This program aims to foster research that creates or expands novel data sets, which are crucial for advancing innovative research in diverse fields. This initiative seeks to address the critical need for high-quality data by providing funding and support for projects that generate, enhance, and annotate data sets to fuel cutting-edge research at the University of Minnesota (UMN). Successful applications will demonstrate how these data sets are valuable beyond any single lab. The value may come from industry partners (particularly those based in MN or those with a strategic value in MN) or other research entities.
Requirements:
The PI will deliver a 2-page progress report to the DSI detailing the current status of the timeline, milestones, and budget information. Upon completion of the project, the data set must be uploaded to a UMN-owned data repository. A manual for usage including an overview of the data set, types of research the data could be used for, detailed meta-data, and methods of data collection must be uploaded with the data set. Evidence that the data set has value to groups other than those generating it. Value can be measured in several ways, including monetarily and related to research advancement. Finally, the PI agrees to provide the DSI with an abstract and optional image which will be used for promotional purposes including in the annual Seed Grant Showcase (annually in December).
Grant Details:
- Funding of up to $100,000 for projects spanning two years.
- Projects intending to pursue external infrastructure grants can apply for up to $200,000.
- The resulting datasets must:
- Be accessible to all UMN researchers, where possible, or easily accessible with appropriate access provisions.
- Adhere to university policies, especially those pertaining to Research Data Management: Archiving, Ownership, Retention, Security, Storage, and Transfer and Data Privacy.
- Expand upon or create novel data sets addressing current research gaps.
- Be thoroughly annotated and cleaned as much as feasible.
- Be available for upload to UMN-owned data repositories.
Eligibility Criteria:
- Faculty members and research teams from diverse disciplines across UMN are encouraged to apply. Faculty or research staff (P&A) with their primary appointment at the University of Minnesota throughout the UMN system are eligible to apply to be a faculty fellow. Adjunct or affiliated faculty are not eligible for funding under this program.
- Proposals must focus on the creation or expansion of data sets necessary for innovative research.
- Collaboration between multiple departments or research centers is highly encouraged.
- Plans should address how infrastructural challenges, such as data storage limits and costs, storage governance, and user access will be addressed.
Application Requirements:
Interested applicants are invited to submit a proposal of no more than 5 pages, addressing the following:
- Project Overview:
- A clear description of the proposed project and its objectives.
- A high-level explanation of the data set and possible research areas it could be used for.
- Relevance to one of the five MnDRIVE areas: Robotics, Global Food, Environment, Brain Conditions, and Cancer Clinical Trials, if applicable.
- Significance:
- Discussion of the importance of the proposed data set for advancing research in the relevant field(s).
- Identification of current gaps in available data sets that the project aims to address.
- Methodology:
- Detailed plan for data generation, expansion, annotation, and cleaning.
- Explanation of the steps to ensure data accessibility, privacy, and compliance with applicable UMN policies.
- Data Repository:
- Description of the proposed UMN-owned data repository for hosting the resulting data set(s).
- Plan for data sharing and accessibility within the UMN research community.
- Budget and Timeline:
- An itemized budget outlining how the funds will be utilized.
- Proposed timeline and milestones for project execution and completion.
The DSI is committed to supporting the creation of high-quality data sets that drive groundbreaking research across the University of Minnesota. We encourage innovative proposals that push the boundaries of knowledge and contribute to the advancement of data science and related fields.
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 Data Sets Grants
This program aims to foster research that creates or expands novel data sets, which are crucial for advancing innovative research in diverse fields. This initiative seeks to address the critical need for high-quality data by providing funding and support for projects that generate, enhance, and annotate data sets to fuel cutting-edge research at the University of Minnesota (UMN). Successful applications will demonstrate how these data sets are valuable beyond any single lab. The value may come from industry partners (particularly those based in MN or those with a strategic value in MN) or other research entities.
Requirements:
The PI will deliver a 2-page progress report to the DSI detailing the current status of the timeline, milestones, and budget information. Upon completion of the project, the data set must be uploaded to a UMN-owned data repository. A manual for usage including an overview of the data set, types of research the data could be used for, detailed meta-data, and methods of data collection must be uploaded with the data set. Evidence that the data set has value to groups other than those generating it. Value can be measured in several ways, including monetarily and related to research advancement. Finally, the PI agrees to provide the DSI with an abstract and optional image which will be used for promotional purposes including in the annual Seed Grant Showcase (annually in December).
Grant Details:
- Funding of up to $100,000 for projects spanning two years.
- Projects intending to pursue external infrastructure grants can apply for up to $200,000.
- The resulting datasets must:
- Be accessible to all UMN researchers, where possible, or easily accessible with appropriate access provisions.
- Adhere to university policies, especially those pertaining to Research Data Management: Archiving, Ownership, Retention, Security, Storage, and Transfer and Data Privacy.
- Expand upon or create novel data sets addressing current research gaps.
- Be thoroughly annotated and cleaned as much as feasible.
- Be available for upload to UMN-owned data repositories.
Eligibility Criteria:
- Faculty members and research teams from diverse disciplines across UMN are encouraged to apply. Faculty or research staff (P&A) with their primary appointment at the University of Minnesota throughout the UMN system are eligible to apply to be a faculty fellow. Adjunct or affiliated faculty are not eligible for funding under this program.
- Proposals must focus on the creation or expansion of data sets necessary for innovative research.
- Collaboration between multiple departments or research centers is highly encouraged.
- Plans should address how infrastructural challenges, such as data storage limits and costs, storage governance, and user access will be addressed.
Application Requirements:
Interested applicants are invited to submit a proposal of no more than 5 pages, addressing the following:
- Project Overview:
- A clear description of the proposed project and its objectives.
- A high-level explanation of the data set and possible research areas it could be used for.
- Relevance to one of the five MnDRIVE areas: Robotics, Global Food, Environment, Brain Conditions, and Cancer Clinical Trials, if applicable.
- Significance:
- Discussion of the importance of the proposed data set for advancing research in the relevant field(s).
- Identification of current gaps in available data sets that the project aims to address.
- Methodology:
- Detailed plan for data generation, expansion, annotation, and cleaning.
- Explanation of the steps to ensure data accessibility, privacy, and compliance with applicable UMN policies.
- Data Repository:
- Description of the proposed UMN-owned data repository for hosting the resulting data set(s).
- Plan for data sharing and accessibility within the UMN research community.
- Budget and Timeline:
- An itemized budget outlining how the funds will be utilized.
- Proposed timeline and milestones for project execution and completion.
The DSI is committed to supporting the creation of high-quality data sets that drive groundbreaking research across the University of Minnesota. We encourage innovative proposals that push the boundaries of knowledge and contribute to the advancement of data science and related fields.
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