Organization profile
Organization profile
GEMS Informatics re-imagines the relationships between public and private data, institutions, and disciplines to inform and accelerate data-driven innovation in the food and agricultural sectors. GEMS is a joint venture led by the College of Food, Agricultural and Natural Resource Sciences (CFANS) and Research Computing (Minnesota Supercomputing Institute and U-Spatial) at the University of Minnesota. We make genomic, environmental, management, and socioeconomic data inter-operable at varying spatial and temporal scales to generate new insights and promote new innovation partnerships. GEMS Informatics uses FAIR(ER)-findable, accessible, interoperable, reusable, ethical and reproducible practices in all it does. Producing and enabling the creation of research-ready, scientific-quality data, and turning that data into actionable information for farmers, scientists, governments or companies is what we do.
GEMS has five service areas:
- GEMS Platform: Smart data sharing, cleaning and analysis platform
- GEMS Sensing: Field-based IoT (Internet of Things) sensors and data services
- GEMS Exchange: Data streaming services via interoperable APIs
- GEMS Learning: Digital and data science education
- GEMS Solutions: Consulting services
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. Our work contributes towards the following SDG(s):
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SDG 2 Zero Hunger -
SDG 3 Good Health and Well-being -
SDG 4 Quality Education -
SDG 6 Clean Water and Sanitation -
SDG 7 Affordable and Clean Energy -
SDG 8 Decent Work and Economic Growth -
SDG 12 Responsible Consumption and Production -
SDG 13 Climate Action -
SDG 14 Life Below Water -
SDG 15 Life on Land -
SDG 16 Peace, Justice and Strong Institutions -
SDG 17 Partnerships for the Goals
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Collaborations and top research areas from the last five years
Profiles
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Graham T Allan
- Research Computing - Research Director
- Minnesota Supercomputing Institute
- GEMS Informatics Initiative
Person: Research Support
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Olena Boiko
- Research Computing - Research Professional 5
- GEMS Informatics Initiative
Person: Researchers
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AI-Enabled Tools for Working and Natural Lands Management
Runck, B. C. (PI)
1/1/26 → 6/14/26
Project: Research project
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Phase II, Potato Biotic Threat Intelligence SystemEvaluation of developed biotic risk prediction models, expanding on data sources and building data pipelines for biotic risk prediction models.
Pardey, P. G. (PI), Ranjan, A. (PI), Senay, S. D. (CoI) & Srivastava, S. (Other Role)
7/1/25 → 6/30/26
Project: Research project
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RCP - Hennepin County Baseline Heat Risk Assessment
Greco, M. (PI), Runck, B. C. (CoI) & Kielsmeier-Cook, S. (Other Role)
1/1/25 → 12/31/26
Project: Research project
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A review of integrated crop growth and hydrological models for sustainable agriculture in the midwest: Challenges and opportunities
Pourmansouri, F., Garg, A., Runck, B., Mulla, D., Sharma, V. & Sharma, V., May 31 2026, In: Agricultural Water Management. 329, 110368.Research output: Contribution to journal › Review article › peer-review
Open Access1 Link opens in a new tab Scopus citations -
Winter damage diagnostic modeling based on synthetic vegetation indices from UAV-based multispectral imaging
Li, X., Sanaeifar, A., Padilla, N., Stover, C., Kowalewski, A., Watkins, E., Runck, B., Qiao, L. & Yang, C., Mar 1 2026, In: Computers and Electronics in Agriculture. 243, 111334.Research output: Contribution to journal › Article › peer-review
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Agricultural policy and crop location: Long-run output and spatial climate risk consequences
Greyling, J. C., Pardey, P. G. & Senay, S., Jan 2025, In: American Journal of Agricultural Economics. 107, 1, p. 181-207 27 p.Research output: Contribution to journal › Article › peer-review
Open Access
Datasets
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SNP Genotyping Data for the Barley Population in "Registration of the S2MET Barley Mapping Population for Multi-Environment Genomewide Selection"
Neyhart, J. & Smith, K. P., Data Repository for the University of Minnesota, 2019
DOI: 10.13020/cp4r-0v95, http://hdl.handle.net/11299/204785
Dataset