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.
2,562
datasets available to search
ShareScore release 0.9.0
Dataset results
2,562 results for “SARS CoV 2”
Supplementary Table 1 - GISAID Accession Numbers of Samples analysed in the first and second waves of SARS-CoV-2 cases in Irish hospitals
<p>The Supplementary Table 1 contains the GISAID accession numbers of samples sequenced in the context of the AIID biobank in the Republic of Ireland during the first and second wave of SARS-CoV-2 cases in hospitals of Dublin.</p>
Dataset of 3D-complexes of SARS-CoV-2:Human proteins
<p><strong>The dataset of human: SARS-CoV-2 protein complexes used for the study.</strong></p>
Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations
<p>Dataset and analysis for:</p> <p>Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations.<br>Arnab Mukherjee, Sharmistha Mishra, Vijaya Kumar Murty, Swetaprovo Chaudhuri<br> </p> <p>For any questions please contact the first author at: arnab.mukherjee@mail.utoronto.ca</p> <p><strong>Contents:</strong></p> <ol> <li><strong>school_active_cases_ON.zip:</strong> Contains datasets for number of COVID-19 infections reported by public schools in Ontario on ten different dates. The data files have been created based on the raw data in the file named 'covidtesting.csv' that has also been shared.</li> <li><strong>school_active_cases_pdf.m:</strong> Matlab code to obtain PDF of secondary infections in schools for a particular date based on the datasets in 'school_active_cases_ON.zip'. To obtain PDF for different dates, the appropriate dataset needs to be loaded. Created in MATLAB R2021b.</li> <li><strong>U_jet2.m:</strong><em> </em>User-defined Matlab function that is required to run the code 'gZ_code.m'. The function simulates the evolution of a simple jet/puff. Created in MATLAB R2021b.</li> <li><strong>gZ_code.m:</strong> Matlab code to obtain the analytical PDF of secondary infections due to long-range transmission, near-field transmission, or both. Created in MATLAB R2021b.</li> <li><strong>covidtesting.zip: </strong>Contains the data file 'covidtesting.csv' that reports the breakdown of COVID-19 infections in different public schools in Ontario on a daily basis. Data obtained from 'https://data.ontario.ca/dataset/summary-of-cases-in-schools/resource/dc5c8788-792f-4f91-a400-036cdf28cfe8'. Contains information licensed under the Open Government License – Ontario.</li> <li><strong>schoolrecentcovid2021_2022.zip:</strong> Contains the data file 'schoolrecentcovid2021_2022.csv<strong>' </strong>that reports the status of COVID-19 cases in Ontario, obtained from 'https://data.ontario.ca/en/dataset/status-of-covid-19-cases-in-ontario/resource/ed270bb8-340b-41f9-a7c6-e8ef587e6d11'. Contains information licensed under the Open Government License – Ontario.</li> </ol> <p> </p> <p> </p> <p> </p>
The nucleotides absent in genes of SARS-CoV-2 non-canonical subgenomic RNAs generate new Programmed -1 Ribosomal Frameshifting
<p>The data correspond to the article entitled: "dNTPs and adjuvant reagent solutions in 3’ RACE improve the characterization of noncanonical RNA SARS-CoV-2 genomes"</p> <p>R1. RACE 3’ Primer Blast Alignment. Contains BLAST alignments against the GenBank database using the consensus nucleotide sequence from the 3’ end of the SARS-CoV-2 genome and the polylinker. In addition, an illustration of the restriction enzyme pattern of the 3' RACE primer RV30AkCOVID19 and its synthesis by MALDI-TOF is included. The red box indicates the nucleotide sequence of the polylinker and the yellow box represents the 3' RACE primer along with the result of primer synthesis and purification.</p> <p>Graphic representation of the procedure for SARS-CoV-2 genome cDNA synthesis and design of the 3’ RACE RV30AkCOVID19 primer. The rectangle with vertical lines and the dots represents the 3’ RACE RV30AkCOVID19 primer and the polylinker, respectively, in the region complementary to the 3’ UTR end. The arrow represents the reverse transcriptase during complementary strand synthesis. The scissors represent RNases used in purification. The black spheres and magnets indicate the purification process using magnetism.</p> <p>R2. Reads and assembles SARS-CoV-2 genomes.</p> <p>The folder "1) Reads - Ion torrent" contains the reads obtained from sequencing via Ion Torrent technology and the reagents used in this study.</p> <p>The folder named "2) FastQC" contains the results of Ion Torrent sequencing. In the file name, the number indicates the sample, and the letters "RNA" indicate the sequencing according to the IonTorrent protocol. The cDNA synthesis procedures for this study correspond to the following nomenclature: dNTPs-R = dNTPs SARS-CoV-2 solution, DES-R = denaturation reagent, and COM PRO = commercial procedure.</p> <p>The folders named "3) IRMA" and "4) Bowtie2" contain the assemblies of the genomes.</p> <p>Regions and/or codons with loss of genomes 07dN120320 and 27sT122620.</p> <p>Mutations and amino acid substitutions of the SARS-CoV-2 genomes.</p> <p>In addition, an Excel document with the nucleotide ratios of each characterized genome is included from SARS-CoV-2.</p> <p>R3. BLAST alignment of assembled SARS-CoV-2 genomes. Contains two folders named "BLAST - IRMA" and "BLAST - Bowtie2," which contain plain text documents with the results of the BLAST alignment for the genomes obtained with each of the assemblies.</p> <p>R4. Pangolin v1.16 and Nextclade v2.9.1 lineages for SARS-CoV-2 genomes. Contains the folders "Pangolin and Nextclade (Bowtie2)" and "Pangolin and Nextclade (IRMA)." Each folder shows the data obtained with the Pangolin v1.16 and Nextclade v2.9.1 software for the classification of the genomes reported in this study, which were assembled with the IRMA and Bowtie2 software.</p> <p>R5. Reference genome alignment and assembled genomes. Contains the folders "1) IRMA genomes," "2) Bowtie2 genomes," and "3) Genomes 07dN120320 and 27St122620." The files show the sequences and alignments of the examined genomes (the file name indicates the analyzed genome) relative to the SARS-CoV-2 reference genome both in FASTA and Clustal W formats.</p> <p>R6. Programmed −1 Ribosomal Frameshifting Structure. The folder "1) Gibbs free energy 2D" contains a plain text document indicating the secondary structures of the open reading frame stimulation element in dot-bracket format. The folder "2) modeling Data Modeling 3D" contains the information for generating the structure of folder 1 in 3D.</p> <p>R7. SARS-CoV-2 Database.</p> <p>1) GISAID_sequences.zip contains a Zip file that contains a folder named GISAID, which in turn contains plain text documents with the genomes of each variant indicated in the filename of each document.</p> <p>2) The depuration of sequences_GISAID contains two subfolders. The first subfolder, named "1) SARS-CoV-2 complete genome" contains plain text documents with the genomes downloaded from GISAID without undetermined nucleotides. The file name of each document corresponds to the analyzed variant. The subfolder "2) SARS-CoV-2 eliminate genome" contains the sequences eliminated from subfolder 1 because they differed from the majority of the analyzed sequences.</p> <p>3) SARS-CoV-2 consensus variants. Contains plain text documents with consensus sequences for each variant, with frequency thresholds of 20 and 100 indicated in the file name of each document.</p> <p>4) SARS-CoV-2 alignment consensus variants. Contains two subfolders, with the number indicating the alignment frequency threshold. The "Alignment 20_" subfolder contains four documents named "with Ns," which correspond to fasta and Clustal formats with undetermined nucleotides, whereas the files named "without" do not have undetermined nucleotides. The "100_" folder has the same file pattern as the previous folder.</p> <p>5) SARS-CoV-2 codons alignment consensus variants and nc-sgRNA. Contains a document with the alignment of the genomes characterized in this study with the reference genome of SARS-CoV-2. A subfolder named “SARS-CoV-2 codons nc-sgRNA” shows each of the nc-sgRNA obtained in this study with the reference genome, and the file name corresponds to the nc-sgRNAs. The subfolder “SARS-CoV-2 Geneious Prime” contains 4 documents. Each document includes the graphical representation of the alignment of the nc-sgRNA obtained with each treatment for the synthesis of SARS-CoV-2 cDNA with respect to the reference genome. The following three documents indicated with the numbers 25, 50, and 100 correspond to the percentage of identity with respect to the number of annotations relative to the reference genome, which is indicated in the title of each document.</p> <p>6) Variant Alignment – Ns. Contains eight documents corresponding to the fasta and clustal formats with SARS-CoV-2 genomes obtained in this study from the reference genome and from genomes containing undetermined nucleotides of the Gamma, Lambda, Mu and Omicron variants.</p> <p>R8. Phylogeny SARS-CoV-2. Contains two subfolders with the results of the phylogenetic analyses conducted via the maximum likelihood method of the genomes characterized in this study compared to the variants. The subfolder named "Phylogeny with Ns" indicates the analysis of genomes containing undetermined nucleotides, whereas "Phylogeny without Ns" corresponds to the analysis of complete genomes.</p>
MD simulations from "#GotGlycans: Role of N343 Glycosylation on the SARS-CoV-2 S RBD Structure and Co-Receptor Binding Across Variants of Concern
<p>This folder contains all the MD simulations (saved in frames of 1 ns in PDB format) analysed and discussed in the paper titled "#GotGlycans: Role of N343 Glycosylation on the SARS-CoV-2 S RBD Structure and Co-Receptor Binding Across Variants of Concern" DOI https://doi.org/10.1101/2023.12.05.570076. The naming reflects the specific variant and the presence ('g' or 'gly') or absence ('ng' or 'nogly') of glycosylation at N343 and N331 sites in the SARS-CoV-2 S RBD. Gaussian accelerated MD simulations are indicated with 'gamd', all others represent conventional (deteriministic) sampling. For all details please refer to the original manuscript.</p>
Sars-Cov-2 and Mers sequences from human host with no unknown characters
<p>The datasets are organized as follows: first column, number of bases in a given sequence; second, third, fourth and fifth columns, number of bases of type A, C, G and T, respectively, in the same sequence. </p> <p><strong>1) Sars-Cov-2 dataset. </strong>This dataset contains number of bases for the complete genome sequences from a human host, with none unknown characters.<span> </span>In the NCBI database, there are about 950.000 sequences with these characteristics.</p> <p><strong>2) Restricted Sars-Cov-2 dataset:</strong> This dataset contains number of bases for the complete sequences from a human host, with no unknown characters, with 29903 bases, that is of the same length as the reference sequence NC045512.2. We obtained, from the NCBI database, about 5600 sequences with such features.</p> <p><strong>3) Mers dataset:</strong> This dataset contains number of bases for the complete sequences of about 200 complete genome sequences from a human host, with no unknown characters.</p>
Data from: Compromise docking power evaluation of liganded crystal structures of Mpro SARS-CoV-2
<p>A set of 406 liganded SARS-CoV-2 M<sup>pro</sup> crystal structures originally downloaded from RCSB PBD database is provided. Ligand and protein files are processed and corrected for various types of structural errors and are provided in pdbqt and mol2 formats for immediate use in molecular docking programs AutoDock, AutoDock Vina, and PLANTS. Data are utilized in calculations of newly defined compromise docking power to monitor the performance of above-mentioned software. The provided dataset can also be used for benchmarking of other software and molecular docking protocols on liganded SARS-CoV-2 M<sup>pro</sup> systems.</p>
Input parameters and output trajectory files for manuscript "Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CLPro: Insights from the Computational and Experimental Investigations"
<p>Input parameters used for molecular dynamics simulations on GROMACS 2022 software and the output trajectory files for calculating binding free energy, in the manuscript with the title: "Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CL<sup>Pro</sup>: Insights from the Computational and Experimental Investigations"</p>
Mapping SARS-CoV-2 antigenic relationships and serological responses
<p>During the SARS-CoV-2 pandemic, multiple variants escaping pre-existing immunity emerged, causing concerns about continued protection. Here, we use antigenic cartography to analyze patterns of cross-reactivity among a panel of 21 variants and 15 groups of human sera obtained following primary infection with 10 different variants or after mRNA-1273 or mRNA-1273.351 vaccination. We find antigenic differences among pre-Omicron variants caused by substitutions at spike protein positions 417, 452, 484, and 501. Quantifying changes in response breadth over time and with additional vaccine doses, our results show the largest increase between 4 weeks and >3 months post-2nd dose. We find changes in immunodominance of different spike regions depending on the variant an individual was first exposed to, with implications for variant risk assessment and vaccine strain selection.</p>
Generative AI in the Advancement of Viral Therapeutics for Predicting and Targeting Immune-Evasive SARS-CoV-2 Mutations
<p>This dataset <strong>encompasses</strong> and describes the following features:</p> <ul> <li>Mutations in viruses like SARS-CoV-2 can make them escape vaccines and treatments.</li> <li>Accurately predicting these mutations is crucial for developing effective countermeasures.</li> <li>The study uses a type of AI called a Generative Adversarial Network (GAN) to analyze the virus's spike protein, which plays a key role in infection.</li> <li>The GAN generates protein sequences similar to natural ones, but which are also likely to evade immune responses.</li> <li>By analyzing these generated sequences, the researchers improve their AI model's ability to predict real-world escape mutations.</li> <li>This improved prediction could help design better vaccines and treatments, and prepare for future viral threats.</li> </ul>
UnCoVar: Benchmarking dataset for SARS-CoV-2 sequence processing software pipelines, Sanger sequences
Open the record for dataset details and reuse information.
Membrane mesh and tomogram with SARS-CoV-2 intact virions
<p>Membrane mesh and tomogram with SARS-CoV-2 intact virions. These data were originally published in <a href="http://dx.doi.org/10.1038/s41586-020-2665-2">Ke et al., Nature, 2020</a>. The tomograms were accessed from the <a href="https://cryoetdataportal.czscience.com/runs/467?prev=%2Fdatasets%2F10006%3Fprev%3D%252Fbrowse-data%252Fdatasets">Cryo-ET Data Portal</a>. Raw data is available from <a href="https://doi.org/10.6019/EMPIAR-10493">EMPIAR-10493</a>. The membrane segmentation was created using <a href="https://doi.org/10.1101/2024.01.05.574336">MemBrain-seg</a>.</p>
PanDDA analysis of fragment screen against the Nsp3 macrodomain of SARS-CoV-2 - P43 crystals at UCSF
<p>This deposition contains the X-ray diffraction data used for the PanDDA analysis of the fragment screen against the NSP3 macrodomain of SARS-CoV-2 described in Schuller et al. 2021 (DOI: 10.1126/sciadv.abf8711).</p> <p>A description of the files can be found in the "README" text file. </p> <p>The data in this deposition is from the fragment screen performed at UCSF using P43 crystals. The data from the fragment screen performed at UCSF using C2 crystals can be found here - https://zenodo.org/record/4716363 - in the zipped directory named "ucsf_nsp3_mac1_C2.zip". </p>
Dataset (I.) related to publication: PIP4K2C inhibition reverses autophagic flux arrest induced by SARS-CoV-2
<p>MD simulation data (PIKfyve simulations) related to publication: </p> <p>Karim, M., Mishra, M., Lo, CW. <em>et al.</em> PIP4K2C inhibition reverses autophagic flux impairment induced by SARS-CoV-2. <em>Nat Commun</em> <strong>16</strong>, 6397 (2025). https://doi.org/10.1038/s41467-025-61759-1</p> <ul> <li>The .zip files contain raw Desmond simulation trajectories of PIKfyve in complex with RMC-113 [18 replicas; each 4 us] (-out.cms files and trajectories).</li> </ul> <p> </p>
Tracking SARS-CoV-2 variants in wastewater in San Pedro de la Paz, Chile
<p>Various studies have shown the presence of SARS-CoV-2 RNA in the feces of patients<br>with COVID-19, both symptomatic and asymptomatic. This allowed determining the<br>viral load in wastewater samples from Wastewater Treatment Plants (WWTPs),<br>carrying out wastewater-based surveillance (WBS) of the virus in the community, as a<br>complement to person-to-person testing. The appearance of SARS-CoV-2 variants,<br>which can increase transmissibility and/or immune evasion, creates an imperative<br>need to implement specific and permanent surveillance methods to control the COVID19 pandemic. For variant detection, we performed a real-time RT-qPCR assay with a<br>commercial kit to detect five virus variants (Alpha, Beta, Gamma, Lambda, and Delta)<br>in the municipality of San Pedro de la Paz, Chile, from January to November 2021.<br>Detection of variants in wastewater was consistent with available clinical data and<br>provided additional information for community surveillance, identifying lambda and<br>delta variants as the most frequently detected during the second and third wave of<br>infections in the population of this area. Furthermore, in some cases we detected<br>specific variants in wastewater before local authorities confirmed the first clinical cases.<br>The study demonstrates that WBS is a tool that allows a rapid and cost-effective<br>detection of specific mutations associated with SARS-CoV-2 variants using RT-qPCR.<br>However, Illumina amplicon sequencing confirms that there are more optimal methods<br>to sequence this type of matrices. This method can be used to complement clinical<br>data during outbreaks and is especially useful when clinical care is insufficient or<br>collapsed and/or cost is very high, as is the case in many countries.</p>
Seroprevalence survey on infection with the SARS-CoV-2 virus after the second wave in Kinshasa, Democratic Republic of the Congo. 2021
<p>Results of population-based age stratified seroepidemiological investigation in the Democratic Republic of the Congo</p>
AMTraC-19 (v7.7d) Dataset: Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia
<p>A preprint paper describing scenarios which generated this dataset can be accessed here: https://arxiv.org/abs/2107.06617. Please cite this work when using the dataset:<br> S. L. Chang, C. Zachreson, O. M. Cliff, M. Prokopenko, Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia, arXiv: 2107.06617, 2021.</p> <p>Abstract. An outbreak of the Delta (B.1.617.2) variant of SARS-CoV-2 that began around mid-June 2021 in Sydney, Australia, quickly developed into a nation-wide epidemic. The ongoing epidemic is of major concern as the Delta variant is more infectious than previous variants that circulated in Australia in 2020. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, including case isolation, home quarantine, school closures, and stay-at-home restrictions (i.e., "social distancing"). Our modelling indicated that the levels of reduced interactions in workplaces and across communities attained in Sydney and other parts of the nation were inadequate for controlling the outbreak. A counter-factual analysis suggested that if 70% of the population followed tight stay-at-home restrictions, then at least 45 days would have been needed for new daily cases to fall from their peak to below ten per day. Our model successfully predicted that, under a progressive vaccination rollout, if 40-50% of the Australian population follow stay-at-home restrictions, the incidence will peak by mid-October 2021. We also quantified an expected burden on the healthcare system and potential fatalities across Australia.</p> <p>The AMTraC-19 source code (v7.7d) is released on Zenodo: https://zenodo.org/record/5778218</p>
Predominance of antibody-resistant SARS-CoV-2 variants in vaccine breakthrough cases from the San Francisco Bay Area, California
<p>This repository contains the pertinent datasets used in the manuscript, <em>Predominance of antibody-resistant SARS-CoV-2 variants in vaccine breakthrough cases from the San Francisco Bay Area, California</em>.</p>
ACE2-IgG1 fusions with improved in vitro and in vivo activity against SARS-CoV-2
<p>These are raw data for figures in ACE2-IgG1 fusions with improved in vitro and in vivo activity against SARS-CoV-2, in iScience: PMID: 34957381</p>
GRAND-SLAM analysis of SARS-CoV-2 data from Finkel et al., Nature 2021 (https://www.nature.com/articles/s41586-021-03610-3)
<p>This is the processed SLAM-seq data from Finkel et al., Nature 2021 (https://www.nature.com/articles/s41586-021-03610-3). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The file sars.tsv.gz is the GRAND-SLAM output table.</p> <p> </p> <p>To generate the GRAND-SLAM output yourself, first <a href="https://github.com/erhard-lab/gedi/wiki/Preparing-genomes">prepare</a> the human (ensembl v90) and the SARS-CoV-2 genome (NC_045512). Then run:</p> <pre><code class="language-bash">gedi -e Slam -trim5p 15 -reads sars.cit -genomic h.ens90 SARS-CoV2 -prefix grandslam_t15/sars -plot -progress </code></pre> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p>You can also start from scratch (i.e., the json file):</p> <ol> <li><a href="https://github.com/erhard-lab/gedi/wiki/Preparing-genomes">Prepare</a> the human genome (ensembl v90), the SARS-CoV-2 genome (NC_045512), the human rRNA sequence (U13369.1), and the Mycoplasma hominis sequence</li> <li>Prepare the joint STAR index for the human and virus genome by calling gedi -e GenomicUtils -p -m star -g h.ens90 SARS-CoV2</li> <li>Modify the starindex entry in the json file to match your file system</li> <li>Run: gedi -e Pipeline -r parallel -j sars.json rnaseq_mapping.sh report.sh grandslam.sh</li> </ol> <p>Software versions:</p> <ul> <li>gedi toolkit 1.0.4</li> <li>GRAND-SLAM 2.0.7</li> <li>cutadapt 3.4</li> <li>Bowtie 2 version 2.3.0</li> <li>STAR version 2.5.3a</li> </ul>
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.