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.
6,410
datasets available to search
ShareScore release 0.9.0
Dataset results
6,410 results for “results”
Fig. 4 in Ephemeral Occurrence of the Echiuran Listriolobus brevirostris (Annelida: Echiura) in Osaka Bay between 1995 and 2002; a New Record for Japan, Probably Resulting from Human-mediated Introduction
Fig. 4. Relationship between trunk length and proboscis length in 52 fixed specimens of Listriolobus brevirostris, collected from Osaka Bay in 1999 (NSMT-Ec 164 to 183).
Fig. 3 in Ephemeral Occurrence of the Echiuran Listriolobus brevirostris (Annelida: Echiura) in Osaka Bay between 1995 and 2002; a New Record for Japan, Probably Resulting from Human-mediated Introduction
Fig. 3. Internal morphology (dorsal view) of Listriolobus brevirostris from Osaka Bay, collected off Kobe on 26 November 2001 (NSMT-Ec 186). A, anterior end of trunk; B, posterior end of trunk (ventral vessel undetectable due to deterioration). Abbreviations: al, alimentary canal; av, anal vesicle; dv, dorsal vessel; gd, gonoduct; gl, gonostomal lip; im, interbasal muscle; nv, neurointestinal vessel; rc, rectal caecum; re, rectum; rv, ring vessel; tb, terminal bulb; vn, ventral nerve cord: vs, ventral seta; vv, ventral vessel. Scale bars: 1 mm.
Fig. 5 in Ephemeral Occurrence of the Echiuran Listriolobus brevirostris (Annelida: Echiura) in Osaka Bay between 1995 and 2002; a New Record for Japan, Probably Resulting from Human-mediated Introduction
Fig. 5. Seasonal changes in trunk length of Listriolobus brevirostris from Osaka Bay in 1999. Solid bars indicate mature specimen(s), dotted lines, measurements from live specimens, and double-headed arrow of dotted line, the length range in live specimens (see text).
TCGA ABSOLUTE auto-calling results with DoAbsolute
<p>The results are calculated with DoAbsolute (https://github.com/ShixiangWang/DoAbsolute) with cancer type specific and pan-cancer mode based on TCGA copy number segments data and mutation data. Take your own risk as the data are auto-processed and no manual check.</p> <p> </p> <p>array sample call status purity ploidy Genome doublings delta Coverage for 80% power Cancer DNA fraction Subclonal genome fraction tau E_CR<br> TCGA-OR-A5JO-01 TCGA-OR-A5JO-01 called 1 5 0 0.2 21 1 0.01 5.00281122783359 0<br> TCGA-OR-A5KQ-10 TCGA-OR-A5KQ-10 called 0.69 5 0 0.17 29 0.85 0 4.99022535717521 0<br> TCGA-OR-A5L8-10 TCGA-OR-A5L8-10 called 0.56 3.99 0 0.18 27 0.72 0 4.02086049427876 0<br> TCGA-OU-A5PI-10 TCGA-OU-A5PI-10 called 0.75 5 0 0.18 28 0.88 0.01 5.01366221195135 0</p> <p> </p> <p>Please cite the following paper if you use it in academic research:</p> <ul> <li>Wang, Shixiang, et al. "The predictive power of tumor mutational burden in lung cancer immunotherapy response is influenced by patients' sex." International journal of cancer (2019).</li> <li>Carter, Scott L., et al. “Absolute quantification of somatic DNA alterations in human cancer.” Nature biotechnology 30.5 (2012): 413.</li> </ul> <p> </p>
Original data and RML mapping used to generate RDF results for K-CAP 2021
<p>These materials include the following:</p> <ul> <li>The csvs used to track the accepted papers, authors and resources in K-CAP 2021</li> <li>The RML mappings generated to transform them to RDF (paths to data will have to be adjusted)</li> <li>The resultant RDF file generated when applying the mapping (out.ttl)</li> </ul>
ABR raw data and results from automated hearing threshold detection
<p><strong>This repository contains: </strong></p> <ol> <li><strong>data.tar.gz</strong>: raw data from Auditory Brainstem Response (ABR) measurements performed at over 4,000 mice at the German Mouse Clinic. It also contains suitably pre-processed ABR raw data of over 8,000 mice from another public data repository (10.5061/DRYAD.CV803RV) of the Wellcome Sanger Institute. All data is intended to be used by and compatible with code published on GitHub (https://github.com/ExperimentalGenetics/ABR_thresholder). <br> </li> <li><strong>results.tar.gz</strong>: results and visualisations - comparison of two new, independent automatic hearing threshold finding methods and comparison with the manual gold standard method.</li> </ol>
CESM model results - Effect of nitrogen limitation and soil processes on Holocene greening of the Sahara
<p>The model results are made in EAPL, Yonsei University, South Korea (https://eapl.yonsei.ac.kr). The purpose of the simulations were to find out the effect of nitrogen limitation and soil processes on Holocene greening of the Sahara. CESM model v1.2 was used for this simulation.</p>
Data Results from Performance Modelling of SLAM methods
<p>Data from runs/bencmarks of SLAM methods GMapping, SLAM Toolbox and Hector SLAM.</p> <p>Results include data for multiple performance metrics, parameters of the robot sensors, and environment features. The data contains results from many runs executed with various combinations of parameters in order to create a statistical model of the SLAM performance in function of characteristics of the robot and environment.</p>
Computational results for the publication "Evolution of water structures in metal-organic frameworks for improved atmospheric water harvesting"
<p>This upload contains the computationally obtained atomic coordinates for MOF-303 and MOF-333 at different water loadings.</p>
Tsunami Simulation: TsunAWI Testcase "Coquimbo 2015", Setups and Example Results
<p>This dataset contains three setups with example results for TsunAWI simulations for the Chilean coast with a focus on<br> the Coquimbo region. They are used in the LEXIS project, WP6 "Earthquake and Tsunami Pilot".</p> <p><strong>coquimbo_150m.tgz </strong>- coarse setup with down to 150m resolution in Coquimbo and other areas of interest along the Chilean coast</p> <p><strong>coquimbo_20m.tgz </strong>- fine<strong> </strong>setup with down to 20m resolution in Coquimbo, also high resolution along other areas of interest along the Chilean coast</p> <p><strong>coquimbo_20m_new.tgz </strong>- setup tuned for the LEXIS workflow aiming at a real time estimate of the inundation on the City of Coquimbo, thus a focus with 20m resolution only in Coquimbo region.</p> <p> </p> <p>Each dataset includes a README explaining the included files. The following description is taken as pars pro toto from coquimbo_20m_new.tgz.</p> <p><strong>Computational Mesh</strong></p> <ul> <li>mesh/Coquimbo_20m/nod2D.out <br> Location of mesh vertices in geographical coordinates, vertices on the open boundary are flagged with "1"<br> Resolution in Coquimbo area: 20m</li> <li>mesh/Coquimbo_20m/nodhn.out<br> Water depth at the respective vertices. Negative values denote elevation on land. <br> In the ocean: ‘The GEBCO_08 Grid, version 20090202 (http://www.gebco.net)’ <br> On land: SRTM, Shuttle radar topography mission, 1 arcsec resolution, https://www2.jpl.nasa.gov/srtm/ Bilinear interpolation to the mesh vertices.</li> <li>mesh/Coquimbo_20m/elem2D.out<br> Mesh geometry: List of the vertices forming the triangular elements.</li> </ul> <p><strong>Input Parameters</strong></p> <ul> <li>namelist.tsunami<br> Parameters read by TsunAWI at run time, e.g., <ul> <li>path to the mesh and the output</li> <li>the time step dt and the time frame of the simulation T_end</li> <li>physical parameters like Manning friction, viscosity</li> <li>toggle the output </li> </ul> </li> <li>Additional command line parameters<br> The results below are for an idealized source (Cosine bell at the location of the 2015 Coquimbo event, started with <br> ompTsuna.x -xy -71.674 -31.573 -mw 8.3 -id coquimbo_20m</li> </ul> <p><strong>Result</strong></p> <ul> <li>coquimbo_20m_new.nc <br> Example output in netcdf format, raw data on the triangular mesh, see<br> ncdump -h coquimbo_20m.nc </li> <li>coquimbo_20m_new_inundation.nc<br> The same raw data but restricted to the inundated area, for faster postprocessing.</li> </ul> <p>The TsunAWI version used is <br> https://gitlab.awi.de/tsunawi/tsunawi/-/tags/LEXIS<br> with one minor bugfix: Add <br> use mpi_parallel<br> to line 881 of tsunawi/src/parameters.F90 </p> <p>Compiled with tsunawi/src/Makefile.in_karolina<br> and run on karolina.it4i.cz in MPI-parallel mode with 128 tasks.</p> <p> </p> <p>This work was supported by the LEXIS project funded by the EU’s Horizon 2020 research and innovation programme (2014-2020) under grant agreement No 825532.</p>
OpenForecast results in 2020-2021
<p><a href="https://openforecast.github.io">OpenForecast</a> is the first national-scale operational runoff forecasting system in Russia. The presented data supports a research article on a long-term assessment of OpenForecast performance in 2020-2021.</p> <p>File listing:</p> <ul> <li>calibration_vs_hindcast.npy -- Python dictionary that provides results of efficiency assessment for calibration and evaluation (hindcast) periods in terms of NSE and KGE metrics for individual hydrological models (GR4J<sub>NSE</sub>, GR4J<sub>KGE</sub>, HBV<sub>NSE</sub>, HBV<sub>KGE</sub>).</li> <li> <p>hindcast_vs_forecast.npy -- Python dictionary that provides results of efficiency assessment for hindcast, pre-operational hindcast, and forecast periods in terms of NSE and KGE metrics for individual hydrological models (GR4J<sub>NSE</sub>, GR4J<sub>KGE</sub>, HBV<sub>NSE</sub>, HBV<sub>KGE</sub>), as well as their ensemble mean (ENS).</p> </li> <li> <p>meteo_forecast.npy -- Python dictionary that reports correlation coefficients between ICON and ERA5 reanalysis for air temperature and precipitation forecasts.</p> </li> <li> <p>users.csv -- daily numbers of OpenForecast users.</p> </li> </ul> <p>Sample code for data access:</p> <pre><code class="language-python">import numpy as np import pandas as pd calibration_hindcast = np.load("calibration_vs_hindcast.npy", allow_pickle=True).item() hindcast_forecast = np.load("hindcast_vs_forecast.npy", allow_pickle=True).item() meteo_forecasts = np.load("meteo_forecast.npy", allow_pickle=True).item() users = pd.read_csv("users.csv", index_col=0, parse_dates=True, dayfirst=True) % pandas dataframe for the GR4J_KGE model efficiency in terms of NSE for calibration and hindcast periods calibration_hindcast["GR4J_KGE"]["NSE"] % pandas dataframe for the ensemble mean efficiency in terms of NSE for hindcast and forecast periods hindcast_forecast["ENS"]["NSE"] % pandas dataframe for correlation coefficients between ICON and ERA5 for precipitation forecasts meteo_forecasts["P"]["Correlation"] % available keys of Python dictionaries could be checked as follows calibration_hindcast.keys()</code></pre> <p> </p>
Uncovering structural ensembles from single particle cryo-EM data using cryoDRGN | Software, datasets, and results
<p>Software, datasets, and results referenced in "Uncovering structural ensembles from single particle cryo-EM data using cryoDRGN"</p>
Fig. 2. Tree resulting from implied weighting analysis using K in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 2. Tree resulting from implied weighting analysis using K = 12 with characters unambiguously optimized. Symmetric resampling values over 51 are represented for trees obtained with K = 3, K = 6, K = 9, and K = 12 analyses. Characters and character states are described in pp. 11–22. Specimens not to scale.
Fig. 1. Strict consensus tree resulting from equal weighting analysis. Jackknife values over 51 in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 1. Strict consensus tree resulting from equal weighting analysis. Jackknife values over 51 are reported for tree one of eight.
Availability of results of interventional trials assessing colorectal cancer over the past seven years
<p>Dataset used for our work "Availability of results of interventional trials assessing colorectal cancer over the past seven years."</p> <p>Part of the dataset was extracted from the AACT database (Clinical Trials Transformation Initiative) and part of the dataset was extracted by the authors.</p> <p>The list of trials are ordered by NTC number (from ClinicalTrials.gov).</p> <p> </p>
Availability of results of trials studying pancreatic adenocarcinoma over the past ten years.
<p>Dataset underlying our work "Availability of results of trials studying pancreatic adenocarcinoma over the past ten years".</p> <p>Data has been extracted either through AACT (Clinical Trials Transformation Initiative) or by the authors</p> <p>Trials are listed and organized by their NCT number (ClinicalTrials.gov)</p>
mDRONES4rivers-project: Portfolios of classification results, UAV and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. </p> <p>Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. </p> <p>In this dataset, the following portfolios of classifications, UAS and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River are available for download:</p> <p>• Multispectral orthophotos produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: MS_ORTHO)</p> <p>• RGB-orthophotos and digital surface models produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PH_SR_ORTHO_DSM)</p> <p>• Multispectral orthophotos and Digital Surface Models produced with the aid of a gyrocopter (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PANX_ORTHO_DSM)</p> <p>• Classification results based on UAV- and a gyrocopter data (PDF, Detailed description of processing procedure for different classification levels; abbreviation: CLASSIF_PROD)</p> <p>• German translated version of all above mentioned product portfolios (PDF, abbreviation: product_portfolio_collection_ger)</p>
mDRONES4rivers-project: Classification results based on UAV data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download:<br> • Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units)<br> • Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands )<br> • Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types)<br> • associated reports (PDF; statistical and additional information on the classifiaction results and workflow)<br> The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided: <br> • Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands, 1x for classification of substrate types)<br> • Color Coding table for the visualization of the classifiaction units (.xlsx)</p>
E3SM simulation results and associated python analysis scripts
<p>This archive contains E3SM Land Model simulation results associated with the <em>Journal of Advances in Modeling Earth Systems (JAMES)</em><em> </em>article titled "More Realistic Intermediate Depth Dry Firn Densification in the Energy Exascale Earth System Model (E3SM)," by Adam M. Schneider, Charles S. Zender, and Stephen F. Price. Also included in the archive are python scripts used to analyze associated data and an offline, statistical firn model.</p>
Enhanced Protein Isoform Characterization Through Long-Read Proteogenomics - Workflow Results
<pre> </pre> <p>The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.</p> <p>Companion Repositories:</p> <ol> <li><a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow GitHub Repository Release</a></li> <li><a href="https://doi.org/10.5281/zenodo.5920847">Long-Read-Proteogenomics Analysis GitHub Repository Release</a></li> </ol> <p>Companion Datasets</p> <ol> <li><a href="https://zenodo.org/deposit/5703754">Long-Read-Proteogenomics Workflow Sample and Reference Data</a></li> <li><a href="https://doi.org/10.5281/zenodo.5234651">TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions</a></li> </ol> <p>This Repository contains the complete output from the execution of the <a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow</a>, using the input from <a href="https://zenodo.org/deposit/5703754">Jurkat Samples and Reference Data</a>. </p> <p>The file <em>jurkat.flnc.bam </em>was 6.5 GB had to be split into 13 separate files and for use should be rejoined -- here are the steps that were used to split the file up. </p> <p>1. Convert <em>jurkat.flnc.bam</em> (binary format) to sam file (text format) without header: <em>samtools view jurkat.flnc.bam > jurkat.flnc.sam</em></p> <p>2. Capture the header: <em>samtools view -H jurkat.flnc.bam > jurkat.flnc.header.sam</em></p> <p>3. Split <em>jurkat.flnc.sam</em> into smaller files (aim to get final size under 2GB): <em>split -l 400000 jurkat.flnc.sam jurkat.flnc.chunk.</em></p> <p>4. Convert each of these files back to bam for uploading: <em>samtools view -b jurkat.flnc.chunk.a* -o jurkat.flnc.chunk.a*.bam (*=a,b,c,d,e,f,g,h,i,j,k,l,m)</em></p> <p>After downloading, reverse this process including using the header file which is found in the LRPG-Manuscript-Results-results-results-jurkat-isoseq3-companion-files.tar.gz file></p> <p>1. Convert the bam files back to sam files: <em>samtools view jurkat.flnc.chunk.a*.bam > jurkat.flnc.chunk.a*.sam (*=a,b,c,d,e,f,g,h,i,j,k,l,m)</em></p> <p>2. Combine the header together with the sam files: <em>cat jurkat.flnc.chunk.a*sam > jurkcat.flnc.sam (</em>verified the same number of lines of the sam files is identical to the number of lines of the original without header: 4,956,761. Header file is 13 lines.</p> <p>3. Convert to bam files if desired: <em>samtools view -b jurkat.flnc.sam -o jurkat.flnc.bam</em></p> <p>4. Rehead with the header file: <em>samtools reheader -P -i jurkat.flnc.header.sam jurkat.flnc.bam</em></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.