High-resolution phenomics dataset collected on a field-grown, EMS-mutagenized sorghum population evaluated in hot, arid conditions
Abstract Objectives The University of Arizona Field Scanner (FS) is capable of generating massive amounts of data from a variety of instruments at high spatial and temporal resolution. The accompanying field infrastructure beneath the system offers capacity for controlled irrigation regimes in a hot...
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2025-07-01
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| Online Access: | https://doi.org/10.1186/s13104-025-07407-9 |
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| author | Jeffrey Demieville Brian Dilkes Andrea L. Eveland Duke Pauli |
| author_facet | Jeffrey Demieville Brian Dilkes Andrea L. Eveland Duke Pauli |
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| description | Abstract Objectives The University of Arizona Field Scanner (FS) is capable of generating massive amounts of data from a variety of instruments at high spatial and temporal resolution. The accompanying field infrastructure beneath the system offers capacity for controlled irrigation regimes in a hot, arid environment. Approximately 194 terabytes of raw and processed phenotypic image data were generated over two growing seasons (2020 and 2022) on a population of 434 sequence-indexed, EMS-mutagenized sorghum lines in the genetic background BTx623; the population was grown under well-watered and water-limited conditions. Collectively, these data enable links between genotype and dynamic, drought-responsive phenotypes, which can accelerate crop improvement efforts. However, analysis of these data can be challenging for researchers without background knowledge of the system and preliminary processing. Data description This dataset contains formatted tabular data generated from sensing system outputs suitable for a wide range of end-users and includes plant-level bounding areas, temperatures, and point cloud characteristics, as well as plot-level photosynthetic parameters and accompanying weather data. The dataset includes approximately 422 megabytes of tabular data totaling 1,903,412 unique unfiltered rows of FS data, 526,917 cleaned rows of FS data, and 285 rows of weather data from the two field seasons. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-e8c2765aed794ba4b34ab408d85a3aba2025-08-20T04:01:44ZengBMCBMC Research Notes1756-05002025-07-011811410.1186/s13104-025-07407-9High-resolution phenomics dataset collected on a field-grown, EMS-mutagenized sorghum population evaluated in hot, arid conditionsJeffrey Demieville0Brian Dilkes1Andrea L. Eveland2Duke Pauli3School of Plant Sciences, The University of ArizonaDepartment of Biochemistry, Purdue UniversityDonald Danforth Plant Science CenterSchool of Plant Sciences, The University of ArizonaAbstract Objectives The University of Arizona Field Scanner (FS) is capable of generating massive amounts of data from a variety of instruments at high spatial and temporal resolution. The accompanying field infrastructure beneath the system offers capacity for controlled irrigation regimes in a hot, arid environment. Approximately 194 terabytes of raw and processed phenotypic image data were generated over two growing seasons (2020 and 2022) on a population of 434 sequence-indexed, EMS-mutagenized sorghum lines in the genetic background BTx623; the population was grown under well-watered and water-limited conditions. Collectively, these data enable links between genotype and dynamic, drought-responsive phenotypes, which can accelerate crop improvement efforts. However, analysis of these data can be challenging for researchers without background knowledge of the system and preliminary processing. Data description This dataset contains formatted tabular data generated from sensing system outputs suitable for a wide range of end-users and includes plant-level bounding areas, temperatures, and point cloud characteristics, as well as plot-level photosynthetic parameters and accompanying weather data. The dataset includes approximately 422 megabytes of tabular data totaling 1,903,412 unique unfiltered rows of FS data, 526,917 cleaned rows of FS data, and 285 rows of weather data from the two field seasons.https://doi.org/10.1186/s13104-025-07407-9SorghumHigh performance computingField phenotypingDroughtEMS mutagenesisThermal |
| spellingShingle | Jeffrey Demieville Brian Dilkes Andrea L. Eveland Duke Pauli High-resolution phenomics dataset collected on a field-grown, EMS-mutagenized sorghum population evaluated in hot, arid conditions BMC Research Notes Sorghum High performance computing Field phenotyping Drought EMS mutagenesis Thermal |
| title | High-resolution phenomics dataset collected on a field-grown, EMS-mutagenized sorghum population evaluated in hot, arid conditions |
| title_full | High-resolution phenomics dataset collected on a field-grown, EMS-mutagenized sorghum population evaluated in hot, arid conditions |
| title_fullStr | High-resolution phenomics dataset collected on a field-grown, EMS-mutagenized sorghum population evaluated in hot, arid conditions |
| title_full_unstemmed | High-resolution phenomics dataset collected on a field-grown, EMS-mutagenized sorghum population evaluated in hot, arid conditions |
| title_short | High-resolution phenomics dataset collected on a field-grown, EMS-mutagenized sorghum population evaluated in hot, arid conditions |
| title_sort | high resolution phenomics dataset collected on a field grown ems mutagenized sorghum population evaluated in hot arid conditions |
| topic | Sorghum High performance computing Field phenotyping Drought EMS mutagenesis Thermal |
| url | https://doi.org/10.1186/s13104-025-07407-9 |
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