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.
143
datasets available to search
ShareScore release 0.9.0
Dataset results
143 results for “deconvolution”
GPR data used to test the efficient deconvolution method of Schmelzbach and Huber (2015)
<p>GPR data recorded with Pulse Ekko Pro from Sensors & Software on the river bed of the Tagliamento River (NE Italy).</p> <p>This data was used to test the efficient deconvolution scheme of Schmelzbar and Huber (2015):</p> <p>C. Schmelzbach, E. Huber (2015) Efficient Deconvolution of Ground-Penetrating Radar Data. IEEE Transactions on Geoscience and Remote Sensing, 53(9): 5209 - 5217<br> doi: <a href="http://dx.doi.org/10.1109/TGRS.2015.2419235">10.1109/TGRS.2015.2419235</a></p>
Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm
<p>A refractive lens is one of the simplest, cost-effective and easily available imaging elements. With a spatially incoherent illumination, a refractive lens can faithfully map every object point to an image point in the sensor plane, when the object and image distances satisfy the imaging conditions. However, static imaging is limited to the depth of focus, beyond which the point-to-point mapping can be only obtained by changing either the location of the lens or the imaging sensor. In this study, the depth of focus of a refractive lens in static mode has been expanded using a recently developed computational reconstruction method, Lucy-Richardson-Rosen algorithm (LRRA). The technique consists of three steps. In this first step, the point spread functions (PSFs) were recorded along different depths and stored in the computer as PSF library. In the next step, the object intensity distribution was recorded. The LRRA was then applied to deconvolve the object information from the recorded intensity distributions in the final step. The results of LRRA were compared against two well-known reconstruction methods namely Lucy-Richardson algorithm and non-linear reconstruction. The data corresponding to experimental analysis is given in the manuscript. (Preprints Link:). The theoretical simulation data is given here.</p>
Deconvolution Test Dataset
<p>This a test dataset, HeLa cells stained for action using Phalloidin-488 acquired on confocal Zeiss LSM710, which contains</p> <p>- Ph488.czi (contains all raw metadata)</p> <p>- Raw_large.tif ( is the tif version of Ph488.czi, provided for conveninence as tif doesn't need Bio-Formats to be open in Fiji )</p> <p>- Raw.tif , is a crop of the large image</p> <p>- PSFHuygens_confocal_Theopsf.tif , is a theoretical PSF generated with HuygensPro</p> <p>- PSFgen_WF_WBpsf.tif , is a theoretical PSF generated with PSF generator</p> <p>- PSFgen_WFsquare_WBpsf.tif, is the result of the square operation on PSFgen_WF_WBpsf.tif , to approximate a confocal PSF</p>
Multi-cell type deconvolution using a probabilistic model for single-molecule DNA methylation haplotypes
<p>Files required to run deconvolution with CelFIE-ISH and Epistate, in U250 regions from Loyfer et al. 2023, in both "pat" and "epiread" formats. </p>
SMART: Spatial transcriptomics deconvolution using marker-gene-assisted topic model
<p>Source code and simulated datasets used in manuscript "SMART: Spatial transcriptomics deconvolution using marker-gene-assisted topic model"</p>
Escherichia coli lineage deconvolution indexes for Themisto, mSWEEP/mGEMS, and demix_check
<h2><strong><em>Escherichia coli</em> lineage deconvolution indexes for Themisto, mSWEEP/mGEMS, and demix_check</strong></h2> <p>This dataset contains the indexes used in a study conducted in Punjab, Pakistan that investigated <em>E. coli</em> colonisation diversity in healthy carriage with the use of CLED enrichment plates. The following files are included in the download:</p> <ul> <li>Themisto v3 pseudoalignment index.</li> <li>PopPUNK database.</li> <li>Demix_check index.</li> <li>Raw assembly data.</li> </ul> <h3><strong>About</strong></h3> <h4><strong>Version history</strong></h4> <p><strong>v0.1.1 (current version)</strong></p> <ul> <li>Added reference to the study.</li> </ul> <p><strong>v0.1.0</strong></p> <ul> <li>Added brief description with a few missing parts.</li> </ul> <h4><strong>Distribution</strong></h4> <p>These files are made available under a CC-BY 4.0 license. If you use these assemblies in your study please cite the source as appropriate (study will be added in a later version).</p> <h4><strong>Citation</strong></h4> <p>Khawaja, T., Mäklin, T., Kallonen, T. et al. Deep sequencing of _Escherichia coli_ exposes colonisation diversity and impact of antibiotics in Punjab, Pakistan. Nature Communications 15, 5196 (2024). <a href="https://doi.org/10.1038/s41467-024-49591-5">https://doi.org/10.1038/s41467-024-49591-5</a></p> <h3><strong>Methods briefly</strong></h3> <h4><strong>Data</strong></h4> <p>Assembly data included originates from the following studies:</p> <ul> <li>Horesh G, et al., A comprehensive and high-quality collection of <em>Escherichia coli</em> genomes and their genes. <em>Microbial Genomics</em> 2021. doi: <a href="https://doi.org/10.1099/mgen.0.000499">10.1099/mgen.0.000499</a></li> <li>Gladstone R, et al., Emergence and dissemination of antimicrobial resistance in <em>Escherichia coli</em> causing bloodstream infections in Norway in 2002–17: a nationwide, longitudinal, microbial population genomic study. <em>The Lancet Microbe</em> 2021. doi: <a href="https://doi.org/10.1016/S2666-5247(21)00031-8">10.1016/S2666-5247(21)00031-8</a></li> <li>Shao Y, et al., Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth. <em>Nature</em> 2019. <a href="https://doi.org/10.1038/s41586-019-1560-1">10.1038/s41586-019-1560-1</a></li> <li>Snaith AE, et al. The highly diverse plasmid population found in <em>Escherichia coli</em> colonizing travellers to Laos and its role in antimicrobial resistance gene carriage. <em>Microbial Genomics</em> 2023. doi: <a href="https://doi.org/10.1099/mgen.0.001000">10.1099/mgen.0.001000</a></li> <li>Habib A, et al. Dissemination of carbapenemase-producing Enterobacterales in the community of Rawalpindi, Pakistan. <em>PLOS ONE</em> 2022. doi: <a href="https://doi.org/10.1371/journal.pone.0270707">10.1371/journal.pone.0270707</a></li> <li>Runcharoen C, et al. Whole genome sequencing of ESBL-producing <em>Escherichia coli</em> isolated from patients, farm waste and canals in Thailand. <em>Genome Medicine</em> 2017. doi: <a href="https://doi.org/10.1186/s13073-017-0471-8">10.1186/s13073-017-0471-8</a></li> <li>Musicha P, et al. Trends in antimicrobial resistance in bloodstream infection isolates at a large urban hospital in Malawi (1998–2016): a surveillance study. <em>The Lancet Infectious Diseases</em> 2017. doi: <a href="https://doi.org/10.1016/S1473-3099(17)30394-8">10.1016/S1473-3099(17)30394-8</a></li> </ul> <h4><strong>Themisto index construction</strong></h4> <p>Assemblies were indexed with <a href="https://github.com/algbio/themisto">Themisto</a> v3.0.0-rc using <em>k</em>-mer size 31 and the `--file-colors` option.</p> <h4><strong>PopPUNK clustering</strong></h4> <p>We followed the approach described in <a href="https://doi.org/10.1038/s41467-022-35178-5">Mäklin et al. 2022</a> using <a href="https://github.com/bacpop/poppunk">PopPUNK</a> v2.5.0.</p> <h4><strong>Demix check indexing</strong></h4> <p>The index was generated using the `setup_reference.sh` script from <a href="https://github.com/tmaklin/coreutils_demix_check">tmaklin/coreutils_demix_check</a>.</p> <h3><strong>Contact</strong></h3> <p>Tommi Mäklin <tommi'at'maklin.fi>.</p>
transynergy: synergistic prediction and pathway deconvolution of drug combinations
<p>Dataset used for transynergy: synergistic prediction and pathway deconvolution of drug combinations</p>
Data for accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy
<p>Simulated and experimental data used in the ReSort manuscript. It is necessary and sufficient to reproduce the results in the paper.</p>
Spatially Resolved Transcriptomics Deconvolutes Prognostic Histological Subgroups in Patients with Colorectal Cancer and Synchronous Liver Metastases
<p>Spatial transcriptomic data (counts.csv) derived using the Nanostring GeoMx digital spatial profiler platform to analyse matched colonic primary and liver metastases from 4 patients with metastatic colorectal cancer. 48 AOIs of cancer transcriptome atlas data. Normalised using Q3 normalisation. In addition, normalised data (Counts - ncounter.csv) from ncounter bulk experiment comparing matched colonic primary and liver metastases</p>
Teleseismic reverse time migration image dataset of southwest Japan in "Three-dimensional teleseismic elastic reverse-time migration with deconvolution imaging condition and its application to southwest Japan"
<p>This dataset contains the teleseismic elastic reverse time migration results of southwest Japan used in the manuscript entitled "Three-dimensional teleseismic elastic reverse-time migration with deconvolution imaging condition and its application to southwest Japan". submitted to Journal of Geophysical Research Letters.</p>
Spatial-DC: a robust deep learning-based method for deconvolution of spatial proteomics
<p>The processed reference and spatial proteomics datasets, along with the processed mIHC imaging data of mouse PDAC tissue are available in the repository.</p> <p>Also, the source code for pre-processing, data analysis, and generating figure and tables has been deposited in both GitHub [<a href="https://github.com/TencentAILabHealthcare/Spatial-DC">https://github.com/TencentAILabHealthcare/Spatial-DC</a>] and Zenodo [<a href="https://doi.org/10.5281/zenodo.14386585">https://doi.org/10.5281/zenodo.14386585</a>].</p> <p> </p>
Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target
<p>Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in </p> <blockquote> <p><strong>Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.</strong></p> </blockquote> <p>The files will be made available upon publication. <br></p> <h4><strong>Description of the files</strong></h4> <ul> <li><strong>01_raw_counts: </strong>count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline</li> <li><strong>10_prepare_adata</strong>: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.</li> <li><strong>20_scrnaseq_qc</strong>: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.</li> <li><strong>30_merge_adata</strong>: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering</li> <li><strong>40_cluster_analysis</strong>: Identify and investigate subclusters representing cell-states that go beyond the major cell-types</li> <li><strong>50_de_analysis</strong>: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)</li> <li><strong>70_downstream_analysis</strong>: Perform pathway analyses and generate figures for publication based on the data generated in the previous steps</li> <li><strong>containers:</strong> Conda environments used for the analysis packed up as singularity containers. </li> </ul>
Target deconvolution of the HDAC pharmacopoeia highlights MBLAC2 as a common off-target
<p>This dataset contains the untargeted lipidomics data for the publication Lechner et al. 2022 "Target deconvolution of the HDAC pharmacopoeia highlights MBLAC2 as a common off-target". The dataset has also been submitted to MetaboLight repository with ID "MTBLS3557". Please refer to the MetaboLight repository for the most up-to-date datasets. </p> <p>Publication abstract:</p> <p>Histone deacetylase (HDAC) targeting drugs have entered the pharmacopoeia in the 2000s. However, some enigmatic phenotypes suggest off-target engagement. Here, we developed a quantitative chemical proteomics assay using immobilized HDAC inhibitors and mass spectrometry that we deployed to establish the target landscape of 53 drugs. The assay covers 9 of the 11 human zinc dependent HDACs, questions the reported selectivity of some widely-used molecules, notably for HDAC6, and delineates how the composition of HDAC complexes influences drug potency. Unexpectedly, metallo-beta-lactamase domain-containing protein 2 (MBLAC2) featured as a frequent off-target of hydroxamate drugs. This poorly characterized palmitoyl-CoA hydrolase is inhibited by 24 HDAC inhibitors at low nM potency. MBLAC2 enzymatic inhibition and knock down led to the accumulation of extracellular vesicles. Given the importance of extracellular vesicle biology in neurological diseases and cancer, this HDAC-independent drug effect may qualify MBLAC2 as a target for drug discovery.</p>
Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution
<p>This is the dataset for the work titled and authored by:</p> <p><strong>Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution</strong></p> <p>Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig</p> <p>We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.</p> <p>Software can be found under this DOI:</p> <p>10.5281/zenodo.846448</p>
Blind Sparse Deconvolution for Spike Inference from Fluorescence: Larval Zebrafish fluorescence recordings
<p>This repository contains the two fluorescence imaging datasets used in the publication "Blind Sparse Deconvolution for Spike Inference from Fluorescence Recordings". Each dataset is a T X N DF/F matrix, where T is the number of fluorescence measurement and N is the number of neurons. They were obtained from light-sheet fluorescence microscopy. Both recordings correspond to spontaneous activity.</p> <p>The first file "DFF_20Hz_GCaMP3.mat" was obtained by a 2D recording acquired at 20 frame/second for 20 minutes of a 5dpf-old zebrafish larva expressing the genetically encoded indicator GCaMP3 (elavl3:GCaMP3). The images were parsed into 8082 neural traces.</p> <p>The second file "DFF_1Hz_GCaMP5.mat" was obtained by a 3D, whole-brain recording acquired at 20 frame/second and 20 stacks, (1 measurement/voxel/s) of a 5dpf-old zebrafish larva expressing the genetically encoded indicator GCaMP5. After segmentation, 255463 fluorescence traces encompassing the brain volume are computed independently.</p>
Machine learning deconvolution of the immune response to dengue
<p>Machine learning deconvolution of the immune response to dengue - dataset of antibody repertoire sequencing</p>
Data from: 3-D deconvolution of human skin immune architecture with Multiplex Annotated Tissue Imaging System (MANTIS)
<p><span class="pre-line-wrapping ng-binding">Routine clinical assays, such as conventional immunohistochemistry, often fail to resolve the regional heterogeneity of complex inflammatory skin conditions. Here we introduce MANTIS (Multiplexed Annotated Tissue Imaging System), a flexible analytic pipeline compatible with routine practice, specifically designed for spatially-resolved immune phenotyping of the skin in experimental or clinical samples. Based on phenotype attribution matrices coupled to alpha-shape algorithms, MANTIS projects a representative digital immune landscape, while enabling automated detection of major inflammatory clusters and concomitant single-cell data quantification of biomarkers. We observed that severe pathological lesions from systemic lupus erythematosus, Kawasaki syndrome, or COVID-19-associated skin manifestations share common quantitative immune features, while displaying a non-random distribution of cells with the formation of disease-specific dermal immune structures. Given its accuracy and flexibility, MANTIS is designed to solve the spatial organization of complex immune environments to better apprehend the pathophysiology of skin manifestations.</span></p>
Data for: Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature
<p><span>Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, </span><span>we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.</span></p>
Data for: Whole genome deconvolution unveils Alzheimer’s resilient epigenetic signature
Open the record for dataset details and reuse information.
Data from: 3-D deconvolution of human skin immune architecture with Multiplex Annotated Tissue Imaging System (MANTIS)
Open the record for dataset details and reuse information.
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.