Data Expeditions

Migration’s Many Forms: Finding Creative Ways to Examine the Movement of Populations

Sarah Dwyer
Migration Lab faculty and student photos.
Directors, teaching and graduate assistants, and fellows of the Representing Migration Humanities Lab (top row: Charlotte Sussman, Tsitsi Jaji, Domenika Baran, Jarvis McInnis, Corina Stan; second row: Sasha Panaram, Karen Little, Sonia Nayak, Catherine Lee, Isabella Arbelaez; third row: Jessica Covill, Kelsey Desir, Nicole Higgins, Jared Junkin, Dana Johnson; bottom row: Andrew Kim, Trisha Remetir, Hannah Borenstein, Grant Glass, Anna Tybinko)

A few years ago, two associate professors in Duke’s English Department started a reading group to explore their shared interest in human mobility and its cultural expressions. Building on their discussions, Charlotte Sussman and Tsitsi Jaji teamed up with fellow faculty members Dominika Baran, Jarvis McInnis, and Corina Stan to direct the Representing Migration Humanities Lab.

The lab received support from Humanities Unbounded, a five-year initiative funded by an Andrew W. Mellon Foundation grant.

“We were lucky to have some great graduate students as part of the group convening the lab,” Sussman says. “They made me really enjoy working collaboratively.”

Sussman is the author of Consuming Anxieties: Consumer Protest, Gender, and British Slavery, 1713–1833 and Eighteenth-Century English Literature. Based on her positive experience with the lab, she says she “started looking for different kinds of pedagogies and also opportunities for graduate students.”

Fifteen students have served as Representing Migration fellows, teaching assistants, or graduate lab assistants. Others have taken part in courses and research with faculty.

One of the lab’s projects explored Migration Memorials. Around the same time, over at the Duke Marine Lab, Cindy Van Dover’s lab was studying the impact of seabed mining. “It occurred to them that [mining] grants from the International Seabed Authority were close to the path of the Middle Passage,” Sussman says. Van Dover’s lab became interested in proposing a memorial to victims of the trans-Atlantic voyages that brought enslaved Africans to the New World.

Phillip Turner, a Ph.D. student in Marine Science and Conservation, convened a meeting with a wide range of experts, including Sussman. “They knew about the geography but were curious how the Middle Passage was recorded or memorialized,” she says.

Phillip Turner (second from left) with Aline Jaeckel, Diva Amon, and Jessica Perelman at the 25th Session of the International Seabed Authority in Kingston, Jamaica.
Phillip Turner (second from left) with Aline Jaeckel, Diva Amon, and Jessica Perelman at the 25th Session of the International Seabed Authority in Kingston, Jamaica

Turner organized a coauthored article on ways to commemorate the enslaved people who came to rest on the Atlantic seabed. In 2018, he received a Graduate Student Training Enhancement Grant to attend a meeting of the International Seabed Authority, where he networked and discussed the Middle Passage project. “The project was positively received,” he reported, “and it will hopefully be discussed in more detail at subsequent ISA sessions once the manuscript has been published.”

Kaylee Alexander.
Kaylee Alexander

Sussman had an idea to explore the Middle Passage from a new angle and involve more students through a Data+ summer research project. To help prepare the project, doctoral student Kaylee Alexander (Art, Art History & Visual Studies) worked with Duke Libraries’ Data and Visualization Services as a Humanities Unbounded Graduate Assistant.

“One of the original goals of the project was to use data representing nearly 36,000 transatlantic slave voyages to see if it would be possible to map a reasonable location for a deep-sea memorial to the transatlantic slave trade,” Alexander reflected. “The promises of these data were great; we just had to figure out how to use them.”

Sussman’s Data+ team set out to locate where and why enslaved Africans died during the sea voyage and analyze patterns of these mortality rates.

Chudi Zong, Ethan Czerniecki, Daisy Zhan, Charlotte Sussman, and Emma Davenport at the Data+ poster session.
Chudi Zong, Ethan Czerniecki, Daisy Zhan, Charlotte Sussman, and Emma Davenport at the Data+ poster session

“It’s been really interesting to fill in the gaps of the Middle Passage and search for patterns,” said Chudi Zhong, a master’s student in Statistical Science. “There is a lot of missing data, and we’ve used current technology to fill gaps. For example, using the Trans-Atlantic Slave Trade Database, we can find records on how many enslaved people died. The Climatological Database for the World’s Oceans has other kinds of data for ships. We merged the two databases and found 35 matching voyages. Then we used our own model to make predictions.”

Dutch Slaving Voyages (1751-1795): The height of each bar corresponds to the average number of deaths per 150km2 grid. The color of the bar corresponds to the number of ship locations recorded in each grid. [From the Data+ team’s executive summary].
Dutch Slaving Voyages (1751-1795): The height of each bar corresponds to the average number of deaths per 150km2 grid. The color of the bar corresponds to the number of ship locations recorded in each grid. [From the Data+ team’s executive summary]
As an undergraduate majoring in Philosophy and Global Cultural Studies, Ethan Czerniecki said the Data+ project “gave me a different way of approaching these topics outside the humanities that proved to be expansive,” he said. “I wouldn’t have thought to treat these individuals as data points, but [the data science approach] opens up new areas like data visualization. Combining a humanities project with data science is really interesting, and the methodologies interact well.”

Prediction of 2,164 trans-Atlantic voyage paths that ended in the northern hemisphere based on the LSTM model; inset map:  prediction of 36 trans-Atlantic voyage paths based on the LSTM model, all of which have reasonably smooth lines. [From the Data+ team’s executive summary].
Prediction of 2,164 trans-Atlantic voyage paths that ended in the northern hemisphere based on the LSTM model; inset map:  prediction of 36 trans-Atlantic voyage paths based on the LSTM model, all of which have reasonably smooth lines. [From the Data+ team’s executive summary]
English Ph.D. student Emma Davenport served as project manager for the Data+ team. “This was my first experience in a real mentorship role,” she said. “It’s different than being part of a team doing the research. Being a mentor calls for a different set of skills and a different orientation.” Davenport is going on the job market this year. “Job committees want to see that you have a set of skills for guiding undergraduate research,” she said, “and both academic and nonacademic jobs are looking for candidates with a well-rounded skillset. I couldn’t have gotten this experience from traditional teaching and research.”

This fall, a Bass Connections project team is continuing the work of the Representing Migration lab and the Data+ project. Doctoral students in English and Romance Studies and undergraduates representing at least six majors are collaborating with faculty and librarians. Some students are creating a map showing where the deaths occurred in the Atlantic; their original data will support a proposal for the Middle Passage memorial.

Also in this academic year, six graduate and undergraduate students will serve as Representing Migration Humanities Fellows.

“I think these opportunities are really great,” says Sussman. “Duke is not a heavy teaching school, at least for English, relative to other Ph.D. programs, but I think what Duke can offer grad students is more unique. This kind of work is useful to them professionally, whether they go into academia or not.”

In addition to the opportunities she has found to engage students in research on migration, Sussman has tapped into other Duke programs as well.

Undergraduates Clifford Haley, Eli Kline, and Bailey Bogle present “Pirating Texts” at the 2019 Story+ Research Symposium. Photo: Jennifer R. Zhou.
Undergraduates Clifford Haley, Eli Kline, and Bailey Bogle present “Pirating Texts” at the 2019 Story+ Research Symposium. Photo: Jennifer R. Zhou

Grant Glass used a Data Expeditions grant to create a data visualization module for Sussman’s course, Queens of Antiquity. A doctoral student at UNC Chapel Hill, Glass also served as project manager on Sussman’s 2018 Data+ project, Pirating Texts, and as graduate mentor on the 2019 Story+ summer research project of the same name.

Most recently, English Ph.D. student Kimberley Dimitriadis received an Archival Expeditions grant to create a module for Sussman’s medical humanities course, Doctors’ Stories.

“Using this [kind of approach] in your classroom setting involves letting go of authority, and sometimes that works better than others,” says Sussman. “You have to be willing to let go.”

Learn more at a free lunchtime event on December 4 at the John Hope Franklin Center for Interdisciplinary and International Studies, featuring Charlotte Sussman and colleagues.

Current Opportunities and Deadlines

Graduate Students Can Propose Data Expeditions for Undergraduate Courses

Sarah Dwyer

Data Expeditions.

Deadlines: August 5, 2019 for Fall 2019; October 10, 2019 for Spring 2020

The Rhodes Information Initiative at Duke (iiD) invites graduate students to submit a Fall 2019 or Spring 2020 Data Expeditions proposal.

Rhodes iiD, in partnership with the Social Science Research Institute, will support pairs of graduate students to prepare a data set for use in an undergraduate class and then assist the faculty instructor by supervising the data expedition within the class. Another useful approach is to prepare several data sets for use in illustrating the ideas behind a particular data analysis technique.

Graduate students who participate receive a (tax-free) grant of $1,500 for academic-related travel (such as conferences or workshops), texts, certain hardware (as long as it does not have a hard drive) and software, and more.

Review past Data Expeditions projects.

See details and apply for Fall 2019 undergraduate Data Expeditions.

See details and apply for Spring 2020 undergraduate Data Expeditions.

Project on Color Vision of Shrimp Helps Biology Students See Data Science in New Light

Sarah Dwyer

Patrick Green and Eleanor Caves

We are all data scientists these days, to one degree or another. The ability to explore and analyze data helps us make sense of our world.

Duke’s Data Expeditions program aims to introduce more undergraduates to data science early in their college education. The Information Initiative at Duke (iiD), in partnership with the Social Science Research Institute (SSRI), supports pairs of graduate students to prepare a dataset for use in an existing undergraduate course.

Patrick Green teaching Data ExpeditionsIn one Data Expedition project, Exploring Cleaner Shrimp Color Vision Capabilities Using R, Biology doctoral students Eleanor Caves and Patrick Green teamed up with Professor Sönke Johnsen to pilot their approach in an introductory summer course called Sensory Systems. Green and his advisor Sheila Patek then adapted it for use in an upper-level lab course, Principles of Animal Physiology.

“Especially if classes have a lab component, getting students some experience with importing, analyzing, and plotting data can be invaluable,” said Caves. “I remember struggling with Excel to write my own lab reports in college, and if someone had just given me the tools to code, and then inspired me to use those tools for a couple of reports, I would have been so much more comfortable with different aspects of data analysis.”

“This is a critical tool for students to learn,” Green added, “whether they use data in their future careers or whether they’re just trying to understand the world around them as they, for example, vote and raise families.”

Cleaner shrimp working on a fishCleaner shrimp are crustaceans that provide handy cleaning services to reef fish by removing ectoparasites. The project’s aim was to investigate how cleaner shrimp perceive the color patterns of other cleaner shrimp and fish. Caves collected the data as part of her doctoral dissertation.

In the class, she and Green introduced the ecology of cleaner shrimp, asked the students to make predictions about color vision capability and taught coding sessions in R.

Along the way, both the undergraduates and the instructors faced challenges.

“What makes coding frustrating on an individual level translates into the classroom,” said Caves. “Typos and minor errors that can send coding errors back at you occur on the students’ computers too, and you have to be ready to troubleshoot on your feet.”

Patrick Green working with Data Expeditions students

“Similar to Eleanor, I learned that these activities move more slowly than we might expect,” noted Green. “It was incredibly useful to have ‘teachable moments’ when students hit error messages. Even if these errors were caused by simple misspellings, it allowed us to show students that this is normal and fixable – not an impassible roadblock. Because we coded in real-time along with the students, we were also able to showcase our own mistakes and humanize the process, something I think is useful for students to see.”

The students soon learned how to subset, index, plot, change the color and shape of data points, add best fit lines, change line width and type, and create smooth spectral sensitivity curves (which show how sensitive photoreceptors are across the visible spectrum of light).

Figure from Data ExpeditionsAt the end, they created a figure of spectral sensitivity for several individuals of the same species. They compared their results to their predictions and discussed how they might use their new skills to analyze data they’ll collect in future lab-based courses.

And they seemed to enjoy the process. Caves noted, “I’ve been pleasantly surprised at how attentive the students remain and how engaged they seem the whole time.”

“It never occurred to me that I would need to learn how to code,” wrote one student in an end-of-class reflection, “but I am glad that I get to learn this.” Another student wrote, “It was actually easier than I expected, since coding seems so out of reach when you don’t know what is happening or what the terms mean. I could definitely use R in the future for projects where I am required to use data.”

At the end of the day, coding gives students a deeper understanding of data to solve real-world problems. “It gives students, even those who won’t go on to do research of their own, a respect for the scientific process, how we analyze our data, and where results come from, so that hopefully they can be more informed citizens and interpreters of the overwhelming number of facts they’re exposed to every day,” said Caves.

Eleanor Caves and Patrick Green with their advisors

Both Caves and Green received the Dean’s Award for Excellence in Mentoring from The Graduate School. They graduated this spring and are now postdoctoral researchers in Duke’s Biology Department with the Nowicki Lab.

“I have been surprised to learn during my Ph.D. that I can code, and that I am somewhat good at it,” Green reflected. “This has taken lots of trial and error, but I am motivated to continue learning and developing these skills in my research. Being able to use the same skills in my teaching is something that expands my teaching abilities and, I hope, will improve my ability to reach new generations of students.”

See other Data Expeditions projects and learn about a new program at Duke called Archival Expeditions. Photos at top and bottom courtesy of The Graduate School; other photos courtesy of Eleanor Caves and Patrick Green.

Graduate Students Can Submit Proposals for Data Expeditions in Undergraduate Courses

Sarah Dwyer

Data Expeditions

Deadline: June 1, 2018

The purpose of this call is to introduce more undergraduate students to exploratory data analysis early in their Duke experience, and to involve graduate students in thinking about the way classes can interact with data. Our hope is that expeditions will encourage students to be more adventurous in exploring the Duke curriculum, and that students with deeper skills will be capable of deeper insights.

What Are Expeditions?

The Information Initiative at Duke (iiD), in partnership with the Social Science Research Institute (SSRI), will support pairs of graduate students to prepare a data set for use in an undergraduate class and then assist the faculty instructor by supervising the data expedition within the class. Another useful approach is to prepare several data sets for use in illustrating the ideas behind a particular data analysis technique.

Graduate students who participate receive a (tax free) grant of $1,500 for academic-related travel (such as conferences or workshops) or computers/technical equipment for research. The funds are not available until the course is complete and all materials have been submitted to Ariel Dawn (ariel.dawn@duke.edu) in the Information Initiative. These materials add to our undergraduate curriculum through expeditions, and we reciprocate by investing in intellectual development.

How Are Expeditions Organized and Funded?

iiD provides resources, SSRI stores the data sets for later use, and representatives from affiliated departments provide direction. Departments interested in participating are encouraged to contact the Data Expeditions Director Paul Bendich (bendich@math.duke.edu).

Application Process

Applications will be reviewed by a faculty committee; those received by June 1, 2018, will receive full consideration, and funding decisions will be made by July 1, 2018.

Graduate students are encouraged to contact Paul Bendich (bendich@math.duke.edu) for help in developing ideas.

We particularly encourage exploration of data sets that bring different intellectual communities together. We place a special emphasis on expeditions that can be used in the introductory undergraduate classroom, as well as those that can be easily adapted for use in multiple pedagogical scenarios.

Application Details

Email Ariel Dawn (ariel.dawn@duke.edu) a PDF, at most 2 pages, with the following information:

  • Sponsoring faculty member and target undergraduate class
  • Title of data set(s)
  • Description: A brief data description that includes (at least) the following information:
    • One two-sentence description of data file
    • Source(s) where the data come from (we greatly prefer data that can be made public without restrictions!)
    • Why the data were collected in the first place
    • How the data set was put together
    • Dimensions of the data set
  • Potential classroom exercises: List of potential questions that can be explored using this data set, and description of pathways toward answers the students can take
  • Techniques: List of computational techniques – this is an opportunity to ask for access to a virtual machine that comes pre-loaded with different software packages
  • Source(s): Properly formatted citation of data source(s)

Expeditions recommended for an award will be asked to provide a Markdown or HTML document that contains, in addition to the information listed above:

  • List of variables: A list of the variable names and brief description for each (with hyperlinks to Codebook below)
  • Codebook: Description of each variable and its values

Learn More

See the iiD website.

Engineering Doctoral Student Teaches Undergraduates about the Power of Data Science

Sarah Dwyer

Chris Tralie wasn’t even working with big data when he came to Duke as a graduate student. But a movement gaining steam here in 2013 helped him realize he had the technical skillset to reveal structures and patterns where others saw chaos—or nothing.

“There were people working on Big Data problems in various departments when I first got to campus,” said Tralie, a doctoral candidate in electrical & computer engineering (ECE) and a National Science Foundation Graduate Research Fellow. “Then the Information Initiative at Duke launched. It was brilliant because it brought everyone together and let them learn from each other’s work. There was real and sudden excitement in the air.”

Tralie found his niche while learning about topology with John Harer, a professor of mathematics with a secondary appointment in ECE. The class boiled down to understanding the “shape” of data. Tralie thought, “Why can’t we do this with music?”

Tralie designed a program that analyzes many different musical parameters of a song and mathematically reduces each time point into 3D space. The resulting shape can help determine which genre of music a song belongs to and can even recognize covers of songs by other bands.

“Nobody thought you could do that, because of the differences in vocals and instruments,” said Tralie.

Tralie took his own academic journey and used it to turn other Duke students on to big data—creating a “Data Expedition” using his method for visualizing songs as a fun and approachable way to teach undergraduates how to design data-crunching algorithms.

Data Expeditions and Data+ both benefit our undergraduates by making technical subjects more relevant and exciting, but they’re also professional development opportunities for our graduate students.

Data Expeditions are projects proposed and taught by graduate students within the context of an existing undergraduate course. “Data Expeditions and Data+ both benefit our undergraduates by making technical subjects more relevant and exciting, but they’re also professional development opportunities for our graduate students,” said Robert Calderbank, director of iiD, which sponsors both programs. “Industry and academia both need people who can lead projects and manage multidisciplinary teams, so these experiences can provide a competitive advantage for Duke graduates.”

“The Data Expeditions were really useful for me growing as a mentor,” said Tralie. “I got to work with really talented students who were still learning the basics and yet had amazing new ideas that I could learn from too. Those skills will translate to my future career, where I hope to be a faculty member advising graduate students of my own someday in engineering or applied math.”

He also developed a new course for graduate students about using data analytics on video recognition challenges, like tracking heartbeats from video clips. Tralie’s own promising work in that arena can potentially add another element to an app developed to recognize signs of autism by another of his advisors, Guillermo Sapiro, the Edmund T. Pratt, Jr. School Professor of Electrical and Computer Engineering.

After defending his dissertation this spring, Tralie plans to stay in academia, at least in part because he loves the teaching experiences he has had while at Duke.

“Mentoring and teaching forces me to explain my work in simple terms, which raises my own understanding of it,” said Tralie. “Plus the students all end up going out and doing their own interesting things, which they can later teach me about in return. They’re like my eyes and ears out there in the fast developing world of Big Data.”

 

By Ken Kingery; originally posted on the Pratt School of Engineering website

Photo: Chris Tralie with advisors John Harer (Math) and Guillermo Sapiro (ECE)