Data From: TERRA-REF, An Open Reference Data Set From High Resolution Genomics, Phenomics, and Imaging Sensors
The ARPA-E funded TERRA-REF project generated 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 fluorescence 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 reused 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 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.
Resources in this dataset:
Resource Title: Link to dataset at Datadryad.org.
File Name: Web Page, url: https://datadryad.org/stash/dataset/doi:10.5061/dryad.4b8gtht99
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.
Funding
U.S. Department of Energy: DE-AR0000598
National Science Foundation: ACI-1548562
History
Data contact name
LeBauer, DavidData contact email
dlebauer@arizona.eduPublisher
DryadIntended use
This data publication consists of data and metadata, as described below. Plot level phenotypes, experimental meta-data, and a catalog of large files are available on Dryad. The file catalog provides the location of larger sensor files that can be accessed using the Globus file transfer service. Because the dataset contains over 400TB of sensor data files, these large files are stored at the National Center for Supercomputing Applications and are made available using the Globus file transfer service. In addition to hosting an archival copy of data on Dryad, the documentation includes instructions for browsing and accessing these data through a variety of online portals. These portals provide access to web user interfaces as well as databases, apis, and R and Python clients. In some cases it will be easier to access data through these portals using web interfaces and software libraries. Except where clearly indicated for sensor data, the structure of directories that contain data and metadata refer to the contents of the Dryad archive. There are four directories: `data`, `metadata`, `code`, and `documentation`. The TERRA-REF documentation is hosted at https://docs.terraref.org. The section "How to Access Data" (https://docs.terraref.org/user-manual/how-to-access-data) provides an overview of methods that can be used to access data beyond what is provided in this repository. There is also a PDF copy of the documentation in the file `docs.terraref.org_2020_04_06.pdf` in the `metadata/` directory. Tutorials for getting started with TERRA-REF data are available at https://terraref.github.io/tutorials and on GitHub at https://github.com/terraref/tutorials. The TERRA-REF YouTube channel hosts 1) video walkthroughs of the tutorials https://www.youtube.com/channel/UComeQAqYR5aZrXN_3K5iFGw and 2) a playlist of videos related to the project https://www.youtube.com/playlist?list=PLNgRX4VLed8213stlJp60MvVx2p6VTv6N.Use limitations
All data are released to the public domain under the CC-0 license (https://creativecommons.org/share-your-work/public-domain/cc0/). All original software are licensed with the BSD 3-clause or MIT/BSD compatible license. All software used for data processing have been archived on Zenodo and are available on GitHub in the `terraref` organization: https://github.com/terraref.Temporal Extent Start Date
2016-02-13Temporal Extent End Date
2019-10-01Theme
- Not specified
Geographic Coverage
{"type":"FeatureCollection","features":[{"geometry":{"type":"Polygon","coordinates":[[[-111.9747967,33.0764953],[-111.9747966,33.0745228],[-111.9750963,33.074485715],[-111.9750964,33.0764584],[-111.9747967,33.0764953]]]},"type":"Feature","properties":{}}]}Geographic location - description
Agricultural research field under the field scanner at the University of Arizona Maricopa Agricultural Center in Maricopa, ArizonaISO Topic Category
- environment
- farming
- geoscientificInformation
- imageryBaseMapsEarthCover
Ag Data Commons Group
- ARDN
National Agricultural Library Thesaurus terms
data collection; genomics; phenomics; image analysis; scanners; algorithms; phenotype; databases; plant breeding; durum wheat; winter; cultivars; artificial intelligence; engineering; Sorghum bicolorPending citation
- No
Public Access Level
- Public
