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1,709 results for “Reflectivity”
Figure 5 in Head morphology reflects the introduction history in a globally invasive carnivore-the small Indian mongoose
Figure 5. Cranial and mandibular shape assignments from the discriminant analyses of principal components.
Dataset - Flux Profile Optimization of a High-Flux Solar Simulator Using the Trust-Region Reflective Method
<p>This dataset accompanies</p> <p>DOI: 10.1080/14786451.2024.2355651</p> <p>Title: Flux Profile Optimization of a High-Flux Solar Simulator Using the Trust-Region Reflective Method</p> <p>Authors: Mohammed Hamid Hussain</p> <p>Journal: International Journal of Sustainable Energy</p> <p> </p>
Hyperspectral Remote-sensing reflectance and Chlorophyll-a concentration from Lakes in North Germany
<p><span>Hyperspectral Remote-sensing reflectance data collected by the </span><span>Leibniz Institute of Freshwater Ecology and Inland Fisheries </span><span>(</span><span>IGB</span><span>) within the</span><span> <span>Connectivity and synchronisation of lake ecosystems in space and time (CONNECT</span></span><span> project</span><span>)</span><span>. </span></p> <p><span>This data set is limited to records for </span><span>one field sampling campaign in 26.07.2019</span><span>. </span></p> <p><span>Field data collection and processing:</span></p> <p><span>Ogashawara, I.; Kiel, C.; Jechow, A.; Kohnert, K.;<span> </span>Ruhtz, T.; Grossart, H.-P.; Hölker, F.; Nejstgaard, J.C.; Berger, S.A.; Wollrab, S. The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany. Remote Sensing, v. 13, 1542, 2021. doi:10.3390/rs13081542 </span></p> <p><span>Data specification</span></p> <p><span>Station name – name of the station</span></p> <p><span>lat, lon - geographical latitude/longitude coordinates in decimal degrees</span></p> <p><span>chl-a</span><span> - </span><span>chlorophyll-a concentration in μg/L</span><span> </span></p> <p><span>Rrs</span><span> 380… Rrs 779 </span><span>- Remote-sensing reflectance (Rrs, units 1/sr). The sequential numbering (</span><span>380 - 779</span><span>) denote</span><span>s the</span><span> wavelength in nm. </span></p> <p> </p>
A 2D hyperspectral library of mineral reflectance, from 900 to 2500nm - Raw 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>@.scan.h5</strong>: HDF5 file containing the raw 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> <p> </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>
Fig. 1 in Red- and yellow-footed tortoises, Chelonoidis carbonaria and C. denticulata (Reptilia: Testudines: Testudinidae), in South American savannahs and forests: do their phylogeographies reflect distinct habitats?
Fig. 1 Approximate ranges of Chelonoidis carbonaria and C. denticulata (modified from Iverson 1992) and geographic distribution of haplotypes. Locality numbers refer to Appendix 1. Question marks indicate that the southern part of the range of C. carbonaria might be connected with the northern part. Symbols for C. carbonaria correspond to haplotype clades (Fig. 2); upper-case letters indicate geographic origin within the range (N, north; NE, northeast; etc.)
Fig. 5 in Red- and yellow-footed tortoises, Chelonoidis carbonaria and C. denticulata (Reptilia: Testudines: Testudinidae), in South American savannahs and forests: do their phylogeographies reflect distinct habitats?
Fig. 5 Chelonoidis carbonaria; left: Brazil (Museum of Zoology Dresden MTD D 3620); right: Filadelfia, Chaco, Paraguay (Museum of Zoology Dresden MTD D 43485). Scale bars: 10 cm. Note distinct shell shapes and colorations
Optical Emission and Reflection Data for Melting Regime Classification in Laser Powder Bed Fusion of 316L Stainless Steel and Ti-6Al-4V
<p>This dataset and accompanying code repository contain the experimental data and analysis scripts used in the study of real-time melting regime classification in Laser Powder Bed Fusion (LPBF) processes. The data includes optical sensor measurements (emission and reflection) collected during LPBF printing of 316L stainless steel and Ti-6Al-4V specimens, along with corresponding process parameters.</p>
A Hyperspectral Reflectance Database of Plastic Debris for River Ecosystems
<div>This is a unique hyperspectral reflectance dataset of weathered and virgin floating plastic debris with different sizes and types under controlled laboratory conditions. The dataset contains the NETCDF and CSV files.</div> <div> </div> <div>Each NETCDF file follows the labeling structure in the following format:</div> <div> </div> <div> ├─ ID # Observation Number</div> <div> └─├─ Polymer # Polymer Type</div> <div> └─├─ Background # Background setting </div> <div> └─├─Sediment Concentration # Sediment concentration</div> <div> └─├─Debris Color # Debris color </div> <div> └─├─Plastic Fraction # Percentage of plastic fraction</div> <div> └──Quality Flag # Quality flag (1: lowest, 5: highest)</div> <div> </div> <div> <p><strong>CSV Files Description:</strong></p> </div> <div> </div> <div>1) spectradictionary.csv: contains the spectra of various debris. <div> </div> </div> <div> <p>- Column 1: Code representing the type of debris and background setting:</p> <p> - 10: PET in Clear water<br> - 11: PET in Turbid water<br> - 12: PET in Foamy water<br> - 20: HDPE in Clear water<br> - 21: HDPE in Turbid water<br> - 22: HDPE in Foamy water<br> - 30: LDPE in Clear water<br> - 31: LDPE in Turbid water<br> - 32: LDPE in Foamy water<br> - 40: PP in Clear water<br> - 41: PP in Turbid water<br> - 42: PP in Foamy water<br> - 50: EPSF in Clear water<br> - 51: EPSF in Turbid water<br> - 52: EPSF in Foamy water<br> - 60: Mix in Clear water<br> - 61: Mix in Turbid water<br> - 62: Mix in Foamy water<br> - 70: Weathered in Clear water<br> - 71: Weathered in Turbid water<br> - 72: Weathered in Foamy water</p> <p>- Column 2: Plastic fraction in percentage (%).<br>- Columns 3 to end: Wavelength numbers ranging from 350 to 2500.</p> </div> <div> </div> <div>2) Background.csv: contains the spectra of various backgrounds (clear, turbid and foamy water))</div> <div> </div> <div> </div> <div>Acknowledgment: The funding support from the Legislative-Citizen Commission on Minnesota Resources (LCCMR, M.L.2021 E812RSM) is greatly acknowledged</div> <div> </div>
AFM measurements of reflective gold and black gold layers
Open the record for dataset details and reuse information.
FIGURE 2 in Is morphology in Cercospora a reliable reflection of generic affinity?
FIGURE 2. Consensus phylogram (50% majority rule) of 622 trees resulting from a Bayesian analysis of the combined 4-gene (ITS, TEF1-α, ACT and HIS) sequence alignment using MrBayes v. 3.2.2. The scale bar represents the average number of substitutions per site, and posterior probability values are shown at the nodes. Clades of different genera are indicated in coloured blocks and names of the genera are shown to the right of the block. The tree is rooted to Cladosporium herbarum (strain CBS 121621).
FIGURE 1 in Is morphology in Cercospora a reliable reflection of generic affinity?
FIGURE 1. Consensus phylogram (50% majority rule) of 512 trees resulting from a Bayesian analysis of the LSU sequence alignment using MrBayes v. 3.2.2. The scale bar represents the average number of substitutions per site, and posterior probability values are shown at the nodes. GenBank accession numbers are shown in brown text and bold accession numbers were generated in this study. Clades of different genera are indicated in coloured blocks and names of the genera are shown in purple text. The tree is rooted to Cladosporium herbarum (GenBank accession DQ678074).
FIGURE 4 in Is morphology in Cercospora a reliable reflection of generic affinity?
FIGURE 4. Neocercospora ammicola (CBS 136450) (in vitro). a. Colony on MEA. b–d. Conidiophores and conidiogenous cells. e. Terminal conidiophore on hypha. f. Conidiophore reduced to a conidiogenous cell. g–l. Solitary and catenate conidia. Scale bars = 10 μm.
FIGURE 3 in Is morphology in Cercospora a reliable reflection of generic affinity?
FIGURE 3. Neocercospora ammicola (CBS 136450) (in vivo). a. Leaf spots on Ammi majus. b–f. Fasciculate conidiophores reduced to conidiogenous cells. g–m. Solitary and catenate conidia. Scale bars = 10 μm.
Figure 2 in What do frog calls tell us about males? The advertisement call of Physalaemus albifrons (Amphibia: Leptodactylidae) in the Brazilian semiarid region does not reflect either gonadal investment or energy storage
Figure 2. On the left, testis (solid arrow) and fat bodies (dashed arrow) of Physalaemus albifrons (CHSA. A 140). On the right, spectrogram (top) and oscillogram (bottom) of the typical harmonic note of the advertisement call of Physalaemus albifrons (CASA 241).
Figure 1 in What do frog calls tell us about males? The advertisement call of Physalaemus albifrons (Amphibia: Leptodactylidae) in the Brazilian semiarid region does not reflect either gonadal investment or energy storage
Figure 1. Male of Physalaemus albifrons calling in a temporary pond in the Brazilian semiarid region. Photograph: Milena Wachlevski.
Seismic reflection data of profile across the Central Basin Fault and the Kyushu-Palau Ridge
<p>Seismic reflection profiles were acquired in 2020 by the R/V "Dayanghao" (Second Institute of Oceanology, Ministry of Natural Resources, China) using a 6040 cubic inch air gun array, discharged at 37.5 m intervals, and a 6-km-long, 480-channel streamer. Data were recorded for 14 s at 2 ms sampling intervals. Seismic data processing involved noise reduction, signal enhancement via filtering and deconvolution, velocity analysis, time migration, and depth migration. </p>
The Data For Spatial Variations of Stellar Elemental Abundances in FIRE Simulations of Milky Way-Mass Galaxies: Patterns Today Mostly Reflect Those at Formation
<p>Spatial patterns of stellar elemental abundances encode rich information about a galaxy’s formation<br>history. We analyze the radial, vertical, and azimuthal variations of metals in stars, both today and at<br>formation, in the FIRE-2 cosmological simulations of Milky Way-mass galaxies, and we compare<br>with the Milky Way. Overall, spatial variations of stellar metallicities show only modest differences between formation and today; spatial variations today primarily reflect the conditions of stars at birth, with spatial redistribution of stars after birth contributing secondarily. </p> <p> </p> <p>This data abides by CC-BY.</p>
Research Data for "Kinetic Analysis of the Self-Discharge of the NiOOH OER Active Phase in KOH Electrolyte: Insights from In-Situ Raman and UV-Vis Reflectance Spectroscopies"
<p>This dataset provides the underlying experimental and simulation raw data supporting the figures of both main text and supporting information of the paper "Kinetic Analysis of the Self-Discharge of the NiOOH OER Active Phase in KOH Electrolyte: Insights from In-Situ Raman and UV-Vis Reflectance Spectroscopies" appearing in <em>Journal of Catalysis</em> under DOI: <a href="https://doi.org/10.1016/j.jcat.2024.115823" rel="noreferrer">https://doi.org/10.1016/j.jcat.2024.115823</a>.</p>
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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
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