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1,709 results for “Reflectivity”
Watching the watchers: Camera identification and characterization using retro-reflections - Dataset
<p>A focused imaging system such as a camera will reflect light directly back at a light source in a retro-reflection (RR) or cat-eye reflection. RRs provide a signal that is largely independent of distance providing a way to probe cameras at very long ranges. We find that RRs provide a rich source of information on a target camera that can be used for a variety of remote sensing tasks to characterize a target camera including predictions of rotation and camera focusing depth as well as cell phone model classification. We capture three RR datasets to explore these problems with both large commercial lenses and a variety of cell phones. This repository contains time-synced videos from the perspective of both a retro-reflective probe and a target camera that can be used to train algorithms for different remote sensing tasks. We include a dataset for cellphone classification, target camera rotation prediction, and target camera focusing depth prediction.</p>
Dataset of micro-roughness, Schmidt hammer and reflectance spectra obtained at Midtre Lovénbreen foreland
<p>The files contain data of micro-roughness (Ra and Rz), raw and corrected data of Schmidt hammer rebound-values, and reflectance spectra obtained at Midtre Lovénbreen glacier foreland in July 2023. </p> <p>Funding provided by National Science Centre, Poland (Preludium Bis-2 2020/39/O/ST10/01068).</p> <p>Micro-roughness, rock strength (Schmidt hammer rebound values), and spectral reflectance were obtained in-situ on glacially abraded rock surfaces along a transect from the glacial snout to the outermost moraines from the Little Ice Age, covering circa 118 years of subaerial weathering in the proglacial polar environment. UAV surveys of the studied area were performed to obtain Digital Elevation Models (DEMs) and allow for detailed comparative studies in the future.</p> <p>Test site 1 was very close to the glacier (undergoes weathering for 3 years), site 2 was in the zone c. 43 years old, site 3 was in the zone c. 63 years old, site 4 was in the zone c. 87 years old, and the last one (site 5) was on the LIA moraines, where the duration of weathering is c. 118 years. The sites were located on biotite gneiss boulders embedded in the moraines with distinct traces of glacial abrasion, allowing us to infer that older weathering rind (developed before glacial accumulation) has been eroded. The sites were selected based on their age, homogenous petrography, accessibility, and suitability for micro-roughness measurements. Within each test site, we selected ten specific rock surfaces (c. 100 cm2 each), with clear signs of glacial abrasion, for the measurements of micro-roughness, Schmidt hammer rebound (rock strength), and spectral reflectance. </p> <p> </p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P50 (2001): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P50 (median) values of corresponding predictors for the year 2001. The median values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Sensory weighting reflects changing patterns of visual investment during ecological divergence in Heliconius butterflies
<p>Integrating information across sensory modalities enables animals to orchestrate a wide range of complex behaviours. The relative importance placed on one sensory modality over another reflects the reliability of cues in a particular environment and corresponding differences in neural investment. As populations diverge across environmental gradients, the reliability of sensory cues may shift, favouring divergence in neural investment and sensory weighting. During their divergence across closed-forest and forest-edge habitats, <em>Heliconius </em>butterflies <em>H. cydno</em> and <em>H. melpomene </em>evolved distinct brain morphologies, with the former<em> </em>investing more in vision. Molecular and anatomical data suggest selection drove these changes, but their behavioural effects remain uncertain. We hypothesised that divergent investment in neuropils may alter sensory weighting during behavioural tasks. To address this, we trained individuals in an associative learning experiment using multimodal colour and odour cues. When positively rewarded stimuli were presented in conflict pairing positively trained colour with negatively trained odour, and vice-versa, <em>H. cydno</em> prioritised visual cues more strongly than <em>H. melpomene</em>. Hence, differences in sensory weighting may evolve early during divergence and are predicted by patterns of neural investment. These findings, alongside other examples, imply that differences in sensory weighting stem from sensory investment as adaptations to local sensory environments.</p>
FIGURE 1 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 1. Geographic sketch showing the location of the plant-bearing sites. For locality numbers see Table 1.
FIGURE 6 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 6. Representation of modern European vegetation formations for the test set of fossil assemblages as delivered by Drudges 1 and 2. Formation H - Hygrophilous thermophytic mixed deciduous broadleaved forests; Formation G - Thermophilous mixed deciduous broadleaved forests; Formation F - Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation D - Mesophytic and hygromesophytic coniferous and mixed broadleaved-coniferous forests; Formation C - Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation. More detailed information on subdivisions and units is available in Appendix 9.
FIGURE 4 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 4. Representation of East Asian and European vegetation types and formations as delivered by Drudges 1 and Drudge 2 for the IPR Similarity, Taxonomic Similarity (TS), and Results Mix. See also Appendix 8.
FIGURE 8 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 8. Mean annual temperature (MAT), warm-month mean temperature (WMMT), and cold-month mean temperature (CMMT) based on CLAMP and the Coexistence Approach (CA) for the fossil plant record (sources are Kvaček et al., 2011; Teodoridis and Kvaček, 2015; Teodoridis et al., 2009, 2012, 2015, 2017). black columns: minimum CA. light grey columns: maximum CA, narrow, dark grey columns: CLAMP result. For more comprehensive climate data see Appendix 10.
FIGURE 9 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 9. Climate parameters of the modern European vegetation Formations F, G, and H based on Bohn et al. (2004) and Traiser and Mosbrugger (2004) represented as columns spanning the minimum and maximum of the respective data. Vegetation of Formation F tends to lower temperatures (note, however, that climate data for formations F.3 – F.1 are more complex). Vegetation of Formation G tends to lower MAP. Asterisks indicate single data points (no climate interval was available). The data are listed in Appendix 11. Abbreviations: MAT = mean annual temperature; WMMT = warm-month mean temperature; CMMT = cold-month mean temperature; MAP = mean annual precipitation.
Replication Package for "Software Quality Assurance Analytics: Enabling Software Engineers to Reflect on QA Practices" Paper (SCAM 2024)
<p>Welcome to our artifact!<br>In here we provide additional information for you to retrace our steps in the interview analysis.<br>It has the following contents:</p> <ul> <li><code>codebook.xlsx</code>: Our full codebook with our open codes, structured after the axial codes that emerged. <code>codebook-statistics.xlsx</code> lists for each code in which participant's interview it can be found.</li> <li><code>generate-figures</code>: The plain data and scripts used to generate the figures in the paper.</li> <li><code>survey.pdf</code>: An printout of our whole online questionnaire that guided the participants through the pretest-posttest study and the interview.</li> <li><code>survey-answers.xlsx</code>: The complete data for our participants answers in the online survey during the interviews.</li> <li><code>repoinsights-dashboard-software</code>: The code of our prototype repoinsights. As it is under active development, this is not yet documented for replicating the study setup or extending it. Still, we are providing the source code for transparency and will publish a version with comprehensive setup instructions later.</li> </ul>
Seismic profiles, migration velocities and tomographic inversion results of the deep reflection profiles in the central South China
<h1><strong>Overview</strong></h1> <p>The following set of data and scripts are meant to accompany the paper:</p> <p>Jiang, W. B., Wang, Q., Zhang, Y.Q., Dong, S.W., Ruan, Y.Q., Cui, J.J., Kuang, Z.Y., Paleoproterozoic collision to Mesozoic crustal reworking in central South China: evidence from borehole data and seismic crustal structure, Submitted to JGR: Solid Earth</p> <p>The data and scripts are intended to reproduce seismic profiles, migration velocities, and tomographic inversion results shown in the paper.</p> <p>The repository contains five directories:</p> <p><strong>./01_Seismic_Profiles/</strong> -> Four seismic profiles shown in the manuscript. (1) psdm_line01.sgy (Figure 7a); (2) psdm_line02.sgy (Figure 7b); (3) SCB_PSDM.segy (Figure 9); (4) SCB_PSTM.segy (Figure S5a).</p> <p><strong>./02_Tomographic_Data_Velocity/</strong> -> picked_traveltimes.tt, picked traveltime for the normal shots (Figure 6a). The file contains location of shots and receivers, picked traveltimes; Tomo_inv.mdl, P-wave velocity model derived from first-arrival traveltime tomography (Figure 8a); Tomo_Fx1_Fz1_ray.mdl, Ray density distribution calculated using the Vp model (Figure 8b); traveltime_data_FILE_FORMAT.pdf, this pdf file describes the format of *.tt file; mdl_data_FILE_FORMAT.pdf, this pdf file describes the format of *.mdl file.</p> <p><strong>./03_Migration_Velocity_Models/ </strong>-> Migration velocity models to produce prestack time migration profile and prestack depth migration profile. VEL_RMS.mdl, RMS velocity field used in prestack time migration (Figure S4a); VEL_INTERVAL.mdl, Interval velocity field used in prestack depth migration (Figure S4b).</p> <p><strong>./04_Bouguer_Gravity_Anomaly/ </strong>-> gravity_data.grv, the Bouguer gravity anomaly data used in 2-D gravity modelling; gravity_data_FILE_FORMAT.pdf, this pdf file describes the format of *.grv file.</p> <p><strong>./05_Scripts/</strong> -> Matlab scripts to reproduce seismic profiles, migration velocities, and tomography inversion results shown in the paper. </p>
FIGURE 3 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 3. Modern vegetation types/formations delivered as proxies by Drudges 1 and 2 for the test set of fossil assemblages. Shown are the five best fitted results for the Taxonomic Similarity (TS) and the overall scores (synthesis of all similarity approaches), i.e., 25 proxies for every plant assemblage. Pastel colours represent East Asian vegetation types, bright colours European vegetation formations. For more detailed information see Appendix 4 which provides interactive colour signature (moving the cursor over the columns provides the designation of the proxies and their relevance for every fossil assemblage).
FIGURE 2 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 2. Modern vegetation types/formations delivered as proxies by Drudges 1 and 2 for the test set of fossil assemblages. Shown are the five best fitted results for the IPR Similarities based on Drudge 1 and Drudge 2 and for the Results Mix based on Drudge 1 and Drudge 2. Pastel colours represent East Asian vegetation types, bright colours European vegetation formations. For more detailed information see Appendix 4 which provides interactive colour signature (moving the cursor over the columns provides the designation of the proxies and their relevance for every fossil assemblage).
FIGURE 7 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 7 (previous page). Representation of modern European vegetation formations for the test set of fossil assemblages as delivered by Drudges 1 and 2 in more detail (see also Appendix 9). Formation H: H001, Colchic lowland to submontane mixed oak forests, in black; H002, Hyrcanian lowland-colline mixed broadleaved forests, in dark grey; H003, Hyrcanian colline to montane oak forests, in light grey. Formation G: G.1 - Subcontinental thermophilous (mixed) pedunculate oak and sessile oak forests, in black; G.2 - Sub-Mediterranean-subcontinental thermophilous bitter oak and Balkan oak and mixed forests, in dark grey; G.3 - Sub-Mediterranean and meso-supra-Mediterranean downy oak and mixed forests, in light grey; G.4 - Iberian supra- and meso-Mediterranean oak forests, in white. Formation F: F.1 - Species-poor acidophilous oak and mixed oak forests, in black; F.2 - Mixed oak-ash forests, in dark grey; F.3 - Mixed oak-hornbeam forests, in light grey; F.4 Lime-pedunculate oak forests, in white; F.5 - Beech and mixed beech forests, hatched lower left to upper right; F.6 - Oriental beech forests and hornbeam-oriental beech forests, hatched upper left to lower right; F.7 - Caucasian mixed hornbeam-oak forests, hatched vertically. Formation F, F.5 - Beech and mixed beech forests: F.5.1.1 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, lowland(-colline) types, in black; F.5.1.2 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, colline-submontane types, in dark grey; F.5.1.3 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, montane-altimontane types, in light grey; F.5.2.1 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, colline-submontane types, in white; F.5.2.2 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, colline-submontane types, hatched lower left to upper right; F.5.2.3 and 4 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, montane-altimontane types, hatched upper left to lower right. Formation D: D.1 - Western boreal spruce forests, in black; D.2 - Eastern boreal pine-spruce and fir-spruce forests, in dark grey; D.3 - Hemiboreal spruce and fir-spruce forests with broad-leaved trees, in light grey; D.4 - Montane to altimontane, partly submontane fir and spruce forests in the nemoral zone, in white; D.5 - Boreal and hemiboreal pine forests, hatched lower left to upper right; D.6 - Montane to altimontane (subalpine) pine forests in the nemoral zone; hatched upper left to lower right.
Models for rapid estimates of leaf litter chemistry using reflectance spectroscopy
<p>Measuring the chemical traits of leaf litter is important for understanding plants' roles in nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression (PLSR) models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validation <em>R<sup>2</sup></em> = 0.543-0.941; %RMSE = 7.49-18.5) and ground-leaf litter spectra (validation <em>R<sup>2</sup></em> = 0.491-0.946; %RMSE = 7.00-19.5). Notably, intact-leaf spectra yielded better predictions of LMA. Our results support the feasibility of building models to estimate multiple chemical traits from leaf litter of a range of species. In particular, the success of intact-leaf spectral models allows non-destructive trait estimation in a matter of seconds, which could enable researchers to measure the same leaves over time in studies of nutrient resorption.</p>
A legacy of submarine slope failure in seismic reflection data along the active Hikurangi Margin, Aotearoa New Zealand
<p><span>We present a database that documents mass transport deposits (MTDs) in 32 marine geophysical surveys, encompassing >38,000 line-km of 2D seismic profiles. We map and characterise 737 MTDs, showing variations in size, location and style of failure, which we attribute to changes in geomorphic setting from north to south. MTDs in the northern Hikurangi margin, characterised by a high taper wedge and seamount subduction, show a broad range in size, with the highest proportion of MTDs displaying blocky or intact internal architecture. The central margin, characterised by lower wedge taper, hosts the most MTDs (51%), albeit with the thinnest (on average) and clustering within interridge basins. The southern Hikurangi margin hosts widespread submarine canyons and the largest (on average) MTDs, based on area and thickness. We demonstrate the importance of seismic archives in providing new insights into MTD preservation and discuss the bias between seafloor geomorphology and subseafloor seismic data in quantifying MTD occurrence. Our findings support the interrogation of the varied and complex causes of submarine landslides along active margins generally, as well as regions prone to cascading geohazards and landslide-induced tsunami. </span></p>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982-2000
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), reconstructed using the AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks. We also </li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>Other LCSPP repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.1 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.1 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Gaia DR3 asteroid reflectance spectra: L-type families, memberships and ages
<p>The Gaia Data Release 3 (DR3) contains reflectance spectra at visible wavelengths for 60,518 asteroids over the range between 374-1034 nm, representing a large sample that is well suited to studies of asteroid families.</p> <p>We wanted to assess the potential of Gaia spectra in identifying asteroid family members. Here, we focus on two L-type families, namely Tirela/Klumpkea and Watsonia. These families are known for their connection to Barbarian asteroids, which are potentially abundant in calcium-aluminum rich inclusions (CAIs).</p> <p>The method we developed to establish family memberships is based (1) on a color taxonomy specifically built on Gaia data and (2) on the similarity of spectra of candidate members with the template spectrum of a specific family.</p> <p>Our work demonstrates the advantage of combining the classical hierarchical clustering method (HCM) approach to spectral properties obtained by Gaia for the study of asteroid families. Future data releases are expected to further expand the capabilities in this domain.</p> <p>The memberships for the Tirela/Klumpkea and Watsonia families are reported here. The columns report, from left to right: the identifier of the asteroid, the absolute magnitude, the proper elements (semi-major axis, eccentricity and sine of the inclination, taken from AFP, Novaković et al., 2022), NEOWISE albedo (Masiero et al., 2011) and spectral type from our color taxonomy. For the objects that are not directly classified into the S and L classes, their most probable spectral type is also reported. </p>
DNA methylation in clonal Duckweed lineages (Lemna minor L.) reflects current and historical environmental exposures.
<p>The following depository contains raw phenotypic data and intermediate DNA methylation data presented in the article <strong>"DNA methylation in clonal Duckweed lineages (<em>Lemna minor </em>L.) reflects current and historical environmental exposures.</strong>" :</p> <p><strong>1) Raw phenotypic data</strong></p> <p>- Frond_area_Phase1_Phase2 -> Frond area measured at different time points (week 1, 6, 7 and 8) during the experiment.</p> <p>- Frond_number_Phase1_Phase2 -> Frond number measured at different time points (week 1, 6, 7 and 8) during the experiment.</p> <p><strong>2) Intermediate files obtained from running the epiGBS2 pipeline. The following files are available:</strong></p> <p>- consensus_cluster.renamed.fa -> epiGBS <em>de novo </em>loci. This file consists of the <em>de novo </em>epiGBS reference sequence file obtained during the <em>de novo </em>reference creation.</p> <p>- methylation.filtMETH -> The filtered DNA methylation data. This data was obtained after filtering the raw DNA methylation data. Cytosines which had a 10X coverage or higher and which were present in 80% of all samples were kept for further analysis.</p> <p>Demultiplexed and raw data were deposited at NCBI: BioProject: <strong>PRJNA883550</strong></p>
GYU-KING Rock art panel Multi-Light Reflectance Dataset, trial02
<p>--------------------------------------------------------------------------------------------------------------<br> GYU-KING ROCK ART PANEL MULTI-LIGHT REFLECTANCE DATASET, TRIAL02 <br> ---------------------------------------------------------------------------------------------------------------</p> <p>GYU-KING_Trial02_RTI</p> <p>Dataset composed during the National Geographic Society 'Tracing Paleolithic Aurochs: Rock Art Survey in Upper Egypt' project<br> directed by Dr. Dirk Huyge (Royal Museums of Art and History, Brussels)</p> <p>________________________________________________</p> <p>WHAT </p> <p>Multi-Light Reflectance (MLR) / Reflectance Transformation Imaging (RTI) dataset<br> - 49 original raw images (.nef, NIKON D800E)<br> - 49 derivates (.jpg)<br> - RTI Builder processing files<br> - MLR/RTI processed result HSH RTI (.rti)<br> - MLR/RTI processed result (.ptm)<br> - MLR/RTI processed result (.scml)<br> - 3D processed result based on conversion of HSH RTI into SCML into 3D (.ply, .obj)</p> <p> >>>>the .rti, .ptm and .scml files can (among others) be consulted via https://www.heritage-visualisation.org/viewer/<<<<</p> <p>DEPICTING</p> <p>The GYU-KING rock art panel and some surrounding rocks at Gebelet Yussef (Edfu, Aswan Governorate - N24.730935,E32.921496)<br> Specifications:<br> - Pictures taken during daytime (time setting on camera, in technical metadata, is incorrect!) under bright light conditions by the sun, date of data acquisition 2014-11-15<br> - A powerful flash light was used for the multi-light reflectance method (see https://doi.org/10.5281/zenodo.48060 for extra information)<br> - The obtained processed MLR/RTI models have, or have not, been cropped with the focus on the rock art panel.</p> <p>DATA ACQUISITION</p> <p>Hendrik Hameeuw & Wouter Claes</p> <p>PROCESSING</p> <p>Hendrik Hameeuw & Vincent Vanweddingen<br> _________________________________________________________<br> </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
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.