Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
642
datasets available to search
ShareScore release 0.9.0
Dataset results
642 results for “multiplexed”
Assessment of a multiplex PCR and Nanopore-based method for portable dengue virus sequencing in Indonesia
<p>Multiplex primer sets for amplification of the complete coding region of Indonesian dengue virus.</p>
Data for "Fast delivery of heralded atom-photon quantum correlation over 12 km fiber through multiplexing enhancement"
<p><span>This dataset is for the research article "Fast delivery of heralded atom-photon quantum correlation over 12 km fiber through multiplexing enhancement".</span></p> <p><span> </span></p>
CD3/CD31/LAMA2 immunofluorescence multiplex data related to Van Hijfte et al.
<p>CD3/CD31/LAMA2 immunofluorescence multiplex images and GeoJSON files used for analysis in R. For more information see related publication.</p>
Data from: A set of plastid loci for use in multiplex fragment length genotyping for intraspecific variation in Pinus (Pinaceae)
Premise of the study: Recently released Pinus plastome sequences support characterization of 15 plastid Simple Sequence Repeat (ptSSR) loci originally published for P. contorta and P. thunbergii. This allows selection of loci for single-tube PCR multiplexed genotyping in any subsection of the genus. Methods: Unique placement of primers and primer conservation across the genus were investigated, and a set of six loci were selected for single-tube multiplexing. We compare interspecific variation between ptSSRs and nucleotide sequences of ycf1 then test intraspecific variation for ptSSRs using 911 samples in the P. ponderosa species complex. Results: The ptSSR loci contain mononucleotide and complex repeats with additional length variation in flanking regions. They are not located in hypervariable regions and most primers are conserved across the genus. A single PCR per sample multiplexed for six loci yielded 45 alleles in 911 samples. Discussion: The protocol allows efficient genotyping of many samples. The ptSSR loci are too variable for Pinus phylogenies but are useful for the study of genetic structure within and among populations. The multiplex method could easily be extended to other plant groups by choosing primers for ptSSR loci in a plastome alignment for the target group.
Data repository accompanying "Rapid microwave-only characterization and readout of quantum dots using multiplexed gigahertz-frequency resonators"
<p>Data repository including raw data and analysis scripts used for generating the figures appearing in the paper 'Rapid microwave-only characterization and readout of quantum dots using multiplexed gigahertz-frequency resonators'</p>
Multiplexed imaging reveals an IFN-γ-driven inflammatory state in Nivolumab-associated gastritis (data)
<p>MIBI-TOF images, segmentation output, cell phenotype maps, and extracted cell tables for Ferrian et al.</p>
Imaging Mass Cytometry of human normal colon mucosa (CLN1-6) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Four µm-thick sections were cut from each block of samples CLN1-CLN6 with a microtome and used for staining with a panel of 26 antibodies targeting the main immune, stromal and epithelial cell populations of the gastrointestinal tract (Supplementary Table 2). The optimal dilution of each antibody in the panel was identified by staining and ablating FFPE appendix sections. The resulting images were reviewed by a mucosal immunologist (J.S.) and the dilution giving the best signal to background ratio was selected for each antibody (Supplementary Table 2). To perform the staining for IMC, slides were dewaxed after a one-hour incubation at 60°C, rehydrated and heat-induced antigen retrieval was performed with a pressure cooker in Antigen Retrieval Reagent-Basic (R&D Systems). Slides were incubated in a 10% BSA (Sigma), 0.1% Tween (Sigma), and 2% Kiovig (Shire Pharmaceuticals) Superblock Blocking Buffer (Thermo Fisher) blocking solution at room temperature for two hours. Each antibody was added to a primary antibody mix at the selected concentration in blocking solution and incubated overnight at 4°C. After two washes in PBS and PBS-0.1% Tween, the slides were treated with the DNA intercalator Cell-ID™ Intercalator-Ir (Fluidigm) (containing the two iridium isotopes 191Ir and 193Ir) 1.25 mM in a PBS solution. After a 30-minute incubation, the slides were washed once in PBS and once in MilliQ water and air-dried. The stained slides were then loaded in the Hyperion Imaging System (Fluidigm) imaging module to obtain light-contrast high resolution images of approximately four mm<sup>2</sup>. These images were used to select the ROI in each slide. For CLN1-CLN6, 1 mm<sup>2 </sup>ROIs were selected to contain the full thickness of the colon mucosa, with epithelial crypts in longitudinal orientation. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.</p> <p>Twenty-eight images from 26 antibodies (Supplementary Table 2) and two DNA intercalators were obtained from the raw .txt files of the ablated regions in CLN1-CLN6 using the data extraction process.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONROL CASE 2 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 3 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (CONTROL CASE 3 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 4 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 4 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 5 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 5 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 2 FOV4
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 2 FOV4)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Image dataset from multiplex IHC stained TMA sections
<p><strong>IHC Cohort:</strong> Multiplex IHC stained histological slides were collected from Liaoning Cancer Hospital and Institute in China. The raw data collected are TMA sections, all of which are obtained from NSCLC patients. Non-overlapping image patches (256*256 pixels) are extracted from TMA sections and manually annotated by our collaborated pathologists via the Qupath software. Initially, seven TMA sections with multiplex stains including CD3, CD20, CD38, CDK4, Cyclin-D1, Ki67, and P53 were used. Except for CD3 stained TMA sections, we randomly cropped 25 image patches from each of these TMA sections, of which 17, 3 and 5 patches are correspondingly used for training, validation, and testing. Note that 18, 3 and 5 CD3 image patches are cropped for building dataset. To suppress over-fitting and enhance generalization of the SRSA-Net, we additionally included 81 annotated image patches from other five TMA sections with stains of CD34, CD68, D2-40, FAP, and SMA into training and validation set. In total, the IHC cohort includes 9,725 manually identified cell nuclei. The numbers of training, validation and testing image patches are 195, 36, and 35, respectively.</p> <p>For the masks, the first channel and the second channel denotes the negative and positive nuclei pixels, respectively. Note that each pixel is labelled from 0 to n, where n is the number of individual nuclei detected. 0 pixels indicate background. Pixel values i indicate that the pixel belongs to the ith nucleus. The last channel marks the information of all the nuclei pixels, where nuclei pixel are left as 0, otherwise 1.</p> <p><strong>fold1: training set</strong></p> <p><strong>fold2: validation set</strong></p> <p><strong>fold3: testing set</strong></p> <p> </p> <h2>if you use this dataset, please cite:</h2> <pre>@article{wang2024simultaneously, title={Simultaneously segmenting and classifying cell nuclei by using multi-task learning in multiplex immunohistochemical tissue microarray sections}, author={Wang, Ranran and Qiu, Yusong and Hao, Xinyu and Jin, Shan and Gao, Junxiu and Qi, Heng and Xu, Qi and Zhang, Yong and Xu, Hongming}, journal={Biomedical Signal Processing and Control}, volume={93}, pages={106143}, year={2024}, publisher={Elsevier} }</pre>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the continuation of dataset 10.5281/zenodo.7494413 and 10.5281/zenodo.7494509.</p> <p>The file tiff_stacks_masks.zip contains the IMC image stacks and single-cell masks as tiff files.</p> <p>The IHC_TMAs.zip contains the scans of the IHC stains of ZTMA25 and the QuPATH projects used to extract the single-cell data (incl. the single-cell measurements as csv files).</p>
Raymond et al, 2023 Multiplexing Dataset
<p>Large videos files to remake the figures in the manuscript:</p> <p>W Raymond, et al, Using mechanistic models and machine learning to design single-color multiplexed Nascent Chain Tracking experiments, 2023.</p>
Continuous multiplexed population representations of task context in the mouse primary visual cortex
<p>Each spiketrain pck file contains a pickled list of single unit activities, each a list of spike times.</p> <p>Each cellinfo contains additional information about putative narrow or broad spiking, wave templates and depths.</p> <p>Trial times (in milliseconds), trial stimuli, action and reward outcome are listed in the accompanying csv files.<br> </p> <p>Movement caches to be unzipped as a single volume.</p>
The contribution of multiplexing single cell RNA sequencing in acute myeloid leukemia
<p>Figures and supplimentary data of article : " The contribution of multiplexing single cell RNA sequencing in acute myeloid leukemia"</p>
Supporting data for "Scalable wavelength-multiplexing photonic reservoir computing"
<p>Supporting data for "Scalable wavelength-multiplexing photonic reservoir computing"</p>
Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering (data)
<p>MIBI-TOF data for lymph node dataset reported in Liu et al., Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering</p> <p>1. mibi_single_channel_tifs.zip: Single-channel MIBI-TOF images</p> <p>Folders are labeled according to the field-of-view (FOV) number. Each folder contains single-channel TIFFs for each marker in the panel. Images are 1024x1024 pixels, 500 um. See paper for details.</p> <p>2. segmentation.zip: Segmentation output of MIBI-TOF images</p> <p>Cell segmentation was performed using Mesmer (Greenwald NF, Nature Biotechnology 2021). Output of Mesmer that delineates the single cells in each of the images is included.</p> <p>3. source_data.zip: Source data files for figures</p> <ul> <li>pixel_ccs_allpreprocessing.csv: Cluster consistency score (CCS) for all pixels using all preprocessing steps, related to Fig. 2d-f, Supp. Fig. 4,5,9,10</li> <li>pixel_ccs_nopixelnorm.csv: CCS for all pixels where pixel normalization was left out, related to Fig. 2f, Supp. Fig. 6</li> <li>pixel_ccs_nochannelnorm.csv: CCS for all pixels where channel normalization was left out, related to Fig. 2f, Supp. Fig. 8</li> <li>pixel_ccs_passes1.csv: CCS for all pixels where 1 pass was used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_passes100.csv: CCS for all pixels where 100 passes were used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_sigma0.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma1.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma3.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma0_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma1_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma2_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 2 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma3_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_nodes15.csv: CCS for all pixels where 15 nodes were used for SOM training, related to Supp. Fig. 9e</li> <li>pixel_ccs_threshold80.csv: CCS for all pixels where a threshold of 80% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_ccs_threshold98.csv: CCS for all pixels where a threshold of 98% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_info_comparison_table.csv: Number of pixels that were assigned to a cluster outside of cell segmentation masks, related to Fig. 3d</li> <li>single_cell_pixel_composition_table.csv: Pixel composition information for each single cell, related to Fig. 5, Supp. Fig 16</li> <li>single_cell_integrated_expression_table.csv: Integrated expression per cell, output by Mesmer, related to Fig. 5, Supp. Fig. 16</li> <li>cell_silhouette_scores.csv: Silhouette scores for comparing integrated expression and pixel composition, related to Fig. 5d</li> <li>cell_silhouette_scores_undefinedremoved.csv: Silhouette scores for comparing integrated expression and pixel composition where undefined cells were removed, related to Fig. 16g</li> <li>cell_silhouette_scores_preprocessed.csv: Silhouette scores for comparing integrated expression and pixel composition where pixels were preprocessed before integrating expression, related to Fig. 17d</li> <li>cell_silhouette_scores_ilastik_cellprofiler.csv: Silhouette scores for comparing integrated expression and pixel composition where segmentation masks were obtained using Ilastik/CellProfiler, related to Fig. 18b</li> <li>cell_ccs_pixel_composition.csv: CCS for all cells using pixel composition for clustering, related to Supp. Fig. 16e, 17c</li> <li>cell_ccs_integrated_expression.csv: CCS for all cells using integrated expression for clustering, related to Supp. Fig 16e-f</li> <li>cell_ccs_integrated_expression_preprocessed.csv: CCS for all cells using integrated expression for clustering where data was preprocessed before integrating, related to Supp. Fig 17c</li> <li>cytof_ccs.csv: CCS of the CyTOF dataset used as a benchmark, related to Supp. Fig. 4c,d</li> <li>scrnaseq_ccs.csv: CCS of the scRNA-seq dataset used as a benchmark, related to Supp. Fig. 4c,e</li> <li>pixel_phenotype_maps: TIFFs where pixel value corresponds to pixel cluster number as reported in the paper</li> <li>cell_phenotype_maps: TIFFs where pixel value corresponds to cell cluster number as reported in the paper</li> <li>runtime_analysis_pixel.csv: Runtime analysis of pixel clustering in Pixie, related to Supp. Fig. 22a</li> <li>runtime_analysis_cell.csv: Runtime analysis of cell clustering in Pixie, related to Supp. Fig. 22b</li> <li>runtime_clustering_algorithm.csv: Runtime analysis of different clustering algorithms, related to Supp. Fig. 22c</li> </ul>
DNA-barcoded signal amplification for imaging mass cytometry enables sensitive and highly multiplexed tissue imaging
<p>Tiff images, single cell data, and cell masks for the publication "DNA-barcoded signal amplification for imaging mass cytometry enables sensitive and highly multiplexed tissue imaging". The code used to produce the results of this study is available at https://github.com/BodenmillerGroup/SABER-IMC_publication</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.