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2,152 results for “outputs”
Outputs from new methods for 3D+time image segmentation and tracking
<p>Segmentation and tracking of 3D+time images of artificially generates spheres. <br> The file named _20_frames_of_moving_spheres.avi is a 20-frame video of artificially generated spheres moving in time, the file <br> named _resulf_of_20_frames_of_moving_spheres.avi has the result of 4D segmentation, using our new segmentation methods, of the spheres (colored blue) moving in time, and the file _tracking_of_artificial_data_in_20_frames.mp4 has the tracking of these spheres. Additionally, the file named _one_moving_sphere.avi is also a 20-frame video of an artificially generated sphere moving in time, the file named _segmentation_result_one_moving_sphere.avi has the result of 4D segmentation of the sphere (colored blue) moving in time, and the file named _segmentation_result_one_moving_sphere_with_some_missing_spheres.avi has the result of 4D segmentation of the sphere (colored blue) moving in time when frames 5, 10, and 15 are missing in the file _one_moving_sphere.avi.</p>
MCMC output files for: Quantitative characterization of population-wide tissue- and metabolite-specific variability in perchloroethylene toxicokinetics in male mice
<p>Quantification of inter-individual variability is a continuing challenge in risk assessment, particularly for compounds with complex metabolism and multi-organ toxicity. Toxicokinetic variability for perchloroethylene (perc) was previously characterized across three mouse strains and in one mouse strain with various degrees of liver steatosis. To further characterize the role of genetic variability in toxicokinetics of perc, we applied Bayesian population physiologically-based pharmacokinetic (PBPK) modeling to the data on perc and metabolites in blood/plasma and tissues of male mice from 45 inbred strains from the Collaborative Cross (CC) mouse population. After identifying the most influential PBPK parameters based on global sensitivity analysis, we fit the model with a hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation. We found that the data from three commonly used strains were not representative of the full range of variability in perc and metabolite blood/plasma and tissue concentrations across the CC population. Using inter-strain variability as a surrogate for human inter-individual variability, we calculated dose-dependent, chemical-, and tissue-specific toxicokinetic variability factors (TKVFs) as candidate science-based replacements for the default uncertainty factor for human toxicokinetic variability of 10<sup>0.5</sup>. We found that TKVFs for glutathione conjugation metabolites of perc showed the greatest variability, often exceeding the default, whereas those for oxidative metabolites and perc itself were generally less than the default. Overall, we demonstrate how a combination of a population-based mouse model such as the CC with Bayesian population PBPK modeling can reduce uncertainty associated with toxicokinetic human variability by deriving the chemical-specific adjustment factors needed to increase accuracy and precision in quantitative risk assessment.</p>
HYCOM 10.2 hourly steric sea surface height outputs for Amazon Shelf domain 2016
<p>This data set contains HYCOM steric sea surface heights for year 2016 subdomain of .04 degree resolution global (non-assimilative) HYCOM. The HYCOM tidal forcing included 5 constituents (s2, n2, m2, o1, k1) with nodal corrections. The subdomain limits are: longitude 305-335E, latitude: -5-20N.</p>
Figure 1 from: Lyal CHC (2016) Digitising legacy zoological taxonomic literature: Processes, products and using the output. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 189–206. https://doi.org/10.3897/zookeys.550.9702
Figure 1 - Outline workflow to acquire, put into a suitable format, retrieve and utilize legacy literature.
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly RACMO fields, Greenland and Antarctica 1979-2014
<p>These model output are those presented in the manuscript that provides the first description of version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly RACMO output from 1979-2014. </p>
MarsWRF outputs
<p>MarsWRF outputs</p>
Model code and outputs of "Modeling the Dispersal of the San Francisco Bay Plume over the Northern and Central California Shelf"
<p>- "model_code.rar" contains the model source code.</p> <p>- "model_grid.nc" contains the grid file.</p> <p>- "model_outputs.nc" contains the model outputs.</p>
Model code and output data for Tang et al. 2022 (doi:10.3389/fenvs.2022.1013875)
<p>flex_extract.tar: The code used to prepare inputdata for flexpart and flexdust.</p> <p>FLEXDUST_default_20210301-20210331.nc: The output of default FLEXDUST.</p> <p>FLEXDUST_updated_20210311-20210320.nc: The output of updated FLEXDUST.</p> <p>flexpart_1h_default.nc: The output of forward FLEXPART simulation using default FLEXDUST.</p> <p>flexpart_1h_erode_topoTc_true_moreveg0.1_76FVThresh_noblock_KOK_AN05.nc: The output of forward FLEXPART simulation using updated FLEXDUST.</p>
E3SM output for evaluating the simulation of CONUS precipitation by storm type
<p>E3SMv1 output for evaluating the simulation of CONUS precipitation by storm type.</p>
Example input and output data for RegCFinder
<p>Example input and output for RegCFinder and example workflow</p>
ESM4 stratospheric aerosol output
<p>AOD and sulfate burden data.</p>
Part 1 of simulation output for WRF-Chem with enabling ARI and ACI
<p>Part 1 of simulation output for WRF-Chem with enabling aerosol-radiation interactions (ARI) and aerosol-cloud interactions (ACI) in eastern China during 2017.</p>
Empirical Models output - RECCAP2 Program
<p>This data set contains the Empirical models' output data provided under RECCAP2 Program</p>
The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019 - 2021: Low-resolution gridded outputs for 2019 - 2021
<p><strong>The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019 – 2021: Low-resolution gridded outputs for 2019 - 2021</strong></p> <p><strong>Roger Teoh, Zebediah Engberg, Marc Shapiro, Lynnette Dray and Marc E.J. Stettler</strong></p> <p>These files contain the low-resolution monthly sum of the global flight distance flown, fuel consumption, and various pollutants from 2019 to 2021. The data is provided in a 4D grid with spatiotemporal resolution of 0.5° (longitude) x 0.5° (latitude), at altitude intervals of 1000 feet, and at a monthly temporal resolution.</p> <p>The global air traffic activity in GAIA was derived from Spire Aviation data. Non-commercial use and research purposes only.</p> <p>For further details, see README.txt</p> <p><strong>Change log</strong></p> <p>- 6-June-2023: The missing file "2021-08-monthly.nc" has been added and can be found in "Zenodo.zip"</p>
model output
<p>model output-NOSIP case</p>
Plassembler v 1.1.0 Benchmarking Output
<p>This repository contains the Plassembler benchmarking v1.1.0 input FASTQ files and outputs.</p>
Intergenic RNAPII Atlas : output data
<p>This dataset represents the RNAPII (RNAP2) Atlas of potentially transcribed intergenic regions of the human genome by integrating 906 high quality human Chromatin-ImmunoPrecipitation sequencing (ChIP-seq) biosamples targeting the RNA Polymerase II, obtained from public data warehouses. </p> <p><strong>Github Code available here : </strong><a href="https://github.com/benoitballester/Pol2Atlas">https://github.com/benoitballester/Pol2Atlas</a> </p> <p><strong>The dataset consists of 5 zipped folders described below: </strong></p> <p><strong>./pol2_consensuses/:</strong><br> consensuses.bed:<br> Location of intergenic RNAP2 consensuses in bed format for the hg38 assembly. <br> First three columns are genomic locations, 4th column is consensus ID, <br> 5th column is the number of datasets with RNAP2 observed at this RNAP2 consensus,<br> 6th column is strand (not used), 7-8th columns is consensus centroid.<br> consensusesHg19.bed:<br> Location of intergenic RNAP2 consensuses in hg19 assembly. ~1000 are missing due to liftover.<br> Consensus ID is matching with the hg38 one.<br> matrix.mtx:<br> RNAP2 occupancy consensus-dataset binary matrix in sparse matrix market format.<br> Corresponding row annotation are RNAP2 consensuses.<br> Corresponding column annotation are datasets stored in dataset.txt.<br> datasets.txt:<br> See matrix.mtx<br> clusterConsensuses_Labels.txt:<br> Assigned cluster for each RNAP2 consensus.<br> intersectIntergPol2.tsv:<br> RNAP2 consensuses with cluster ID and intersections with reference databases.<br> cluster_bed/:<br> consensuses.bed splitted per cluster.<br> saf_files/:<br> Files typically used for read counting with featureCounts. Suffixes:<br> _500 : RNAP2 consensuses standardized to 1kbp.<br> _all : All RNAP2 consensuses including genic.<br> Hg19 : Intergenic RNAP2 Lifted to Hg19.</p> <p> </p> <p><strong>./rnap2_all_peaks/:</strong><br> all_peaks.bed.gz:<br> Concatenated bed file with all POLR2A peaks from all experiments, genome wide, for the hg38 assembly. <br> Peaks are filtered with a MACS2 qvalue > 1e-5, datasets with less than 100 peaks in intergenic regions are removed.<br> First three columns are genomic locations, 4th column contains sample of origin of the peak, 5th column is the<br> MACS2 q-value, 6th column is dna strand (not used), 7-8th are peak "summit". 9th column contains an r,g,b value<br> corresponding to the biotype of origin (Blood / Immune, Brain, Embryo...) for easy visualization in a genome browser.<br> Legend is available in legend.png. Conversion table between rgb values and biotype in palette.csv.<br> Note that singletons are removed when creating consensus peaks.<br> all_peaks_interg.bed.gz:<br> Same as above, but for intergenic regions only (excluding 1kb before TSS and 1kb after TES).</p> <p> </p> <p><strong>./count_tables_rnaseq/:</strong><br> ENCODE/:<br> counts.mtx.gz:<br> Count table in sparse matrix market format. Row corresponds to samples, columns to Pol II probes (Pol2_500.saf).<br> samples.csv.gz:<br> Matching row annotation for count matrix.<br> encode_total_rnaseq_annot.tsv.gz:<br> Sample annotation (not ordered!).<br> GTEx/:<br> counts.mtx.gz:<br> Count table in sparse matrix market format. Row corresponds to samples, columns to Pol II probes (Pol2_500.saf).<br> samples.csv.gz:<br> Matching row annotation for count matrix.<br> sample_annot.tsv.gz:<br> Sample annotation (not ordered!).<br> TCGA/:<br> counts.mtx.gz:<br> Count table in sparse matrix market format. Row corresponds to samples, columns to Pol II probes (Pol2_500.saf).<br> samples.csv.gz:<br> Matching row annotation for count matrix.<br> annotation_table.tsv.gz:<br> Sample annotation (not ordered!).<br> </p> <p><br> <strong>./cancer_markers/:</strong><br> bed/:<br> DE_Tumor_vs_Normal/:<br> TCGA-*/:<br> allWithStats.bed:<br> FDR, mean difference in pearson residuals and log2(FC) for each RNAP2 probe. Warning: probes are prefiltered to have > 1 read in 3 samples, make sure to use row index to match <br> with RNAP2 consensuses.<br> allDE.bed:<br> All DE (cancer vs normal) probes in bed format for this cancer. <br> 5th column has been replaced by enrichment p-value.<br> DE_downreg.bed:<br> Downregulated (cancer vs normal) probes in bed format for this cancer. <br> 5th column has been replaced by enrichment p-value.<br> DE_upreg.bed:<br> Upregulated (cancer vs normal) probes in bed format for this cancer. <br> 5th column has been replaced by enrichment p-value.<br> classifier_TCGA-*:<br> Performance of a machine learning tumor-normal tissues classifier using Pol II probes as input.<br> globally_DE.bed:<br> Probes DE in 7+ cancers (FPR permutation threshold). Last column indicates the number of cancers this probe is DE in.<br> globally_Down regulated.bed:<br> Probes DE in 6+ cancers (FPR permutation threshold). Last column indicates the number of cancers this probe is Down regulated in.<br> globally_Up regulated.bed:<br> Probes DE in 5+ cancers (FPR permutation threshold). Last column indicates the number of cancers this probe is Up regulated in.<br> subtypes/:<br> BRCA/:<br> allWithStats_BRCA.*.bed:<br> FDR, mean difference in pearson residuals and log2(FC) for each RNAP2 probe for DE test of sample from this subtype against normal samples. <br> Warning: probes are prefiltered to have > 1 read in 3 samples, make sure to use row index to match <br> with RNAP2 consensuses.<br> bed_BRCA.*.bed:<br> All DE (subtype vs normal) probes in bed format for this cancer. <br> bed_uniqueDE_BRCA.*.bed:<br> All DE (subtype vs normal) probes in bed format for this cancer and not DE in any other subtype. </p> <p> TCGA_survival/:<br> TCGA-*/:<br> prognostic.bed:<br> All probes associated with survival for this cancer. <br> 5th column has been replaced by p-value.<br> stats.csv:<br> Cox linear model statistics for each Pol II probe. <br> Warning: probes are prefiltered to have > 1 read in 3 samples, make sure to use row index to match <br> with RNAP2 consensuses.<br> globally_prognostic.bed:<br> Probes associated with survival in 5+ cancers (FPR permutation threshold). 5th column has been replaced with <br> the number of cancers this probe is associated with survival in.<br> tabular/:<br> Same as above but stored in a tabular binary format for DE and survival.</p> <p> </p> <p><strong>./metacluster_markers/:</strong><br> bed/:<br> allPol2_datasetCount:<br> For each tissue, all Pol II consensuses, with 5th column indicating the number of<br> datasets (RNAP2, GTEx, ENCODE, TCGA tumour and normal) in which the RNAP2 consensus is<br> considered a marker.<br> robust_2_datasets_per_tissue:<br> For each tissue, Pol II consensuses considered marker in 2+ datasets out of 5 <br> (RNAP2, GTEx, ENCODE, TCGA tumour and normal).<br> tabular/:<br> Each Pol II consensus with marker information stored in a binary format.<br> </p>
Model output after parameter optimization for NEMO4.0-SI3
<p>Using the default configuration of NEMO4.0-SI3 (version 4.0.7 revision 15731), the 2003-2009 seasonal mean Antarctic sea ice concentration budget was obtained after adjusting the following three parameters: rn_pstar=3.298e+04, rn_cio=0.006094, Cd_ice=0.0009563.</p> <p>Both files are in npz format and can be read using python. ''Benchmark_budget_1to12_NEMO4-SI3-optim.npz'' file contains 4 fields: 'dadt_seasonal', 'adv_seasonal', 'div_seasonal' and 'res_seasonal', each of size [145, 145, 4], corresponding to lon, lat and season. The meaning of each field can be found in Nie et al. (2023), noting that here is the seasonal average for the years 2003-2009.</p> <p>The ''Klonlat.npz'' file contains 3 fields, 'Klon' (longitude), 'Klat' (latitude) and 'K_A' (grid area), all of size [145, 145].</p>
Paladino et al. 2024 raw PCM output Set # 3
<p>Set #3 of PCM output from Paladino et al., (2024).</p>
JULES-RED simulation outputs and evaluation at Harwood Forest, UK
<p>Repository contains JULES-RED simulation output and processed observations at a mature Sitka spruce plantation (Harwood Forest, Northumberland, UK). Additionally, we have included python files used to process the data, these files may have incomplete comments/documentation. To fully compile please see the JULES rose suite (u-cn548) at the Met Office code repository: code.metoffice.gov.uk. Access can be applied for at <a href="http://jules-lsm.github.io/access_req/JULES_access.html">http://jules-lsm.github.io/access_req/JULES_access.html</a>.</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.