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ShareScore release 0.9.0
Dataset results
8 results for “High-throughput experimentation”
Data for: Whole Animal Feed FLat (WAFFL): A complete and comprehensive validation of a novel high-throughput fly experimentation system
<p>Non-mammalian model organisms have been essential for our understanding of the mechanisms that control development, disease, and physiology, but they are underutilized in pharmacological and toxicological phenotypic screening assays due to their low throughput in comparison with cell-based screens. To increase the utility of using <em>Drosophila melanogaster</em> in screening, we designed the Whole Animal Feeding FLat (WAFFL), a novel, flexible, and complete system for feeding, monitoring, and assaying flies in a high-throughput format. Our 3-D printed system is compatible with inexpensive and readily available, commercial 96-well plate consumables and equipment. Experimenters can change the diet at will during the experiment and video record for behavior analysis, enabling precise dosing, measurement of feeding, and analysis of behavior in 96-well plate format. </p>
Data for: Whole Animal Feed FLat (WAFFL): A complete and comprehensive validation of a novel high-throughput fly experimentation system
Open the record for dataset details and reuse information.
FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data (Part 1)
<p>Data for FLIGHTED (Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data). This data contains the model weights for FLIGHTED-Selection and FLIGHTED-DHARMA, the training data for both, and fits on the GB1 landscape. It does not contain anything related to the TEV protease landscape.</p> <p>The data is arranged in the following folders:</p> <ol> <li>DHARMA_Input: contains the input for the DHARMA models, with the canvas sequence, the DHARMA reads, and the FACS data.</li> <li>DHARMA_Models: contains the weights, hyperparameters, and model training history for the FLIGHTED-DHARMA model.</li> <li>Fitness_Landscapes: the GB1 landscape, with and without FLIGHTED, as well as splits published by FLIP.</li> <li>Landscape_Models: models trained on the GB1 landscape with and without FLIGHTED under the various FLIP splits. Each model folder contains hyperparameters, training history, and predictions on the test set which can be used to evaluate model performance. Raw model parameters are not provided for fine-tuned models due to size; contact us if you want them.</li> <li>FLIGHTED_Selection: contains the weights, hyperparameters, and model training history for the FLIGHTED-Selection model.</li> <li>Selection_Simulations: contains the simulated training data for the FLIGHTED-Selection model.</li> </ol>
FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data (Part 2)
<p>Data for FLIGHTED (Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data). This data contains the TEV protease landscape and models trained on it. All other FLIGHTED data is in the other Zenodo repository (refer to the paper for details).</p> <p>The data is arranged in the following folders:</p> <ol> <li>TEV_Landscape: contains the TEV landscape (in flighted_fitnesses.csv) and splits thereof in Splits/. The main files for model training are flighted_fitnesses.csv and the files labeled one_vs_rest, two_vs_rest, and three_vs_rest. The files labeled three_vs_rest_control within Splits/ and the read count CSV files refer to further information about the read count in the landscape; see the Supplement for details. The dictionary files are the original raw data prior to processing with FLIGHTED.</li> <li>TEV_Models: contains models trained on the TEV landscape under the various splits. Each model folder contains hyperparameters, training history, and predictions on the test set which can be used to evaluate model performance. Raw model parameters are not provided for fine-tuned models due to size; contact us if you want them. The control_run/ refers to the run described in the supplement on just read counts.</li> </ol>
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>
A streamline experimental and computational approach for inexpensive, accurate, efficient and high-throughput annotation of 5’ and 3’ ends
GEO Series GSE60215. Drosophila melanogaster. 54 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
High-throughput identification of adaptive mutations in experimentally evolved yeast populations
GEO Series GSE58497. Saccharomyces cerevisiae. 8 samples. Type: Genome variation profiling by genome tiling array.
Experimental identification of apoptosis-associated miR-21 target genes in Jurkat T-cells using a high-throughput experimental approach
GEO Series GSE85116. Homo sapiens. 2 samples. Type: Expression profiling by array.
ScienceDex guides
Understand access before you commit
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.