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Data From: TERRA-REF, An open reference data set from high resolution genomics, phenomics, and imaging sensors

Citation

LeBauer, David et al. (2020), Data From: TERRA-REF, An open reference data set from high resolution genomics, phenomics, and imaging sensors, Dryad, Dataset, https://doi.org/10.5061/dryad.4b8gtht99

Abstract

The ARPA-E funded TERRA-REF project is generating open-access reference datasets for the study of plant sensing, genomics, and phenomics. Sensor data were generated by a field scanner sensing platform that captures color, thermal, hyperspectral, and active flourescence imagery as well as three dimensional structure and associated environmental measurements. This dataset is provided alongside data collected using traditional field methods in order to support calibration and validation of algorithms used to extract plot level phenotypes from these datasets.

Data were collected at the University of Arizona Maricopa Agricultural Center in Maricopa, Arizona. 
This site hosts a large field scanner with fifteen sensors, many of which are capable of capturing mm-scale images and point clouds at daily to weekly intervals.

These data are intended to be re-used, and are accessible as a combination of files and databases linked by spatial, temporal, and genomic information. In addition to providing open access data, the entire computational pipeline is open source, and we enable users to access high-performance computing environments.

The study has evaluated a sorghum diversity panel, biparental cross populations, and elite lines and hybrids from structured sorghum breeding populations. 
In addition, a durum wheat diversity panel was grown and evaluated over three winter seasons.
The initial release includes derived data from from two seasons in which the sorghum diversity panel was evaluated.
Future releases will include data from additional seasons and locations.

The TERRA-REF reference dataset can be used to characterize phenotype-to-genotype associations, on a genomic scale, that will enable knowledge-driven breeding and the development of higher-yielding cultivars of sorghum and wheat. 
The data is also being used to develop new algorithms for machine learning, image analysis, genomics, and optical sensor engineering.

Methods

See terraref-dryad.pdf, metadata/methods.csv, and metadata/docs.terraref.org_2020_04_06.pdf

Usage Notes

See terraref-dryad.pdf

Funding

Advanced Research Projects Agency - Energy (ARPA-E), U.S. Department of Energy, Award: DE-AR0000598