Max Burnette

National Center for Supercomputing Applications

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David LeBauer

University of Arizona

Rob Kooper

National Center for Supercomputing Applications

Noah Fahlgren

Donald Danforth Plant Science Center

Abby Stylianou

Saint Louis University

Kenton McHenry

National Center for Supercomputing Applications

Michael C. Dietze

Boston University

Craig Willis

University of Illinois Urbana-Champaign

Scott Rohde

University of Illinois Urbana-Champaign

Zhengqi Yang

Zhejiang Medical and Health Group Hangzhou Hospital

Jimmy Chen

University of California, San Diego

Cooperative Institutions

University of Illinois Urbana-Champaign

National Center for Supercomputing Applications

Saint Louis University

University of Arizona

University of Illinois System

University of California, Irvine

Max Planck Society

Michigan Technological University

Urbana University

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Clowder

Proceedings of the Practice and Experience on Advanced Research Computing (2018)

Luigi Marini Indira Gutierrez-Polo Rob Kooper Sandeep Puthanveetil Satheesan Max Burnette

Clowder is an open source data management system to support data curation of long tail data and metadata across multiple research domains and diverse data types. Institutions and labs can install and customize their own instance of the framework on local hardware or on remote cloud computing resources to provide a shared service to distributed communities of researchers. Data can be ingested directly from instruments or manually uploaded by users and then shared with remote collaborators using a web front end. We discuss some of the challenges encountered in designing and developing a system that can be easily adapted to different scientific areas including digital preservation, geoscience, material science, medicine, social science, cultural heritage and the arts. Some of these challenges include support for large amounts of data, horizontal scaling of domain specific preprocessing algorithms, ability to provide new data visualizations in the web browser, a comprehensive Web service API for automatic data ingestion and curation, a suite of social annotation and metadata management features to support data annotation by communities of users and algorithms, and a web based front-end to interact with code running on heterogeneous clusters, including HPC resources.

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10.1145/3219104.3219159

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Citations (27)

TERRA REF: Advancing phenomics with high resolution, open access sensor and genomics data

AGU Fall Meeting Abstracts (2017)

David LeBauer Rob Kooper Max Burnette Clinton Willis

Phenomics

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TERRA-REF Data Processing Infrastructure

Proceedings of the Practice and Experience on Advanced Research Computing (2018)

Max Burnette Rob Kooper J. D. Maloney Gareth S. Rohde Jeffrey Terstriep

The Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) provides a data and computation pipeline responsible for collecting, transferring, processing and distributing large volumes of crop sensing and genomic data from genetically informative germplasm sets. The primary source of these data is a field scanner system built over an experimental field at the University of Arizona Maricopa Agricultural Center. The scanner uses several different sensors to observe the field at a dense collection frequency with high resolution. These sensors include RGB stereo, thermal, pulse-amplitude modulated chlorophyll fluorescence, imaging spectrometer cameras, a 3D laser scanner, and environmental monitors. In addition, data from sensors mounted on tractors, UAVs, an indoor controlled-environment facility, and manually collected measurements are integrated into the pipeline. Up to two TB of data per day are collected and transferred to the National Center for Supercomputing Applications at the University of Illinois (NCSA) where they are processed.

Data Processing

10.1145/3219104.3219152

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bety: BETYdb 4.8

Scott Rohde MarvinJ andrewshirk Jeremy Kemball 董伊琳

10.5281/zenodo.50884

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bety: BETYdb 4.9

Scott Rohde Carl Crott mulroony Jeremy Kemball David LeBauer

10.5281/zenodo.51898

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Terraref/Extractors-Stereo-Rgb: Test Time

Max Burnette ZongyangLi Solmaz Hajmohammadi Nick Heyek

RGB color model

10.5281/zenodo.866388

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Pressure test analysis of 200-inch multicell test tank

Max Burnette Roman Šturm

Hydrostatic test

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What Does TERRA-REF's High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer Vision Community?

arXiv (Cornell University) (2021)

David LeBauer Max Burnette Noah Fahlgren Rob Kooper Kenton McHenry

A core objective of the TERRA-REF project was to generate an open-access reference dataset for the evaluation of sensing technologies to study plants under field conditions. The TERRA-REF program deployed a suite of high-resolution, cutting edge technology sensors on a gantry system with the aim of scanning 1 hectare (10$^4$) at around 1 mm$^2$ spatial resolution multiple times per week. The system contains co-located sensors including a stereo-pair RGB camera, a thermal imager, a laser scanner to capture 3D structure, and two hyperspectral cameras covering wavelengths of 300-2500nm. This sensor data is provided alongside over sixty types of traditional plant phenotype measurements that can be used to train new machine learning models. Associated weather and environmental measurements, information about agronomic management and experimental design, and the genomic sequences of hundreds of plant varieties have been collected and are available alongside the sensor and plant phenotype data. Over the course of four years and ten growing seasons, the TERRA-REF system generated over 1 PB of sensor data and almost 45 million files. The subset that has been released to the public domain accounts for two seasons and about half of the total data volume. This provides an unprecedented opportunity for investigations far beyond the core biological scope of the project. The focus of this paper is to provide the Computer Vision and Machine Learning communities an overview of the available data and some potential applications of this one of a kind data.

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Workshop Report: Container Based Analysis Environments for Research Data Access and Computing

Craig Willis Matt Turk Gregory H. Bauer Max Burnette M. Carrasco Kind

Data access

10.5281/zenodo.1045240

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What Does TERRA-REF's High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer Vision Community?

arXiv (Cornell University) (2021)

David LeBauer Max Burnette Noah Fahlgren Rob Kooper Kenton McHenry

10.48550/arxiv.2107.14072

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