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Dataset results
6,410 results for “results”
PLD results with Archives of the Old University of Leuven: 22
<p>Combination of screenshots with 'color' and 'sketch1' shader of Archives of the Old University of Leuven: 22 (KU Leuven University Archives), i.e. the front of the pendant seal of Emperor Charles V, rendered in PLDviewer 7.0.05.</p>
bin3C - Cluster report and CheckM result for bin3C solution of the real human gut microbiome
<p>Supplementary data table S3 from the manuscript</p> <p>bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)</p> <p>A real human gut microbiome was deconvoluted using bin3C. Subsequently, bin3C produced a report detailing per-cluster statistics for the entire solution. The largest 296 clusters were then analyzed with CheckM and joined to the report.</p>
Simulated Neutral Landscape Models and Scaling Results for Testing of Multi-Dimensional Grid-Point Scaling Algorithm
<p>The data package contains (1) simulations of neutral landscape models of categorical data for benchmark testing of scaling algorithms, and (2) scaling results for testing consistency and sensitivity of the newly developed Multi-Dimensional Grid-Point (MDGP) scaling algorithm.</p> <p>Neutral landscapes were generated using the "nlmpy" python module. The MDGP scaling algorithm and the test framework were implemented in R (https://github.com/gannd/landscapeScaling). </p>
bin3C - CheckM result for bin3C solution of the simulated community at full depth
<p>Supplementary data table S2 from the manuscript</p> <p><strong>bin3C :</strong><strong> Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)</strong></p> <p>CheckM validation result for the simulated community at full shotgun and Hi-C depth of coverage.</p>
QT-IGC Meta-Analysis Results
<p>Meta-analysis results from the QT-IGC meta-analysis of QT interval duration. The manuscript detailing the generation and analysis of these results can be found here (https://www.nature.com/articles/ng.3014, Nature Genetics) and here (<a href="https://paperpile.com/shared/JEVaXW">https://paperpile.com/shared/JEVaXW</a>).</p> <p>Column names:</p> <ol> <li>SNP - SNP identifier (note that these will be on a much older dbSNP build!)</li> <li>CHR - Chromosome</li> <li>POS - Position, hg18</li> <li>A1 - minor allele in HapMap 2</li> <li>A2 - other allele in HapMap 2</li> <li>HAPMAP_A1_FREQ - frequency of A1 in HapMap</li> <li>CODED_ALLELE - the coded allele in the meta. All stats refer to this allele.</li> <li>NONCODED_ALLELE - other allele</li> <li>CODED_ALLELE_FREQ - frequency of the coded allele</li> <li>N_EFF - effective sample size</li> <li>Z_SQRTN - Z-score of the SNP in sample size meta-analysis</li> <li>P_SQRTN - P-value of the SNP in sample size meta-analysis</li> <li>BETA_FIXED - Beta of coded allele in fixed effects meta-analysis</li> <li>SE_FIXED - standard error of coded allele in fixed effects meta-analysis</li> <li>Z_FIXED - z-score of coded allele in fixed effects meta-analysis</li> <li>P_FIXED - p-value in fixed effects meta-analysis</li> <li>BETA_LOWER_FIXED - lower estimate of the beta, fixed effects</li> <li>BETA_UPPER_FIXED - upper estimate of the beta, fixed effects</li> <li>BETA_RANDOM - beta of coded allele in random effects meta-analysis</li> <li>SE_RANDOM - standard error of the coded allele in random effects meta-analysis</li> <li>Z_RANDOM - z-score from random effects meta-analysis</li> <li>P_RANDOM - p-value from random effects meta-analysis</li> <li>BETA_LOWER_RANDOM - lower estimate of the beta, random effects meta</li> <li>BETA_UPPER_RANDOM - upper estimate of the beta, random effects meta</li> <li>COCHRANS_Q - Cochran's test of heterogeneity, Q statistic</li> <li>DF - degrees of freedom for Cochran's Q test</li> <li>P_COCHRANS_Q - p-value of Cochran's Q test</li> <li>I_SQUARED - I-squared stat, test of heterogeneity</li> <li>TAU_SQUARED - Tau-squared, test of heterogeneity</li> <li>DIRECTIONS - directions of the beta in each of the cohorts used in meta-analysis. Cohorts are in the same order as in the params file provided in the related GitHub repo (https://github.com/saralpulit/QTIGCmeta)</li> <li>GENES_1000KB - Genes within 1000kb of the SNP, according to RefSeq</li> <li>NEAREST_GENE - Gene closest to the SNP, according to RefSeq</li> <li>FUNCTION - function of the SNP, if relevant</li> <li>CAVEAT - any relevant caveats, e.g., A/T or C/G SNPs with >40% minor allele frequency</li> </ol>
Model results for `Surface pond energy absorption across four Himalayan glaciers accounts for 1/8 of total catchment ice loss'
<p>Model setup (setup.mat) and outputs (allkeyres.mat, postproc.mat) for 5000 runs of Monte Carlo supraglacial pond energy-balance modelling in the Langtang catchment of Nepal. The full set of results are included for the median model run (run_..._n1645.zip).</p> <p>Also included are flux gate results for calculation of emergence velocity (fgates...zip).</p>
The Results of Applying Potential Predictability (PP)
<p>Author: Mao-Lin Shen (E-mail: maolin.shen@uib.no)</p> <p>Figures of the potential predictability based on signal-to-noise ratio.</p>
ERASER project: experimental results
<p>This package contains the experimental results obtained in the ERASER project. ERASER stands for Experimenting with Real Application-specific QoS Guarantees in a Large-scale RINA Demonstrator and is a project funded by the 3rd Open Call of the Fed4FIRE+ project (https://www.fed4fire.eu).</p> <p>Experiments have been conducted over a 5G metro/regional RINA network scenario spanning from the end-user terminal until the server where applications run in the datacentre has been defined. Over the last years, the clean-slate Recursive InterNetwork Architecture (RINA) (http://pouzinsociety.org) has attracted significant interest from the Future Internet research community, seen as a viable solution to solve the well-known limitations of the current TCP/IP-based Internet, e.g., in terms of routing scalability, application-specific Quality of Service (QoS) delivery or built-in security.</p> <p>The ERASER project targeted experimental evaluations of the QoS guarantees that RINA can deliver to heterogeneous applications. These experiments have been conducted over the Fed4FIRE+ Virtual Wall experimentation facility hosted and operated by imec in Ghent (Belgium). </p> <p>First experiments in ERASER have contemplated a 10-node small-scale RINA scenario, aiming to integrate all RINA-related open source implementations and tools needed for the entire experiments and validate their operation to meet the ERASER objectives. Two alternative QTA-Mux (i.e., the policy responsible for the RINA QoS support) deployment scenarios have been considered. This has been followed by a definition of the QoS classes (referred to as QoS Cubes in RINA) that QTA-Mux has to enforce, with their relative requirements in terms of losses and delay, as well as the characteristics of the synthetic application flows that will be injected in the network.</p> <p>Second experiments consider a large-scale metro/regional RINA network scenario composed of 37 nodes, eventually, where measurements of delay and packet losses experienced by synthetic traffic flows end-to-end have been obtained for the two considered QTA-Mux deployment scenarios and under different load conditions (links operating at 70, 80 and 90% of their effective capacity). </p> <p>The obtained results highlight the need for configuring QTA-Mux at both Service Provider Network and Metro-Regional network layers in order to provide effective QoS differentiation in terms of both delay and losses, particularly under high network congestion (link loads of 80% and 90%). Conversely, configuring QTA-Mux only at the Service Provider Network layer falls short of delivering adequate QoS differentiation, as best-effort packet treatment at the underlying Metro-Regional network layer deteriorates it, with all synthetic application flow types experiencing similar delay and packet loss outcomes end-to-end.</p>
Structural modelling results to accompany the paper "Uncommon mutational profiles of metastatic colorectal cancer detected during routine genotyping using next generation sequencing: an update"
<p>This repository contains the results of modelling missense mutants in KRAS, NRAS and BRAF observed in our study in the corresponding protein structures. Modelling was performed using FoldX.</p>
Data and results for manuscript: "Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurements"
<p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript titled:<br> <br> Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurement</p>
Data and results for manuscript "Small scale characterization of vine plant root water uptake via 3D electrical resistivity tomography and Mise-à-la-Masse method"
<p>This package contains measured raw ERT and MALM data used to generate the plots in the manuscript.</p> <p> </p>
TARGET BioBombe Results - Randomly Permuted Data
<p>BioBombe analysis applied to <strong>randomly permuted</strong> gene expression data from The Therapeutically Applicable Research To Generate Effective Treatments (TARGET) program. Method and results described in https://github.com/greenelab/BioBombe</p>
TARGET BioBombe Results
<p>BioBombe analysis applied to gene expression data from The Therapeutically Applicable Research To Generate Effective Treatments (TARGET) program. Method and results described in https://github.com/greenelab/BioBombe</p>
TCGA BioBombe Results - Randomly Permuted Data
<p>BioBombe analysis applied to <strong>randomly permuted</strong> gene expression data from The Cancer Genome Atlas (TCGA) PanCanAtlas. Method and results described in https://github.com/greenelab/BioBombe</p>
GTEX BioBombe Results
<p>BioBombe analysis applied to gene expression data from The Genotype-Tissue Expression (GTEx) project. Method and results described in https://github.com/greenelab/BioBombe</p>
VERTEX 2.0 Rat Neocortex Simulation Results
<p>The results of simulations of electric field stimulation in rat neocortex.</p> <p>Three stimulation paradigms have been used, a single pulse, a paired pulse, and theta burst stimulation.</p> <p>The VERTEX simulator code that generated these results is available at https://github.com/haeste/Vertex_2.</p>
Overview of available toxicity data for calystegines - results of the in silico genotoxicity predictions
<p>Results of the<em> in silico</em> genotoxicity predictions complementing the EFSA scientific report on calystegines: https://doi.org/10.2903/j.efsa.2019.5574</p>
Dataset of the publication "Cross-sectional study of prevalence of dementia, behavioural symptoms, mobility, pain and other health parameters in nursing homes in Austria and the Czech Republic: results from the DEMDATA project"
<p>Dataset of the publication Stefanie R. Auer , Margit Höfler, Elisabeth Linsmayer, Anna Beránková, Doris Prieschl, Paulina Ratajczak,<br> Michal Šteffl and Iva Holmerová (2018) "Cross-sectional study of prevalence of dementia, behavioural symptoms, mobility, pain and other health parameters in nursing homes in Austria and the Czech Republic: results from the DEMDATA project"</p>
Screw and hole detection output results dataset in an autonomous screwing operation
<p>This dataset contains a set of csv files containing the results of a screwing and hole detection developed for a screwing mobile robot. It also contains a set of images representing the inputs, the outputs and some relevant images in the hole detection algorithm.</p> <p>Thee different scenarios were built, and a total of 25 missions were executed in order to build the datasets presented in appendix 5.7. The first scenario (missions 1 to 15), represents a normal operation procedure, meaning, a screwing 6 hole screwing and 5 screws refilling. The second scenario (missions 16 to 20), represents an operation with an exception handling during the refill (the second, third and fourth screws are missing in the refill support). Lastly, the third scenario (missions 21 to 25) has another exception handling, this time on both hole and screw detections (the third, fourth and fifth holes are already screwed, and the screwdriver trigger is not pressed, so the screw is not tightened resulting in a screw detecting after the refilling phase.</p> <p>Also three different group of csv files can be found:</p> <ul> <li><em>hole_detection_mission_xx.csv</em><sup><em><a href="#sdfootnote1sym"><sup>1</sup></a></em></sup>: contains the hole detection result (detected or non detected) and the located hole coordinates (u,v), in pixels;</li> <li> <p><em>detect_screw_screwing_result_mission_xx</em>.csv: contains the screw detection result performed after a screwing to detect if the screw was screwed successfully. 1 means the screwing was ok (there is no screw on the screwdriver), 0 means the screwing was not ok (there was a screw on the screwdriver);</p> </li> <li> <p><em>detect_screw_refill_mission_xx</em>.csv: contains the screw detection result performed after a refill attempt, to check if there was any screw missing in the refill support. 0 means that no screw was missing on the refill support (there was a screw on the screwdriver), 1 means there was a screw missing on the support (there was no screw on the screwdriver).</p> </li> </ul> <p>The <em>mission_xx</em> suffix represents which mission file (rosbag) the mission was acquired in and, each csv file contains the timestamp in which it happens.</p> <p><a href="#sdfootnote1anc">1</a> xx represents the mission number</p>
Simulation results from HadGEM-UKCA perturbed parameter ensembles for Yoshioka et al. 2019 JAMES
<p>This dataset was created from perturbed parameter ensembles (PPEs) using HadGEM-UKCA atmospheric composition climate model and used in Yoshioka et al. (Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and their Radiative Forcing) submitted to The Journal of Advances in Modeling Earth Systems (JAMES). It contains the following data;</p> <p>N50_sfc_data.tar.gz contains simulated number concentrations of particles larger than 50 nm from one-at-a-time screening experiments used in Figure 1 of the paper. teaca-teacl are job IDs of different experiments where values of parameters RAIN_FRAC and CLOUD_ICE_THRESH were varied;<br> teaca: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.7<br> teacb: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.5<br> teacc: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.3<br> teacd: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.1<br> teace: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.7<br> teacf: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.5<br> teacg: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.3<br> teach: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.1<br> teaci: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.7<br> teacj: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.5<br> teack: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.3<br> teacl: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.1</p> <p>CCN0p2_Stations_for_figure6.xlsx contains cloud condensation nuclei concentration at 0.2% supersaturation calculated for the AER ensemble of simulations at station locations used to create Figure 6 of the paper.</p> <p>Mean_Variance_CCN0p2_L9_AER_2008ANN.nc and Mean_Variance_CCN0p2_L9_AER-ATM_2006ANN.nc are emulator means and variances of annual average cloud condensation nuclei concentration at 0.2% supersaturation at model level 9 (~650m) in AER and AER-ATM ensembles and used in Figures 7 and 8.</p> <p>Mean_Variance_AOD550_AER_2008ANN.nc and Mean_Variance_AOD550_AER-ATM_2006ANN.nc are emulator means and variances of annual average aerosol optical depth at 550 nm in AER and AER-ATM ensembles and used in Figures 7 and 8.</p> <p>Mean_Variance_RFnet_AER_2008ANN.nc and Mean_Variance_ERF_AER-ATM_2006ANN.nc are emulator means and variances of annual average aerosol radiative forcing in AER ensemble and aerosol effective radiative forcing in AER_ATM ensemble and used in Figures 7, 8 and 9.</p> <p>Mean_Variance_SeaSalt_Load_AER_2008ANN.nc and Mean_Variance_SeaSalt_Load_AER-ATM_2006ANN.nc are emulator means and variances of annual average column mass loading of sea salt aerosol in AER and AER-ATM ensembles and used in Figure 9.</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.