# Accelerating COVID-19 modeling research by improving the discovery and new use of data: leveraging community engagement and automation of curation workflows

> **NIH NIH U24** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $334,466

## Abstract

Project Summary
The Models of Infectious Disease Agent Study (MIDAS) research network has been highly productive, and a
key challenge faced by the MIDAS and the general scientific community is how to make its models and
datasets accessible to others so as to amplify and accelerate the research and discovery process. The value of
data and software as research products has been widely acknowledged, but individual researchers can face
persistent barriers to data sharing, including the prevailing “publish or perish” paradigm as the main driver for
academic tenure and promotion. While new technology can enable data sharing, a social-cultural, human-
based approach is essential to improve data access and reuse in a community. We propose to create a
MIDAS Coordinating Center (MCC) that is investigator-focused, with the long-term goal of increasing
the use of MIDAS research products for new research and discovery. Our approach will follow FAIR Data
Principles developed by the NIH Data Commons Consortium to specify requirements for Findable, Accessible,
Interoperable, and Reusable research products. We will leverage FAIR-enabling technology developed by the
Informatics Services Group (the current MIDAS Information Technology Resource) and add community-based
research, outreach, education, and governance. We propose the following specific aims: (1) Facilitate
compliance of MIDAS datasets and software with FAIR Data Principles; (2) Create FAIR "gold standard
datasets" (GSD) to improve testing of MIDAS models; (3) Create a dynamic infrastructure and support services
for data storage and high-performance computing; (4) Coordinate outreach through an annual network meeting
and improved electronic communication channels; (5) Educate MIDAS trainees in open science and research
design principles; and (6) Create executable workflow representations of MIDAS models to improve model
testing and reproducibility. The MCC will augment the impact of NIGMS investments in basic scientific
research by improving the use of MIDAS research products. Other scientists or computer algorithms will be
able to discover, access, and integrate MIDAS products and increasingly, machine-driven access to, and use
of, datasets and software will accelerate the rate of new discoveries and innovation for control of infectious
disease threats. The MCC will be led by Dr. Wilbert van Panhuis, MD, PhD, who has worked as
epidemiological modeler in the Pitt MIDAS Center of Excellence, and who has collaborated as data scientist
with the ISG. Dr. Van Panhuis has a unique track record of unlocking access to valuable datasets previously
unavailable to MIDAS and a proven ability to design, and successfully lead, large-scale international
collaborations. As PI of the MCC Dr. Van Panhuis will proactively collaborate with MIDAS investigators and the
MIDAS Steering Committee. The other MCC team members are also firmly rooted into the MIDAS community
and have complementary expertise in infectious disease and...

## Key facts

- **NIH application ID:** 10146599
- **Project number:** 3U24GM132013-02S1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Willem Gijsbert Van Panhuis
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $334,466
- **Award type:** 3
- **Project period:** 2019-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10146599

## Citation

> US National Institutes of Health, RePORTER application 10146599, Accelerating COVID-19 modeling research by improving the discovery and new use of data: leveraging community engagement and automation of curation workflows (3U24GM132013-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10146599. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*