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1,709 results for “Reflectivity”
Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"
<p>RHEED raw dataset and Gaussia fitting parameter dataset for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
IODP Expedition 369 Color reflectance
<p>Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.</p>
Data for: Wolverine density distribution reflects past persecution and current management in Scandinavia
<p>After centuries of intense persecution, several large carnivore species in Europe and North America have experienced a rebound. Today's spatial configuration of large carnivore populations has likely arisen from the interplay between their ecological traits and current environmental conditions, but also from their history of persecution and protection. Yet, due to the challenge of studying population-level phenomena, we are rarely able to disentangle and quantify the influence of past and present factors driving the spatial distribution and density of these controversial species. Using spatial capture-recapture models and a data set of 742 genetically identified wolverines <em>Gulo gulo</em> collected over ½ million km<sup>2</sup> across their entire range in Norway and Sweden, we identify landscape-level factors explaining the current population density of wolverines in the Scandinavian Peninsula. Distance from the relict range along the Swedish-Norwegian border, where the wolverine population survived a long history of persecution, remains a key determinant of wolverine density today. However, regional differences in management and environmental conditions also played an important role in shaping spatial patterns in present-day wolverine density. Specifically, we found evidence of slower recolonization in areas that had set lower wolverine population goals in terms of the desired number of annual reproductions. Management of transboundary large carnivore populations at biologically relevant scales may be inhibited by administrative fragmentation. Yet, as our study shows, population-level monitoring is an achievable prerequisite for a comprehensive understanding of the distribution and density of large carnivores across an increasingly anthropogenic landscape.</p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the agricultural land at Demmin, Germany
<p>The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Demmin, Germany [53°52'5.80"N,13°16'6.80"E] (DEGE). It is a subset of the complete data record, consisting of the measurements withEthaturements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 degrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-XR sensor was installed on 22 July 2021 at the top of a 10m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 53.868278°N, 13.268556°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angles.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with an FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with an FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full DEGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
Data and code for: The welfare problems of wide-ranging Carnivora reflect naturally itinerant lifestyles
<p class="MsoNormal"><span>Carnivora with naturally small annual home ranges can adjust well to the evolutionarily new environment of captivity, but wider-ranging species are vulnerable to stress. To investigate why, we identified eight correlates of home range size (reflecting energetic needs, movement, intra-specific interactions, and itinerant lifestyles). We systematically assessed whether these correlates predict welfare better than range size itself, using data on captive juvenile mortality (from 13,518 individuals across 42 species) and stereotypic route-tracing (456 individuals, 27 species). Rather than large ranges </span><em><span>per se</span><span>, </span></em><span>i</span><span>tinerant lifestyles (quantified via ratios of daily to annual travel distances in nature) were found to confer risk, predicting greater captive juvenile losses and stereotypic time-budgets. </span><span><span>This result<em><span> </span></em>advances<span class="apple-converted-space"> </span>our<em><span> </span></em></span></span><span><span>understanding of the evolutionary basis for welfare problems in captive Carnivora, helping to explain why naturally sedentary species (e.g. American mink) may breed even in intensive farm conditions, while others (e.g. polar bears, giant pandas) can struggle even in modern zoos/breeding centres. Naturally itinerant lifestyles involve decision-making and strategic shifts to new locations, suggesting that supplying more novelty, cognitive challenge and/or opportunities for control will be effective ways to meet these animals' motivational needs in captivity. Such findings could therefore assist with both collection planning and enclosure design.</span></span></p>
Light intensity reflected from PAAO containing samples (AoI 45 deg., 60 deg., two polarisations)
<p>The file contains raw intensity data measured in reflection mode for various material combinations: porous anodized aluminium oxide (PAAO), gold nanoparticles (Au NPs), diamond-like carbon with silver nanoparticles composite (DLC:Ag), quartz, and silicon.</p> <p>List of investigated samples:</p> <table> <thead> <tr> <th scope="col">Sample name</th> <th scope="col">Sample description</th> </tr> </thead> <tbody> <tr> <td>AJ1</td> <td>Aluminium substrate, ~241 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 16 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ2</td> <td>Aluminium substrate, ~259 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 37 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ3</td> <td>Aluminium substrate, ~293 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 8 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ4</td> <td>Aluminium substrate, ~317 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 30 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ5</td> <td>Aluminium substrate, ~345 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 57 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ6</td> <td>Aluminium substrate, ~276 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 42 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>AJ7</td> <td>Aluminium substrate, ~316 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 25 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>AJ8</td> <td>Aluminium substrate, ~345 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 5 min 4 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>Q140</td> <td>Quartz substrate, ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>Si140</td> <td>Silicon substrate, ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> </tbody> </table> <p>The file also contains data for lamp and dark spectra. Images are for illustrative purposes and contain measured sample intensity minus dark intensity.</p> <p>The spectra were collected by a custom-made computer-controlled motorized goniometer set-up with an incandescent lamp light source and an AvaSpec-2048 (Avantes) spectrometer covering a wavelength range of 360–860 nm with 1.2 nm resolution.</p> <p>Notations inside the file: AoI - angle of incidence (45 deg. or 60 deg.), AoD - angle of detection (90 deg. is mirror reflection from 45 deg. incidence, while 120 deg. is the same for 60 deg.), pol - angle of the polarizing prism (0 deg. or 90 deg., light intensity was lower for the 0 deg.).</p>
Reflective Microscopy Dataset on the Formation of Zr-Based Conversion Coating on Al2024
<p>The accompanying files contain the raw optical images and initial data processing results, conducted in Python environments, related to the spontaneous formation of a Zirconium-based conversion coating (CC) on an Al2024 alloy.</p> <p>The files designated as 16-47-51.761.zip and similar, contain the raw optical images that chronicle the continuous observation of CC formation over a span of up to 40 minutes. The naming convention of these files is representative of the hours-minutes-seconds.milliseconds timestamp when the data recording began.</p> <p>Image capture commenced at a rate of 5 frames per second (Hz), and subsequent images were acquired at a rate of 1 Hz. This timestamped naming approach also extends to the individual raw images, reflecting the precise time at which each image was taken. Each image measures 2000x2000 pixels, with an individual pixel resolution of 240 nanometers and a pixel depth of 12 bit.</p> <p>The file labeled Image_processing.ipynb is a Jupyter lab notebook containing the image processing routine, implemented in Python 3 programming language. A PDF version of this notebook, for ease of printing and viewing, is available in the file named IMAGE_PROCESSING.pdf. This script has generated a selection of images and videos, which can be found within the Generated_images.zip and Generated_videos.zip folders.</p> <p>The normalized_intensity.png file contains the most pivotal plot, which displays the normalized intensity as a function of time. This graph is designed to reflect the progression of the CC thickness, interpreted through Fresnel equations.</p> <p>The video files provide unedited footage of the first 3 minutes of the Al surface's exposure to the CC solution, along with the normalized data. A side-by-side comparison of raw and normalized data for a 300x300 pixel image cut is also available in these video files.</p>
IODP Expedition 382 Color reflectance
<p>Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.</p>
IODP Expedition 392 Color reflectance
<p>Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.</p>
Munsell soil color chart: a spectral reflectance dataset
<p>This dataset contains the calculated spectral reflectance of the 2009 edition of Munsell soil colour chart measured using Konika Minolta 2000 spectroradiometer with a tungsten lamp as the light source. The spectroradiometer has a 380-780nm spectral range with a 1nm spectral sampling.</p> <p>Reflectance is obtained by using the following equation with the assumption of a uniformly-illuminated surface.<br> <span class="math-tex">\(R(\lambda) = \frac{\Phi(\lambda)_{Reflected}}{\Phi(\lambda)_{Incident}} \)</span></p> <p><span class="math-tex">\( \Phi(\lambda)_{Incident} = \frac{\Phi(\lambda)_{sample}}{R(\lambda)_{sample}} \)</span><br> using a known <span class="math-tex">\(R(\lambda)\)</span> of white reference tile used to obtain <span class="math-tex">\(\Phi(\lambda)_{Incident}\)</span> of the tungsten lamp.</p> <p> </p> <p>If you have any questions regarding this dataset, kindly contact me on riestiya.z.fadillah@ntnu.no</p>
Examining Validity and Sensitivity of Pressure-Mediated Reflection Spectroscopy
ClinicalTrials.gov study NCT04056624. IPD Sharing: YES. Countries: 1. Publications: 3.
Predicting the distribution of serotonergic axons: A supercomputing simulation of reflected fractional Brownian motion in a 3D-mouse brain model
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Data for: Do ectomycorrhizal exploration types reflect mycelial foraging strategies?
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Selection maintains floral color polymorphism in the scarlet paintbrush, <em>Castilleja coccinea</em>, reflecting combined ecological factors
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Molecular assays of pollen use consistently reflect pollinator visitation patterns in a system of flowering plants
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Data from: Bateman gradients reflect variation in sexual selection in a species with dynamic sex roles
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Humpback whale genomes reflect the increased efficiency of commercial whaling
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Watching the watchers: Camera identification and characterization using retro-reflections - Dataset
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Data from: Responses of C4 grasses to aridity reflect species-specific strategies in a semiarid savanna
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Exposure to thermal extremes favours higher solar reflectivity in intertidal gastropods
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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)
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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.