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1,274 results for “High-Throughput”
Multipad Agarose Plate (MAP): A Rapid and High-Throughput Approach for Antibiotic Susceptibility Testing
<p>The datasets used for the Multipad Agarose Plate (MAP) paper. Each experiment was labelled with BE followed by a number. </p> <p>The file <em>BE_condition_map.json</em> describes what was placed on each pad for the experiments. Attached here are JSON files with Pandas data frames that contain all segmentation information, along with debug videos showing how the segmentation aligns with the images. Contact us for access to the raw data.</p> <p>Datasets used for validation experiments:</p> <ul> <li>Leakage test: BE100, BE102</li> <li>Agarose concentration: BE103</li> <li>Illumination wavelength verification: BE138</li> <li>Seeding density verification: BE162</li> </ul> <p>Datasets used for AST:</p> <ul> <li>Chloramphenicol and Rifampicin: BE140, BE141, BE142, BE144, BE145</li> <li>Vancomycin, Ampicillin, Kanamycin: BE148, BE149</li> <li>Ciprofloxacin, Tetracycline, Carbenicillin, Mecillinam: BE150, BE151</li> </ul> <p>Broth microdilution data used for AST validation:</p> <ul> <li>BE139, BE143, BE160</li> </ul> <p>Some datasets also include data that was discarded. </p>
Raw data for the article: High-throughput computational solvent screening for lignocellulosic biomass processing
<p>This data set contains the raw data for the article "High-throughput computational solvent screening for lignocellulosic biomass processing" published in <em>Chemical Engineering Journal</em>, DOI: <a href="https://doi.org/10.1016/j.cej.2022.139476">https://doi.org/10.1016/j.cej.2022.139476</a></p>
High-throughput in-situ plankton imaging from the East China Sea: raw images and acantharian ROIs
<p>Vertical imaging profiles were performed at four stations (3, 10, 15, 17; closed circles on the map) during the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) MR17-03C cruise from May 29 to June 13, 2017 with an ISIIS small-imager (<a href="https://www.planktonimaging.com/smaller-imagers">https://www.planktonimaging.com/smaller-imagers</a>) attached to the JAMSTEC DEEP TOW 6KCTD (<a href="https://www.jamstec.go.jp/e/about/equipment/ships/deeptow.html">https://www.jamstec.go.jp/e/about/equipment/ships/deeptow.html</a>). The ISIIS camera was programmed to take 1 photo per second coinciding with an LED flash. Each photo imaged 0.39 L (st. 3 and 10) or 0.35 L (st. 15 and 17) parcels of water in 2448 x 2050 pixel resolution, with each pixel being 22.5 µm. A Sea-Bird SBE 9 CTD was deployed with the DEEP TOW and the ISIIS internal clock was calibrated to match the CTD’s so that CTD data could be used to determine the depth at which each image was taken. Raw images are labeled with the time stamp. Acantharian ROIs are labeled with the timestamp for the raw image from which they were cropped. If more than one acantharian ROI was found in a single raw image, a letter was appended to the ROI file name. </p> <p>Accompanying data (CTD, sequencing) and analyses are available from the GitHub repository: <a href="https://github.com/maggimars/Acanth_ImageSeq">https://github.com/maggimars/Acanth_ImageSeq</a>.</p> <p> </p>
Homogeneous multifocal excitation for high-throughput super-resolution imaging - Expanded centriole particles
<p>Datasets containing the segmented expanded centriole particles. The prefix Hs is used to denote particles acquired in synchronized RPE-1 human cells. Otherwise particles were collected from expanded isolated centrioles from <em>Chlamydomoanas reinhardtii</em>. Resized datasets have uniform voxel size of 14x14x14 nm3 after expansion (56x56x56 nm3 before expansion). Non-resized datasets have 14x14x30 pixel size (56x56x120 nm3 before expansion). All files should be mirrored horizontally/vertically to account for the chirality inversion due to the imaging process</p> <p>The channels in different datasets are:</p> <ul> <li>Chlamy acetylated sample: <ul> <li>C1: acetylated tubulin-Alexa488</li> <li>C2: aTubulin-Alexa568</li> </ul> </li> <li>Chlamy MonoE sample <ul> <li>C1: aTubulin-Alexa488</li> <li>C2: GT335-Alexa568</li> </ul> </li> <li>Chlamy PolyE sample <ul> <li>C1: PolyE-Alexa488</li> <li>C2: aTubulin-Alexa568</li> </ul> </li> <li>Hs sample: <ul> <li>C1: PolyE-Alexa488</li> <li>C2: acetylated tubulin-Alexa586</li> </ul> </li> </ul>
SI data: A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al
<p>Journal: ACS Combinatorial Science<br> Title: A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al<br> Author(s): Joress, Howie; DeCost, Brian; sarker, suchismita; Braun, Trevor; Jilani, Sidra; Smith, Ryan; Ward, Logan; Laws, Kevin; Mehta, Apurva; Hattrick-Simpers, Jason</p>
High-Throughput Sequencing of Human Immunoglobulin Variable Regions with Subtype Identification
<p>Raw Illumina MiSeq data in zipped FASTQ format. The data set includes demultiplexed samples from three different time points (_wk*_) of patient ZA159 (159_*), samples from four different preps of a healthy donor (HD1_*), and samples from IgG subtype sorted cells of a healthy donor (HD3_*). Every sample consists of forward (_R1_), reverse (_R2_) and index read 1 (_I1_). </p>
Dataset of Chen et al. (2023) "MCount: An automated colony counting tool for high-throughput microbiology"
<p>Folder "96 well colonies" contains 10 microplate images with the original resolution. Folder "results" contains a segmentation and quantification results from the microplate images. Folder "Codes" includes Python source code.</p>
TREXIO files used for the validation tests in the paper entitled 'TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods'.
<p>The TREXIO files used for the validation tests in the paper entitled TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods. The detail about the TREXIO library is described in the JCP article [J. Chem. Phys. 158, 174801 (2023)] and the GitHub repository [https://github.com/TREX-CoE/trexio]. The TREXIO files were generated using TREXIO version 2.3.2 (and the corresponding Python API version 1.3.2).</p>
Supplementary Data for "Sequencing the Pandemic: Rapid and High-Throughput Processing and Analysis of COVID-19 Clinical Samples for 21st Century Public Health"
<p>Supplementary material for F1000 methods manuscript. Includes raw sequencing metrics for two COVID sequencing methodologies, as well as a complete cost breakdown for each methodology.</p>
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
<p>Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at 5 time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into 2 groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.</p>
Strong piezoelectric response in stable TiZnN2, ZrZnN2, and HfZnN2 found by ab initio high-throughput approach
<p>The phase diagrams of the Ti-Zn-N, Zr-Zn-N, and Hf-Zn-N systems are determined using large-scale high-throughput density functional calculations. In total 12,815 relaxed structures are shared alongside their energy calculated using the VASP DFT code. The High-Throughput Toolkit was used to manage the calculations.</p> <p>A README file is included that describes how to load the data and the contents of the DataFrame.</p>
Computational Analysis of Two-dimensional High-throughput Data from Large-scale RNAi Screens and Single-cell Transcriptomics
<p>This publication provides a singularity definition file to reproduce the computational environment along with the scripts to reproduce every figure or table in the revised manuscript using ZetaSuite Perl module and R package.</p> <p>First, generate a new folder and then download all the files into the folder.</p> <p>Then, uncompressed the files DataSets_part1.tar.gz,DataSets_part2.tar.gz,DataSets_part3.tar.gz,DataSets_part4.tar.gz, and scripts.tar.gz. within the folder.</p> <p>Next, move all the files in DataSets_part1 folder, DataSets_part2 folder,DataSets_part3 folder and DataSets_part4 folder to a new folder called DataSets.</p> <p>Finally, run the following scripts to generate the figures and tables in our manuscript.</p> <p>Regeneration of Figure2 and S2: singularity exec ZetaSuite.sif sh Figure2andS2.sh </p> <p>Regeneration of Figure3 and S3: singularity exec ZetaSuite.sif sh Figure3andS3.sh </p> <p>Regeneration of Figure4 and S4: singularity exec ZetaSuite.sif sh Figure4andS4.sh </p> <p>Regeneration of Figure5 and S5: singularity exec ZetaSuite.sif sh Figure5andS5.sh </p> <p>Regeneration of Figure6 and S6: singularity exec ZetaSuite.sif sh Figure6andS6.sh </p> <p>Regeneration of Figure7 and S7: singularity exec ZetaSuite.sif sh Figure7andS7.sh </p> <p> </p>
Codes for "High-throughput parallel optofluidic 3D-imaging flow cytometry"
<p>Codes used in Ugawa & Ota. "High-throughput parallel optofluidic 3D-imaging flow cytometry". Small size data is also included.</p>
Raw data: High-throughput screening of soybean di-nitrogen fixation and seed nitrogen content using spectral sensing
<p>Symbiotic di-nitrogen fixation of grain legumes has a substantial impact on crop performance, harvest product quality, and nitrogen (N) balance of crop rotations, particularly under organic management regimes. In soybean breeding, selection for increased nitrogen fixation is desirable for improving seed protein content and N balance of cropping systems. However, the lack of high-throughput screening methods for direct measurement of N 2 fixation rates prohibits practical breeding efforts. Therefore, hyperspectral canopy reflectance measurement as a field-based phenotyping method was evaluated in three environments for indirect estimation of N fixation and uptake of soil nitrogen in a set of early maturity soybean genotypes exhibiting a wide range in seed protein content. Reflectance spectra were collected in repeated measurements during flowering and early seed filling stages. Subsequently, various spectral reflectance indices (SRIs) were calculated for characterizing nitrogen accumulation of individual genotypes. Moreover, prediction models for seed protein content as an end-of-season target trait were developed utilizing full spectral information in partial-least-square regression (PLSR) models. A number of N-related SRIs calculated from spectral reflectance data recorded at the beginning of the seed filling stage were significantly correlated to seed protein content. The best prediction of seed protein content, however, was achieved in PLSR models (validation R 2 =0.805 across all three environments). Environments lower in initial soil mineral N content appeared as more favorable selection sites in terms of prediction accuracy, because N fixation is not masked by soil N uptake in such environments. Hyperspectral reflectance data proved to be a valuable method for determining genetic variation in crop N accumulation, which might be implemented in high-throughput screening protocols for N fixation in plant breeding programs.</p>
Raw data for "Fluorescence crosstalk reduction by modulated excitation-synchronous acquisition for multispectral analysis in high-throughput droplet microfluidics."
<p>Raw data to quantify the crosstalk reduction and signal resolution improvement by MESA used in Figure 3 and 4.</p> <p><br> </p>
High-throughput metabolomics for the design and validation of a diauxic shift model
<p>Untargeted metabolomics on ten different regulatory strains in <em>Saccharomyces cerevisiae, </em>(BY4741). Samples were taken before and after the diauxic shift, to investigate regulatory consequences of gene deletions and their roles during the substantial metabolic reconfiguration that is the diauxic shift. The analysis of samples was performed on an Agilent UHPLC-qTOF-MS system which consisted of a 1290 II Infinity series UHPLC system with a 6550 UHD iFunnel accurate-mass qTOF spectrometer.</p> <p>Data-set used in: <a href="https://www.nature.com/articles/s41540-023-00274-9">High-throughput metabolomics for the design and validation of a diauxic shift model</a></p>
Data from: Towards drift-free high-throughput nanoscopy through adaptive intersection maximization
<p>Single-molecule localization microscopy (SMLM) often suffers from suboptimal resolution due to imperfect drift correction. Existing marker-free drift-correction algorithms often struggle to reliably track high-frequency drift and lack the computational efficiency to manage large, high-throughput localization datasets. We present an adaptive intersection maximization-based method (AIM) that leverages the entire dataset's information content to minimize drift correction errors, particularly addressing high-frequency drift, thereby enhancing the resolution of existing SMLM systems. We demonstrate that AIM can robustly and efficiently achieve an angstrom-level tracking precision for high-throughput SMLM datasets under various imaging conditions, resulting in an optimal resolution in simulated and biological experimental datasets. We offer AIM as simple and model-free software for instant resolution enhancement with standard CPU devices.</p>
The Pan-Canadian Chemical Library: A Mechanism to Open Academic Chemistry to High-Throughput Virtual Screening
<h1>Pan-Canadian Chemical Library</h1> <p>This Zenodo repository contains the cheap and druglike subset of the Pan-Canadian Chemical Library (PCCL) project. For more information, visit <a href="https://pccl.thesgc.org/" rel="nofollow">https://pccl.thesgc.org</a>.</p> <h2>PCCL library</h2> <p>The PCCL library is splitted by reaction, then by number of heavy atoms. Two types of files are available in zip archives:</p> <ul> <li>The SMILES format files, with the SMILES string and their product name,</li> <li>The CSV format file, with all the information generated during their enumeration: reagents, druglike properties, etc.</li> </ul> <p>Note: Purchasability is defined according to two integers: 1 for products only composed of BB-50 reagents, and 2 for products composed of BB-40 or BB-50 reagents. Read more about the meaning of these reagents groups in the article below.</p> <h2>Citation</h2> <p>If you find the PCCL useful or if you use it, please cite our paper:</p> <p>Bedart, C. <em>et al.</em> The Pan-Canadian Chemical Library: A mechanism to open academic chemistry to high-throughput virtual screening. Scientific Data 11, (2024).<br>doi: <a title="10.1038/s41597-024-03443-5" href="https://www.nature.com/articles/s41597-024-03443-5">10.1038/s41597-024-03443-5</a></p> <p> </p> <p> </p>
Dataset for High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating
<p>This is a dataset for the manuscript High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating. This mansucript is currently under peer-review in ACS Nano. </p>
Dataset of "Key Aspects in Designing High-Throughput Workflows in Electrocatalysis Research: A Case Study on IrCo Mixed-Metal Oxidese"
<p>With the growing interest of the electrochemical community in high-throughput (HT) experimentation as a powerful tool in accelerating materials discovery, the implementation of HT methodologies and the design of HT workflows has gained traction. We identify 6 aspects essential to HT workflow design in electrochemistry and beyond to ease the incorporation of HT methods in the community’s research and to assist in their improvement. We study IrCo mixed-metal oxides (MMOs) for the oxygen evolution reaction (OER) in acidic media using the mentioned aspects to provide a practical example of possible workflow design pitfalls and strategies to counteract them. </p>
ScienceDex guides
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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.