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1,661 results for “Throughput”
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Microscopy Data
<p>Microscopy dataset of multipoint-multichannel images of giant unilamellar vesicles (GUVs) suspensions analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>Three folders concerning different sections of the work are included. "preliminary analysis.zip" contians the raw files and analysis scripts for recall computation and imaging setup optimization as described in the paper. Timelapse data was excluded due to file size restrictions (available upon request at the corresponding authors of the work). "IFC comparison.zip" contains raw files and analysis scripts used to optimize colocalization computation in lipid exchange and content exchange experiments. "GUV fusion analysis" contains raw files and analysis scripts for the quantification of lipid and content exchange upon sodium chloride-induced aggregation.</p> <p>Further details on the analysis are provided in the paper. The R scripts require files saved upon analysis of the raw files by the ImageJ macro "CE_analysis_CPU.ijm" included here. The R environment of the complete analysis are included in each folder to provide easier access to the elaborated data.</p>
Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study VCF files
<p>VCF files used and cited in the article: Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study</p>
Formation and cycling data for Na-ion batteries from high-throughput synthesis, coating, and assembly
<p>Formation and cycling data from a combinatorial/high-throughput upscaling process for the production and characterization of sodium-ion batteries. The process involves batch synthesis, screen printing of electrodes, robotic cell assembly, and battery cycling. The goal of this study was to test how fast a new chemistry (to the group) could be introduced into the workflow and if we are able to enhance efficiency, accuracy, and reproducibility. The cathode material, Na0.9[Cu0.22Fe0.30Mn0.48]O2, was synthesized through a solid-state reaction (Na2CO3 (purity 99.5 %), CuO (purity 99.7 %), Fe2O3 (purity 99.9 %) and Mn2O3 (purity 98 %) at 850°C for 15h) in a pressed pellet (10 MPa) that was ground up again to make a slurry. The electrodes were prepared using screen printing, which offers simplicity, low cost, and quick coating of large areas in a reproducible manner. The binder was sodium carboxymethyl cellulose to make the electrodes water processable in air. The assembled batteries utilized the synthesized cathode material and hard carbon as the anode, with a glass fiber separator and a 1M NaPF6 EC:EMC 3:7 with 2 wt% FEC electrolyte.</p>
A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications - Underlying Data
<p><strong>Video files and logs</strong></p> <p>Single-slice and thick-slice roll* source videos are included. Each video is accompanied by a .txt log that contains information about the source file, slice thickness, and a brief description of the visualization mode.</p> <p>List of files:</p> <ul> <li>20211019-23h59m_20xAvgInt.mp4</li> <li>20211019-23h59m_20xAvgInt.txt</li> <li>20211019-23h59m_20xMaxInt.mp4</li> <li>20211019-23h59m_20xMaxInt.txt</li> <li>20211019-23h59m_20xStDev.mp4</li> <li>20211019-23h59m_20xStDev.txt</li> <li>20211019-23h59m_XYSliceRoll.mp4</li> <li>20211019-23h59m_XYSliceRoll.txt</li> <li>20211019-23h59m_XZSliceRoll.mp4</li> <li>20211019-23h59m_XZSliceRoll.txt</li> <li>20211019-23h59m_YZSliceRoll.mp4</li> <li>20211019-23h59m_YZSliceRoll.txt</li> </ul> <p>* <em>Thick-slice rolling is a 2D thick-slice viewing that allows rolling of a pre-selected number of slices (n) along the z-axis of the 3D data. A single thick-slice roll forwards is accomplished by translating the thick-slice by one single slice forwards; that is moving forward by one (+1) slice from the first and nth element and reapplying the criteria or operations to the new slice sub-stack.</em></p> <p><strong>Volume XRH data</strong><br> These are processed raw volume file saved in .raw and/or .tiff format, which are resliced to a histology-relevant orientation and/or have been enhanced using noise reduction (3D median filter) and/or ct-artefact removal techniques (e.g. cBC identifies a bandpass filter used to remove intensity variations originating from the histology cassette).</p> <p>List of volume files:</p> <ul> <li><strong>32220_20200703_XRH_2504_OLK_DEMO02019-FFPE_1620x1959x164x16bit.raw</strong> <ul> <li>sample: Human lung adenocarcinoma</li> <li>histology-relevant resliced volume (2x2x2 3D medial filter applied)</li> <li>import as 1620 x 1959 x 164 x 16-bit, big-endian; voxel edge size (mm): 0.0160042 isotropic</li> </ul> </li> <li><strong>cBC_32220_20200703_XRH_2504_OLK_DEMO02019-FFPE_1588x1674x164x16bit.raw</strong> <ul> <li>sample: Human lung adenocarcinoma</li> <li>cassette artefacts background correction (bandpass) of volume 32220_20200703_XRH_2504_OLK_DEMO02019-FFPE_1620x1959x164x16bit.raw</li> <li>import as 1620 x 1959 x 164 x 16-bit, big-endian; voxel edge size (mm): 0.0160042 isotropic</li> </ul> </li> <li><strong>Med3D_HPass_2111_20190606_MEDX_2234_EH_HN2_recon_2000x1952x501x32bit.raw</strong> <ul> <li>sample: Human head and neck tumour</li> <li>histology-relevant resliced volume (1x1x1 3D medial filter applied)</li> <li>import as 2000 x 1952 x 501 x 32-bit, big-endian; voxel edge size (mm): 0.00999782 isotropic</li> </ul> </li> </ul> <p><strong>Conventional Histology and correlative imaging</strong></p> <ul> <li><strong>HN2_Level001_MEDX080_Manual_BW_Series4.tif</strong> <ul> <li>H&E histology slice of the human head and neck tumour sample shown in "Med3D_HPass_2111_20190606_MEDX_2234_EH_HN2_recon_2000x1952x501x32bit.raw"</li> </ul> </li> <li><strong>HN2_Level001_MEDX080_Manual_BW</strong> <ul> <li>manual landmark selection used for registering the conventional histology slice onto the μCT slice</li> </ul> </li> <li><strong>HN2_MEDX_rotated_0080.tif</strong> <ul> <li>Slice 80 from volume "Med3D_HPass_2111_20190606_MEDX_2234_EH_HN2_recon_2000x1952x501x32bit.raw" that corresponds to histological slice "HN2_Level001_MEDX080_Manual_BW"</li> </ul> </li> </ul>
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>
anTraX: high throughput video tracking of color-tagged insects (benchmark datasets)
<p>Datasets used to benchmark anTraX tracking software. Each dataset contains the raw videos, a configured anTraX session with all parameters required to reproduce the tracking results from the paper, as well as the tracking output for the first video in each dataset.</p> <p> </p> <p> </p>
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>
Quantum Cascade Laser Spectral Histopathology: Breast Cancer Diagnostics Using High Throughput Chemical Imaging
<p>Fourier transform infrared (FT-IR) microscopy, coupled with machine learning approaches, has been demonstrated to be a powerful technique for identifying abnormalities in human tissue. The ability to objectively identify the prediseased state, and diagnose cancer with high levels of accuracy, has the potential to revolutionise current histopathological practice. Despite recent technological advances in FT-IR microscopy, sample throughput and speed of acquisition are key barriers to clinical translation. Wide-field quantum cascade laser (QCL) infrared imaging systems with large focal plane array detectors utilising discrete frequency imaging, have demonstrated that large tissue microarrays (TMA) can be imaged in a matter of minutes. However this ground breaking technology is still in its infancy and its applicability for routine disease diagnosis is, as yet, unproven. In light of this we report on a large study utilising a breast cancer TMA comprised of 207 different patients. We show that by using QCL imaging with continuous spectra acquired between 912 and 1800 cm<sup>-1</sup>, we can accurately differentiate between 4 different histological classes. We demonstrate that we can discriminate between malignant and non-malignant stroma spectra with high sensitivity (93.56%) and specificity (85.64%) for an independent test set. Finally, we classify each core in the TMA and achieve high diagnostic accuracy on a patient basis with 100% sensitivity and 86.67% specificity. The absence of false negatives reported here opens up the possibility of utilising high throughput chemical imaging for cancer screening, thereby reducing pathologist workload and improving patient care.</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>
Autonomous millimeter scale high throughput battery research system
<p>In this study, we present data from high-throughput cyclic voltammetry (CV) test, derived by our digital workflow, Auto-MISCHBARES, complemented by XPS analysis. This research is part of the pre-print publication named "Autonomous millimeter scale high throughput battery research system", showcasing Cathode Electrolyte Interphase (CEI) investigation. The electrolyte used for this experiment is 1M of LiPF6 solution in an Ethylene Carbonate (EC): Ethyl Methyl Carbonate (EMC) mixture with a 3:7 weight ratio, along with LFP as our electrode material. The CV tests were conducted through a high-throughput sequential process for two cycles, each with varying stop potentials. After the experimentation phase, XPS analysis was applied to characterize the synthesized CEI.</p>
refering rawdata and code of "Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE)"
<p>Corresponding raw data and codes that produce all relative video and imaging results for real-time monitoring the physicochemical phenomena of microfluidics, microorganism's group, and chemical reactions, supporting and verifying the research article "Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE)".</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>
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.