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38
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Dataset results
38 results for “high-throughput phenotyping”
Cosegmentation for Plant Phenotyping+ (CosegPP+) Data Repository Collected Via a High-Throughput Imaging System
<p>CosegPP+ is an extension of CosegPP (https://doi.org/10.5281/zenodo.5117176) with binary masks for a collection of cosegmentation and segmentation algorithms. </p> <p>We thank Vincent Stoeger for acquiring the dataset using LemnaTec at the University of Nebraska-Lincoln.</p> <p>If you use this dataset, please cite this paper:</p> <table> <tbody> <tr></tr> <tr> <td> <div>Quiñones, R., Samal, A., Das Choudhury, S., & Muñoz-Arriola, F. (2023). OSC-CO2: coattention and cosegmentation framework for plant state change with multiple features. <em>Frontiers in Plant Science</em>, <em>14</em>, 1211409.</div> </td> </tr> <tr> <td> </td> </tr> </tbody> </table> <p> </p>
Data files associated with the article entitled: "High-throughput behavioral phenotyping of tiny arthropods: chemosensory traits in a mesostigmatic hematophagous mite" (accepted in Journal of Experimental Zoology - part A))
<p>Here are provided three groups of datasets, corresponding respectively to the data obtained from MiteMap bioassays (1) with the monomolecular reference substances (geraniol and NH<sub>3</sub>), (2) with the patented blend MIX1.0, (3) with the odors emitted by the mites' bodies. These datasets were analyzed using the script provided in RMarkdown format in the Zenodo repository DOI 10.5281/zenodo.6109388. A folder is also provided with the heatmaps associated with these data grouped in subfolders by farm x modality.</p>
High-throughput phenotyping of salt-stress responses of the selected Arabidopsis hormone mutants
<p>This data was collected using Hight Throughput Phenotyping machine (psi.cz) purchased by KAUST in January 2018. The plants were germinated and grown under 12/12 light/dark light regime, 22C, 60% humidity for two weeks. After two weeks, half of the plants were exposed to approximately 100 mM NaCl (protocol described in Awlia et al., 2016, Frontiers in Plant Sci, <a href="https://doi.org/10.3389/fpls.2016.01414">https://doi.org/10.3389/fpls.2016.01414</a>). The rosettes of the plants were subsequently phenotyped using the Thermal Camera, RGB, and Chlorophyll Fluorescence camera (in this order). Chlorophyll fluorescence was measured after 15 minutes of dark adaptation using the "LightCurve2" protocol. </p> <p>The description of the traits is available in the notebook available at <a href="https://github.com/mmjulkowska/PSI_notebook">https://github.com/mmjulkowska/PSI_notebook</a> </p>
Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research
Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.
Cosegmentation for Plant Phenotyping (CosegPP) Data Repository Collected Via a High-Throughput Imaging System
<p>CosegPP is a data repository that contains four datasets for plant phenotyping. Each dataset contains: </p> <ol> <li>two species physically different for challenging segmentation. Buckwheat is a thin plant with a variety sizes of leaves and Sunflower is a bushy plant that contains flowering;</li> <li>the most commonly used induced environments in plant phenotyping such as a control and drought-induced; </li> <li>a temporal resolution that begins with the plants vegetative stage and ends with the plant fully matured;</li> <li>modalities (infrared, visible, near infrared) that are commonly used in plant phenotyping analysis; and </li> <li>multiple perspectives that are becoming widely acquired in plant phenotyping analysis due to its potential for three dimensional analysis.</li> </ol> <p>We thank Vincent Stoeger for acquiring the dataset using LemnaTec at the University of Nebraska-Lincoln.</p> <p>If you use this dataset, please cite this paper:</p> <p>Quiñones R, Munoz-Arriola F, Choudhury SD, Samal A (2021) Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. PLoS ONE 16(9): e0257001. <a href="https://doi.org/10.1371/journal.pone.0257001">https://doi.org/10.1371/journal.pone.0257001</a></p>
Data from: Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping
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Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research
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Data from: Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding
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Data from: Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice
Manual phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, automated, non-destructive phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale assessment of rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We could explain 30% of the grain yield variance from 2 tiller traits assessed in the early growth stages. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and rice accessions containing haplotype H3 displayed much smaller tiller angles. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting.
High-throughput phenotyping of drought response in Phaseolinae
<p><span>Using a high-throughput phenotypic imaging system, we evaluated the drought tolerance of 151 bean accessions (Phaseolinae; Fabaceae) by quantifying five different traits simultaneously: biomass, water use efficiency, relative water content, chlorophyll content, and root/shoot ratio. </span>The base data for calculating the drought response indicators and the accessions used in the study are listed in these datasets</p>
Data from: Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice
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Data from: Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis
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Assessing the Effects of Silver Nanoparticles on ARPE-19 Cells via High-Throughput Phenotypic Profiling with the Cell Painting Assay.
GEO Series GSE297342. Homo sapiens. 88 samples. Type: Expression profiling by high throughput sequencing.
Tumor immunological phenotype signature-based high-throughput screening for the discovery of combination immunotherapy compounds
GEO Series GSE160071. Homo sapiens; Mus musculus. 22 samples. Type: Expression profiling by high throughput sequencing.
High-throughput phenotyping of lung cancer somatic mutations [main experiment]
GEO Series GSE83744. Homo sapiens. 3738 samples. Type: Expression profiling by array.
Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling
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Optimization of Human Neural Progenitor Cells for an Imaging-Based High-Throughput Phenotypic Profiling Assay for Developmental Neurotoxicity Screening
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High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes
GEO Series GSE198047. Homo sapiens; synthetic construct. 18 samples. Type: Other.
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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)
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.