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Integrative modeling results of in-cell architecture of an actively transcribing-translating expressome
<p>Repository containing good-scoring models, input files, modeling and analysis protocols for the integrative modeling of the M. pneumoniae expressome from in-cell cryo-electron tomography and crosslinking mass spectrometry using IMP.</p>
Data Release: Aurorasaurus Science Products Inventory & Survey Results (2014-2019)
<p>Aurorasaurus is an eight-year-old project: the first and only citizen science initiative that tracks auroras around the world via reports on our website, mobile apps, and social media. (See Kosar et al., (2018). Aurorasaurus Real-Time Citizen Science Aurora Data (Version v1.0) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.1255196">http://doi.org/10.5281/zenodo.1255196</a>.)</p> <p>At the American Geophysical Union Fall Meeting 2019, MacDonald and Brandt (2019) gave a talk entitled "Towards developing appropriate and diverse metrics for citizen science – a case study." In the presentation, they examined our 2015 user survey, used more recent metrics to assess Aurorasaurus' current status, and began to lay a framework for their next round of evaluation. </p> <p>In order to frame the process, MacDonald and Brandt (2019) utilized the Science Products and Data Practices inventories created by Wiggins et al., (2018). The authors constructed lists of items and practices that should be present in citizen science projects. While the Science Products and Data Practices inventories are excellent for quantitative analysis, MacDonald and Brandt (2019) wanted to include qualitative analysis of past evaluation as well. To that end, they informally and retrospectively mapped Aurorasaurus' 2015 survey questions to the BASIK framework.</p> <p>Aurorasaurus is publishing this dataset as a case study for how the Wiggins et al. framework can be applied and combined with other systems.</p>
Results of the 1st International Competition on Software Testing (Test-Comp 2019)
<p>This file describes the contents of an archive of the<br> 1st Competition on Software Testing (Test-Comp 2019)<br> <a href="https://test-comp.sosy-lab.org/2019/">https://test-comp.sosy-lab.org/2019/</a></p> <p>The competition was run by Dirk Beyer, LMU Munich, Germany.<br> More information is available in the following article:<br> Dirk Beyer. First International Competition on Software Testing: Test-Comp 2019.<br> International Journal on Software Tools for Technology Transfer, 2020.</p> <p>Copyright (C) Dirk Beyer<br> <a href="https://www.sosy-lab.org/people/beyer/">https://www.sosy-lab.org/people/beyer/</a></p> <p>SPDX-License-Identifier: CC-BY-4.0<br> <a href="https://spdx.org/licenses/CC-BY-4.0.html">https://spdx.org/licenses/CC-BY-4.0.html</a></p> <p> </p> <p>To browse the competition results with a web browser, there are two options:<br> - start a local web server using<br> php -S localhost:8000<br> in order to view the data in this archive, or<br> - browse <a href="https://test-comp.sosy-lab.org/2019/results/">https://test-comp.sosy-lab.org/2019/results/</a><br> in order to view the data on the Test-Comp web page.</p> <p><br> Contents:</p> <p>index.html directs to the overview web page<br> LICENSE.txt specifies the license<br> README.txt this file<br> results-validated/ results of validation runs<br> results-verified/ results of verification runs and aggregated results</p> <p><br> The folder results-validated/ contains the results from validation runs:</p> <p>- *.xml.bz2 XML results from BenchExec<br> - *.logfiles.zip output from tools<br> - *.json.gz mapping from files names to SHA 256 hashes for the file content</p> <p><br> The folder results-verified/ contains the results from test-generation runs and aggregated results:</p> <p>index.html overview web page with rankings and score table<br> design.css HTML style definitions<br> *.xml.bz2 XML results from BenchExec<br> *.merged.xml.bz2 XML results from BenchExec, status adjusted according to the validation results<br> *.logfiles.zip output from tools<br> *.json.gz mapping from files names to SHA 256 hashes for the file content<br> *.xml.bz2.table.html HTML views on the detailed results data as generated by BenchExec's table generator<br> *.All.table.html HTML views of the full benchmark set (all categories) for each tool<br> META_*.table.html HTML views of the benchmark set for each meta category for each tool, and over all tools<br> <category>*.table.html HTML views of the benchmark set for each category over all tools<br> iZeCa0gaey.html HTML views per tool</p> <p>quantilePlot-* score-based quantile plots as visualization of the results<br> quantilePlotShow.gp example Gnuplot script to generate a plot<br> score* accumulated score results in various formats</p> <p><br> The hashes of the file names (in the files *.json.gz) are useful for<br> - validating the exact contents of a file and<br> - accessing the files from the witness store.</p> <p> </p> <p>Overview over archives from Test-Comp 2019 that are available at Zenodo:</p> <p><a href="https://doi.org/10.5281/zenodo.3856669">https://doi.org/10.5281/zenodo.3856669</a> Witness store (containing the generated test suites)<br> <a href="https://doi.org/10.5281/zenodo.3856661">https://doi.org/10.5281/zenodo.3856661</a> Results (XML result files, log files, file mappings, HTML tables)<br> <a href="https://doi.org/10.5281/zenodo.3856478">https://doi.org/10.5281/zenodo.3856478</a> Test tasks, version testcomp19<br> <a href="https://doi.org/10.5281/zenodo.2561835">https://doi.org/10.5281/zenodo.2561835</a> BenchExec, version 1.18</p> <p>All benchmarks were executed<br> for Test-Comp 2019, <a href="https://test-comp.sosy-lab.org/2019/">https://test-comp.sosy-lab.org/2019/</a><br> by Dirk Beyer, LMU Munich<br> based on the components<br> git@github.com:sosy-lab/sv-benchmarks.git testcomp19-0-g6a770a9c1<br> git@gitlab.com:sosy-lab/test-comp/bench-defs.git testcomp19-0-g1677027<br> git@github.com:sosy-lab/benchexec.git 1.18-0-gff72868</p> <p><br> Feel free to contact me in case of questions:<br> <a href="https://www.sosy-lab.org/people/beyer/">https://www.sosy-lab.org/people/beyer/</a></p> <p> </p>
Experiment Results: Evaluation of different parallelisation strategies with regard to the performance of parallel program execution
<p><br> Modern processors achieve an increase in performance by adding multiple cores. This means that during software development, care must be taken to parallelise the program sequences. To make predictions about the performance of a software design, there is Palladio. This is very accurate for single core processors.<br> In this bachelor thesis the influence of the chosen parallelization strategy on the performance of software is examined. Different hardware requirements are used for this purpose. They are used to generate individual work packages. These are executed by different parallelization strategies. The used parallelization strategies are: Java Threads, Java ParallelStreams, OpenMp and Akka Actor. Runtime and cache behavior are measured during each execution. In addition, the experiments are performed on different servers. The evaluation is done using acceleration curves and the Cache Miss Rate. The results show that the parallelization strategies differ only slightly in the work packages used.</p>
SPEC CPU 2017 Benchmark Suite Results for the HPE ProLiant DL385 Gen10 server
<p>Benchmark results for the SPEC CPU 2017 benchmark suite with the full power and temperature data. Data is collected on a state-of-the-art HPE ProLiant DL385 Gen10 server. Results contain two full runs of the SPEC CPU 2017 benchmark suite. One result set of all four benchmark suites with compiler optimizations matching official results and a second set with moderate, real-world, compiler settings.</p>
Performance results of LeanMD on the Joliot-Curie supercomputer using different load balancing algorithms
<p>This dataset contains the raw output files generated from the execution of LeanMD on the Joliot-Curie supercomputer (20 SKL Irene nodes), the scripts used to generate them, and the scripts used to parse these results for statistical analysis and plotting.</p> <p><strong>Software information</strong>:</p> <ul> <li>OS: Red Hat Enterprise Linux 7.6</li> <li>OpenMPI: version 2.0.4</li> <li>Compilers: C/C++ Intel 17.0.6.256</li> <li>Charm++ version: v6.9.0-rc3, build mpi-linux-x86_64 --with-production</li> <li>LeanMD source: <a href="https://charm.cs.illinois.edu/gerrit/gitweb?p=benchmarks/leanmd.git">https://charm.cs.illinois.edu/gerrit/gitweb?p=benchmarks/leanmd.git</a></li> <li>Additional load balancers source: <a href="https://github.com/viniciusmctf/packing-schemes/tree/packs_2019-v1">https://github.com/viniciusmctf/packing-schemes/tree/packs_2019-v1</a></li> <li>Charm++, LeanMD, and the load balancers were compiled with -O3</li> </ul> <p><strong>File information</strong>:</p> <p>The raw result files are organized in four directories (oct18, oct23, oct24, and oct24_2).<br> Each directory contains the results of one batched execution in the supercomputer.<br> Each batch is composed of 10 repetitions of a set of experiments.<br> Each set of experiments includes different load balancing algorithms and different problem sizes.<br> Each set is randomly ordered to avoid interference coming from a specific order of execution.<br> Each raw file contains the appended output of the application and its load balancer for all 10 repetitions.<br> The name of the files indicate the load balancer and size of the problem.<br> For instance, `PackStealLB.240` means that the application was run with PackStealLB and the problem size parameter is 240.</p> <p><strong>Problem sizes</strong>:</p> <ul> <li>80: 80×11×5 cells of dimensions 15×15×30</li> <li>120: 120×11×5 cells of dimensions 15×15×30</li> <li>160: 160×11×5 cells of dimensions 15×15×30</li> <li>240: 240×11×5 cells of dimensions 15×15×30</li> <li>320: 320×11×5 cells of dimensions 15×15×30</li> </ul> <p>Each execution of LeanMD ran for 301 iterations with load balancing calls at iterations 40, 140, and 240.</p> <p><strong>Raw output files</strong>:</p> <p>Each raw output file starts with a Charm++ header providing information on the execution.</p> <p>For each step of the application, its execution time in ms is provided.</p> <p>Load balancing calls usually provide information about their start time, end time, and duration. Depending on the load balancer, more information is provided.</p> <p>Each raw file contains ten executions of the application with a given load balancer and input size.</p> <p><strong>Generating plots</strong>:</p> <p>The analysis of the results can be done by running the Jupyter notebook named "Analysis of load balancing results.ipynb"</p>
Experimental Results of "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms"
<p>This repository contains the experimental results of the article "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms" by Duarte Campos, D., Lindsay, C., Roth, J., LeSavage, B., Seymour, A., Krajina, B., Ribeiro, R., Costa, P., Heilshorn, S., published in <em>Front. bioeng. biotechnol. </em><strong>8, 374</strong> (2020). https://doi.org/10.3389/fbioe.2020.00374</p>
bimmer results for 405 phenotypes from the UKBB
<p>Full data analysis results corresponding to the manuscript "Phenome-scale causal network discovery with bidirectional mediated Mendelian randomization"</p>
Global Storm Tide Modeling on Unstructured Meshes with ADCIRC v55 - Simulation Results and Model Setup
<p>Simulation results and model setup for the paper entitled "Global Storm Tide Modeling with ADCIRC v55: Unstructured Mesh Design and Performance". Simulations conducted using <a href="http://adcirc.org/">ADCIRC</a> (pre-release Version 55) on unstructured triangular meshes of the global Earth's ocean. </p> <p>Contains:</p> <ol> <li>Global Tide Harmonics: Simulated harmonic constituents of global astronomical tide on various mesh designs (*.53.nc).</li> <li>Local Storm Tide: ADCIRC model setup and simulation results from storm tides forced by Hurricane Katrina and Super Typhoon Haiyan on meshes with different local refinements (1.5 km, 500 m, 150 m) in the storm landfall region. <ul> <li>ADCIRC model input files (*.13, *.14, *.15, *.22, *.221, *.222, fort.rotm)</li> <li>Global maximum storm tide elevations (*_maxele.63.nc)</li> <li>Global 3-hourly storm tide elevation time series (*.63.nc)</li> <li>Storm tide elevation and velocity time series (20-min intervals) at selected stations (*.61.nc, *.62.nc)</li> </ul> </li> <li>Zipped archive of the ADCIRC code (adcirc-cg-GLOBAL.zip) used to produce the results archived here.</li> </ol> <p>See the README files for further details.</p>
MUSTEC Deliverable 9.1 Inputs and results. MUSTEC project.
<p>This dataset contains the main inputs needed to perform sustainability assessment of concentrated solar power (CSP) plants through an input-output analysis. Results are also included. Additional information is available in the document <em>Deliverable 9.1. Sustainability assessment of future CSP cooperation projects in Europe.</em></p> <p>For information on the project see: https://www.mustec.eu/</p>
Recombination detection program (RDP4) results for ICMV and SLCMV DNA-B
<p>A DNA-B multiple alignment (Zenodo record 3964977) was scanned with RDP4.100 (D. Martin et al. 2015). Recombination breakpoints for events with statistical support from at least 5 of 7 methods were adjusted per the manual (high-confidence accepted events). A CSV file with the results for the accepted events was exported and then saved a second time with Excel to normalize format (a comma in between every column).</p> <p>Note added 2020-09-07: AJ575821 is listed in the files as ICMV based on its assignment in the NCBI Taxonomy database (taxa 341701 and 31600) but it is better classified as SLCMV.</p> <p>These results will be described in a manuscript by Crespo-Bellido et al.</p>
UniDAM results for RAVE dr6
<p>Results for RAVE DR6 Madera data + Gaia DR2 + 2MASS + AllWISE</p> <p>For Gaia parallax an offset of -0.054 was applied.</p>
Computed results for Bayesian genome scale modelling temperature effect on yeast metabolism
<p>This repository contains the computed results for reproducing the figures in the manuscript "Li G., et al. Bayesian genome scale modelling identifies thermal determinants of yeast metabolism". The scripts can be found in Github (<a href="https://github.com/Gangl2016/BayesianGEM">https://github.com/Gangl2016/BayesianGEM</a>)</p>
Final results from McCoy et al., 'Global Observations of Submesoscale Coherent Vortices in the Ocean', submitted to Progress in Oceanography.
<p>Submesoscale coherent vortices (SCVs) are small-scale, subsurface eddies that are ubiquitous in the ocean. Observations suggest that they efficiently trap and transport water, nutrients, and other properties thousands of kilometers away from their formation regions. However, the weak sea-surface signature restricts SCV observations to mostly chance encounters with shipboard subsurface instrumentation. Thus, the global occurrence, properties, and generation frequency of SCVs remain poorly constrained. Here we present results from a new algorithm used to identify SCVs from Argo float data, applied to roughly 2 million profiles conducted globally from August 1997 to January 2020.</p> <p>After application of the SCV detection algorithm to the global Argo array, we identify 2501 casts piercing spicy-core SCVs (those with anomalously hot and salty water mass characteristics), and 1583 casts piercing minty-core SCVs (anomalously cold and fresh cores) over more than 20 years of available data. The Matlab file 'final_individual_scvs.mat' contains various data for each SCV identified.</p> <p>By grouping detections from consecutive Argo casts, we are also able to record 383 spicy-core SCV time-series and 169 minty-core SCV time-series. The Matlab file 'final_timeseries_scvs.mat' contains the data for these time-series. </p> <p>For a more detailed description of each Matlab file, please see 'README.rtf'. </p> <p>Reach out to Daniel McCoy (dmccoy801@gmail.com) or Daniele Bianchi (dbianchi@atmos.ucla.edu) for inquiries. </p>
Results for eQTL and sQTL meta-analysis and colocalization
<p>This dataset is part of the manuscript: "<em>Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies</em>", by Lopes KP, Snijders GJL, Humphrey J, et al.</p> <p> </p> <p>Description of files:</p> <p><em>COLOC_supp_table_all_results.tsv.gz - </em>Table with results from <strong>COLOC</strong><em> </em>(gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>disease - disease name (Alzheimer’s disease - AD, Bipolar Disorder - BPD, Multiple sclerosis - MS, Parkinson’s disease - PD, Schizohphrenia - SCZ)</li> <li>GWAS - GWAS study (IMSGC_2019, Jansen_2018, Kunkle_2019, Lambert_2013, Marioni_2018, Nalls23andMe_2019, Ripke_2014, Stahl_2019)</li> <li>locus - locus id according to each GWAS study</li> <li>GWAS_SNP - SNP reported in the GWAS study</li> <li>GWAS_P - <em>P</em>-value of the GWAS_SNP reported in the GWAS study</li> <li>GWAS_chr - chromosome of the GWAS_SNP (hg38)</li> <li>GWAS_pos - genomic position in the chromosome of the GWAS_SNP (hg38)</li> <li>QTL - id for the QTL study</li> <li>type - the type of QTL (eQTL or sQTL)</li> <li>QTL_SNP - SNP id from the QTL association</li> <li>QTL_P - <em>P</em>-value for the QTL association </li> <li>QTL_Beta - Slope (beta) for the QTL association</li> <li>QTL_MAF - minor allele frequency for the QTL_SNP in each QTL study. If not available, values were obtained from the European superpopulation of 1000 Genomes phase 3</li> <li>QTL_chr - chromosome for the QTL_SNP (hg38)</li> <li>QTL_pos - genomic position in the chromosome of the QTL_SNP (hg38)</li> <li>QTL_junction - splicing junction tested in the association (for sQTLs only)</li> <li>QTL_Gene - gene name for the QTL association</li> <li>QTL_Ensembl - Ensembl gene id for the QTL_gene (GENCODE v30)</li> <li>nsnps - number of SNPs tested </li> <li>PP.H0.abf - posterior probability for H0 (no causal variant)</li> <li>PP.H1.abf - posterior probability for H1 (causal variant for trait 1 only)</li> <li>PP.H2.abf - posterior probability for H2 (causal variant for trait 2 only)</li> <li>PP.H3.abf - posterior probability for H3 (two distinct causal variants)</li> <li>PP.H4.abf - posterior probability for H4 (one common causal variant)</li> <li>cell_type - cell type of the QTL study</li> <li>SNP_distance - the absolute distance between GWAS_SNP and QTL_SNP</li> <li>LD - linkage disequilibrium between the GWAS_SNP and the QTL_SNP according to 1000 genomes phase 3 European reference panel 3 (only for PP4>0.5, -Inf otherwise)</li> </ol> <p><em>mashR_lfsr_eQTL.txt.gz - </em><strong>mashR </strong>results for <strong>eQTL</strong><em> </em>(gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>ensembl_snp - Ensembl ID and the SNP prioritized by mashR (best SNP per gene)</li> <li>MFG_eur_expression_peer10.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the MFG region</li> <li>STG_eur_expression_peer10.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the STG region</li> <li>SVZ_eur_expression_peer5.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the SVZ region</li> <li>THA_eur_expression_peer10.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the THA region</li> </ol> <p><em>mashR_lfsr_eQTL.txt.gz - </em><strong>mashR </strong>results for <strong>sQTL</strong><em> </em>(gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>pos_ensembl_rsnp - splicing junction coordinates, Ensembl ID, and SNP ID prioritized by mashR (best SNP per junction)</li> <li>MFG_eur_rsplicing_peer5_gene.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the MFG region</li> <li>STG_eur_rsplicing_peer5_gene.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the STG region</li> <li>SVZ_eur_rsplicing_peer0_gene.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the SVZ region</li> <li>THA_eur_rsplicing_peer5_gene.cis_qtl_nominal - local false sign rate (lfsr) of the gene-SNP pair for the THA region</li> </ol> <p><em>out_mfg_stg_svz_tha.metasoft.gz - </em><strong>METASOFT</strong> results<strong> </strong>for <strong>eQTLs</strong> meta-analysis from MiGA four brain regions<em> </em>(gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>RSID - Id composed by gene Ensembl and SNP ID separated by an underscore for each gene-SNP pair tested in the eQTL study</li> <li>#STUDY - number of studies included in the meta-analysis</li> <li>PVALUE_FE - <em>P</em>-value of the fixed-effects model (FE) according to METASOFT</li> <li>BETA_FE - Estimated Beta under the fixed-effects model according to METASOFT</li> <li>STD_FE - Standard error of BETA_FE</li> <li>PVALUE_RE - <em>P</em>-value of the random effects model (RE) according to METASOFT</li> <li>BETA_RE - Estimated Beta under the random-effects model (RE) according to METASOFT</li> <li>STD_RE - Standard error of BETA_RE</li> <li>PVALUE_RE2 - <em>P</em>-value of the Han and Eskin's Random Effects model (RE2) according to METASOFT</li> <li>STAT1_RE2 - RE2 statistic mean effect part</li> <li>STAT2_RE2 - RE2 statistic heterogeneity part</li> <li>PVALUE_BE - BE P-value (“NA” in all row, -binary_effects option is not used)</li> <li>I_SQUARE - I-square heterogeneity statistic</li> <li>Q - Cochran's Q statistic</li> <li>PVALUE_Q - Cochran's Q statistic's <em>P</em>-value</li> <li>TAU_SQUARE - Tau-square heterogeneity estimator of DerSimonian-Laird</li> <li>PVALUES_OF_STUDIES(Tab_delimitered) - <em>P</em>-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA</li> <li>MVALUES_OF_STUDIES(Tab_delimitered) - M-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA</li> </ol> <p><em>out_miga_young_mynd_fairfax.metasoft.gz - </em><strong>METASOFT</strong> results<strong> </strong>for <strong>eQTL</strong> meta-analysis from MiGA four brain regions plus microglia eQTL from Young et al. (2019), and monocytes eQTL from Navarro et al. (2020) and Fairfax et al. (2014)<em> </em>(gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>RSID - Id composed by gene Ensembl and SNP ID separated by an underscore for each gene-SNP pair tested in the eQTL study</li> <li>#STUDY - number of studies included in the meta-analysis</li> <li>PVALUE_FE - <em>P</em>-value of the fixed-effects model (FE) according to METASOFT</li> <li>BETA_FE - Estimated Beta under the fixed-effects model according to METASOFT</li> <li>STD_FE - Standard error of BETA_FE</li> <li>PVALUE_RE - <em>P</em>-value of the random effects model (RE) according to METASOFT</li> <li>BETA_RE - Estimated Beta under the random-effects model (RE) according to METASOFT</li> <li>STD_RE - Standard error of BETA_RE</li> <li>PVALUE_RE2 - <em>P</em>-value of the Han and Eskin's Random Effects model (RE2) according to METASOFT</li> <li>STAT1_RE2 - RE2 statistic mean effect part</li> <li>STAT2_RE2 - RE2 statistic heterogeneity part</li> <li>PVALUE_BE - BE P-value (“NA” in all row, -binary_effects option is not used)</li> <li>I_SQUARE - I-square heterogeneity statistic</li> <li>Q - Cochran's Q statistic</li> <li>PVALUE_Q - Cochran's Q statistic's <em>P</em>-value</li> <li>TAU_SQUARE - Tau-square heterogeneity estimator of DerSimonian-Laird</li> <li>PVALUES_OF_STUDIES(Tab_delimitered) - <em>P</em>-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA, 5-Young et al., 6-Navarro et al., 7-Fairfax et al.</li> <li>MVALUES_OF_STUDIES(Tab_delimitered) - M-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA, 5-Young et al., 6-Navarro et al., 7-Fairfax et al.</li> </ol> <p><em>out_mfg_stg_svz_tha_sClusters.metasoft.gz - </em><strong>METASOFT</strong> results<strong> </strong>for <strong>sQTLs</strong> meta-analysis from MiGA four brain regions (gzip-compressed). Table columns are formatted as follows:</p> <ol> <li>RSID - Id composed by splicing junction coordinates, gene Ensembl ID, and SNP ID separated by underscores for each junction-SNP pair tested in the sQTL study (e.g. chr1_962047_962355_ENSG00000187961.14_1:11008:C:G)</li> <li>#STUDY - number of studies included in the meta-analysis</li> <li>PVALUE_FE - <em>P</em>-value of the fixed-effects model (FE) according to METASOFT</li> <li>BETA_FE - Estimated Beta under the fixed-effects model according to METASOFT</li> <li>STD_FE - Standard error of BETA_FE</li> <li>PVALUE_RE - <em>P</em>-value of the random effects model (RE) according to METASOFT</li> <li>BETA_RE - Estimated Beta under the random-effects model (RE) according to METASOFT</li> <li>STD_RE - Standard error of BETA_RE</li> <li>PVALUE_RE2 - <em>P</em>-value of the Han and Eskin's Random Effects model (RE2) according to METASOFT</li> <li>STAT1_RE2 - RE2 statistic mean effect part</li> <li>STAT2_RE2 - RE2 statistic heterogeneity part</li> <li>PVALUE_BE - BE P-value (“NA” in all row, -binary_effects option is not used)</li> <li>I_SQUARE - I-square heterogeneity statistic</li> <li>Q - Cochran's Q statistic</li> <li>PVALUE_Q - Cochran's Q statistic's <em>P</em>-value</li> <li>TAU_SQUARE - Tau-square heterogeneity estimator of DerSimonian-Laird</li> <li>PVALUES_OF_STUDIES(Tab_delimitered) - <em>P</em>-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA</li> <li>MVALUES_OF_STUDIES(Tab_delimitered) - M-values of each study in the respective order 1-MFG, 2-STG, 3-SVZ, 4-THA</li> </ol> <p><strong>NOTE:</strong> The effect sizes of eQTLs and sQTL are defined as the effect of the alternative allele (ALT) relative to the reference (REF) allele in the human genome reference (GRCh38). A file containing that information for all alleles tested is available at 10.5281/zenodo.4301005</p>
Simulation results: CryoGrid 3 set-up for ice-wedge polygons
<p>This dataset contains numerical simulation results obtained with the CryoGrid 3 permafrost model for a field site in northeast Siberia.</p> <p>The model code and settings which were used to create these results are deposited at <a href="http://doi.org/10.5281/zenodo.2601973">10.5281/zenodo.2601973</a>.</p> <p>A journal article which describes the simulation results scientifically has been published in the scientific journal <em>The Cryosphere</em> as <a href="http://doi.org/10.5194/tc-13-1089-2019">10.5194/tc-13-1089-2019</a>.</p> <p> </p> <p>The dataset contains two types of simulations described in the article</p> <p><em>VALIDATION_200710-201412_*</em>: These simulations of a 7-year period which been used to evaluate the model performance by comparison to field observations.</p> <p><em>LONGTERM_197910-204412_*</em>: These simulations of a 65-year period have been used to investigate the long-term dynamics of the model as described in the article.</p>
Alternate Results Dataset in Comparative Study of Four Scenarios Measuring Visual Pollution in Intramuros, Manila
<p>Alternate results dataset tabulated in all the methods and procedures done in the study 'Comparative Study of Four Scenarios Measuring Visual Pollution in Intramuros, Manila'.</p>
Bacterial and archaeal communities in the abyssal Clarion-Clipperton Zone: spatial synthesis results
<p>Datasets associated with a manuscript synthesizing previously published results and a new dataset on bacterial and archaeal community composition from the abyssal Clarion-Clipperton Zone, including communities from deep seawater, sediments, and polymetallic nodules.</p>
WRF-Chem simulation results of Aerosol Optical Depth and PM2.5 concentrations
<p>This is the data for the manuscript submitted to JGR-Atmosphere: Simulations and characteristics of extreme aerosol events over eastern North America. </p>
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite"
<p>Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.</p> <p>Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet. </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.