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305 results for “Barley”
Barley as a production platform for oral vaccines in sustainable fish aquaculture
<p>Experimental data for the study "Barley as a production platform for oral vaccines in sustainable fish aquaculture"</p>
EoRNA, a barley gene and transcript abundance database
<p>A high-quality, barley gene reference transcript dataset (BaRTv1.0, Rapazote-Flores et al. 2019), was used to quantify gene and transcript abundances from 22 RNA-seq experiments, covering 843 separate samples. Using the abundance data we developed a Barley Expression Database (EoRNA* – Expression of RNA) to underpin a visualisation tool that displays comparative gene and transcript abundance data on demand as transcripts per million (TPM) across all samples and all the genes. EoRNA provides gene and transcript models for all of the transcripts contained in BaRTV1.0, and these can be conveniently identified through either BaRT or HORVU gene names, or by direct BLAST of query sequences. Browsing the quantification data reveals cultivar, tissue and condition specific gene expression and shows changes in the proportions of individual transcripts that have arisen via alternative splicing. TPM values can be easily extracted to allow users to determine the statistical significance of observed transcript abundance variation among samples or perform meta analyses on multiple RNA-seq experiments. * Eòrna is the Scottish Gaelic word for Barley</p>
Four Reference Quality Genome Assemblies of Pyrenophora teres f. maculata: A Resource for Studying the Barley Spot Form Net Blotch Interaction
<p>Updated draft genome assembly (FASTA) and annotation (GFF) for the <em>P. teres </em>f.<em> maculata </em>isolate FGOB10Ptm-1. </p>
Four Reference Quality Genome Assemblies of Pyrenophora teres f. maculata: A Resource for Studying the Barley Spot Form Net Blotch Interaction
<p>Updated draft genome assembly (FASTA) and annotation (GFF) for the <em>P. teres </em>f.<em> maculata </em>isolate P-A14. </p>
Oregon Wolfe Barley (Hordeum vulgare) Informative & Spectacular Subset (ISS) vegetative stage growth data
<p>Oregon Wolfe Barley Informative & Spectacular Subset (ISS) was raised at the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University (West Lafayette, Indiana, USA) for 42 days. There were 18 genotypes, with two replicates for each genotype (total plants: 36). AAPF is a controlled environment high-throughput phenotyping facility with automated imaging and irrigation systems. A virtual tour of AAPF can be found at <a href="https://ag.purdue.edu/aapf/virtual-tour.html">https://ag.purdue.edu/aapf/virtual-tour.html</a>.</p> <p>Seeds were sown in a 6 L pot with 2.8 L of Profile Porous Ceramic Greens Grade and Berger BM6 each with 10g of Osmocote. Five hundred ml of Turface was laid on top of each pot to avoid effect of algae for RGB data derivation. The growth temperature in the chamber was 72/68 degrees Fahrenheit day/night. Relative humidity was set at 60%. Lighting was 16 h day/8 h night.</p> <p>Plants were imaged with RGB camera from one top and 12 side views three times a week, ranging between 10 days from planting (equivalent to sowing, Dfp) to 42 Dfp. Ground reference data of plant height and tiller count were measured twice a week. </p> <p> </p> <p>RGB imaging data were stored in “OWB_RGB.xlsx”. Datasheet “Information” describes the variables in datasheets for top view, side average view and every side view.</p> <p> </p> <p>Ground reference data for plant height and tiller count were stored in “OWB_ground_reference.xlsx”. Datasheet “Information” describes the variables in datasheet “Data”.</p>
BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq
<p>Background<br> Time consuming computational assembly and quantification of gene expression and splicing analysis from RNA-seq data vary considerably. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants.</p> <p>Results<br> A high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts – BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al., 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al., 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5’ and 3’ UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2,791 differentially alternatively spliced genes and 2,768 transcripts with differential transcript usage.</p> <p>Conclusion<br> A high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.</p>
Raw data used in Kumar et al. 2020: Barley shoot biomass responds strongly to N:P stoichiometry and intraspecific competition, whereas roots only alter their foraging
<p>Raw data used in Kumar et al. 2020: Barley shoot biomass responds strongly to N:P stoichiometry and intraspecific competition, whereas roots only alter their foraging</p>
Bymovirus Diversity in Barley Crops in France from 2013 to 2016
<p>A study was undertaken to determine the distribution and diversity of viruses causing yellow mosaic disease in barley in France. 241 samples from symptomatic susceptible, <em>rym4</em> and <em>rym5</em> varieties were gathered. The viruses present in all samples were identified by specific RT-PCR assays and, for selected samples, by double stranded RNA metagenomic analyses.</p>
Additional files for manuscript titled 'The double round-robin population unravels the genetic architecture of grain size in barley'
<p>Additional file 1: Parental allele for barley orthologs of genes controlling grain size in rice</p> <p>Additional file 2: Cross-validation of quantitative trait loci (QTLs) detected for grain size characters in rice</p> <p>Additional file 3: Adjusted entry means of recombinant inbred lines of 45 HvDRR sub-populations</p>
Data from: Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat
<div> <div> <p>Barley yellow dwarf is one of the major viral diseases of cereals. Phenotyping barley yellow dwarf in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to (1) evaluate the use of high-throughput phenotyping to improve barley yellow dwarf assessment; (2) identify genomic regions associated with barley yellow dwarf resistance, and (3) evaluate the ability of genomic selection models to predict barley yellow dwarf resistance. Up to 107 wheat lines were phenotyped during each of 5 field seasons under both insecticide treated and untreated plots. Across all seasons, barley yellow dwarf severity was lower within the insecticide treatment along with increased plant height and grain yield compared with untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying the resis- tance gene Bdv2. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a poten- tially novel genomic region for barley yellow dwarf resistance on chromosome 5AS. Given the variable heritability of the trait (0.211–0.806), we obtained a predictive ability for barley yellow dwarf severity ranging between 0.06 and 0.26. Including the presence or absence of Bdv2 as a covariate in the genomic selection models had a large effect for predicting barley yellow dwarf but almost no effect for other ob- served traits. This study was the first attempt to characterize barley yellow dwarf using field-high-throughput phenotyping and apply geno- mic selection to predict disease severity. These methods have the potential to improve barley yellow dwarf characterization, additionally identifying new sources of resistance will be crucial for delivering barley yellow dwarf resistant germplasm.</p> </div> </div>
Fig. 2 in Allelic Diversity Of The Beta-Amylase Gene Bmy1 In Latvian Barley Breeding Lines
Fig. 2. Genotyping on the (1+6) bp Indel. A – (1+6) bp insertion; B – (1+6) bp deletion; C – heterozygote.
Data on Crop Yield, Nutrient Content of Barley (Hordeum vulgare L.) and Weather of a Vertical Agrivoltaic System in Sweden
<p>The dataset location is latitude 59.55° N and longitude 16.76° E in Kärrbo Prästgård, Sweden. </p> <p>Crop data:</p> <p>Raw data of barley related to yield kernels and straws (kg DM/ha), nitrogen content in kernels (%), crude protein in kernels (%), kernels yield (kg DM/ha), straws yield (kg DM/ha), starch content in kernels (%), and thousand kernel weight (%) from the harvest on September 12<sup>th</sup>, 2023, at the agrivoltaics research site in Kärrbo Prästgård, Sweden. Fifty squared samples (each 0.25 m<sup>2</sup>) distributed in 5 groups (A, B, C, D, E) were collected according to the layout presented in the corresponding publication. Groups A, B and C are based on the spatial location in the crop area between the three vertical rows of PV modules: west side (A), center side (B) and east side (C). Group R corresponds to the reference control plot conditions. Group D represent the crops that are growing in the space between the rows of the conventional ground-mounted PV system with 30° tilt. </p> <p> </p> <p>Weather data:</p> <p>1-hour timeseries averaged data measurements at local time, raw data, not quality controlled from the barley growing season at Kärrbo Prästgård, Sweden from May 7<sup>th</sup> to September 12<sup>th</sup>,2023.</p> <p>Temperature of air (°C), relative humidity (%), relative air pressure (hPa), wind speed (m/s), and precipitation (mm/h) are measured with a Lufft WS600-UMB Smart Weather Sensor located on-site on a 5 m height mast.</p> <p>Global and diffuse horizontal irradiance (W/m<sup>2</sup>) are measured with a Delta-T SPN1 Sunshine Pyranometer.</p> <p>Photosynthetically active radiation (µmol/m<sup>2</sup>/s) is measured with an Apogee PAR Quantum sensor SQ-500.</p>
Experimental barley data
<p>Barley plot heading</p>
SNP call data for: The current epidemic of the barley pathogen Ramularia collo-cygni derives from a recent population expansion and shows global admixture
<p>Ramularia Leaf Spot is becoming an ever increasing problem in main barley growing regions since the 1980s, causing up to 70% yield loss in extreme cases. Yet, the causal agent <em>Ramularia collo-cygni</em>, remains poorly studied. The diversity of the pathogen in the field thus far remains unknown. Furthermore, it is unknown to which extend the pathogen has a sexual reproductive cycle. To date, the teleomorph of <em>R. collo-cygni</em> has not been observed.</p> <p>To study the genetic diversity of <em>R. collo-cygni </em>and to get more insights into its biology, we sequenced the genomes of 19 <em>R. collo-cygn</em>i isolates from multiple geographic locations and diverse hosts. Here we share the SNP call data as well as the reference genome.</p> <p>The reference genome files and assembly can be found on ENI: GCA_900074925.1</p> <p>https://www.ebi.ac.uk/ena/data/view/GCA_900074925.1</p> <p>The raw sequence data is also available through ENI: ERX2296228</p> <p>https://www.ebi.ac.uk/ena/data/view/ERX2296228</p>
Fig. 3 in Epigeic beetle (Coleoptera) communities in summer barley agrocenoses
Fig. 3. Mean (± SD) number of some recedent, subrecedent and subdominant ground beetle species in spring barley
Fig. 2 in Epigeic beetle (Coleoptera) communities in summer barley agrocenoses
Fig. 2. Mean (± SD) number of eudominant, some dominant and subdominant carabid species in spring barley
Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform
<p>Shape plays a fundamental role in biology. Traditional phenotypic analysis methods measure some features but fail to measure the information embedded in shape comprehensively. To extract, compare and analyse this information embedded in a robust and concise way, we turn to topological data analysis (TDA), specifically the Euler characteristic transform. TDA measures shape comprehensively using mathematical representations based on algebraic topology features. To study its use, we compute both traditional and topological shape descriptors to quantify the morphology of 3121 barley seeds scanned with X-ray computed tomography (CT) technology at 127 μm resolution. The Euler characteristic transform measures shape by analysing topological features of an object at thresholds across a number of directional axes. A Kruskal–Wallis analysis of the information encoded by the topological signature reveals that the Euler characteristic transform picks up successfully the shape of the crease and bottom of the seeds. Moreover, while traditional shape descriptors can cluster the seeds based on their accession, topological shape descriptors can cluster them further based on their panicle. We then successfully train a support vector machine to classify 28 different accessions of barley based exclusively on the shape of their grains. We observe that combining both traditional and topological descriptors classifies barley seeds better than using just traditional descriptors alone. This improvement suggests that TDA is thus a powerful complement to traditional morphometrics to comprehensively describe a multitude of 'hidden' shape nuances which are otherwise not detected.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Red, Green, Blue, Infrared, and Near Infrared). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.