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I-GUIDE: Increasing Sustainability by Harnessing Data

By Raleigh Butler

Gravity dam in Marion County, Oregon. Photo by Dan Meyers.
Photo by Dan Meyers/Unsplash

Sustainability is not just achieved through solar panels and windmills. Of course these help, but one organization is working to tackle sustainability on a larger scale: I-GUIDE is a collaborative environment for sharing and using geospatial data. It is community-oriented and works to address sustainability challenges.

“I-GUIDE” stands for “Institute for Geospatial Understanding through an Integrative Discovery Environment.” This project is funded by the National Science Foundation (NSF) under the Harnessing the Data Revolution program. Awarded in 2021, the institute is led by PI Shaowen Wang, head of the Department of Geography and Geographic Information Science at the University of Illinois. The institute has partners from across the country, including MBDH collaborators such as EarthCube, CUAHSI, the University of Minnesota, Columbia University, and the Discovery Partners Institute.

As the I-GUIDE site states, “most challenging sustainability and resilience problems today require expertise from multiple domains and geospatial data science.” I-GUIDE acts as a main point for qualified entities to access varying types of data. For example, I-GUIDE allows other participating entities to access the data stored in HydroShare, a system from CUAHSI, the Consortium of Universities for the Advancement of Hydrologic Science, Inc. The HydroShare infrastructure can be used to share data as well as analyze and visualize those data. I-GUIDE brings together other related programs. This allows increased knowledge on the subjects of sustainability, and the supporting data. I-GUIDE currently has data being added to it in the fields of water, geospace, geography, and the atmosphere.

“The institutional collaborations facilitated by this project will enable the I-GUIDE team as well as the broader community to explore a wide range of interdisciplinary science questions that leverage an interconnected network of software and cloud infrastructure,” said Dr. Anthony Castronova,
Senior Research Hydrologist at CUAHSI. “These types of institutional connections are critical to support water science research around pressing environmental issues that require modern software, data, and modeling approaches.”

Environmental issues often present themselves in one way (e.g., a drought) when the problem at hand is much larger than the assumed cause (a lack of rainfall). As the climate changes, droughts and other environmental changes can become increasingly harmful to current ecosystems. HydroShare cultivates collaboration in water-focused areas such as drought conditions, water quality, temperature, and soil moisture. These data act as the first step to help promote sustainability and resilience.

I-GUIDE holds regular webinars. The first in the series, held on March 23, 2022, explored the need for geospatial education when sustainability is growing more important every day. Led by Eric Shook from the University of Minnesota, the webinar emphasized the need for building diverse communities of instructors and learners to build best practices for cyberinfrastructure (CI) literacy, and lower the barriers for learners new to CI.

“The Midwest Big Data Innovation Hub is pleased to be a partner on the I-GUIDE project,” said MBDH Executive Director John MacMullen. “This is a diverse and talented team that will have important impacts on key areas of focus for the MBDH, including water data, CI workforce development, and data-enabled resilient communities.”

“MDBH is a great example of how our I-GUIDE Partners are organizations and institutions that share common goals and objectives,” said George Percival, co-lead of I-GUIDE’s Engagement and Partnership Team. “The I-GUIDE Partnership Program provides the pathway for Partners to contribute to and gain from the I-GUIDE activities based on mutually beneficial agreements. As the MBDH objective “to build and cultivate communities around data” is highly aligned with I-GUIDE, it is anticipated that the MBDH and I-GUIDE partnership will benefit both activities.”

If you’re interested in getting involved with I-GUIDE, please take a look at their News & Events page. The site often lists such events as webinars and symposiums. The I-GUIDE team held its first All-Hands Meeting in May 2022.

Get Involved

Activities to build the community of Midwest researchers and practitioners in the Smart & Resilient Communities priority area of the Midwest Big Data Innovation Hub are continuing throughout 2022. Contact the Hub if you’re interested in participating, or are aware of other people or projects we should profile here. The MBDH has a variety of ways to get involved with our community and activities.

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.

Water Data Forum Webinar Series

Header for the Water Data Forum web series presented by the Cleveland Water Alliance, Water Environment Federation, and Midwest Big Data Innovation Hub.

Water Data Forum, the virtual series presented by the Cleveland Water Alliance, Water Environment Federation, and Midwest Big Data Innovation Hub, is returning for a second season in 2022!
In 2021, the Forum assembled expert panels to engage in timely topics such as new sensor and control technologies as well as water data for environmental justice and climate resilience. This year, interactive web sessions will engage a diverse array of experts across sectors in an exploration of topics ranging from the intersection of cyber security and water to STEM and youth empowerment.

2022 Sessions

The new season will kick off this March with a session titled: Innovations in Water Quality: The Real-Time Revolution on March 30 at 12 p.m. ET. This session will convene industry, government, and research experts to explore the next generation of water quality sensing technologies. In a facilitated discussion, panelists will use specific case studies to examine the challenges posed by new, or more recently understood, sources of water pollution and the opportunities surrounding real-time networks and new sensing modalities.



May Session: Cyber and Water: Driving Digital Security across the Water Sector
July Session: Smart Stormwater: Data-Driven Response to Flooding, Erosion and other Natural Hazards
September Session: Water Education: STEM, Youth Empowerment and Workforce Development
November Session: Smart Water Equity: Data-Enabled Affordability, Justice and Sovereignty

Robust, accurate data are crucial for the future of water resource management, economic and workforce development, and technological advancement. Water Data Forum aims to demystify the complexities of water data for seasoned experts as well as the general public. For more information and updates around speakers and registration, visit https://clevelandwateralliance.org/wdf.

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, which include Water Quality. The MBDH has a variety of ways to get involved with our community and activities.

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.

Community Engagement through Open Watersheds

By Qining Wang

Lake Michigan and the Chicago skyline. Photo by Muzammil Soorma.
Photo by Muzammil Soorma via Unsplash

Many living in and outside of the USA presume clean water to be universally accessible in the USA. In reality, many people are living in a water crisis.

A recent study published in Nature Communications assesses the degree of the clean water crisis. Researchers found that as of August 18, 2020, more than 22,000 community water systems are either serious violators of or in significant noncompliance with the Safe Drinking Water Act. As the researchers point out, “our findings demonstrate that the problem of water hardship in the United States is hidden, but not rare.”

A huge underlying cause is inaccessible data on water quality. Different governmental and state sectors, such as the National Oceanic and Atmospheric Association and the Environmental Protection Agency, collect data on various water sources. Yet, the lack of communication among different sectors creates fragmentation of watershed data. As a result, watershed data is widespread, difficult to locate, and sometimes wholly inaccessible. Such fragmentation significantly limits policymakers’ ability to make informed decisions to improve water quality. Neither would consumers be able to tell when their water is unsafe.

One solution is to create open data hubs that centralize accessible and interpretable data, which would require both governmental and nongovernmental efforts. As such, the creation of open watersheds manifests in interesting intersections of community empowerment, resident engagement, and watershed management. In a recent panel discussion titled “Open watersheds: Innovations in Community Water Data,” four panelists involved in open watersheds in the Midwest discussed the benefits, challenges, and opportunities in open-source environmental monitoring.

This panel discussion is part of the monthly Water Data Forum webinar series, co-hosted by the Midwest Big Data Innovation Hub, the Cleveland Water Alliance, and the Water Environment Federation. The participating panelists were Whitnye Long-Jones, founder and executive director of Organic Connects; Mark App, project manager of the Great Lakes Data Watershed; Barb Horn, expert facilitator and steering committee member of the Water Data Collaborative; and Brandon P. Wong, president and co-founder of Hyfi.

Envisioning Open Watersheds

Each panelist had their own vision for open watersheds. Despite coming from different backgrounds, all agreed on the importance of recruiting community effort. Wong spoke from a technological standpoint by relating open-source technologies to open watersheds. He said: “I think there’s a responsibility to see what’s going on underneath the hood.” When it comes to open watersheds, there should be transparency in the process, from data collection from physical devices to data storage. Wong believes that open technologies will allow people to speak openly about what they know about the watershed, knowing how the data is collected and processed.

Long-Jones mentioned creating connections with community members. Since data scientists tend to use jargon and terminologies to describe water data, it is crucial to train community members to make water data more interpretable. Horns reciprocated this point and talked about cultivating relationships between residents and institutions.

Tools for Open Watersheds

Panelists further discussed building connections and relationships when talking about different tools that can benefit the communities. Horn made a crucial point about building healthy relationships with new technologies that facilitate data collection: “Too often technology comes in with its excitement [. . .], but no one has spent that time helping them build the context on how to use it, so it just becomes a strategy that actually has a short-term gain but not long still sustainability even if it could have.”

Horn also explained using data and technology to serve “wholism.” In other words, we should use new technologies to collect data that foster collaboration and innovation. New technologies should not mean to create competitions that only profit a minority of people. “It’s not the community versus the agency or the Agency. It’s not the company industry against the community. It’s like, how can this [new technology] serve wholism? How can this technology serve us coming together in a whol[ly] innovative way?” Horn said.

Regarding the current challenges and barriers, App discussed how the lack of consistent standards in water data collection creates difficulty in data integration. He suggested creating new visions around watershed data. In those visions, data collection would not merely be the responsibility of isolated entities but would be up to the whole community. Skillful community members such as retired NASA engineers, motivated high school students, and computer professionals in pattern recognition can all contribute to monitoring water quality. He believes that those new visions will be the driving force to create open standards in open watersheds.

Open Watersheds and Beyond

The panelists also discussed the benefits of open watersheds beyond open-source data collection and environmental monitoring. Long-Jones emphasized that the community efforts in open watersheds can greatly benefit areas experiencing disinvestment due to historical redlining. “Now you’re seeing some of that even still continuing when we’re talking about cities [that] are experiencing dirty, unclear drinking water,” she said. Many residents in those communities struggle to meet their basic needs, and their survival priorities come before monitoring the contaminants coming out of their faucets.

In this regard, Long-Jones encourages us to envision communities beyond geographic boundaries and be open-minded and humble when engaging with community members bearing diverse backgrounds. Only by truly listening to each other and understanding where each of us comes from can we realize that open watersheds and improving water quality require everyone’s involvement.

Get involved

You can get more involved with open watersheds by participating in the Cleveland Water Alliance’s Smart Citizen Scientist Initiative, a movement that encourages youth, elder, and underrepresented citizen scientists to collect open-source data on Lake Erie with simple technologies. You can also join upcoming Water Data Forum sessions.

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, which include Water Quality. The MBDH has a variety of ways to get involved with our community and activities.

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.

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.