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.
1,007
datasets available to search
ShareScore release 0.9.0
Dataset results
1,007 results for “single use”
Data from: Distances and their visualization in studies of spatial-temporal genetic variation using single nucleotide polymorphisms (SNPs)
<p>Distance measures are widely used for examining genetic structure in datasets that comprise many individuals scored for a very large number of attributes. Genotype datasets composed of single nucleotide polymorphisms (SNPs) typically contain bi-allelic scores for tens of thousands if not hundreds of thousands of loci.</p> <p>We examine the application of distance measures to SNP genotypes and sequence tag presence-absences (SilicoDArT) and use real datasets and simulated data to illustrate pitfalls in the application of genetic distances and their visualization.</p> <p>The datasets used to illustrate points in the associated review are provided here together with the R script used to analyse the data. Data are either simulated internal to this script or are SNP data generated as part of other studies and included as compressed binary files readily accessable by reading into R using R base function readRDS(). Refer to the analysis script for examples.</p>
Raw data for "Deep mouse brain two-photon near-infrared fluorescence imaging using a superconducting nanowire single-photon detector array"
<p>Two-photon microscopy (2PM) has become an important tool in biology to study the structure and function of intact tissues in-vivo. However, adult mammalian tissues such as the mouse brain are highly scattering, thereby putting fundamental limits on the achievable imaging depth, which typically resides around 600-800um. In principle, shifting both the excitation as well as (fluorescence) emission light to the shortwave near-infrared (SWIR, 1000-1700 nm) region promises substantially deeper imaging in 2PM, yet has proven challenging in the past due to the limited availability of detectors and probes in this wavelength region. To overcome these limitations and fully capitalize on the SWIR region, in this work we introduce a novel array of superconducting nanowire single-photon detectors (SNSPDs) and associated custom detection electronics for the use in near-infrared 2PM. The SNSPD array exhibits high efficiency and dynamic range, as well as low dark-count rates over a wide wavelength range. Additionally, the electronics and software permit seamless integration into typical 2PM systems. Together with a fluorescent dye emitting at 1105 nm, we report imaging depth of > 1.1mm in the in-vivo mouse brain, limited only by available labeling density and laser power. Our work further establishes SWIR 2PM approaches and SNSPDs as promising technologies for deep tissue biological imaging. </p>
An enhanced single Gaussian point continuum finite element formulation using automatic differentiation: Source code and data
<p>This dataset contains the source code and the data with an example of uniaxial strain of an enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</p> <p> </p> <p>This contribution presents a low-order 3D finite element formulation with hourglass stabilization using automatic differentiation. Here, the former Q1STc element formulation is enhanced by an approximation-free computation of the inverse of the Jacobian. The improved version is termed "Q1STc+."</p> <p> </p> <p>The corresponding publication is:</p> <p><br>Pacolli, N., Awad, A., Kehls, J., Sauren, B., Klinkel, S., Reese, S., Holthusen, H.<br><em>An enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</em></p> <p>Standalone_Elementroutine: <em>Q1STc+_Codes</em> contains:</p> <ul> <li><strong>main.f90</strong>: Standalone routine for local uniaxial strain test</li> <li><strong>Makefile</strong>: Makefile to create executable "Q1STc+"</li> <li><strong>elem40.f90</strong>: Element routine "Q1STc+" with elem_sub.f90 as the subroutine written in AceGen</li> <li><strong>mat52.f90</strong>: Elasto-plastic material routine with all subroutines written in AceGen</li> <li><strong>elem_mat_select.f90</strong>: The selected material routine (Here: mat52)</li> <li><strong>elem_subs.f90</strong>: Subroutines for elem40.f90</li> <li><strong>mat_subs.f90</strong>: Subroutines for mat52.f90</li> </ul>
Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model
<p>This dataset contains the processed scRNA-seq data and code for single-cell unified polarization assessment. Please refer to the article "Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model" for the detailed data and method description.</p> <p>*.rds: the processed scRNA-seq datasets with Universal Cell Embeddings saved as a Seurat object. The Universal Cell Embeddings are saved in the assay 'uce'.</p> <ul> <li>immune_dict_uce.rds: Immune Dictionary.</li> <li>ifnb_treatment_uce.rds: human PBMCs treated with IFN-beta.</li> <li>macrophage_stimulation_uce.rds: human macrophages treated with one or two of IFN-beta, IFN-gamma, TNF-alpha and IL-4.</li> <li>il2_treatment_uce.rds: mouse spleen CD8+ T cells treated with IL-2 or anti-PD-L1.</li> <li>pan_cancer_myeloid_uce.rds: tumor-infiltrating myeloid cells across seven cancer types.</li> </ul> <p>notebooks.zip: Jupyter notebooks containing code for training models and applications to multiple datasets.</p>
Single-Shot Three-Dimensional Orientation Imaging of Nanorods Using Spin to Orbital Angular Momentum Conversion_experimental_dataset
<p>This is the datafile containing the raw experimental data to the article Fordey et al., Single-Shot Three-Dimensional Orientation Imaging of Nanorods Using Spin to Orbital Angular Momentum Conversion, <em>Nano Lett.</em> 2021, 21, 17, 7244–7251. </p>
Quantitative sodium MRI in foods: addressing sensitivity issues using single quantum Chemical Shift Imaging at high field
<p>Quantitative sodium MRI in foods: addressing sensitivity issues using single quantum Chemical Shift Imaging at high field</p>
Evaluating Augmented Reality Light Probe Estimation Techniques using a Single Camera (Supplemental Material)
<p>This repository contains the dataset for the paper "Evaluating Single Camera Augmented Reality Light Probe Estimation Techniques".</p> <p>The data archive includes:</p> <ul> <li>The complete image set (3 scenes with 6 techniques and ground truth in 90 time points)</li> <li>SSIM data of the panoramas as a CSV file</li> <li>SSIM data of the rendered images as a CSV file</li> <li>Various graphs to visualize the data</li> <li>R files used to plot the graphs</li> </ul> <p>The data archive includes additional plots that are not included in the paper.</p>
Processing single-cell RNA-seq datasets using SingCellaR
<ul> <li>cellranger_output.zip : Zipped files for cellranger output</li> <li>Code.zip: This contains the code for Step2-7</li> <li>Human_genesets.zip: This includes the geneset signatures (.gmt) files we used in the protocol and original study (Roy et al, 2021)</li> <li>Human_HSPC_All.SingCellaR.rdata: The SingCellaR objects generated in Step 4 on a local computer </li> <li>ABM_1.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>eFL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>PBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>meta.data.txt: meta data includes the donor and batch information </li> </ul>
Data sets used to demonstrate the software MadHitter in the manuscript "The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via a Single-Cell Perspective"
<p>This is a zip archive of nine single-cell RNASeq data sets used in the manuscript entitled:</p> <p>"The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via A Single-Cell Perspective" by Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schaffer, Eytan Ruppin,</p> <p>The README.txt describes the data sets in detail.</p> <p>The associated software can be found at https://github.com/ruppinlab/madhitter</p>
Raw Data for the article: Transjugular Intrahepatic Portosystemic Shunt: A Single-Centre Mid-term Experience Using the Viatorr Controlled-Expansion Stent
<p>Authors reported a high incidence of hepatic encephalopathy (HE) using the Viatorr Controlled Expansion stent (VCX) (Gore, Flagstaff AZ) in a cohort of 33 patients underwent transjugular intrahepatic portosystemic shunt (TIPS) creation. In detail 6 and 12 month overall HE rate was 53% and 61%. Those results are different as compared to our preliminary experience, with the same stent, in which the overall HE rate was 22% in a cohort of 75 patients and with a mean follow up of 5.8 months.</p>
Kiessling et al. Plastic Pirates single use plastics and other litter Dataset
<p>Full dataset of the citizen science data from riversides in Germany collected in the project Plastic Pirates and used within the study "What potential has the EU Single-Use Plastic Directive in reducing plastic pollution at coastlines and riversides? An evaluation based on citizen science data". Litter data for individual sampling sites and metadata can be found within the file. See the main text of the manuscript for a full description of methods for the collection of these data.</p>
Long-term testing of alkaline electrolyser single cell using Raney Ni electrodes
<p>Chronopotentiometry and polarization curves of alkaline electrolyser single cell using Raney Ni electrodes.</p>
Exploring cell diversity and fidelity in crustacean limb regeneration using single-nucleus RNA sequencing
<p>R objects containing datasets generated by snRNA-seq on Parhyale hawaiensis limbs. These datasets have been generated during my Phd thesis.</p>
Merger seismology: distinguishing massive merger products from genuine single stars using asteroseismology (online data)
<h1><strong>Input and output files for "Merger seismology: distinguishing massive merger products from genuine single stars using asteroseismology" (Henneco et al. 2024b)</strong></h1> <p>This repository contains the MESA and GYRE input files required to reproduce the models used in Henneco et al. (2024b), as well as some of the output files.</p> <p><strong>[MESA version]</strong><br>MESA r12778<br>MESA SDK 20.3.2</p> <p><strong>[GYRE version]</strong><br>GYRE 7.0<br>MESA SDK 22.6.1</p> <p> </p> <h2><strong>input_files</strong></h2> <p>This directory contains the template input files for the MESA and GYRE models.</p> <h3><strong>gyre</strong></h3> <p>- gyre_nonrot_template.in: GYRE inlist for computations without rotation<br>- gyre_rot_template.in: GYRE inlist for computations, including rotation using the TAR<br>- gyre_rot_pert_template: GYRE inlist for computations including rotation using the perturbative approach</p> <h3><strong>mesa</strong></h3> <p>- <strong>genuine_single</strong>: MESA work directory for genuine single stars<br>- <strong>merger_product</strong>: MESA work directory for merger products via the fast accretion method<br>- <strong>zams_z0142_y2703.data</strong>: ZAMS models used to start all MESA computations from</p> <h2> </h2> <h2><strong>output</strong></h2> <h3><strong>mesa</strong></h3> <p>In this directory, we provide the MESA history and profile (GYRE format only) output for the MESA models used in our work.<br>The more detailed regular profile files are left out because of storage constraints, but these can be transferred upon reasonable request.</p> <p>- mXX_plus_mYY_at_rZZ: XX + YY Msol merger product model where the fast accretion method was invoked when the HG star had a radius of ZZ Rsol<br>- mXX: genuine single-star model of XX Msol</p> <h3><strong>gyre</strong></h3> <p>This directory contains the GYRE summary files and input files (with the frequency ranges specific to these models). The detail files are left out because of storage constraints, but these can be transferred upon reasonable request. </p> <p><strong>[suffixes]</strong><br>NAD: nonadiabatic computations<br>pmodes: computations in frequency ranges appropriate for pressure modes<br>Om20_PERT: computations including rotation (20% of critical) using the perturbative approach<br>Om20_TAR: computations including rotation (20% of critical) using the TAR</p> <p>Except for the computations in `m6.0_plus_m2.4_at_r9.0`, the GYRE computations have been made only for a specific MESA profile (the profile at the time when the models were seismically compared).<br>These are:</p> <p>-<strong> m6.0_plus_m2.4_at_r9.0</strong>: profile 38<br>- <strong>m7.8</strong>: profile 14<br>- <strong>m9.0_plus_m6.3_at_r10.4</strong>: profile 62<br>- <strong>m13.6</strong>: profile 16</p> <p>For the `m6.0_plus_m2.4_at_r9.0` model, GYRE computations have been made for profiles 27 -- 52 (see Section 4.2).</p>
Raw data for figures used in manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures
<p>Raw Image files used for generating the figures (Fig 2, Fig3, Fig4, Fig5 and Fig6, Supplementary figures, files needed for background subtraction and image processing tutorial (docker image)) in the manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures</p> <p>Use command tar xfz[v] *.tar.gz to retain the file structure. </p> <p>Docker image in image processing tutorial works on Linux platforms only.</p> <p>File structure after un-compressing each *.tar.gz is as follows - </p> <p>1. acetylated_background_signalsFiles.tar.gz</p> <p> - Folders for the different experiments with the name expt[1..30]</p> <p> - *SIGNALS.pkl - Pickle file (python encoded) containing the information of the histogram of the peptide step-drops</p> <p> - acetylated_backgroundFiles_list.csv (file formatted for performing iterative_background.py)</p> <p> - README.txt (information on the contents and the use of the files in the directory)</p> <p>2. fig2.tar.gz</p> <p> - fig2A/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig2B/ (contains raw image folders, processed_results and README.txt)</p> <p>2. fig3and4.tar.gz</p> <p> - acPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - bocPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - README.txt</p> <p>3. fig5.tar.gz</p> <p> - fig5A_panel1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5A_panel2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A3/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5C/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5D/ (contains raw image folders, processed_results and README.txt)</p> <p>5. fig6.tar.gz</p> <p> - fig6B_top/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig6B_bottom/ (contains raw image folders, processed_results and README.txt)</p> <p>6. fig_supplementary08.tar.gz</p> <p> - (contains raw image folders, processed_results and README.txt)</p> <p>7. fig_supplementart12.tar.gz</p> <p> - supplementary_fig14A/ (contains raw image folders, processed_results and README.txt)</p> <p> - supplementary_fig14B/ (contains raw image folders, processed_results and README.txt)</p> <p>8. imageProcessingTutorial.tar.gz</p> <p> - walkthrough_docker_image.tar.xz (contains the docker image with necessary code pre-installed. Includes small example dataset; Works only in linux docker and not macOS)</p> <p> - README.txt (information on the image processing tutorial). </p>
Online Resources Chapter 3 - Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate
<p>Online Resources to Chapter 3 "Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate" of PhD thesis from Ciska Overbeek, "Peat formation on a former landfill - Production and decomposition of aquatic pioneer vegetation". </p> <p>Published by Overbeek et al in 2019 in Wetlands 39(1): 113-125. https://doi.org/10.1007/s13157-018-1081-y. </p>
Single-shot multi-keV X-ray absorption spectroscopy using an ultrashort laser wakefield accelerator source
<p>The data contained in this repository was used to generate the figures for the publication "Single-shot multi-keV X-ray absorption spectroscopy using an ultrashort laser wakefield accelerator source".</p>
Single-pass STEM-EMCD on a zone axis using apatterned aperture: Data and methodology
<p>Raw data and code accompanying the paper "Experimental progress towards single-pass STEM-EMCD on a zone axis using a patterned aperture"</p>
Data for: Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH Cardiovascular Magnetic Resonance
<p>Magnetic Resonance Imaging measurement data used in our paper about model-based myocardial T1 mapping with sparsity constraints. The data was obtained using a single-short inversion-recovery radial FLASH sequence and is provided in a file format used by the BART toolbox (<a href="http://doi.org/10.5281/zenodo.592960">DOI: 10.5281/zenodo.592960</a>).</p>
Time-resolved single-cell RNA-seq using scSLAM-seq and GRAND-SLAM
<p>This is the example data for the protocol "Time-resolved single-cell RNA-seq using scSLAM-seq and GRAND-SLAM".</p> <p>The test data comprises sequencing data from 12 different cells. Five of them in mock condition with 4sU, five infected with MCMV with 4sU and 2 uninfected cells without 4sU. For each sample, paired-end sequencing was conducted and therefore two files exist for each sample. The files can be found at data/fastq.</p> <p>The reads should be mapped against the mouse genome and the MCMV genome, which are located in data/genome. The sequence is in fasta-fomat and the features in gtf-format.The scripts conducting quality control, read mapping and GRAND-SLAM analysis can be found in the processing folder. For each computational step in this protocol there is a shell script for running the necessary steps. Executing all scripts will reproduce the analysis of our test data</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.