TERRA-REF Season 4 and 6 Sorghum phenotypes and agronomic metadata in BrAPI format: ARDN Products
ARDN (Agricultural Research Data Network) annotations for "TERRA-REF Season 4 and 6 Sorghum phenotypes and agronomic metadata in BrAPI format". The ARDN project ([https://data.nal.usda.gov/ardn](https://data.nal.usda.gov/ardn)) is a network of datasets harmonized and aggregated using the ICASA vocabulary, as recommended by USDA NAL ([https://data.nal.usda.gov/data-dictionary-examples](https://data.nal.usda.gov/data-dictionary-examples)) and described in detail here: [www.tinyurl.com/icasa-mvl](www.tinyurl.com/icasa-mvl)
This data represents a small subset of the TERRA-REF release available on Dryad (LeBauer et al 2020), including harvested biomass for each cultivar, plot location, planting date, harvest date, fertilizer application, genotype / accession names and metadata, and additional agronomic management metadata for a population of Sorghum bicolor evaluated over two growing seasons. The data can be accessed through a BrAPI-compliant endpoint at terraref.org/brapi. This dataset is a snapshot of the TERRA-REF BrAPI endpoint contents, representing the minimum data and metadata required to run a crop model.
Data from polishCLR: Example input genome assemblies
In order to produce the best possible *de novo*, chromosome-scale genome assembly from error prone Pacific BioSciences continuous long reads (CLR) reads, we developed a publicly available, flexible and reproducible workflow that is containerized so it can be run on any conventional HPC, called polishCLR. This dataset provides example input primary contig assemblies to test and reproduce the demonstrated utility of our workflow.
Data from: Solenopsis invicta virus 3 infection alters worker foraging behavior in its host, Solenopsis invicta
Data collected to compare the foraging/food consumption and impacts of Solenopsis invicta virus 3 on fire ant colonies. Ant colonies infected with Solenopsis invicta virus 3 were compared with uninfected (control) colonies. Four data sets include foraging/food consumption, brood changes, queen fecundity, and virus quantity.
- 4x csv
Data from: Dataset of de novo assembly and functional annotation of the transcriptome of blueberry (Vaccinium spp.)
To enrich available transcriptome data and identify genes potentially involved in fruit quality, RNA sequencing was performed on fruit tissue from two northern-adapted hybrid blueberry breeding populations.
Data from: Draft genome of the rice coral Montipora capitata obtained from linked-read sequencing
Gene models for protein-coding genes in the genome of the rice coral Montipora capitata, Hawaii Island. Annotation was performed with Augustus v3.3.1, using RNA-seq data as extrinsic evidence. Gene structures (.gff), coding sequences (_cds.fas), and amino acid sequences (_aa.fas) are provided.
- 3x bin
Data from: Identification and functional characterization of immunity-suppressing, candidate effector proteins in the parasitic weed Phelipanche aegyptiaca
All source data from the referenced paper (Figures 1b and Table 1). 22 Excel files of data from each experimental block of the reactive oxygen species assay, 1 Excel file of the combined data from the bacterial growth enhancement assay, 1 Excel file of the RT-qPCR data.
Mixed Linear Model Approaches for Quantitative Gen
Computer software for estimating variance and covariance components, correlations, and predicting genetic effects.
AgroAtlas
The Russian-English Agricultural Atlas is the world’s most comprehensive source of information on the geographic distribution of plant-based agriculture in Russia and neighboring countries. The Atlas contains 1500 maps that illustrate the distribution of 100 crops, 560 wild crop relatives, 640 diseases, pests and weeds, and 200 environmental parameters. Additionally, the Atlas provides detailed biological descriptions, illustrations, metadata and reference lists. Currently, individual maps can be downloaded and viewed using freely available AgroAtlas GIS Utility software, which can also be downloaded at this site.
Non-dominated Sorting Genetic Algorithm-II
This code is implements the nondominated sorting genetic algorithm (NSGA-II) in the R statistical programming language. The function is theoretically applicable to any number of objectives without modification. The function automatically detects the number of objectives from the population matrix used in the function call. NSGA-II has been applied in ARS research for automatic calibration of hydrolgic models (whittaker link) and economic optimization (whittaker link).