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22 results for “bioRxiv”
Datasets from Prokkola et al. 2021 bioRxiv, doi: https://doi.org/10.1101/2021.08.23.457324).
<p>Standard and maximum metabolic rate and aerobic scope datasets for Atlantic salmon juveniles.</p> <p><strong>In raw data files</strong> ("xxx_bgCorrected_alldata_xx"), oxygen saturations (O2.correct) are shown in mg O2/L after correcting for background respiration. </p> <p><strong>The analysed datasets </strong>("Analysed_xxx_data.txt"), contain values for each fish, combined with information of fixed and random effects for the models. These are the files for statistical analysis.</p> <p>See linked Github repository and the manuscript for details.</p>
bioRxiv 10k
<p>This dataset is a CC-BY 4.0 subset of what bioRxiv kindly made available: <a href="https://www.biorxiv.org/tdm">https://www.biorxiv.org/tdm</a></p> <p>It is randomized and split into train (6,000), validation (2,000) and test (2,000) subsets - 10,000 PDF / XML pairs in total.</p> <p>The zip files further contain file lists of smaller subsets that used the subject area to potentially create a balanced subset.</p> <p>The zip is similar in structure to the "PMC sample 1943" dataset that was created as part of: <a href="https://doi.org/10.1145/2494266.2494271">https://doi.org/10.1145/2494266.2494271</a> (a working link is available from: <a href="https://grobid.readthedocs.io/en/stable/End-to-end-evaluation/">https://grobid.readthedocs.io/en/stable/End-to-end-evaluation/</a>).</p> <p>Therefore it is well suited for evaluation of <a href="https://github.com/elifesciences/sciencebeam/wiki/Related-Projects">PDF to XML conversion tools</a>, such as <a href="https://github.com/kermitt2/grobid">GROBID</a>. The dataset was created as part of <a href="https://elifesciences.org/">eLife</a>'s <a href="https://github.com/elifesciences/sciencebeam">ScienceBeam</a> project.</p>
data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation
<p>This is supporting data and software code for the following preprint in bioRxiv</p> <p><strong>Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation</strong></p> <p>https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1</p>
Data for: Tang et al., Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. bioRxiv 2018.
<p>Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations.</p> <p><strong>Paper</strong>: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: <a href="https://doi.org/10.1101/454793">https://doi.org/10.1101/454793</a>.</p> <p><strong>Details:</strong> A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows:</p> <ol> <li><em>Development (Phases I-II)</em>. 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).</li> <li><em>Hold-out (Phase III)</em>. 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.</li> <li><em>CERAD-like hold-out</em>. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.</li> </ol> <p>Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented.</p> <p>Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files.</p> <p><strong>Slide source and preparation:</strong> All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification.</p> <p><strong>Code:</strong> Please visit <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p> <p> </p>
Data from: Tracking the popularity and outcomes of all bioRxiv preprints
<p>The data used to generate figures in the manuscript titled <a href="https://www.biorxiv.org/content/early/2019/01/13/515643">"Tracking the popularity and outcome of all bioRxiv preprints,"</a> posted to bioRxiv 13 Jan 2019.</p> <ul> <li><strong>22 Mar 2019:</strong> PDFs of each figure from the paper have been added to the repository. In addition, the license has been changed from CC-BY-NC to CC0.</li> </ul>
bioRxiv 10k figure bounding boxes
<p>This dataset contains figure bounding boxes corresponding to the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>.</p> <p>It provides annotations in two formats:</p> <ul> <li><a href="https://cocodataset.org/#format-data">COCO format (JSON)</a></li> <li>JATS XML with <a href="https://github.com/kermitt2/grobid/blob/0.7.0/doc/Coordinates-in-PDF.md">GROBID's "coords" attribute</a></li> </ul> <p>The COCO format contains bounding boxes in rendered pixel units, as well as PDF user units. The latter uses field names with the "pt_" prefix.</p> <p>The "coords" attribute uses the PDF user units.</p> <p>The dataset was generated by using an algorithm to find the figure images within the rendered PDF pages. The main algorithm used for that purpose is <a href="https://en.wikipedia.org/wiki/Scale-invariant_feature_transform">SIFT</a>. As a fallback, <a href="https://docs.opencv.org/4.5.3/d4/dc6/tutorial_py_template_matching.html">OpenCV's Template Matching</a> (with multi scaling) was used. There may be some error cases in the document. Very few documents were excluded, were neither algorithm was able to find any match for one of the figure images (six documents in the train subset, two documents in the test subset).</p> <p>Figure images may appear next to a figure description, but they may also appear as "attachments". The latter usually appears at the end of the document (but not always) and often on pages with dimensions different to the regular page size (but not always).</p> <p>This dataset itself doesn't contain any images. The PDF to render pages can be found in the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>.</p> <p>The dataset is intended for training or evaluation purposes of the semantic Figure extraction. The evaluation score would be calculated by comparing the extracted bounding boxes with the one from this purpose. (example implementation <a href="https://github.com/elifesciences/sciencebeam-judge">ScienceBeam Judge</a>)</p> <p>The dataset was created as part of <a href="https://elifesciences.org/">eLife</a>'s <a href="https://github.com/elifesciences/sciencebeam">ScienceBeam</a> project.</p>
bioRxiv 10k with assets
<p>This dataset is a superset of the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>. It additionally includes the assets (usually images) that are linked by the XML files.</p> <p>The assets were retrieved from the <a href="https://www.biorxiv.org/tdm">bioRxiv's tdm bucket</a>. Assets that are not linked by the XML were omitted. Examples of those would be some large videos.</p> <p>This dataset serves a similar purpose as the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>, but for use-cases that require the assets. e.g. training and evaluation of figure image extraction.</p> <p>This dataset mirrors the exact same documents and structure as the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>. But rather than just containing the PDF and XML files, it also contains the linked assets (often images, but not necessarily).</p> <p>The dataset was created as part of <a href="https://elifesciences.org/">eLife</a>'s <a href="https://github.com/elifesciences/sciencebeam">ScienceBeam</a> project.</p>
Image data for bioRxiv article named: mtFociCounter - Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci
<p>Raw imaging data to reproduce and test the findings of the bioRxiv article: <strong>mtFociCounter </strong>- Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci. It contains data from three imaging days and 2 or three technical replicates on each day.</p> <p> </p>
dataset for bioRxiv preprint titled 'Evolution of drug resistance drives progressive destabilizations in functionally conserved molecular dynamics of the flap region of the HIV-1 protease'
<p>This data supports the Figures in the preprint titled</p> <p><strong>Evolution of drug resistance drives progressive destabilizations in functionally conserved molecular dynamics of the flap region of the HIV-1 protease</strong></p> <p><strong>working abstract</strong></p> <p>The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS). During the progression of the disease, some individual patients acquire -drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the ‘flap region’ of the HIV-1 protease, which is a critical dynamic region involved in non-specific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and polypeptide binding of the main protease. We use a multi-agent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs (SIV and FIV). We also investigate changes in dynamics due to common drug-resistant mutations in many patients. We find that a key functional site in the flap region, a solvent-exposed isoleucine (ILE50) and surrounding sites that control flap dynamics is often targeted by drug-resistance mutations, likely leading to malfunctional molecular dynamics affecting the overall flexibility of the flap region. We conclude that better long term patient outcomes may be achieved by designing drugs that target protease regions which are less dependent upon single sites with large functional binding effects.</p>
Metabolomics data from Li et al., 2022 BioRxiv
<p>Metabolomics data from Li et al., 2022 BioRxiv</p> <p>Prerpint: https://www.biorxiv.org/content/10.1101/2022.07.04.498697v1 </p>
Data from: International authorship and collaboration across bioRxiv preprints
<p>Data and supplementary tables for <a href="https://doi.org/10.7554/eLife.58496">"International authorship and collaboration across bioRxiv preprints,"</a> a paper first posted to <a href="https://doi.org/10.1101/2020.04.25.060756">bioRxiv</a> and now published in <em>eLife</em>.</p> <ul> <li><strong>"reproduce.md"</strong> includes all R code used to generate figures and perform analyses described in the paper.</li> <li><strong>"biorxiv_countries.postgres.backup"</strong> is a database snapshot that can be loaded into a PostgreSQL database to access all data collected and used in the study.</li> <li><strong>"schema.pdf"</strong> describes each field in each table of the database.</li> <li><strong>"manual_edits.sql"</strong> describes all corrections made to the automated inference of the country-level affiliations inferred for all authors.</li> <li><strong>"affiliation_corrections.csv"</strong> lists every unique affiliation string that was re-categorized after institutional corrections. The consequences of the corrections described in "manual_edits.sql."</li> <li><strong>"institution_corrections_summary.csv"</strong> summarizes "affiliation_corrections.csv" by listing each "before" and "after" correction one time. It is important to note that each before/after pair does not necessarily indicate that <em>every</em> affiliation string from the "before" institution was reassigned to the "after" institution, just that at least one affiliation string was switched from one to the other. <ul> <li>Note that the final two "corrections" files describe steps taken to correct the institution-level associations between authors and countries. The final set of corrections assigned authors to countries <strong>using heuristics that did not take institution-level accuracy into account</strong>.</li> </ul> </li> </ul> <p><strong>Version history:</strong></p> <ul> <li><strong>1.0.0: </strong>New files uploaded reflecting substantial corrections to the data, mostly linked to classification of authors and preprints previously without a country classification. (26 Jun 2020)</li> <li><strong>0.2.1:</strong> Added "schema.pdf" file, previously only in the manuscript.</li> <li><strong>0.2.0:</strong> Added new files "affiliation_corrections.csv" and "institution_corrections_summary.csv"</li> <li><strong>0.1.1: </strong>Database snapshot added.</li> <li><strong>0.1.0: </strong>First version with supplementary tables added.</li> </ul>
Dump of bioRxiv S3 bucket
<pre><code class="language-bash">aws s3 ls s3://biorxiv-src-monthly/ --request-payer --recursive --human-readable --summarize > dump.txt && xz dump.txt</code></pre> <p> </p>
Supplementary datasets for manuscript bioRxiv https://doi.org/10.1101/2022.06.03.494642
<p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 1.</strong> </a> Database of reference human messenger RNAs and SARS-CoV-2 genomes used for mapping reads. Raw count matrices.</p> <p> </p> <p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 2</strong></a><strong>.</strong> Expression matrices and transcript levels in uninfected cells.</p> <p> </p> <p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 3.</strong> </a> Differential Expressed Gene analysis used in the volcano plots analysis. Datasets and results used in the Gene Ontology analyses. Classification of outliers.</p> <p> </p> <p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 4.</strong> </a> Transcriptional profiles for all genes in each cell type.</p> <p> </p> <p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 5.</strong> </a> Networks used in the analyses presented in Figs. 5 and 6.</p> <p> </p> <p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 6.</strong> </a> Transcriptional analysis of human astrovirus 1 infection in ileum organoids.</p>
bioRxiv and medRxiv 2023 user survey
<p>This dataset presents the anonymized results of the 2023 bioRxiv and medRxiv user survey, which received responses from 7135 participants. Conducted between October 25th and December 19th, 2023, the survey aimed to understand community usage of the preprint servers, with a focus on improving service delivery and functionality. To protect respondent anonymity, all free-text entries have been removed from the dataset. </p>
bioRxiv preprint and publication details, 2014-2023
<p>bioRxiv preprint and publication details, 2014-2023</p> <p>Details at: <a href="https://blog.stephenturner.us/p/exploring-the-biorxiv-api-with-r-httr2-rvest-tidytext-datawrapper" target="_blank" rel="noopener">https://blog.stephenturner.us/p/exploring-the-biorxiv-api-with-r-httr2-rvest-tidytext-datawrapper</a></p> <p>Code at: <a href="https://gist.github.com/stephenturner/e1487c90a98e6d5805a3211f0140e198" target="_blank" rel="noopener">https://gist.github.com/stephenturner/e1487c90a98e6d5805a3211f0140e198</a></p> <p>Original data pulled from the bioRxiv API: <a href="https://api.biorxiv.org/" target="_blank" rel="noopener">https://api.biorxiv.org/</a></p>
Dataset associated with A. Hallou, R. He, et al. A computational pipeline for spatial mechano-transcriptomics. bioRxiv 2023.08.03.551894
<p>Dataset associated with:</p> <p>Adrien Hallou, Ruiyang He, Benjamin David Simons and Bianca Dumitrascu. A computational pipeline for spatial mechano-transcriptomics. bioRxiv 2023.08.03.551894; doi: <a href="https://doi.org/10.1101/2023.08.03.551894">https://doi.org/10.1101/2023.08.03.551894</a></p> <p>Licence</p> <p>This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
Raw IMC images associated with De Lima et al. 2023, BioRxiv
<p>This dataset contains the raw IMC images associated with the paper "Spatial mapping of rheumatoid arthritis synovial niches reveals specific macrophage networks associated with response to therapy" (De Lima et al. 2023, BioRxiv). The output files by CyTOF IMC were exported to one TIFF per channel. This dataset contains 40 directory files corresponding to 40 regions of interest (see ImageNb_PatientID.xlsx file for additional information). Each directory contains 46 TIFF images, including the 34 channels used in our study and 1 TIFF of the cell segmentation mask.</p>
Supplemental tables for bioRxiv submission
<p>Supplemental tables for the bioRxiv manuscript titled "Reduction of SynGAP-γ, disrupted splicing of Agap3, and oligodendrocyte deficits in Srrm2 mice, a genetic model of schizophrenia and neurodevelopmental disorder"</p>
Prider testing data and code, Biorxiv
<p> </p> <p>The FASTA file sets and the R script used for the testing of the processing time of R package Prider.</p>
Supplemental data for bioRxiv protocol on proteomic data analysis
<p>Table output from MSstats for the data analysis described in the BioRxiv paper, and the R script used to create the volcano plots in the manuscript. </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
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DANDI Archive for NWB datasets
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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.