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10 results for “compressed files”
Example input files and output data for 1D hydrodynamic simulations of shock compressed iron
<p>Example input files and output data for 1D hydrodynamic simulations of shock compressed iron. Input files consists of 3 examples from the SIMEX github wiki page for a 50 micron CH ablator with 5 micro Fe foil (laser pulse is a 6 ns flat top pulse, 1064 nm with 0.3 TW/cm<sup>2</sup>). Output data are from Esther hydrocode in .txt format and the SIMEX opmd.h5 format.</p>
Example data map compressed with ISO29500-2 with 3 entry point for ArcGIS, MiraMon and OWS context file.
<p>A simple map consisting of a 1:1 000 000 country boundaries vector file, produced by the FAO (United Nations – FAOStat; geodata.grid.unep.ch/options.php?selectedID=2135) on top of a 5’ digital elevation model raster file (produced by the NOAA and NGDC; geodata.grid.unep.ch/options.php?selectedID=1414). Data has been obtained from the UNEP EDE Data Portal (UNEP 2013). Vector file is a Shapefile (a de facto standard) (ESRI 1998), while the raster file consists of either a raw signed 16-bit data or a long known TIFF file (Adobe 1992, Perkins 1995). Metadata and symbolization files are included. OPC specifies how to explicitly relate different parts using .rels files. These files are XML files with the same name as that of its respective source part, adding “.rels” and placed in a “rels” folder. Each of these files lists the target parts related to its source and the semantics of this relation.</p> <p>OPC can define entry points to the data by listing them in a “.rels” part in the root “rels” folder. In this file, three map files: for the ESRI software a world.mxd map, for the MiraMon software a world.mmm map, and a world.xml map in the form of an atom file following the new Web Service common standard (OGC OWS) context document. A geospatial application reading the package will determine which entry part it better supports to start recovering the data.</p>
Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"
<p>Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"</p> <p>The source files are to be used with the code on https://github.com/MaximeMaW/CompressedSensingMicroscopy3D (also archived in https://zenodo.org/record/439690)</p> <ol> <li>The files prefixed with "VIZ" are high resolution TIF visualizations.</li> <li>The files come from three experiments on two different setups: <ol> <li>A lattice light sheet microscope (LLSM): beads sample (filed termed "<strong>lattice-beads</strong>" and actin-labelled mESCs (files termed "<strong>lattice-phalloidin</strong>")</li> <li>An epifluorescence microscope: beads sample (files termed "<strong>epifluorescence</strong>")</li> </ol> </li> <li>The acquisitions were either performed using an identity measurement matrix (mimicking the plane-by-plane acquisition mode of a traditional z-stack): files termes "<strong>reference</strong>" or with a Fourier measurement matrix (described in the code mentioned above) with a compression ratio of 2 (files termed "<strong>compressed</strong>".</li> <li>The reconstructions were performed as described in the paper with the code mentioned above. Several reconstructions were computed from the same compressed images by simulating increasing compression ratios. To do so, reconstructions were performed by selecting a subset of the acquired planes (number indicated as "<strong>**frames</strong>")</li> <li>Reconstructions were sparsified using a 2D PSF model computed for our epifliuorescence setup and the LLSM (files termed "<strong>PSF_model</strong>"). These are provided as numpy arrays.</li> </ol> <p> </p>
Dataset of Synchrotron Low Energy XRF and STXM files used in a manuscript on "Compressive Sensing for Dynamic XRF Scanning"
<p>Synchrotron Low Energy XRF and STXM Dataset used in a research manuscript on "Compressive Sensing for Dynamic XRF Scanning". This dataset includes HDF5 files with XRF (/dante) and STXM (/andor) maps and metadata such as XRF lifetime and sample stage positions (/sample_motors). The dataset also includes as TIFF images various outputs such as the sparse maps, the masked areas and the results of in-painting methods. In the DAT file, there is the output of the fitted XRF data as ASCII from PyMCA. In HTML there is included the relevant part of the electronic logbook (DonkiLOG). These data were acquired during the beamtime experiments 20180178 and 20192072 in the <a href="http://www.elettra.eu/elettra-beamlines/twinmic.html">TwinMic</a> soft X-ray microscopy beamline of Elettra Sincrotrone Trieste.</p> <p> </p> <p> </p>
Dataset (.esg files) of a multiphase aggregate representative of a lower mantle composition compressed at High P/T in an R rDAC
<p>A collection of .esg files for use in the software package Materials Analysis Using Diffraction (MAUD; Lutterotti et al., 1997). Seven files are included, spanning the range used in data analysis. All are post-conversion from San Carlos olivine. Files are named in the following format:</p> <p>MPBrgFp: Stands for Multiphase, primary phases bridgmanite and ferropericlase<br> ###: Number of the diffraction pattern taken during the experiment<br> ##GPa: Approximate temperature, rounded to the nearest whole number. This is to cross-correlate .esg files with experimental Pt pressures cited within the paper.</p>
Antidepressant mice behavioral video compression file
<p>Antidepressant mice behavioral video compression file(autonomous activity, forced swimming, tail suspension experiment)</p>
Ground-Based Global Navigation Satellite System (GNSS) GLObal NAvigation Satellite System (GLONASS) Hatanaka-Compressed Observation Data (30-second sampling, daily files) from NASA CDDIS - D
This dataset consists of ground-based Global Navigation Satellite System (GNSS) GLONASS Compact Observation Data (30-second sampling, daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS GNSS observation files (compact) contain one day of GLONASS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.
Ground-Based Global Navigation Satellite System (GNSS) Hatanaka-compressed Observation Data (1-second sampling, sub-hourly files) from NASA CDDIS
This dataset consists of ground-based Global Navigation Satellite System (GNSS) Observation Data (1-second sampling, sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly GNSS observation files (compact) contain 15 minutes of GPS or multi-GNSS observation (1-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.
Ground-Based Global Navigation Satellite System (GNSS) Hatanaka-compressed Observation Data (30-second sampling, hourly files) from NASA CDDIS
This dataset consists of ground-based Global Navigation Satellite System (GNSS) Observation Data (30-second sampling, hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly GNSS observation files (compact) contain one hour of GPS or multi-GNSS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per hour per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.
Ground-Based Global Navigation Satellite System (GNSS) GLObal NAvigation Satellite System (GLONASS) Hatanaka-Compressed Observation Data (30-second sampling, daily files) from NASA CDDIS
GNSS receivers collect the signals from orbiting satellites to determine their location in three dimensions and calculate precise time. GNSS receivers detect, decode, and process both pseudorange (code) and phase transmitted by the GNSS satellites. The satellites transmit the ranging codes on two or more radio-frequency carriers, allowing the locations of GNSS receivers to be determined with varying degrees of accuracy, depending on the receiver and post-processing of the data. The receivers also calculate current local time to high precision facilitating time synchronization applications.This dataset consists of ground-based Global Navigation Satellite System (GNSS) GLONASS Compact Observation Data (30 second sampling, daily, 24 hour files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS compact observation data files contain one day of GLONASS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site.
ScienceDex guides
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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.