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9 results for “High Dynamic Range”
Supplementary material for "High turn-over rates at the upper range limit and elevational source-sink dynamics in a widespread songbird"
<p><strong>Abstract</strong></p> <p>The formation of an upper distributional range limit for species breeding along mountain slopes is often based on environmental gradients resulting in changing demographic rates towards high elevations. However, we still lack an empirical understanding of how the interplay of demographic parameters forms the upper range limit in highly mobile species. Here, we study apparent survival and within-study area dispersal over a 700 m elevational gradient in barn swallows (<em>Hirundo rustica</em>) by using 15 years of capture-mark-recapture data. Annual apparent survival of adult breeding birds decreased while breeding dispersal probability of adult females, but not males increased towards the upper range limit. Individuals at high elevations dispersed to farms situated at elevations lower than would be expected by random dispersal. These results suggest higher turn-over rates of breeding individuals at high elevations, an elevational increase in immigration and thus, within-population source-sink dynamics between low and high elevations. The formation of the upper range limit therefore is based on preference for low-elevation breeding sites and immigration to high elevations. Thus, shifts of the upper range limit are not only affected by changes in the quality of high-elevation habitats but also by factors affecting the number of immigrants produced at low elevations.</p>
DEVILS: a tool for the visualization of large datasets with a high dynamic range
<p>This repository accompanying the article “DEVILS: a tool for the visualization of large datasets with a high dynamic range” contains the following:</p> <ul> <li>Extended Material of the article</li> <li>An example raw dataset corresponding to the images shown in Fig. 3</li> <li>A workflow description that demonstrates the use of the DEVILS workflow with BigStitcher.</li> <li>Two scripts (“CLAHE_Parameters_test.ijm” and a “DEVILS_Parallel_tests.groovy”) used for Figure S2, S3 and S4.</li> </ul>
Test data for RAW high dynamic range merging
<p>* the data contains a set of .CR2 raw images taken with a Canon 5D mark II<br> * the exposures where done on a tripod in manual mode, the EXIF information is correct<br> * only the exposure times were varied:</p> <p>IMG_7220.CR2<br> Exposure Time : 1<br> IMG_7221.CR2<br> Exposure Time : 1/5<br> IMG_7222.CR2<br> Exposure Time : 1/20<br> IMG_7223.CR2<br> Exposure Time : 1/80<br> IMG_7224.CR2<br> Exposure Time : 1/320<br> IMG_7225.CR2<br> Exposure Time : 1/1250</p> <p>* Author: Ivo Ihrke, ~2011, Universität des Saarlandes / MPI Informatik</p> <p> </p>
Supplementary data: Bayesian multi-exposure image fusion for robust high dynamic range ptychography
<p>Accompanying supplementary data for the paper. To download this data automatically and use the software, please refer to the details in the README of the linked github repository. </p> <p><strong>Github URL: </strong><a href="https://github.com/microscopic-image-analysis/bayes-mef"><strong>https://github.com/microscopic-image-analysis/bayes-mef</strong></a></p>
Data archive for the publication: Multi-variable compensated quantum yield measurements of upconverting nanoparticles with high dynamic range: a systematic approach
<p>The two compressed repositories contain raw and analysed data for the following publication on Optics Express: <strong><a href="https://doi.org/10.1364/OE.452874">Multi-variable compensated quantum yield measurements of upconverting nanoparticles with high dynamic range: a systematic approach</a></strong></p> <p>The code for processing and analysing these data is available on GitHub and it has its own DOI. </p> <p>To access the code and for instructions on how to run the it follow the link: <a href="https://github.com/Biophotonics-Tyndall/QY-System-paper">Biophotonics-Tyndall/QY-System-paper (github.com)</a>. </p>
HDRIHaven: Public domain high dynamic range image dataset
<p>High quality datasets are essential for image processing research.<br> This set contains 201 high dynamic range panoramic images (360 x 180 degrees) with jpeg previews.<br> <br> The images are originally published at HDRIHaven under CC0 license / waiver:<br> - HDRIHaven: https://hdrihaven.com/<br> <br> The images are named using the SOURCE_IDX_DESCRIPTION.EXTENSION format, e.g. <br> hdrihaven_001_ahornsteig_16k.hdr.xz to ease further reference (e.g. "hdrihaven_001").</p> <p><br> <strong>HDRIHaven:</strong><br> The photos were taken and processed by Greg Zaal, and he generously published the images under CC0 license.<br> The images were originally published at https://hdrihaven.com.</p> <p>The files have Radiance HDR type and 16K resolution (16384x8192, 134Mpixel)<br> <br> <strong>Bugfix:</strong></p> <p>One file was missing from the first version (hdrihaven_200_yellow_field_16k.hdr.xz).<br> This updated version should be used instead of the original.</p> <p> </p> <p><strong>Downloading data:</strong><br> Due to the large size, the .hdr images were compressed with xz, which is not especially fast, but has a high compression ratio. <br> <br> It could be quite time consuming to download each file individually.<br> If all of the files are required, then it might be a better approach to download only the "file_list.txt",<br> and use a download manager to download the rest of the files. E.g. on Unix systems: </p> <pre><code class="language-bash">wget -i file_list.txt </code></pre> <p><br> Direct link to the file list: <a href="https://zenodo.org/record/1285800/files/file_list.txt">https://zenodo.org/record/1285800/files/file_list.txt</a></p> <p>Alternatively, zenodo_get ( <a href="https://gitlab.com/dvolgyes/zenodo_get">http://gitlab.com/dvolgyes/zenodo_get</a> ) could be used to download the whole record, e.g.<br> </p> <pre><code>zenodo_get.py 10.5281/zenodo.1285800</code></pre> <p> </p> <p><strong>Related links:</strong></p> <p>You might also be interested in: <a href="https://doi.org/10.5281/zenodo.1245790">https://doi.org/10.5281/zenodo.1245790</a> which is a similar dataset.</p>
A 2D hyperspectral library of mineral reflectance, from 900 to 2500nm - High dynamic range data
<p>Each <strong>zip</strong> file contains the text description file: <strong>description.txt</strong>. It also contains the data of one or two measurement, <strong>A</strong> and possibly B. The files are named as follows (<strong>####</strong> is the sample ID, <strong>@</strong> the letter of the measurement):</p> <ul> <li><strong>####-@.ply</strong> and <strong>####-@.png</strong>: 3D reconstruction in Stanford PLY format, and associated texture file.</li> <li><strong>@-im-000.jpg</strong>, <strong>@-im-010.jpg</strong>, ..., <strong>@-im-360.jpg</strong>: JPEG images of the sample, the angle is in degrees. 0 degree correspond to the position used during scanning.</li> <li><strong>@.hdr.h5</strong>: HDF5 file containing the HDR scan data.</li> </ul> <p>The zip files correspond to the sample IDs, according to the following table:</p> <pre><code>Mineral Sample IDs Datapoints ---------------------------------------------------------------- Actinolite 0020, 0021, 0064 56840 Albite 0107 22521 Almandine 0025, 0026, 0073, 0074 17425 Andalusite 0014 18862 Anhydrite 0004 30832 Apatite 0089(2), 0090(2) 69062 Aragonite 0061 40111 Arsenopyrite 0087(2) 66333 Augite 0038 8503 Barite 0006 37130 Beryl 0075(2) 37743 Biotite 0049(2), 0050 45400 Blende 0086(2) 15596 Bronzite 0112 50452 Bytownite 0103 14666 Calcite 0010, 0011, 0052, 0078, 0079 112501 Cassiterite 0119, 0120 38535 Celestite 0000, 0001, 0002 56075 Chalcedony 0108(2), 0109(2) 96431 Chalcopyrite 0106 19375 Chlorite 0013 75802 Clinochlore 0126 45301 Coal 0081, 0082 44664 Copper 0101 14556 Diopside 0069 58959 Dolomite 0091(2), 0092(2) 76810 Enstatite 0047 31072 Epidote 0023, 0024 47306 Fluorite 0003(2), 0012(2) 98638 Galena 0053, 0054 17931 Garnet 0115 27331 Glaucophane 0016, 0017, 0076, 0077 200830 Goethite 0114 10717 Graphite 0083, 0084 20047 Grossular 0030, 0031 46592 Gypsum 0005(2), 0007, 0063 159829 Halite 0008, 0056 66201 Halloysite 0121, 0122 102028 Hematite 0039(2), 0085(2), 0095, 0096 165228 Hornblende 0046 9175 Hypersthene 0111 62932 Ilmenite 0116 16256 Kaolinite 0113 20764 Kyanite 0029 12320 Labradorite 0088(2), 0104(2) 62762 Limonite 0128, 0129 64550 Magnetite 0055, 0072 7103 Microcline 0071 60699 Montmorillonite 0123, 0124, 0125 87986 Muscovite 0034 62481 Nepheline 0097(2) 51930 Olivine 0065 7965 Omphacite 0019, 0067 108032 Opal 0102 34404 Orthoclase 0057 49824 Phlogopite 0045, 0070 105119 Pyrite 0042, 0048 31598 Pyrolusite 0117, 0118 45534 Pyrrhotite 0051 21572 Quartz 0009(2), 0035 126760 Rutile 0093 4959 Sanidine 0099(2) 49260 Serpentine 0018, 0068 78937 Siderite 0080 11651 Silicified wood 0127(2) 92935 Sillimanite 0032, 0033 70546 Sodalite 0043, 0060 61852 Sphalerite 0105 27485 Staurolite 0027, 0028, 0066 30628 Sulfur 0036, 0037 34751 Talc 0022, 0040, 0041, 0058, 0059 138317 Titanite 0094 4121 Tourmaline 0044, 0062 68419 Tremolite 0098(2), 0100 70913 Zircon 0110(2) 5110 </code></pre>
A 2D hyperspectral library of mineral reflectance, from 900 to 2500nm - Masked high dynamic range data
<p>Each <strong>zip</strong> file contains the text description file: <strong>description.txt</strong>. It also contains the data of one or two measurement, <strong>A</strong> and possibly B. The files are named as follows (<strong>####</strong> is the sample ID, <strong>@</strong> the letter of the measurement):</p> <ul> <li><strong>####-@.ply</strong> and <strong>####-@.png</strong>: 3D reconstruction in Stanford PLY format, and associated texture file.</li> <li><strong>@-im-000.jpg</strong>, <strong>@-im-010.jpg</strong>, ..., <strong>@-im-360.jpg</strong>: JPEG images of the sample, the angle is in degrees. 0 degree correspond to the position used during scanning.</li> <li><strong>@.mhdr.h5</strong>: HDF5 file containing masked HDR raw scan data. Masked values are represented by nan.</li> </ul> <p>The zip files correspond to the sample IDs, according to the following table:</p> <pre><code>Mineral Sample IDs Datapoints ---------------------------------------------------------------- Actinolite 0020, 0021, 0064 56840 Albite 0107 22521 Almandine 0025, 0026, 0073, 0074 17425 Andalusite 0014 18862 Anhydrite 0004 30832 Apatite 0089(2), 0090(2) 69062 Aragonite 0061 40111 Arsenopyrite 0087(2) 66333 Augite 0038 8503 Barite 0006 37130 Beryl 0075(2) 37743 Biotite 0049(2), 0050 45400 Blende 0086(2) 15596 Bronzite 0112 50452 Bytownite 0103 14666 Calcite 0010, 0011, 0052, 0078, 0079 112501 Cassiterite 0119, 0120 38535 Celestite 0000, 0001, 0002 56075 Chalcedony 0108(2), 0109(2) 96431 Chalcopyrite 0106 19375 Chlorite 0013 75802 Clinochlore 0126 45301 Coal 0081, 0082 44664 Copper 0101 14556 Diopside 0069 58959 Dolomite 0091(2), 0092(2) 76810 Enstatite 0047 31072 Epidote 0023, 0024 47306 Fluorite 0003(2), 0012(2) 98638 Galena 0053, 0054 17931 Garnet 0115 27331 Glaucophane 0016, 0017, 0076, 0077 200830 Goethite 0114 10717 Graphite 0083, 0084 20047 Grossular 0030, 0031 46592 Gypsum 0005(2), 0007, 0063 159829 Halite 0008, 0056 66201 Halloysite 0121, 0122 102028 Hematite 0039(2), 0085(2), 0095, 0096 165228 Hornblende 0046 9175 Hypersthene 0111 62932 Ilmenite 0116 16256 Kaolinite 0113 20764 Kyanite 0029 12320 Labradorite 0088(2), 0104(2) 62762 Limonite 0128, 0129 64550 Magnetite 0055, 0072 7103 Microcline 0071 60699 Montmorillonite 0123, 0124, 0125 87986 Muscovite 0034 62481 Nepheline 0097(2) 51930 Olivine 0065 7965 Omphacite 0019, 0067 108032 Opal 0102 34404 Orthoclase 0057 49824 Phlogopite 0045, 0070 105119 Pyrite 0042, 0048 31598 Pyrolusite 0117, 0118 45534 Pyrrhotite 0051 21572 Quartz 0009(2), 0035 126760 Rutile 0093 4959 Sanidine 0099(2) 49260 Serpentine 0018, 0068 78937 Siderite 0080 11651 Silicified wood 0127(2) 92935 Sillimanite 0032, 0033 70546 Sodalite 0043, 0060 61852 Sphalerite 0105 27485 Staurolite 0027, 0028, 0066 30628 Sulfur 0036, 0037 34751 Talc 0022, 0040, 0041, 0058, 0059 138317 Titanite 0094 4121 Tourmaline 0044, 0062 68419 Tremolite 0098(2), 0100 70913 Zircon 0110(2) 5110 </code></pre> <p> </p>
Chemically-inducible CRISPR/Cas9 circuits for ultra-high dynamic range gene perturbation
GEO Series GSE302722. Homo sapiens. 172 samples. Type: Other.
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Allen Brain Atlas
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OpenNeuro
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