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44 results for “Colocalization”
Source Data and Scripts - MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy
<p>Experimental and simulated STED data and scripts associated with Naas et al. "<em>MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy.</em>" <em>bioRxiv</em> (2024): 2024-02. </p> <p>The MultiMatch Python package and further illustrative examples are available on GitHub repository <a href="https://github.com/gnies/multi_match">https://github.com/gnies/multi_match</a>.</p>
Satellite-based measurements of brightness temperatures (AMSR2 sensor) colocated to MOSAiC ground measurements
<p>The file contains measurements of brightness temperatures of satellite overpasses of the research vessel Polarstern during the MOSAiC expedition from October 26, 2019 - May 26, 2020 as well as co-located measurements of different parameters. For every overpass of Polarstern, the satellite measurement closest to the hourly position of Polarstern is taken.</p> <p>The satellite sensor is AMSR2 (six frequencies between 6.9 and 89 GHz and both polarizations) and we use the Level 1R (<em>Madea et al., 2016)</em> product available at JAXA <a href="https://gportal.jaxa.jp/gpr/">https://gportal.jaxa.jp/gpr/</a></p> <p>The co-located parameters are liquid water path, total water vapor, sea ice concentration, multi-year ice fraction, snow depth, snow-air interface temperature, snow-ice interface temperature, wind speed and sea surface temperature. In addition to the co-located parameters as ground truth, the dataset also contains their “uncertainties” given as temporal and/or spatial variability.</p> <p>Note: The dataset contains <strong>only</strong> satellite overpasses where co-located data is available.</p> <p>More information on the parameters are found below and they are described in more detail in <em>Rückert et al., 2023</em><em> </em>and the references given therein.</p> <ul> <li> <p><strong>scantime</strong>: time of satellite observation as included in the satellite data from JAXA</p> </li> <li> <p><strong>lon</strong>: longitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA</p> </li> <li> <p><strong>lat</strong>: latitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA</p> </li> <li> <p><strong>distance</strong>: distance to the hourly Polarstern position</p> </li> <li> <p><strong>TB6.9V, TB6.9H, TB10.7V, TB10.7H, TB18.7V, TB18.7H, TB23.8V, </strong><strong>T</strong><strong>B23.8H, TB36.5V, TB36.5H, TB89V, TB89H</strong>: Brightness temperatures (TB) measured by AMSRE2, the name includes the frequency in GHz and the polarization (either H for horizontal or V for vertical polarization), e.g, TB6.9V is the brightness temperature at 6.9 GHz and vertical polarization</p> </li> <li> <p><strong>LWP</strong>: liquid water path in kg/m² measured by a radiometer onboard the ship (<em>Walbröl et al., 2022</em>), averaged within +/- 10 minutes of the satellite observations</p> </li> <li> <p><strong>sigma_LWP</strong>: temporal variability of liquid water path (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>TWV</strong>: total water vapor (integrated water vapor) in kg/m² measured by a radiometer onboard the ship (<em>Walbröl et al. (2022)</em>), averaged within +/- 10 minutes satellite observation time</p> </li> <li> <p><strong>sigma_TWV</strong>: temporal variability of total water vapor (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>WSP</strong>: wind speed in m/s from the vessel’s meteorological observatory (<em>Schmithüsen et al., 2021</em>), averaged within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>sigma_WSP</strong>: temporal variability of wind speed (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>SST</strong>: sea water temperature in K from the vessel’s meteorological observatory (<em>Schmithüsen et al., 2021</em>), averaged within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>sigma_SST</strong>: temporal variability of sea water temperature (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>SND</strong>: snow depth in m obtained from the median of daily snow depth from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (<em>Lei et al., 2021</em><em>a</em>) in the proximity of Polarstern.</p> </li> <li> <p><strong>sigma_SND</strong>: spatial variability of snow depth (see previous point) given as standard deviation of all buoys available on that day.</p> </li> <li> <p><strong>Tsi:</strong> Snow-ice interface temperature in K obtained from the median of daily measurements from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (e.g. <em>Lei et al., 2021b</em>, for references of all buoys the reader is referred to the references given in <em>Rückert et al., 2023</em>) in the proximity of Polarstern.</p> </li> <li> <p><strong>sigma_Tsi:</strong> spatial variability of snow-ice interface temperature (see previous point) given as standard deviation of all buoys available on that day.</p> </li> <li> <p><strong>MYIF:</strong> multi-year ice fraction (from 0 to 1) based on classified TerraSAR-X scenes (<em>Guo et al., 2023</em>) in the proximity of Polarstern, interpolated to daily values.</p> </li> <li> <p><strong>sigma_MYIF:</strong> estimated (constant) uncertainty of multi-year ice fraction (see previous point).</p> </li> <li> <p><strong>SIC</strong>: sea ice concentration (from 0 to 1) based on classified TerraSAR-X scenes (<em>Guo et al., 2023</em>) in the proximity of Polarstern, interpolated to daily values.</p> </li> <li> <p><strong>sigma_SIC:</strong> estimated (constant) uncertainty of sea ice concentration (see previous point).</p> </li> <li> <p><strong>Tsa</strong>: Snow-air interface temperature in K based on infrared thermometer data (<em>Cox et al., 2023 a)-d)</em>) installed at four positions in the proximity of Polarstern, averaged within +/- 20 minutes of the satellite observation time.</p> </li> <li> <p><strong>sigma_Tsa:</strong> spatial variability of snow-ice interface temperature (see previous point), given as spatial (4 sites) and temporal (within +/- 20 minutes of the satellite observation time) standard deviation.</p> </li> </ul>
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>
Coloc summary results for "Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets"
<p>Text files containing coloc results between MS GWAS loci and CD4 T and B cell cis-eQTLs from DICE. These results accompany the paper "<strong>Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets"</strong></p>
Bayesian machine learning analysis of single-molecule fluorescence colocalization images
<p>Data files for the "Bayesian machine learning analysis of single-molecule fluorescence colocalization images" manuscript.</p>
DNA curtain assay for dCas12a/CS10B colocalization
<p>A self-assembling polypeptide (C-S<sub>10</sub>-B, labeled in green) binds to a DNA that has been pre-decorated with CRISPR-dCas12a (labeled in magenta) via multiple CRISPR-RNAs (crRNAs). C-S<sub>10</sub>-B binds and diffuses along DNA but cannot move beyond stably bound dCas12a. During self-assembly, large clusters of C-S<sub>10</sub>-B commonly form and colocalize with dCas12a.</p> <p>Bacteriophage λ DNA (λDNA) (NEB, N3011S) was mixed in T4 DNA ligase (NEB, M0202S) reaction buffer with biotinylated oligos complementary to λDNA cohesive ends, for 15 min at 70°C, followed by a cool down to 15 °C for over 2 h. Ligation took place overnight at room temperature. After T4 DNA ligase inactivation with 2 M NaCl, the biotinylated DNA was purified on a Sephacryl S-1000 size exclusion column (GE Healthcare).</p> <p>Using a lipid solution (1.954% DOPC, 0.04% DOPE-mPEG2k and 0.006% DOPE-biotin) in buffer (10 mM Tris-HCL pH 8, 100 mM NaCl), the flowcell was passivated at room temperature for 30 min. Next, BSA buffer (40 mM Tris-HCl pH 8, 2 mM MgCl<sub>2</sub>, 0.2 mg/mL BSA) was used to wash the flowcell, followed by incubation for 10 min. BSA buffer containing biotinylated DNA was injected into the flowcell, before washing out all the non-tethered DNA material. Imaging was done in BSA buffer supplemented with 100 mM NaCl, 5 mM MgCl<sub>2</sub>, 2 mM DTT.</p> <p>dCas12a was mixed with the crRNA pool at a 1:10 molar ratio in buffer (20 mM Tris-HCl pH 8.0, 100 mM NaCl, 5 mM MgCl<sub>2</sub>, 2% glycerol, 2 mM DTT) for 30 min at 37°C. The formed ribonucleoprotein particle complexes (10 nM) were injected into the flowcell to induce DNA binding for 30 min at room temperature. Monoclonal ANTI-FLAG ® BioM2-Biotin (Sigma-Aldrich, F9291) conjugated to quantum dots (Thermo, Q21361MP) were used to label dCas12a. C-S<sub>10</sub>-B was labeled at a single N-terminal cysteine with maleimide-Alexa-488.</p> <p>Imaging was carried out in an inverted Nikon Ti-E microscope at 60X magnification. Excitation of the sample was provided by a 488 nm laser. Emission light was split with a 638 nm dichroic beam splitter (Chroma) and registered by two EM-CCD cameras (Andor iXon DU897). Image processing was done in FIJI.</p>
Data from: Precise colocalization of sorghum’s major chilling tolerance locus with Tannin1 due to tight linkage drag rather than antagonistic pleiotropy
Open the record for dataset details and reuse information.
Extended data: Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx
<p>eQTL summary statistics and GWAS colocalization posterior probabilities from eQTL calling in an admixed subcohort of GTEx v8 with local and global ancestry adjustments. For the original, non-peer-reviewed preprint, see <a href="https://www.biorxiv.org/content/10.1101/836825v1">https://www.biorxiv.org/content/10.1101/836825v1</a>. </p> <p>For the related source code, see <a href="https://doi.org/10.5281/zenodo.3924788">https://doi.org/10.5281/zenodo.3924788</a> or <a href="https://github.com/nicolerg/gtex-admixture-la">https://github.com/nicolerg/gtex-admixture-la</a>. </p>
Figure 3: Rubicon - Rab7 binding colocalization [FINAL]
Open the record for dataset details and reuse information.
Determination of protein stoichiometries via dual-color colocalization with single molecule localization microscopy
<p>This entry contains the datasets for the manuscript titled as listed.</p>
[Dataset] ColocZStats: A Z-Stack Signal Colocalization Extension Tool for 3D Slicer
<p>To showcase the capabilities of ColocZStats discussed in its manuscript, we utilized confocal z-stack data collected during a study on the colocalization of DSS1 nuclear bodies with other nuclear body types. DSS1, also known as SEM1, is a gene that encodes a protein crucial for various cellular processes, most notably the function of the 26S proteasome complex in protein degradation. More specifically, a human ovarian clear cell carcinoma cell line (RMG-I) was seeded at 100,000 cells per well onto a coverslip in a 6-well plate and allowed to grow till 70\% confluency. On the day of staining, the cells were fixed with 4\% ice-cold paraformaldehyde for 15 mins at room temperature (RT) and blocked with 2\% Bovine Serum Albumin (BSA) in 0.1\% Phosphate Buffer Saline containing 0.1\% Triton-X (PBSTx) for 30 mins. Following fixation and blocking, the cells were incubated with a primary antibody cocktail containing anti-DSS1 (Catalogue\# NB100-1334, Novus Biologicals) and anti-PML (Catalogue\#sc-966, SCBT) for 1h at RT. After that, the cells were washed 3 times with 0.1\% PBSTx for 5 mins each and incubated with a secondary antibody cocktail containing anti-goat Alexa FluorTM 647 (for DSS1), anti-mouse Alexa FluorTM 488 (for PML) and Hoechst 33342 (Catalogue\# H3570, Invitrogen) for 1h at RT. Following this incubation, the cells were subjected to 3 washes, each lasting 5 mins, with 0.1\% PBSTx to ensure thorough cleansing. Subsequently, z-stack imaging was performed using a Zeiss LSM800 confocal microscope with Airyscan. The above process utilized three distinct dyes to specifically label DSS1 nuclear bodies (Red), promyelocytic leukemia (PML) nuclear bodies (Green), and the nucleus (Blue). The data file was named ‘Sample Image Stack.tif’.</p>
Colocalization and Interaction Study of Neuronal JNK3, JIP1, and -Arrestin2 Together with PSD95
<p>The dataset contains the raw images of the publication.</p>
Reverse Colocated Integrated Care Intervention Among Persons With Severe Persistent Mental Illness at US-Mexico Border
ClinicalTrials.gov study NCT03881657. IPD Sharing: NO. Countries: 0. Publications: 2.
GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura UV-2 1-Orbit L2 Support Swath 13x24km V3 (OMUFPITMET) at GES DISC
The GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura UV-2 1-Orbit L2 Support Swath 13x24km (OMUFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPITMET. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. To reduce the size of each orbital file, FP-IT data fields with a vertical dimension of 72 layers have been reduced to 47 layers in OMUFPITMET by combining layers above the troposphere. The OMUFPITMET files are in netCDF4 format which is compatible with most HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.
GEOS-5 FP-IT 3D Time-Averaged Single-Level Diagnostics Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km V4 (OMUFPSLV) at GES DISC
The GEOS-5 FP-IT 3D Time-Averaged Single-Level Diagnostics Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPSLV) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.The fields in this product include boundary layer top pressure, tropopause pressure, surface pressure, surface skin temperature, and vertical wind profiles at 10m. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPSLV. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.The OMUFPSLV files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.
GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura VIS 1-Orbit L2 Support Swath 13x24km V3 (OMVFPITMET) at GES DISC
The GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura VIS 1-Orbit L2 Support Swath 13x24km (OMVFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI VIS swath. The fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial. The OMI team also provides a corresponding product for the OMI UV-2 swath, OMUFPITMET. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI VIS spatial resolution is 13km x 24km at nadir. To reduce the size of each orbital file, FP-IT data fields with a vertical dimension of 72 layers have been reduced to 47 layers in OMVFPITMET by combining layers above the troposphere. The OMVFPITMET files are in netCDF4 format which is compatible with most HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.
GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km V4 (OMUFPMET) at GES DISC
The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.The OMUFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.
Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km V4 (OMUANC) at GES DISC
The Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI VIS swath, OMVANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.The OMUANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.
Primary Ancillary Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km V4 (OMVANC) at GES DISC
The Primary Ancillary Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.The OMVANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.
GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km V4 (OMVFPMET) at GES DISC
The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.The OMVFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.
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.