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Midwest researchers address food insecurity and transportation access during the pandemic

By Raleigh Butler and Qining Wang

This story is part of a series on coronavirus research in the Midwest region. To explore other NSF-funded research addressing the COVID-19 pandemic, please visit the COVID Information Commons, a project of the four NSF Big Data Innovation Hubs.

The University of Michigan received a RAPID award from the National Science Foundation in the early stages of the pandemic to explore improving food-insecurity conditions driven by the pandemic. The project, titled “Improving Transportation Equity to Enhance Food Security for Families Vulnerable to COVID-19,” is led by Robert Hampshire, in collaboration with H. V. Jagadish, Tayo Fabusuyi, and Aditi Misra.

The project builds on earlier NSF-funded research that developed the Transportation Equity Open Knowledge Network (OKN). The researchers integrated data from the Food Security Index and other sources into the Transportation Equity OKN. The researchers proposed to “investigate, and begin to develop mechanisms to address, the lack of access to food (i.e., food insecurity) associated with COVID-19 and the role of transportation challenges leading to food insecurity.” The research builds on prior work to support the development and evaluation of a meal-delivery program, as well as the identification of people and places most at risk of food insecurity due to a lack of access to transportation.

PROJECT GOALS

As a part of the project, the research team provided background context and technical assistance to the City of Detroit’s pilot program that delivers meals to vulnerable families.

The project aims to address the food insecurity as a result of the underlying inequalities exacerbated by the COVID-19 pandemic. During the pandemic, many low-income, marginalized, and vulnerable households struggled with access to food because of reduced public-transit services and inability to access internet services. Consequently, these households cannot place food orders or call for food delivery. Fearful of contracting COVID, many also avoided in-person grocery shopping. Considering the underlying broader social inequality, the food-insecurity situation isn’t just about food. In times of COVID-19, it translates into broader issues of health insecurity.

To address this issue, Hampshire’s team takes a data-driven approach to estimate the number and the key demographics of households facing food insecurity. In addition, they also worked with the City’s pilot program—Covid Food Delivery Program (CFDP)—to provide meal-delivery services for identified food-insecure households that rely on public transit in the City of Detroit or based on health referrals. By making their results publicly available, the team hopes their findings could inform policy makers to create more effective mitigation measures.

PROJECT OUTCOMES

Choosing the City of Detroit as their case study, the team used data from multiple sources to identify the key demographic characteristics of households receiving Supplemental Nutrition Assistance Program/Electronic Benefits Transfer (SNAP/EBT) benefits. The team estimated that 71,600 households across Michigan met criteria for both food and transportation insecurity based on the US Census Public Use Microdata Sample (PUMS), of which 20,800 are from the City of Detroit. Finer segmentation based on geography and household composition were also carried out. By narrowing down the sample size to a finer geographic region, the team can easily replicate their approach for other local regions with more accurate Census tracts and more consistent information on food services.

The team randomly selected 350 patrons from the CFDP data dashboard to investigate the benefits of food delivery during the pandemic. They found that even though the program’s service only accounted for roughly 70% of the households’ weekly food consumption, 86% of the recipients of the program’s service reported having sufficient food each week.

However, many also reported that the food deliveries lack refrigerated items such as dairy and meat. Alarmingly, they also found more than a third of the patrons were first-time beneficiaries of CFPD, suggesting the pandemic is creating new cases of food insecurity in Detroit.

Through the analysis, the research team was able to identify the key benefits and issues of CFDP, which enabled CFDP to secure additional resources to redesign and expand their program. This program has now received an additional $1.5 million that can sustain the program until 2024. The work of the research team serves as an example of data for social good, in which a data-driven approach provides insightful guidance on how to mitigate issues around food insecurity.

Tayo Fabusuyi, the lead author of the project’s report, stated that “by documenting the program’s process issues and demonstrating how food insecurity severity could be estimated for different geographic areas, the program could easily be replicated at city or neighborhood level across the US. The project allows for learnings and adaptations not only by the City of Detroit, but also other cities that may be grappling with similar challenges.” The report closes by saying, “We believe that other cities will benefit from our documentation, learn from our experience and be able to modify similar program designs to address local peculiarities.”

Learn more

If you’re interested in learning more about how data directly connects to societal issues and human lives, consider attending MBDH’s “Smart & Resilient Communities / Data for Social Good” panel discussion, which will be held Thursday, October 28, 2021 – 2:00–3:00 p.m. CT / 3:00–4:00 p.m. ET. This panel includes one of the project’s Co-Principal Investigators, Tayo Fabusuyi.

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other projects we should include here, or to participate in any of our community-led Priority Areas.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, the University of Michigan, the University of Minnesota, Iowa State University, Indiana University, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the NSF Big Data Innovation Hubs community.

Big Data Neuroscience Workshop Brings Together a Transdisciplinary Research Community

By Erica Joo

Researchers working at the interface of computational neuroscience, big data science, and health analytics held the latest in a series of workshops designed to virtually bring together their community to explore new research and opportunities. The 2021 Advanced Computational Neuroscience Network (ACNN) meeting was held September 2–3, 2021. This is the sixth year that this workshop series was organized since the initial seed funding from the Midwest Big Data Innovation Hub in 2016. Despite this being the second year that the COVID-19 pandemic has led to this being an online meeting, participation remained strong, with over 180 participants from over 40 institutions across the Midwest, USA, and several other countries.

“The success of this workshop series in bringing together researchers across the Midwest has gone beyond our initial expectations,” said co-organizer and ACNN co-founder Franco Pestilli. “Every year for the last six years we have had students, postdocs, and faculty join the events. There is a thirst for connection across the Midwest.”

“This is how I have come to think about the Midwest region: It is similar to Boston or New York City but with a geographical barrier,” Pestilli said. “Large hubs such as those in the East Coast have an incredible amount of talent compressed within a small urban area. That allows researchers to share scientific ideas, results, and resources just by walking into a building at the other side of town. The Midwest has similar talent but spread across an incredibly large geographic region. What our workshop series aimed at doing is to break the barriers to scientific research and education created by the geography of the Midwest region. We did so first by using support by the NSF to bring students and scientists together from across the Midwest.”

“We learned a lot by going virtual,” Pestilli continued. “In 2020, we had over 450 participants, and double that of the years before. This year’s event was hybrid and we learned that it is possible to successfully bring together talent across the Midwest using hybrid events. We think that if more of these events are organized, the data science and neuroscience talents across large U.S. regions can come together more often, and effectively, just like it can more naturally happen in the East Coast hubs. We can break the geographical barriers to science and education in the Midwest. We also think that the southern States possibly have a similar challenge, with talent dispersed across a large geographic area. I am looking forward to expanding our Neuroscience network to the South.”

The 2021 meeting included multiple research presentation sessions, lightning talks, and keynote talks. Dr. Kamil Ugurbil from the University of Minnesota delivered the “Nalbandov Public Lecture” on Harnessing Imaging towards meeting a Central Scientific Challenge of the 21st Century: Understanding Human Brain Function. And Charles Springer from the Oregon Health and Science University presented a keynote lecture on Celebrating the 50th anniversary of first human MRI for non-invasive 3D imaging of water molecules, or protons, bones and soft tissues.

Reports of some exciting new research included recent work of Monica Rosenberg from the University of Chicago on building generalizable models of human behavior using Big Data neuroimaging data, and Archana Venkataraman from Johns Hopkins University, who demonstrated novel strategies for understanding structural and functional brain connectivity and its applications to multidimensional clinical phenotyping.

“The ACNN workshop was fantastic,” said Rosenberg. “It was a great way to hear about cutting-edge theoretical and methodological work in the field and connect with the computational and network neuroscience communities here in the Midwest. I’d love to participate in the future.”

A number of talks and lightning presentations introduced powerful multimodal techniques for data-driven inference in structural, functional, and diffusion imaging (Shella Keilholz), contrasting population-based and individual differences in functional brain networks (Caterina Gratton), and deriving and utilizing proxy measures of brain connectivity (Joaquin Goni). One lightning talk held by Dr. Bradly Alicea from the University of Illinois at Urbana-Champaign was on network science and application to neuroscience and biology. One such presenter, Paul Camacho, who is a neuroscience doctoral student at the University of Illinois at Urbana-Champaign, shared his experience from the workshop.

“The workshop was a fantastic event with a rare balance of world-class keynote speakers and a well-curated set of lightning talks from our Midwest community,” said Camacho. “The level of discussion in each session was greater than I had come to expect from virtual conferences over the past couple of years. Although I did not personally know all of my fellow presenters, there was a sense of camaraderie that is emblematic of the Midwest and very appreciated in the scientific community. As a mark of how successful the workshop was in fostering collaboration, I have noticed an uptick in traffic to the GitHub repositories for the work I presented in my lightning talk.”

Dr. Bradly Alicea from the University of Illinois at Urbana-Champaign held a lightning talk on his research in the application of network science on neuroscience and biology. “The conference went well. I’ve attended other conferences before, and there were some great keynote speakers as well as interesting discussions at this one,” Alicea notes. “I’m looking forward to next year’s conference and hope to present again.”

Next year, the 2022 ACNN meeting is scheduled to be held in person in Texas. “After five tremendous years in the Midwest I relocated to the South, to the University of Texas at Austin,” said Franco Pestilli. “I am currently in the process of exporting the model for the Big Data Neuroscience workshops to the South, building a new team of collaborators across the Southern states. I am sure the Midwest team, Ivo Dinov, Rich Gonzalez, and the others, will continue the work we have initiated in the region. The Midwest Big Data Hub has been fundamental in supporting our activities and I am sure it has interests at stake to continue the ‘good’ that it has been started and to connect the human infrastructure resources the Midwest has available.”

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led Priority Areas.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Iowa State University, Indiana University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

Building a Midwest Carpentries Community

By Raleigh Butler

The Midwest Big Data Innovation Hub is committed to building data science instructional capacity in the Midwest region, particularly at smaller colleges and universities, such as predominantly undergraduate institutions (PUIs).

One avenue for this is the Midwest Carpentries Community, a partnership between the MBDH and the University of Wisconsin-Madison, under the Hub’s Community Development and Engagement (CDE) incubator program.

The project aims to build “hands-on data science instruction capacity,” by using the existing curriculum and workshop model of The Carpentries, an international member-supported organization that strives to teach data science and coding skills on a global scale. The organization is structured around three lesson programs: Software Carpentry, Data Carpentry, and Library Carpentry, which are “communities of Instructors, Trainers, Maintainers, helpers, and supporters who share a mission to teach foundational computational and data science skills to researchers.”

In this post, we will focus on a discussion with Sarah Stevens, who leads the Midwest Carpentries Community. Stevens is a 2021 member of the Executive Council for The Carpentries. She is also a Data Science Facilitator at the University of Wisconsin–Madison, in the Data Science Hub within the Wisconsin Institute for Discovery and American Family Insurance Data Science Institute.

How did you get involved with The Carpentries?
“I did my undergrad at the University of Illinois. My degree was in molecular and cellular biology, but I did a minor in informatics. And when I came to graduate school, I found that none of my classmates had done any coding and they didn’t know computation. And almost all of them had to learn how to do some computational analysis over the course of grad school. So to help support [them], I started a community of practice around helping each other with our computational needs and learning from one another. I was trying to bring people together not just to discuss the biology in our research, but actually the computation in our research, and in doing so I also got connected with The Carpentries community. There’s been an ongoing Carpentries community since long before my time at the University of Wisconsin-Madison. And my advisor recommended ‘maybe you should sign up for instructor training so you can learn how to teach these things better.’”

What are some of the main projects you’ve worked on during your time there, specifically in the Midwest?
“I’ve been trying to bring together researchers in the Midwest who are either running Carpentries communities of their own or want to get started with Carpentries communities. We’ve been hosting a monthly call to bring those people together to help each other, similar to the community of practice I started in grad school. I’d say probably instructor training is one of the things that I find the most useful and interesting in The Carpentries. I think it’s really cool to talk to other instructors about how to teach, and how to teach using evidence-based research, and how to teach computational skills and learn from one another.”

What are some of the skills that people develop in Carpentries workshops?
“They [the learners] come to learn R, Python, the Unix shell, and Git, but what I really want them to get is a foundation where they believe that they can learn more. I feel like a lot of people come to our workshops feeling like computing and technology is not for them. Maybe they’ve even had bad experiences trying to learn coding in the past. What I really want people to learn and come away with from our workshops is that they can learn this.”

What has been different about doing Carpentries-related activities specifically during the pandemic?
“Moving online has its own challenges. Being a part of a community of instructors, who are also all dealing with this transition to online at the same time, I got to learn a lot from what other people did and how it worked for them. So, as a community, we were able to share tips and tricks and best practices for moving online and learn from one another. That’s really one of the things I love most about The Carpentries community is being able to benefit from other instructors’ experiences.”

“I will say the worst part about moving online is that while I totally respect folks not turning on their video, it’s a little less rewarding to teach to a screen. You do get feedback, like the sticky note feedback we collect in Google forms and people typing in chat, ‘this was a great workshop.’ But you don’t get to see them actually overcome that boundary of ‘I didn’t think I could do it—and I can do it now or this makes sense to me suddenly.’ And so it’s a little less rewarding to teach online, I will say, but I do feel like it’s been a good learning experience of having to pivot and practice these skills in a different way of teaching and checking in with learners.”

You proposed the Midwest Carpentries Community project for the MBDH CDE program—what did you perceive as the need for that?
“I’m seeing communities start to form in other places across the world. And I think it’s really great for creating new Carpentry communities and teaching these important skills across the globe. I was running into people from other institutions who had interacted with The Carpentries in some way. I wanted to be able to share my experience with The Carpentries like at UW–Madison; what works well with the UW–Madison Carpentries community, with other folks in the Midwest and working to learn from them as well.”

“So, what works well at Illinois, what are they doing that we can learn from? Are they creating new workshops that we too could use? That’s where I saw the need—I wanted to be able to support these new instructors and new communities that we’re developing in the Midwest, and learn from the existing communities that have been teaching Carpentries workshops for a while and doing new and interesting things.”

What would you say to someone new to The Carpentries world about why it’s valuable to participate in the community beyond attending a workshop?
In addition to offering the teaching of various skills, Stevens says “I think it’s really valuable. There’s so many things you get from it, you learn a lot about building an inclusive community as that is a big part of the Carpentry community.”

She adds, “I see a lot of networking—developing an interpersonal network and being able to find employment in the future is also a benefit of this, but you make connections with other institutions and learn from them and other organizations across the globe, really, and so it’s a great opportunity to learn from others, not just being in the workshop, but observing other people in our community and their activities they’re up to.”

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in our activities, which include a data science student community and the national BD Hubs monthly webinar on data science education and workforce development.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Iowa State University, Indiana University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

Meet the MBDH Fall 2021 science writing and coordination interns

For Fall 2021, the Midwest Big Data Innovation Hub has four new interns joining the team to work on a variety of projects. One intern, Sushma Mahadevaswamy, will be working on project and events coordination. Three others, Raleigh Butler, Erica Joo, and Qining Wang, will be science writers, helping to amplify the many community-led projects in the Hub’s 12-state region. All will learn about the range of activities and communities the MBDH is involved in, and will receive mentoring and have opportunities for career development.

The MBDH has a number of events planned for Fall 2021, including ongoing webinar series (Water Data Forum, Data Science Student Groups), a new research development series called the Collaboration Cafe, and a two-day Regional Community Meeting, open to all.

To help develop these events, and do outreach to our student community, Sushma Mahadevaswamy has joined the MBDH team as a project coordination and events intern. She’s currently pursuing her master’s degree in information management at UIUC. Previously, she was a software developer for 3 years at Cisco. Hailing from the silicon city of India, she’s well versed in cloud computing, problem solving and algorithms (she knows her Big O’s), and software development.

While working at Cisco, she handled application security, across six cross-geographical teams based in India and the USA, through collaboration and communication. She loves to organize events to motivate her team. She’s a vibrant individual, who was an MC for various global events. Her strengths lie in development as well as efficient management of projects.

Her goal is to bridge the gap between technical and business aspects of product/project management. She’s excited to put her skill set to good use at MBDH. She will be engaging with the student community to organize knowledge-sharing events that will enrich the data science community.

In her spare time, she usually paints or goes on a hike. She’s done three Himalayan treks and hopes to ascent Mt. Everest one day. She also believes in giving back to the society and she regularly volunteers to teach underprivileged children. Her favorite quote is, “Make a difference, not a living.”

With programmatic activities ranging from the MBDH’s partnerships in its Community Development and Engagement (CDE) program, to other Priority Area work, exciting new projects in the region, and the events described above, there is a lot for the science-writing interns to draw from. They will be focused on telling the stories of the projects and the people—researchers, students, partners, and collaborators—and how the work they are doing is impacting the Midwest region, the nation, and the world.

Raleigh Butler is one of the three science writers interning at MBDH for the fall semester. Her undergraduate degree was a dual major in Linguistics and French at the University of Tennessee, Knoxville. She recently got her MS degree in UIUC’s Journalism program, graduating summa cum laude. Between the two degrees, she pursued a post-bac, focusing on introductory science courses.

Raleigh views science writing as a wonderful opportunity to combine STEM and the humanities. She aspires to “translate” technical verbiage into phrasing easily understood by the average reader. She emphasizes, “during these times of great scientific developments—not to mention health-related developments—it’s critical that the wider population have an understanding of what’s going on. By providing a reliable source of information that is also more understandable, perhaps we can assist in this education process.” Indeed, people frequently want to learn without necessarily reading a full-length technical article.

She believes that access to easy-to-understand material instead of difficult-to-parse journal articles will reach the population more successfully and wants to do her best on that front. For example, recently, she has been writing about COVID-19.

Raleigh says “I’m extremely excited about this opportunity to begin pursuing my dream job and to learn more about the field.”

Qining Wang (she/her) also joins MBDH this semester as a science-writing intern. Born and raised in China, Qining moved to the USA in 2013 and received her BA degree in chemistry from Rutgers University in 2018. She is now in her fourth year of pursuing a PhD in chemistry at Northwestern University. Co-advised by Prof. Joe Hupp and Prof. Justin Notestein, she synthesizes heterogeneous catalysts supported on metal-organic frameworks and investigates their gas-phase reactivities.

Aside from conducting scientific research, Qining is also conscious of the broader impact of science. She strives to inform the public of the progress in science and technology by making cutting-edge science more accessible to a lay audience. She wants to tell the stories of scientific discoveries and scientists through a curious lens without invoking intimidating equations and jargon. Therefore, in addition to writing, she also explores different approaches to effectively communicate science, such as videos, podcasts, and social media.

Qining says, “there are so many barriers to accessing and understanding science, from the intricate language scientists use to talk about science to the academic publications behind paywalls. As a scientist, I am responsible for removing those barriers.”

Erica Joo (she/her) is the third science-writing intern at MBDH this semester. As a junior at the University of Illinois at Urbana-Champaign, Erica is pursuing her BS degree in Molecular and Cellular Biology with a minor in Journalism. Additionally, she is an undergraduate researcher in Dr. Joe Qiao’s lab, and her research project is focused on meiotic checkpoint pathways and investigating certain enzymes involved with DNA repair pathways.

While being involved on the frontlines as a healthcare worker during the pandemic, she noticed a disparity in information about COVID-19, especially with the perpetuation of misinformation across the media. Erica recalls. “I felt that I wanted to be a part of the change that the world desperately needed at the time.” Combining her two passions, science and writing stories, was a catalyst in the evolution of her life. Erica has a strong interest in social issues and science research, and as a biology student herself, she understands the difficulty in understanding science at face value. “Navigating from one discipline to the other, I’m ultimately trying to create a common ground in my versatility.”

She aspires to take her experiences and academic background to not only help readers make sense of the science behind various types of research but to also address questions that the general public may wonder about and make it easily accessible. With high hopes and ambitions, Erica imparts, “from my experience in both fields, my job is always to write effectively so that audiences without extensive knowledge on a particular field can also learn and develop their own thoughts.”

MBDH Executive Director John MacMullen said, “We’re excited to have such a talented group of interns who bring a diverse set of skills and experiences to the Hub this semester. We look forward to seeing the work they produce and having the community engage with them on the wide range of data science activities happening across the region.”

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in our activities, which include a data science student community.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Iowa State University, Indiana University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

Researcher Profile: Aditya Kulkarni

By Raleigh Butler

On May 19, 2021, five researchers joined the COVID Information Commons (CIC) “Lightning Talks” webinar hosted by the National Science Foundation-funded Big Data Innovation Hubs. Each speaker was involved in COVID-19 research and gave a brief presentation on their project.

One of the presenters, Minnesota high school student Aditya Kulkarni, was almost indistinguishable from the other researchers in terms of his preparation and professional presentation.

Kulkarni is currently about to go into his senior year. He has been taking college classes since seventh grade. He started off just taking dual-enrollment math courses and now takes all of his classes at the University of Minnesota.

Though he has always been fascinated with programming and data science, the COVID-19 pandemic spurred Kulkarni on to explore data related to that specific issue. He submitted a paper entitled “Human Mobility Patterns Linked to COVID-19 Prone Locations” to the COVID Information Commons (CIC) Student Paper Challenge. His paper won third place, and he was invited to present the research alongside his more senior colleagues on the CIC webinar.

Needless to say, all this is an impressive feat, so I sat down and spoke with him a bit about his interests, school life, and hopes for the future.

How has taking college courses so early in your school career affected you? Do you think you’re more driven or serious than normal?
“Yeah, I think it’s actually been pretty helpful, because . . . I do feel like there’s some differences between taking high school classes and college classes. I mean, high school classes are like, fine—you have your different social groups, but with college, you’re also able to get exposed [to] the cutting-edge research that’s happening, [in] these fields that you’re learning about.”

Do you do dual-enrollment classes where the professors come to your high school, or do you go to the university?
“In this term, the high school isn’t really involved. I’m basically just like a college student traveling to campus coming back later in the evenings. And I’m still in the class with the other college students interacting with them, doing projects.”

Yeah, I was going to ask, if you were socially involved with college students; if you’re more mature than most people your age, then that would be something to appreciate.
“Yeah, and . . . it’s not like people even treat me weird. I just blend in with everyone else, just participating in things.”

Did you take any programming classes? And if so, like, did you enjoy them?
Kulkarni stated that his school offered a small programming course. “It was called Hour of Code. So there was a website, and we would have around an hour a day for one week. And we would just spend [time] seeing how to develop code, mainly block code. But at that time, it was kind of interesting to me seeing how I was able to create things just by dragging and dropping things. And yeah, it was pretty interesting. And [I] think it was mainly animation based . . . just making things move on the screen doing simple tasks. But from there, I think I saw the power and the capabilities that were there with coding.”

Do you and your peers participate in datathons, hackathons, and other kinds of science and computing activities?
This coming term, Kulkarni said, “the high school [won’t be] really involved,” but in the past, he started a STEM-related club at his high school and was very active in terms of connecting fellow students with professionals. Students from the club also team up to participate in hackathons and datathons. Kulkarni says he finds these competitions interesting “especially if there’s a sponsor, I’ll do something related to what they’re doing.”

For the CIC Student Paper Challenge, Kulkarni focused on a data set obtained from SafeGraph.com. This site tracks device movement (no personal information tied in) across the U.S. Kulkarni used the available information to create related datasets and compare similar locations in Minnesota. For instance, he found 15 public places with June-July outbreaks and 15 places with no June-July outbreaks. His results show that longer-duration visits to an establishment are associated with COVID outbreaks. He received feedback and mentoring from Midwest Big Data Hub co-PI Shashi Shekhar, a professor of computer science at the University of Minnesota. His final paper is available online in the Columbia University Academic Commons repository.

Are there opportunities for you to build on this specific project that you submitted?
Currently, Kulkarni is pursuing “another direction of economic metrics.” “Even though it’s a human mobility data set, seeing the economic aspect in terms of socioeconomic groups, how [those people] were affected during the pandemic, and then their mobility in terms of that.”

So, I get the feeling you’re wanting to officially pursue computer science and data. If you had to choose a specific subfield to go into, what would you choose?
“I think I would actually go [into] data science. I think that’s the main thing. Then AI, with data sets, just seeing what are the possibilities to explore.” He went on to emphasize how technology could be of use in terms of bettering health situations and other human issues, “there’s just so much [COVID] data going further beyond into the predictive capabilities that can just be done with this much data. Because if there’s a future pandemic, which even though happens pretty rarely, if it happens, then maybe there’s something that we can learn from this one and apply it to the future.”

So, basically, what you like about research is the ability to help and provide insight into what can make the world a better place; is that how you would say it?
“Just because I can, through this mode . . . I can help the community as . . . a broader world or even as a small, small subsection. That’s a way where I can contribute to society, I guess.”

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led activities. The MBDH also has a data science student community, with a monthly webinar. Learn more about the COVID Information Commons webinar series and community.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Iowa State University, Indiana University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

Integrating Regional Water Quality Data with the Upper Mississippi Information System (UMIS) Project

By KJ Naum

Photo of the Mississippi river near Fort Snelling & Minnehaha, Minnesota
Photo by Mathew Benoit on Unsplash

As the Mississippi River flows from its source in northern Minnesota to its mouth on the Louisiana coast, its waters cross the boundaries of ten states, picking up a lot along the way. This includes nutrients such as nitrogen and phosphorous, which contribute to “dead zones” where the river drains into the Gulf of Mexico. Dead zones occur when too much nutrient pollution causes algae to grow excessively. When they die, the decaying cells consume oxygen, depriving other life forms of the oxygen they need to survive. This condition, known as hypoxia, can lead to the devastation of entire ecosystems if left unchecked.

There’s not a lot of mystery about what causes nutrient pollution. Widespread agricultural practices in the Midwest’s Corn Belt encourage the plentiful use of nutrient-based fertilizer, so much so that much of it washes away even before the crops can use it. But trying to understand how it’s happening remains a challenge. The data on the river is as free-flowing as the water itself—and often just as slippery.

“Lots of people are doing water quality monitoring, and there are maybe hundreds or thousands of water quality parameters that can be tracked,” says Chris Jones. Jones is a research engineer at the University of Iowa, who works with the Upper Mississippi Information System (UMIS), an online platform that aims to make this deluge of data more accessible and manageable. Jones also works on the Iowa Water Quality Information System (IWQIS), an ongoing effort that informs this newer project. IWQIS makes real-time water quality data from within the state of Iowa available to researchers and the general public. However, the UMIS team is thinking bigger than that. Jones notes, “Watershed boundaries are different from political boundaries. We have to think within their context if we’re going to improve water quality, and so our vision was to bring the IWQIS concept to a larger geographical area.” The Upper Mississippi Information System aims to do exactly that. A team of researchers at the University of Iowa, Iowa State University, and the University of Illinois at Urbana-Champaign are working together on building the UMIS platform and wrangling the data for public consumption. The online platform provides one-stop access to independently managed data streams—both real-time and historical.

The initial site is live, and Jones characterizes it as about halfway complete. The biggest task for the team is to acquire still more data through building partnerships with other organizations. “We’re mainly focused on nutrients like nitrogen and phosphorus right now, but some other data will likely be available,” Jones says. “We had to start somewhere. This is a good place to start because it’s what many people are most interested in.”

Despite the widespread interest, combating nutrient pollution in the Midwest is an uphill battle. Unlike other U.S. water systems like the Chesapeake Bay, the states of the Mississippi basin have chosen not to regulate nutrient reduction, thanks to a powerful agricultural lobby that is opposed to such mandates. Instead, the state governments each try to promote and incentivize more widespread adoption of practices that reduce nutrient flow. 

Jones, however, is skeptical that meaningful change can happen without collaboration. “The states will have to work in concert in order to have any meaningful impact on solving hypoxia,” he says. “That means giving scientists access to a lot of data. Having access to sound scientific data is critical for making policy.”

Individuals and organizations that are interested in the UMIS project can sign up to be a data partner or beta user via the UMIS website, or contact the team via email. Jones and the team are hopeful that UMIS will help drive change at the scale that is needed. “Nutrient pollution is one of the wicked problems, along with climate change, but we know there are solutions out there,” he says. “Solving this is a sociological and economic issue. Hopefully, UMIS can be a tool for policymakers to do just that.”


Get involved

Contact the Midwest Big Data Innovation Hub to suggest other projects we should highlight on this blog, or to participate in any of our community-led Priority Areas.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Indiana University, Iowa State University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing data science collaborations in the 12-state Midwest region. Learn more about the NSF Big Data Innovation Hubs community.

Big data aids PPE research

By Barbara Jewett

This story is part of a series on coronavirus research in the Midwest region

Many researchers in the Midwest received awards from the National Science Foundation last year for developing novel masks and other personal protective equipment.

One of those researchers, Leonardo P. Chamarro, an associate professor in the Department of Mechanical Engineering at the University of Illinois at Urbana-Champaign, was awarded a special one-year, $200,000 RAPID grant to design a 3D-printable medical mask inspired by the nasal structures of animals. Working with Associate Professor Sunghwan Jung at Cornell University and Assistant Professor Saikat Basu at South Dakota State University, the team hopes their design addresses mask shortages and improves existing face protection by providing an open-source template for use with 3D printers.

The team captured small aerosol droplets that can carry viruses from inhaled air using a combination of copper-based filters and twisted periodic thermal gradients induced by spiral copper wires that mimic nasal pathways. The aerosol capture was articulated by modulating the dynamics of flow structures in the convoluted geometry (a vortex trap) and by thermophoresis action along the respirator’s internal walls (a thermal trap). Cyclic cold/hot temperature changes on the walls, along with ionic activity from the copper material, is used to inactivate the trapped viruses.

Dr. Chamorro took time away from his research to answer five questions about his COVID-19 research:

What’s the problem you’re trying to solve, and how is your team addressing it?
We are focused on exploring ways to mitigate the COVID-19 pandemic transmission and understand the role of turbulence [in virus spread]. In particular, we are collaborating with Sunny Jung at Cornell University and Saikat Basu at South Dakota State University in the development of a novel bio-inspired protective mask based on thermal and vortex traps. [We are also collaborating] with researchers at Purdue, Rensselaer Polytechnic Institute, the National Autonomous University of Mexico, and Tsinghua University in Beijing in the development of an autonomous robot for scanning, data mining, and disinfection. [In another project] we are also collaborating with a team at Northwestern on the description of contaminated droplet dynamics. My team uses theory, state-of-the-art flow diagnostics tools at various scales, and in-house analysis tools.

What’s changed since this project started last year?
It is a question that has many layers. The more we learn, the more we realize that several fundamental gaps need to be addressed to prepare for the next pandemic. Changes have occurred at various levels.

What data are you working with? Are there data challenges you’re dealing with? Are you using public data resources? Are you producing data that others are using?
We focus on the dynamics of droplets and aerosols and the interaction with closed domains at a range of scales. It requires performing experiments, capturing three-dimensional particle and flow dynamics, and, consequently, we produce our data. High-fidelity tracking of many particles and flow filed simultaneously in space and time is not trivial; however, my team has developed the needed technology to face those challenges.

Is your team seeking collaborators, subject matter experts, or other resources that you’d like to put a call out for?
Yes, we would very much like to collaborate at the fundamental and applied levels on various pressing problems, including, but not limited to, the role of turbulence across scales, ventilation, and boundary conditions.

Where can people learn more about your progress?
So far, we have contributed to two peer-reviewed papers. One paper in Extreme Mechanics Letters on the performance of various fabrics in homemade masks and another paper is in advanced stages of review in PNAS. My group also gave four technical talks on COVID research at the last American Physical Society in November, and we are updating our webpage to share recent findings.

Other PPE Projects
There are numerous other PPE projects in the Midwest that received Rapid Response Research grants. Here are a few of them:

  • Safely returning to using reusable equipment, including some PPE, is the focus of an award to Andrea Hicks, an assistant professor of civil and environmental engineering at the University of Wisconsin–Madison. You can read more about her work here.
  • Producing masks that capture and neutralize viral pathogens by adapting a decade of work developing a proprietary composite nanofiber material for water filtration is the focus of collaborators David Cwiertny, a professor of civil and environmental engineering and director of the Center for Health Effects of Environmental Contamination at the University of Iowa, and Nosang Myung, the Keating Crawford Endowed Professor in Chemical and Biomolecular Engineering at Notre Dame. Cwiertny received an award for this research project and Myung also received an award. You can read more about their work here and also here.
  • Developing smart face masks embedded with battery-free sensors to assess proper fit and monitor health is the focus of the award received by Northwestern’s Josiah Hester, an assistant professor of electrical and computer engineering. You can read about his work here.
  • Developing a new self-sanitizing medical face mask that deactivates viruses on contact earned an award for Northwestern materials science professor Jiaxing Huang. You can read about his work here.
  • Exploring coating the surface of PPE with copper and zinc oxide nanoparticles to limit the spread of viral particles is the subject of an award for Robert DeLong, an associate professor in the Nanotechnology Innovation Center at Kansas State.

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other projects we should include here, or to participate in any of our community-led Priority Areas.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, the University of Michigan, the University of Minnesota, Iowa State University, Indiana University, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the NSF Big Data Hubs community.

MBDH Learning Innovation Fellows program – first cohort projects

The Midwest Big Data Innovation Hub Learning Innovation Fellows Program, housed at the University of Michigan School for Environment and Sustainability, enables teams to form for work toward better understanding of the intersections of the Hub’s “Cyberinfrastructure and Data Sharing” and “Data Science Education and Workforce Development” themes.

Our fellows work with faculty and teaching staff to create innovative interactive data analysis activities that can nest within sustainability science case studies. They design, prototype, and pilot these features in classrooms within the MBDH network. The program leverages talent and resources from two existing, open-source science learning environments. Gala (www.learngala.com) is a community-based, responsively designed sustainability science learning environment. Quantitative Undergraduate Biology Education and Synthesis (QUBESHub, or Qu) is a virtual center for faculty development and open educational resource sharing (https://qubeshub.org) that has had long-term support from NSF, formalizing and professionalizing open educational resources.

Through a series of virtual “Networkshops,” we connect undergraduate data science majors, graduate/professional students, faculty, and professionals. We can thus be inclusive, incorporating into classrooms problem-driven, data-rich material that speaks to lived infrastructural and environmental challenges from a range of communities across our region, and beyond. The team includes the following:

Leadership—

Rebecca Hardin (PI) is an anthropologist and Associate Professor at the University of Michigan School for Environment and Sustainability (UMSEAS), where she leads collaborations on the open-source, open-access learning platform Gala (www.learngala.com) and research group on Digital Justice. Rebecca also coordinates the Environmental Justice Field of Specialization and related Certificate program at UMSEAS.



Ann E. Russell (Co-PI) is an ecosystems ecologist, with special expertise in the biogeochemistry of tropical ecosystems. She is an Associate Adjunct Professor in the Department of Natural Resource Ecology and Management at Iowa State University, and PI of the NSF Research Collaborative network ALIVE: Authentic Learning in Virtual Environments.





M. Drew Lamar (Co-PI) is a mathematician and Associate Professor of Biology at William & Mary. His teaching and research are highly interdisciplinary in nature, using techniques and concepts from mathematics, statistics, biology, and computational sciences. Drew is Co-PI and Director of Cyberinfrastructure for the Quantitative Undergraduate Biology Education and Synthesis (QUBES) virtual center, with an interest and passion in open-source software development, quantitative biology education, and development of education gateways.

Ed Waisanen (Program Manager) is Program and Platform Lead for Gala (learngala.com). He has a master’s degree in Natural Resources and Environment from the University of Michigan, with a focus in Environmental Informatics and a background in multimedia production. Ed is focused on developing tools and communities that emphasize curation, open exchange, and narrative approaches to deepen learning.





Teams—

Data Learning for Restoration Ecology

Kyra Hull (Fellow) is a native of Grand Rapids, Michigan, and a first-year graduate student at Grand Valley State University, studying Biostatistics. Kyra is working on the following case about forest restoration, which is bilingual (Spanish and English versions): https://www.learngala.com/cases/a3224235-cdc0-44fc-a98b-46735dfef6c9




Karen Holl (Faculty Advisor) is a Professor of Environmental Studies at the University of California, Santa Cruz. Her research focuses on understanding how local and landscape-scale processes affect ecosystem recovery from human disturbance and using this information to restore damaged ecosystems. She advises numerous public and private agencies on land management and restoration; recently, she has been working to improve outcomes of the effort of the many large-scale tree-growing campaigns.




Data Learning to Address Groundwater Contamination

Saba Ibraheem (Fellow) is a second-year Health Informatics student at the University of Michigan, focusing on data analytics and research in health care. Saba is working on the following case, which is bilingual (English and French versions): https://www.learngala.com/cases/dioxane-plume





Rita Loch-Caruso (Faculty Advisor) is a toxicologist in the Department of Environmental Health Sciences at the University of Michigan School of Public Health, with a research focus in female reproductive toxicology and, in particular, mechanisms of toxicity related to adverse pregnancy outcomes such as premature birth.





Alan Burton (Faculty Advisor) is a Professor at the School for Environment and Sustainability and the Department of Earth and Environmental Sciences at the University of Michigan. His research focuses on sediment and stormwater contaminants and understanding contaminant bioavailability processes, effects, and ecological risk at multiple trophic levels. He is also a specialist in ranking stressor importance in human-dominated watersheds and coastal areas.





Data Learning in Livestock Ecologies

Daniel Iddrisu (Fellow) is a second-year student in Masters in International and Regional Studies, with a specialization in Africa, at the University of Michigan. He earned a BA degree in Integrated Community Development from the University for Development Studies, Tamale, Ghana. His research focuses on health, development, gender, and environmental health. The case he is working on takes place on the Greek Island of Naxos, but comprises skills for modeling and analyzing human/livestock interactions more broadly: https://www.learngala.com/cases/livestock-grazing

Johannes Foufopoulos (Faculty Advisor) is an Associate Professor at University of Michigan’s School for Environment and Sustainability, who focuses his lab research on fundamental conservation biology questions and on issues related to the ecology and evolution of infectious diseases. Major research projects examine how habitat fragmentation, invasive organisms, and global climate change result in species extinction.





Data Learning on Safari

Rahul Agrawal Bejarano (Fellow) has a background in computer science and he is currently working on a master’s degree at the University of Michigan School of Environment and Sustainability, with a concentration in Sustainable Systems. Rahul uses data from a diverse range of sources to shed light on today’s environmental challenges and develop innovative solutions, and is working on identifying climate-related vulnerabilities to our supply chains. He is working on this case, about the interactions of various wildlife species in the Serengeti: https://www.learngala.com/magic_link?key=oOTYOXyDRpmY_yM4AFlnXQ


Charles Willis (Faculty Advisor) is a Teaching Assistant Professor, Biology Teaching and Learning at the University of Minnesota. He is currently interested in the research and development of pedagogy practices for non-major biology students. In particular, he is focused on studying student-student and instructor-student feedback in online spaces. His research is also concerned with understanding how changing environments shape plant diversity on both evolutionary and ecological time scales. Currently, he is focused on using historical specimen data to study how historic climate change (over the past century) has impacted plant phenology and diversity across North America.

Jeffrey A. Klemens (Faculty Advisor) is an Assistant Professor of Biology at Thomas Jefferson University, where he serves as program director for the undergraduate biology curriculum. His current research activities are focused on the use of agent-based models to describe habitat use by organisms in the urban environment and the role of active learning in science education, particularly the use of systems thinking and other modeling techniques to improve student understanding of complex phenomena.




Data Learning in Detroit’s Eastern Market

Ghalia Ezzedine (Fellow) is a second-year master’s student studying Health Informatics. She is interested in leveraging data and digital tools to improve population health. In her free time, she likes to try new recipes, work out, and occasionally jump off a bridge or airplane. She chose this case study because of her interest in nutrition, and the shift in foods available at this iconic marketplace: https://www.learngala.com/cases/2b92db37-de87-4321-a531-510dea225189



Josh Newell (Faculty Advisor) is an Associate Professor in the School for Environment and Sustainability at the University of Michigan. He is a broadly trained human-environment geographer, whose research focuses on questions related to urban sustainability, resource consumption, and environmental and social justice. His research approach is often multiscalar and integrative and, in addition to theory and method found in geography and urban planning, he draws upon principles and tools of industrial ecology and spatial analysis.


Profile: Crystal Lu

Nitrogen reduction in the Upper Mississippi River Basin

By Katie Naum

As extreme climate events become more frequent, some of their impact is visible—like the derecho that tore through Iowa in August 2020, leaving a wake of destruction in its path. Other impacts—including nutrient pollution in water systems—are less understood. In what ways will climate change affect the world around us? How can we use data science to better understand and adapt to the impact of climate extremes? 

Chaoqun (Crystal) Lu portrait
Chaoqun (Crystal) Lu

Chaoqun (Crystal) Lu is a quantitative ecosystem ecologist and assistant professor at Iowa State University, and a collaborator of the Midwest Big Data Innovation Hub. Her work focuses on water quality modeling, including the impact of extreme climate events and human activities on nutrient pollution. Her recent NSF CAREER award is titled “Understanding the dynamics and predictability of land-to-aquatic nitrogen loading under climate extremes by combining deep learning with process-based modeling”. The project will bridge the gaps between science and practice, sharing the most current knowledge of Earth system modeling to the public and making the complex concept of watershed management more concrete for the next generation of scientists, land managers, policy makers, and voters.

I spoke with Lu recently via Zoom to learn more about her work with water quality data. The following conversation has been edited and condensed for clarity.

Why is it important to study water quality here and now?

In the United States, nearly 60% of coastal rivers and bays have been degraded by nutrient pollution. Here in the Midwest, people have invested a lot of money and effort over the years to reduce nitrogen pollution. At the same time, climate-driven variations may far outweigh the effects of these nitrogen reduction practices. Increasing summer humidity, more frequent heavy rainfalls, and extreme floods have become a new normal in the central United States over the past few decades. There are a lot of unknowns about how extreme climate events have affected nitrogen leaching from soil and nitrogen loading through tiles, streams and rivers. Lots of data exist, though! 

Policymakers need science-based management suggestions. As a researcher, I would like to benchmark my model with long-term measurements of water quality, and scale up from site-specific measurements to a broader region such as the Upper Mississippi River Basin. If we can figure out how to reduce nitrogen pollution here in the Midwest, the solution we come up with will be very likely to be effective elsewhere. 

Can you tell readers more about the focus of your work, including your recent NSF CAREER award? (Congrats!)

I’m engaged in water quality modeling projects—studying, for example, the impact of nitrogen reduction practices on water quality. Our research team uses mathematical models to represent the physical processes involved in connected systems—the flow of water, the amount of nutrients used by plants or lost to runoff. We also quantify how climate change, land uses, and human management practices could affect nitrogen loading, and assess the effectiveness of nitrogen reduction practices in cleaning water.

The focus of this CAREER award is on how extreme climate events may affect nitrogen loading. My team wants to see how sensitive nitrogen leaching and loading are to events like these, which are increasing in the Midwest. We’re integrating machine learning approaches with a traditional process-based hydroecological model, using a large volume of water quality monitoring data that drains from various sized watersheds in the upper Mississippi–Ohio river basin. I want the key processes represented by traditional process-based models to be kept for water quality prediction, and at the same time improve the models’ outputs with “big data” and machine learning. Our integrated model uses data on water quality, weather, land cover, and human management practices, to better understand whether and where there are nitrogen pollution hotspots in the region. 

What are some of the challenges in working with water data? What are the insights you hope to gain from your research?

One important challenge is just the enormous amount of variation in the data. If you look at a time series for hydrological flow, you see huge variation in the relationship between flow and nitrogen concentration. The challenge we have is to quantify how varied and why. Why do some small watersheds have larger variations than others? Why are some regions more sensitive to climate than others? Is this pattern we’re seeing caused by a specific event, or the legacy of many such events over time? We want to get the whole picture on nitrogen dynamics, from vegetation to soil to water to rivers, from small to large watersheds, at daily time steps, using modeling to recreate such processes.

In our work under this award, we’re planning to include more small watersheds and high frequency data sets. I’m looking forward to new insights from such data analysis. There is so much data over the past few decades to work with, and the technology of water quality monitoring has really improved.

How does deep learning contribute to watershed management?

Deep learning has been transformative for hydrological science and earth system science, yet few studies have used it to digest the big data of water quality monitoring. Meanwhile, high-frequency water quality monitoring data are increasingly available, especially in smaller watersheds and at shorter time scales. This brings new opportunities to test the relationship between flow and nitrogen concentration in response to climate extreme events. All of this motivates me.

Do you consider yourself a data scientist as well as an ecologist? 

I consider myself an ecosystem ecologist, with data science skills. The question I want to find answers to are mostly ecological questions. Sustainability science, biogeochemical cycles, climate variability, natural and human drivers—these are all ecology questions. I say this even though I received training in ecosystem modeling and geospatial analysis for many years—but I consider these tools, the same way I consider machine learning a tool. I always keep my eyes open for tools that can help answer the ecological questions I care about. I tell my students this too: even if their degree or job title says ‘ecosystem modeler,’ I always hope they will step back and see the big picture.

How might interested stakeholders learn more or get involved?

We’ll be developing a project webpage where we will release research findings, future publications, and other relevant materials. Our results will be presented and disseminated to interested stakeholders through our collaborating institutions—not only to academic investigators, but also to the general public, because they are the people who actually make decisions on managing the land and improving the environment. 

This is a very multidisciplinary project, and others may have different ways of thinking about and analyzing the problem that we haven’t considered. We would love to hear from other researchers interested in analyzing the problem from another angle. We are also working actively to seek collaborators and more grants to leverage this project, putting available data sources online to allow easy access.

What do you love most about your research?

Being a modeler is a very precious role. Through multi-scale modeling, we try to connect a lot of different people—field scientists, computational experts, engineers, economists, stakeholders, and policy makers—who can work together to understand and build a more sustainable world for us to live in. This provides a lot of opportunity to collaborate with people in different fields. As a quantitative ecosystem ecologist and ecosystem modeler, I can serve as a bridge between field scientists, extrapolating their findings, and decision makers, who want to see and understand ecological outcomes. The work is really useful and applicable in real life. I enjoy the endless possibilities and the feeling that my research is useful and applicable for our world.


Katie Naum writes on science & technology, climate change, and culture. Follow her @naumstrosity and read more at katienaum.com.


Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led Priority Areas.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, the University of Michigan, the University of Minnesota, Iowa State University, Indiana University, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the NSF Big Data Hubs community.

Midwest water researchers explore COVID-19 in wastewater

This story is part of a series on coronavirus research in the Midwest region

Researchers in the Midwest are looking in a surprising place for clues about the COVID-19 pandemic: wastewater.

Because so many people who are infected with COVID-19 are asymptomatic, scientists are interested in measuring the prevalence of the SARS-CoV-2 coronavirus in wastewater as a way to understand the population-level spread of the virus in communities. In-person testing can be problematic for a variety of reasons, so researchers are interested in alternatives.

Minnesota Public Radio interviewed one research group that is exploring new ways to explore coronavirus spread without directly testing people. “We’ve decided that one of the easiest ways to do that would be to noninvasively kind of scan the population for the presence of the virus,” University of Minnesota professor Glenn Simmons Jr. said. “And one easy way of doing that would be to look at the wastewater.”

Simmons, along with his collaborator Richard Melvin at UMN Duluth, are testing samples collected from wastewater treatment facilities for the presence of genetic material from the SARS-CoV-2 virus. Other researchers in the Midwest are working on similar sample collection, data analysis, and developing new tools and resources.

One resource under development is a publicly accessible, web-based Wastewater Pathogen Tracking Dashboard (WPTD). Dr. Rachel Spurbeck, research scientist at the non-profit Battelle Memorial Institute in Columbus Ohio, leads the creation of this project.

“The WPTD program is tracking SARS-CoV-2 and other viral pathogens found in the wastewater of four different locations in Toledo, Ohio over time and comparing the sequencing results to the public health and demographic data for these sites”, Spurbeck said. “This comparison will be used to generate risk models for COVID-19 spread in the community as well as other viruses present. We will also be identifying mutations in SARS-CoV-2 which will not only tell us that the virus is in the communities being studied, but also if there are any differences in the virus that could enable identification of how the virus is affecting the population and where the virus came from geographically.”

The data collected will be entered into the Wastewater Pathogen Tracking Dashboard for use by local public health officials to aid in identifying where contact tracing will be most useful. The project is funded by the National Science Foundation (NSF).

Since March 2020, the NSF has made hundreds of new awards focused on COVID-19 research to help address the pandemic. The NSF and the four regional Big Data Innovation Hubs collaborated on the creation of the COVID Information Commons resource to bring together information on these projects. Researchers can use the site to help find tools and resources, and to develop collaborations with other researchers.

Other wastewater tracking projects in the Midwest include two led by Kyle Bibby, Associate Professor of Engineering at Notre Dame university in Indiana. Bibby is leading an effort to develop methods to monitor for the presence of SARS-CoV-2 in wastewater and to connect these measurements to epidemiology models. Bibby also leads a project to create a national Research Coordination Network (RCN) focused on wastewater surveillance, in collaboration with partners from Howard University, Stanford University, Arizona State University, and the Water Research Foundation.

At the national level, the U.S. Centers for Disease Control and Prevention (CDC) has announced the development of a National Wastewater Surveillance System (NWSS) that collects data from local, state, tribal, and territorial health departments to supplement the efforts above.

Get involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other projects we should include here, or to participate in any of our community-led Priority Areas.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, the University of Michigan, the University of Minnesota, Iowa State University, Indiana University, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the NSF Big Data Hubs community.

Guest post – Diverse programs from ISU address sustainable cities challenges

By Iowa State University’s Sustainable Cities team

Researchers with the Sustainable Cities team at Iowa State University recognize the difficulty that public officials face in transforming vast amounts of climate and energy research into contextualized public policy. In attempting to address this critical issue, the team’s mission goes beyond the creation of new climate analysis tools to also investigate new methods for integrating communities into the discourse of data creation and energy conservation. To accomplish this agenda, our team engages in various research avenues that range from the creation of new spatial-data tools to enabling community youth activism. Here are just a few highlights of the team’s most recent achievements:

Sustainable Cities’ team leader Ulrike Passe, associate professor of architecture, presented our hybrid physics data modeling framework at the National Science Foundation-sponsored Research Coordination Networking (RCN) workshop held at Carnegie Mellon University on May 17, 2018. The presentation, which capstones one of the major branches of the Sustainable Cities initiatives, demonstrated the integration of our recently developed thermo-physical data simulator with our research into human energy-use behavior to demonstrate how a more holistic neighborhood energy model could be constructed. This same model was presented by graduate research assistant Himanshu Sharma at the fifth High Performance Building’s Conference on July 9, 2018, at Purdue University.

image from Krejci et al. (2016)

The Community Growers Program, a public-engagement initiative started back in March of 2017, has become another core pillar of the Sustainable Cities group research. Spanning a course of eight weeks, researchers worked with 22 leadership-minded youth in the Baker Chapter of the Boys and Girls Club at Hiatt Middle School in Des Moines, Iowa, to create a community garden based on a methodology of spatial, socio-technical storytelling. Through this process, the youth participants were able to learn more about their community through access to geographic information system (GIS) and spatial mapping tools. Associate English professor Linda Shenk, our community engagement lead, and Mallory Riesberg, a collaborator with the Baker Chapter of the Boys and Girls Club, presented this methodology in a presentation titled, “Fostering the Next Generation of Big Data Scientists and Sustainable City Planners” at The Growing Sustainable Communities Conference in Dubuque, Iowa, on Oct. 4, 2017. Team members Linda Shenk, Passe and Alenka Poplin, assistant professor of community and regional planning, would later be published in the 35th Journal of Interaction Design and Architectures for the inclusion of this work in their entry, titled, Engaging Youth with Pervasive Technologies for Resilient Communities.

Poplin, an established researcher in the field of geo-spatial mapping, also leads a research group that seeks to understand how to better develop feedback loops through innovative user-interfaces. An inquiry into mapping places of emotional power was highlighted in a 2017 paper entry to the second edition of Kartographische Nachrichten on Empirical Cartography Journal, titled, “Mapping Expressed Emotions: Empirical Experiments on Power Places.” More recently, Poplin and her researcher team have begun testing an energy survey game they have developed called E-Footprints. The framework of this game includes the extraction of user-performance data to measure and analyze what learning opportunities may help guide more environmentally efficient decision making. This feedback is then generated back into learning mini-games throughout the game, such that the user gets more “energy savvy” as they play. This project begins field-testing in November 2018.

With a diverse, multifaceted research team of nearly 50 members, the Sustainable Cities group continues to advance the capabilities of communities and cities to think sustainably about a better future.

 

Image reference:

Krejci, C. C., Passe, U., Dorneich, M. C., & Peters, N. (2016), “A Hybrid Simulation Model for Urban Weatherization Programs”, Proceedings of the 2016 Winter Simulation Conference, Arlington, VA, December 11–14. T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, and S. E. Chick, eds. (pdf)

 

Read more about the MBDH’s Smart, Connected, and Resilient Communities initiatives.

Guest post – Data Science Education at Two-Year Colleges

By Matt Fall

Executive Director, Center for Data Science, Lansing Community College

Recently, the American Statistical Association (ASA), with support from the National Science Foundation (NSF), hosted a two-day summit in Washington D.C. to discuss outcomes and curricula for data science programs at two-year colleges. The Two-Year College Data Science Summit (TYCDSS) was intended to help spur the growth of data science programs at these institutions and included representatives from two and four-year institutions, government, and industry.

Sallie Keller (Virginia Tech) plenary talk (photo: Nicholas Horton)

The summit included several plenary talks discussing the role of two-year colleges in addressing the need for data scientists as well as a brief presentation from a graduate of a community college data science program. The majority of the summit, however, was devoted to a series of working sessions where the participants discussed ideal outcomes and competencies for three categories of students:

  • Category 1: students intending to complete an Associate’s degree and begin working
  • Category 2: students intending to earn an Associate’s degree and transfer to a 4-year program
  • Category 3: students seeking a certificate

The working discussions provided an opportunity for the summit participants to discuss what was expected and feasible for a student from each category to complete. The discussions were captured by a designated writing group and there will be a forthcoming write-up summarizing the recommendations of the summit participants with guidelines for two-year college data science programs.

This summit was particularly timely for my colleagues at Lansing Community College (LCC) as we have recently begun development of a data science program. Prior to the summit, participants were provided access to a list of resources that included relevant research, reports from related workshops, and sample syllabi. Of particular interest to us, as we design the layout of our program, were the Park City Math Institute’s Curriculum Guidelines for Undergraduate Programs in Data Science (2016) [PDF], the Oceans of Data Profile of the Data Practitioner (2016), and the Oceans of Data workshop report on Building Global Interest in Data Literacy (2016). The resources provided, candid discussions with other two-year colleges regarding their programs, and the discussions about realistic competency expectations were also of interest and informative to our program design.

The intent of the TYCDSS directly supports the MBDH’s priority area of interest in data science, education and workforce development. Two-year colleges provide higher education accessibility to many students who could not or would not otherwise pursue an advanced degree. An increasing number of these schools are offering certificate and Associate’s degree programs in data science and analytics to support growing workforce demand. Growth in these types of programs should naturally lead to an increase in data competency, enrollment in university programs, and larger hiring pools for data science based careers.

Related information:

Guest post – URSSI: Conceptualizing a US Research Software Sustainability Institute

First URSSI workshop attendees (Credit: Mike Hucka)

Contributed by Daniel S. KatzJeff CarverSandra GesingKarthik RamNic Weber

 

The NSF-funded conceptualization of a US Research Software Sustainability Institute (URSSI) is making the case for and planning a possible institute to improve science and engineering research by supporting the development and sustainability of research software in the US.

Research software is essential to progress in the sciences, engineering, humanities, and all other fields. In many fields, research software is produced within academia, by academics who range in experience and status from students and postdocs to staff members and faculty. Although much research software is developed in academia, important components are also developed in national laboratories and industry. Wherever research software is created and maintained, it can be open source (most likely in academia and national laboratories) or commercial/closed source (most likely in industry, although industry also produces and contributes to open source.)

The open source movement has created a tremendous variety of software, including software used for research and software produced in academia. This plethora of solutions is not easy for researchers to find and use out-of-the-box. Standards and a platform for categorizing software for communities are lacking, which often leads to novel developments rather than reuse of solutions. Three primary classes of concern are pervasive across research software in all research disciplines and have stymied research software from achieving maximum impact:

  • Functioning of the individual and team: issues such as training and education, ensuring appropriate credit for software development, enabling publication pathways for research software including novel methods beyond “classical” academic publications, fostering satisfactory and rewarding career paths for people who develop and maintain software, increasing the participation of underrepresented groups in software engineering, and creating and sustaining pipelines of diverse developers.
  • Functioning of the research software: supporting sustainability of the software; growing community, evolving governance, and developing relationships between organizations, both academic and industrial; fostering both testing and reproducibility, supporting new models and developments (for example, agile web frameworks, software as a service), and supporting contributions of transient contributors (for example, students).
  • Functioning of the research field itself: growing communities around research software and disparate user requirements, avoiding siloed developments, cataloging extant and necessary software, disseminating new developments, and training researchers in the usage of software.

The goal of this conceptualization project is to create a roadmap for a URSSI to minimize or at least decrease these types of concerns. To do this, the two aims of the URSSI conceptualization are to:

  1. Bring the research software community together to determine how to address the issues about which we have already learned. In some cases, there are already subcommunities working together on a specific problem, including those that we are part of, but those subcommunities might not be working with the larger community. This leads to a risk of developing solutions that solve one issue but don’t reduce (or might even deepen) other concerns.
  2. Identify additional issues URSSI should address, identify communities for whom these issues are relevant, determine how we should address the issues in coordination with the communities, and determine how to prioritize all the issues in URSSI.

We are not working in a vacuum, but with other like-minded projects. In addition to Better Scientific Software (BSSw) and activities around research facilitators (ACI-REF) in the US, there are two ongoing institutes in science gateways (SGCI) and molecular sciences (MolSSI); a recently completed conceptualization in high energy physics (S2I2-HEP); two other conceptualization projects now underway in geospatial software and fluid dynamics; and a large number of software development and maintenance projects. In the UK, the Software Sustainability Institute (SSI), which has been in operation since 2010, is an inspiration and a potential model for our work.

Given these existing activities, part of our challenge is to define how we will work with these other groups. For example, we might decide that they perform an activity so well that we should point to it, such as the SSI’s software guides. Or we might decide to either duplicate or enhance an activity they do to expand its impact, such as working with the SGCI to offer incubator services to a wider community than just gateway developers. Or we might decide to collaborate with one or more groups, such as on policy campaigns aimed at providing better career paths for research software developers in universities.

We have held one workshop and are planning three more, in addition to a community survey we plan to have out soon, and a set of ethnographic studies of specific projects. We are communicating through our website, a series of newsletters, and a community discussion site.

URSSI welcomes members of the research software community to join us, both to help us determine how to proceed and to directly contribute. Please sign up for the URSSI mailing listcontribute to our discussions, and potentially publish a guest blog post on the URSSI blog on a topic around software sustainability.

Welcome to the new MBDH Community Blog

Greetings!

Today we are launching a new MBDH Community Blog, which is intended to extend information sharing around events and projects, as well as expand our channels for Community conversation.

We plan to run 1-2 posts per month, and we are now seeking submissions from the MBDH Community – including the Spokes and our other collaborative projects – that describe your contributions and developments in the broader data ecosystem. Of interest are short reports and highlights from data-related meetings, events, or project outcomes, inclusive of the role and impact of the MBDH for these efforts.

We welcome contributions from the Social Sciences and Humanities, including short contributions that address data and algorithmic ethics, or coming changes for work, daily life, and public engagement in U.S data policy.

We encourage submissions from practitioner and NGO perspectives, as well as those from academia, industry, or government. We will provide additional guidelines shortly. If you are interested in submitting a Blog post, please send your contact information and the subject area to: info@midwestbigdatahub.org

Our first guest post is by Daniel Katz, Assistant Director for Scientific Software and Applications at the National Center for Supercomputing Applications (NCSA). Check out his post on the US Research Software Sustainability Institute (URSSI) project.

Finally, I’ll note a couple of activities where we are currently seeking input and engagement:

Add your voice to our Midwest Big Data Hub evaluation

  • To create a robust strategic plan for the Midwest Hub.
  • To plan toward long-term sustainability, especially financial sustainability, for the Midwest Hub.
  • Provide your input here: https://www.surveymonkey.com/r/MBDHSurvey

Participate in our election of five (5) At-large representatives for the MBDH Steering Committee:  https://midwestbigdatahub.org/2018-steering-committee-at-large-nominees/

As always, please contact us with any ideas or questions.
Thank you for your continued support!

All the best,
Melissa Cragin
Executive Director, Midwest Big Data Hub