The following protocols have been contributed by TERRA-REF team members:

• Field Scanner - Coming 2017

• Genomics - Coming 2017

• Manually Collected Field Data

• Phenotractor

• UAV

A template for documenting protocols is available here.

Field phenotyping research sites

Maricopa Agricultural Center (MAC), Arizona

The Maricopa field site is located at the the University of Arizona Maricopa Agricultural Center and USDA Arid Land Research Station in Maricopa, Arizona. At this site, we have deployed the following phenotyping platforms.

• The Lemnatec Scanalyzer Field System is the largest field crop analytics robot in the world. This high-throughput phenotyping field-scanning robot has a 30-ton steel gantry that autonomously moves along two 200-meter steel rails while continuously imaging the crops growing below it with a diverse array of cameras and sensors.

Lemnatec Field Sensors

• The PhenoTractor is fitted with a sensor frame that supports a real time kinematic (RTK) satellite navigation antenna, a sonar transducer, an infrared temperature (IRT) scanner, and three GreenSeeker crop sensing systems.

• UAV data: weekly flights with RGB and multispectral cameras

• Manually Collected Field Data - Data will are collected manually using standard field methods. These measurements are used to calibrate and validate phenotypes derived from sensor-collected data.

Controlled-environment phenotyping

Donald Danforth Plant Science Center, Missouri

The Bellwether Foundation Phenotyping Facility is a climate controlled 70 m2 growth house with a conveyor belt system for moving plants to and from fluorescence, color, and near infrared imaging cabinets. This automated, high-throughput platform allows repeated non-destructive time-series image capture and multi-parametric analysis of 1,140 plants in a single experiment.

Genomics

Genomic data includes whole-genome resequencing data from the HudsonAlpha Institute for Biotechnology, Alabama for 384 samples for accessions from the sorghum Bioenergy Association Panel (BAP) and genotyping-by-sequencing (GBS) data from Kansas State University for 768 samples from a population of sorghum recombinant inbred lines (RIL).

TERRA-REF uses a suite of databases and software components that are described below.

Clowder (sensor data and computation management with web user interface)

Clowder is the primary system used to organize, annotate, and process raw data generated by the phenotyping platforms as well as information about sensors. Use Clowder to explore the raw TERRA-REF data, perform exploratory analysis, and develop custom extractors. For more information, see Using Clowder.

Globus Connect (large data transfer)

Raw data is transferred to the primary TERRA-REF compute pipeline using Globus Online. Globus also provides access to TERRA REF files, but this is not a primary portal and metadata in Clowder may be required to locate and interpret these files. Use Globus Online when you want to transfer data from the TERRA-REF system for local analysis by accessing the Terraref endpoint. For more information, see Using Globus.

BETYdb (phenotype data)

BETYdb is a database and web interface to the trait / phenotype data and agronomic metadata. This is where you can find plant and plot level trait data as well as plot locations and other information associated with agronomic experimental design. Use BETYdb to access derived trait and agronomic data. For more information, see Using BETYdb.

Algorithms (a.k.a. 'extractors')

Plant CV

Plant CV is an imaging processing package specific for plants that is built upon open-source software platforms OpenCV, NumPy, and MatPlotLib. Plant CV is used for trait identification, the output is stored in both Clowder and BETYdb.

Other Algorithms

Each step in the pipeline is performed by an algorithm. These are maintained in the TERRA REF GitHub organization in repositories with names that begin in extractors-* such as github.com/terraref/extractors-hyperspectral.

Analysis Tools

The NDS Workbench enables users to access the large filesystem and databases with familiar development environments. We provide a variety of environments for developing new algorithms and integrating them into the TERRA REF pipeline. These include RStudio and Jupyter Notebooks configured for specific use cases such as sensor data processing, trait analysis, database queries, and piepline development.

CoGe

CoGe contains genomic information and sequence data. For more information, see Using CoGe.

Abstract

Automated VIS and NIR imaging in a controlled growth environment

Materials

ProMix BRK20 + 14-14-14 Osmocote pots; pre-filled by Hummert Sorghum seed

Equipment

• Conviron Growth House

• LemnaTec moving field conveyor belt system

• Scanalyzer 3D platform

Procedures

Planting

• Plant directly into phenotyping pots 

Chamber Conditions

Pre-growth (11 days) and Phenotying (11 days)

• 14 hour photoperiod

• 32oC day/22oC night temperature

• 60% relative humidity

• 700 umol/m2/s light

Watering Conditions

• Prior to phenotyping, plants watered daily

• The first night after loading, plants watered 1× by treatment group to 100% field capacity (fc)

• Days 2 – 12, plants watered 2× daily by treatment group (100% or 30% FC) to target weight

Automation

• Left shift lane rotation within each GH, during overnight watering jobs

• VIS (TV and 2 x SV), NIR (TV and 2 x SV) imaging daily

Recipes

• Field capacity = 200% GWC (200 g water/100 g soil), based upon extensive GWC testing done by Skyler Mitchell

• Target weight (fc) = [(water weight at % fc) + [(average weight of carrier/saucer) + (dry soil weight) + (pot weight)]

• Water weight at 100% fc = dry soil weight * (%GWC/100)

• Water weight at 30% fc = water weight at 100% fc * 0.30

References

This section describes sensor calibration processes and how to access additional information about specific calibration protocols, calibration targets, and associated reference data.

LemnaTec Field Scanalyzer

Calibration protocols

Calibration protocols have been defined by LemnaTec in cooperation with vendors and the TERRA-REF Sensor Steering Committee. Draft calibration protocols are currently in and have been incorporated into the .

A detailed calibration process is also provided for the , with further information below.

Calibration targets

The following calibration targets are available:

• Aluminum 3D test object

Sensor Calibration

Environmental sensor calibration

The environmental sensor has been calibrated by LemnaTec. The output of the spectrometer is raw counts, users will need to use the calibration files to convert to units of µW m-2 s-1, taking into account the bandwidth of the chip (0.4nm) if converting to µmol m-2 s-1.

Calibration reference data is available via Globus /sites/ua-mac/EnvironmentLogger/CalibrationDataor in Github

Hyperspectral calibration

Sources:

For the SWIR and VNIR sensors, factory calibration is repeated each year using the calibration lamp provided by Headwall. To convert the hyperspectral exposure image to reflectance requires the wavelength-dependent, factory calibrated reflectance of the spectralon at all VNIR and SWIR wavelengths and a good image of a spectralon panel from each camera. This includes periodic measurements of a white spectralon reflectance panel run with 20ms exposure to match panel calibration.

Dark reference measurement:

• VNIR

  • Dark measurement for VNIR camera is taken at exposure times 20, 25, 30, 35, 40, 45, 50, 55ms.

  • Data is in the same hypercube format with 180-200 lines, 955 bands, and 1600 pixel samples.

  • Measurement was done using Headwall software, so there is no LemnaTec json file.

  • The name of the folder is the exposure time. "current setting exposure" is showing the exposure time in ms.

  • Custom workflow to process the calibration files.

• SWIR;

  • Dark counts handled internally, so no calibration files are necessary.

White reference measurement:

• VNIR

  • White measurement for VNIR camera is taken at exposure times 20, 25, 30, 25, 40, 25, 50, 55 ms.

  • The name of the folder is the exposure time. Data are 1600 sample, 955 bands and 268-298 lines. White reference is located in the lines between 60 to 100 and in the samples between 600 to 1000.

  • The white reference scans was done at around 1pm ( one hour after solar noon). I don’t see the saturation with 20ms and 25ms exposure time.

  • For the calibration, this needs to be subtracted from the dark current in the same sample, band and exposure time.

  • In the following file, I stored an extra file named "CorrectedWhite_raw". This file includes only a single white pixel( one line, one sample) in 955 bands for each exposure time. Data is stored in the similar format but it doesnot include any extra files like frameIndex, image, header ,..

    https:\/\/drive.google.com\/file\/d\/0ByXIACImwxA7dVNHa3pTYkFjdWc\/view?usp=sharing

    Let me know if you have issue with opening the files.

Stereo 3D height scanner

LemnaTec applied calibration matrix to the 3D scanners.

UAV calibration

• There are calibrated reference panels and blackbody images taken with UAV sensors before and\/or after the each flight mission.

• There are also 4 white,grey and black panels laid on the ground during the flight. Knowing the proprieties of these targets would helps us radiometrically correct the UAV images.

• What are the reflectance properties of calibrated reference panels for multispectral camera?

• What are the thermal properties of reference target for thermal camera?

• What are the reflectance properties of the reference panels laid on the ground during the flight?

• Is there any other ground truth data collected during the flight for aerial data processing, such as surface reflectance, temperature and other environmental data? These type of data would be helpful for further atmospheric correction.

• There are two sets of reference reflectance panels: one that PDS uses, it is small, PDS will need to provide the specs; the second set consists of 4 8m x 8m canvas tarps, nominally 4%, 8%, 48% and 64% reflectance across vnir bands.

• We have data from an ASD spectrometer on many but not all flight days that can be used to give the most accurate actual reflectances for each. Kelly Thorp can provide the numbers. The tarps are old and the dark targets are more reflective than nominal and light targets darker than nominal.

• The thermal target is a passive black body, I dont know the surface emissivity, it is around 0.97. There are thermistors in the back of the metal plate to provide physical temperature of the body. The black body is stored in a wood box, insulated, to dampen thermal variations. Id guess it is accurate to 2C.

• There is a met station on farm for air temperature, humidity, wind speed, wind direction, solar radiation. we have a sun photometer that can be used for atmospheric water vapor content but currently dont deploy it routinely.

Halogen spectrum

Spectral response data

Relative spectral response data is available for the following sensors:

• NDVI

• PRI

• PAR

Calibration data

About this book

This book describes the TERRA-REF data collection, computing, and analysis pipelines. The following links provide quick access to:

About TERRA-REF

The ARPA-E-funded Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) program aims to transform plant breeding by using remote sensing to quantify plant traits such as plant architecture, carbon uptake, tissue chemistry, water use, and other features to predict the yield potential and stress resistance of 300+ diverse Sorghum lines.

The data storage and computing system provides researchers with access to the reference phenotyping data and analytics resources using a high performance computing environment. The reference phenotyping data includes direct measurements and sensor observations, derived plant phenotypes, and genetic and genomic data.

Our objectives are to ensure that the software and data in the reference data and computing pipeline are interoperable, reusable, extensible, and understandable. Providing clear definitions of common formats will make it easier to analyze and exchange data and results.

Versions

• The first edition (alpha release) was published November 2016.

• The second edition (beta release) will be published November 2018

• The third edition (version 1.0) will be published November 2019

Authors: Rick Ward

Abstract

Materials

Platforms

• SenseFly eBee fixed-wing drone

• Hexacopter

Cameras

UAV data are collected using one of three cameras:

• 5-band MicaSense RedEdge

• 4-band + RGB Parrot Sequoia

• SenseFly thermal thermoMap

Cameras are carried singly or in tandem on the SenseFly eBee fixed-wing drone (Sequoia and thermoMap, individually only), or a hexacopter (RedEdge or Sequoia, individually or in tandem).

Procedure

Flight

Standard flight altitude is 44m with 75% image overlap (both sequentially and laterally), and missions are programmed and managed by either Mission Planner or senseFly eMotion.

Calibration

No radiometric calibration was conducted as of Nov 5, 2016.

Analysis

Pix4D software was used to generate gray-scale orthomosaic geotiff files containing NDVI data after georegistration to the WGS84/UTM 12 N coordinate reference system using three to five 2D geo-located ground control points. These are manually matched to 5-40 images each. Ground control points for the Lemnatec Field Scanner are on the concrete pylons and were geolocated using an RTK base station maintained by the USDA-ARS at Maricopa (see section on geospatial information).

QGIS software was used to confirm geospatial alignment of NDVI geotiffs with shape files containing geolocated positions of the rail foundations. A shape file containing polygons aligning with the middle two rows of each of the 350 experimental units (for sorghum crop Aug-Nov 2016) was kindly generated by Dr. A French of USDA-ARS. Zonal Statistics in QGIS was used to calculate NDVI means for each plot polygon.

References

• MicaSense: https://www.micasense.com

• SenseFly https://www.sensefly.com

• QGIS https://www.qgis.org

Phenotyping

The TERRA-REF project is phenotyping the same genotypes of sorghum at multiple locations

• Automated Lemnatec Scanalyzer Field System at Maricopa Agricultural Center (MAC)

• PhenoTractors on parallel plots at MAC and Kansas State University (KSU)

• UAV platform on parallel plots at KSU

• Controlled-environment phenotyping systems at the Danforth Center

  • Manually collected field data at all locations

Genotyping

Whole genome resequencing is being carried out on ~400 sorghum accessions to understand the landscape of genetic variation in the selected germplasm and enable high-resolution mapping of bioenergy traits with genome wide association studies (GWAS). Additionally, ~200 sorghum recombinant inbred lines (RILs) will be characterized with ~400,000 genetic markers using genotyping-by-sequencing (Morris et al., 2013) for trait dissection in the RIL population and testcross hybrids of the RIL population.

Maricopa Agricultural Center (MAC), Arizona

Three hundred thirty one lines were planted in 2016. Plantings occurred both under and west of the gantry system.

• Experiments planned for 2016

• Field layouts under the gantry and west of the gantry in 2016.

Automated Field Scanner System

The Lemnatec Scanalyzer Field Scanner System is the largest field crop analytics robot in the world. This high-throughput phenotyping field-scanning robot has a 30-ton steel gantry that autonomously moves along two 200-meter steel rails while continuously imaging the crops growing below it with a diverse array of cameras and sensors.

Twelve sensors are attached to the system. Detailed information for each sensor including name, variable measured, and field of view are available here. The planned sensor missions and their objectives for 2016 are available here.

Phenotractor

The PhenoTractor at MAC is fitted with a sensor frame that supports a real time kinematic (RTK) satellite navigation antenna, a sonar transducer, an infrared temperature (IRT) scanner, and three GreenSeeker crop sensing systems.

UAV

Coming 2017

Manually collected field data

In progress

Automated controlled-environment phenotyping, Missouri

The Scanalyzer 3D platform at the Bellwether Foundation Phenotyping Facility at the Donald Danforth Plant Science Center consists of multiple digital imaging chambers connected to the Conviron growth house by a conveyor belt system, resulting in a continuous imaging loop. Plants are imaged from the top and/or multiple sides, followed by digital construction of images for analysis.

• RGB imaging allows visualization and quantification of plant color and structural morphology, such as leaf area, stem diameter and plant height.

• NIR imaging enables visualization of water distribution in plants in the near infrared spectrum of 900–1700 nm.

• Fluorescent imaging uses red light excitation to visualize chlorophyll fluorescence between 680 – 900 nm. The system is equipped with a dark adaptation tunnel preceding the fluorescent imaging chamber, allowing the analysis of photosystem II efficiency.

The LemnaTec software suite is used to program and control the Scanalyzer platform, analyze the digital images and mine resulting data. Data and images are saved and stored on a secure server for further review or reanalysis.

Kansas State University

• PhenoTractor - Coming 2017

• UAV - Coming 2017

• Manually collected field data - Coming 2017

HudsonAlpha - Genomics

• Coming 2017

Experiment LT1A (TM015)

ATLAS LEOTI PI_144134 PI_145619 PI_145626 PI_145632 PI_145633 PI_146890 PI_147224 PI_152591 PI_152651 PI_152694 PI_152727 PI_152728 PI_152730 PI_152733 PI_152751 PI_152771 PI_152816 PI_152828 PI_152860 PI_152862 PI_152923 PI_152961 PI_152963 PI_152965 PI_152966 PI_152967 PI_152971 PI_153877 PI_154750 PI_154844 PI_154846 PI_154944 PI_154987 PI_154988 PI_155149 PI_155516 PI_155760 PI_155885 PI_156178 PI_156203 PI_156217 PI_156268 PI_156326 PI_156330 PI_156393 PI_156463 PI_156487 PI_156871 PI_156890 PI_157030 PI_157033 PI_157035 PI_157804 PI_167093 PI_170787 PI_175919 PI_176766 PI_179749 PI_180348 PI_181080 PI_181083 PI_195754 PI_196049 PI_196583 PI_196586 PI_196598 PI_197542 PI_19770 PI_213900 PI_217691 PI_218112 PI_221548 PI_221651 PI_226096 PI_22913 PI_229841 PI_251672 PI_253986 PI_255239 PI_255744 PI_257599 PI_257600 PI_266927 PI_267573 PI_273465 PI_273969 PI_276837 PI_297130 PI_297155 PI_297171 PI_302252 PI_303658 PI_329256 PI_329286 PI_329299 PI_329300 PI_329301 PI_329310 PI_329319 PI_329326 PI_329333 PI_329338 PI_329351 PI_329394 PI_329403 PI_329435 PI_329440 PI_329465 PI_329466 PI_329471 PI_329473 PI_329478 PI_329480 PI_329501 PI_329506 PI_329510 PI_329511 PI_329517 PI_329518 PI_329519 PI_329541 PI_329545 PI_329546 PI_329550 PI_329569 PI_329570 PI_329584 PI_329585 PI_329605 PI_329614 PI_329615 PI_329618 PI_329632 PI_329644 PI_329645 PI_329646 PI_329665 PI_329673 PI_329699 PI_329702 PI_329710 PI_329711 PI_329841 PI_329843 PI_329864 PI_329865 PI_330168 PI_330169 PI_330181 PI_330182 PI_330184 PI_330185 PI_330195 PI_330196 PI_330199 PI_330796 PI_330803 PI_330807 PI_330833 PI_330858 PI_337680 PI_337689 PI_35038 PI_365512 PI_452542 PI_452619 PI_452692 PI_453696 PI_455217 PI_455221 PI_455280 PI_455301 PI_455307 PI_505717 PI_505722 PI_505735 PI_506030 PI_506069 PI_506114 PI_506122 PI_508366 PI_510757 PI_511355 PI_513898 PI_514456 PI_521019 PI_521152 PI_521280

Experiment LT1B (TM016)

PI_521290 PI_524475 PI_525049 PI_52606 PI_526905 PI_527045 PI_533792 PI_533902 PI_533998 PI_534120 PI_534165 PI_535783 PI_535785 PI_535792 PI_535793 PI_535794 PI_535795 PI_535796 PI_540518 PI_542718 PI_550604 PI_561840 PI_562730 PI_562732 PI_562781 PI_562897 PI_562970 PI_562971 PI_562981 PI_562982 PI_562985 PI_562990 PI_562991 PI_562994 PI_562997 PI_562998 PI_563002 PI_563006 PI_563009 PI_563020 PI_563021 PI_563022 PI_563032 PI_563196 PI_563222 PI_563295 PI_563329 PI_563330 PI_563331 PI_563332 PI_563338 PI_563348 PI_563350 PI_563355 PI_564163 PI_566819 PI_568717 PI_569090 PI_569097 PI_569148 PI_569244 PI_569264 PI_569416 PI_569418 PI_569419 PI_569420 PI_569421 PI_569422 PI_569423 PI_569425 PI_569427 PI_569433 PI_569435 PI_569443 PI_569444 PI_569445 PI_569447 PI_569452 PI_569453 PI_569454 PI_569455 PI_569457 PI_569458 PI_569459 PI_569460 PI_569462 PI_569465 PI_569886 PI_570031 PI_570038 PI_570042 PI_570047 PI_570053 PI_570071 PI_570073 PI_570074 PI_570075 PI_570076 PI_570085 PI_570087 PI_570090 PI_570091 PI_570096 PI_570106 PI_570109 PI_570110 PI_570114 PI_570145 PI_570254 PI_570371 PI_570373 PI_570388 PI_570393 PI_570400 PI_570431 PI_573193 PI_576399 PI_576401 PI_583832 PI_585406 PI_585448 PI_585452 PI_585454 PI_585461 PI_585467 PI_585577 PI_585608 PI_585954 PI_585961 PI_585966 PI_586435 PI_586443 PI_586541 PI_593916 PI_619807 PI_619838 PI_620072 PI_620157 PI_63715 PI_641807 PI_641810 PI_641815 PI_641817 PI_641821 PI_641824 PI_641829 PI_641830 PI_641835 PI_641836 PI_641850 PI_641860 PI_641862 PI_641892 PI_641909 PI_642998 PI_643008 PI_643016 PI_646242 PI_646251 PI_646266 PI_651491 PI_651493 PI_651495 PI_651496 PI_651497 PI_653616 PI_653617 PI_655972 PI_655978 PI_655981 PI_655983 PI_656015 PI_656026 PI_656035 PI_656065 PI_92270 PI_329471 PI_329506 PI_329569 PI_337680 PI_452692 PI_455217 PI_152730 PI_329311 NTJ2 M81e CK60B B_Az9504 ICSV700 China 17

Abstract

Materials

• barcode scanning protractor

• barcode scanning ruler

• ceptometer (Decagon AccuPAR LP-80)

• digital caliper

• drying oven

• forage chopper

• hand shears

• infrared thermometer

• juice extractor

• leaf area meter (Li-Cor 3100, Li-Cor Inc.)

• leaf porometer (SC-1 Leaf Porometer, Decagon Devices)

• leaf punch

• meter stick

• paper bags

• portable photosynthesis system (Li-Cor 6400, Li-Cor Inc.)

• scale

• SPAD Meter (SPAD 502 Plus Chlorophyll Meter, Minolta)

• spray paint

Equipment

Procedures

References

• Pérez-Harguindeguy N., Díaz S., Garnier E., Lavorel S., Poorter H., Jaureguiberry P., Bret-Harte M. S., CornwellW. K., Craine J. M., Gurvich D. E., Urcelay C., Veneklaas E. J., Reich P. B., Poorter L., Wright I. J., Ray P., Enrico L.,Pausas J. G., de Vos A. C., Buchmann N., Funes G., Quétier F., Hodgson J. G., Thompson K., Morgan H. D., ter Steege H., van der Heijden M. G. A., Sack L., Blonder B., Poschlod P., Vaieretti M. V., Conti G., Staver A. C.,Aquino S., Cornelissen J. H. C. (2013) New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany 61 , 167–234. http://dx.doi.org/10.1071/BT12225

• Vanderlip RL. 1993. How a sorghum plant develops. Manhattan, KS, USA: Kansas State University Cooperative Extension. Field Experiments in Crop Physiology. 2013, Jan 13. In PrometheusWiki. Retrieved 15:03,June 21, 2016, from http://www.publish.csiro.au/prometheuswiki/tiki-pagehistory.php?page=Field Experiments in Crop Physiology&preview=41

Photosynthesis / leaf chemistry from hyperspectral data references:

• Shawn Serbin et al - Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature 2011 J Exp Bot

• Mapping biochemistry and photosynthetic metabolism in ecosystems using imaging spectroscopy (Presentation) - Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy 2015 Remote Sensing of the Environment

• Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species 2014 Ecological Applications

• Additional Draft Protocols are available at https://docs.google.com/document/d/1iP8b97kmOyPmETQI_aWbgV_1V6QiKYLblq1jIqXLJ84/edit#

Authors: Matthew Maimaitiyiming, Wasit Wulamu, and David LeBauer

Center for Sustainability, Saint Louis University, St. Louis, MO 63108

Abstract

This document provides a brief summary of methods, procedures, and workflows to process the tractor data.

Content modified from Andrade-Sanchez et al 2014.

Materials

• Tractor

• Sensors

  • Sonar Transducer

  • GreenSeeker Multispectral Radiometer

  • Infrared Thermal Sensor

Methods

Tractor

The Tractor-based plant phenotyping system (Phenotractor) was built on a LeeAgra 3434 DL open rider sprayer. The vehicle has a clearance of 1.93 m. A boom attached to the front end of the tractor frame holds the sensors, data loggers, and other instrumentation components including enclosure boxes and cables. The boom can be moved up and down with sensors remaining on a horizonal plane. An isolated secondary power source supplies 12-V direct current to the electronic components used for phenotyping.

Sensors

The phenotractor was equipped with three types of sensors for measuring plant height, temperature and canopy spectral reflectance. A RTK GPS was installed on top of the tractor, see the figure below.

Plant Height with Sonar

The distance from canopy to sensor position was measured with a sonar proximity sensor ($S\rm{output}$, in mm). Canopy height ($CH$) was determined by combining sonar and GPS elevation data (expressed as meter above sea level). An elevation survey was conducted to determine a baseline reference elevation ($E\rm{ref}$) for the gantry field. CH was computed according to the following equation:

where $E_rm{s}$ is sensor elevation, which was calculated by subtracting the vertical offset between the GPS antenna and sonar sensor from GPS antenna elevation.

Thermal Sensor

An Apogee SI-121 Infrared radiometer (IRT) sensors were used measure canopy temperature and temperature values were recoded as degree Celsius (°C).

Multispectral Radiometer

Canopy spectral reflectance was measured with GreenSeeker sensors and the reflectance data were used to calculate NDVI (Normalized Difference Vegetation Index). GreenSeeker sensors record reflected light energy in near infrared (780 ± 15 nm) and red (660 ± 10 nm ) portion electromagnetic spectrum from top of the canopy by using a self-illuminated light source. NDVI was calculated using following equation:

Where $\rho\rm{NIR}$ and $\rho\rm{red}$ and ρ_red represent fraction of reflected energy in near infrared and red spectral regions, respectively.

Georeferencing

Georefencing was carried out using a specially developed Quantum GIS (GGIS, www.qgis.org ) plug-in by Andrade-Sanchez et al. (2014) during post processing. Latitude and longitude coordinates were converted to UTM coordinate system. Offset from the sensors to the GPS position on the tractor heading were computed and corrected. Next, the tractor data, which uses UTM Zone 12 (MAC coordinates), was transformed to EPSG:4326 (WGS84) USDA coordinates by performing a linear shifting as follows:

• Latitude: $U_y = M_y – 0.000015258894$

• Longitude: $U_x = M_x + 0.000020308287$

where $U_y$ and $U_x$ are latitude and longitude in USDA coordinate system, and $M_y$ and $M_x$ are latitude and longitude in MAC coordinate system (see section on geospatial coordinate systems). Finally, georeferenced tractor data was overlaid on the gantry field polygon and mean value for each plot/genotype was calculated using the data points that fall inside the plot polygon within ArcGIS Version 10.2 (ESRI. Redlands, CA).

References

Andrade-Sanchez, Pedro, Michael A. Gore, John T. Heun, Kelly R. Thorp, A. Elizabete Carmo-Silva, Andrew N. French, Michael E. Salvucci, and Jeffrey W. White. "Development and evaluation of a field-based high-throughput phenotyping platform." Functional Plant Biology 41, no. 1 (2014): 68-79. doi:10.1071/FP13126

Location: The Automated controlled-environment phenotyping at the Donald Danforth Plant Science Center Bellwether Foundation Phenotyping Facility

The consists of multiple digital imaging chambers connected to the Conviron growth house by a conveyor belt system, resulting in a continuous imaging loop. Plants are imaged from the top and/or multiple sides, followed by digital construction of images for analysis.

• RGB imaging allows visualization and quantification of plant color and structural morphology, such as leaf area, stem diameter and plant height.

• NIR imaging enables visualization of water distribution in plants in the near infrared spectrum of 900–1700 nm.

• Fluorescent imaging uses red light excitation to visualize chlorophyll fluorescence between 680 – 900 nm. The system is equipped with a dark adaptation tunnel preceding the fluorescent imaging chamber, allowing the analysis of photosystem II efficiency.

The LemnaTec software suite is used to program and control the Scanalyzer platform, analyze the digital images and mine resulting data. Data and images are saved and stored on a secure server for further review or reanalysis.

Experiments LT1A (TM015) and LT1B (TM016)

Duration: 10 days on LemnaTec platform

Experimental Design:

• 3 replicates of were grown in a randomized complete block design

• Watering regimes = 30% FC and 100% FC

• Drought conditions were imposed 10 days after planting

• Plants were imaged daily for 10 days (11-20 DAP) and sampled at 20 days after planting

• Experiment was repeated twice to phenotype the full BAP (Reps 1A and 1B)

Field phenotyping

• Kansas State University - coming 2017

Controlled-environment phenotyping

Genomics

• HudsonAlpha Institute for Biotechnology, Alabama - coming 2017

Genomic data includes whole-genome resequencing data from the HudsonAlpha Institute for Biotechnology, Alabama for 384 samples for accessions from the sorghum Bioenergy Association Panel (BAP) and genotyping-by-sequencing (GBS) data from Kansas State University for 768 samples from a population of sorghum recombinant inbred lines (RIL).

Whole-genome resequencing

Experimental Design:

• were sequenced to an average depth of ~25x.

• Shotgun sequencing (127-bp paired-end) was done using an Illumina X10 instrument at the HudsonAlpha Institute for Biotechnology.

• Variant calling was done using a at the Danforth Center.

• See the page to get access to raw and derived data products.

Genotyping-by-sequencing

Experimental Design:

• were sequenced using a GBS approach.

Real-time sensor data transfer by file number and size can be viewed here.

See Data Products for more information about individual data products and How to Access Data for instructions to access the data products.

Summary

Fluorescence intensity data is collected using the PSII camera.

Raw data access

Fluorescence intensity data is available via Clowder and Globus:

• Clowder: __ps2Top collection

• Globus path: /sites/ua-mac/raw_data/ps2top

• Sensor information: LemnaTec PSII

For details about using this data via Clowder or Globus, please see Data Access section.

Computational pipeline

Multispectral extractor

• Description: Raw image output is converted to a raster format (netCDF\/GeoTIFF)

• Output: /sites/ua_mac/Level_1/ps2top

Details

There are 102 bin files. The first (index 0) is an image taken right before the LED are switched on (dark reference). Frame 1 to 100 are the 100 images taken, with the LEDs on. In binary file 102 (index 101) is a list with the timestamps of each frame of the 100 frames.

Right now the LED on timespan is 1s thus the first 50 frames are taken with LEDs on the latter 50 frames with LED off..

See also

• Geospatial information

see

Genomic data includes whole-genome resequencing data from the HudsonAlpha Institute for Biotechnology, Alabama for 384 samples for accessions from the sorghum Bioenergy Association Panel (BAP) and genotyping-by-sequencing (GBS) data from Kansas State University for 768 samples from a population of sorghum recombinant inbred lines (RIL).

These data are available to Beta Users and require permission to access. The form to sign up for our beta user program is at http://terraref.org/beta. Once you have signed up for our beta user program you can access genomics data in one of the following locations:

• Download via Globus.

• The NDS Analysis Workbench, which provides container-based computing environments including Jupyter, Rstudio, and Python IDE.

• The CyVerse Data Store for download or use within the CyVerse computing environment.

• The CoGe computing environment.

See Data use policy before continuing.

The data is structured on both the TERRA-REF strorage (accessible via Globus and Workbench) and CyVerse Data Store infrastructures as follows:

|-terraref
|  |-genomics
|  |  |-raw_data
|  |  |  |-bap
|  |  |  |  |-resequencing
|  |  |  |-ril
|  |  |  |  |-gbs
|  |  |-derived_data
|  |  |  |-bap
|  |  |  |  |-resequencing
|  |  |  |  |  |-danforth_center
|  |  |  |-ril
|  |  |  |  |-gbs
|  |  |  |  |  |-kansas_state

Whole-genome resequencing

Raw data

Raw data are in bzip2 FASTQ format, one per read pair (*_R1.fastq.bz2 and *_R2.fastq.bz2). 384 samples are available. For a list of the lines sequenced, see the sample table.

Derived data

Data derived from analysis of the raw resequencing data at the Danforth Center (version1) are available as gzipped, genotyped variant call format (gVCF) files and the final combined hapmap file.

Genotyping-by-sequencing (GBS)

Raw data

Raw data are in gzip FASTQ format. 768 samples are available. For a list of lines sequenced, see the sample table.

Derived data

Combined genotype calls are available in VCF format.

Overview

This user manual is divided into the following sections:

• Data Products: A summary of the available data products and the processes used to create them

• Data Access: Instructions for how to access the data products using Clowder, Globus, BETYdb, and CoGe

• Description of the scientific objectives and experimental design

• Data use policy: Information about data use and attribution

• User Tutorials: In-depth examples of how to access and use the TERRA-REF data

What data is available?

• Raw output from sensors deployed on Lemnatec field and greenhouse systems, UAVs and tractors

• Manually-collected fieldbooks and associated protocols

• Derived data, including phenomics data, from computational approaches

• Genomic pipeline data

Audience

The TERRA-REF reference dataset may be of interest to a variety of research communities including:

• Computer vision\/remote sensing\/image analysis (raw sensor data and metadata)

• Physiologists (plants and how they are growing)

• Robotics (gantry\/location\/orientation)

• Breeders (derived traits)

• Genomics\/bioinformaticians (genomics data)

The following table lists available TERRA-REF data products. The table will be updated as new datasets are released. Links are provided to pages with detailed information about each data product including sensor descriptions, algorithm (extractor) information, protocols, and data access instructions.

See also

• What data is available?

• Sensor calibration

• Fieldbooks and Protocols

• Data standards

• Geospatial information

Authors:

Abstract

Materials

Equipment

Procedure

Recipes

References

Summary

3D point cloud data is collected using the Fraunhofer 3D laser scanner. Custom software installed at Maricopa converts .png output to the .ply point clouds. The .ply point clouds are converted to georeferenced .las files using the 3D point cloud extractor

Level 1 data products are provided in both .las and .ply formats.

For each scan, there are two .ply files representing two lasers, one on the left and the other on the right. These are combined in the .las files.

Sensor information

• Fraunhofer 3D scanner collection

For details about using this data via Clowder or Globus, please see Data Access section.

Data access

Data is available via Clowder, Globus, and Workbench.

• Clowder: Laser Scanner 3D LAS

• Globus or Workbench File System:

  • LAS /sites/ua_mac/raw_data/laser3D_las

  • PLY /sites/ua_mac/raw_data/laser3D

Computational pipeline

Raw sensor output (PLY) is converted to LAS format using the ply2las extractor

ply2las extractor

See also

• Geospatial information

Known issues

• The position of the lasers is affected by temperature. We plan to add a correction for temperature that will adjust for this effect. See terraref/reference-data#161

Meteorological data will use Climate Forecasting 'standard names' and 'canonical units' conventions. CF is widely used in climate, meteorology, and earth sciences.

Here are some examples (note that we can change from canonical units to match the appropriate scale, e.g. "C" instead of "K"; time can use any base time and time step (e.g. hours since 2015-01-01 00:00:00 UTC, etc. But the time zone has to be UTC, where 12:00:00 is approx (+/- 15 min). solar noon at Greenwich.

• standard_name is CF-convention standard names (except irrigation)

• units can be converted by udunits, so these can vary (e.g. the time denominator may change with time frequency of inputs)

Running The Pipeline

Before the Running

The pipepline is developed in Python, so a Python Interpreter is a must. Other than the basic Python standard librarys, the following third-party libraries are required:

• netCDF4 for Python

• numpy

Other than official CPython interpreter, Pypy is also welcomed, but please make sure that these third-party modules are correctly installed for the target interpreter. The pipeline can only works in Python 2.X versions (2.7 recommended) since numpy does not support Python 3.X versions.

Cloning from the Git:

git clone https://github.com/terraref/computing-pipeline.git
cd  computing-pipeline/scripts/environmental_logger
git checkout master

The extractor for this pipeline is developed and maintained by Max in branch "EnvironmentalLogger-extractor" under the same repository.

Get the Environmental Logger Pipeline to Work

To trigger the pipeline, use the following command:

python ${environmental_logger_source_path}/environmental_logger_json2netcdf.py ${input_JSON_file} ${output_netCDF_file}

Where:

• ${environmental_logger_source_path} is where the three environmental_logger files are located

• ${input_JSON_file} is where the input JSON files are located

• ${output_netCDF_file} is where the users want the pipeline to export the product (netCDF file)

Please note that the parameter for the output file can be a path to either a directory or a file, and it is not necessarily to be existed. If the output is a path to a folder, the final product will be in this folder as a netCDF file that has the same name as the imported JSON file but with a different filename extension (.nc for standard netCDF file); if this path does not exist, environmental_logger pipeline will automatically make one.

Calculation

The calculation in the Environmental Logger is mainly finished by the module environmental_logger_calculation.py under the support of numpy.

Summary

Hyperspectral imaging data is collected using the Headwall VNIR and SWIR sensors. In the Nov 2017 Beta Release only VNIR data is provided because we do not have the measurements of downwelling spectral radiation required by the pipeline.

Please see the README hyperspectral pipeline README for more information about how the data are generated and known issues.

Hyperspectral Algorithm and pipeline

See Hyperspectral extractor

Data Access

Level 1 data access

Hyperspectral data is available via Clowder, Globus #Terraref endpoint, the TERRA REF Workbench, and our THREDDS server:

• Clowder:

  • VNIR Hyperspectral NetCDFs

  • SWIR Collection: Level 1 data not available

• Globus and Workbench:

  • VNIR: /sites/ua-mac/Level_1/vnir_netcdf

  • SWIR: Level 1 data not available

• Sensor information:

  • Headwall SWIR

  • Headwall VNIR

For details about using this data via Clowder or Globus, please see Data Access section.

Level 2 data access

Level 2 data are spectral indices computed at the same resolution as Level 1. These can be found in the same Level 1 directories as their parents, but the files are appended *_ind.nc.

To get a list of hyperspectral indices currently generated:

traits::

Level 3 data access

Raw Data

Raw data is available in the filesystem, accessible via Workbench and Globus in the following directories:

• VNIR: /sites/ua-mac/raw_data/VNIR

• SWIR: /sites/ua-mac/raw_data/SWIR

These files are uncalibrated; see the hyperspectral pipeline repository for information on how these can be processed.

See also

• Sensor calibration

• Hyperspectral data pipeline

• Geospatial information

Several different sensors include geospatial information in the dataset metadata describing the location of the sensor at the time of capture.

Coordinate reference systems The Scanalyzer system itself does not have a reliable GPS unit on the sensor box. There are 3 different coordinate systems that occur in the data:

• Most common is EPSG:4326 (WGS84) USDA coordinates

• Tractor planting & sensor data is in UTM Zone 12

• Sensor position information is captured relative to the southeast corner of the Scanalyzer system in meters

EPSG:4326 coordinates for the four corners of the Scanalyzer system (bound by the rails above) are as follows:

• NW: 33° 04.592' N, -111° 58.505' W

• NE: 33° 04.591' N, -111° 58.487' W

• SW: 33° 04.474' N, -111° 58.505' W

• SE: 33° 04.470' N, -111° 58.485' W

Scanalyzer coordinates Finally, the Scanalyzer coordinate system is right-handed - the origin is in the SE corner, X increases going from south to north, and Y increases from east to the west.

In offset meter measurements from the southeast corner of the Scanalyzer system, the extent of possible motion for the sensor box is defined as:

• NW: (207.3, 22.135, 5.5)

• SE: (3.8, 0, 0)

Scanalyzer -> EPSG:4326 1. Calculate the UTM position of known SE corner point 2. Calculate the UTM position of the target point, using SE point as reference 3. Get EPSG:4326 position based on UTM

MAC coordinates Tractor planting data and tractor sensor data will use UTM Zone 12.

Scanalyzer -> MAC Given a Scanalyzer(x,y), the MAC(x,y) in UTM zone 12 is calculated using the linear transformation formula:

Assume Gx = -Gx', where Gx' is the Scanalyzer X coordinate.

MAC -> Scanalyzer

MAC -> EPSG:4326 USDA We do a linear shifting to convert MAC coordinates in to EPSG:4326 USDA

Sensors with geospatial metadata

• stereoTop

• flirIr

• co2

• cropCircle

• PRI

• scanner3dTop

• NDVI

• PS2

• SWIR

• VNIR

Available data All listed sensors

stereoTop

cropCircle

co2Sensor

flirIrCamera

ndviSensor

priSensor

SWIR

field scanner plots

The range.csv should be in the following format:

And the row.csv should look like:

The output will be something look like:

Interested researchers can access BETYdb directly from GIS software such as ESRI ArcMap and QGIS. In some cases direct access can simplify the use of spatial data in BETYdb data, but this convenience must be weighed against a more complex setup, limits of GIS software compatibility, and additional complexity of extracting data from a PostGIS SQL database.

Overview

Accessing the production BETYdb used by the TERRA REF program requires creating a secure shell tunnel (SSH) to a remote server. After creating the tunnel, the database is accessed as if it were available on the local machine. A step-by-step process is given below.

Configuration used for these instructions

• ArcMap 10.3 or later (Requires Windows operating system)

• Instructions for using QGIS and other GIS software are provided below

• PuTTY: ssh client for Windows that can be downloaded here:

Setup

Request Access

Request access by following the link. This will take you to the NCSA identity service. If you do not have an NCSA account, you will be asked to create one. This account and password will be used to login to the database server. Access will generally be granted within 24-hours.

Confirm Access

Use PuTTY or your preferred SSH client and your NCSA account. First open the terminal and then login to bety6.ncsa.illinois.edu using ssh from the command line:

After confirming access to bety6 logout by typing exit.

Create SSH Tunnel to BETYdb

The following command will create an SSH tunnel from your computer to the BETYdb server:

Note if have a postgres running on your desktop computer (using the default port 5432), you will need to stop it first.

The above will bind the local port 5432 (first parameter) to port 5432 (second parameter), the default Postgres listening port, on the remote server. All traffic bound for port 5432 on your local machine will be automatically forwarded to the remote server. As a result, programs such as ArcGIS running on your computer will connect to the remote BETYdb as if it were on your computer.

Note you will need to create the SSH connection with the tunnel every time you wish to access BETYdb from your local machine.

To keep the tunnel open, use

note for PuTTY Users: you can configure Putty to remember these settings. In the navigation tree on the left-hand side, click Connection > SSH > Tunnels. Enter '5432' under Source port and 'localhost:5432' in the Destination field. Then click session and save this configuration for future use.

The next section of the guide will discuss accessing BETYdb using ArcMap, querying plots and joining these to the traits and experiments tables. The instructions for setting up a SSH tunnel will also work psql, pgAdmin3, QGIS, and other clients. Instructions for connecting via QGIS and ArcGIS Pro are provided below.

Using ArcMAP

Add BETYdb Layer or Table to ArcMap

BETYdb is configured with PostGIS geometry support. This allows ArcGIS Desktop clients to access geometry layers stored within BETYdb.

Warning: ArcGIS releases prior to 10.3 required you to place the PostgreSQL libpq files in the ArcGIS client's bin directory. This is no longer required for the ArcGIS Desktop clients but some ESRI tools may still require the library be installed.*

1. Click on the ArcCatalog icon (on right edge of ArcMap window) to open the ArcCatalog Tree

2. In the tree, click on 'Database Connections' and then "Add Database Connnections". A Database Connection dialog window will open.

3. Within the dialog box:
4. Click OK

5. The connection will be saved as "Connection to localhost.sde", right

   click and rename to it to "TERRA REF BETYdb trait database" to allow easy reuse.

6. Click on the Add Layer icon (black cross over yellow diamand) button to open the Add Data dialog window.

7. Under 'Look in' on the second line choose 'Database Connections'.

8. Select the "TERRA REF BETYdb trait database" that created above

9. Select the bety.public.sites table and click 'Add'.

   • This 'sites' table is the only table in the database with a geospatial 'geometry' data type.

   • Any of the other tables can also be added, as described below.

10. The New Query Layer dialog will be displayed asking for the Unique Identifier Field for the layer. For the bety.public.sites table, the unique identifier is the "sitename" field.

11. Click Finish.

Warning: ArcMap does not support the big integer format used by BETYdb as primary keys and those fields will not be visible or available for selection. In most cases you should be able to use other fields as unique identifiers.*

Modifying the Query Layer

BETYdb contains one geometry table called betydb.public.sites containing the boundaries for each plot. Because the plot boundaries can change each season, and even within season, different plot definitions may be used (e.g. to subset plots or exclude boundary rows), there is significant overlap that can cause confusion when displayed. In general, you will want to use the query layer to limit plots to a single season and a single definition.

1. Right click the bety.public.sites layer and choose properties.

2. Choose the Definition Query tab

3. Add the line sitename LIKE 'MAC Field Scanner Season 1%' or sitename LIKE 'MAC Field Scanner Season 2%' to limit the layer to Season 1 or Season 2 respectively.

4. Click 'OK'

For more advanced selection of sites by experiment or season, you can join the experiments and experiments_sites tables. This is beyond the scope of the present tutorial.

Joining Additional BETYdb Tables

Additional tables can be added and joined to the sites table. Tables can be added just like any other layer. In this case, we'll add bety.public.traits_and_yields_view and join it to the bety.public.sites layer.

1. To create a join with other tables, start by adding the desired table.

2. Follow instructions above to add the bety.public.traits_and_yields_view

3. On this table the unique identifier is a group of columns, so select sitename, cultivar, scientificname, trait, date, entity, and method as the unique identifiers.

4. Right click on the bety.public.sites layer.

5. Under 'Joins and Relates' select 'Join'.

6. Choose sitename (from bety.public.sites) in part 1

7. Choose bety.public.traits_and_yields_view in part 2

8. Choose sitename in part 3

9. Click OK

Creating a Thematic View

The final section describes how to create a thematic view of the bety.public.sites layer based on the mean attribute where the trait is NDVI from the bety.public.traits_and_yields_view. Remove any previous joins from bety.public.sites (right click bety.public.sites --> joins and relates --> remove join) prior to performing this procedure because we will be selecting the NDVI data by creating a query layer from bety.public.traits_and_yields_view prior to the join.

1. Right click bety.public_traits_and_yields_view table and select properties

2. Click on the Definition Query tab

3. Add the line "trait = 'NDVI'" to the Definition Query box

4. Click OK

5. Follow the steps defined in Joining Additional BETYdb Tables

6. Right click on the bety.sites layer and choose properties

7. Choose the Symbology tab

8. Under the Show section, choose Quantities --> Graduated Colors

9. Under the Fields Value selection choose mean

10. Click OK

Connecting to Other GIS Software

Below connection instructions assume an SSH tunnel exists.

ArcGIS Pro

This assumes you have followed instructions for ArcMAP to create a database connection file.

• Open ArcCatalog

  • Under database connections, you will find the connection made above, called 'TERRA REF BETYdb.sde'

  • right click this and select 'properties'

  • copy the file path (it should look like C:\Users\<USER NAME>\AppData\Roaming\ESRI\Desktop10.4\ArcCatalog\TERRA REF BETYdb.sde

• Open ArcGIS Pro

  • Under the Insert tab, select connections --> 'add database'

  • paste the path to 'TERRA REF BETYdb.sde' in the directory navigation bar

  • select 'TERRA REF BETYdb.sde'

QGIS

• Open QGIS

• In left 'browser panel', right-click the PostGIS icon

• select 'New Connection'

• Enter connection properties

  • Name: TERRA REF BETYdb trait database

  • Service: blank

  • Host: localhost

  • Port: 5432

  • Database: bety

  • SSL mode: disable

  • Username: viewer

  • Password: DelchevskoOro

  • Options: select 'Also list tables with no geometry'

How to export plots from PostGIS as a Shapefile

This does not require GIS software other than the PostGIS traits database. While connecting directly to the database within GIS software is handy, it is also straightforward to export Shapefiles.

After you have connected via ssh to the PostGIS server, the pgsql2shp function is available and can be used to dump out all of the plot and site definitions (names and geometries) thus:

About Globus Connect

The service provides high-performance, secure, file transfer and synchronization between endpoints. It also allows you to securely share your data with other Globus users.

Installing Globus

To access data via Globus, you must first have a Globus account and endpoint.

1. Sign up for Globus at

2. Download and install Globus Connect or

Requesting Access

To request access to the Terraref endpoint, send your Globus id (or University email) to David LeBauer (dlebauer@illinois.edu) with 'TERRAREF Globus Access Request' in the subject. You will be notified once you have been granted access.

Accessing Data via Globus

To transfer data to your computer or server:

1. Log into Globus

2. Add an endpoint for the destination (e.g. your local computer)

3. Go to the 'transfer files' page:

4. Select source

   • Endpoint: Terraref

   • Path: Navigate to the subdirectory that you want.

   • Select (click) a folder

   • Select (highlight) files that you want to download at destination

   • Select the endpoint that you set up above of your local computer or server

   • Select the destination folder (e.g. /~/Downloads/)

5. Click 'go'

6. Files will be transfered to your computer

See also

About Clowder

Clowder is an active data repository designed to enable collaboration around a set of shared datasets. TERRAREF uses Clowder to organize, annotate, and process data generated by phenotyping platforms. Datafiles are available via the Clowder or .

See the for more information about the software and its applications.

Requesting Access

To create an account, sign up at the and wait for your account to be approved. Once access is granted, you can explore collections and datasets.

Data organization

Data is organized into spaces, collections, and datasets, collections.

• Spaces contain collections and datasets. TERRA-REF uses one space for each of the phenotyping platforms.

• Collections consist of one or more datasets. TERRA-REF collections are organized by acquisition date and sensor. Users can also create their own collections.

• Datasets consist of one or more files with associated metadata collected by one sensor at one time point. Users can annotate, download, and use these sensor datasets.

Searching the database

Clowder allows users to search metadata and filter datasets and files with particular attributes. Simply enter your search terms in the search box.

Analyzing data in Clowder

Clowder includes support for launching integrated analysis environments from your browser, including RStudio and Jupyter Notebooks.

After selecting a dataset, under the "Analysis Environment Instances", select the "Launch new instance with dataset" drop-down, select the desired tool, then the "Launch" button. Select the "Environment manager" link to view the list of active instances. Find your instance and select the title link. This will display the tool with the selected dataset mounted. If you have a running instance, you can also "Upload dataset to existing instance".

Clowder Extractors

About BETYdb

is used to manage and distribute agricultural and ecological data. It contains phenotype and agronomic data including plot locations and other geolocations of interest (e.g. fields, rows, plants).

Requesting access

To request access to BETYdb, register on the. You will be notified once you have been granted access.

Data organization

The primary BETYdb is largely relevant here, noting the following usages:

• Genotypes are stored in the cultivars table

• Plots are stored in the sites table. Plots are nested hierarchically based on geolocation.

Using the Advanced Search box

Most tables in BETYdb have search boxes. We describe below how to use the Advanced Search box to query data from these tables and download the results as a CSV file.

The Advanced Search box is the easiest way to download summary datasets designed to have enough information (location, time, species, citations) to be useful for a wide range of use cases.

(For more information about querying data from specific tables, see the BETYdb .)

Using the Search Box

On the Welcome page of BETYdb there is a search option for trait and yield data (Figure 1). This tool allows users to search the entire collection of trait and yield data for specific sites, citations, species, and traits.

The results page provides a map interface and the option to download a file containing search results. The downloaded file is in CSV format. This file provides meta-data and provenance information, including the SQL query used to extract the data, the date and time the query was made, the citation source of each result row, and a citation for BETYdb itself.

Instructions

Using the search box to search trait and yield data is very simple: Type the site (city or site name), species (scientific or common name), cultivar, citation (author and/or year), or trait (variable name or description) into the search box and the results will show contents of BETYdb that match the search. The number of records per page can be changed to accord with the viewer's preference and the search results can be downloaded in the Excel-compatible CSV format.

The search map may be used in conjunction with search terms to restrict search results to a particular geographical area—or even a specific site—by clicking on a map. Clicking on a particular site will restrict results to that site. Clicking in the vicinity of a group of sites but not on a particular site will restrict the search to the region around the point clicked. Alternatively, if a search using search terms is done without clicking on the map, all sites associated with the returned results are highlighted on the map. Then, to zero in on results for a particular geographic area, click on or near highlighted locations on the map.

See also

Produced with Gitbook version

LemnaTec Scanalyzer 3D platform at the Donald Danforth Plant Science Center

Phenotype data is derived from images generated by the indoor LemnaTec Scanalyzer 3D platform at the Donald Danforth Plant Science Center using . PlantCV is an image analysis package for plant phenotyping. PlantCV is composed of modular functions in order to be applicable to a variety of plant types and imaging systems. PlantCV contains base functions that are required to examine images from an excitation imaging fluorometer (PSII), visible spectrum camera (VIS), and near-infrared camera (NIR). PlantCV is a fully open source project: . For more information, see:

Project website:

Full documentation:

Publications:

To learn more about PlantCV, you can find examples in the repository, which is accessible on GitHub and in the TERRA REF under tutorials/plantcv

• an ipython notebook demonstration of PlantCV .

For the TERRA-REF project, a PlantCV Clowder extractor was developed to analyze data from the at the Donald Danforth Plant Science Center. Resulting phenotype data is stored in BETYdb.

Computational pipeline

• Description: Processes VIS/NIR images captured at several angles to generate trait metadata. The trait metadata is associated with the source images in Clowder, and uploaded to the configured BETYdb instance.

• Output CSV: /sites/danforth/Level_1/<experiment name>

Input

• Evaluation is triggered whenever a file is added to a dataset

• Following images must be found

  • 2x NIR side-view = NIR_SV_0, NIR_SV_90

  • 1x NIR top-view = NIR_TV

  • 2x VIS side-view = VIS_SV_0, VIS_SV_90

  • 1x VIS top-view = VIS_TV

• Per-image metadata in Clowder is required for BETYdb submission; this is how barcode/genotype/treatment/timestamp are determined.

Output

• Each image will have new metadata appended in Clowder including measures like height, area, perimeter, and longest_axis

• Average traits for the dataset (3 VIS or 3 NIR images) are inserted into a CSV file and added to the Clowder dataset

• If configured, the CSV will also be sent to BETYdb

Data access

Level 1

Raw Data

• Globus and Workbench:

  • /sites/danforth/raw_data/<experiment name>

Summary

Infrared heat imaging data is collected collected using the FLIR SC615 thermal sensor. These data are provided as geotiff image raster files as well as plot level means.

• Algorithms are in the repository; see the readme for details.

• Sensor information:

Level 1 Data Access

Filesystem (Globus and Workbench)

• ua-mac/Level_1/ir_geotiff

Clowder

To be created

Level 3 Data

Plot level summaries are named in the trait database. In the future this name will be used for the Level 1 data as well. This name from the Climate Forecast (CF) conventions, and is used instead of 'canopy_temperature' for two reasons: First, because we do not (currently) filter soil in this pipeline. Second, because the CF definition of surface_temperature distinguishes the surface from the medium: "The surface temperature is the temperature at the interface, not the bulk temperature of the medium above or below."

Raw data access

Thermal imaging data is available via Clowder and Globus:

/ua-mac/raw_data/flirIrCamera

Known Issues

• Data are unavailable for Season 4 (summer 2017 sorghum) and season 5 (winter 2017-2018 wheat).

See also

Season 1 sorghum (April - July 2016) Season 2 sorghum (August - November 2016) Durum wheat (January 2017 -

Season 1 soghum (April - July 2016)

.

Planting maps

Planting Design

Under scanner system

West of scanner system

Automated Phenotpying

Manually Collected Field Data

emergence vigor emergence final stand counts plant heights node and tiller counts on marked plants phenology growth stage data leaf desiccation ratings radiation interception managements Incomplete harvest yield data

Season 2 soghum (August - November 2016)

Planting map

Planting Design

Under scanner system - same as season 1

Automated Phenotpying

same as season 1

Manually Collected Field Data

plant heights managements emergence vigor emergence final stand counts node and tiller counts on marked plants leaf length and width on marked plants, one date

Durum wheat

Environment conditions data is collected using the Vaisala CO2, Thies Clima weather sensors as well as lightning, irrigation, and weather data collected at the Maricopa site.

Data formats follow the for variable names and units. Environmental data are stored in the Geostreams database.

Data sources

• WeatherStation coordinates are 33.074457 N, 111.975163 W

• EnvironmentLogger is on top of the gantry system and is moveable.

• Irrigation is managed at the field level. There are four regions that can be irrigated at different rates.

Data access

Level 1 data

Level 1 meteorological data is aggregated to from 1 Hz raw data to 5 minute averages or sums.

netCDF: 5s (12 per minute) observations

On Globus or Workbench you can find these data provided in both hourly and daily files. These files contain data at the original temporal resolution of 1/s. In addition, they contain the high resolution spectral radiometer data.

sites/ua-mac/Level_1/envlog_netcdf

• hourly files: YYYY-MM-DD_HH-MM-SS_environmentallogger.nc

• daily files: envlog_netcdf_L1_ua-mac_YYYY-MM-DD.nc

Geostreams: 5 minute observations

Here is the json representation of a single five-minute observation:

Data can be accessed using the geostreams API or the PEcAn meteorological workflow.

Here is the json representation of a single five-minute observation from Geostreams:

Variable names and units

Raw Data

Data is available via Globus or Workbench:

• /ua-mac/raw_data/co2sensor

• /ua-mac/raw_data/EnvironmentLogger

• /ua-mac/raw_data/irrigation

• /ua-mac/raw_data/lightning

• /ua-mac/raw_data/weather

Sensor information:

Computational pipeline

• Description: EnvironmentalLogger raw files are converted to netCDF.

Known Issues

When the full field is irrigated (as is typical), the irrigated area is 5466.1 m2 (=215.2 m x 25.4 m)

In 2017:

• Full field irrigated area from the start of the season to August 1 (103 dap) is 5466.1 m2 (=215.2 m x 25.4 m).

• Well-watered treatment zones from August 1 to 15 (103 to 116 dap): 2513.5 m2 (=215.2 m x 11.68 m) in total, combined areas of non-contiguous blocks

• Well-watered treatment zones from August 15 - 30 (116 to 131 dap): 3169.9 m2 (=215.2 m x 14.73 m), again in total as the combined areas of non-contiguous blocks

Github Issues

We will release the data in stages or tiers.

First Tier

The first tier will be an internal release to the TERRA-REF team and the standards committee. This first tier release will be to initially quality check and calibrate the data and will take place as data sets are produced and compiled.

• By November 2016, it is an objective of the TERRA-REF team to establish a data release pipeline, wherein the release of data to this first tier will be within 21 days from the date of collection.

• Access to the data will be arranged for by the resource producer (i.e. limiting access to selected users).

Second Tier

The second tier will enable the release of the data generated solely by the TERRA-REF team to other TERRA teams as well as non-TERRA entities.

• By November 2017, it is an objective of the TERRA-REF team to establish a data release pipeline, wherein the release of data to this second tier will be within 10 days from the data of collection.

• It is noted that release of the data to the second tier may occur prior to publication and that access is granted with the understanding that the contributions and interests of the TERRA-REF team should be recognized and respected by the users of the data. The TERRA-REF team reserves the right to analyze and published its own data, provided that this is done in a timely fashion. Resource users should appropriately cite the source of the data and acknowledge the resource produces. The publication the data, as suggested in the TERRA-REF Authorship Guidelines, should specify the collaborative nature of the project, and authorship is expected to include all those contributing significantly to the work.

• Access to the data will be determined by the resource producers and may be governed by separate license or other agreements.

• It is an objective of the TERRA-REF team to enable the release of the data to the public by November 2018 but no later than the date of close-out of the awarded funds.

Release with Attribution

We plan to make data from the Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) project available for use with attribution. Each type of data will include or point to the appropriate attribution policy.

Timing and Control of Release

We plan to release the data in stages or tiers. For pre-release access please complete the alpha tester application.

1. The first tier will be an internal release to the TERRA-REF team and the standards committee. This first tier release will be to initially quality check and calibrate the data and will take place as data sets are produced and compiled.

   1. By November 2016, it is an objective of the TERRA-REF team to establish a data release pipeline, wherein the release of data to this first tier will be within 21 days from the date of collection.

   2. Access to the data will be arranged for by the resource producer (i.e. limiting access to selected users).

2. The second tier will enable the release of the data generated solely by the TERRA-REF team to other TERRA teams as well as non-TERRA entities.

   1. By November 2017, it is an objective of the TERRA-REF team to establish a data release pipeline, wherein the release of data to this second tier will be within 10 days from the data of collection.

   2. It is noted that release of the data to the second tier may occur prior to publication and that access is granted with the understanding that the contributions and interests of the TERRA-REF team should be recognized and respected by the users of the data. The TERRA-REF team reserves the right to analyze and published its own data. Resource users should appropriately cite the source of the data and acknowledge the resource produces. The publication of the data, as suggested in the TERRA-REF Authorship Guidelines, should specify the collaborative nature of the project, and authorship is expected to include all those TERRA-REF team members contributing significantly to the work.

3. Access to the data will be determined by the resource producers and may be governed by separate license or other agreements. 1. iii)It is an objective of the TERRA-REF team to enable the release of the data to the public by November 2018 but no later than the date of close-out of the awarded funds.

Genomic Data

Restrictions on dataset usage

Genomic data for the Sorghum bicolor Bioenergy Association Panel (BAP) from the TERRA-REF project is available pre-publication to maximize the community benefit of these resources. Use of the raw and processed data that is available should follow the principles of the Fort Lauderdale Agreement and the Department of Energy's Joint Genome Institute (JGI) early release policies.

By accessing these data, you agree not to publish any articles containing analyses of genes or genomic data on a whole genome or chromosome scale prior to publication by TERRA-REF and/or its collaborators of a comprehensive genome analysis ("Reserved Analyses"). "Reserved analyses" include the identification of complete (whole genome) sets of genomic features such as genes, gene families, regulatory elements, repeat structures, GC content, or any other genome feature, and whole-genome- or chromosome-scale comparisons with other species. The embargo on publication of Reserved Analyses by researchers outside of the TERRA-REF project is expected to extend until the publication of the results of the sequencing project is accepted. Scientific users are free to publish papers dealing with specific genes or small sets of genes using the sequence data. If these data are used for publication, the following acknowledgment should be included: 'These sequence data were produced by the US Department of Energy Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) Project'. These data may be freely downloaded and used by all who respect the restrictions in the previous paragraphs. The assembly and sequence data should not be redistributed or repackaged without permission from TERRA-REF. Any redistribution of the data during the embargo period should carry this notice: "The TERRA-REF project provides these data in good faith, but makes no warranty, expressed or implied, nor assumes any legal liability or responsibility for any purpose for which the data are used. Once the sequence is moved to unreserved status, the data will be freely available for any subsequent use."

We prefer that potential users of these sequence data contact the individuals listed under Contacts with their plans to ensure that proposed usage of sequence data are not considered Reserved Analyses.

Software and Algorithms

For algorithms, we intend to release via BSD 3 clause or MIT / BSD compatible license.

Images, Phenotypes, and Other Raw Data

For other raw data, such as phenotypic data and associated metadata, we intend to release data under CC0: Creative Commons with No Rights Reserved - Public Domain). This is to enable reuse of these data, but Scientists are expected cite our data and research publications. For more information, see related discussion and links in https://github.com/terraref/reference-data/issues/216.

Contacts

Todd Mockler, Project/Genomics Lead (email: tmockler@danforthcenter.org)

David LeBauer, Computing Pipeline Lead (email: dlebauer@email.arizona.edu)

Nadia Shakoor, Project Director (email: nshakoor@danforthcenter.org)

CoGe contains genomic data.

About CoGe

CoGe is a platform for performing Comparative Genomics research. It provides an open-ended network of interconnected tools to manage, analyze, and visualize next-gen data.

Requesting Access

Coming soon

Overview

TERRA’s data standards facilitate the exchange of genomic and phenomic data across teams and external researchers. Applying common standards makes it easier to exchange analytical methods and data across domains and to leverage existing tools.

When practical, existing conventions and standards have been used to create data standards. Spatial data adopts Federal Geographic Data Committee (FGDC) and Open Geospatial Consortium (OGC) data and meta-data standards. CF variable naming convention was adopted for meteorological data and biophysical data. Data formats and variable naming conventions were adapted from NEON and NASA.

Feedback from data creators and users were used to define the types of data formats, semantics, and interfaces, file formats, and representations of space, time, and genetic identity based on existing standards, commonly used file formats, and user needs.

We anticipate that standards and data formats will evolve over time as we clarify use cases, develop new sensors and analytical pipelines, and build tools for data format conversion and feature extraction and tracking provenance. Each year we will re-convene to assess our standards based on user needs. The Standards Committee will assess the trade-off between the upfront cost of adoption with the long-term value of the data products, algorithms, and tools that will be developed as part of the TERRA program. The specifications for these data products will be developed iteratively over the course of the project in coordination with TERRA funded projects. The focus will be to take advantage of existing tools based on these standards, and to develop data translation interfaces where necessary.

See also

• Agronomic and Phenotype Data Standards

• Environmental Data Standards

• Genomic Data Standards

• Sensor Data Standards

• Data Standards Committee

About the Analysis Workbench

The Analysis Workbench allows you to launch private Jupyter Notebook and RStudio instances to explore and analyze TERRA-REF data products.

Requesting Access

To create an account, sign up at the TERRA-REF Analysis Workbench site and wait for your account to be approved. Once access is granted, you can launch analysis environments.

Scratch Space

Each user has a "home" directory mounted into the analysis tools under /home/userid. This is read-write scratch space.

Data Access

Data access is provided via a read-only NFS mount to the TERRA-REF dataset on ROGER. The data is mounted to each container under /data/terraref and linked to the analysis environment working directory. For example, in Jupyter this is /home/jovyan/work/data.

About CyVerse

CyVerse is a National Science Foundation funded cyberinfrastructure that aims to democratize access to supercomputing capabilities.

Accessing Data via CyVerse

TERRA-REF genomics data is accessible on the CyVerse Data Store and Discovery Environment. Accessing data through the CyVerse Discovery Environment requires signing up for a free CyVerse account. The Discovery Environment gives users access to software and computing resources, so this method has the advantage that TERRA-REF data can be utilized directly without the need to copy the data elsewhere. During the TERRA-REF beta release period, users will need to request access to the TERRA-REF CyVerse Community Data folder through the TERRA-REF beta user application. The TERRA-REF Community Data folder can be found at /iplant/home/shared/terraref.

Overview

TERRA-REF data is available through four different approaches: Globus Connect, Clowder, BETYdb, and CoGe. Raw data is transfered to the primary compute pipeline using Globus Online. Data is ingested into Clowder to support exploratory analysis. The Clowder extractor system is used to transform the data and create derived data products, which are either available via Clowder or published to specialized services, such as BETYdb.

For more information, see the Architecture Documentation.

Clowder

Clowder is the primary system used to organize, annotate, and process raw data generated by the phenotyping platforms as well as information about sensors.

Use Clowder to explore the raw TERRA-REF data, perform exploratory analysis, and develop custom extractors.

For more information, see Using Clowder.

Globus Connect

Raw data is transferred to the primary TERRA-REF compute pipeline on the Resource Open Geospatial Education and Research (ROGER) system using Globus Online. Data is available for Globus transfer via the Terraref endpoint. Direct access to ROGER is restricted.

Use Globus Online when you want to transfer data from the TERRA-REF system for local analysis.

For more information, see Using Globus.

BETYdb

BETYdb contains the derived trait data with plot locations and other information associated with agronomic experimental design.

Use BETYdb to access about derived trait data.

For more information, see Using BETYdb.

CoGe

CoGe contains genomic information and sequence data.

For more information, see Using CoGe.

Other Data

• Field protocols

• Calibration protocols

• Field scanner operational log https://github.com/terraref/computing-pipeline/issues/128

Current Practice

In TERRA-REF v0 release, agronomic and phenotype data is stored and exchanged using the BETYdb API. Agronomic data is stored in the sites, managements, and treatments tables. Phenotype data is stored in the traits, variables, and methods tables. Data is ingested and accessed via the BETYdb API formats.

Standardization Efforts

In cooperation with participants from AgMIP, the Crop Ontology, and Agronomy Ontology groups, the TERRA-REF team is pursuing the development of a format to facilitate the exchange of data across systems based on the ICASA Vocabulary and AgMIP JSON Data Objects. An initial draft of this format is available for comment on Github.

In addition, we plan to enable the TERRA-REF databases to import and export data via the Plant Breeding API (BRAPI).

This section includes the following:

• Data Standards

• Directory Structure

• Data Storage

• Data Backup

• Data Transfer

• Data Processing Pipeline

• Data Collection

• Data Product Creation

• Sensor Calibration

• Quality Assurance and Quality Control

• Systems Configuration

The data processing pipeline transmits data from origination sites to a controlled directory structure on the ROGER CyberGIS supercomputer.

The data is generally structured as follows:

/sites
  /ua-mac
    /raw_data
      /sensor1
        /timestamp
          /dataset
      /sensor2
      ...
    /Level_1
      /extractor1_outputs
      /extractor2_outputs
      ...
  /danforth
    /raw_data
      /sensor3
      ...
    /Level_1
      /extractor3_outputs

...where raw outputs from sensors per site are stored in a raw_data subdirectory and corresponding outputs from different extractor algorithms are stored in Level_1 (and eventually Level_2, etc) subdirectories.

When possible, sensor directories will be divided into days and then into individual datasets.

This directory structure is visible when accessing data via the Globus interface.

Summary

The willingness of many scientists to cooperate and collaborate is what makes TERRA REF possible. Because the platform encompasses a diverse group of people and relies on many data contributors to create datasets for analysis, writing scientific papers can be more challenging than with more traditional projects. We have attempted to lay out ground rules to establish a fair process for establishing authorship, and to be inclusive while not diluting the value of authorship on a manuscript. Please engage with the TERRA REF manuscript writing process knowing you are helping to forge a new model of doing collaborative scientific research.

This document is based on the Nutrient Network Authorship Guidelines, http://nutnet.org/authorship and used with permission. Described in Borer, Elizabeth T., et al. "Finding generality in ecology: a model for globally distributed experiments."; Methods in Ecology and Evolution 5.1 (2014): 65-73.

Copyright, Attribution, and Conditions of Use:

We plan to quickly make data and software available for use with attribution, under CC-By 4.0, MIT compatable license, or Ft. Lauderdale Agreement as described in our Data Use Guidelines. Such data can be used with attribution (e.g. citation); co-authorship opportunities are welcome where warranted (see below) by specific contributions to the manuscript (e.g. help in interpreting data beyond technical support).

We are making data available early for users under the condition that manuscripts led within the team not be scooped. In these cases, people who wish to use the data for publication prior to official open release date of November 2018 should coordinate co-authorship with the person responsible for collecting the data.

Overview of the TERRA REF authorship process:

Inclusive but not gratuitous

Our primary goals in the TERRA REF authorship process are to consistently, accurately and transparently attribute the contribution of each author on the paper, to encourage participation in manuscripts by interested scientists, and to ensure that each author has made sufficient contribution to the paper to warrant authorship.

Steps:

1. Read these authorship policies and guidelines.

2. Consult the current list of manuscripts (http://terraref.org/manuscripts) for current proposals and active manuscripts, contact the listed lead author on any similar proposal to minimize overlap, or to join forces. Also carefully read these guidelines.

3. Prepare a manuscript proposal. Your proposal will list the lead author(s), the title and abstract body, and the specific data types that you will use. You can also specify more detail about response and predictor variables (if appropriate), and indicate a timeline for analysis and writing. Submit your proposal through this form.

   Proposed ideas are reviewed by the authorship committee primarily to facilitate appropriate collaborations, identify potential duplication of effort, and to support the scientists who generate data while allowing the broader research community access to data as quickly and openly as possible. The authorship committee may suggest altering or combining analyses and papers to resolve issues of overlap.

4. Circulate your draft analysis and manuscript to solicit Opt-In authorship.

   For global analyses, the lead author should circulate the manuscript to the Network by submitting a email to the TERRA REF team.

   For analyses of more limited scope, the lead author should circulate the manuscript to network collaborators who have indicated interest at the abstract stage, those who have contributed data, and any others who the lead author deems appropriate.

   In both cases, the subject line of the email should include the phrase "OPT-IN PAPER"; This email should also include a deadline by which time co-authors should respond.

   The right point to share your working draft and solicit co-authors is different for each manuscript, but in general:

   1. sharing early drafts or figures allows for more effective co-author contribution. While ideally this would mean circulating the manuscript at a very early stage for opt-in to the entire network, it is acceptable and even typical to share early drafts or figures among a smaller group of core authors.

   2. circulating essentially complete manuscripts does not allow the opportunity for meaningful contribution from co-authors, and is discouraged.

5. Potential co-authors should signal their intention to opt-in by responding by email to the lead author before the stated deadline.

6. Lead authors should keep an email list of co-authors and communicate regularly about progress including sharing drafts of analyses, figures, and text as often as is productive and practical.

7. Lead authors should circulate complete drafts among co-authors and consider comments and changes. Given the wide variety of ideas and suggestions provided on each TERRA REF paper, co-authors should recognize the final decisions belong to the lead author.

8. Final manuscripts should be reviewed and approved by each co-author before submission.

9. All authors and co-authors should fill out their contribution in the authorship rubric and attach it as supplementary material to any TERRA REF manuscript. Lead authors are responsible for ensuring consistency in credit given for contributions, and may alter co-author's entries in the table to do so.

   The authorship rubric provides a framework for the opt-in process. Lead authors should copy the template and edit the contents for a specific manuscript, then circulate to potential co-authors.

   Note that the last author position may be appropriate to assign in some cases. For example, this would be appropriate for advisors of lead authors who are graduate students or postdocs and for papers that two people worked very closely to produce.

10. The lead author should carefully review the authorship contribution table to ensure that all authors have contributed at a level that warrants authorship and that contributions are consistently attributed among authors. Has each author made contributions in at least two areas in the authorship rubric? Did each author provide thoughtful, detailed feedback on the manuscript? Authors are encouraged to contact the TERRA REF PI (Mockler) or authorship committee (Jeff White, Geoff Morris, Todd Mockler, David LeBauer, Wasit Wulamu, Nadia Shakoor) about any confusion or conflicts.

Contributions Warranting Co-authorship

Authorship must be earned through a substantial contribution. Traditionally, project initiation and framing, data analysis and interpretation, and manuscript preparation are all authorship-worthy contributions, and remain so for TERRA REF manuscripts. However, TERRA REF collaborators have also agreed that collaborators who lead a site from which data are being used in a paper can also opt-in as co-authors, under the following conditions: (1) the collaborators' site has contributed data being used in the paper's analysis; and (2) that this collaborator makes additional contributions to the particular manuscript, including data analysis, writing, or editing. For coauthorship on opt-out papers, each individual must be able to check at least two boxes in the rubric, including contribution to the writing process. These guidelines apply equally to manuscripts led by graduate students.

Manuscripts published by TERRA REF will be accompanied by a supplemental table indicating authorship contributions. You can copy and share the authorship rubric. For opt-in papers, a co-author is expected to have at least two of the following areas checked in the authorship rubric.

Publications committee

Members: David LeBauer, Todd Mockler, Geoff Morris, Nadia Shakoor, Jeff White, Wasit Wulamu

The publications committee ensures communication across projects to avoid overlap of manuscripts, works to provide guidance on procedures and authorship guidelines, and serves as the body of last resort for resolution of authorship disputes within the Network.

Acknowledgments

Please use the following text in the acknowledgments of TERRA REF manuscripts:

The [information / data / work] presented here is from the TERRA REF experiment, funded by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000594. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Keywords

Please use "TERRA REF"; as one of your keywords on submitted manuscripts, so that TERRA REF work is easily indexed and searchable.

This page summarizes existing standards, conventions, controlled vocabularies, and ontologies used for the representation of crop physiological traits, agronomic metadata, sensor output, genomics, and other inforamtion related to the TERRA-REF project.

Metadata standards

International Consortium for Agricultural Systems Applications (ICASA)

The ICASA Version 2.0 data standard defines an abstract model and data dictionary for the representation of agricultural field expirements. ICASA is explicitly designed to support implementations in a variety of formats, including plain text, spreadsheets or structured formats. It is important to note that ICASA is both the data dictionary and a format used to describe experiments.

The Agricultural Model Intercomparison Project (AgMIP) project has developed a JSON-based format for use with the AgMIP Crop Experiment (ACE) database and API.

Currently, the ICASA data dictionary is represented as a Google Spreadsheet and is not suitable for linked-data applications. The next step is to render ICASA in RDF for the TERRA-REF project. This will allow TERRA-REF to produce data that leverages the ICASA vocabulary as well as other external or custom vocabularies in a single metadata format.

The ICASA data dictionary is also being mapped to various ontologies as part of the Agronomy Ontology project. With this, it may be possible in the future to represent ICASA concepts using formal ontologies or to create mappings/crosswalks between them.

See also:

• White et al (2013). Integrated Description of Agricultural Field Experiments and Production: The ICASA Version 2.0 Data Standards. Computers and Electronics in Agriculture.

• AgMIP JSON Data Objects format description

• ICASA Master Variable List

  </small>

Minimum Information About a Plant Phenotyping Experiment (MIAPPE)

MIAPPE was developed by members of the European Phenotyping Network (EPPN) and the EU-funded transPLANT project. It is intended to define a list of attributes necessary to fully describe a phenotyping experiment.

The MIAPPE standard is available from the transPlant standards portal and is compatible with the ISA-Tools suite framework. The transPLANT standards portal also provides example configuration for the ISA toolset.

MIAPPE is currently the only standard listed in biosharing.org for the phenotyping domain. While several databases claim to support MIAPPE, the standard is still nascent.

MIAPPE is based on the ISA framework, building on earlier “minimum information” standards, such as MIAME (Minimum Information about a Microarray Experiment). If the MIAPPE standard is determined to be useful for TERRA-REF, it would be worth reviewing the MIAME steandard and related formats such as MAGE-TAG, MINiML, and SOFT accepted by the Gene Expression Omnibus (GEO). GEO is a long-standing repository for genetic research data and might serve as another model for TERRA-REF.

It is worth noting that linked-data methods are supported but optional when depositing data to GEO. The MAGE-TAB format, similar to the MIAPPE ISA Tab format, does support sources for controlled vocabulary terms or ontologies.

See also:

• Minimum Information about a Plant Phenotyping Experiment

  </small>

Dublin Core Application Profiles

While some communities define explicit metadata schema (e.g., Ecological Metadata Language), another approach is the use of "application profiles." An application profile is declaration of metadata terms adopted by a community or an organization along with the source of the terms. Application profiles are composed of terms drawn from multiple vocubularies or ontologies to define a "schema" or "profile" for metadata. For example, the Dryad metadata profile draws on the Dublin Core, Darwin Core, and Dryad-specific elements.

See also:

• DCMI Guidelines for Dublin Core Application Profiles.

• Example Dryad Metadata Profile

• DCMI Singapore Framework

  </small>

Trait Dictionary Format (Crop Ontology)

The Crop Ontology curation tool supports import and export of trait information in a trait dictionary format.

See also:

• The Crop Ontology Improving the Quality of 18 Crop Trait Dictionaries

  </small>

Vocabularies and Ontologies

This section reviews related controlled vocabularies, data dictionaries, and ontologies.

Biofuel Ecophysiological Traits and Yields Database (BETYdb)

While BETYdb is not a controlled vocabulary itself, the relational schema models a variety of concepts including managements, sites, treatments, traites, and yields.

The BETYdb “variables” table defines variables used to represent traits in the BETYdb relational model. There has been some effort to standardize variable names by adopting Climate Forecasting (CF) convention standard names where variables overlap. A variable is represented as a name, description, units, as well as min/max values.

For example:

"variable": {
    "created_at": "2016-03-07T11:23:58-06:00",
    "description": "",
    "id": 604,
    "label": "",
    "max": "1000",
    "min": "0",
    "name": "NDVI",
    "notes": "",
    "standard_name": "normalized_difference_vegetation_index",
    "standard_units": "ratio",
    "type": "",
    "units": "ratio",
    "updated_at": "2016-03-07T11:26:07-06:00"
}

See also:

• The full suite of variables supported by BETYdb

• Trait variables used in the TERRA Ref BETYdb instance

  </small>

DCMI Metadata terms

Controlled vocabulary for the representation of bibliographic information. See also:

• DCMI Terms

  </small>

Climate and Forecast Standard Name Table

Standard variable names and naming convention for use with NetCDF. The Climate and Forecast metadata conventions are intended to promote sharing of NetCDF files. The CF conventions define metadata that provide a definitive description of what the data in each variable represents, and the spatial and temporal properties of the data. This enables users of data from different sources to decide which quantities are comparable, and facilitates building applications with powerful extraction, regridding, and display capabilities.

Basic conventions include lower-case letters, numbers, underscores, and US spelling.

Information is encoded in the variable name itself. The basic format is (optional components in []):

[surface] [component] standard_name [at surface] [in medium] [due to process] [assuming condition]

For example:

Standard names have optional canonical units, AMIP and GRIB (GRidded Binary) codes.

The CF standard names have been converted to RDF by several communities, including the Marine Metadata Interoperability (MMI) project.

Dimensions: time, lat, lon, other specify time first (unlimited) lat, lon or x, y extent to field boundaries.

See also:

• CF Conventions

• CF Conventions FAQ mentions RDF conversions.

  </small>

ICASA master variable list

Vocabulary and naming conventions for agricultural modeling variables, used by AgMIP. The ICASA master variable list is included, at least in part, in the AgrO ontology. The NARDN-HD Core Harmonized Crop Experiment Data is also taken from the ICASA vocabulary.

ICASA variables have a number of fields, including name, description, type, min and max values.

See also:

• ICASA Master Variable List

• White et al (2013). Integrated Description of Agricultural Field Experiments and Production: The ICASA Version 2.0 Data Standards. Computers and Electronics in Agriculture.

  </small>

NARDN-HD Core Harmonized Crop Experiment Data

A subset of the ICASA data dictionary representing set of core variables that are commonly collected in field crop experiments. These will be used to harmonize data from USDA experiments as part of a National Agricultural Research Data Network.

CSDMS Standard Names

Variable naming rules and patterns for any domain developed as part of the CSDMS project as an alternative to CF. CSDMS standard names is considered to have a more flexible community approval mechanism than CF. CSDMS names include object, quantity/attribute parts.

CSDMS names have been converted to RDF as part of the Earth Cube Geosemantic Server project.

See also:

• CSMDS Standard Names

  </small>

International Plant Names Index (IPNI)

http://www.ipni.org/

IPNI is a database of the names and associated basic bibliographical details of seed plants, ferns and lycophytes. It's goal is to eliminate the need for repeated reference to primary sources for basic bibliographic information about plant names.

NCBI Taxonomy

http://www.ncbi.nlm.nih.gov/taxonomy

A curated classification and nomenclature for all of the organisms in the public sequence databases that represents about 10% of the described species of life on the planet. Taxonomy recommended by MIAPPE.

Ontologies

Agronomy Ontology (AGRO)

The Agronomy Ontology “describes agronomic practices, agronomic techniques, and agronomic variables used in agronomic experiments.” It is intended as a complementary ontology to the Crop Ontology (CO). Variables are selected out of the International Consortium for Agricultural Systems Applications (ICASA) vocabulary and a mapping between AgrO and ICASA is in progress. AgrO is intended to work with the existing ontologies including ENVO, UO, PATO, IAO, and CHEBI. It will be part of an Agronomy Management System and fieldbook modeled on the CGIAR Breeding Management System to capture agronomic data.

See also:

• OBO Foundry. Agonomy Ontology

• FAO. Crop Ontology: harmonizing semantics for phenotyping and agronomy data

• RDA. Interest Group on Agricultural Data (IGAD)

  </small>

Crop Ontology (CO)

The Crop Ontology (CO) contains "Validated concepts along with their inter-relationships on anatomy, structure and phenotype of crops, on trait measurement and methods as well as on Germplasm with the multi-crop passport terms." The ontology is actively used by the CGIAR community and a central part of the Breeding Management System. MIAPPE recommends the CO (along with TO, PO, PATO, XEML) for observed variables.

Shrestha et al (2012) describe a method for representing trait data via the CO.

See also:

• Crop Ontology

• Shrestha et al (2012). Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice. Front Physiol. 2012 Aug 25;3:326.

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Crop Research Ontology (CRO)

Describes experimental design, environmental conditions and methods associated with the crop study/experiment/trial and their evaluation. CRO is part of the Crop Ontology platform, originally developed for the International Crop Information System (ICIS). CRO is recommended in the MIAPPE standard for general metadata, environment, treatments, and experimental design fields.

See also:

• Crop Research Ontology

• International Crop Information System

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Extensible Observation Ontology (OBOE)

Cited in Kattge et al (2011) as an example of an ontology used in ecology and environmental sciences to represent measurements and observation. However, the CRO may be better suited for TERRA-REF.

See also:

• Kattge, J.(2011). A generic structure for plant trait databases

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Gene Ontology (GO)

Defines concepts/classes used to describe gene function, and relationships between these concepts. GO is a widely-adopted ontology in genetics research, supported by databases such as GEO. This ontology is cited in Krajewski et al (2015) and might be relevant for the TERRA genomics pipeline.

See also:

• Gene Ontology

• Krajewski et al (2015). Towards recommendations for metadata and data handling in plant phenotyping. Journal of Experimental Botany, 66(18), 5417–5427.

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Information Artifact Ontology (IAO)

Information entities, originally driven by work by OBI (e.g., abstract, author, citation, document etc). IAO covers similar territory to the Dublin Core vocabulary.

Ontology for Biomedical Investigations (OBI)

Integrated ontology for the description of biological and clinical investigations. This includes a set of 'universal' terms, that are applicable across various biological and technological domains, and domain-specific terms relevant only to a given domain. Recommended by MIAPPE for general metadata, timing and location, and experimental design.

See also:

• Minimum Information about a Plant Phenotyping Experiment

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Phenotype and Attribute Ontology (PATO)

Phenotypic qualities (properties).

Recommended in MAIPPE for use in the observed values field.

See also:

• Minimum Information about a Plant Phenotyping Experiment

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Plant Environment Ontology (EO)

Part of the Plant Ontology (PO), standardized controlled vocabularies to describe various types of treatments given to an individual plant / a population or a cultured tissue and/or cell type sample to evaluate the response on its exposure.

Plant Ontology (PO)

Describes plant anatomy and morphology and stages of development for all plants intended to create a framework for meaningful cross-species queries across gene expression and phenotype data sets from plant genomics and genetics experiment. Recommended by MIAPPE for observed values fields. Along with EO, GO, and TO make up the Gramene database. Links plant anatomy, morphology and growth and development to plant genomics data.

See also:

• Minimum Information about a Plant Phenotyping Experiment

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Plant Trait Ontology (TO)

Along with EO, GO, and PO, make up the Gramene database to link plant anatomy, morphology and growth and development to plant genomics data. Recommended by MIAPPE for observed values fields.

Example trait entry:

[Term]
id: TO:0000019
name: seedling height
def: "Average height measurements of 10 seedlings, in centimeters from the base of the shoot to the tip of the tallest leaf blade." [IRRI:SES]
synonym: "SH" RELATED []
is_a: TO:0000207 ! plant height

See also:

• Minimum Information about a Plant Phenotyping Experiment

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Statistics Ontology (STATO)

General purpose statistics ontology coveraging processes such as statistical tests, their conditions of application, and information needed or resulting from statistical methods, such as probability distributions, variables, spread and variation metrics. Recommended by MIAPPE for experimental design.

See also:

• Minimum Information about a Plant Phenotyping Experiment

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Units of Measurement Ontology (UO)

Metric units for PATO. This OBO ontology defines a set of prefixes (giga, hecto, kilo, etc) and units (area/square meter, volume/liter, rate/count per second, temperature/degree Fahrenheit). The two top-level classes are prefixes and units.

UO is mentioned in relation to the Agronomy Ontology (AGRO), but PATO is also recommended by MIAPPE for observed values fields

While there are general standard units, it seems unlikely that these would ever be gathered in a single place. It seems more useful to define a high-level ontology to represent a "unit" and allow domains and communities to publish their own authoritative lists.

XEML Environment Ontology (XEO)

Created to help plant scientists in documenting and sharing metadata describing the abiotic environment.

DDI-RDF Discovery Vocabulary

Data Catalog Vocabulary (DCAT)

The Data Catalog Vocabulary is an RDF vocabulary intended to facilitate interoperability between data catalogs published on the Web. DCAT defines a set of classes including Dataset, Catalog, CatalogRecord, and Distribution.

Data Cite Ontology

The DataCite Ontology

Data Cube Vocabulary

The Data Cube Vocabulary is an RDF-based model for publishing multi-dimentional datasets, based in part on the SDMX guidelines. DataCube defines a set of classes including DataSet, Observation, and MeasureProperty that may be relevant to the TERRA project.

Statistical Data and Metadata Exchange (SDMX)

SDMX is an international initiative for the standarization of the exchange of statistical data and metadata among international organizations. Sponsors of the initiative include Eurostat, European Central Bank, the OECD, World Bank and the UN Statistical Division. They have defined a framework and an exchange format, SDMX-ML, for data exchange. Community members have also developed RDF encodings of the SDMX guidelines that are heavily referenced in the Data Cube vocabulary examples.

Related Software, Services, and Databases

Standard formats, ontologies, and controlled vocabularies are typically used in the context of specific software systems.

Agricultural Model Inter-Comparison and Improvement Project (AgMIP) Crop Experiment (ACE) Database

AgMIP "seeks to improve the capability of ecophysiological and economic models to describe the potential impacts of climate change on agricultural systems. AgMIP protocols emphasize the use of multiple models; consequently, data harmonization is essential. This interoperability was achieved by establishing a data exchange mechanism with variables defined in accordance with international standards; implementing a flexibly structured data schema to store experimental data; and designing a method to fill gaps in model-required input data."

The data exchange format is based on a JSON rendering of the ICASA Master Variable List. Data are transfer into and out of the AgMIP Crop Experiment (ACE) and AgMIP Crop Model (ACMO) databases via REST apis using these JSON objects.

See also

• AgMIP Crop Expirement Database

• Porter et al (2014). Harmonization and translation of crop modeling data to ensure interoperability. Environmental Modelling and Software. 62:495-508.

• AgMIP Data Products presentation

• AgMIP on Github

• AgMIP Crop Experiment Database data variables

• AgMIP API

• AgMIP using ICASA standards</small>

Biofuel Ecophysiological Traits and Yields Database (BETYdb)

BETYdb is used to store TERRA meta-data, provenance, and traits information.

BETYdb traits are available as web-page, csv, json, xml. This can be extended to allow spatial, temporal, and taxonomic / genomic queries. Trait vectors can be queries and rendered in several output formats. For example:

Here are some examples from betydb.org.

• HTML output

• csv output

• xml output

• Json-compatible output

A separate instance of BETYdb is maintained for use by TERRA Ref at terraref.ncsa.illinois.edu.org/bety. The scope of the TERRA Ref database is limited to high througput phenotyping data and metadata produced and used by the TERRA program. Users can set up their own instances of BETYdb and import any public data in the distributed BETYdb network.

See also: BETYdb documentation

• BETYdb Data Access includes accessing data with web interface, API, and R traits package

• BETYdb constraints, see section "uniqueness constraints"

• BETYdb Data Entry

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Gramene

Gramene is a curated, open-source, integrated data resource for comparative functional genomics in crops and model plant species

Integrated Breeding Platform/Breeding Management System

System for managing the breeding process including lists of germplasms, defining crosses, managing nurseries, trials, as well as ontologies and statistical analysis.

See also:

• BMS Site

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TERRA Ref has an instance of BMS hosted by CyVerse (requires login).

International Crop Information System

ICIS is "a database system that provides integrated management of global information on crop improvement and management both for individual crops and for farming systems." ICIS is developed by Consultative Group for International Agricultural Research (CGIAR).

See also

• Fox and Skovmand (1996). "The International Crop Information System (ICIS) - connects genebank to breeder to farmer’s field." Plant adaptation and crop improvement, CAB International.

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MODAPS NASA MODIS Satellite data