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342 results for “sorghum”
Sorghum bicolor (L.) Moench (BR0000005698028)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000011615651)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000012354511)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000014444913)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000011616191)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000011614968)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000012354610)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000012389469)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000011615293)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000014444999)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000024508445)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Annotated genes harboring major effect markers (R2 ≥ 15%). Highlighted in green are genes annotated from Rhodes et al. 2014,2017, in orange genes annotated as similar to Peroxidase, in yellow new annotations from sorghum genome in Atlas. In the first three columns start and stop position on the sorghum genome and transcript name, followed by the nearest marker name and the distance of the gene from the nearest marker, then a column where are shown the GWAS methods and target traits for which the linked SNP was significant, the last column shows the category of the genes.
<p><strong>We conducted a comprehensive genomics study to map genomic loci determining the production of antioxidants in sorghum grains. Encouraging results were obtained and published in peer-reviewed article with impact factor (https://doi.org/10.1371/journal.pone.0225979). Annotated genes harboring major effect markers (R<sup>2</sup> ≥ 15%) were identified and will be of worldwide interest. </strong></p>
Dartseq SNP data of Uganda sorghum germplasm
<p>SNP data generated DArTseq for Ugandan S. bicolor germplasm accessions (UG set) from the Plant Genetic Resources Centre at the Uganda National Genebank.</p>
Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor
<p>Metabolomics data from the study: Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor. Files are raw chromatograms.</p> <p>Metabolite profiling analysis<br>Metabolite Extraction and Quantification. The whole metabolite extraction and quantification pipeline followed the guidelines as described in Giavalisco et al. (2011) and Salem et al. (2020). Briefly, 10-25 mg of fresh plant tissue was harvested and immediately frozen in N2, and ground using zirconia beads with the help of Tissue Lyser Mixer-Mill (Qiagen) 3 min at 25 Hz. After samples were extracted in 100% cold methanol, they were centrifuged for 10 min at 14000 rpm (Room temperature) and 3:1 chloroform was added. After vortexing thoroughly, 1:1 volume of water was added and the samples were subsequently vortexed and centrifuged. The semi-polar phase was used for analysis of semi-polar secondary and primary metabolites. For secondary semi-polar metabolites, the dried polar aliquots were resuspended in water:methanol (1:1 v/v).<br>3 Analysis of semi-polar metabolites was performed using a Thermo Q Exactive Focus coupled to a reverse-phase C18 column. The column was maintained at 40°C with a flow rate of 400 μl/min, and the eluent system consisted of water (eluent A) and acetonitrile (eluent B), both containing 0.1% formic acid. Mass spectra were acquired in full scan mode over a range of 100-1500 m/z using data-independent acquisition (DIA) with high-energy collisional dissociation (HCD) energy set at 30 eV in positive mode.<br>9 For primary metabolite metabolites, the dried polar was derivatized as described in Lisec et al. (2006). Derivatization was carried out at 37°C for 120 min using 40 μl of 20 mg/ml methoxyamine hydrochloride in pyridine, followed by a 30-min treatment at 37°C with 70 μl of trimethylsilyl-N-methyl trifluoroacetamide (MSTFA). The derivatized samples (1 μl) were injected in splitless mode into a gas chromatograph coupled to a time-of-flight mass spectrometer (Pegasus HT TOF-MS). Gas chromatography was performed on a 30-m DB-35 column using helium as the carrier gas. The initial temperature of the oven was 85°C, and it was ramped up at a rate of 15°C/min to a final temperature of 360°C. Mass spectra were recorded in the range of 70-600 m/z at a rate of 20 scans/s. Data Processing and Compound Annotation. LC-MS full scan data were processed using MS Refiner (Expressionist 14.0). Processing of chromatograms, peak detection, and integration were performed using RefinerMS (version 5.3; GeneData). Metabolite identification and annotation were performed using in-house reference compound library, tandem MS (MS/MS) 22 fragmentation, and metabolomics databases (Alseekh et al., 2021). For the annotation of metabolites measured by GC-MS the Golm Metabolome Database was used (Kopka et al., 2005).<br><br></p> <div> <div> <p><span><span>#heatmap of fold changes</span></span><span> </span></p> </div> <div> <p><span><span>ts</span><span> <</span><span>-read.csv(</span><span>"all_log2fc_heat_all.csv", </span><span>sep</span><span>=";", </span><span>fileEncoding</span><span>='latin1</span><span>',check</span><span>.names=F, header=TRUE)</span></span><span> </span></p> </div> <div> <p><span><span>ts1 <- </span><span>ts</span></span><span> </span></p> </div> <div> <p><span><span>ts1$class <- NULL </span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>row.names</span><span>(ts1) <- ts1$compound</span></span><span> </span></p> </div> <div> <p><span><span>ts1$compound <- NULL</span></span><span> </span></p> </div> <div> <p><span><span>m <- </span><span>as.matrix</span><span>(ts1)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>ann</span><span> <- </span><span>ts</span><span>[1:2]</span></span><span> </span></p> </div> <div> <p><span><span>row.names</span><span>(</span><span>ann</span><span>) <- </span><span>ann$compound</span></span><span> </span></p> </div> <div> <p><span><span>ann$compound</span><span> <- NULL</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("</span><span>pheatmap</span><span>")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>library("</span><span>pheatmap</span><span>")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>m1 <- m[</span><span>rownames</span><span>(</span><span>ann</span><span>)</span><span>, ]</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>pheatmap</span><span>(m1, </span><span>annotation_row</span><span> = </span><span>ann</span><span>, </span><span>cluster_rows</span><span> = </span><span>F,cluster_cols</span><span> = F, </span><span>cellheight</span><span> = 10, </span></span><span> </span></p> </div> <div> <p><span><span> </span><span>cellwidth</span><span> = 10, </span><span>gaps_row</span><span> = </span><span>cumsum</span><span>(</span><span>c(</span><span>3,105,30,11,21,4,32,5,17)), </span><span>gaps_col</span><span> = </span><span>cumsum</span><span>(</span><span>c(</span><span>2,2,2)), scale = 'none')</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>#boxplots</span></span><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("ggplot2")</span></span><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("</span><span>readr</span><span>")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>library(ggplot2)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>readr</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("</span><span>tidyverse</span><span>")</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>tidyverse</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>grid <- </span><span>read.csv(</span><span>"sec_exp1_logfc_20.csv", </span><span>sep</span><span>=";", header=TRUE)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>grid_g</span><span> <- grid%>% </span><span>gather(</span><span>"compound", "value", 6:25)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>d=</span><span>ggplot</span><span>(data = </span><span>grid_g</span><span>, </span><span>aes</span><span>(x=compound, y=value, fill=factor(group)</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_boxplot</span><span>(</span><span>outlier.shape</span><span> = </span><span>NA)+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_point</span><span>(position = </span><span>position_jitterdodge</span><span>(0.1))</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>d+theme</span><span>(text = </span><span>element_text</span><span>(size = 15</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>scale_fill_manual</span><span>(values = </span><span>c(</span><span>"#ca5826","#e4ab92", "#008080", "#99cccc"</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>labs(</span><span>y="log2</span><span>fc(</span><span>metabolite content)</span><span>",fill</span><span>="</span><span>gtype</span><span>")+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>theme(</span><span>axis.text.x</span><span> = </span><span>element_text</span><span>(angle = 60, </span><span>hjust</span><span> = 1, size = 5)) + </span></span><span> </span></p> </div> <div> <p><span><span> </span><span>theme_classic</span><span>() +</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>coord_flip</span><span>() +</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>facet_grid</span><span>(condition </span><span>~ .</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>ggsave</span><span>("logfcsum_exp1_15_bri1.png", width = 10, height = 10)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>#boxplots all data</span></span><span> </span></p> </div> <div> <p><span><span>library(ggplot2)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>readr</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>viridis</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>tidyverse</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>dplyr</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA</span><span> <</span><span>-read.csv(</span><span>"GCMS_polar.csv", </span><span>sep</span><span>=",", </span><span>fileEncoding</span><span>='latin1', </span><span>check.names</span><span> = F, header=TRUE)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>row.names</span><span>(</span><span>sPCA</span><span>) <- </span><span>sPCA$Sample</span></span><span> </span></p> </div> <div> <p><span><span>sPCA_D</span><span> <- </span><span>sPCA</span><span>[6:155]</span></span><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog</span><span> <- log(</span><span>sPCA_D</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog10 <- log10(</span><span>sPCA_D</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog</span><span> <- </span><span>cbind</span><span>(</span><span>sPCA_Dlog</span><span>, group = </span><span>sPCA$Sample_group</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog</span><span> <- </span><span>sPCA_Dlog</span><span>%>% </span><span>select(</span><span>group, </span><span>everything(</span><span>))</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog</span><span> <- </span><span>cbind</span><span>(</span><span>sPCA_Dlog</span><span>, sample = </span><span>sPCA$Sample</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog</span><span> <- </span><span>sPCA_Dlog</span><span>%>% </span><span>select(</span><span>sample, </span><span>everything(</span><span>))</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>write.csv(</span><span>sPCA_Dlog10, "GCMS_polar_lg.csv")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog_g</span><span> <- </span><span>sPCA_Dlog</span><span>%>% </span><span>gather(</span><span>"compound", "value", 3:152)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>d <- </span><span>ggplot</span><span>(</span><span>sPCA_Dlog_g</span><span>, </span><span>aes</span><span>(x=sample, y=value, fill=factor(group)</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_boxplot</span><span>(</span><span>outlier.shape</span><span> = </span><span>NA)+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_point</span><span>(position = </span><span>position_jitterdodge</span><span>(0.1))</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>d + </span><span>theme(</span><span>text = </span><span>element_text</span><span>(size = 15</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>scale_fill_manual</span><span>(values = </span><span>c(</span><span>"#CC6677","#882255","#44AA99", "#117733"</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>labs(</span><span>y="</span><span>lg</span><span>(normalized metabolite content)", x= "sample</span><span>" ,</span><span> fill="group</span><span>")+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>theme(</span><span>axis.text.x</span><span> = </span><span>element_text</span><span>(angle = 60, </span><span>hjust</span><span> = 1)) + </span></span><span> </span></p> </div> <div> <p><span><span> </span><span>theme_classic</span><span>() +</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_hline</span><span>(</span><span>yintercept</span><span>=0, </span><span>linetype</span><span>="dashed", </span><span>color</span><span> = "black")</span></span><span> </span></p> </div> <div> <p><span><span>#PCA</span></span><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("</span><span>plotly</span><span>")</span></span><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("</span><span>ggfortify</span><span>")</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>plotly</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>ggfortify</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA</span><span> <</span><span>-read.csv(</span><span>"prim.csv", </span><span>sep</span><span>=";", header=TRUE)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA</span><span> <- </span><span>subset(</span><span>sPCA</span><span>, </span><span>experiment!=</span><span>"2")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCAtd</span><span> <- </span><span>all_d</span><span>[7:240]</span></span><span> </span></p> </div> <div> <p><span><span>sPCAtdn</span><span> <- </span><span>sPCAtd</span><span>[-</span><span>c(</span><span>24)</span><span>, ]</span></span><span> </span></p> </div> <div> <p><span><span>sPCAn</span><span> <- </span><span>sPCA</span><span>[-</span><span>c(</span><span>24)</span><span>, ]</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>#normal</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec.pcat</span><span> <- </span><span>prcomp</span><span>(</span><span>sPCAtd</span><span>, </span><span>center</span><span> = </span><span>TRUE,scale</span><span>. = TRUE)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>summary(</span><span>sec.pcat</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>p <- </span><span>autoplot</span><span>(</span><span>sec.pcat</span><span>, data = </span><span>all_d</span><span>, colour = 'condition', shape = 'Genotype') + </span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_text</span><span>(</span><span>aes</span><span>(label = Replicate), </span><span>nudge_y</span><span> = 0.02) +</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>theme_classic</span><span>() +</span></span><span> </span></p> </div> <div> <p><span><span> </span></span><span><span>scale_color_manual</span><span>(</span><span>values</span><span>=</span><span>c(</span><span>"#56B4E9", "#E69F00"))</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>ggplotly</span><span>(p)</span></span><span> </span></p> </div> <div> <p><span><span>#</span><span>experiment</span><span> wise fold changes</span></span><span> </span></p> </div> <div> <p><span><span>write.csv(</span><span>sec_fc_mean</span><span>, "sec_foldchange_mean.csv")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1 <- </span><span>sect[</span><span>1]</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.drought <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.drought</span><span>, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.drought1 <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.drought</span><span>.1, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.drought2 <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.drought</span><span>.2, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.control <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.control</span><span>, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.control1 <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.control</span><span>.1, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.control2 <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.control</span><span>.2, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.drought <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..drought</span><span>, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.drought1 <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..drought</span><span>.1, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.drought2 <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..drought</span><span>.2, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.control <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..control</span><span>, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.control1 <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..control</span><span>.1, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.control2 <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..control</span><span>.2, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>write.csv(</span><span>sec_fc_exp1, "sec_fc_exp1.csv")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>#</span><span>all</span><span> compounds for </span><span>RNAsq</span> <span>comarision</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>all_R</span><span> <</span><span>-read.csv(</span><span>"all_RNAsq.csv", </span><span>sep</span><span>=";", </span><span>fileEncoding</span><span>='latin1</span><span>',check</span><span>.names=F, header=T)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>all_fc_exp2 <- </span><span>all_R</span><span>[1]</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>all_fc_exp2$drought <- </span><span>foldchange(</span><span>all_R$bri1_drought, </span><span>all_R$WT_drought</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>all_fc_exp2$control <- </span><span>foldchange(</span><span>all_R$bri1_control, </span><span>all_R$WT_control</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>write.csv(</span><span>all_fc_exp2, "all_foldchange_exp2.csv")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>#students </span><span>t</span><span> test</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>secfc_2_t <</span><span>-read.csv(</span><span>"sec_exp1_logfc.csv", </span><span>sep</span><span>=";", </span><span>fileEncoding</span><span>='latin1</span><span>',check</span><span>.names=F, header=T)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>secfc_2_t <- secfc_2_</span><span>t[</span><span>-2]</span></span><span> </span></p> </div> <div> <p><span><span>secfc_2_t <- secfc_2_</span><span>t[</span><span>-(14:25)]</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>st2<- secfc_2_t %>% </span><span>gather(</span><span>"compound", "value", 6:171)</span></span><span> </span></p> </div> <div> <p><span><span>stat.test1 <- st2 %>%</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>group_by</span><span>(compound) %>%</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>t_test</span><span>(value ~ group)</span></span><span> </span></p> </div> <div> <p><span><span>add_significance</span><span>()</span></span><span> </span></p> </div> <div> <p><span><span>stat.test1</span></span><span> </span></p> </div> </div> <p> </p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJ-GUESS sorghum
<p>This is model output from LPJ-GUESS for sorghum as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: CGMS-WOFOST sorghum
<p>This is model output from CGMS-WOFOST for sorghum as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: pDSSAT sorghum
<p>This is model output from pDSSAT for sorghum as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: pAPSIM sorghum
<p>This is model output from pAPSIM for sorghum as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
Rainout shelter in a Walnut-Sorghum agroforestry system with the tarpaulin folded
<p>Picture of a rainout shelter with a fixed tunnel structure, a folded tarpaulin, and gutters to evacuate water, used in a 28 year old agroforestry system with walnut trees and arable crops in plot A2 of Domaine de Restinclières (coordinates: 43.704274 , 3.860958). Picture taken on 2023-06-29</p>
GRAS Family Transcription Factor Binding Behaviors in Sorghum bicolor, Oryza, and Maize
<p>Supplemental Data Files for the manuscript entitled "GRAS Family Transcription Factor Binding Behaviors in Sorghum bicolor, Oyrza, and Maize".</p>
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.