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.
36,943
datasets available to search
ShareScore release 0.9.0
Dataset results
36,943 results for “HUMANE”
A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog - Supporting Data
<p><strong>Description </strong></p> <p>The following files are available : </p> <ul> <li>Simulated non-redundant Gene Catalog (SGC) composed of 128267 genes;</li> <li>Gene abundance profiles across 40 samples: raw read counts, gene length normalized base counts, depth file computed by the jgi_summarize_bam_contig_depth script provided by MetaBAT;</li> <li>Gold Standard (GS) and Gold Standard Single Assignment (GS_SA) binning results;</li> <li>Binning results obtained on the SGC with nine binning methods: MSPminer, MGS-canopy, DAS Tool, MaxBin2, MetaBAT2, SolidBin, CONCOCT, COCACOLA and MyCC.</li> </ul> <p><strong>License</strong></p> <p>These files are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
Characteristics of human and viral RNA binding sites and site clusters recognized by SRSF1 and RNPS1
<p>This dataset was developed for the following article:</p> <p> Rogan PK, Mucaki EJ and Shirley BC. A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections [version 1; peer review: awaiting peer review]. <em>F1000Research</em> 2020, <strong>9</strong>:943 (<a href="https://doi.org/10.12688/f1000research.25390.1">https://doi.org/10.12688/f1000research.25390.1</a>)</p> <p><strong>Section 1. Extended Data Tables</strong></p> <p>This archive contains the extended data tables for the research article "A proposed mechanism for molecular pathogenesis of severe RNA-viral pulmonary infections". These tables provide SRSF1, RNPS1 and hnRNP A1 binding site and information-dense cluster counts across various RNA viral genomes [including multiple SARS-CoV-2 and influenza strains] and the human transcriptome, the estimated SARS-CoV-2 doubling time necessary for viral genome SRSF1 binding site availability to exceed sites within the host transcriptome, and an analysis of influenza, dengue, and aplastic anemia patients misdiagnosed as irradiated by established radiation gene signatures.These tables are:</p> <p><strong>Section 1 - Table 1.</strong> RNPS1 and hnRNPA1 binding sites and Information-Dense Clusters for RNPS1 and<br> hnRNPA1 in RNA Virus Genomes<br> <strong>Section 1 - Table 2A.</strong> Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 1) in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 2B.</strong> Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 2) in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 2C.</strong> Detailed Analysis of Information-Dense Clusters for RNPS1 in RNA Virus Genomes<br> <strong>Section 1 - Table 2D.</strong> Detailed Analysis of Information-Dense Clusters for hnRNP A1 in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 3.</strong> Binding Site Analysis of Multiple Coronavirus Strains (Both Strands)<br> <strong>Section 1 - Table 4A.</strong> Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Negative Strand Only)<br> <strong>Section 1 - Table 4B.</strong> Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Both Strands)<br> <strong>Section 1 - Table 5.</strong> SRSF1, RNPS1 and hnRNPA1 Binding Sites and Information-Dense Clusters by Gene<br> <strong>Section 1 - Table 6A.</strong> Transcriptome-Wide Information Dense Clusters Intersecting DRIP- and DRIPc-seq<br> Intervals<br> <strong>Section 1 - Table 6B. </strong>Exome-Wide Information Dense Clusters within DRIP- and DRIPc-seq Intervals<br> <strong>Section 1 - Table 6C.</strong> Transcriptome-Wide Scan of Strong Binding Sites Intersecting DRIP- and DRIPc-seq<br> Intervals<br> <strong>Section 1 - Table 6D. </strong>Exome-Wide Scan of Strong Binding Sites within DRIP- and DRIPc-seq Intervals<br> <strong>Section 1 - Table 7.</strong> Rate of False Positives for Influenza, Dengue Virus and Aplastic Anemia Using<br> Radiation Signatures<br> <strong>Section 1 - Table 8.</strong> Radiation Model Genes Contributing to False Positives for Patients with Influenza A,<br> Dengue Virus, and Aplastic Anemia<br> <strong>Section 1 - Table 9A.</strong> Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding<br> Sites (Positive-Strand Sites Only)<br> <strong>Section 1 - Table 9B.</strong> Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding<br> Sites (Both Strands Considered)</p> <p><strong>Section 2. All SRSF1, hnRNPA1 and RNPS1 binding site tracks for human and viral genomes</strong></p> <p>We provide bedgraph tracks which provide the location and strength of binding sites (and binding site clusters) for SRSF1, RNPS1 and hnRNPA1 across the human transcriptome (GRCh37), the human exome (including +/-300nt surrounding the exon; non-intergenic only), and for all viral genome investigated in this study (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [two strains]). Note that if no clusters were found for a particular viral genome, a file for said genome will not be present in the Zenodo archive.</p> <p>Folder “Cluster-to-DRIPseq-Intersection-Tracks” contain tracks which indicate where binding site clusters have been identified, intersected with DRIP-seq and DRIPc-seq intervals which indicate where there is evidence of R-Loop formation in the human genome. The DRIP-seq dataset (GSE68845) is not strand specific. DRIPc-seq (GSE70189) is strand specific, and has been taken into account in the intersection (e.g. tracks only list positive strand clusters found in positive-strand DRIPc-seq intervals).</p> <p>Due to sheer size, the human transcriptome and exome tracks which indicate the location of individual binding sites are split into two separate files (separated by strand). While the custom tracks containing human binding site information are designed to be uploaded to the UCSC Genome Browser, files containing transcriptome-wide binding site information may be too large to be uploaded and may require further filtering (i.e. by chromosome).</p> <p>To be classified as a cluster, binding sites on the same strand must have <em>Ri</em> values which sum to >50 bits, each binding site must have a neighboring site within 25nt, and all binding sites in the cluster must have <em>R<sub>i</sub></em> greater than a minimum bit threshold. For human transcriptomes and exomes, this bit minimum was set to <em>R<sub>sequence</sub></em>. The bit minimum for viral binding sites was set to 0.1 * <em>R<sub>sequence</sub></em>. The information density-based clustering algorithm utilized in this work is described in Lu and Rogan 2018 (<a href="https://f1000research.com/articles/7-1933/v2">https://f1000research.com/articles/7-1933/v2</a>) and archived source code is available through Zenodo (<a href="https://dx.doi.org/10.5281/zenodo.1892051">https://dx.doi.org/10.5281/zenodo.1892051</a>).</p> <p><strong>Section 3. Binding site clusters - lollipop plots</strong></p> <p>Lollipop plots present the genomic coordinates and information densities of clusters across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). The height of the "lollipop" corresponds to the information density of a cluster. Labels above "lollipops" present the start and end genomic coordinate (GRCh37) of the cluster followed by the number of sites in the cluster enclosed in brackets. Lollipop plots associated with human transcriptomes/exomes each contain a single gene. Influenza has 8 segments and each segment requires its own plot, other viral genomes examined are presented in a single plot.</p> <p>File naming convention for human plots:</p> <ul> <li>RBP_Gene.png</li> <li>e.g. RNPS1_ADK.png</li> </ul> <p>File naming convention for viral plots (elements in square brackets do not always appear):</p> <ul> <li>Virus[.InfluenzaSegment].RiThreshold.Strand.RBP.png</li> <li>e.g. Wuhan-Hu-1.complete-genome.4.2-bits.PosStrand.hnRNPA1.png</li> </ul> <p>The specified Ri threshold indicates all binding sites which comprise a cluster have <em>R<sub>i</sub></em> greater-than or equal to the threshold.</p> <p><strong>Section 4. Ri(b,l) matrices for all binding sites scanned</strong></p> <p>The information theory-based position weight matrices for the following RNA binding proteins (RBP) used in this study: SRSF1, hnRNPA1 and RNPS1. We investigated binding using two different RNPS1 binding models. While similar, these two models contained binding site information on opposing sides of the binding site motif which is why we found it prudent to scan with both models.</p> <p>Structure of each file:</p> <p>Line #1: Start position, End position and<em> R<sub>sequence</sub></em> [average strength of sequences used to generate the model]</p> <p>Subsequent lines describe the information on each position of the binding site:</p> <ul> <li>First four columns: <em>R<sub>i</sub></em> contribution of nucleotide at this position of the matrix [A, C, G, T]</li> <li>Row #5: Position of the matrix</li> <li>Last four columns: Number of binding sites used to generate model with a particular nucleotide at this position of the matrix [A, C, G, T]</li> </ul> <p>Example:</p> <p>-2.965775 1.282153 0.034225 -4.906891 0 1 19 8 0</p> <p>At zero position of the matrix (first nucleotide), a ‘C’ would have a positive contribution to binding site strength, a ‘G’ would be relatively neutral, and an ‘A’ or ‘T’ would negatively contribute to binding site strength.</p> <p>Generation of R<sub>i</sub>(b,l) matrices and computation of <em>R<sub>i</sub></em> values and can be accomplished by utilizing the Delila package (<a href="https://alum.mit.edu/www/toms/delila/delilaprograms.html">https://alum.mit.edu/www/toms/delila/delilaprograms.html</a>).</p> <p><strong>Section 5. Ri and intersite distance - histograms</strong></p> <p>Two sets of histograms present <em>R<sub>i</sub></em> distribution and intersite distance distribution across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). </p> <p>File naming convention for human plots (elements in square brackets do not always appear):</p> <ul> <li>[IntersiteDistancesThreshold-]Human-[DRIPc]-AllChrs-RBP[-RiThreshold].png</li> <li>e.g. IntersiteDistances500-Human-AllChrs-hnRNPA1-4.6-bits.png</li> </ul> <p>File naming convention for viral plots (elements in square brackets do not always appear):</p> <ul> <li>[IntersiteDistancesThreshold-]Strand-RBP-Virus[.InfluenzaSegment][-RiThreshold].png</li> <li>e.g. IntersideDistances1000-PosStrandOnly-SRSF1-top50000sitesReplicate1-HIV-1-Strain-B.png</li> </ul> <p>Intersite distance thresholds of 500 or 1000 were assigned for all intersite distance histograms. Any distances above the corresponding threshold were excluded from the plot. Plots presenting <em>R<sub>i</sub></em> distributions contain a dashed line indicating <em>R<sub>sequence</sub></em> if it is visible within the scope of the plot.</p> <p><strong>Section 6. Perl Scripts and Descriptions</strong></p> <p>This archive contains all Perl scripts discussed in this archive's associated manuscript and a document file which describes them ("Perl-Script-Descriptions-Page.docx"). The programs and their general functions are as follows:</p> <p>“ClusterToDRIPseqAnalysisProgram.pl” – reports which information-dense clusters are located within DRIPc- and/or DRIP-seq intervals (individually and by gene)</p> <p>“ClusterToDRIPseqAnalysisProgram.GeneDensityFinder.pl” – uses the output from script “ClusterToDRIPseqAnalysisProgram.pl” to determine the number and the density of information-dense clusters within a gene (total clusters within the gene and those within DRIPc-seq intervals)</p> <p>“calculateIntersiteDistance.pl” – determines the distance between all binding sites in the same gene from a list of genomic coordinates</p> <p>“removeOutliersHigherThanN.pl” – discards intersite distances computed by script “calculateIntersiteDistance.pl” that are greater than a specified threshold</p> <p>“getStatisticsOnCol.pl” – calculates the count, geometric mean, median, arithmetic mean, and standard deviation of values from the output of script “removeOutliersHigherThanN.pl”</p> <p>“ScanDataSummaryProgram.pl” – determines the number of binding sites (above a specified <em>R<sub>i</sub></em> threshold) found within known genes (the program also reports the total expression of those genes using external A549 and pneumocyte expression datasets) from binding site coordinate data</p> <p>“TotalBindingSitePerCellCalculator.pl” – estimates the number of binding sites expressed in a single A549 or pneumocyte cell at any given time.</p>
Human assemblies evaluated in the hifiasm paper
<p>Human assemblies evaluated in the <a href="https://www.nature.com/articles/s41592-020-01056-5">Cheng et al (2021)</a>. Non-human assemblies are available at <a href="https://zenodo.org/record/4393750">doi:10.5281/zenodo.4393750</a>. File "*.ONT.*" were generated by <a href="https://www.nature.com/articles/s41587-020-0503-6">Shafin et al (2020)</a>, "*.DipAsm*" by <a href="https://www.nature.com/articles/s41587-020-0711-0">Garg et al (2020)</a> and "*.PAGS*" by <a href="https://www.nature.com/articles/s41587-020-0719-5">Porubsky et al (2020)</a>. The rest of the assemblies were generated by Cheng et al.</p>
New Generation UV-A Filters: Understanding Their Photodynamics on a Human Skin Mimic
<p>The sparsity of efficient commercial ultraviolet-A (UV-A) filters is a major challenge towards developing effective broadband sunscreens with minimal human- and eco-toxicity. To combat this, we have designed a new class of Meldrum-based phenolic UV-A filters. We explore the ultrafast photodynamics of coumaryl Meldrum, CMe, and sinapyl Meldrum, SMe, both in an industry standard emollient and on a synthetic skin mimic, using femtosecond transient electronic and vibrational absorption spectroscopies, and computational simulations. Upon photoexcitation to the lowest excited singlet state (S<sub>1</sub>), these Meldrum-based phenolics undergo fast and efficient non-radiative decay to repopulate the electronic ground state (S<sub>0</sub>). We propose an initial ultrafast twisted intramolecular charge transfer mechanism as these systems evolve out of the Franck-Condon region towards an S<sub>1</sub>/S<sub>0</sub> conical intersection, followed by internal conversion to S<sub>0</sub> and subsequent vibrational cooling. Importantly, we correlate these findings to their long-term photostability upon irradiation with a solar simulator and conclude that these molecules surpass the basic requirements of an industry standard UV filter.</p>
Fibrinogen-like globe domain of human Tenascin-C (hFBG-C); A Target Enabling Package
<p>Chronic activation of the innate immune system by the damage-associated molecular pattern FBG-C (C-terminal fibrinogen-like globe domain of Tenascin-C) contributes to a variety of inflammatory diseases including arthritis, systemic sclerosis, and cancer. This TEP summarizes the first reported efforts to develop small-molecule FBG-C binders, with the aim to disrupt FBG-C-mediated pro-inflammatory protein-protein interactions (PPIs). We present the soluble expression of disulphide-containing human FBG-C (hFBG-C) in <em>E. coli</em>, the novel structure of hFBG-C, and preliminary chemical matter against hFBG-C derived from a crystallographic fragment screen. Finally, we introduce two robustly validated cellular assays, in either immortalized monocytes or primary human macrophages, which provide a route to development of small molecules which inhibit hFBG-C-activated inflammation.</p>
Immunofluorescence staining of a human kidney (#4, peri-tumor area) obtained by MELC
<p>19 marker MELC run in a human peri-tumor kidney sample (#4). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immunofluorescence staining of a human kidney (#3, tumor area) obtained by MELC
<p>19 marker MELC run in a human tumor kidney sample (#3). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immunofluorescence staining of a human kidney (#3, peri-tumor area) obtained by MELC
<p>19 marker MELC run in a human peri-tumor kidney sample (#3). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immunofluorescence staining of a human kidney (#2, tumor area) obtained by MELC
<p>19 marker MELC run in a human tumor kidney sample (#2). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immunofluorescence staining of a human kidney (#2, peri-tumor area) obtained by MELC
<p>19 marker MELC run in a human peri-tumor kidney sample (#2). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immunofluorescence staining of a human kidney (#1, peri-tumor area) obtained by MELC
<p>19 marker MELC run in a human peri-tumor kidney sample (#1). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Image captioning dataset for human activities
<p>An image captioning dataset including images of humans performing various activities. The included images include the following activities: <code>walking, running, sleeping, swimming, sitting, jumping, riding, climbing, drinking and reading.</code></p>
A dataset of human and Plasmodium falciparum genotypes in severe malaria cases from The Gambia and Kenya
<p>This data release contains human and <em>Plasmodium falciparum</em> malaria genotypes from the article:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The release contains genotypes from human and <em>Plasmodium falciparum</em> genetic variants, genotyped using blood samples from 4,171 children ascertained with severe symptoms of malaria at the Royal Victoria Teaching Hospital (now the Edward Francis Small Teaching Hospital), The Gambia, and from the Kilifi District Hospital (now Kilifi County Hospital), Kenya in the period 1995-2009.</p> <p>An accompanying set of association test summary statistics has also been released on Zenodo (doi: <a href="https://doi.org/10.5281/zenodo.5722497">10.5281/zenodo.5722497</a>). Please see <a href="http://www.malariagen.net/resource/32">www.malariagen.net/resource/32</a> for full details of other resources associated with the above manuscript.</p> <p> </p>
Data and scripts related to: Rapid coordination of effective learning by the human hippocampus
<p>This data set contains intracranial EEG data (ASCII format), eye-tracking data from an EyeLink 1000 remote system (edf format), behavioral data, and MATLAB code to reproduce the analyses reported in the manuscript, “Rapid coordination of effective learning by the human hippocampus” published in <em>Science Advances.</em></p> <p>The file <strong>KragelEtal21_SciAdv.zip</strong> contains the raw data divided into folders according to content type, for each of the six participants in the study, and the MATLAB code necessary to reproduce all analyses. MATLAB live scripts provide examples of how to reproduce the main analyses reported in the manuscript.</p> <p>External datasets:</p> <p>In addition to the dataset provided here, three open-access datasets are analyzed in the manuscript.</p> <p> - The <a href="http://figrim.mit.edu/">FIGRIM Dataset</a> contains eye-tracking data during a continuous recognition task.</p> <p> - Two additional eye-tracking datasets during free viewing of repeated scenes are provided in “<a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.9pf75">An extensive dataset of eye movements during viewing of complex images</a>,” namely the Memory I and Memory II datasets.</p> <p>To reproduce region of interest analyses outside of the hippocampus, both the seven-network cortical parcellation developed by <a href="https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation_Yeo2011">Yeo, Krienen et al.</a>, and the <a href="https://identifiers.org/neurovault.image:1702">Harvard-Oxford cortical atlas</a> are required.</p> <p>Stimuli:</p> <p>The scenes used in this study are part of <a href="https://cocodataset.org">Microsoft COCO</a>. Scenes were selected from the 2017 Train images. Image identifiers are maintained.</p> <p>Salience model:</p> <p>To reproduce analyses that consider the visual salience of each scene, DeepGaze II model predictions for each stimulus are required. Tensorflow models and a Jupyter notebook demonstrating their use are available for <a href="https://deepgaze.bethgelab.org/">download</a>.</p> <p>Software dependencies:</p> <p>The code in this project was developed using MATLAB r2017b. The following external packages are required for code execution. Some external packages are included in the repository.</p> <p>- fieldtrip (<a href="https://github.com/fieldtrip/fieldtrip">https://github.com/fieldtrip/fieldtrip</a>)<br> - spm12 (<a href="https://github.com/spm/spm12">https://github.com/spm/spm12</a>)<br> - BOSC (<a href="https://doi.org/10.1016/j.neuroimage.2010.08.064">https://doi.org/10.1016/j.neuroimage.2010.08.064</a>)<br> - Edf2Mat (<a href="https://github.com/uzh/edf-converter">https://github.com/uzh/edf-converter</a>)<br> - boundedline (<a href="https://github.com/kakearney/boundedline-pkg">https://github.com/kakearney/boundedline-pkg</a>)<br> - export_fig (https://github.com/altmany/export_fig)</p> <p>License:</p> <p>The included code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version. See the file COPYING for more details. The release of this software includes functions from other toolboxes that are covered under their respective licenses.</p>
Processed NOMe-seq data for four human cell lines
<p>Raw NOME-seq data (Gene Expression Omnibus accession GSE57498) for the human cell lines HMEC, MCF7, PrEC, PC3 were aligned to hg19 using bwa-meth. Methylation and occupancy calls were made at WCG and GCH sites respectively using bwa-meth, BisSNP and bespoke, tailor made scripts (https://github.com/astatham/NOMe-seq-analysis).</p>
Diffraction images of crystals of the first and second spectrin repeats (mutant C420A/C435A) of human plectin (PDB code 2ODV)
<p>Diffraction images of a native crystals of a fragment of human plectin that includes the first and second spectrin repeats (SR1-SR2) of the plakin domain. The two Cys in the wild type sequence were replaced by Ala.</p> <p>Images correspond to the dataset used to refine the pdb entry 2ODV (http://www.rcsb.org/pdb/explore/explore.do?structureId=2ODV).</p> <p> </p> <p>Data was collected at the BM14 beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) using radiation of 0.9785 Å wavelength and a Mar CCD detector. The dataset consists of 360 images (1 degree oscillation per image). Data extend to ~1.85 Å resolution.</p>
Raw data employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot"
<p>This file contains the raw data necessary to perform the algorithm introduced in the scientific paper:</p> <p>PAPER: Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot</p> <p>Authors: Arturo Bertomeu-Motos, Ricardo Morales, Luis D. Lledó, Jorge A. Díez, Jose M. Catalan, Nicolas Garcia-Aracil.</p> <p>Conference: EMBC 2015, IEEE 37th International Conference in Medicine and Biology Society, August 2015.</p> <p>Raw data acquired necessary to perform thee algorithm introduced in this paper.</p> <p>a) Robot Joints: Robot joints generated to develop the simulation, in radians (j1-j7 colums). This robot is referenced in the paper.<br> b) Direct Upper Limb Joints: Upper limb joints generated to develop the simulation, in radians (q1-q7 columns). This data is used to simulate the accelerometer value.</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - volume filtering verification
<p>For each of the four ROIs (R1 to R4 in file names) we pick few sub-regions (annotated in R?_legend.png) and show the changes between sections (temporally encoded) from initial (registered) input, marked "1" in video to the final result of volume filtering, marked "5" in video.</p> <p>Subregion number j in ROI number i is bears the video file name "R<i>_verification_volume_-_region_<j>_FullHD.mov".</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - registered sections (input data)
<p>These are the 3500x3500 ROI data, selected from complete scans.</p> <p>Following procedure was applied:</p> <p>- Coarse registration (Ulrich et al, 2014)<br> - ROI selection, 4k x 4k regions<br> - Normalisation<br> - Fine-grain registration (Lobachev et al, 2016)<br> - Crop to the center to obtain 3500x3500 size.</p> <p>We estimated the slice thickness to be 7 µm.</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - overview videos
<p>We reconstruct the 3D shape of the micro-vasculature in human bone marrow from serial sections.</p> <p>This upload shows three kinds of overview data.</p> <p>- R[n]_overview_... shows an overview of the mesh from ROI n as video<br> - R[n]_input_animated_... shows a thumb cinema overlay of initial input data<br> - R[n]_both.png shows a still with a frame from overview video (blue) and diameter measurement video (red).</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.