to the Matt Might PhD model.
Countless cultural and religious traditions associate knowledge with trees; indeed, in the Bible, the fruit of one tree is knowledge itself.
During the Roman Empire and the Middle Ages, the sturdy metaphor of trees provided a sense of lineage and order to the world that matched perfectly with the neatly structured cosmos of the time. Common figures of speech we use today like “the root of the problem” or “branches of knowledge” betray the strength with which we connected these structures to one another. Visual representations of knowledge, obviously, were also tree-like.
See, it’s impossible to differentiate the visual from the essential here. The visualization wasn’t a metaphor, it was an instantiation of essence. There are three important concepts that link knowledge to trees, which at that time were inseparable.
One: putting knowledge on a tree implied a certain genealogy of ideas. What we discovered and explored first eventually branched into more precise subdisciplines, and the history of those branches are represented on the tree. This is much like any family tree you or I would put together with our parents and grandparents and so forth. The tree literally shows the historical evolution of concepts.
Two: putting knowledge on a tree implied a specific hierarchy that would by the Enlightenment become entwined with how we understood the universe. Philosophy separates into the theoretical and the practical; basic math into geometry and arithmetic. This branching hierarchy gave an importance to the root of the tree, be that root physics or God or philosophy or man, and that importance decreased as you reached the further limbs. It also implied an order of necessity: the branches of math could not exist without the branch of philosophy it stemmed from. This is why today people still think things like physics is the most important discipline.
Three: As these trees were represented, there was no difference between the concept of a branch of knowledge, the branch of knowledge itself, and the object of study of that branch of knowledge. The relationship of physics to chemistry isn’t just genealogical or foundational; it’s actually transcendent. The conceptual separation of genealogy, ontology, and transcendence would not come until much later.
It took some time for the use of the branching tree as a metaphor for knowledge to take hold, competing against other visual and metaphorical representations, but once it did, it ruled victorious for centuries. The trees spread and grew until they collapsed under their own weight by the late nineteenth century, leaving a vacuum to be filled by faceted classification systems and sprawling network visualizations. The loss of a single root as the source of knowledge signaled an epistemic shift in how knowledge is understood, the implications of which are still unfolding in present-day discussions of interdisciplinarity.
By visualizing knowledge itself as a tree, our ancestors reinforced both an epistemology and a phenomenology of knowledge, ensuring that we would think of concepts as part of hierarchies and genealogies for hundreds of years. As we slowly moved away from strictly tree-based representations of knowledge in the last century, we have also moved away from the sense that knowledge forms a strict hierarchy. Instead, we now believe it to be a diffuse system of occasionally interconnected parts.
Of course, the divisions of concepts and bodies of study have no natural kind. There are many axes against which we may compare biology to literature, but even the notion of an axis of comparison implies a commonality against which the two are related which may not actually exist. Still, we’ve found the division of knowledge into subjects, disciplines, and fields a useful practice since before Aristotle. The metaphors we use for these divisions influence our understanding of knowledge itself: structured or diffuse; overlapping or separate; rooted or free; fractals or divisions; these metaphors inform how we think about thinking, and they lend themselves to visual representations which construct and reinforce our notions of the order of knowledge.
Given all this, it should come as no surprise that medieval knowledge was shaped like a tree – God sat at the root, and the great branching of knowledge provided a transcendental order of things. Physics, ethics, and biology branched further and further until tiny subdisciplines sat at every leaf. One important aspect of these illustrations was unity – they were whole and complete, and even more, they were all connected. This mirrors pretty closely that circle from Matt Might.
Speaking of that circle I had up earlier, many of these branching diagrams had a similar feature. Notice the circle encompassing this illustration, especially the one on the left here: it’s a chain. The chain locks the illustration down: it says, there are no more branches to grow.
This and similar illustrations were also notable for their placement. This was an index to a book, an early encyclopedia of sorts – you use the branches to help you navigate through descriptions of the branches of knowledge. How else should you organize a book of knowledge than by its natural structure?
We start seeing some visual, rhetorical, and ontological changes by the time of Francis Bacon, who wrote “the distributions and partitions of knowledge are […] like branches of a tree that meet in a stem, which hath a dimension and quantity of entireness and continuance, before it come to discontinue and break itself into arms and boughs.”
The highly influential book broke the trends in three ways:
it broke the “one root” model of knowledge.
It shifted the system from closed to open, capable of growth and change
it detached natural knowledge from divine wisdom.
Bacon’s uprooting of knowledge, dividing it into history, poesy, and philosophy, each with its own root, was an intentional rhetorical strategy. He used it to argue that natural philosophy should be explored at the expense of poesy and history. Philosophy, what we now call science, was now a different kind of knowledge, worthier than the other two.
And doesn’t that feel a lot like today?
Bacon’s system also existed without an encompassing chain, embodying the idea that learning could be advanced; that the whole of knowledge could not be represented as an already-grown tree. There was no complete order of knowledge, because knowledge changes.
And, by being an imperfect, incomplete entity, without union, knowledge was notably separated from divine wisdom.
Of course, divinity and transcendence wasn’t wholly exorcised from these ontological illustrations: Athanasius Kircher put God on the highest branch, feeding the tree’s growth. (Remember, from my earlier circle metaphor, the importance of the poking holes in the fabric of the cosmos to let the light of heaven shine through?). Descartes as well continued to describe knowledge as a tree, whose roots were reliant on divine existence.
But even without the single trunk, without God, without unity, the metaphors were still ontologically essential, even into the 18th century. This early encyclopedia by Ephraim Chambers uses the tree as an index, and Chambers writes:
“the Origin and Derivation of the several Parts, and the relation in which [the disciplines] stand to their common Stock and to each other; will assist in restoring ‘em to their proper Places”
Their proper places. This order is still truth with a capital T.
It wasn’t until the mid-18th century, with Diderot and d’Alembert’s encyclopedia, that serious thinkers started actively disputing the idea that these trees were somehow indicative of the essence of knowledge. Even they couldn’t escape using trees, however, introducing their enyclopedia by saying “We have chosen a division which has appeared to us most nearly satisfactory for the encyclopedia arrangement of our knowledge and, at the same time, for its genealogical arrangement.”
Even if the tree wasn’t the essence of knowledge, it still represented possible truth about the genealogy of ideas. It took until a half century later, with the Encyclopedia Britannica, for the editors to do away with tree illustrations entirely and write that the world was “perpetually blended in almost every branch of human knowledge”. (Notice they still use the word branch.) By now, a philosophical trend that began with Bacon was taking form through the impossibility of organizing giant libraries and encyclopedia: that there was no unity of knowledge, no implicit order, and no viable hierarchy.
It took another century to find a visual metaphor to replace the branching tree. Herbert Spencer wrote that the branches of knowledge “now and again re-unite […], they severally send off and receive connecting growths; and the intercommunion is ever becoming more frequent, more intricate, more widely ramified.” Classification theorist S.R. Ranganathan compared knowledge to the Banyan tree from his home country of India, which has roots which both grow from the bottom up and the top down.
The 20th century saw a wealth of new shapes of knowledge. Paul Otlet conceived a sort of universal network, connected through individual’s thought processes. H.G. Wells shaped knowledge very similar to Matt Might’s illustrated PhD from earlier: starting with a child’s experience of learning and branching out. These were both interesting developments, as they rhetorically placed the ontology of knowledge in the realm of the psychological or the social: driven by people rather than some underlying objective reality about conceptual relationships.
Around this time there was a flourishing of visual metaphors, to fill the vacuum left by the loss of the sturdy tree.There was, uncoincidentally, a flourishing of uses for these illustrations. Some, like this map, was educational and historical, teaching students how the history of physics split and recombined like water flowing through rivers and tributaries. Others, like the illustration to the right, showed how the conceptual relationships between knowledge domains differed from and overlapped with library classification schemes and literature finding aids.
By the 80s, we start seeing a slew of the illustrations we’re all familiar with: those sexy sexy network spaghetti-and-meatball graphs. We often use them to illustrate citation chains, and the relationship between academic disciplines. These graphs, so popular in the 21st century, go hand-in-hand with the ontological baggage we’re used to: that knowledge is complex, unrooted, interconnected, and co-constructed. This fits well with the current return to a concept we’d mostly left in the 19th century: that knowledge is a single, growing unit, that it’s consilient, that everyone is connected. It’s a return to the Republic of Letters from the C.P. Snow’s split of the Two Cultures.
It also notably departs from genealogical, transcendental, and even conceptual discussions of knowledge. These networks, broadly construed, are social representations, and while those relationships may often align with conceptual ones, concepts are not what drive the connections.
Interestingly, there is precedent in these sorts of illustrations in the history of evolutionary biology. In the late 19th-century, illustrators and scientists began asking what it would look like if you took a slice from the evolutionary tree – or, what does the tree of life look like when you’re looking at it from the top-down?
What you get is a visual structure very similar to the network diagrams we’re now used to. And often, if you probe those making the modern visualizations, they will weave a story about the history of these networks that is reminiscent of branching evolutionary trees.
There’s another set of epistemological baggage that comes along with these spaghetti-and-meatball-graphs. Ben Fry, a well-known researcher in information visualization, wrote:
“There is a tendency when using [networks] to become smitten with one’s own data. Even though a graph of a few hundred nodes quickly becomes unreadable, it is often satisfying for the creator because the resulting figure is elegant and complex and may be subjectively beautiful, and the notion that the creator’s data is ‘complex’ fits just fine with the creator’s own interpretation of it. Graphs have a tendency of making a data set look sophisticated and important, without having solved the problem of enlightening the viewer.”
Actually, were any of you here at last night’s Pink Floyd light show in the planetarium? They’re a lot like that. [Yes, readers, HASTAC put on a Pink Floyd light show.]
And this is where we are now.
Which brings us back to the outline, and HASTAC. Cathy Davidson has often described HASTAC as a social network, which is (at least on the web) always an intentionally-designed medium. Its design grants certain affordances to users: is it easier to communicate individually or in groups? What types of communities, events, or content is prioritized? These are design decisions that affect how the HASTAC community functions and interacts.
“We believe that a new configuration in the humanities must be championed to ensure their centrality to all intellectual enterprises in the university and, more generally, to understanding the human condition and thereby improving it; and that those intellectual changes must be supported by new institutional structures and values.”
This was a HASTAC rallying cry: how can the humanities constructively inform the world? Notice especially how they called for “New Institutional Structures.”
Remember earlier, how I talked about the problem if isolation? While my story about it was problematic, it doesn’t make disciplinary superspecialization any less real a problem. For all its talk of interdisciplinarity, academia is averse to synthesis on many fronts, superspecialization being just one of them. A dissertation based on synthesis, for example, is much less likely to get through a committee than a thorough single intellectual contribution to one specific field.
The academy is also weirdly averse to writing for public audiences. Popular books won’t get you tenure. But every discipline is a popular audience to most other disciplines: you wouldn’t talk to a chemist about history the same way you’d talk to a historian. Synthetic and semi-public work is exactly the sort of work that will help with HASTAC’s goal of a truly integrated and informed academy for social good, but the cards are stacked against it. Cathy and David hit the nail on the head when they target institutional structures as a critical point for improvement.
This is where design comes in.
Recall again the theme this year: The Art and Science of Digital Humanities. I propose we take the next few days to think about how we can use art and science to make HASTAC even better at living up its intent. That is, knowing what we do about collaboration, about visual rhetoric, about the academy, how can we design an intentional community to meet its goals? Perusing the program, it looks like most of us will already be discussing exactly this, but it’s useful to put a frame around it.
When we talk about structure and the social web, there’s many great examples we may learn from. One such example is that of Tara McPherson and her colleagues, in designing the web publishing platform Scalar. As opposed to WordPress, its cousin in functionality, Scalar was designed with feminist and humanist principles in mind, allowing for more expressive, non-hierarchical “pathways” through content.
When talking of institutional, social, and web-based structures, we can also take lessons history. In Early Modern Europe, the great network of information exchange known as the Republic of Letters was a shining example of the influence of media structures on innovation. Scholars would often communicate through “hubs”, which were personified in people nicknamed things like “the mailbox of Europe”. And they helped distribute new research incredibly efficiently through their vast web of social ties. These hubs were essential to what’s been called the scientific revolution, and without their structural role, it’s unlikely you’d see references to a scientific revolution in the 17th century Europe.
Similarly, at that time, the Atlantic slave trade was wreaking untold havoc on the world. For all the ills it caused, we at least can take some lessons from it in the intentional design of a scholarly network. There existed a rich exchange of medical knowledge between Africans and indigenous Americans that bypassed Europe entirely, taking an entirely different sort of route through early modern social networks.
If we take the present day, we see certain affordances of social networks similarly used to subvert or reconfigure power structures, as with the many revolutions in North Africa and the Middle East, or the current activist events taking place around police brutality and racism in the US. Similar tactics that piggy-back on network properties are used by governments to spread propaganda, ad agencies to spread viral videos, and so forth.
The question, then, is how we can intentionally design a community, using principles we learn from historical action, as well as modern network science, in order to subvert institutional structures in the manner raised by Cathy and David?
Certainly we also ought to take into account the research going into collaboration, teamwork, and group science. We’ve learned, for example, that teams with diverse backgrounds often come up with more creative solutions to tricky problems. We’ve learned that many small, agile groups often outperform large groups with the same amount of people, and that informal discussion outside the work-space contributes in interesting ways to productivity. Many great lessons can be found in Michael Nielsen’s book, Reinventing Discovery.
We can use these historical and lab-based examples to inform the design of social networks. HASTAC already work towards this goal through its scholars program, but there are more steps that may be taken, such as strategically seeking out scholars from underrepresented parts of the network.
So this covers covers the science, but what about the art?
Well, I spent the entire middle half of this talk discussing how visual rhetoric is linked to ontological metaphors of knowledge. The tree metaphor of knowledge, for example, was so strongly held that it fooled Descartes into breaking his claims of mind-body dualism.
So here is where the artists in the room can also fruitfully contribute to the same goal: by literally designing a better infrastructure. Visually. Illustrations can be remarkably powerful drivers of reconceptualization, and we have the opportunity here to affect changes in the academy more broadly.
One of the great gifts of the social web, at least when it’s designed well, is its ability to let nodes on the farthest limbs of the network to still wield remarkable influence over the whole structure. This is why viral videos, kickstarter projects, and cats playing pianos can become popular without “industry backing”. And the decisions we make in creating illustrations, in fostering online interactions, in designing social interfaces, can profoundly affect the way those interactions reinforce, subvert, or sidestep power structures.
So this is my call to the room: let’s revisit the discussion about designing the community we want to live in.
Well, it looks like Digital Humanities Now scooped me on posting my own article. As some of you may have read, I recently did not submit a paper on the Republic of Letters, opting instead to hold off until I could submit it to a journal which allowed authorial preprint distribution. Preprints are a vital part of rapid knowledge exchange in our ever-quickening world, and while some disciplines have embraced the preprint culture, many others have yet to. I’d love the humanities to embrace that practice, and in the spirit of being the change you want to see in the world, I’ve decided to post a preprint of my Republic of Letters paper, which I will be submitting to another journal in the near future. You can read the full first draft here.
The paper, briefly, is an attempt to contextualize the Republic of Letters and the Scientific Revolution using modern computational methodologies. It draws from secondary sources on the Republic of Letters itself, especially from my old mentor R.A. Hatch, some network analysis from sociology and statistical physics, modeling, human dynamics, and complexity theory. All of this is combined through datasets graciously donated by the Dutch Circulation of Knowledge group and Oxford’s Cultures of Knowledge project, totaling about 100,000 letters worth of metadata. Because it favors large scale quantitative analysis over an equally important close and qualitative analysis, the paper is a contribution to historiopgraphic methodology rather than historical narrative; that is, it doesn’t say anything particularly novel about history, but it does offer a (fairly) new way of looking at and contextualizing it.
At its core, the paper suggests that by looking at how scholarly networks naturally grow and connect, we as historians can have new ways to tease out what was contingent upon the period and situation. It turns out that social networks of a certain topology are basins of attraction similar to those I discussed in Flow and Empty Space. With enough time and any of a variety of facilitating social conditions and technologies, a network similar in shape and influence to the Republic of Letters will almost inevitably form. Armed with this knowledge, we as historians can move back to the microhistories and individuated primary materials to find exactly what those facilitating factors were, who played the key roles in the network, how the network may differ from what was expected, and so forth. Essentially, this method is one base map we can use to navigate and situate historical narrative.
Of course, I make no claims of this being the right way to look at history, or the only quantitative base map we can use. The important point is that it raises new kinds of questions and is one mechanism to facilitate the re-integration of the individual and the longue durée, the close and the distant reading.
The project casts a necessarily wide net. I do not yet, and probably could not ever, have mastery over each and every disciplinary pool I draw from. With that in mind, I welcome comments, suggestions, and criticisms from historians, network analysts, modelers, sociologists, and whomever else cares to weigh in. Whomever helps will get a gracious acknowledgement in the final version, good scholarly karma, and a cookie if we ever meet in person. The draft will be edited and submitted in the coming months, and if you have ideas, please post them in the comment section below. Also, if you use ideas from the paper, please cite it as an unpublished manuscript or, if it gets published, cite that version instead.
A few months back, I posted a series of pledges about being a good scholarly citizen. Among other things, I pledged to keep my data and code open whenever possible, and to fight to retain the right to distribute materials pending and following their publication. I also signed the Open Access Pledge. Since then, a petition boycotting Elsevier cropped up with very similar goals, and as of this writing has nearly 7,000 signatures.
As a young scholar with as-yet no single authored publications (although one is pending in the forward-thinking Journal of Digital Humanities, which you should all go and peer review), I had to think very carefully in making these pledges. It’s a dangerous world out there for people who aren’t free to publish in whatever journal they like; reducing my publication options is not likely to win me anything but good karma.
With that in mind, I actually was careful never to pledge explicitly that I would not publish in closed access venues; rather, I pledged to “Freely distribute all published material for which I have the right, and to fight to retain those rights in situations where that is not the case.” The pressure of the eventual job market prevented me from saying anything stronger.
Today, my resolve was tested. A recent CFP solicited papers about “Shaping the Republic of Letters: Communication, Correspondence and Networks in Early Modern Europe.” This is, essentially, the exact topic that I’ve been studying and analyzing for the past several years, and I recently finished a draft of a paper on this topic precisely. The paper utilizes methodologies not-yet prevalent in the humanities, and I’d like the opportunity to spread the technique as quickly and widely as possible, in the hopes that some might find it useful or at least interesting. I also feel strongly that the early and open dissemination of scholarly production is paramount to a healthy research community.
I e-mailed the editor asking about access rights, and he sent a very kind reply, saying that, unfortunately, any article in the journal must be unpublished (even on the internet), and cannot be republished for two years following its publication. The journal itself is part of a small press, and as such is probably trying to get itself established and sold to libraries, so their reticence is (perhaps) understandable. However, I was faced with a dilemma: submit my article to them, going against the spirit – though not the letter – of my pledge, or risk losing a golden opportunity to submit my first single-authored article to a journal where it would actually fit.
In the end, it was actually the object of my study itself – the Republic of Letters – that convinced me to make a stand and not submit my article. The Republic, a self-titled community of 17th century scholars communicating widely by post, was embodied by the ideal of universal citizenship and the free flow of knowledge. While they did not live up to this ideal, in large part because of the technologies of the time, we now are closer to being able to do so. I need to do my part in bringing about this ideal by taking a stand on the issues of open access and dissemination.
The below was my e-mail to the editor:
Many thanks for your fast reply.
Unfortunately, I cannot submit my article unless those conditions are changed. I fear they represent a policy at odds with the past ideals and present realities of scholarly dissemination. The ideals of the Republic of Letters, regarding the free flow of information and universal citizenship, are finally becoming attainable (at least in some parts of the world) with nigh-ubiquitous web access. In a world as rapidly changing as our own, immediate access to the materials of scholarly production is becoming an essential element not just of science, in the English sense of the word, but wissenschaft at large. Numerousstudieshaveshown that the open availability of electronic prints for an article increases readership and citations (both to the author and to the journal), reduces the time to the adoption of new ideas, and facilitates a more rapidly innovating and evolving literature in the scholarly world. While I empathize that you represent a fairly small press and may be worried that the availability of pre-prints would affect 1 sales, I have seen no studies showing this to be the case, although I would of course be open to reading such research if you know of some. In either case, it has been shown that pre-prints at worst do not affect scholarly use and dissemination in the least, and at best increase readership, citation, and impact by up to 250%.
Good luck with your journal, and I look forward to reading the upcoming issue when it becomes available.
It’s a frightening world out there. I considered not posting about this interaction, for fear of the possibility of angering or being blacklisted by the editorial or advisory board of the press, some of whom are respected names in my intended field of study. However, fear is the enemy of change, and the support of Bethany Nowviskie and a host of tweeters convinced me that this was the right thing to do.
With that in mind, I herewith post a draft of my article analyzing the Republic of Letters, currently titled The Networked Structure of Scientific Growth. Please feel free to share it for non-commercial use, citing it if you use it (but making sure to cite the published version if it eventually becomes so), and I’d love your comments if you have any. I’ll dedicate a separate post to this release later, but I figured you all deserved this after reading the whole post.
Big thanks to Andrew Simpson for pointing out the error of my ways! ↩
Early modern history! Science! Letters! Data! Four of my favoritest things have been combined in this brand new beta release of Early Modern Letters Online from Oxford University.
EMLO (what an adorable acronym, I kind of what to tickle it) is Oxford’s answer to a metadata database (metadatabase?) of, you guessed it, early modern letters. This is pretty much a gold standard metadata project. It’s still in beta, so there are some interface kinks and desirable features not-yet-implemented, but it has all the right ingredients for a great project:
Information is free and open; I’m even told it will be downloadable at some point.
The interface is fast, easy, and includes faceted browsing.
Has a fantastic interface for adding your own data.
Actually includes citation guidelines thank you so much.
Visualizations for at-a-glance understanding of data.
Links to full transcripts, abstracts, and hard-copies where available.
Lots of other fantastic things.
Sorry if I go on about how fantastic this catalog is – like I said, I love letters so much. The index itself includes roughly 12,000 people, 4,000 locations, 60,000 letters, 9,000 images, and 26,000 additional comments. It is without a doubt the largest public letters database currently available. Between the data being compiled by this group, along with that of the CKCC in the Netherlands, the Electronic Enlightenment Project at Oxford, Stanford’s Mapping the Republic of Letters project, and R.A. Hatch‘s research collection, there will without a doubt soon be hundreds of thousands of letters which can be tracked, read, and analyzed with absolute ease. The mind boggles.
Bodleian Card Catalogue Summaries
Without a doubt, the coolest and most unique feature this project brings to the table is the digitization of Bodleian Card Catalogue, a fifty-two drawer index-card cabinet filled with summaries of nearly 50,000 letters held in the library, all compiled by the Bodleian staff many years ago. In lieu of full transcriptions, digitizations, or translations, these summary cards are an amazing resource by themselves. Many of the letters in the EMLO collection include these summaries as full-text abstracts.
The collection also includes the correspondences of John Aubrey (1,037 letters), Comenius (526), Hartlib (4,589 many including transcripts), Edward Lhwyd (2,139 many including transcripts), Martin Lister (1,141), John Selden (355), and John Wallis (2,002). The advanced search allows you to look for only letters with full transcripts or abstracts available. As someone who’s worked with a lot of letters catalogs of varying qualities, it is refreshing to see this one being upfront about unknown/uncertain values. It would, however, be nice if they included the editor’s best guess of dates and locations, or perhaps inferred locations/dates from the other information available. (For example, if birth and death dates are known, it is likely a letter was not written by someone before or after those dates.)
In the interest of full disclosure, I should note that, much like with the CKCC letters interface, I spent some time working with the Cultures of Knowledge team on visualizations for EMLO. Their group was absolutely fantastic to work with, with impressive resources and outstanding expertise. The result of the collaboration was the integration of visualizations in metadata summaries, the first of which is a simple bar chart showing the numbers of letters written, received, and mentioned in per year of any given individual in the catalog. Besides being useful for getting an at-a-glance idea of the data, these charts actually proved really useful for data cleaning.
Because I can’t do anything with letters without looking at them as a network, I decided to put together some visualizations using Sci2 and Gephi. In both cases, the Sci2 tool was used for data preparation and analysis, and the final network was visualized in GUESS and Gephi, respectively. The first graph shows network in detail with edges, and names visible for the most “central” correspondents. The second visualization is without edges, with each correspondent clustered according to their place in the overall network, with the most prominent figures in each cluster visible.
The graphs show us that this is not a fully connected network. There are many islands of one or two letters or a small handful of letters. These can be indicative of a prestige bias in the data. That is, the collection contains many letters from the most prestigious correspondents, and increasingly fewer as the prestige of the correspondent decreases. Put in another way, there are many letters from a few, and few letters from many. This is a characteristic shared with power law and other “long tail” distributions. The jumbled community structure at the center of the second graph is especially interesting, and it would be worth comparing these communities against institutions and informal societies at the time. Knowledge of large-scale patterns in a network can help determine what sort of analyses are best for the data at hand. More on this in particular will be coming in the next few weeks.
It’s also worth pointing out these visualizations as another tool for data-checking. You may notice, on the bottom left-hand corner of the first network visualization, two separate Edward Lhwyds with virtually the same networks of correspondence. This meant there were two distinct entities in their database referring to the same individual – a problem which has since been corrected.
Notice that the EMLO site makes it very clear that they are open to contributions. There are many letters datasets out there, some digitized, some still languishing idly on dead trees, and until they are all combined, we will be limited in the scope of the research possible. We can always use more. If you are in any way responsible for an early-modern letters collection, meta-data or full-text, please help by opening that collection up and making it integrable with the other sets out there. It will do the scholarly world a great service, and get us that much closer to understanding the processes underlying scholarly communication in general. The folks at Oxford are providing a great example, and I look forward to watching this project as it grows and improves.
Last post, I talked about combining textual and network analysis. Both are becoming standard tools in the methodological toolkit of the digital humanist, sitting next to GIS in what seems to be becoming the Big Three in computational humanities.
Data as Context, Data as Contextualized
Humanists are starkly aware that no particular aspect of a subject sits in a vacuum; context is key. A network on its own is a set of meaningless relationships without a knowledge of what travels through and across it, what entities make it up, and how that network interacts with the larger world. The network must be contextualized by the content. Conversely, the networks in which people and processes are situated deeply affect those entities: medium shapes message and topology shapes influence. The content must be contextualized by the network.
At the risk of the iPhonification of methodologies 1, textual, network, and geographic analysis may be combined with each other and traditional humanities research so that they might all inform one another. That last post on textual and network analysis was missing one key component for digital humanities: the humanities. Combining textual and network analysis with traditional humanities research (rather than merely using the humanities to inform text and network analysis, or vice-versa) promises to transform the sorts of questions asked and projects undertaken in Academia at large.
Just as networks can be used to contextualize text (and vice-versa), the same can be said of networks and maps (or texts and maps for that matter, or all three, but I’ll leave those for later posts). Now, instead of starting with the maps we all know and love, we’ll start by jumping into the deep end by discussing maps as any sort of representative landscape in which a network can be situated. In fact, I’m going to start off by using the network as a map against which certain relational properties can be overlaid. That is, I’m starting by using a map to contextualize a network, rather than the more intuitive other way around.
Using Maps to Contextualize a Network
The base map we’re discussing here is a map of science. They’ve made their rounds, so you’ve probably seen one, but just in case you haven’t here’s a brief description: some researchers (in this case Kevin Boyack and Richard Klavans) take tons on information from scholarly databases (in this case the Science Citation Index Expanded and the Social Science Citation Index) and create a network diagram from some set of metrics (in this case, citation similarity). They call this network representation a Map of Science.
We can debate about the merits of these maps ’till we’re blue in the face, but let’s avoid that for now. To my mind, the maps are useful, interesting, and incomplete, and the map-makers are generally well-aware of their deficiencies. The point here is that it is a map: a landscape against which one can situate oneself, and with which one may be able to find paths and understand the lay of the land.
In Boyack, Börner 2, and Klavans (2007), the three authors set out to use the map of science to explore the evolution of chemistry research. The purpose of the paper doesn’t really matter here, though; what matters is the idea of overlaying information atop a base network map.
The images above are the funding profiles of the NIH (National Institutes of Health) and NSF (National Science Foundation). The authors collected publication information attached to all the grants funded by the NSF and NIH and looked at how those publications cited one another. The orange edges show connections between disciplines on the map of science that were more prevalent within the context a particular funding agency than they were compared to the entire map of science. Boyack, Börner 3, and Klavans created a map and used it to contextualize certain funding agencies. They and other parties have since used such maps to contextualize universities, authors, disciplines, and other publication groups.
From Network Maps to Geographic Maps
Of course, the Where’s The Beef™ section of this post still has yet to be discussed, with the beef in this case being geography. How can we use existing topography to contextualize network topology? Network space rarely corresponds to geographic place, however neither of them alone can ever fully represent the landscape within which we are situated. A purely geographic map of ancient Rome would not accurately represent the world in which the ancient Romans lived, as it does not take into account the shortening of distances through well-trod trade routes.
Enter Stanford DH ninja Elijah Meeks. In two recent posts, Elijah discussed the topology/topography divide. In the first, he created a network layout algorithm which took a network with nodes originally placed in their geographic coordinates, and then distorted the network visualization to emphasize network distance. The visualization above shows the network laid out geographically. The one below shows the Imperial Roman trade routes with network distances emphasized. As Elijah says, “everything of the same color in the above map is the same network distance from Rome.”
Of course, the savvy reader has probably observed that this does not take everything into account. These are only land routes; what about the sea?
Elijah’s second post addressed just that, impressively applying GIS techniques to determine the likely route ships took to get from one port to another. This technique drives home the point he was trying to make about transitioning from network topology to network topography. The picture below, incidentally, is Elijah’s re-rendering of the last visualization taking into account both land and see routes. As you can see, the distance from any city to any other has decreased significantly, even taking into account his network-distance algorithm.
The above network visualization combines geography, two types of transportation routes, and network science to provide a more nuanced at-a-glance view of the Imperial Roman landscape. The work he highlighted in his post transitioning from topology to topography in edge shapes is also of utmost importance, however that topic will need to wait for another post.
The Republic of Letters (A Brief Interlude)
Elijah was also involved in another Stanford-based project, one very dear to my heart, Mapping the Republic of Letters. Much of my own research has dealt with the Republic of Letters, especially my time spent under Bob Hatch, and Paula Findlen, Dan Edelstein, and Nicole Coleman at Stanford have been heading up an impressive project on that very subject. I’ll go into more details about the Republic in another post (I know, promises promises), but for now the important thing to look at is their interface for navigating the Republic.
The team has gone well beyond the interface that currently faces the public, however even the original map is an important step. Overlaid against a map of Europe are the correspondences of many early modern scholars. The flow of information is apparent temporally, spatially, and through the network topology of the Republic itself. Now as any good explorer knows, no map is any substitute for a thorough knowledge of the land itself; instead, it is to be used for finding unexplored areas and for synthesizing information at a large scale. For contextualizing.
If you’ll allow me a brief diversion, I’d like to talk about tools for making these sorts of maps, now that we’re on the subject of letters. Elijah’s post on visualizing network distance included a plugin for Gephi to emphasize network distance. Gephi’s a great tool for making really pretty network visualizations, and it also comes with a small but potent handful of network analysis algorithms.
I’m on the development team of another program, the Sci² Tool, which shares a lot of Gephi’s functionality, although it has a much wider scope and includes algorithms for textual, geographic, and statistical analysis, as well as a somewhat broader range of network analysis algorithms.
This is by no means a suggestion to use Sci² over Gephi; they both have their strengths and weaknesses. Gephi is dead simple to use, produces the most beautiful graphs on the market, and is all-around fantastic software. They both excel in different areas, and by using them (and other tools!) together, it is possible to create maps combining geographic and network features without ever having to resort to programming.
The above image was generated by combining the Sci² Tool with Gephi. It is the correspondence network of Hugo Grotius, a dataset I worked on while at Huygens ING in The Hague. They are a great group, and another team doing fantastic Republic of Letters research, and they provided this letters dataset. We just developed this particular functionality in Sci² yesterday, so it will take a bit of time before we work out the bugs and release it publicly, however as soon as it is released I’ll be sure to post a full tutorial on how to make maps like the one above.
This ends the public service announcement.
These maps are not without their critics. Especially prevalent were questions along the lines of “But how is this showing me anything I didn’t already know?” or “All of this is just an artefact of population densities and standard trade routes – what are these maps telling us about the Republic of Letters?” These are legitimate critiques, however as mentioned before, these maps are still useful for at-a-glance synthesis of large scales of information, or learning something new about areas one is not yet an expert in. Another problem has been that the lines on the map don’t represent actual travel routes; those sorts of problems are beginning to be addressed by the type of work Elijah Meeks and other GIS researchers are doing.
To tackle the suggestion that these are merely representing population data, I would like to propose what I believe to be a novel idea. I haven’t published on this yet, and I’m not trying to claim scholarly territory here, but I would ask that if this idea inspires research of your own, please cite this blog post or my publication on the subject, whenever it comes out.
We have a lot of data. Of course it doesn’t feel like we have enough, and it never will, but we have a lot of data. We can use what we have, for example collecting all the correspondences from early modern Europe, and place them on a map like this one. The more data we have, the smaller time slices we can have in our maps. We create a base map that is a combination of geographic properties, statistical location properties, and network properties.
Start with a map of the world. To account for population or related correlations, do something similar to what Elijah did in this post, encoding population information (or average number of publications per city, or whatever else you’d like to account for) into the map. On top of that, place the biggest network of whatever it is that you’re looking at that you can find. Scholarly communication, citations, whatever. It’s your big Map of YourFavoriteThingHere. All of these together are your base map.
Atop that, place whatever or whomever you are studying. The correspondence of Grotius can be put on this map, like the NIH was overlaid atop the Map of Science, and areas would light up and become larger if they are surprising against the base map. Are there more letters between Paris and The Hague in the Grotius dataset then one would expect if the dataset was just randomly plucked from the whole Republic of Letters? If so, make that line brighter and thicker.
By combining geography, point statistics, and networks, we can create base maps against which we can contextualize whatever we happen to be studying. This is just one possible combination; base maps can be created from any of a myriad of sources of data. The important thing is that we, as humanists, ought to be able to contextualize our data in the same way that we always have. Now that we’re working with a lot more of it, we’re going to need help in those contextualizations. Base maps are one solution.
It’s worth pointing out one major problem with base maps: bias. Until recently, those Maps of Science making their way around the blogosphere represented the humanities as a small island off the coast of social sciences, if they showed them at all. This is because the primary publication venues of the arts and humanities were not represented in the datasets used to create these science maps. We must watch out for similar biases when constructing our own base maps, however the problem is significantly more difficult for historical datasets because the underrepresented are too dead to speak up. For a brief discussion of historical biases, you can read my UCLA presentation here.
putting every tool imaginable in one box and using them all at once ↩
Full disclosure: she’s my advisor. She’s also awesome. Hi Katy! ↩