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ShareScore release 0.9.0
Dataset results
37 results for “Sugar beet”
Data from: An antagonistic pair of FT homologs mediates the control of flowering time in sugar beet
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Transcriptomic and metabolomic analyses reveal mechanisms of adaptation to salinity in which carbon and nitrogen metabolism is altered in sugar beet roots
GEO Series GSE114968. Beta vulgaris. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic analysis of bolting tolerance in six sugar beet genotypes
GEO Series GSE55951. Beta vulgaris. 6 samples. Type: Expression profiling by array.
LncRNAs and mRNAs of sugar beet in response to drought stress
GEO Series GSE205413. Beta vulgaris. 6 samples. Type: Expression profiling by high throughput sequencing.
Comparative study of gene expression in Aspergillus niger solid and submerged growth during sugar beet pulp utilization
GEO Series GSE175954. Aspergillus niger; Aspergillus niger CBS 513.88. 8 samples. Type: Expression profiling by array; Third-party reanalysis.
The first transcriptome analysis of sugar beet in compatible and incompatible interactions with Heterodera schachtii
GEO Series GSE135555. Beta vulgaris. 16 samples. Type: Expression profiling by high throughput sequencing.
Transcript profiling of sugar beet colonized by Rhizoctonia solani, the crown and root rot pathogen
GEO Series GSE92859. Beta vulgaris. 36 samples. Type: Expression profiling by high throughput sequencing.
DNA methylation variations between six sugar beet genotypes with distinct bolting tolerance levels
GEO Series GSE56413. Beta vulgaris; Beta vulgaris subsp. vulgaris. 6 samples. Type: Methylation profiling by array.
Proteomics Approach to Differentiate Protein Extraction Methods in Sugar Beet Leaves
<p>Interest in alternative plant-based protein sources is continuously growing. Sugar beet leaves have the potential to satisfy that demand due to their high protein content. They are considered as agricultural waste and utilizing them as protein sources can bring them back to the food chain. In this study, isoelectric-point-precipitation, heat-coagulation, ammonium-sulfate precipitation, high-pressure-assisted isoelectric-point precipitation, and high-pressure-assisted heat coagulation methods were used to extract proteins from sugar beet leaves. A mass spectrometry-based proteomic approach was used for comprehensive protein characterization. The analyses yielded 817 proteins, the most comprehensive protein profile on sugar beet leaves to date. Although the total protein contents were comparable, there was a significant difference between the methods for low-abundance proteins. High-pressure-assisted methods showed elevated levels of proteins predominantly located in the chloroplast. Here we showed for the first time that the extraction/precipitation methods may result in different protein profiles that potentially affect the physical and nutritional properties of functional products.</p>
RNA sequence reveal gene expression mechanism of sugar beet in response to alkaline stress
GEO Series GSE107627. Beta vulgaris. 9 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Transcriptome and DNA methylation variations between six sugar beet genotypes with distinct bolting tolerance levels
GEO Series GSE56415. Beta vulgaris subsp. vulgaris; Beta vulgaris. 12 samples. Type: Expression profiling by array; Methylation profiling by array.
Combinatorial control of gene expression in Aspergillus niger grown on sugar beet pectin
GEO Series GSE97974. Aspergillus niger. 32 samples. Type: Expression profiling by high throughput sequencing.
Spatial differentiation of gene expression in Aspergillus niger colonies grown for sugar beet pulp utilization
GEO Series GSE66641. Aspergillus niger; Aspergillus niger CBS 513.88. 10 samples. Type: Expression profiling by array.
Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images
<p>We present the Deep Nutrient Deficiency for Sugar Beet (DND-SB) dataset, which contains 5648 RGB images of sugar beet captured over the whole growth period in 2019 at the long term fertilizer experiment (LTFE) Dikopshof near Bonn. The images were annotated with seven types of fertilizer treatments. The dataset is used for image classification and domain adaptation. For more details, please refer to the link: <a href="https://github.com/jh-yi/DND-SB">https://github.com/jh-yi/DND-SB</a>. </p>
Small RNA Sequencing Reveals a Potential miRNA-Mediated Interaction Network in Sugar Beet Response to Alkaline Stress
GEO Series GSE113161. Beta vulgaris. 3 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Small RNA of sugar beet in response to drought stress
GEO Series GSE205328. Beta vulgaris. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
CircRNA of sugar beet in response to drought stress
GEO Series GSE205327. Beta vulgaris. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
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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.