SEED Grant Program
Overview: Data sciences can enable transformative research in many disciplines. However, initiating such work can be difficult and needs partners and expertise that may be difficult to find. The Data Intensive Studies Center (DISC) will help Tufts researchers overcome these barriers. Through this effort we seek to accelerate well defined X+Data sciences efforts on campus that can target high impact research and in appropriate areas large sponsored research efforts e.g. NSF TRIPODS Phase II, NIH BIG Data to Knowledge, AI Institutes.
In this first round DISC will support such efforts with a seed grant of up to $15,000 and access to data scientists, data sets and other expertise at DISC. Project should initiate a new group effort or provide new direction and significant enhancement to an existing group’s work. Primary review criteria will be:
- - potential impact of the research proposed;
- - necessity of existing or new data science methodologies to enable the proposed research;
- - qualifications of the team for conducting the research proposed;
- - potential for follow-on scholarship and funding resulting from the preliminary work -proposed here;
- - educational efforts enabled;
- - impact beyond the primary domain;
- - support for diversity and broadening participation in data sciences.
- - Description of project including clearly outlined deliverables for the seed investment and potential outcomes in longer term effort that may follow;
- - Potential impact of project;
- - Potential follow on efforts and clearly identified future sponsors;
- - Education efforts supported;
- - Plan for broadening participation.
SEED Grant Winners
Modeling Cholera Toxin Impact on Intestinal Goblet Cells Using Novel Trajectory Analysis Approaches
Team Lead: Shuchin Aeron (SoE)
Team Members: Carol Kumamoto (TUSM), Joan Mecsas (TUSM), and Wai-Leung Ng (TUSM)
Rapid Classification of Transportation Infrastructure Damage and Loss Estimation using Image Libraries, Mobility Data, and Machine Learning
Team Lead: Laurie Baise (SoE)
Team Members: Shan Jiang (A&S) and Babak Moaveni (SoE)
Reinventing the Study of Ancient Languages
Team Lead: Gregory Crane (A&S)
Data for All: Empowering Communities to Improve Health
Team Lead: Christina Economos (Friedman)
Team Members: Erin Hennessy (Friedman) and James Intriligator (SoE)
Building a Theoretical Framework for Microbiomedata
Team Lead: Colin Edwards (A&S)
Team Members: Elizabeth Crone (A&S) and Benjamin Wolfe (A&S)
The Priority Area Research Group on Equity
Team Lead: Wenhui Feng (TUSM)
Team Members: Jennifer Allen (A&S), Peter Levine (Tisch), and Thomas Stopka (TUSM)
Data Science Methods to Enable Label-free, Morphofunctional Imaging in Human Tissue
Team Lead: Irene Georgakoudi (SoE)
Team Member: Liping Liu (SoE)
Real-time Forecast for COVID-19 Laboratory Confirmed Cases
Team Lead: Elena Naumova (Friedman)
A Systems Biology Approach to Study Dysfunctional Neural Network Connectivity in Brain Disorders
Team Lead: Thomas Nieland (SoE)
Team Member: Donna Slonim (SoE)
Using Agent-Based Models to Investigate Countermeasures for False Information Spread
Team Lead: Matthias Scheutz (SoE)
Team Member: Jan P. deRuiter (A&S)
Overview: Data sciences can enable transformative research in many disciplines. However, initiating such work can be difficult and needs partners and expertise that may be difficult to find. The scope, challenges and outcomes are often poorly defined without active and engaged partnerships among disciplinary and data scientists.
The Data Intensive Studies Center (DISC) will help Tufts researchers overcome these barriers and facilitate the formation of groups to explore and define new high impact areas for scholarship enabled by data sciences.
In this first round the DISC will support such efforts with support of up to $10,000 that may be used for supporting group activities like external speakers and student time for activities like data collection and literature survey. Project should initiate a new group effort in a broad area. Groups will be led by disciplinary researchers and closely supported by DISC staff and fellows. Groups will be encouraged to define “sandbox” like structure for exploring preliminary concepts using new methodologies like machine learning with support from DISC staff.
Primary review criteria will be:
- - Potential impact of the research proposed;
- - Necessity of existing or new data science methodologies to enable the proposed research;
- - Qualifications of the team for conducting the research proposed;
- - Potential for follow-on scholarship and funding resulting from the preliminary work proposed here;
- - Impact beyond the primary domain.
Application Process: We seek 2-page applications including a cover page and NSF or NIH style biosketches of the PI. Applications in one pdf file are due by May 15 to the email email@example.com and should provide the following information:
- - Title of group and a short abstract (elevator pitch!);
- - Names and affiliations of all team members (no funding available for non-Tufts partners except costs incurred in collaboration e.g. travel to Tufts);
- - Direct cost budget and brief justification.
- - Description of area of research and potential outcomes in longer term effort that may follow;
- - Potential follow on efforts and clearly identified future sponsors.
Eligibility: Tufts university faculty with eligibility to be PI on externally funded grants.
Overview: Data sciences can enable transformative research in many disciplines. Tufts has invested strategically in establishing the Data Intensive Studies Center to catalyze the growth of data science and data science enabled scholarship and education. To engage Tufts faculty in the development and growth of DISC and to provide them an opportunity to grow their research interests in this domain DISC will appoint a number of faculty fellows. Salient points of such appointments are:
- - Salary support for 10% of time.
- - Fellows will generally be appointed in annual cohorts with renewals possible.
- - Fellows will be expected to anchor new DISC initiatives and thrust areas and will work closely with the SEED grant and Study group programs as appropriate.
- - Fellows will have access to DISC resources including staff.
We encourage faculty to nominate colleagues and/or self-nominate with a current CV and a short description (less than 1 page) of what they hope will be accomplished using the fellowship. Please note that we will be communicating with the unit head (department chair) to ensure compatibility of the fellowship with current responsibilities and commitments. These nominations may be made at any time and will be considered as positions open up.