The Data Science Initiative (DSI) is pleased to announce a rapid response seed grant opportunity to foster innovative research and collaboration in data science and artificial intelligence (AI). With a maximum of $15,000 available per project, this initiative aims to catalyze projects that address emerging research needs, including projects that involve outreach and engagement, in data science and AI. Priority will be given to those proposals that align with specific external funding opportunities. Applications will be reviewed and award notifications will be made within 3 weeks of submission.
Eligibility:
Interdisciplinary Approach: Proposals should involve interdisciplinary collaboration, at least cross-departmental, however, cross-collegiate levels are preferred and demonstrate a clear integration of data science and AI methodologies.
Faculty and Researcher Teams: The principal investigator (PI) must be a faculty or research staff (P&A) with their primary appointment at the University of Minnesota. Collaborators from diverse backgrounds and disciplines are strongly encouraged. Adjunct or affiliated faculty are not eligible for funding under this program.
Two-Page Application: Applications must be concise, with no more than two pages of content (single-spaced, 12-point font), excluding references.
Proposal Requirements:
Research Need: Proposals must articulate a clear and compelling case for how the project addresses an emerging research need in data science or AI. There must be a specific funding opportunity identified for which the result of the rapid response work is needed. The proposal must demonstrate relevance to the funding opportunity and potential for external funding. Projects should outline innovative approaches or methodologies that push the boundaries of current data science and AI research.
Proposal Components:
Applications should include the following components:
Title of the Project
Principal Investigator(s) and Collaborator(s) Information
Project Summary (maximum 250 words)
Relevance to one of the five MnDRIVE areas: Robotics, Global Food, Environment, Brain Conditions, and Cancer Clinical Trials, if applicable
Research Plan, including:
Background and Significance
Project Objectives
Methods and Approach
Expected Outcomes and Impact
References (not included in page count)
- A 1-page budget justification with clear budget breakdown outlining anticipated expenses noting any current and pending support, and/or existing funding to support for the proposed work
Equipment, non-PI salary, supplies, and services are all allowable expenses. For a Rapid Response grant to be successful, an investigator must justify how the expenses will help to advance their data science research and perhaps also align long term with the data science landscape at the University.
About DSI and MnDRIVE
The DSI has the current topic focus areas, but funding is not restricted to them.
- Foundational Data Sciences: Topics that are foundational to data science applications including data-intensive and data-informed topics and applications along with methodological research in areas such as signal processing, data mining, statistics, machine learning, and artificial intelligence (including GenAI and AI literacy) as well as topics in ethics and privacy. Fundamental methods dealing with data storage, archiving, sharing, acquisition, compression, or transmission are included. This includes but is not limited to, disciplines that underlie data science and AI such as statistics, mathematics, computer science, philosophy, and social and behavioral sciences.
- Digital Health and Personalized Health Care Delivery: The broad scope of digital health includes disciplines such as mobile health (mHealth), real-world observational healthcare data, public health, health information technology (HIT), wearable devices or technology, virtual care, and personalized healthcare and medicine. It includes enhancements to patient and consumer health and healthcare delivery through capacity-building activities and continuous, personalized, predictive, participative, and preventive approaches.
- Agriculture and the Environment: Agriculture and the environment are closely intertwined, topics that touch either or both areas of interest. The agriculture sector faces the challenge of feeding a growing global population while minimizing environmental impact and preserving natural resources for future generations. Research challenges in this area can reduce the consequences of climate and pest risks on agricultural production, lessen the impact of pollution, soil degradation, and water contamination or trapping greenhouse gasses, and mitigating flood risks.
Applications to all funding opportunities should be relevant to one of the five MnDRIVE areas: Robotics, Global Food, Environment, Brain Conditions, and Cancer Clinical Trials. While it is not required, it will improve the chances of success.
Acknowledgment Statements
Please include the following acknowledgment in any resulting publications:
The authors acknowledge the Data Science Initiative (DSI) at the University of Minnesota for providing seed funds that contributed to the research results reported in this paper.
For questions about this program, please email dsi-grants@umn.edu
DSI Rapid Response Seed Grants
The Data Science Initiative (DSI) is pleased to announce a rapid response seed grant opportunity to foster innovative research and collaboration in data science and artificial intelligence (AI). With a maximum of $15,000 available per project, this initiative aims to catalyze projects that address emerging research needs, including projects that involve outreach and engagement, in data science and AI. Priority will be given to those proposals that align with specific external funding opportunities. Applications will be reviewed and award notifications will be made within 3 weeks of submission.
Eligibility:
Interdisciplinary Approach: Proposals should involve interdisciplinary collaboration, at least cross-departmental, however, cross-collegiate levels are preferred and demonstrate a clear integration of data science and AI methodologies.
Faculty and Researcher Teams: The principal investigator (PI) must be a faculty or research staff (P&A) with their primary appointment at the University of Minnesota. Collaborators from diverse backgrounds and disciplines are strongly encouraged. Adjunct or affiliated faculty are not eligible for funding under this program.
Two-Page Application: Applications must be concise, with no more than two pages of content (single-spaced, 12-point font), excluding references.
Proposal Requirements:
Research Need: Proposals must articulate a clear and compelling case for how the project addresses an emerging research need in data science or AI. There must be a specific funding opportunity identified for which the result of the rapid response work is needed. The proposal must demonstrate relevance to the funding opportunity and potential for external funding. Projects should outline innovative approaches or methodologies that push the boundaries of current data science and AI research.
Proposal Components:
Applications should include the following components:
Title of the Project
Principal Investigator(s) and Collaborator(s) Information
Project Summary (maximum 250 words)
Relevance to one of the five MnDRIVE areas: Robotics, Global Food, Environment, Brain Conditions, and Cancer Clinical Trials, if applicable
Research Plan, including:
Background and Significance
Project Objectives
Methods and Approach
Expected Outcomes and Impact
References (not included in page count)
- A 1-page budget justification with clear budget breakdown outlining anticipated expenses noting any current and pending support, and/or existing funding to support for the proposed work
Equipment, non-PI salary, supplies, and services are all allowable expenses. For a Rapid Response grant to be successful, an investigator must justify how the expenses will help to advance their data science research and perhaps also align long term with the data science landscape at the University.
About DSI and MnDRIVE
The DSI has the current topic focus areas, but funding is not restricted to them.
- Foundational Data Sciences: Topics that are foundational to data science applications including data-intensive and data-informed topics and applications along with methodological research in areas such as signal processing, data mining, statistics, machine learning, and artificial intelligence (including GenAI and AI literacy) as well as topics in ethics and privacy. Fundamental methods dealing with data storage, archiving, sharing, acquisition, compression, or transmission are included. This includes but is not limited to, disciplines that underlie data science and AI such as statistics, mathematics, computer science, philosophy, and social and behavioral sciences.
- Digital Health and Personalized Health Care Delivery: The broad scope of digital health includes disciplines such as mobile health (mHealth), real-world observational healthcare data, public health, health information technology (HIT), wearable devices or technology, virtual care, and personalized healthcare and medicine. It includes enhancements to patient and consumer health and healthcare delivery through capacity-building activities and continuous, personalized, predictive, participative, and preventive approaches.
- Agriculture and the Environment: Agriculture and the environment are closely intertwined, topics that touch either or both areas of interest. The agriculture sector faces the challenge of feeding a growing global population while minimizing environmental impact and preserving natural resources for future generations. Research challenges in this area can reduce the consequences of climate and pest risks on agricultural production, lessen the impact of pollution, soil degradation, and water contamination or trapping greenhouse gasses, and mitigating flood risks.
Applications to all funding opportunities should be relevant to one of the five MnDRIVE areas: Robotics, Global Food, Environment, Brain Conditions, and Cancer Clinical Trials. While it is not required, it will improve the chances of success.
Acknowledgment Statements
Please include the following acknowledgment in any resulting publications:
The authors acknowledge the Data Science Initiative (DSI) at the University of Minnesota for providing seed funds that contributed to the research results reported in this paper.
For questions about this program, please email dsi-grants@umn.edu