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30 results for “dependency resolution”
Images and Crater Data for "Crater Detection Dependence on Resolution, Incidence Angle, Emission Angle, and Phase Angle"
<p>Images are from the LROC-NAC and have been cartographically controlled to each other and the <em>Apollo 11</em> landing site as described in Supporting Information Text S1. Images are cropped so that the cover ±0.025° from the landing site when coordinates have three significant figures. The images are provided as .png files with .pgw ("PNG World"). The images are at 1 mpp (contain a "1mpp" string in the file name), 2.5 mpp (contain a 2p5mpp" string in the file name), and 6.25 mpp (contain a "6p25mpp" string in the file name). Additionally, the three <em>e</em> > 10° images are included as unprojected .cub files; these files omit the "l2" (map projected, Level-2 data) string and any "l4" (mosaicked) string from the file name, but they instead include "trim" to indicate the image has been trimmed from its full extent to the area of interest.</p> <p>Crater data are formatted as .csv (comma-separated values) files and are one file per image per researcher. File names have the exact same name as the image file that was used to map crater data, with two differences: The initials of the author are appended, and the file extension is "csv" instead of "png". The files do not have headers, but they are formatted such that the first column is latitude (decimal degrees north), second column is longitude (decimal degrees east), and diameter (kilometers). Crater data are entirely in one .zip file.</p>
Video Supplement for "Understanding the dependence of mean precipitation on convective treatment and horizontal resolution in tropical aquachannel experiments"
<p>A time series of snapshots of precipitable water (shading) and rainfall rate (contour) in the tropical aquachannel simulations. </p>
Data for GRL manuscript "Vertical Coordinate and Resolution Dependence of the Second Moment Turbulent Closure Models"
<p>The zipped directory contains data used for analysis and figures in the GRL manuscript "Vertical Coordinate and Resolution Dependence of the Second Moment Turbulent Closure Models"</p> <p>### OSP observations:</p> <p>Air Temperature: airt50n145w_hr.ascii </p> <p>Ocean Temperature: t50n145w_hr.ascii </p> <p>Ocean Salinity: s50n145w_hr.ascii</p> <p>Total Heat Flux: heatflux_papa.dat</p> <p>10m Wind Speed: wspd_papa.dat</p> <p>u and v components of Stokes drift velocity hourly time series given on vertical grid vertical_grid_128: uvstk_papa.dat</p> <p>### NCOM simulation results</p> <p>out_Model_LayerGrid.dat</p> <p>Model:</p> <p> h15 - use harcourt (2015) </p> <p> kc04 - use Kantha & Clayson (2004)</p> <p> myl2p5 - use Mellor & Yamda (1982) level 2.5</p> <p>Layer:</p> <p> 030, 040, 050, 080, 100</p> <p>Grid:</p> <p> s - uniform stretched grid</p> <p> u - uniform grid</p> <p> y - mixed layer enhanced grid</p> <p>The following variables are provied in all out_*.dat files as given in their headers</p> <p> depth (m) tke (cm2/s2) dis (cm2/s3) tl (cm) zkm (cm2/s) zkh (cm2/s) T (C) S (psu) pd (kg/m3) z-mid (m)</p> <p>### LES output</p> <p>Eddy viscosity computed using LES model</p> <p> LES simulation with all foring - Km_les_AllForcing.dat</p> <p> LES simulation without Stokes drift - Km_les_NoStk.dat</p> <p> LES simulation without Heat Flux - Km_les_NoHF.dat</p>
Data and scripts (2) for Storkey et al, "Resolution dependence of interlinked Southern Ocean biases in global coupled HadGEM3 models", GMD (2024)
<p>================================================================<br> Data and scripts for producing plots from Storkey et al (2024):<br> "Resolution dependence of interlinked Southern Ocean biases in<br> global coupled HadGEM3 models"<br> ================================================================</p> <p>The plots in the paper consist of 10-year mean fields from the third <br>decade of the spin up and timeseries of scalar quantities for the first<br>150 years of the spin up. The data to produce these plots are stored<br>in the MEANS_YEARS_21-30 and TIMESERIES_DATA directories respectively.</p> <p>Note that due to the size limit on records on Zenodo, the 10-year mean <br>output from the N216-ORCA12 integration has been stored as a separate<br>record.</p> <p>Scripts to produce the plots are in SCRIPT, with section definitions<br>in SECTIONS. Bespoke plotting scripts are included in SCRIPT. They use<br>python 3 including the Matplotlib, Iris and Cartopy packages. The <br>plotting of the timeseries data used the Marine_Val VALSO-VALTRANS <br>package which is available here:</p> <p> https://github.com/JMMP-Group/MARINE_VAL/tree/main/VALSO-VALTRANS </p> <p>Much of the processing of the model output data was performed with the<br>CDFTools package, which is available here:</p> <p> https://github.com/meom-group/CDFTOOLS</p> <p>and the NCO package:</p> <p> https://web.mit.edu/course/13/13.715/nco-2.8.1/doc/</p>
Data for "On the resolution-dependence of cloud fraction in radiative-convective equilibrium"
<p>Cloud-resolving model output and analysis scripts for the paper "On the resolution-dependence of anvil cloud fraction and precipitation efficiency in radiative-convective equilibrium"</p>
Data for "Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation"
<p>The datasets for the manuscript "Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation".</p> <p>model: WRF3.1.1</p> <p>location: Southern Great Plains</p> <p>time: from 2100 UTC 23 May to 0600 UTC 24 May</p> <p>time interval: 6 minutes</p> <p>domain size: 512km x 512km</p> <p>vertical layer: 500hpa</p> <p>variables: P, PB, PH, PHB, U, V, W, T, QCLOUD, QICE, QVAPOR</p> <p>calculated data: mse, up_only(only consider updraft), up_down(consider both updrafts and downdrafts)</p> <p> </p>
Data from: Cholesterol and ORP1L dependent clustering of dynein on endolysosmes in cells 2 revealed by super resolution microscopy
<p>The sub-cellular positioning of endolysosomes is crucial for regulating their function. Particularly, the positioning of endolysosomes between the cell periphery versus the peri-nuclear region impacts autophagy, mTOR (mechanistic target of rapamycin) signaling and other processes. The mechanisms that regulate the positioning of endolysosomes at these two locations are still being uncovered. Here, using quantitative super-resolution microscopy in intact cells, we show that the retrograde motor dynein forms nano-clusters on endolysosomal membranes containing 1-2 dyneins, with an average of ~3 nanoclusters per endolysosome. These data suggest that a very small number of dynein motors (1-6) drive endolysosome motility inside cells. Surprisingly, dynein nano-clusters are slightly larger on peripheral endolysosomes having higher cholesterol levels compared to peri-nuclear ones. By perturbing endolysosomal membrane cholesterol levels, we show that dynein copy number within nano-clusters is influenced by the amount of endolysosomal cholesterol while the total number of nano-clusters per endolysosome is independent of cholesterol. Finally, we show that the dynein adapter protein ORP1L (Oxysterol Binding Protein Homologue) regulates the number of dynein motors within nano-clusters in response to cholesterol levels. We propose a new model by which endolysosomal transport and positioning is influenced by the cholesterol sensing adapter protein ORP1L, which influences dynein's copy number within nano-clusters.</p>
Data and scripts (1) for Storkey et al, "Resolution dependence of interlinked Southern Ocean biases in global coupled HadGEM3 models", GMD (2024)
<p><br> ================================================================<br> Data and scripts for producing plots from Storkey et al (2024):<br> "Resolution dependence of interlinked Southern Ocean biases in<br> global coupled HadGEM3 models"<br> ================================================================</p> <p>The plots in the paper consist of 10-year mean fields from the third <br>decade of the spin up and timeseries of scalar quantities for the first<br>150 years of the spin up. The data to produce these plots are stored<br>in the MEANS_YEARS_21-30 and TIMESERIES_DATA directories respectively.</p> <p>Note that due to the size limit on records on Zenodo, the 10-year mean <br>output from the N216-ORCA12 integration has been stored as a separate<br>record.</p> <p>Scripts to produce the plots are in SCRIPT, with section definitions<br>in SECTIONS. Bespoke plotting scripts are included in SCRIPT. They use<br>python 3 including the Matplotlib, Iris and Cartopy packages. The <br>plotting of the timeseries data used the Marine_Val VALSO-VALTRANS <br>package which is available here:</p> <p> https://github.com/JMMP-Group/MARINE_VAL/tree/main/VALSO-VALTRANS </p> <p>Much of the processing of the model output data was performed with the<br>CDFTools package, which is available here:</p> <p> https://github.com/meom-group/CDFTOOLS</p> <p>and the NCO package:</p> <p> https://web.mit.edu/course/13/13.715/nco-2.8.1/doc/</p>
Data from: Cholesterol and ORP1L dependent clustering of dynein on endolysosmes in cells 2 revealed by super resolution microscopy
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High-resolution HIV-1 m6A epitranscriptome reveals splicing-dependent methylation clusters and unique 2-LTR transcript modifications
GEO Series GSE291168. Human immunodeficiency virus. 11 samples. Type: Expression profiling by high throughput sequencing.
Base-resolution analyses of parent-of-origin and sequence dependent allele specific DNA methylation in the mouse genome (MRE-seq)
GEO Series GSE33579. Mus musculus; Homo sapiens. 5 samples. Type: Methylation profiling by high throughput sequencing.
Resolution of ntla-dependent transcriptome using caged molecules
GEO Series GSE31882. Danio rerio. 20 samples. Type: Expression profiling by array.
High-Resolution Nucleosome Mapping Reveals Transcription-Dependent Promoter Packaging
GEO Series GSE18530. Saccharomyces cerevisiae. 5 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Base-resolution analyses of parent-of-origin and sequence dependent allele specific DNA methylation in the mouse genome (ChIP-seq and Methyl-seq)
GEO Series GSE30199. Mus musculus. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
A virally encoded high resolution screen of cytomegalovirus host dependencies
GEO Series GSE246735. Homo sapiens. 51 samples. Type: Other.
Resolution of ntla-dependent transcriptome at 16 hpf using caged molecules
GEO Series GSE31881. Danio rerio. 10 samples. Type: Expression profiling by array.
Resolution of ntla-dependent transcriptome at 9 hpf using caged molecules
GEO Series GSE31880. Danio rerio. 10 samples. Type: Expression profiling by array.
Sustained macrophage reprogramming is required for CD8 T cell-dependent long-term tumor resolution
GEO Series GSE276345. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Base-resolution analyses of parent-of-origin and sequence dependent allele specific DNA methylation in the mouse genome (RNA-seq)
GEO Series GSE33468. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding
GEO Series GSE111936. synthetic construct. 14 samples. Type: Other.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.