2019 Propelling Original Data Science (PODS) Grants

A primary mission of the University of Michigan Institute for Data Science (MIDAS) is to foster innovative and groundbreaking multi-disciplinary research in data science.  MIDAS is pleased to announce our next round of funding for innovative data science.  MIDAS plans to fund research in the broad area of data science, including: 

  • Developing the theoretical foundations of data science;
  • Developing data science methodology and tools;
  • The innovative application of data science methods in any research area;
  • Examining and managing the implications of data science for society and the public interest;
  • Especially encouraged are proposals that combine a data science methodology component and a clear research question.

We are particularly interested in funding pioneering work based on innovative concepts that promises high reward, major impact, promotion of public interest, and potential for major expansion.  In other words, we aim to fund “disruptive” instead of incremental research.  All projects should be collaborative and interdisciplinary, with faculty from at least two departments/research units identified as PI and Co-PI(s) respectively.

Award Information: 12 – 15 projects will be awarded for a duration of 12-18 months, based on a competitive process.  Two levels of funding are available: 1) $30K, MIDAS will provide 100% of the funding, with no cost-share required.  2) Up to $90K, with cost-share above the first $30K in the ratio of 2:1 (MIDAS:Investigators or their units). For example, if you request $60K, your cost-share portion will be $10K and MIDAS will provide $50K.  Please email  for questions regarding the cost-share.

Who May Apply: Principal Investigators (PIs) and co-PIs should be faculty members at the University of Michigan (Ann Arbor, Dearborn, or Flint campus), and should not have been a  PI or co-PI of a previous MIDAS grant.  An individual may participate as PI/co-PI on only one proposal. Co-investigators, consultants and other personnel are not limited by this restriction. 

Important Dates:

  • Sept. 10, 2019: Submission of PI name(s) and tentative proposal title (Pre-submission)
  • Sept. 20, 2019: Proposals due (Proposal Submission) before 11:59 pm.
  • Nov. 15, 2019: Awards announced
  • By Jan. 1, 2020: Projects start

Proposal Content:

  • Project description, up to 4 pages, in Arial with minimum font size 10.  This should include Specific Aims, background, significance and innovation, and methods.  Most importantly, a description of why this project is “pioneering work based on innovative concepts that promises high reward, major impact, and potential for major expansion”.
  • A transition plan (up to ½ page) that describes the strategy for follow-on funding and activities, such as the next phase of research, curriculum development, data products and/or commercialization.
  • A Statement of Societal Impact (up to ½ page) that describes how this project will benefit the society, and, if any, potential negative impact that needs to be carefully avoided.
  • References (no page limit).
  • Two-page biosketch in NSF or NIH format for PI, co-PI and senior personnel;
  • A detailed budget and budget justification.  A generic budget template is available, but you can use any budget template that you prefer.
  • If cost-share is applicable, include proof that you have secured it (e.g. a letter from your department chair).

Review criteria

  • Innovative concept and/or approach;
  • The significance of the research questions;
  • Multi-disciplinary collaboration;
  • Likelihood of success;
  • Impact to the research field;
  • Broad impact to UM data science research community.
  • Potential for continuation, external funding and/or commercialization.

Post-award expectations

  • PIs, co-PI’s and senior investigators are expected to become MIDAS affiliate members.
  • All teams will be expected to present at MIDAS events and participate in MIDAS activities for data science research, education and community building. 
  • All publications, public presentations and products from this award should acknowledge MIDAS.

For questions, please contact: