Tyson L Swetnam
- Assistant Research Professor
- Assistant Research Professor, Natural Resources
- Member of the Graduate Faculty
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- Gillan, J., Ponce-Campos, G. E., Swetnam, T. L., Gorlier, A., Heilman, P., & McClaran, M. P. (2021). Innovations to expand drone data collection and analysis for rangeland monitoring. Ecosphere.
- Guo, W., Carroll, M. E., Singh, A., Swetnam, T. L., Merchant, N., Sarkar, S., Singh, A. K., & Ganapathysubramanian, B. (2021). UAS-Based Plant Phenotyping for Research and Breeding Applications. Plant Phenomics, 2021.
- Mart'inez-Meyer, E., Gonz'alez-Bernal, A., Velasco, J. A., Swetnam, T. L., Gonz'alez-Saucedo, Z. Y., Serv'in, J., L'opez-Gonz'alez, C. A., Oakleaf, J. K., Liley, S., & Heffelfinger, J. R. (2021). Rangewide habitat suitability analysis for the Mexican wolf (Canis lupus baileyi) to identify recovery areas in its historical distribution. Diversity and Distributions, 27(4), 642--654.
- Nagy, R. C., Balch, J. K., Bissell, E. K., Cattau, M. E., Glenn, N. F., Halpern, B. S., Ilangakoon, N., Johnson, B., Joseph, M. B., Marconi, S., & others, . (2021). Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community. Ecosphere, 12(12), e03833.
- Rengers, F. K., McGuire, L. A., Kean, J. W., Staley, D. M., Dobre, M., Robichaud, P. R., & Swetnam, T. (2021). Movement of sediment through a burned landscape: Sediment volume observations and model comparisons in the San Gabriel Mountains, California, USA. Journal of Geophysical Research: Earth Surface, 126(7), e2020JF006053.
- Sahneh, F., Balk, M. A., Kisley, M., Chan, C., Fox, M., Nord, B., Lyons, E., Swetnam, T., Huppenkothen, D., Sutherland, W., & others, . (2021). Ten simple rules to cultivate transdisciplinary collaboration in data science. PLoS Computational Biology, 17(5).
- Swetnam, T. L., Yool, S. R., Roy, S., & Falk, D. A. (2021). On the Use of Standardized Multi-Temporal Indices for Monitoring Disturbance and Ecosystem Moisture Stress across Multiple Earth Observation Systems in the Google Earth Engine. Remote Sensing, 13(8), 1448.
- Bartelme, R. P., Behrisch, M., Cain, E. J., Chang, R., Debnath, I., Heidorn, B., Jaiswal, P., LeBauer, D. S., Mosca, A. b., Munoz-Torres, M., & others, . (2020). Do androids dream of electric sorghum?: Predicting Phenotypes from Multi-Scale Genomic and Environmental Data using Neural Networks and Knowledge Graphs. Open Science Framework Preprint. doi:10.31219/osf.io/yx7t9
- Gedir, J. V., Cain, I., Swetnam, T. L., Krausman, P. R., & Morgart, J. R. (2020). Extreme drought and adaptive resource selection by a desert mammal. Ecosphere, 11(7), e03175.
- Nust, D., Eddelbuettel, D., Bennett, D., Cannoodt, R., Clark, D., Daroczi, G., Edmondson, M., Fay, C., Hughes, E., Kjeldgaard, L., & others, . (2020). The Rockerverse: Packages and Applications for Containerisation with R. R JOURNAL, 12(1), 437--461.
- Ponsero, A., Bartelme, R., Oliveira, A. G., Bigelow, A., Tuteja, R., Ellingson, H., Swetnam, T., Merchant, N., Oxnam, M., & Lyons, E. (2020). Ten simple rules for organizing a data science workshop. PLoS computational biology.
- Hancock, D. Y., Stewart, C. A., Vaughn, M., Fischer, J., Lowe, J. M., Turner, G., Swetnam, T. L., Chafin, T. K., Afgan, E., Pierce, M. E., & others, . (2019). Jetstream—Early operations performance, adoption, and impacts. Concurrency and Computation: Practice and Experience, 31(16), e4683.
- Heilman, P., Swetnam, T. L., Mcclaran, M. P., & Gillan, J. (2019). Estimating forage utilization with drone-based photogrammetric point clouds. Rangeland Ecology and Management, 72, 575-585.
- Mitra, B., Papuga, S. A., Alexander, M. R., Swetnam, T. L., & Abramson, N. (2019). Allometric relationships between primary size measures and sapwood area for six common tree species in snow-dependent ecosystems in the Southwest United States. Journal of Forestry Research, 1--10.
- Swetnam, T. L. (2021, November). The Airborne Environmental Observations Laboratory for Unoccupied Systems (AEOLUS). Research Insights in Semi-Arid Ecosystems. University of Arizona: USDA Agricultural Research Service.
- Webley, P., Durden, D., Swetnam, T. L., & Sullivan, D. (2021, December). Autonomous Scientific Observations: Building Reproducible Research Today and Into the Future. AGU Fall Meeting 2021. New Orleans, LA: American Geophysical Union Meeting.
- Swetnam, T. L. (2020, January). W673 The Airborne Environmental Observations Laboratory for Unoccupied Systems (AEOLUS). Plant and Animal Genome. San Diego, CA: CyVerse BIO5 Institute.More infoSmall Unmanned Aerial Systems (sUAS) are now ubiquitous and increasingly valuable tools for observing biological, ecological, and geophysical phenomena. For example, linking in situ observations from sUAS of organism phenotype to their genomic information, and environmental factors, is critical for so-called 'GxE' research. The Airborne Environmental Observations Laboratory for Unoccupied Systems (AEOLUS) establishes a cloud native cyberinfrastructure for sUAS data analyses and related research on publically-funded cloud and high performance computing resources. AEOLUS has three integrated Specific Aims: 1) the data management lifecycle through ingestion of data, automation of metadata collection, curation, and support of data publication with attribution; 2) enablement of high throughput computing for data processing, orchestration of computational resources, and enablement of interactive scientific analyses through cloud native computing, containers, and machine learning; and 3) training and adoption of these workflows via in person workshops, on-line tutorials and step-by-step instructional manuals and materials. AEOLUS utilizes CyVerse, a national academic and research cyberinfrastructure. CyVerse provides computational resources and federation to other state and national resources (XSEDE), for processing these research sUAS data, hosting data sets that researchers are required to publish, and supports open source software for locating and reusing any sUAS data from local to continental scale. This talk will focus on the first and second aims of AEOLUS, and provide information about how to access CyVerse resources.
- Swetnam, T. L. (2020, July). CyVerse Learning Institute’s foundational open science skills workshop 🍐. BCC 2020. BCC 2020 Virtual: CyVerse, BIO5 Institute.More infoCyVerse is a research cyberinfrastructure funded by the National Science Foundation’s Directorate for Biological Sciences. CyVerse provides life scientists with computational infrastructure to handle big datasets and complex analyses, thus enabling data-driven discovery. Principal investigators have reported that access to computing resources is not the bottleneck to data-driven discovery, rather the requisite skills in utilizing cyberinfrastructure and access to training are the most limiting. Our “Foundational Open Science Skills (FOSS)” was designed as a weeklong, camp-style training to address these problems. The focus of FOSS is on computational research strategies, full lifecycle data management, the FAIR data principles, collaboration skills, and using open-source software. FOSS prepares researchers to meet the growing expectations of funding agencies, publishers, and research institutions for scientific reproducibility, data accessibility, and advanced analytics. In this talk, I will discuss our lessons learned, how participants become familiar with productivity software for organizing their data science lab group, communications, and research; and how we approach teaching computational skills from laptop to cloud and high-performance computing (HPC) systems. In the last twelve months, FOSS has been taught twice to over forty early career researchers. Participants have gone on to begin their tenure-track positions, conduct funded research, written new proposals utilizing FOSS techniques and have won competitive grant awards. To contribute back to the community, we have placed our training materials online in GitHub in ReadTheDocs format, where anyone can learn from them or contribute back to the project.
- Swetnam, T. L. (2020, 12). NH028-0007 - Wrestling the four V's of small unoccupied aerial systems data in the cloud and on national cyberinfrastructure. American Geophysical Union Meeting. AGU Fall Meeting (Virtual): CyVerse, BIO5.More infoSmall unoccupied aerial systems (sUAS), colloquially referred to as ‘drones’, are essential tools for observing our world. Despite the increasing ubiquity of drone data from thousands of research projects, their availability for (re)use is limited by the complexity of managing, integrating, processing, and accessing them. The overall goal of the Airborne Environmental Observations Laboratory for Unoccupied Systems (AEOLUS), is to address the computational challenges presented in drone-based research by bridging existing national investments in cyberinfrastructure and computational hardware with open-source cloud software. By making drone data more FAIR (findable, accessible, interoperable, and reusable) and leveraging existing computational systems, AEOLUS enables advancement across a wide array of scientific applications.
- Swetnam, T. L. (2020, July). CYVERSE: INFORMATICS CYBERINFRASTRUCTURE FOR THE EARTH SCIENCES. Earth Science Information Partners Summer 2020. ESIP virtual: CyVerse, BIO5 Institute.More infoCyVerse is an NSF funded cyberinfrastructure for the Life Sciences with numerous applications in Earth Sciences. CyVerse resources are free to US based researchers and can leverage modern research computing software and hardware. This presentation was given at the Earth Science Information Partners (ESIP) Summer Meeting held online in July 2020.