Community Development and Engagement (CDE) projects are active partnerships with the Midwest Big Data Innovation Hub that incubate community-building activities in at least one of the Hub’s five Priority Areas [Advanced Materials and Manufacturing (AMM), Digital Agriculture (DA), Smart & Resilient Communities (SRC), Water Quality (WQ), and Big Data in Health (BDH)] and/or one of the Hub’s Crosscutting Theme Areas [Data Science Education and Workforce Development (DSEWD), and Cyberinfrastructure and Data Sharing (CDS)].
The projects below were established through the first round of CDE proposals in 2020, and are launching during fall 2020 and throughout 2021.
Midwestern Consortium for Computational Pathology
PI: Dhabaleswar Panda, The Ohio State University
Partner(s): The Ohio State University (OSU), University of Michigan (UMich), University of Pittsburgh (Pitt), Case Western Reserve University (CWRU), Cincinnati Children’s Hospital Medical Center (CCHMC), Mayo Clinic
Priority and/or Crosscutting Theme Area(s): BDH, DSEWD, and CDS
Project description: The goal of this consortium is to foster a community of practice around computational pathology in the Midwest and beyond. The challenges posed by a shortage of pathologists, the sheer volume and velocity of data (huge data) that must be harvested and managed, and the strong demand for better and faster diagnostics have created an enormous opportunity for the application of innovative human-machine teaming, deep learning, machine learning, and AI technologies. This consortium brings together experts from pathology, data science, and computer science; from academia, government laboratories, and industry; and aims to position the Midwest at the cutting edge of this new age of digital pathology. The consortium held its first interdisciplinary community workshop in January 2021. Slides and recordings are available.
Midwest Carpentries Community (MCC)
PI: Sarah Stevens, Midwest Carpentries Community
Partner(s): University of Wisconsin, The Carpentries, small institutions & minority-serving institutions (MSIs)
Priority and/or Crosscutting Theme Area(s): DSEWD
Project description: The Midwest Carpentries Community (MCC) is focused on building hands-on data science instruction capacity within the region through sharing best practices, community outreach, and train-the-trainer sessions. The MBDH is also interested in curriculum development that aligns with the Hub’s priority domain areas. The MCC has a monthly community call that is open to all.
Midwest Sustainable Transportation Datathon
PI: Eleftheria Kontou, University of Illinois at Urbana-Champaign
Partner(s): University of Illinois at Urbana-Champaign (UIUC), Iowa State University (ISU), Purdue University
Priority and/or Crosscutting Theme Area(s): SRC, DSEWD, and CDS
Project description: This project is focused on our Smart & Resilient Communities priority area and our Data Science Education theme. The project will develop a student-centered datathon with mentoring, and is intended to catalyze a community of interdisciplinary researchers who are interested in sustainable transportation systems.
Building Data Science Education Capacity at Central State University in Digital Agriculture and Water Quality
PI: Sakthi Subburayalu, Central State University
Partner(s): Central State University, other small institutions & minority-serving institutions (MSIs)
Priority and/or Crosscutting Theme Area(s): DA, WQ, and DSEWD
Project description: This project is focused on building research capacity at small institutions in the region, particularly MSIs, through multiple proposal-development workshops and a webinar series. These activities are centered on the overlapping areas of Digital Agriculture and Water Quality.
Democratize Neuroscience Education via Open Data and Cloud Technology
PI: Franco Delogu, Lawrence Technical University
Partner(s): Lawrence Technical University (LTU), Indiana University, minority-serving institutions (MSIs)
Priority and/or Crosscutting Theme Area(s): BDH and DSEWD
Project description: This project brings an established online neuroscience course to a broader audience of MSIs and small colleges and universities through instructor training and outreach. Community development and sustainability will be addressed through related collaborative proposal-development activities.
MBDH Learning Innovation Fellows program
PI: Rebecca Hardin, University of Michigan
Partner(s): University of Michigan (UMich), Iowa State University (ISU), William & Mary, Quantitative Undergraduate Biology Education and Synthesis Hub (QUBESHub)
Priority and/or Crosscutting Theme Area(s): DSEWD and CDS
Project description: This project brings together students and mentors to do small-team hands-on development of open learning modules in the Gala environment, in a context of sustainability and environmental science. Like the neuroscience project, this work builds capacity in data science education and curriculum development that can be leveraged by smaller institutions that don’t currently have resources to build them in-house.
Augmented Reality/Mixed Reality Working Institute
PI: Christopher Fasano, Monmouth College
Partner(s): Monmouth College + industry partner
Priority and/or Crosscutting Theme Area(s): AMM and DSEWD
Project description: Bringing together data science and augmented reality/mixed reality (AR/MR) concepts within a manufacturing context, this project addresses undergraduate training and workforce development through a hands-on workshop for students from small institutions in the region, faculty mentors, and an industry partner.
Community Development and Engagement for Viral Surveillance and Big Data Analytics towards Precision Livestock Agriculture
PI: Ashfaq Khokar, Iowa State University
Partner(s): Iowa State University (ISU)
Priority and/or Crosscutting Theme Area(s): BDH, DA, and DSEWD
Project description: The goal of the project is to develop a long-standing engagement with the livestock agricultural community that will (1) help to identify emerging issues and needs in research, education, and management towards a center-level effort in digital viral surveillance, diagnostics, and data analytics for animal agriculture; (2) advise of desired skill sets, potential approaches, and broad animal-production industry experiences; and (3) develop a program to train a data science workforce in this area.