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Problem-Solving Lab: Interpret Scientific Illustrations

Think Critically:

Each wavelength in the hydrogen’s Balmer series corresponds with one of the parts in problem 1. For example, part d corresponds with the wavelength closest to the series limit, in pink. Problem d’s solution was 4.12e3 Angstroms, which would be best represented with the pink wavelength.

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October 25, 2018

Visualizing Science: Illustration and Beyond

Where does the illustrator end and the infographer begin? How does data visualization fit in? And what does science have to say about the design decisions we make? 

By Jen Christiansen

This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American

In July I presented at the 50th anniversary conference of the Guild of Natural Science Illustrators (GNSI). Here’s a lightly-edited transcript of that talk, with an abridged set of companion images. 

*** Thanks for including me in the annual meeting. Particularly this year, here in Washington, DC. Although I’ve been a bit of an absentee family member, this feels something like a homecoming, in that the first GNSI meeting I attended was also in Washington DC, 22 years ago.

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With the goal of reinforcing connections between communities and to strengthen some of those family ties, I plan to use this time to prompt you to think about what you [scientific illustrators] can learn from—and teach to—a few different sub-disciplines within the broader orb of visual science communication. For example: Where does the illustrator end….

….and the infographer begin?

How does data visualization fit in?

And what does science have to say about the design decisions we make?

But first, I should provide some context. The thoughts to follow are very much rooted my own experiences. This is a bit of a reflection upon what I’ve observed over the last 20 years as an art director. This is not a comprehensive literature review or a rigorous look at the entire field of science visualization. These are musings related to my journey—rooted in the world of publishing—with shout-outs to some of the resources and communities that I’ve found helpful or thought-provoking along the way. I am inevitably going to get myself into some trouble, as I’m not a specialist in all of the sub-disciplines that I’ll be speaking about. But I do work at the intersection of these sub-disciplines, and over time I have developed a set of resources that may be of interest to you.

As background, for context on what has shaped my point of view, I should say that I self define as a graphics editor, science communicator, and an infographer. I was trained primarily in traditional (or physical) media. Here’s one of my first published scientific illustrations—ink drawings of hydrothermal vent shrimp mouthparts. It’s one of the products of a few summers in college, working in a crustacea lab at the Natural History Museum of Los Angeles.

But I did get a chance to dabble in the digital realm starting in the late 1980's with a Commodore Amiga, thanks to my high school art teacher, Robert Tartter. I then shifted over to tablets and virtual drawing at Smith College, where my instructor Gary Niswonger led exercises like blind contour figure drawing. Model in the middle of the room, our backs towards our monitors, and Wacom tablets in our laps.

As much as I loved my art and design courses, I also loved my courses in science. I resisted choosing one discipline at the expense of the other, and pursued a double major in geology and studio art. Then, a scientific illustration graduate program at the University of California, Santa Cruz (now hosted by California State University at Monterey Bay) caught my eye, and allowed me to officially merge the two disciplines under the tutelage of Ann Caudle, Jenny Keller, and Larry Lavendal. Instead of dedicating myself to a single scientific line of research, I’d help make other people’s research accessible to wider audiences through a visual language.

I entered the publishing scene after graduate school, as an intern in the art department at Scientific American , thanks to my mentor, Ed Bell. This is my first illustration for the magazine; a watercolor cockroach. 

My internship turned into a full-time job, and as my role shifted from artist to art director, I found myself setting my watercolors and Rapidograph pens aside and turning to the computer as my primary tool. In 1998, I left Scientific American to work as an assistant art director, then a designer, at National Geographic . All the while, growing as an art director, and learning about print production from some of the best in the business. Five years later, I left DC and established a freelance science communication business, focusing mainly on magazine and book projects.

In 2007, I returned to Scientific American , where I’m now the senior graphics editor. Today, there are two of us on the graphics team, Amanda Montañez and me. We art direct all of the information graphics in the magazine, from data visualizations (like the sample on the left by Pitch Interactive), to illustrated explainers (like the sample on the right, by Cherie Sinnen). Occasionally we develop the final images ourselves, but most often we hire freelance artists, and manage the project.

We also manage digital versions of all the graphics for web and phone viewing, and Amanda develops graphics specifically for the web environment…

…but I’m going to remain rooted in print examples for what I’ll be sharing today.

Editorial illustrations, like these examples by Maria Corte Maidagan and Jay Bendt, are art directed by other folks in the art department (design director Michael Mrak and art director Jason Mischka).

What’s the difference? By my definition, information graphics are illustrations, built on a foundation of research, that exist primarily to convey information. Whereas editorial illustrations are thematic illustrations that are inspired by the text, and which entice readers to engage more fully with the magazine’s content.

For example, for an article on the microbiome, illustrator Bryan Christie developed an opening image that is not a literal representation of the concept, but instead nods to the idea of a human as being defined by the microbes within. Bryan is telling a story with visual symbols and thoughtful composition. This is not an information graphic. Instead, it’s a metaphorical illustration that nods to the theme of the text.

Within the article, Bryan and his collaborator Joe Lertola shift into information-graphic mode, using visual symbols and thoughtful composition to convey very specific information that is rooted in research. This is not an editorial illustration. It’s an information graphic or explanatory diagram.

Some artists, like Bryan Christie, are comfortable in both of these worlds.

But this is where I reside, when it comes to my own illustration work and in my role as a graphics editor. 

That said, I can learn from, and often work with, artists who navigate both sides. For example, for an article on malaria vaccines, I built out the content plan and composition, shown here. 

But knew that it would take an adept artist to breathe life into the spread. So I hired the illustration team of Peter and Maria Hoey to develop the final rendering. Peter and Maria have a great way with icons and develop rich textures and depth in their editorial work, attributes that served this information graphic well.

Similarly, I leaned heavily on editorial illustrator Leandro Castelao’s styling and color palette to make this schematic of interferometers feel like it belongs in a consumer magazine, and not a scientific journal…

…and Gavin Potenza’s style provided a fresh look at a topic that lots of media outlets covered, giving us a stylistically distinctive timeline of Mars exploration.

When working on my own illustrations, I find that I sometimes get hyper-focused on details related to accuracy, and the illustration style can fall flat. Things can get very academic looking and anonymous really fast. Editorial illustrators like Jillian Ditner and Chad Hagen remind me that injecting the artist’s voice and point of view and sense of style is OK, particularly in the context of a magazine.

Let’s revisit this slide…

…and dive in further to some lessons learned and specific resources related to information graphics.

I tend to think of information graphics as a continuum, with figurative representations at one end and abstract representations on the other.

In the world of science visualization, you could argue that the full continuum can also be referred to as data visualizations. 

After all, essentially all of our work is rooted in data collection at some stage in the process: from bone length measurements in dinosaur reconstructions, …

…to meticulously documented laboratory experiments that build up to a more complete understanding of processes like photosynthesis …

…to representations of mathematical expressions, like Feynman diagrams…

…to straight-up plotting of the raw data itself, in chart form. 

But, outside of the world of science visualization, it may be more useful to think of the continuum like this:

When I flip through old issues of Scientific American , it strikes me that many artists worked across this full spectrum. Regular contributor Bunji Tagawa’s body of work astounds me. Here’s a small sample, to give you a glimpse at the breadth of his skillset. It ranges from figurative depictions of objects…

…to medical cutaways…

…technical schematics…

…and data visualizations. [For more, see “ Remembering Bunji Tagawa ” by Amanda Montañez].

But as a graphics editor at the magazine now, I find myself maintaining discrete freelance pools for each of the different points along the continuum. 

Perhaps this is an artifact of my own biases, but when I flip through old issues of Scientific American , it occurs to me that this increased specialization may also be in part due to the shifting tools of each of these areas. When the primary tool for developing representative illustrations, explanatory diagram, and data visualizations for print magazines was pen and ink, an artist could become a master at pen and ink, then explore different methods of problem solving in each of these areas. Here are more examples from Tagawa. Again, from organics…

...to technical cutaways…

…abstract representations of processes…

…and number-based plots. All in pen and ink.

As desktop publishing became ubiquitous and digital rendering tools diversified and became more widely available, it seems to me that the simple act of choosing a primary tool starts to define the edges of the artist’s scope. As an art director, I find myself specifically looking for 3D artists to build physical objects, like this example by Don Foley.

And I look for artists like Tami Tolpa, who hone in on composition and the flow of information by iterating with tools like Adobe Illustrator for explanatory diagrams.

And data designers that build solutions with code for visualizing large datasets, like Jan Willem Tulp.

Each of these tools, mediums, styles and genres take lots of time to master, and tend to favor certain portions of the continuum.

Many of the conferences I attend and communities that I engage with seem to reinforce these divisions, by focusing on the tools. And it seems that artists that span more than one of these orbs are harder and harder to find. 

Perhaps that way of thinking about things is a bit overly dramatic. The reality is probably much more like this.

And perhaps this is a completely natural and fine state to be in—particularly since the primary tools for these different sub-disciplines have bifurcated over time. And perhaps there isn’t value in trying to force discrete clusters to reconnect.

After all, some of my favorite recent Scientific American graphics are the result of bringing together different artists—plucking experts from each of those groups and matching them up to create a final image that draws upon all of their strengths, not forcing one artist to excel in all areas. For example, I love to take an artist who can develop spot illustrations with a stylus or pen, and pair them up with an artist who can custom code data visualization solutions, as in this example by Moritz Stefaner and Jillian Walters.

But it also applies to graphics like these, in which different pieces of the full story are simply better served by different types of visualizations and areas of artist expertise.

That said, I argue that even if you don’t have the desire to work across the full continuum—or the time to dedicate to becoming proficient across the full continuum—there is lots to be learned from each of these clusters, and I’d love to see more cross pollination of ideas between them. I think we’d all benefit, if things looked more like this:

And even better, if things looked like this:

I’m not arguing that everyone along this continuum should learn how to code. Or that everyone along the continuum should build clay maquettes and paint from life. I’m arguing that we can—and should—learn how science visualizers from cross the full spectrum think through and solve problems.

In that spirit, I’d like to share some of the things that I’ve learned from individuals within these different clusters. 

First, I should note that at this web address: http://bit.ly/jenGNSI , you’ll find a Google Sheet of more complete citations and links to sources that directly relate to the rest of my presentation. I’ll be showing some examples from the magazine that exemplify the lessons that I’ve learned, but in many cases, I’ve absorbed those lessons through a variety of books, blog posts, presentations, podcasts and workshops. This Google Sheet will guide you to specific references that you might find inspiring as more context for each of the following points.

And at this web address: http://bit.ly/SciVizResources , you’ll find a more general list of references that I’ve built in response to specific requests from students, scientists, and artists interested in learning more about scientific visualization—all the way from illustration to data visualization—as well as references related to the work I do as a science graphics editor.

Back to representative illustrations. Here are 5 key lessons that I’ve gleaned largely from the scientific illustration community.

Lesson number 1: Every detail is worthy of intense attention and care. Every feature should be observed and considered.

For a graphic about the impact of mosquitoes on public health, artist Immy Smith demonstrates that yes, a lineup of different mosquito species can, in fact, be visually interesting and informative…

…and reinforces the idea that different mosquito species are connected to different diseases in humans. Some of my colleagues were a bit skeptical at this plan—they worried that the mosquitos would all look the same. But the careful and exacting eye of artist Immy Smith revealed the variations between them. 

It’s clear here that every pencil mark is intentional and considered. To my mind, the thoughtful and meticulous rendering of every single detail is a challenge to the reader to, in return, carefully and thoughtfully consider each insect depicted.

Lesson 2: Get your eyes on the subject matter

For an article on the American burying beetle, artist Kelly Murphy knew she’d need to render the insect from a variety of angles. Existing images online made for great references for top-down views. But in order to get a better look at the mouthparts and in order to more accurately represent the beetle in different positions, she tracked down a specimen at the California Academy of Sciences and spent some time photographing it from all angles.

Lesson 3: if you can’t get your eyes on the subject matter, consider making an approximation.

Artist James Gurney’s scenes are incredibly convincing in part due to the dimensional models he develops to inform his composition and lighting.

Lesson 4: There is power in photorealistic and hyper-real renderings

As Ed bell wrote in 2010, “Artist Ron Miller takes us on a journey to eight of the most breathtaking views that await intrepid explorers of our solar system. The scale of these natural wonders dwarfs anything Earth has to offer. What might we see and feel if we could travel to these distant domains? The artist’s eye—interpreting data from probes such as NASA’s Cassini, which is now exploring the Saturnian system, and MESSENGER, which has flown by Mercury three times and goes into permanent orbit next March—allows us an early visit to these unforgettable locales.”

Lesson 5: There is power in non-photorealistic renderings

Here, artist Carol Donner edits out non-essential details and employs transparency to create an aesthetically appealing and clear look at the heart. Unlike Ron Miller’s objective in the view of Saturn, the goal here is not to help the reader to experience an environment. The goal here is to represent the behavior of an object.

As Ian Suk and his co-authors wrote in The Journal of Biocommunication , “Medical illustration, by its very essence, requires some degree of simplification and idealization of the subject. It is the role of the illustrator to visually educate the reader regarding a complex biomedical procedure, structure, or process in an aesthetic and efficient manner.”

Let’s start to slide over more into the realm of information graphics and explanatory diagrams.

Lesson 1: There’s power in knowing when and how to distort the physical form of your subject to best serve the informational content

This particular example was rendered by Bunji Tagawa. It’s a variation on a classic schematic representation of the cardiovascular system. Rather than get caught up in the details of a human figure, the information is streamlined in a way that highlights the closed and circular flow of blood through the system. 

Lesson 2: Composition is key: the artist should be acutely aware of how they are guiding the reader’s eye through an illustration

Here, John Grimwade very intentionally guides the reader's eye through the page with spare linework, selective color, and setting up the flow of action in a way that reinforces the flow of information.

Lesson 3: Text is vital. It is just as important as images.

Here, artist Emily Cooper presents an elegant set of four Atlantic Ocean views. Labels help orient the reader to a certain degree, and you can piece together parts of the story: We’re looking at how winds and the gulf stream relate to temperature. But it’s hard to sort out what the take-home message is.

That’s where text comes in. The headline and annotation subheads immediately provide context.

The graphic aims to explain why winter is warmer in Europe, according to four different models. Through that lens, the reader is now primed to compare and contrast the four models.

Lesson 4: Welcoming gestures, in the form of relatable details or moments of joy, can engage readers

Nigel Holmes is an expert at including touches of humor and humanity to explanatory graphics. Here, he manages to inject life into a graphic about group theory and the mathematical underpinnings of symmetry. But the figures aren’t simply decorative. They help reinforce the concept of the cube rotation around different axes.

Lesson 5: Context is key. Your audience and the goal of the information graphic should guide both content and style.

For an article on artificial photosynthesis, a scientist provided us with the reference material on the left. It’s a schematic of the device they had developed to mimic the process. The image is perfectly serviceable for the scientist’s peers, and for the context of a scientific paper. But for a consumer magazine, we needed to not only engage non-specialist readers, but also help them more immediately see the parallels between this technology and natural photosynthesis. Our version is on the right. Note that we introduced a photosynthesis primer of sorts for basic context. And the context of a consumer magazine granted us the freedom to go a bit more playful with the rendering style, making it a great match for botanical artist Cherie Sinnen.

Let’s continue sliding over more into the realm of abstract renderings and data visualization.

Lesson 1: Fully explore your dataset. Then explore it some more.

For a graphic about changing bee populations over time, data visualizer Moritz Stefaner started by exploring the raw data, in forms ranging from bar charts to tables and heat maps to network diagrams, all in the service of examining the shape of the data through many different lenses. The data sketches on the left are just a small sample of the total. Only after he had thoroughly examined the data in different forms did he start to hone in on what final form might serve the data—and our readers—the best.

Lesson 2: It’s OK to embrace complexity

But when presenting people with a complicated, layered, and rich look at a topic, always provide the reader the tools they need to interpret the visualization. Here, for an article on the evolution of mythical stories, the design studio Accurat presents many levels of data in a single visual. It’s a complicated and rich data visualization, rooted in a complicated and rich dataset from a scientist. Some folks might argue that we’re requiring too much effort on behalf of the reader. There’s not a quick way to read this.

However, we set things up with a clear “How to Read this Figure” key, using informal language that you might use when talking a friend through the graphic. The idea being that once the reader dedicates a bit of guided effort into learning how to read things, they can then pop over and see a few annotated main takeaway points, then explore further on their own. This type of approach also allowed us to present the full dataset, rather than distilling things down into an over-simplistic summary.

Lesson 3: Novel forms can draw a reader in

For a piece on when babies are born, data scientist Zan Armstrong and visualizer Nadieh Bremer collaborated to develop this custom solution for the magazine. The information here could be presented in a more classic and linear chart form as well. But I doubt it would’ve captured the readers’ attention nearly as well. The circles are not only captivating, but they also reinforce the cyclical nature of the data, like a clock. As with the complex example, however, the reader should be given a clear guide on how to read the graphic.

Lesson 4: But sometimes a straightforward line graph or bar chart is best

For this chart nugget on bias in coronary artery disease research, there was no need to push the boundaries or try to be clever. A straightforward and simple and familiar chart form was all that was needed.

Lesson 5: Your source data is not objective. Nor is it complete. 

I’ve included links to some great resources about this in my main reference list, but I think it’s worth nodding directly to a few specific writings here.

As researcher, artist and software developer Catherine D’Ignazio writes in her post “What would feminist data visualization look like?”

“What’s the issue? Feminist standpoint theory would say that the issue is that all knowledge is socially situated and that the perspectives of oppressed groups including women, minorities and others are systematically excluded from ‘general’ knowledge…

…Until we acknowledge and recognize that power of inclusion and exclusion, and develop some visual language for it, we must acknowledge data visualization as one more powerful and flawed tool of oppression.”

And on the topic of missing data, artist and researcher Mimi Onuoha writes, “Datasets are the end product of classification systems, the clean outputs of intentional orderings. Datasets point to their own contrasts—specifically the things that we haven’t collected…

…And if it is true that we make sense of the world through exclusion, then perhaps there is a special type of meaning to be found in the things that we leave out.”

These ideas have made me approach some projects with both more trepidation and clearer vision. For an article on infectious diseases, we hoped to give folks a sense of the global state of things. How have infectious diseases impacted public health over time and space? 

Art researcher Amanda Hobbs was a huge help on this front, doing a large literature survey and identifying some of the known pitfalls. We ended up using pretty standard and authoritative sources for the bulk of the spread: The Centers for Disease Control and Prevention, and the World Health Organization.

But even seemingly well-meaning authoritative sources can’t control for data collection variations over time and space. And data collection methodologies are developed by individuals with inherent biases. So, I made sure to read the data documentation carefully, avoiding known pitfalls and making note of when methodologies changed. That helped guide decisions like the time interval we used, as well as some on-chart annotations. And we ran things by a few experts in the field for a reality check.

I’m still thinking that we should have done more to help readers understand that this sort of data is far from perfect. In the future, I plan to fight a little harder to keep more explanatory text intact. But we at least left a pretty clear crumb trail with the source citation, so that it’s possible for people to inspect the original data for themselves.

And now, four bonus lessons from across and around the continuum.

Lesson 1: Question design “rules” 

Many of us can probably recite oft-quoted “rules” from well-known authors, like Edward Tufte’s proclamation that “Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.” But how do we know that’s true? And if it is true in one context, is it true in another? Perception scientists are steadily chipping away at some of these sorts of questions.

I’d encourage you to check out the Datastories podcast for interviews with some of those researchers, Robert Kosara’s blog Eagereyes for leads on where else to look, and Kennedy Elliot’s post entitled “39 studies about human perception in 30 minutes.”

Lesson 2: Share

I am often struck by how generous the data journalism community is when it comes to sharing visualization best-practices, story ideas, techniques, tools and resources. If you’re interested in learning more, a great place to start is the Computer Assisted Reporting program of the Investigative Reporters and Editors organization.

A few more specific resources include Jennifer LaFleur’s guide to bulletproofing your data…

….and ProPublica’s “The Nerd Blog.”

Lesson 3: Create spaces for dialogue

The intersection of science and the fine and performing arts is a bit further outside of my personal wheelhouse, but I’m excited to continue to learn more about how artists and performers engage with broad audiences in different kinds of spaces. And I’m interested trying to figure out how to apply those lessons of engagement within my magazine work.

For more, I’d encourage you to check out SciArt Center, an online platform and event organization that promotes cross-disciplinary collaborations, art exhibits and events.

And for more information about the performing arts and science, see Jamé McCray’s talk at the SciVizNYC conference in 2017, as well as one of the communities she references in that talk, SuperHero Clubhouse. As I mentioned a moment ago, I’m trying to figure out how to apply some of their lessons of audience engagement and dialogue within my magazine work.

But meanwhile, I’m trying to actively create spaces for dialogue for the science visualization community as a member of the organizing team of SciVizNYC. I’d love to see this model put into action in other locations. 

The idea for this event rolled out of conversations with medical illustrator Jill Gregory. She and I enjoyed visiting each other’s workplace, to see how we each put our training in scientific illustration to use. We wondered if a roving meet-up event could work, in which a group moved from one office to another studio to another newsroom over the course of a day, seeing how different folks put scientific visualization to use in their daily work. As the idea grew, Jill and I were joined by co-organizers Christopher Smith, Amanda Montañez, and Ni-ka Ford. We ended up moving forward with a more traditional—and logistically easier—all-day event in a single location featuring 14 speakers. Our first event, in 2016, was free. We charged $35 a seat for our second event to discourage no-shows. But it was important for us to keep the cost of entry as low as possible, while still paying our speakers a small honorarium for their time. Thanks to funding from the Icahn School of Medicine at Mount Sinai, we were able to make that happen. Our event brings together a wide variety of visual science communicators together—primarily from the New York City area—to learn about the topic from many different angles.

Which brings me to Lesson 4: We’re stronger together

I’d like to tip my hat to platforms and communities that encourage idea and tool sharing. I’ve grown a great deal by seeing the data visualization community problem solve in real time on Twitter, watching panel discussions about technology and interaction through the lens of ethics at Eyeo Festival, listening to talks about the open web at OpenVisConf, learning about how visual journalists from around the world solve problems at the Malofiej World infographics Summit,….the list is too long to call them all out individually here, but I’ve included some of my favorites in my general resource list.

There’s certainly already evidence of lesson crossover between communities. For example, the idea of using annotations in information graphics has infiltrated the data visualization world, as demonstrated by tools developed in 2017 by data visualization engineer Susie Lu. Her work facilitates annotation integration for charts built with code.

And you could argue that the photo-real versus non-photo-real dynamic in scientific illustration, is mirrored by the complex versus simple dynamic in data visualization.

But I think we still have so much to learn from each other, especially when it comes to the challenge of visualizing scientific uncertainty—a communications challenge that cuts across all areas of science, and thus, all areas of science visualization.

At the magazine, we often work with academics and research scientists. Communities that regularly fall back on conventions like error bars. And all too often, I faithfully reproduce those error bars (shown here in gray), figuring that adding a few labels might be enough to unpack meaning.

The same goes for confidence intervals, like these. I’m not convinced that I did enough here to really help the reader fully understand what they’re looking at.

A few years ago, I had a pivotal conversation with a cartographer at a Gordon Research Conference for visualization in science and education. Diana Sinton (from Cornell University) caused me to step back, and think about how I depict uncertainty more critically. We kicked off a collaboration, with support by a mini grant funded by NASA that resulted in this poster presentation, in the hopes of adding another perspective to the broader conversation on visualizing uncertainty.

At the time, conversations with regards to uncertainty tended to swirl around climate projections, but more recently, I think it's fair to say that election forecasting has taken center stage.

We started by diving into the literature, and that got me wondering, in data visualization, do we lean too heavily on conventions like the whisker box plot, error bars, and violin plots? And are conversations about which of these approaches are better than others too circular in nature?

What if we borrowed from qualitative, rather than quantitative examples? At Scientific American, we deal with representing different types of uncertainty all the time across different disciplines in explanatory diagrams. Can we borrow from those, and apply to data visualization?

What if data visualizers took inspiration from scientific illustrators? And scientific illustrators took inspiration from data visualizers? Maybe we could work together to figure out more intuitive ways to represent statistical uncertainty.

Quantum mechanics, and Heisenberg's uncertainty principle seems like a great place to start. Specifically, the idea that “events at the atomic level cannot be observed with certainty.” It’s worth underscoring that the uncertainty principle describes quantum behavior and uncertainty at the particle level. And isn’t a perfect analogy for uncertainty at the macro or classical physics level. 

But I’m more concerned with how we *illustrate* the uncertainty associated with the idea. So I think that studying figures like this one, which depict quantum mechanics, can still be really useful beyond the quantum world.

I don’t want to get too bogged down in the science behind many of these examples, and truth be told, I only have a fleeting grasp on some of the physics they represent. But I still think that they can be informative, and hopefully inspiring. On the left, the classical view of light as a combination of oscillating electric and magnetic fields. On the right, light as viewed through the lens of quantum mechanics—in which that line fluctuates within a larger envelope of uncertainty (shaded region).

The level of uncertainty not only varies over space, but also varies over time. Here’s a neat example of three different ways to visualize that concept.

Neuroscience is another field filled with uncertainty. Here, regions of the brain are labeled like mountain ranges on a map. No clear boundaries are drawn because the boundaries aren’t precisely known.

Around 70 years later, some structures and functions are known with greater precision. But we still rely on fuzzy edges quite a lot.

Here’s another strategy for dealing with the uncertain geography of the brain. The entire structure is abstracted.

And here, the brain is rendered realistically, but process arrows float above the structure, suggesting that the exact details of those interactions are still a work in progress.

I think that this one (from 1969) is my favorite. Brain inputs—A B and C—and outputs —X, Y, and Z—are more or less understood. However, “far less is known about the workings of regions in between, which make up most of the brain.”

And those of you working in the realm of animal reconstructions have to deal with the challenge of building forms based on incomplete information all of the time. Here, artist Rudolf Freund uses strong line-work to signal what is known, and lighter line-work to signal what is uncertain.

And many of us deal with illustrating processes that aren’t yet fully understood, or hypotheses that aren’t yet proven. Sometimes it may make sense to simply resort to the use of a question mark and inset box, to help flag that a particular interpretation is still not fully embraced.

I’m not trying to suggest that the samples I just shared are perfect examples of visualizing uncertainty. But I think it would be exciting to see how qualitative solutions in general might inform quantitative solutions, and vice-versa.

So with that, I’ll end with a request.

I hope that you’ll join me in thinking on the topic of visualizing uncertainty, as well as developing other ways of strengthening ties and sharing lessons across the broader world of science visualization.


Thanks to Jonathan Corum for inspiring this transcript post format. If you made it to the bottom of this one, I think you’ll find his “ Design for an Audience: Science graphics at The New York Times ” transcript an enjoyable and informative read.

* * * Image Credits  * * *

1-4: Illustrations by April Neander, Art Direction and Graphics by 5W Infographics, in “ The Ascent of Mammals ” by Stephen Brusatte and Zhe-Xi Luo, Scientific American, June 2016.

5: Illustration by Jen Christiansen, in “ A new species of the shrimp genus Chorocaris Martin & Hessler, 1990 (Crustacea Decapoda: Bresiliidae) from hydrothermal vent fields along Mid-Atlantic Ridge ” by J. W. Martin and J. C. Christiansen. Proc. Biol. Soc. Wash. 108:220–227, 1995.

6. Illustration by Jen Christiansen, in “ Exoskeletal Sensors for Walking ” by Sasha N. Hill and Ernst-August Seyfarth, Scientific American, July 1996.

7. LEFT: Graphic by Pitch Interactive, in “ The Steady Rise of Ebola ,” Scientific American, February 2015 (Sources: “A Comprehensive Database of the Geographic Spread of Past Human Ebola Outbreaks,” by Adrian Mylne et al., in Scientific Data, Vol. 1, Article no. 140042; October 23, 2014 (historical data); Centers for Disease Control and Prevention (2013-2014 data)); RIGHT: Illustration by Cherie Sinnen, in “ The End of Orange Juice ,” by Anna Kuchment, Scientific American, March 2013

8. LEFT: Interactive by Jan Willem Tulp (Source: Eclipse Predictions by Fred Espenak, NASA GSFC Emeritus), for Scientific American; MIDDLE: Graphic by Amanda Montañez (Source: “ The social and cultural roots of whale and dolphin brains ,” by Kieran C. R. Fox et al., in Nature Ecology & Evolution, Vol. 1, No. 11; November 2017), for Scientific American; RIGHT: Bryan Christie Design, in “ How Seashells Take Shape ” by Derek E. Moulton, Alain Goriely and Régis Chirat, Scientific American, April 2018

9. Illustration by Maria Corte Maidagan, in “ The Emptiest Place in Space ,” by István Szapudi, Scientific American, December 2016

10: Illustration by Jay Bendt, in “ The Digital-Subscription Dilemna ,” by David Pogue, Scientific American, December 2017

11. LEFT:  Illustration by Cherie Sinnen, in “ The End of Orange Juice ,” by Anna Kuchment, Scientific American, March 2013; RIGHT: Illustration by Maria Corte Maidagan, in “ The Emptiest Place in Space ,” by István Szapudi, Scientific American, December 2016

12-13. Illustration by Bryan Christie Design, in “ The Ultimate Social Network ,” by Jennifer Ackerman, Scientific American, June 2012

14. Sketch by Jen Christiansen15. Illustration by Peter and Maria Hoey, in " Halting the World's Most Lethal Parasite ," by Mary Carmichael, Scientific American, November 2010

16. Illustration by Leandro Castelao, in “ Is Space Digital? ” by Michael Moyer, Scientific American, February 2012

17. Illustration by Gavin Potenza, in “ Digging Mars ,” by Peter H. Smith, in Scientific American, November 2011

18. LEFT: Illustration by Jillian Ditner, in “ Black Holes and Wormholes and the Secrets of Quantum Spacetime ,” by Juan Maldacena, Scientific American, November 2016; RIGHT: Illustration by Chad Hagen, in “ A Geometric Theory of Everything ,” by A. Garrett Lisi, and James Owen Weatherall, Scientific American, December 2010

19. Illustration by Bunji Tagawa, Scientific American, March 1975

20. Illustration by Bunji Tagawa, in “ The Transplantation of the Kidney, ” by John P. Merrill, Scientific American, October 1959

21. Illustration by Bunji Tagawa, in “ Computer Inputs and Outputs ” by Ivan E. Sutherland, Scientific American, September 1966

22. Graphic by Bunji Tagawa, in “ Molecular Motions ” by B.J. Alder and Thomas E. Wainwright, Scientific American, October 1959

23. Illustration by Bunji Tagawa, in “ The Most Poisonous Mushrooms ,” by Walter Litten, Scientific American, March 1975

24. Illustration by Bunji Tagawa, in “ An Ancient Greek Computer ,” by Derek J. de Solla Price, Scientific American, June 1959

25. Illustration by Bunji Tagawa, in “ The Adrenal Chromaffin Cell, ” by Stephen W. Carmichael and Hans Winkler, Scientific American, August 1985

26. Graphic by Bunji Tagawa, in “ Rocket Astronomy, ” by Herbert Friedman, in Scientific American, June 1959

27. Illustration by Don Foley, in “ Reading the Red Planet ,” by John P. Grotzinger and Ashwin Vasavada, Scientific American, May 2012

28. Illustration by Tami Tolpa, in “ Lab-Built Brains ,” by Juergen A. Knoblich, Scientific American, January 2017 (Source: “Generation of Cerebral Organoids from Human Pluripotent Stem Cells,” by Madeline A. Lancaster and Juergen A. Knoblich, in Nature Protocols, Vol. 9; October 2014)

29. Graphic by Jan Willem Tulp, in “ Planets Everywhere , “by John Matson, Scientific American, December 2012

30. Graphic by Moritz Stefaner, Illustrations by Jillian Walters, in “ Where the Wild Bees Are, ” by Ferris Jabr, Scientific American, December 2013 (source: “Plant-Pollinator Interactions over 120 Years: Loss of Species, Co-occurrence, and Function,” by Laura A. Burkle, John C. Marlin and Tiffany M. Knight, in Science, Vol. 339; March 29, 2013) 

31: Illustrations by April Neander, Art Direction and Graphics by 5W Infographics, in “ The Ascent of Mammals ” by Stephen Brusatte and Zhe-Xi Luo, Scientific American, June 2016.

32-33. Illustrations by Immy Smith, Research by Amanda Hobbs, in “ Buzz Kill, ” by Dan Strickman, Scientific American, July 2018

34. Photographs and illustration by Kelly Murphy 

35. Photographs and illustration by James Gurney

36. Illustration by Ron Miller, in “ 8 Wonders of the Solar System ,” by Edward Bell, Scientific American, April 2010

37. Illustration by Carol Donner, in “ The Heart as a Suction Pump ,” by Edmund H. Sonnenblick, Stephen M. Factor and Thomas F. Robinson, Scientific American, June 1986

38. Illustration by Bunji Tagawa, in “ The Heart ,” by Carl J. Wiggers, Scientific American, May 1957

39. Illustration by John Grimwade, in “ Summon the Rain, ” by Dan Baum, in Scientific American, June 2014

40-42. Illustration by Emily Cooper, in “ Rethinking the Gulf Stream ,” by Stephen C. Riser and M. Susan Lozier, Scientific American, February 2013

43. Illustration by Nigel Holmes, in “ The Whole Universe Catalog ,” by Stephen Ornes, Scientific American, July 2015

44. LEFT: Reyes-Gil, Karla & M. Spurgeon, Joshua & S. Lewis, Nathan. (2009). Silicon and tungsten oxide nanostructures for water splitting. Proc SPIE. 7408. 10.1117/12.825545; RIGHT: Illustration by Cherie Sinnen, in “ Reinventing the Leaf ,” by Antonio Regalado, Scientific American, October 2010

45. Sketches  and Graphic by Moritz Stefaner, Illustrations by Jillian Walters, in “ Where the Wild Bees Are ,” by Ferris Jabr, Scientific American, December 2013 (source: “Plant-Pollinator Interactions over 120 Years: Loss of Species, Co-occurrence, and Function,” by Laura A. Burkle, John C. Marlin and Tiffany M. Knight, in Science, Vol. 339; March 29, 2013

46-47. Graphic by Accurat, in " Scientists Trace Society's Myths to Primordial Origins ,” by Julien d'Huy, Scientific American, December 2016

48. Graphic by Zan Armstrong and Nadieh Bremer, in “ The Baby Spike, ” by Mark Fischetti and Zan Armstrong, Scientific American, July 2017

49. Graphic by Jen Christiansen, in " Not Just for Men ,” by Marcia L. Stefanick, Scientific American, September 2017 (Source: Treatment Strategies for Women with Coronary Artery Disease: Future Research Needs, by Rowena J. Dolor et al. Comparative Effectiveness Review No. 66 Agency for Healthcare Research and Quality, Department of Health and Human Services, August 2012)

50-51. Excerpt from “ What would feminist data look like? ” By Catherine D’Ignazio, December 2015

52-53. Excerpt from “ What is Missing is Still There ” by Mimi Onuoha

54. Graphics by Jen Christiansen, Research by Amanda Hobbs, in “ Infections by the Numbers ,” Scientific American, May 2018

55. TOP LEFT: Illustration by Ron Miller; BOTTOM LEFT: Illustration by Carol Donner; TOP RIGHT: Graphic by Accurat; BOTTOM RIGHT: Graphic by Jen Christiansen

56. Graphic by Jen Christiansen, in “ The Neutron Enigma ,” by Geoffrey L. Greene and Peter Geltenbort, Scientific American, April 2016

57. Graphic by Jen Christiansen, in “ Up, Up and Away, ” by Mark Fischetti, in Scientific American, December 2014

58. Illustration by Katy Wiedemann, in “ Where We Came From, ” in Scientific American, September 2014 (source: Bernard Wood)

59. Illustration by Jen Christiansen, in " Crossing the Quantum Divide ,” by Tim Folger, Scientific American, July 2018

60. Illustration by Jen Christiansen, in “ Beyond the Quantum Horizon ,” by  David Deutsch and Artur Ekert, Scientific American, September 2012

61. Illustration by George Kelvin, in “ Squeezed Light ,” By Bernard Yurke and Richart E. Slusher, Scientific American, May 1988

62. Illustration by George Kelvin, in “ Quantum Gravity, ” by Bryce S. DeWitt, Scientific American, December 1983

63. Illustration by Eric Mose, in “ The Great Ravelled Knot ,” by George W. Gray, Scientific American, October 1948

64. Illustration by AXS Studio, in “ The Case of the Sleeping Slayer, ” by James Vlahos, Scientific American MIND, September 2012

65. Illustration by Bunji Tagawa, in “ Brain Mechanisms of Movement ,” By Edward V. Evarts, Scientific American 1979

66. Illustration by AXS Studio, in “ Good Habits, Bad Habits, ” by Ann M. Graybiel and Kyle S. Smith, Scientific American, June 2014

67. Illustration by Ilil Arbel, In “ The Brain ,” By David H. Hubel, Scientific American, September 1979

68. Illustration by Rudolf Freund, In “ The Early Relatives of Man, ” By Elwyn L. Simons, Scientific American, July 1964

69. Illustration by Chad Hagen, In “ Goldilocks Black Holes, ” By Jenny E. Greene, Scientific American, December 201

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Problem-solving lab: interpret scientific illustrations

Today I learned that while utilizing Rydberg’s constant many numbers and conversions are involved and when you are calculating for wavelength or energy in joules you must be organized and willing to check your work jut in case there are any small errors.

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Scientific illustration: The key to a world of visual science

Learn everything you need to know about scientific illustrations, how to make them and their crucial role in science here.

In science, visual communication is fundamental. A lot depends on the subject matter, from an atom to a gene to a crystal or even an entire galaxy. It aims to illuminate knowledge with visuals. Hence, to share discoveries, ideas, and observations, researchers have used both image and text to communicate.   In other words, art contributes greatly to advancements in science and how knowledge is spread.  Science and art are often seen as opposites, yet scientific illustration lies at the intersection of the two fields. 

You have probably seen numerous scientific illustrations even if you were unfamiliar with the discipline.  Take a look at your science books, a museum exhibit you experienced, or even that chart in the doctor’s office displaying the outlines of various body parts.

We have so much more information on that in this article. Learn everything you need to know about scientific illustrations and their crucial role in science.

What is scientific illustration?

Scientific illustration is a method of communication that allows complex ideas, details, and theories to be presented in an interesting and informative approach.

It is possible to more clearly communicate scientific concepts through illustrations rather than the technical jargon often used to describe them. Drawing or rendering scientific images in an accurate manner is how these artists or illustrators communicate science and inform audiences. 

In order to create accurate illustrations, scientific illustrators use a combination of aesthetics and technical expertise.  By using illustrations, graphics, and multimedia, concepts can be clearly presented and portrayed visually, making them more accessible and appealing to a wide range of audiences. 

By using it, users can explain complex concepts to a wider audience, as well as educate doctors or scientists through exemplary illustrations or animations.  After all, the power of pictures cannot be understated.

A brief History

From prehistoric times, humans have depicted the world around them through art. Cave paintings from Paleolithic times illustrating animals are evidence of this long-established practice.

Chauvet cave paintings in Southern France are a good example of Paleolithic natural illustration. It is estimated that the cave was painted approximately 33000 to 30000 years ago.

Back in 1994, a group of hikers discovered the cave. Throughout the world, Paleolithic carvings of animals have been discovered in caves, ranging from Bhimbetka, India (lions, bisons, and paintings) to Ubirr, Australia (kangaroo-themed paintings).

Historically, illustrations have served to document discoveries and inform others. An illustration from more than fifteen thousand years later depicted a mammoth with a heart-shaped smudge on its shoulder: the smudge was situated close to the heart.

Perhaps this is the oldest example of an anthropological illustration. It has been hypothesized that the illustration may have been a way to guide early hunters on where the mammoth’s heart was located during the hunt.

Only centuries later did scholars at Alexandria city in Egypt created a dedicated library for observing. Greek physician Herophilus , who practiced in Alexandria, dissected and drew his subjects to better understand the anatomy of the body. A pioneer in using illustration as a technical tool, he is credited as being one of the first.

Illustrations were crucial to the nascent development of science. Early on in human history, from Greece and Egypt to the Enlightenment in Europe, visual representations were essential for conveying information that was sometimes complex. Illustrations of great artwork were also found on the walls of pyramids.

Until the early 1800s, we had to use scientific illustrations to share visual information . Illustration still holds a lot of power today, even with cell phones and cameras at our disposal. A picture can only portray one perspective at a time; an illustration can depict many.

With the skills of a scientific illustrator, one can illustrate complex designs with multiple lines of focus, draw attention to details, or reassemble disassembled objects in a way that’s impossible with a camera. 

Taking a look at galleries of scientific illustrations from centuries past , you will discover intricate illustrations that seem bizarre and perfect at the same time. Scientists often depict more than just a single specimen in their scientific illustrations. Nevertheless, scientific illustrations have become more precise and clearer with the advancement of innovations and technology.

Various types of scientific illustrations

Natural history illustrations and mappings.

It goes without saying, this is an illustration of historic scientific theories and observations.  The collection includes illustrations about creatures, plants, and microbes in their environment.  Many of the oldest illustrations in science were in this field since it is heavily observation-based. 

It is through their work that illustrators present illustrations of past and present subjects as a way to better understand and study certain natural phenomena. The facts about fossils, animal habitats, and life on the planet can be seen or communicated through these pieces. Several of the older ones include maps and illustrations of the habitat.

Restoration and Illustration of Extinct Species

Although more specific than natural history illustration, this is similar.  There are many illustrations of dinosaurs and other extinct species in collections, including wooly mammoths and saber-toothed tigers. 

The artwork you see today were made with the help of scientists and fossil evidence found thousands of years after these creatures went extinct.  In addition to these popular extinct species, a number of lesser-known extinct species also exist, which some illustrators explore in order to illustrate extinct species.

The Forensic Reconstruction of Facial Structures 

In a similar way to our previous illustration, this one is also more focused. An anthropological reconstruction of the face using a skull or bones from our early hominid ancestors as a basis for this type of illustration.  As a result, we are able to get a sense of who our ancestors were, as well as learn about them.

Botanical Illustrations

Natural history illustrations often depict botanical subjects and their anatomical characteristics.  It is always interesting to illustrate new subjects because there are so many plant species and discoveries. Illustrations in this section detail the entire process of how the plant performs, as well as its unique features.

Models and images for museum exhibits

The artistic creation of exhibitions for museums contributes greatly to the educational purposes of scientific art.  Content varies widely between exhibits, ranging from interactive exhibits including fossils to text-based exhibits. Since the public can easily comprehend the content, these serve a large part of the public education.

Infographics

The creation of functional art is heavily influenced by data and information.  As a visual artist, it is important to select relevant and precise data that can be portrayed in a comprehensible and aesthetically pleasing fashion.

Frequently, you can find infographics, such as those made in Mind the Graph’s Infographic Maker , in newspapers, on websites, and even on your mobile device. Depending on the complexity of the design, it could be used for scientific research or for everyday use by the general public.

Digital 3-D models, animations, and videos

3D modeling and animation have gained in popularity with the advancement of technology.  With it, you can model the organism, processes, plants, and animals in their entirety rather than just visually. 

The user can interact with the subject and see how body parts work, as well as how it looks from different perspectives. Processes can be demonstrated, and working models can also be made at a smaller scale to show how it works.

Examples of scientific illustrations in practice

Modern scientific illustrations are undeniably very distinctive from cave paintings. Throughout history, the methods of depicting nature have evolved, from primitive art and engravings to digital art, data graphics, and photographs. Scientists’ illustrations have evolved in line with the advancement of technology and the evolution of science itself. Take a look at some examples.

Botanical scientific illustration examples

Some of the most famous science illustrators have worked on natural history illustrations, which depict observations of plant and insect life. 

During the 16th and 17th centuries, European naturalists ventured to South America, Australia, and the Galápagos Islands to gather and examine the natural world. 

As the ‘New World’ came to be known, a greater number of illustrations documented its newly discovered environment. Scientist and illustrator Maria Sibylla Merian illustrated many plants and insects. It was she who studied metamorphosis and explained the relationship between organisms and the host plant.

Animals’ scientific illustrations examples

Ernst Haeckel was a zoologist who focused on nature’s symmetry and natural beauty, which helped redefine scientific illustrations. The images he drew helped define modern scientific illustration. 

In fact, they still appear in science books and had a profound influence on the arts and architecture of the 20th century. Additionally, he proposed a classification system based on evolutionary history for the kingdom he named “Protista” and discovered a variety of new species within it. 

His work paved the way for terms such as phylum, phylogeny, and ecology that have become commonplace in biology. Throughout his life, he was fascinated by the origin of non-random forms, which led him to create Kunstformen der Natur, an illustrated book of life forms.

Medical scientific illustrations examples

Leonardo Da Vinci illustrated the human heart (around 1511-1513). While Da Vinci is best known for his paintings, he was also an avid stargazer, a skilled architect, a skilled weapons designer, and a serious researcher of human anatomy and botany during his illustrious career. 

He found that the heart has four chambers as opposed to two chambers, and most importantly, observed how the aortic valve opens and closes, so blood can only flow one way. Da Vinci’s illustration, now part of the Royal Collection, has survived in remarkably good condition.

How to make a scientific illustration

1.  the importance of research is immeasurable.

After you’ve chosen a topic for a new research, it’s time to start it.  Scientific illustrators begin by researching the topic.  It is only possible to create a good illustration if you have a clear vision of what you are trying to illustrate and recognize how important it is.

In the end, sharing accurate information can take a long time, but it’s crucial.  Illustrators need to conduct research on the topic and the surroundings around their focal point in order to produce high-quality illustrations.

2.  An initial sketch

There will be a lot of roughness in initial sketches. Almost every piece of art goes through rough sketching and conceptual stages before it finally comes to fruition, no matter what the expectation may be. Embrace the process! In order to accomplish a work of art, you will have to revise and edit it for a long time.

3.  Experiment with selected tools

Depending on the medium employed by the illustrator, it is essential for them to practice using the tool selected (for example, pen and ink, illustrating software, 3D software, etc.) in order to achieve visual effects and to perfect their technique.  Consider using a drawing tablet and digital brushes in Adobe Photoshop before settling on Adobe Illustrator. But if you need easy smooth illustrations, think about Mind the Graph , a platform made specially for scientific art projects with the world’s largest gallery of scientific illustrations to work from.

4.  Decide on the composition & transfer drawings

It depends on what type of media is used, but typically in conventional drawing or painting, the finished composition or outline is transferred onto the surface. To transfer a sketch, tracing paper must be used. Light boxes can be used by illustrators to expedite the transfer process.

Over the original sketch is placed the paper for the final illustration, allowing light to shine through for tracing a final illustration into the paper. Digital illustrations work a bit differently from traditional sketches. The majority of the work is done through software, so it can be done easily and cleanly. At Mind the Graph’s tool, you may edit composition freely with many assets and customizable elements to achieve the intended goal.

5.  Finish the final illustration

This step, although seemingly simple, takes a lot of time.  During the process of creating the final scientific illustration, an illustrator must continually reevaluate the content, layout, design, and appearance of the product.

You can use reference images to illustrate various subjects realistically and to observe how lighting and positions influence a subject’s appearance. The inclusion of a scale and measurements is also prevalent in scientific illustrations, depending on the domain or content.

Creating a science figure with Mind the Graph

Here are some common mistakes to avoid

Mistakes with scientific illustrations arise from confusing them with naturalistic drawings, or even extremely realistic drawings. There is a great debate among professionals of illustration over the limit of these disciplines.

While there are semantic debates over the meaning of each branch, there are certain points where there is a degree of consensus.

Avoid subjectiveness

Making an illustration has a tendency to be subjective, which is one of the most common errors discussed in this area. The main problem can be found in the overly subjective approach of the author, who mixes up the work’s informative function and its personal interpretation.

Don’t let aesthetics overtake

Scientists, researchers, museums, editors, and journalists use scientific illustrations to explain something. Illustrations of scientific information are not created by themselves or by their authors. Therefore, valuing aesthetics over functionality is a grave mistake. It is imperative that scientific illustrations are both accurate and visually appealing.

One mistake that is often made is giving excessive weight to the exactitude of certain features of the image and overlooking the synthesis of the message. There are times when an illustration acquires a work of art status more than that of a scientific illustration due to its interdisciplinary aspects, thus compromising the scientific information in lieu of other aesthetic considerations.

Also, don’t do the opposite

The opposite difficulty occurs when too much attention is paid to scientific veracity and not enough to aesthetic aspects. One can find works that are very useful for academic research, but not necessarily for informational purposes, which is yet another of the various uses for scientific illustrations.

Attention to Details

Additionally, errors are made in scientific illustration because of a low degree of consistency in color, pattern, and format, incoherence between concepts and designs, disregard for discipline-specific codes, inconsistent proportions and angles, the use of difficult-to-understand words and symbols, and the absence of graphs with accurate scales. 

So, it’s important to do thorough research on the subject, understand it, and then begin illustrating it. If you want an easier path to achieve that, try creating from a ready to use template that provides the ideal layout for you to enhance. You may find a nice gallery of scientific templates to edit from in Mind the Graph .

Today, scientists have at their disposal a variety of visualization tools, with each contributing to science communication in different ways. The use of macro-and microphotographs, as well as images derived from scientific data, is an integral part of scientific practice because they are objective and convey the knowledge about a subject.

Illustrations, however, can also obscure the bigger picture by focusing on in-depth details. As in textbooks and scientific journals, illustrations simplify complex systems so that audiences can grasp scientific concepts. Illustrations’ artistic quality and creative quality increase reader interest in scientific texts and make science more approachable for everyone.

If you seek inspiration for scientific illustration, you must check the world’s largest scientific illustrations gallery , where you can choose from many categories to use in creations. Moreover, as a Mind the Graph user, you can also request specific custom illustrations from a team of illustration experts.

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About Fabricio Pamplona

Fabricio Pamplona is the founder of Mind the Graph - a tool used by over 400K users in 60 countries. He has a Ph.D. and solid scientific background in Psychopharmacology and experience as a Guest Researcher at the Max Planck Institute of Psychiatry (Germany) and Researcher in D'Or Institute for Research and Education (IDOR, Brazil). Fabricio holds over 2500 citations in Google Scholar. He has 10 years of experience in small innovative businesses, with relevant experience in product design and innovation management. Connect with him on LinkedIn - Fabricio Pamplona .

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I. developing scientific literacy.

• 1 - Building a Scientific Vocabulary
• 2 - Developing Science Reading Skills
• 3 - Developing Science Writing Skills
• 4 - Science, Technology & Society

II. Developing Scientific Reasoning

• 5 - Employing Scientific Methods
• 6 - Developing Scientific Reasoning
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III. Developing Scientific Understanding

• 8 - Organizing Science Information
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IV. Developing Scientific Problem Solving

• 14 - Science Word Problems
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• 19 - Scientific Databases
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Chapter 16 - Diagramming and Visualizing Problems in Science

(16.1) vector diagrams.

• Vector addition

(16.2) Interpreting Scientific Diagrams

• Samples from chapter with simulations

(16.3) Pictorial Riddles

• Far Side cartoons
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(16.4) Analyzing Photographs

• Analyzing for similarities & differences
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(16.6) Extrapolation

• Sample extrapolation activity (from book)
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(16.7) Interactive Scientific Simulations

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Scientific Illustrations: A Complete Guide for Researchers

As a researcher, writing reports and articles is a big part of your job. Academics and scientists everywhere spend the majority of their time either doing research or writing about it. However, there is one aspect of reports and articles that we don’t think about very often, and that is scientific illustrations. We write articles and reports to convey our ideas to a variety of audiences—academics, fellow scientists, and the public. Everyone who has read a report or article recognizes that illustrations, figures, graphs, and infographics are an invaluable factor in clearly presenting ideas. Until recently, there haven’t been many good tools that make it easy to create clear and interesting scientific illustrations. But with the rise of digital technology, this is changing. In this article, we will look at ways you can create detailed and helpful illustrations to better present your ideas and research. We will also examine the latest tools for scientists and researchers to make such illustrations.

Scientific Illustrations: Where to Begin?

The first place to begin with a scientific illustration is research about the subject. Before you can draw something, you need to have a thorough understanding of it. If you are illustrating a process or an organ, it can help to break it down into smaller parts first so you can think about the best way to present it. Once you have all the information you need, make some preliminary sketches on paper. You can think of this step as similar to writing an outline for an article. You will need to have a good idea of what you want your illustration to look like before you start dealing with digital illustration tools.

When you’re making preliminary sketches, it’s important to keep your target audience grain mind first and foremost. Are you making this illustration for fellow academics or other scientists who already have a detailed understanding of the material? Or are you creating it for the general public? Determining your target audience will make it clear what information you need to include and what information you can leave out of your illustration.

Finally, before you turn to digital tools, remember that the purpose of a scientific illustration is not to introduce new materials. Scientific illustrations are used to present the information in your article in a way that is easier to understand for your audience. The illustration should clarify the topic of your research. For example, if you are writing about the way a stent opens up blocked arteries in a heart, then you can create an illustration showing the key components and highlighting the function of the stent.

Digital Tools for Scientific Illustrations

Once you have your preliminary ideas sketched out, it’s time to turn to digital tools. Most researchers are familiar with tools such as PowerPoint and Adobe Photoshop or Illustrator. While PowerPoint is good for making slideshows, it falls short when it comes to making actual illustrations. Adobe Photoshop and Illustrator are wonderful tools, but they are difficult to learn and can be expensive. Fortunately, some recent innovators have created illustration tools especially for scientists .

The hottest tool right now is BioRender , which was created by a group of women in Toronto who recognized the need for a simple illustration program for scientists. BioRender has a library of more than 30,000 life-science icons, which includes anatomical drawings and pictures of everything from SARS-CoV-2 virus particles to fruit flies. The program allows you to drag and drop icons easily and also has special drawing tools. The founders created the program after recognizing the need for it in fast-growing fields like immunology and microbiology that don’t have standard tools for illustrating, unlike engineering or chemistry. However, it does come with an institutional price tag of USD $35 a month or USD $99 for five users.

But BioRender isn’t the only company trying to meet the need for better tools to illustrate scientific concepts. EDrawSoft offers professional scientific diagrams and templates and is great for physics, mathematics, and chemistry. Gimp is another popular illustration software offering tools for high-quality image manipulation . Gimp’s popularity isn’t only because of its variety of tools and ability to work across platforms like Linux. It is also free. However, it’s often considered an alternative to Adobe Photoshop, meaning beginners might have a difficult time getting started. InkScape is another free tool popular for scientists who want to edit vector graphics. Cacoo is great for collaboration as it has the ability to create diagrams as well as edit and comment in real-time. These are just a few of the many tools available now to make high-quality images and scientific illustrations.

Tips for Making Eye-Catching Scientific Illustrations

Once you have identified the best tool for your purpose and learned the basics, it’s time to think about what makes a good scientific illustration. The short answer is: a good scientific illustration should present information clearly in an eye-catching way. It should enhance the understanding of the viewer, not make it more confusing. You can use color, but there’s no need to use the whole rainbow—color sparingly applied can highlight the most important aspects of your illustration. Scientific illustrations can also make use of text to help viewers understand what they are looking at. A great illustration can add a whole new dimension to the presentation of your research, leaving a strong impression on your audience.

As more journals accept illustrations along with articles, the demand for the skill of scientific illustration is also growing. The founders of BioRender highlight what a well-paying field scientific illustration is . Shiz Aoki is one of very few people in the world with an actual degree in medical illustration from Johns Hopkins University. The program only produces six graduates per year. Before Aoki started BioRender, she ran a medical illustration firm that charged between USD $1000 to USD $10,000 for commissions. For many scientists and researchers struggling to make ends meet due to the fallout from the coronavirus epidemic, freelancing in scientific illustration may offer a lucrative side hustle. With the number of tools available, investing some time in learning this new skill now can boost your career in more ways than one.

Do you create illustrations for your articles and presentations? What tools have you found helpful or difficult? Let us know your thoughts in the comments below.

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Problem-solving lab: Interpret scientific illustrations

2. The wavelengths (if the calculations are done correctly) match. The calculations we did in class matched, but mine (shown below) were incorrect.

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• Jun 8, 2021

How to draw your research with simple scientific illustrations

Turn sketchbook ideas into scientific masterpieces: a student’s journey

You know the phrase. A picture speaks a 1000 words.

And often, a research paper speaks for much longer than it really needs to. SEVERAL thousand words more beyond what you may want to know. So why don’t we try and make your long story short with your very own scientific illustrations and infographics? And the good news is that you don’t need to be a fancy high-level artist to draw for YOUR science.

Not a Picasso? No problem! But you could be a Da Vinci - most people know him as a famous painter, but he was equally versed in the sciences.

Let us take you through the process of becoming a scientist just like him, one step at a time.

In this blog, Dr. Juan Miguel Balbin, Science Communicator at Animate Your Science, talks about his experiences and life lessons growing up with a sketchbook, and the fundamentals of making simple scientific illustrations to add visibility to your research.

The boy with a sketchbook, now a scientist with a lab book

As a scientist you’ve gone through school. Several levels of school more than what you originally intended. For now let’s cast our minds back to primary school (or elementary for our global readers!). We all had a pencil case with several coloured pencils, broken and blunt ones, and maybe some notes you’d sneakily pass around in class.

For me, I had a sketchbook in there that was just a little bigger than the size of my hand.

Artist lesson #1 : No piece of art in the world is completely original

I liked to draw, but I wasn’t the best at it. I had friends who could draw hyperrealistic animals or put together entire comic book strips. Me? I wasn’t super original. I’d draw characters from my favourite video games or TV shows growing up. But I always felt like I was “copying” from something that already existed. Was I a fraud because I couldn’t come up with my own unique ideas? Little did I know at the time that every artist “copies” and dare I say “steals” ideas as inspiration for their own style. It’s only human to be influenced.

So anyone can draw if your imagination is up for the task!

Artist lesson #2 : Start doodling with a simple medium that’s accessible to you

Eventually my sketchbook ran out of pages, so I wondered if I could go digital. I first tinkered a lot with Microsoft Paint (the classic one that needed Windows XP or older!) as well as Microsoft PowerPoint. These were great starting points for someone wanting to test out digital art and to learn about bitmap vs vector graphics .

Artist lesson #3 : Refine your way of drawing with new tools as you progress

In the end, doodles in Paint and PowerPoint could only go so far when it came to looking professional. So, in high school I picked up classes for Adobe Illustrator (AI) which was industry-standard stuff in graphic design. AI was a fantastic tool to equip myself with to really get that polished look in my work.

But one thing didn’t change. I still drew very simple things, just using new toys.

Artist lesson #4 : Thinking like a scientist makes art easier

I realised that I had a very methodological way of drawing where I would reverse-engineer an image in my mind and list the shapes it was made up of. Wait, was this how an artist thinks? I wasn’t sure. Perhaps this style of thinking paved the way for me on the path to becoming a scientist with a little bit of art and graphic design under my belt. Take the Twitter bird for example!

Artist lesson #5 : If you can draw, you fill a very special niche on a team

Fast forward to University, and I came across the concept of scientific posters. I had a group assignment where we needed to make a poster about insecticide resistance in moths. Nobody else wanted to be responsible for making the poster, so I put my hand up for the job. My group was thankful for someone with a graphic design skillset. I didn’t know what a poster was really meant to look like, except that it shouldn’t be an intimidating wall of text where you would have to squint to see the Size 8 No Spacing Times New Roman.

Instead, we filled it up half-way with pictures and catchy titles while giving a good oral commentary. No intimidating text, just a gigantic moth in the middle of the poster (apologies to those with a phobia!). We scored a very high mark, and it set the bar high for every science poster after.

Artist lesson #6 : Art is your ticket to a good first impression

Heading into my PhD, I was being trained to be a clear and concise scientist. Creativity was gauged on research novelty, not by how prettily I could label up some tubes. What was an artist doing here? Then came my first lab meeting where I presented my initial project proposal. I’ve seen everyone else do theirs, but I wanted to try something different - my own way.

My slides had colourful illustrations of genetically-modified malaria parasites that I would engineer to glow green and red - this was the moment I made my artwork known to my research group and they loved it! However for more formal seminars, the “traditional” slides were needed. Yes that meant reverting back to a bunch of statistics and references. Oh well.

Artist lesson #7 : A story is told better when you use art to show what’s happening

The next step was to present at scientific conferences and excite people with my research! But how could I possibly do this with a project that had mostly negative results? Why was hypothesis A wrong? Because of reasons B and C? How could I tell people this was really hard? With little data on me, I sought to fill up the gaps in my posters and PowerPoints with visual introductions to my topic, drawn schematics of my experiments and used these to tell my story.

And it worked well. Really well.

My storytelling worked well enough to be awarded two prizes at two separate events for the same seemingly basic research project. You don’t need to cure cancer or make a Da Vinci-level painting to make an impact, I certainly didn’t. There’s room for artists of all skill levels in science.

Hopefully at this point you’ve been inspired to give scientific illustrations a try! Let’s now talk about the process of making your graphics and why scientists might hesitate to give drawing a go. I guarantee your next grant or presentation will be GLOWING with these tips.

Identify what shapes make up your research object

“but i haven’t got any drawing skills”.

If you can draw basic shapes, you’re all set. Really, that’s it, plus a healthy dose of imagination. Basic shapes form the basis of any complicated (or simple) drawing.

Okay sure, maybe an owl’s a bit too much. But you can see it’s just made up of a million different shapes. And just like any science experiment there’s method to the madness, so hold on to your pencil and paper. What shapes make up your “owl”?

Let’s draw a cell for example, a red blood cell (my specialty!). A simple red circle is a good place to start. But then you go back into Google Images and find that these cells aren’t just red circles, they’ve got some dimension to them, with a little bit of a dip in the center. So, draw another red circle, but make it a little darker to make it fancy.

Voila! You now have a mostly medically-accurate red blood cell. Of course, you could always add more details, but the point is that beauty lies in simplicity, and science loves to keep things clear, concise and simple . But simple doesn’t need to mean boring and made in a rush. See our article on graphical abstracts to see why you don’t rush these things.

So no, we’re not drawing owls unless you specifically work on owls. Be relieved.

“My work is too complicated for me to turn into a picture.”

In that case, let’s make it less complicated by using symbols.

Symbols are easy to understand and will allow your audience to quickly get a hold on the topic you’re presenting. You can use symbols to illustrate your literature review, methodology, or even as icons for your dot points. Let’s try and make these, using shapes.

microscope (circles and rectangles)

chemical flask (triangles and rectangles)

viral particle (triangles in an icosahedron)

leaf (pointy oval)

atomic models (three ovals and circles)

For researchers who work on more abstract or non-tangible topics, we’ll have to be a little more creative. But this is the fun part! Allow me to introduce metaphorical symbols - your new best friend. These represent broader concepts and methods that could closely tie with your topic and methods. Take these for example.

magnifying glass (representing “investigation”, circles and rectangles)

gears (representing “mechanisms”, circles and squares)

keys (representing the keys to “unlocking the unknown”, circles and rectangles)

thought bubble (representing “hypotheses”, circles)

stopwatch (representing “time needed for a study”, circles and rectangles)

lightbulb (representing “novelty”, circles, rectangles and lines)

ladder (representing “progression”, rectangles)

stick figures (representing “participants”, you know how to make this!)

check boxes (representing “tasks” in your study, squares and rectangles)

Once you have your individual symbols together, you could display them as a scientific infographic like this.

Then give yourself a pat on the back, you’ve earned it for making your first set of scientific illustrations!

“I don’t know what software to use to make my drawings”

Worry not, you likely already have something you can use! Many researchers love to use Microsoft PowerPoint to arrange figures because they’ve already been trained in it. PowerPoint is a fantastic starting point for making illustrations using the Insert shape tool.

Levelling up past PowerPoint? Try out InkScape for free to gain that edge in your vector artwork. We also recommend Affinity Designer which you can access with a one-time payment! Affinity Designer allows you to tinker with both bitmap and vector graphics for that added flexibility.

The holy grail is definitely the Adobe Creative Suite of software products, including Adobe Photoshop, Adobe Illustrator and Adobe InDesign. For starters, try out Illustrator! A free trial is available, so give it a try before you commit to a subscription!

“I haven’t got the time to learn to make these myself”

Understandable, completely understandable. Though I would bet that if you’re reading this blog right now that you would be keen to give it a try with some trial and error.

There are also online resources, such as BioRende r , which provide you with base illustrations that you can move around and assemble into a figure yourself.

Alternatively, we’re at your beck and call. Have a look at our gallery to get an idea of the services we provide so we could draw your research for you!

Other tips for new venturing scientific illustrators

An illustration is only good if it can be easily understood! Pair it with an equally descriptive figure legend and/or very clear labels.

Visibility is everything - make sure it is suitably large for your purpose, and is coloured in a way that matches the palette for your poster/presentation etc.

Your pictures tell a story , but they need you to narrate them. Use your illustrations as a tool to better structure your oral narrative.

Once you’re confident with illustrating, why not breathe life into them in a video abstract or animation ?

Take-away points

Every artist starts out simple!

You can draw anything if you can pick out what shapes to use to make an image.

You can tell a story by drawing simple symbols and icons.

We’re only at the tip of the iceberg with what you could do to make scientific illustrations. If you found this blog useful, perhaps you'd consider subscribing to our newsletter ?

Until next time!

Dr Juan Miguel Balbin

Dr Tullio Rossi

#scientificillustration #Twitter

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Scientific Illustration: What Is It?

Where Science and Art Become One

Art plays a significant role in the development of science and how information and discoveries are shared.  Most people consider science and art to be on opposite sides of the spectrum, but at the conjunction of art and science is the lesser-known field of scientific illustration.  Even if you have never heard of scientific illustration before, you have likely seen it in many places.  Think about the science textbooks you used in school, the images and models in a museum exhibit you visited, or maybe even that poster at your doctor’s office that shows what your organs look like.

So what is Scientific Illustration?   

Scientific illustration is more than just artwork.  It is a way to communicate complex concepts, details, and subjects in an engaging and easily comprehensible way.  Artwork can go beyond the complex language often used to describe scientific topics and allow for a greater understanding of the subject. 

Scientific illustration is art in the service of science.

These artists draw or render images of scientific subjects in an accurate way to inform and communicate.  A scientific illustrator must use technical art and aesthetic skills in combination with scientifically informed observations and research to make accurate representations of the subject.  Illustrations, models, or videos provide visual explanations and depictions to help make the topics accessible and engaging to audiences of all levels.  It can be used to explain complicated topics to the general public in a simple way, but it can also to help educate scientists or medical professionals in training with highly detailed images or animations.  As the saying goes, a picture is worth a thousand words.

"Scientific illustration takes the viewer to the often unobservable — from molecules and viruses to the universe, from depiction of the internal anatomy of arthropods and plants to geologic cross sections and reconstruction of extinct life forms, ranging from realistic to abstract portrayal."

- The Guild Handbook of Science Illustration, ©GNSI 1989, 2003

Due to the growing diversity of artistic media (the materials or tools used to create artwork) used in our current digital age, scientific illustration can often be referred to simply as scientific art.  Though it may seem like a small field of study, the subjects of scientific artwork are very diverse, spanning nearly all fields of science, from microscopic biology to astrophysics and everything in between. 

Digital ​illustration of simplified atomic fusion by Kara Perilli

Two main international organizations work to connect these artists, The Guild of Natural Science Illustrators (GNSI) and the Association of Medical Illustrators (AMI).  Each group holds their own annual conference, where hundreds of scientific and medical illustrators from around the world attend workshops and classes to continue their education and share their knowledge with others in this small field of study.

One of the largest and most popular types scientific artwork is medical illustration.  This can include artwork, illustrations, and animations depicting surgical procedures, internal anatomy, or biological processes that are too small to even see in microscopes.  Medical illustrators specialize in biomedical communications, and they require further training in anatomy and life sciences.  There is a very practical usage of this artwork in the medical field for education and resources. 

"A medical illustrator is a professional artist with advanced education in both the life sciences and visual communication."

- Association of Medical Illustrators

A few other categories within Scientific Illustration include the following:

Natural History Illustrations and Models:

This is likely what you may first think of as scientific illustration.  It includes artwork relating to animals, plants, and fungi in their environment.  A lot of early scientific illustration was in this field because it is largely based on observation.  In order to better understand and study the histories of certain animals, plants, and life cycles, artists use their skills to render past and present subjects.  These pieces help depict or communicate information on fossils, the habitats and lives of animals, and the planet.  

​Illustrations by Mary Koger  © Oyster Life Cycle (top) and Fossil Skull (bottom)  

Reconstruction or Illustration of Extinct Species:

This is similar to natural history, though it is more specific.  Most likely, you would think of dinosaur illustrations and other popular extinct animals such as woolly mammoths or saber-toothed tigers.  Since these animals became extinct in distant history, illustrators work with scientists and fossil records to create the images you see today.  There are many more extinct species that are not as popular, and some illustrators dedicate their studies to creating reconstructions and images of these extinct animals.

Mauritian Giant Skink   by Kara Perilli These pen & ink illustrations are done to provide information on the Mauritian Giant Skink, a species that became extinct in the 1600s. 

Forensic Facial Reconstruction:   

If you have ever watched the television series Bones , the character Angela specialized in this.  This type of reconstruction involves using a skull or bones of humans, our early hominid ancestors, and remodeling a face based on certain markers.  This has allowed us to see what the faces of our early ancestors looked like, and help us engage with these people of the past.

Museum Exhibit Image and Model Design:

Design and artwork done for museum exhibits is highly important to the educational goals of scientific artwork.  Exhibit designs vary greatly in their content, from interactive exhibits such as the  Giant Heart here at The Franklin Institute that children can run around in, to fossils, and to text and image based exhibits.  

The Giant Heart  at The Franklin Institute

Botanical Illustrations:

Artwork depicting botanical subjects and the different anatomy of these plants is another popular area within natural history illustrations.  Due to the large number of plant species and new discoveries, there is always another subject to render.

Illustration of  Coccoloba uvifera  (sea grapes) by Scott Rawlins

Infographics and Data Visualization:

This type artwork is very functional, and can be highly data and information driven.  In order to make sense of data, it is the artist's job to choose the accurate and relevant information and display it in an understandable and visually compelling way.  You have likely seen infographics used in magazines, on websites, and even on your phone apps.  These designs can be either very complex and used for scientists to process information, or they can be simple for the public to use in daily life.

Ad Astra  designs by Kara Perilli

Digital 3-D Modeling, Animation, and Video:

With advances in technology, 3-D modeling and animation has grown as an art form.  It is a way to create models of the human body, processes, plants, and animals in full form instead of just on paper.  This allows more interaction and understanding of how body parts fit together, and what the subject looks like from all directions.        

From the Past until Now

 Sketchbook Pages  by Leonardo da Vinci, c. 1500s Leonardo da Vinci, a more familiar name, commonly used artistic depictions to understand science. His notebooks are filled with anatomical studies and illustrations of the human body and surgeries, as well as designs for mechanical inventions . 

For thousands of years, illustrations have been used to depict observations and inform others. Some early “medical illustrations” date back to nearly 400 BC, when they were created on individual sheets of papyrus and were used for instructional purposes to depict anatomy, rudimentary “surgery” and medicinal plants (Alan E. Branigan, The History of the Association of Medical Illustrators ). 

Early scientific illustration was the primary way to capture images of newly discovered animal species, to show internal human anatomy, or to create an index of plant or animal species.  This was necessary before the imaging capabilities of the modern were invented.  However, with the advent of technological advances, the field of scientific illustration has changed to meet the needs of scientist and educators alike. 

"Science illustrators are artists in the service of science."

-  The Guild of Natural Science Illustrators

About the author

Kara Perilli

Kara is the Digital Media Intern at the Franklin Institute for the spring of 2019.  As an intern, she works primarily on website editing and digital content creation, in addition to an assortment of other work within the department.  

She holds a B.A. in Scientific Illustration from Arcadia University, and is pursuing a career in this field.  Her goal is to promote science education and share information with a wide range of audiences through museum work and scientific illustration.

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The Art of Scientific Illustration

A picture is worth a thousand words. Scientific illustration is more than just a cool art form. It conveys technical details about research that other tools cannot.

By Shayna Keyles

Scientific illustration is more than just cool artwork. It’s a way of conveying technical detail that other tools can’t provide; of capturing the complexities of organisms or fabrications that are too small, too far away, too extinct, or too difficult to dissect; of sharing intimate research in a globally understood language.

Pictures of discovery

Illustrations have always been used to track observations and inform others. There are drawings of animals in the Chauvet-Pont-d’Arc caves in France that date back 30,000 years and are accurate enough for researchers to identify at least 13 of the species depicted. Fifteen millennia later, a depiction of a mammoth with a smudge near its shoulder might have been the first intentional scientific illustration: that smudge had the shape of a heart and was located near where the heart would have been. Fast-forward another fifteen millennia; the scholars in Alexandria had an entire library devoted to their observations. Herophilus, a Greek physician in Alexandria, regularly performed dissections and illustrated his subjects to learn more about human anatomy. He is believed to be one of the earliest pioneers in using illustration as a technical tool.

Early scientific discoveries were solidified and propelled by illustration. From the early experiments in Greece and Egypt to the later innovations of the European Enlightenment, visual representation was necessary for conveying often complicated information. In the words of The Guild Handbook for Science Illustration , “Imagine a description of a yellow butterfly in words only! What shade of yellow? What is the wing shape? What does the color pattern look like? These deficiencies in communication made obvious the need for illustration. Thus, artists accompanied early exploring expeditions to record discoveries visually.”

Cameras were not in use until the early 1800s, so to share visual data prior to that point, scientific illustration was necessary. But even now, when cameras are just a cell phone away, illustration still holds power. Unlike a photograph, an illustration can showcase numerous perspectives at once. “The skilled scientific illustrator can clarify multiple focal depths and overlapping layers, emphasize important details, and reconstruct broken specimens on paper—results unattainable through photography. Structure and detail may be depicted with cutaway drawings, transparencies, and exploded diagrams,” according to The Guild Handbook .

Flipping through collections of scientific illustrations from the past few centuries, you’ll find fascinating images that seem both meticulously rendered and strangely abstract at the same time. This is because many scientific illustrations are drawn so as to represent not the single specimen used as a reference, but a generalized version of a species or event. This much is said in the preface to a collection by seventeenth-century illustrator and collector Albertus Seba: “The elements important for human identification are rendered better and considerably more clearly in the picture than in the actual specimen itself.”

Scientific illustration in practice

The goal of scientific illustrations varies depending on the artist and the need. Some illustrations are used to identify poisonous versus edible flora and fungi, for example, and the immaculate detail afforded by illustration, as opposed to photography, is especially useful in the field. Medical textbooks will often have scientific illustrations instead of photographs for this same reason.

Minneapolis-based paleoartist Emily Willoughby illustrates prehistoric organisms and has a keen interest in depicting dinosaurs that are not quite bird , but aren’t too far off, either. Without illustrations like hers, it would be difficult to visualize and learn from dromaeosaurs , troodontids, and oviraptorosaurs. Researchers point to these creatures as evidence that there is an evolutionary link between dinosaurs and birds.

“For me,” Emily says, “the attraction to this field has always been the unique power that artworks hold in the field of paleontology , which does not enjoy the benefit of direct observation or photography of its living subjects.”

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Emily’s illustrations help researchers, students, and educators alike grasp concepts that otherwise don’t lend themselves to visualization. Paleoart especially helps fill in the blanks in the fossil record. Emily’s illustrations of non-avian dinosaurs, for example, were recently published in a book that addresses the evolution/creation debate. The depictions of the evolutionary chain of events makes the argument for evolution much stronger.

Laura Macias, a scientific illustrator in San Francisco, also uses her illustrations to trace evolutionary history. Her preferred subjects are cnidarians, echinoderms, and cephalopods (including jellyfish, urchins, and octopus, among others), which have some of the oldest recorded fossil records.

RELATED: PREHISTORIC CROCODILES RULED ANCIENT PERU

For Laura, science illustration is an essential teaching tool, especially for people who are visual learners. Visual stimuli can engage people and introduce new subjects, spark new interests, and reinforce new ideas. “No matter how extensive your knowledge of a subject can be, we humans always respond to what is visually alluring,” she explains. “Once you have piqued an interest and engaged your audience, you can then proceed to expanding the understanding.”

RELATED: PALEONTOLOGISTS DIGITALLY RESTORE DINOSAUR FOSSILS

Science communicators have the responsibility of conveying a complicated reality to numerous individuals, most of whom don’t come from an expert background. Accuracy is important, but so is helping the public achieve an understanding. Scientific illustration is an excellent tool that can not only foster that understanding, but also cultivate interest, highlight details, and make complicated ideas more accessible. And you know what they say: a picture is worth a thousand words!

Sena, Albertus. Cabinet of Natural Curiosities. Reproduction. Cologne: Taschen, 2011.

Interviews with Emily Willoughby and Laura Macias.

https://gnsi.org/science-illustration

Featured image:  Dakotaraptor steini . Hell Creek Formation. 66 Ma. Artwork by Emily Willoughby.

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Natural Science Illustration

Online Certificate: Courses in this certificate are delivered completely online.

Natural science illustrators accurately translate and visually communicate scientific subject matter to inform and educate about the natural world. This certificate will equip you with the necessary skills to document and detail that world: careful observation, precise rendering techniques and natural subject curiosity and knowledge. You will explore various media and materials including pencil, pen and ink, acrylic, watercolor and gouache to discover your individual style. You will also learn composition and color strategies that enable you to fully interpret, capture and portray subjects—including birds, animals and botanicals—in natural environments. You’ll have access to RISD’s Edna Lawrence Nature Lab and Digital 3D Specimen Library (see naturelab.risd.edu for more information). Botanical gardens, zoos, natural history museums, aquariums and nature centers in your local area are also ideal resources for observing live animals and plants, as well as preserved specimens. By the end of this program, you’ll progress toward creating portfolio-ready pieces to pursue a freelance illustration career, exhibit opportunities, as well as print and web publications.

Certificate Guide and Cost Breakdown (pdf)

Certificate Program Policies

Certificate Program FAQs

Skill Development

• Drawing, rendering, design, color theory and its applications
• Hand-eye coordination and drawing realistically from observation, as well as using students' own photos as reference
• Media handling proficiency using pencil, ink and paint (acrylic, gouache and watercolor)
• Understanding of strong composition
• Portrayal of subjects in natural habitats

Learning Outcomes

• Learn fundamentals of design and color as they relate to natural science illustration subjects and environments
• Gain problem-solving skills by conceptualizing, planning and executing an illustration, from preliminary drawing to final product
• Learn how to observe, interpret, research and portray subjects including birds, animals, insects, specimens and botanicals. 
• Receive an introduction to, and familiarity with, Adobe software methods to enhance imagery – both of individual artwork and reference materials
• Understand how to build a portfolio toward freelance career opportunities including commissions, working with naturalists or natural science organizations, and working as an independent illustrator

Program Cost

$9,735 total Current tuition rates; subject to change over the course of your program. Supplies, software subscription fees and other materials are not included.

Additional Details

Application, level i: core.

• 0637 Drawing I (Part A)
• 0638 Drawing I (Part B)
• 0623 Design (Part A)
• 0624 Design (Part B)
• 1466 Digital Design Fundamentals
• 1186 Applied Color
• 3580 Illustration: Introduction

Level II: Concentration

• 2849 Botanical Illustration
• 2260 Drawing II
• 2533 Adobe Photoshop I
• 9035 Drawing from Nature (Part A)
• 9036 Drawing from Nature (Part B)
• 0918 The Artful Bird: Ornithological Illustration
• 2287 The Artful Insect: Entomological Illustration
• 0626 Painting from Nature (Part A)
• 0627 Painting from Nature (Part B)
• 0928 Final Studio: Natural Science Illustration

Students in the Natural Science Illustration certificate program must complete 54 contact hours in elective courses of their choice. Electives may be taken at any time in the program.

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Identifying problems and solutions in scientific text

Kevin heffernan.

Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD UK

Simone Teufel

Research is often described as a problem-solving activity, and as a result, descriptions of problems and solutions are an essential part of the scientific discourse used to describe research activity. We present an automatic classifier that, given a phrase that may or may not be a description of a scientific problem or a solution, makes a binary decision about problemhood and solutionhood of that phrase. We recast the problem as a supervised machine learning problem, define a set of 15 features correlated with the target categories and use several machine learning algorithms on this task. We also create our own corpus of 2000 positive and negative examples of problems and solutions. We find that we can distinguish problems from non-problems with an accuracy of 82.3%, and solutions from non-solutions with an accuracy of 79.7%. Our three most helpful features for the task are syntactic information (POS tags), document and word embeddings.

Introduction

Problem solving is generally regarded as the most important cognitive activity in everyday and professional contexts (Jonassen 2000 ). Many studies on formalising the cognitive process behind problem-solving exist, for instance (Chandrasekaran 1983 ). Jordan ( 1980 ) argues that we all share knowledge of the thought/action problem-solution process involved in real life, and so our writings will often reflect this order. There is general agreement amongst theorists that state that the nature of the research process can be viewed as a problem-solving activity (Strübing 2007 ; Van Dijk 1980 ; Hutchins 1977 ; Grimes 1975 ).

One of the best-documented problem-solving patterns was established by Winter ( 1968 ). Winter analysed thousands of examples of technical texts, and noted that these texts can largely be described in terms of a four-part pattern consisting of Situation, Problem, Solution and Evaluation. This is very similar to the pattern described by Van Dijk ( 1980 ), which consists of Introduction-Theory, Problem-Experiment-Comment and Conclusion. The difference is that in Winter’s view, a solution only becomes a solution after it has been evaluated positively. Hoey changes Winter’s pattern by introducing the concept of Response in place of Solution (Hoey 2001 ). This seems to describe the situation in science better, where evaluation is mandatory for research solutions to be accepted by the community. In Hoey’s pattern, the Situation (which is generally treated as optional) provides background information; the Problem describes an issue which requires attention; the Response provides a way to deal with the issue, and the Evaluation assesses how effective the response is.

An example of this pattern in the context of the Goldilocks story can be seen in Fig.  1 . In this text, there is a preamble providing the setting of the story (i.e. Goldilocks is lost in the woods), which is called the Situation in Hoey’s system. A Problem in encountered when Goldilocks becomes hungry. Her first Response is to try the porridge in big bear’s bowl, but she gives this a negative Evaluation (“too hot!”) and so the pattern returns to the Problem. This continues in a cyclic fashion until the Problem is finally resolved by Goldilocks giving a particular Response a positive Evaluation of baby bear’s porridge (“it’s just right”).

Example of problem-solving pattern when applied to the Goldilocks story.

Reproduced with permission from Hoey ( 2001 )

It would be attractive to detect problem and solution statements automatically in text. This holds true both from a theoretical and a practical viewpoint. Theoretically, we know that sentiment detection is related to problem-solving activity, because of the perception that “bad” situations are transformed into “better” ones via problem-solving. The exact mechanism of how this can be detected would advance the state of the art in text understanding. In terms of linguistic realisation, problem and solution statements come in many variants and reformulations, often in the form of positive or negated statements about the conditions, results and causes of problem–solution pairs. Detecting and interpreting those would give us a reasonably objective manner to test a system’s understanding capacity. Practically, being able to detect any mention of a problem is a first step towards detecting a paper’s specific research goal. Being able to do this has been a goal for scientific information retrieval for some time, and if successful, it would improve the effectiveness of scientific search immensely. Detecting problem and solution statements of papers would also enable us to compare similar papers and eventually even lead to automatic generation of review articles in a field.

There has been some computational effort on the task of identifying problem-solving patterns in text. However, most of the prior work has not gone beyond the usage of keyword analysis and some simple contextual examination of the pattern. Flowerdew ( 2008 ) presents a corpus-based analysis of lexio-grammatical patterns for problem and solution clauses using articles from professional and student reports. Problem and solution keywords were used to search their corpora, and each occurrence was analysed to determine grammatical usage of the keyword. More interestingly, the causal category associated with each keyword in their context was also analysed. For example, Reason–Result or Means-Purpose were common causal categories found to be associated with problem keywords.

The goal of the work by Scott ( 2001 ) was to determine words which are semantically similar to problem and solution, and to determine how these words are used to signal problem-solution patterns. However, their corpus-based analysis used articles from the Guardian newspaper. Since the domain of newspaper text is very different from that of scientific text, we decided not to consider those keywords associated with problem-solving patterns for use in our work.

Instead of a keyword-based approach, Charles ( 2011 ) used discourse markers to examine how the problem-solution pattern was signalled in text. In particular, they examined how adverbials associated with a result such as “thus, therefore, then, hence” are used to signal a problem-solving pattern.

Problem solving also has been studied in the framework of discourse theories such as Rhetorical Structure Theory (Mann and Thompson 1988 ) and Argumentative Zoning (Teufel et al. 2000 ). Problem- and solutionhood constitute two of the original 23 relations in RST (Mann and Thompson 1988 ). While we concentrate solely on this aspect, RST is a general theory of discourse structure which covers many intentional and informational relations. The relationship to Argumentative Zoning is more complicated. The status of certain statements as problem or solutions is one important dimension in the definitions of AZ categories. AZ additionally models dimensions other than problem-solution hood (such as who a scientific idea belongs to, or which intention the authors might have had in stating a particular negative or positive statement). When forming categories, AZ combines aspects of these dimensions, and “flattens” them out into only 7 categories. In AZ it is crucial who it is that experiences the problems or contributes a solution. For instance, the definition of category “CONTRAST” includes statements that some research runs into problems, but only if that research is previous work (i.e., not if it is the work contributed in the paper itself). Similarly, “BASIS” includes statements of successful problem-solving activities, but only if they are achieved by previous work that the current paper bases itself on. Our definition is simpler in that we are interested only in problem solution structure, not in the other dimensions covered in AZ. Our definition is also more far-reaching than AZ, in that we are interested in all problems mentioned in the text, no matter whose problems they are. Problem-solution recognition can therefore be seen as one aspect of AZ which can be independently modelled as a “service task”. This means that good problem solution structure recognition should theoretically improve AZ recognition.

In this work, we approach the task of identifying problem-solving patterns in scientific text. We choose to use the model of problem-solving described by Hoey ( 2001 ). This pattern comprises four parts: Situation, Problem, Response and Evaluation. The Situation element is considered optional to the pattern, and so our focus centres on the core pattern elements.

Goal statement and task

Many surface features in the text offer themselves up as potential signals for detecting problem-solving patterns in text. However, since Situation is an optional element, we decided to focus on either Problem or Response and Evaluation as signals of the pattern. Moreover, we decide to look for each type in isolation. Our reasons for this are as follows: It is quite rare for an author to introduce a problem without resolving it using some sort of response, and so this is a good starting point in identifying the pattern. There are exceptions to this, as authors will sometimes introduce a problem and then leave it to future work, but overall there should be enough signal in the Problem element to make our method of looking for it in isolation worthwhile. The second signal we look for is the use of Response and Evaluation within the same sentence. Similar to Problem elements, we hypothesise that this formulation is well enough signalled externally to help us in detecting the pattern. For example, consider the following Response and Evaluation: “One solution is to use smoothing”. In this statement, the author is explicitly stating that smoothing is a solution to a problem which must have been mentioned in a prior statement. In scientific text, we often observe that solutions implicitly contain both Response and Evaluation (positive) elements. Therefore, due to these reasons there should be sufficient external signals for the two pattern elements we concentrate on here.

When attempting to find Problem elements in text, we run into the issue that the word “problem” actually has at least two word senses that need to be distinguished. There is a word sense of “problem” that means something which must be undertaken (i.e. task), while another sense is the core sense of the word, something that is problematic and negative. Only the latter sense is aligned with our sense of problemhood. This is because the simple description of a task does not predispose problemhood, just a wish to perform some act. Consider the following examples, where the non-desired word sense is being used:

• “Das and Petrov (2011) also consider the problem of unsupervised bilingual POS induction”. (Chen et al. 2011 ).
• “In this paper, we describe advances on the problem of NER in Arabic Wikipedia”. (Mohit et al. 2012 ).

Here, the author explicitly states that the phrases in orange are problems, they align with our definition of research tasks and not with what we call here ‘problematic problems’. We will now give some examples from our corpus for the desired, core word sense:

• “The major limitation of supervised approaches is that they require annotations for example sentences.” (Poon and Domingos 2009 ).
• “To solve the problem of high dimensionality we use clustering to group the words present in the corpus into much smaller number of clusters”. (Saha et al. 2008 ).

When creating our corpus of positive and negative examples, we took care to select only problem strings that satisfy our definition of problemhood; “ Corpus creation ” section will explain how we did that.

Corpus creation

Our new corpus is a subset of the latest version of the ACL anthology released in March, 2016 1 which contains 22,878 articles in the form of PDFs and OCRed text. 2

The 2016 version was also parsed using ParsCit (Councill et al. 2008 ). ParsCit recognises not only document structure, but also bibliography lists as well as references within running text. A random subset of 2500 papers was collected covering the entire ACL timeline. In order to disregard non-article publications such as introductions to conference proceedings or letters to the editor, only documents containing abstracts were considered. The corpus was preprocessed using tokenisation, lemmatisation and dependency parsing with the Rasp Parser (Briscoe et al. 2006 ).

Definition of ground truth

Our goal was to define a ground truth for problem and solution strings, while covering as wide a range as possible of syntactic variations in which such strings naturally occur. We also want this ground truth to cover phenomena of problem and solution status which are applicable whether or not the problem or solution status is explicitly mentioned in the text.

To simplify the task, we only consider here problem and solution descriptions that are at most one sentence long. In reality, of course, many problem descriptions and solution descriptions go beyond single sentence, and require for instance an entire paragraph. However, we also know that short summaries of problems and solutions are very prevalent in science, and also that these tend to occur in the most prominent places in a paper. This is because scientists are trained to express their contribution and the obstacles possibly hindering their success, in an informative, succinct manner. That is the reason why we can afford to only look for shorter problem and solution descriptions, ignoring those that cross sentence boundaries.

To define our ground truth, we examined the parsed dependencies and looked for a target word (“problem/solution”) in subject position, and then chose its syntactic argument as our candidate problem or solution phrase. To increase the variation, i.e., to find as many different-worded problem and solution descriptions as possible, we additionally used semantically similar words (near-synonyms) of the target words “problem” or “solution” for the search. Semantic similarity was defined as cosine in a deep learning distributional vector space, trained using Word2Vec (Mikolov et al. 2013 ) on 18,753,472 sentences from a biomedical corpus based on all full-text Pubmed articles (McKeown et al. 2016 ). From the 200 words which were semantically closest to “problem”, we manually selected 28 clear synonyms. These are listed in Table  1 . From the 200 semantically closest words to “solution” we similarly chose 19 (Table  2 ). Of the sentences matching our dependency search, a subset of problem and solution candidate sentences were randomly selected.

Selected words for use in problem candidate phrase extraction

Selected words for use in solution candidate phrase extraction

An example of this is shown in Fig.  2 . Here, the target word “drawback” is in subject position (highlighted in red), and its clausal argument (ccomp) is “(that) it achieves low performance” (highlighted in purple). Examples of other arguments we searched for included copula constructions and direct/indirect objects.

Example of our extraction method for problems using dependencies. (Color figure online)

If more than one candidate was found in a sentence, one was chosen at random. Non-grammatical sentences were excluded; these might appear in the corpus as a result of its source being OCRed text.

800 candidates phrases expressing problems and solutions were automatically extracted (1600 total) and then independently checked for correctness by two annotators (the two authors of this paper). Both authors found the task simple and straightforward. Correctness was defined by two criteria:

• An unexplained phenomenon or a problematic state in science; or
• A research question; or
• An artifact that does not fulfil its stated specification.
• The phrase must not lexically give away its status as problem or solution phrase.

The second criterion saves us from machine learning cues that are too obvious. If for instance, the phrase itself contained the words “lack of” or “problematic” or “drawback”, our manual check rejected it, because it would be too easy for the machine learner to learn such cues, at the expense of many other, more generally occurring cues.

Sampling of negative examples

We next needed to find negative examples for both cases. We wanted them not to stand out on the surface as negative examples, so we chose them so as to mimic the obvious characteristics of the positive examples as closely as possible. We call the negative examples ‘non-problems’ and ‘non-solutions’ respectively. We wanted the only differences between problems and non-problems to be of a semantic nature, nothing that could be read off on the surface. We therefore sampled a population of phrases that obey the same statistical distribution as our problem and solution strings while making sure they really are negative examples. We started from sentences not containing any problem/solution words (i.e. those used as target words). From each such sentence, we at random selected one syntactic subtree contained in it. From these, we randomly selected a subset of negative examples of problems and solutions that satisfy the following conditions:

• The distribution of the head POS tags of the negative strings should perfectly match the head POS tags 3 of the positive strings. This has the purpose of achieving the same proportion of surface syntactic constructions as observed in the positive cases.
• The average lengths of the negative strings must be within a tolerance of the average length of their respective positive candidates e.g., non-solutions must have an average length very similar (i.e. + / -  small tolerance) to solutions. We chose a tolerance value of 3 characters.

Again, a human quality check was performed on non-problems and non-solutions. For each candidate non-problem statement, the candidate was accepted if it did not contain a phenomenon, a problematic state, a research question or a non-functioning artefact. If the string expressed a research task, without explicit statement that there was anything problematic about it (i.e., the ‘wrong’ sense of “problem”, as described above), it was allowed as a non-problem. A clause was confirmed as a non-solution if the string did not represent both a response and positive evaluation.

If the annotator found that the sentence had been slightly mis-parsed, but did contain a candidate, they were allowed to move the boundaries for the candidate clause. This resulted in cleaner text, e.g., in the frequent case of coordination, when non-relevant constituents could be removed.

From the set of sentences which passed the quality-test for both independent assessors, 500 instances of positive and negative problems/solutions were randomly chosen (i.e. 2000 instances in total). When checking for correctness we found that most of the automatically extracted phrases which did not pass the quality test for problem-/solution-hood were either due to obvious learning cues or instances where the sense of problem-hood used is relating to tasks (cf. “ Goal statement and task ” section).

Experimental design

In our experiments, we used three classifiers, namely Naïve Bayes, Logistic Regression and a Support Vector Machine. For all classifiers an implementation from the WEKA machine learning library (Hall et al. 2009 ) was chosen. Given that our dataset is small, tenfold cross-validation was used instead of a held out test set. All significance tests were conducted using the (two-tailed) Sign Test (Siegel 1956 ).

Linguistic correlates of problem- and solution-hood

We first define a set of features without taking the phrase’s context into account. This will tell us about the disambiguation ability of the problem/solution description’s semantics alone. In particular, we cut out the rest of the sentence other than the phrase and never use it for classification. This is done for similar reasons to excluding certain ‘give-away’ phrases inside the phrases themselves (as explained above). As the phrases were found using templates, we know that the machine learner would simply pick up on the semantics of the template, which always contains a synonym of “problem” or “solution”, thus drowning out the more hidden features hopefully inherent in the semantics of the phrases themselves. If we allowed the machine learner to use these stronger features, it would suffer in its ability to generalise to the real task.

ngrams Bags of words are traditionally successfully used for classification tasks in NLP, so we included bags of words (lemmas) within the candidate phrases as one of our features (and treat it as a baseline later on). We also include bigrams and trigrams as multi-word combinations can be indicative of problems and solutions e.g., “combinatorial explosion”.

Polarity Our second feature concerns the polarity of each word in the candidate strings. Consider the following example of a problem taken from our dataset: “very conservative approaches to exact and partial string matches overgenerate badly”. In this sentence, words such as “badly” will be associated with negative polarity, therefore being useful in determining problem-hood. Similarly, solutions will often be associated with a positive sentiment e.g. “smoothing is a good way to overcome data sparsity” . To do this, we perform word sense disambiguation of each word using the Lesk algorithm (Lesk 1986 ). The polarity of the resulting synset in SentiWordNet (Baccianella et al. 2010 ) was then looked up and used as a feature.

Syntax Next, a set of syntactic features were defined by using the presence of POS tags in each candidate. This feature could be helpful in finding syntactic patterns in problems and solutions. We were careful not to base the model directly on the head POS tag and the length of each candidate phrase, as these are defining characteristics used for determining the non-problem and non-solution candidate set.

Negation Negation is an important property that can often greatly affect the polarity of a phrase. For example, a phrase containing a keyword pertinent to solution-hood may be a good indicator but with the presence of negation may flip the polarity to problem-hood e.g., “this can’t work as a solution”. Therefore, presence of negation is determined.

Exemplification and contrast Problems and solutions are often found to be coupled with examples as they allow the author to elucidate their point. For instance, consider the following solution: “Once the translations are generated, an obvious solution is to pick the most fluent alternative, e.g., using an n-gram language model”. (Madnani et al. 2012 ). To acknowledge this, we check for presence of exemplification. In addition to examples, problems in particular are often found when contrast is signalled by the author (e.g. “however, “but”), therefore we also check for presence of contrast in the problem and non-problem candidates only.

Discourse Problems and solutions have also been found to have a correlation with discourse properties. For example, problem-solving patterns often occur in the background sections of a paper. The rationale behind this is that the author is conventionally asked to objectively criticise other work in the background (e.g. describing research gaps which motivate the current paper). To take this in account, we examine the context of each string and capture the section header under which it is contained (e.g. Introduction, Future work). In addition, problems and solutions are often found following the Situation element in the problem-solving pattern (cf. “ Introduction ” section). This preamble setting up the problem or solution means that these elements are likely not to be found occurring at the beginning of a section (i.e. it will usually take some sort of introduction to detail how something is problematic and why a solution is needed). Therefore we record the distance from the candidate string to the nearest section header.

Subcategorisation and adverbials Solutions often involve an activity (e.g. a task). We also model the subcategorisation properties of the verbs involved. Our intuition was that since problematic situations are often described as non-actions, then these are more likely to be intransitive. Conversely solutions are often actions and are likely to have at least one argument. This feature was calculated by running the C&C parser (Curran et al. 2007 ) on each sentence. C&C is a supertagger and parser that has access to subcategorisation information. Solutions are also associated with resultative adverbial modification (e.g. “thus, therefore, consequently”) as it expresses the solutionhood relation between the problem and the solution. It has been seen to occur frequently in problem-solving patterns, as studied by Charles ( 2011 ). Therefore, we check for presence of resultative adverbial modification in the solution and non-solution candidate only.

Embeddings We also wanted to add more information using word embeddings. This was done in two different ways. Firstly, we created a Doc2Vec model (Le and Mikolov 2014 ), which was trained on  ∼  19  million sentences from scientific text (no overlap with our data set). An embedding was created for each candidate sentence. Secondly, word embeddings were calculated using the Word2Vec model (cf. “ Corpus creation ” section). For each candidate head, the full word embedding was included as a feature. Lastly, when creating our polarity feature we query SentiWordNet using synsets assigned by the Lesk algorithm. However, not all words are assigned a sense by Lesk, so we need to take care when that happens. In those cases, the distributional semantic similarity of the word is compared to two words with a known polarity, namely “poor” and “excellent”. These particular words have traditionally been consistently good indicators of polarity status in many studies (Turney 2002 ; Mullen and Collier 2004 ). Semantic similarity was defined as cosine similarity on the embeddings of the Word2Vec model (cf. “ Corpus creation ” section).

Modality Responses to problems in scientific writing often express possibility and necessity, and so have a close connection with modality. Modality can be broken into three main categories, as described by Kratzer ( 1991 ), namely epistemic (possibility), deontic (permission / request / wish) and dynamic (expressing ability).

Problems have a strong relationship to modality within scientific writing. Often, this is due to a tactic called “hedging” (Medlock and Briscoe 2007 ) where the author uses speculative language, often using Epistemic modality, in an attempt to make either noncommital or vague statements. This has the effect of allowing the author to distance themselves from the statement, and is often employed when discussing negative or problematic topics. Consider the following example of Epistemic modality from Nakov and Hearst ( 2008 ): “A potential drawback is that it might not work well for low-frequency words”.

To take this linguistic correlate into account as a feature, we replicated a modality classifier as described by (Ruppenhofer and Rehbein 2012 ). More sophisticated modality classifiers have been recently introduced, for instance using a wide range of features and convolutional neural networks, e.g, (Zhou et al. 2015 ; Marasović and Frank 2016 ). However, we wanted to check the effect of a simpler method of modality classification on the final outcome first before investing heavily into their implementation. We trained three classifiers using the subset of features which Ruppenhofer et al. reported as performing best, and evaluated them on the gold standard dataset provided by the authors 4 . The results of the are shown in Table  3 . The dataset contains annotations of English modal verbs on the 535 documents of the first MPQA corpus release (Wiebe et al. 2005 ).

Modality classifier results (precision/recall/f-measure) using Naïve Bayes (NB), logistic regression, and a support vector machine (SVM)

Italicized results reflect highest f-measure reported per modal category

Logistic Regression performed best overall and so this model was chosen for our upcoming experiments. With regards to the optative and concessive modal categories, they can be seen to perform extremely poorly, with the optative category receiving a null score across all three classifiers. This is due to a limitation in the dataset, which is unbalanced and contains very few instances of these two categories. This unbalanced data also is the reason behind our decision of reporting results in terms of recall, precision and f-measure in Table  3 .

The modality classifier was then retrained on the entirety of the dataset used by Ruppenhofer and Rehbein ( 2012 ) using the best performing model from training (Logistic Regression). This new model was then used in the upcoming experiment to predict modality labels for each instance in our dataset.

As can be seen from Table  4 , we are able to achieve good results for distinguishing a problematic statement from non-problematic one. The bag-of-words baseline achieves a very good performance of 71.0% for the Logistic Regression classifier, showing that there is enough signal in the candidate phrases alone to distinguish them much better than random chance.

Results distinguishing problems from non-problems using Naïve Bayes (NB), logistic regression (LR) and a support vector machine (SVM)

Each feature set’s performance is shown in isolation followed by combinations with other features. Tenfold stratified cross-validation was used across all experiments. Statistical significance with respect to the baseline at the p  < 0.05 , 0.01, 0.001 levels is denoted by *, ** and *** respectively

Taking a look at Table  5 , which shows the information gain for the top lemmas,

Information gain (IG) in bits of top lemmas from the bag-of-words baseline in Table  4

we can see that the top lemmas are indeed indicative of problemhood (e.g. “limit”,“explosion”). Bigrams achieved good performance on their own (as did negation and discourse) but unfortunately performance deteriorated when using trigrams, particularly with the SVM and LR. The subcategorisation feature was the worst performing feature in isolation. Upon taking a closer look at our data, we saw that our hypothesis that intransitive verbs are commonly used in problematic statements was true, with over 30% of our problems (153) using them. However, due to our sampling method for the negative cases we also picked up many intransitive verbs (163). This explains the almost random chance performance (i.e.  50%) given that the distribution of intransitive verbs amongst the positive and negative candidates was almost even.

The modality feature was the most expensive to produce, but also didn’t perform very well is isolation. This surprising result may be partly due to a data sparsity issue

where only a small portion (169) of our instances contained modal verbs. The breakdown of how many types of modal senses which occurred is displayed in Table  6 . The most dominant modal sense was epistemic. This is a good indicator of problemhood (e.g. hedging, cf. “ Linguistic correlates of problem- and solution-hood ” section) but if the accumulation of additional data was possible, we think that this feature may have the potential to be much more valuable in determining problemhood. Another reason for the performance may be domain dependence of the classifier since it was trained on text from different domains (e.g. news). Additionally, modality has also shown to be helpful in determining contextual polarity (Wilson et al. 2005 ) and argumentation (Becker et al. 2016 ), so using the output from this modality classifier may also prove useful for further feature engineering taking this into account in future work.

Number of instances of modal senses

Polarity managed to perform well but not as good as we hoped. However, this feature also suffers from a sparsity issue resulting from cases where the Lesk algorithm (Lesk 1986 ) is not able to resolve the synset of the syntactic head.

Knowledge of syntax provides a big improvement with a significant increase over the baseline results from two of the classifiers.

Examining this in greater detail, POS tags with high information gain mostly included tags from open classes (i.e. VB-, JJ-, NN- and RB-). These tags are often more associated with determining polarity status than tags such as prepositions and conjunctions (i.e. adverbs and adjectives are more likely to be describing something with a non-neutral viewpoint).

The embeddings from Doc2Vec allowed us to obtain another significant increase in performance (72.9% with Naïve Bayes) over the baseline and polarity using Word2Vec provided the best individual feature result (77.2% with SVM).

Combining all features together, each classifier managed to achieve a significant result over the baseline with the best result coming from the SVM (81.8%). Problems were also better classified than non-problems as shown in the confusion matrix in Table  7 . The addition of the Word2Vec vectors may be seen as a form of smoothing in cases where previous linguistic features had a sparsity issue i.e., instead of a NULL entry, the embeddings provide some sort of value for each candidate. Particularly wrt. the polarity feature, cases where Lesk was unable to resolve a synset meant that a ZERO entry was added to the vector supplied to the machine learner. Amongst the possible combinations, the best subset of features was found by combining all features with the exception of bigrams, trigrams, subcategorisation and modality. This subset of features managed to improve results in both the Naïve Bayes and SVM classifiers with the highest overall result coming from the SVM (82.3%).

Confusion matrix for problems

The results for disambiguation of solutions from non-solutions can be seen in Table  8 . The bag-of-words baseline performs much better than random, with the performance being quite high with regard to the SVM (this result was also higher than any of the baseline performances from the problem classifiers). As shown in Table  9 , the top ranked lemmas from the best performing model (using information gain) included “use” and “method”. These lemmas are very indicative of solutionhood and so give some insight into the high baseline returned from the machine learners. Subcategorisation and the result adverbials were the two worst performing features. However, the low performance for subcategorisation is due to the sampling of the non-solutions (the same reason for the low performance of the problem transitivity feature). When fitting the POS-tag distribution for the negative samples, we noticed that over 80% of the head POS-tags were verbs (much higher than the problem heads). The most frequent verb type being the infinite form.

Results distinguishing solutions from non-solutions using Naïve Bayes (NB), logistic regression (LR) and a support vector machine (SVM)

Each feature set’s performance is shown in isolation followed by combinations with other features. Tenfold stratified cross-validation was used across all experiments

Information gain (IG) in bits of top lemmas from the bag-of-words baseline in Table  8

This is not surprising given that a very common formulation to describe a solution is to use the infinitive “TO” since it often describes a task e.g., “One solution is to find the singletons and remove them”. Therefore, since the head POS tags of the non-solutions had to match this high distribution of infinitive verbs present in the solution, the subcategorisation feature is not particularly discriminatory. Polarity, negation, exemplification and syntactic features were slightly more discriminate and provided comparable results. However, similar to the problem experiment, the embeddings from Word2Vec and Doc2Vec proved to be the best features, with polarity using Word2Vec providing the best individual result (73.4% with SVM).

Combining all features together managed to improve over each feature in isolation and beat the baseline using all three classifiers. Furthermore, when looking at the confusion matrix in Table  10 the solutions were classified more accurately than the non-solutions. The best subset of features was found by combining all features without adverbial of result, bigrams, exemplification, negation, polarity and subcategorisation. The best result using this subset of features was achieved by the SVM with 79.7%. It managed to greatly improve upon the baseline but was just shy of achieving statistical significance ( p = 0.057 ).

Confusion matrix for solutions

In this work, we have presented new supervised classifiers for the task of identifying problem and solution statements in scientific text. We have also introduced a new corpus for this task and used it for evaluating our classifiers. Great care was taken in constructing the corpus by ensuring that the negative and positive samples were closely matched in terms of syntactic shape. If we had simply selected random subtrees for negative samples without regard for any syntactic similarity with our positive samples, the machine learner may have found easy signals such as sentence length. Additionally, since we did not allow the machine learner to see the surroundings of the candidate string within the sentence, this made our task even harder. Our performance on the corpus shows promise for this task, and proves that there are strong signals for determining both the problem and solution parts of the problem-solving pattern independently.

With regard to classifying problems from non-problems, features such as the POS tag, document and word embeddings provide the best features, with polarity using the Word2Vec embeddings achieving the highest feature performance. The best overall result was achieved using an SVM with a subset of features (82.3%). Classifying solutions from non-solutions also performs well using the embedding features, with the best feature also being polarity using the Word2Vec embeddings, and the highest result also coming from the SVM with a feature subset (79.7%).

In future work, we plan to link problem and solution statements which were found independently during our corpus creation. Given that our classifiers were trained on data solely from the ACL anthology, we also hope to investigate the domain specificity of our classifiers and see how well they can generalise to domains other than ACL (e.g. bioinformatics). Since we took great care at removing the knowledge our classifiers have of the explicit statements of problem and solution (i.e. the classifiers were trained only on the syntactic argument of the explicit statement of problem-/solution-hood), our classifiers should in principle be in a good position to generalise, i.e., find implicit statements too. In future work, we will measure to which degree this is the case.

To facilitate further research on this topic, all code and data used in our experiments can be found here: www.cl.cam.ac.uk/~kh562/identifying-problems-and-solutions.html

Acknowledgements

The first author has been supported by an EPSRC studentship (Award Ref: 1641528). We thank the reviewers for their helpful comments.

1 http://acl-arc.comp.nus.edu.sg/ .

2 The corpus comprises 3,391,198 sentences, 71,149,169 words and 451,996,332 characters.

3 The head POS tags were found using a modification of the Collins’ Head Finder. This modified algorithm addresses some of the limitations of the head finding heuristics described by Collins ( 2003 ) and can be found here: http://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/trees/ModCollinsHeadFinder.html .

4 https://www.uni-hildesheim.de/ruppenhofer/data/modalia_release1.0.tgz.

Contributor Information

Kevin Heffernan, Email: [email protected] .

Simone Teufel, Email: [email protected] .

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