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4,681 results for “light”
A large-scale wide-baseline light field dataset - Part I
<p>This dataset is the Part I of a large-scale, synthetic wide-baseline light field dataset (called WLF), including 345 light fields. </p> <p>Each light field provides 9x9 angular (RGB) images and ground truth disparities. This light field dataset involves the spatial resolution (512x512) images only.</p> <p>The dataset is originally created for training the deep learning-based models for depth estimation. You might use this dataset for other tasks if possible.</p> <p>You might also have a try to play with this dataset using our code in https://github.com/YanWQ/LLF-Net.</p>
Fig. 3 in New acoustic and molecular data shed light on the poorly known Amazonian frog Adenomera simonstuarti (Leptodactylidae): implications for distribution and conservation
Fig. 3. Preserved male of nominal Adenomera simonstuarti (Angulo & Icochea, 2010) (= genetic lineage 3): call voucher INPA-H 40967 (SVL = 23.4 mm) from the upper Juruá River, in Tarauacá, Brazilian state of Acre. This specimen corresponds to a call voucher (see Fig. 5). A−B. Body in dorsal and ventral views, not to scale. C−D. Detail of the ventral surface of right foot and hand, respectively. Note the nearly solid, dark-colored stripe along the underside of the forearm. Photographs by J. Magnusson. Scale bar = 5 mm.
Fig. 5. Cobbionema cylindrolaimoides Schuurmans Stekhoven, 1950. Light micrographs. A in Revision of the genus Cobbionema Filipjev, 1922 (Nematoda, Chromadorida, Selachinematidae)
Fig. 5. Cobbionema cylindrolaimoides Schuurmans Stekhoven, 1950. Light micrographs. A. Female stoma showing stoma armament, arrow pointing to the right ventrosublateral tine (SMNH-179211). B. Anterior part of a female showing sphincter muscle surrounding the anterior stoma chamber (arrow) (SMNH-179212). C. Anterior part of a female with arrow showing the ampulla of the secretory-excretory system (SMNH-179211). D. Male stoma showing buccal armament (SMNH-179213). E. Cuticle showing lateral ala (SMNH-179211). F. Caudal region of a male showing spicule and crura (SMNH-179214). G. Male tail (SMNH-179214). H. Female tail (SMNH-179212).
Data and code for: High light alongside elevated pCO2 alleviates thermal depression of photosynthesis in a hard coral (Pocillopora acuta)
<p>Data and R scripts of analyses performed for the manuscript "<strong>High light alongside elevated pCO<sub>2</sub> alleviates thermal depression of photosynthesis in a hard coral (<em>Pocillopora acuta</em>)</strong>"</p>
Pupillary response to representations of light in paintings
<p>“.mat” files contain:</p> <ul> <li><strong>BUF2:</strong> Recording during fixation slide (column 1: time; column 2: right pupil width; column 3: right pupil height; column 4: left pupil width; column 5: left pupil height; column 6: glint data from right eye; column 7: glint data from right eye; column 8: image number)</li> <li><strong>BUF</strong>: Recording during stimulus presentation (same columns of BUF2)</li> <li><strong>c</strong>: block’s specifics (eye-tracker information, trials number and duration, start and end times of eye-tracker registrations)</li> <li><strong>smile</strong>: images code</li> <li><strong>Response</strong>: block’s information (first column: stimulus presentation time; variable number of other columns: smile variable; last column: trials number);</li> </ul> <p> </p>
Data Associated with the paper "Bell correlations between light and vibration"
<p>Data Associated with the <a href="http://doi.org/10.1126/sciadv.abb0260">following paper</a></p> <blockquote> <p>S. Tarrago Velez, V. Sudhir, N. Sangouard, C. Galland, Bell correlations between light and vibration at ambient conditions. Sci. Adv. 6, eabb0260 (2020).</p> </blockquote> <p>A thorough explanation of the experiment performed is available there.</p> <p> </p> <p>The file <strong>RawData_DelaySweep.zip</strong> contains the raw count information as obtained during the experiment. It is organized in different folders, each containing the data associated with one particular position of the delay stage. The file naming convention has the position, followed by the angles in detection (thA corresponds to the Stokes detection arm and thB corresponds to the anti-Stokes detection arm), and the acquisition time. As explained in the Supplementary Information Sec. 2.1, there are multiple measurements for each combination of delay and detection settings. The zip file also contains the file AnalysisBellv3.py, which was used to analyze the data. Running the file produces 'resultsBell.txt' and 'results_g2.txt', which contain the results for the CHSH parameter and the second order cross correlation, respectively.</p> <p> </p> <p>The file <strong>RawData_Visibility.zip</strong> contains the raw count information obtained for the visibility curves, as expained in the Supplementary Information Sec. 2.1. The files follow the same naming convention as those in RawData_DelaySweep.zip. It also contains the file AnalysisCounts.py, which analyzes the data to obtain the correlation parameters.</p> <p> </p> <p>The file <strong>BellCorrelations_CompiledData.xlsx</strong> is a spreadsheet containing the results of the analysis of the previous two sets of data.</p>
Fig. 3. Halichoanolaimus ovalis Ditlevsen, 1921. Light micrographs. A in New and known Halichoanolaimus de Man, 1886 species (Nematoda: Selachinematidae) from New Zealand's continental margin
Fig. 3. Halichoanolaimus ovalis Ditlevsen, 1921. Light micrographs. A. Female cuticle showing lateral differentiation and pore complexes (arrow). B. Vulva, showing vaginal glands. C. Copulatory apparatus. Scale bar: A, C = 5 µm; B = 15 µm.
Thiosulfonylation of Unactivated Alkenes with Visible-Light Organic Photocatalysis
<p><strong>Origin of the data: </strong>Experimental spectroscopic measurements<br> <strong>Data Type: </strong>experimental measurements, open access supporting information</p> <p>The data are in CSV, DSW and FBSW format. Supporting information are supplied in PDF format.</p> <p>Data <strong>generated </strong>by instruments: </p> <p>Varian Cary 5E-UV-Vis-NIR spectrophotometer for UV-Vis measurements,<br> Varian Cary Eclipse fluorescence spectrophotomer for fluorescence quenching measurements.</p> <p><strong>Analytical and procedural information: </strong>Stern-Volmer fluorescence quenching experiments, UV-Vis measurements and Fluorescent Quantum Yield determination via ferrioxalate actinometry.</p> <p><strong>Definition of variables: </strong>Wavelength, Absorbance, Concentration<br> <strong>Units of measurement: </strong>nanometers (nm), moles-per-litre (mol/l)</p> <p><strong>Abbreviations: </strong><br> File names and data headers use the following abbreviations:</p> <ul> <li><strong>FQY </strong>refers to Fluorescence Quantum Yield determination experiments</li> <li><strong>Light </strong>refers to irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information.</li> <li><strong>Dark </strong>refers to non-irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information.</li> <li><strong>SVQuench </strong>refers to Stern-Volmer quenching experiments</li> <li><strong>RAxx </strong>refer to measurements related to allylbenzene. <strong>Xx </strong>is the amount of quencher in mol/l (05 should be intended as 0.5 mol/l and so on).</li> <li><strong>RTxx </strong>refer to measurements related to <em>S</em>-(4-methylphenyl) 4-methylbenzenethiosulfonate. <strong>Xx </strong>is the amount of quencher in mol/l as above.</li> <li><strong>RExx </strong>refer to measurements related to 1,2-dimethoxy-4-(prop-2-en-1-yl)benzene. <strong>Xx </strong>is the amount of quencher in mol/l as above.</li> <li><strong>RSxx </strong>refer to measurements related to styrene. <strong>Xx </strong>is the amount of quencher in mol/l.</li> <li><strong>RTFxx </strong>refer to measurements related to <em>S</em>-(4-fluorophenyl) 4-fluorobenzenethiosulfonate. <strong>Xx </strong>is the amount of quencher in mol/l as above.</li> <li><strong>MesAcrMe Xx </strong>refers to data related to catalyst 9-mesityl-10-methylacridinium. <strong>Xx </strong>is the amount of catalyst in mol/l as above.</li> <li><strong>DMC </strong>for measurements employing dimethylcarbonate as solvent.</li> <li><strong>ACN </strong>for measurements employing acetonitrile as solvent.</li> </ul>
PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset
<p>The PICCOLO White-Light and Narrow-Band Imaging Colonoscopic dataset comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion.</p>
Kaneohe Bay Light Data 2014 and 2015 - superseded
<p>PAR light data from 2 meters depth on different patch reefs in Kaneohe Bay, Oahu Hawaii during 2014 and 2015.</p>
Leaf growth response to mild drought: natural variation sheds light on trait architecture
<p>Plant growth and crop yield are negatively affected by a reduction in water availability. However, a clear understanding of how growth is regulated under non-lethal drought conditions is lacking. Recent advances in genomics, phenomics and transcriptomics allow in-depth analysis of natural variation. In this study, we conducted a detailed screening of leaf growth responses to mild drought in a worldwide collection of <em>Arabidopsis thaliana</em> accessions. </p> <p>The raw phenotyping can be found in:<br> - cellularData.txt -> mature (23 days after stratification; DAS) leaf epidermis (third leaf) analysed for cell area, cell number, pavement cell area, pavement cell number, stomatal index and leaf area of the analysed leaf.</p> <p>- leaf3AreaMaturity.txt -> area of the third leaf at maturity (23DAs) in mm<sup>2.</sup></p> <p>- leaf3AreaProliferation.txt -> area of the third leaf at proliferation (last day of full cell proliferation; 8-10 DAS) in mm<sup>2</sup>.</p> <p>- rosetteArea Maturity.txt -> projected rosette area at maturity (22DAS)</p> <p>The phenotyping results have been normalised for batch effects ('experiment' in raw data)</p> <p>- allPhenotypesNormalised.txt -> contains the normalised data for all the measured phenotypes</p> <p>All datafiles indicate the accession name ('Accession'), the unique identifier for each accessions ('Ecotype_ID') as used in the 1001genomes project (www.1001genomes.org) and the treatment ('C' indicate well-watered plants, 'S' the mild-drought treated plants).</p> <p>These results and methodological results are described in Clauw et al. (2016, The Plant Cell).</p> <p>Citation:</p> <p><strong>Clauw, Pieter, Frederik Coppens, Arthur Korte, Dorota Herman, Bram Slabbinck, Stijn Dhondt, Twiggy Van Daele, et al. 2016. “Leaf Growth Response to Mild Drought: Natural Variation in Arabidopsis Sheds Light on Trait Architecture.” The Plant Cell, October. doi:10.1105/tpc.16.00483.</strong></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy
<p>Data for submission to Wellcome Open Research entitled "ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy".</p> <p>Data_ultraLM.tif is an image stack from the fluorescence microscope mounted on the ultramicrotome.</p> <p>Data_miniLM.tif is an image stack from the fluorescence microscope mounted in the SBF-SEM.</p> <p>Data_miniLM_EM.tif is an image stack from the SBF-SEM while the miniLM was in-situ.</p>
Photonics4All - OmniLightLaboratory: the art of light
<p>This video is presenting the OmniLight Laboratory, a tool which was developed by the International Laser Center in Slovakia within the EU funded project Photonics4All.</p>
Seeing lightness in the dark
<p>These are data files for a manuscript about lightness perception under scotopic viewing conditions. The main finding was that observers do not perceive white during scotopic adaptation, implicating that the cones must be activated to produce the appearance of white. The publication will be published in Current Biology as a Correspondence under the title, "Seeing lightness in the dark," on June 19th, 2017. The data file contents are as follows:</p> <p>chipBlocks.csv - This CSV file contains the reflectances of our paper stimuli, relative to the reflectance of our PR650 white reference. Those numbers are in the first column. The remaining columns are for plotting purposes (third column = y-coordinate of squares along bottom of Fig. 1A in the associated paper, remaining columns = RGB values for coloring those squares).</p> <p>chipsVSA4.tsv - A TSV file containing the relevant ratings for Fig. 1B in the associated paper. The first column is the x-axis position for plotting, the second column is population mean white rating, and the third column is standard error of the mean for that rating. The first set of two rows are for peripheral viewing of the chips, the second set of two rows are for foveal viewing of the chips, and the third set of two rows are for ratings given to the larger A4 papers. The ratings are grouped in sets of two because each x-position is repeated twice: once for the rating of the darkest stimulus and a second time for the lightest stimulus. The gray values given to the data points in Fig. 1B were computed by taking the average white rating for that point and dividing it by 100, giving a value in the range of 0-1, which can be used as a normalized grayscale RGB value.</p> <p>obsDataChips.csv - This is a CSV file containing each individual white rating (from 0%-100% in steps of 10%) that observers gave to the chips, with two trials per chip across four different lightness levels and at two different positions of fixation. In the final report, fixation position was only considered for the scotopic condition. A header is in the file labelling the columns.</p> <p>fovealPopDataChips.csv - A CSV file containing white ratings of the chip stimuli when viewed with the fovea. First column is the reflectance of our paper stimuli, as in chipBlocks.csv. The remaining columns are the population mean and standard error of the mean (SEM) for the different paper stimuli under the different adaptation conditions, as follows: 2nd/3rd column = population mean and SEM for scotopic adaptation, 4th/5th column = population mean and SEM for mesopic adaptation, 6th/7th column = population mean and SEM for dim photopic adaptation, 8th/9th column = population mean and SEM for bright photopic adaptation.</p> <p>obsDataA4.csv - Same as obsDataChips.csv, but for the A4 sized papers. Observers were not given explicit instructions about different fixation positions during this experiment, so fixation was always listed as "center" and this column was ignored in later analysis.</p> <p>popDataA4.tsv - Same as fovealPopDataChips.csv, but for the A4 sized papers.</p> <p>whiteOLED.zip - A collection of MATLAB MAT files with data for each of the 17 subjects that performed the white patch adjustment task on an OLED, as described in the Supplementary Info of the associated paper. Each MAT file contains a 2x3 matrix variable named, "v", which holds the "luminance (v)alue" that each observer choose as their impression of white (see Supplementary Info for more details). The first row contains values for the larger patch and the second row contains the values for the smaller patch (each tested 3 times). The values are in normalized units of 0-1 for our monitor. To convert to luminance, use the following transformation: 2.04698e-5*((v*1023)^2.28231).</p>
Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"
<p>Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves. </p> <h2> </h2> <h2>Interactive Figure Data</h2> <p>Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII).</p> <p><strong>clouds_{line_name}.csv</strong>: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight.</p> <p><strong>transfer_function_velocity_{line_name}.csv</strong>: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity.</p> <p><strong>transfer_function_tau_{line_name}.csv</strong>: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days.</p> <p><strong>transfer_function_{line_name}.csv</strong>: A CSV file containing the transfer function <span lang="el">Ψ.</span></p> <p> </p> <h2>Model-Related Figures</h2> <p><strong>fitplot_low.pdf</strong>: Same as Figure 4 in the publication, but for the low state.</p> <p><strong>fitplot_high.pdf</strong>: Same as Figure 4 in the publication, but for the high state.</p> <p><strong>geoplot_low.pdf</strong>: Same as Figure 5 in the publication, but for the low state.</p> <p><strong>geoplot_high.pdf</strong>: Same as Figure 5 in the publication, but for the high state.</p> <p><strong>lagplot_low.pdf</strong>: Same as Figure 6 in the publication, but for the low state.</p> <p><strong>lagplot_high.pdf</strong>: Same as Figure 6 in the publication, but for the high state. </p> <p> </p> <h2>Spectral Reduction Method Comparison</h2> <p><strong>spec_decomp_pyqsofit.pdf</strong>: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels.</p> <p><strong>input_method_comp.pdf</strong>: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.</p> <p> </p> <h2>Published Value Comparison</h2> <p><strong>pubval_table.pdf</strong>: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024). </p> <p> </p> <h2>Joint Posterior Analysis</h2> <p><strong>joint_line_posterior_table.pdf</strong>: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species. </p> <p> </p> <h2>Virial Factor Analysis</h2> <p><strong>fcomp.pdf</strong>: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states.</p> <p><strong>fcorr_table.pdf</strong>: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.</p>
A Pixel-scale Corrected Nighttime Light Dataset (PCNL, 1992-2024) Combining DMSP-OLS and NPP-VIIRS
<h1><strong>Updated to 2024! Welcome to download!</strong></h1> <p>We proposed a set of DMSP-OLS and NPP-VIIRS inter-correction methods and produced a pixel-scale corrected nighttime light dataset (PCNL).</p> <p>Two sets of reliable global nighttime light datasets were chosen as the basis. CCNL-DMSP (1992-2013) is a consistent and corrected nighttime light dataset produced from DMSP-OLS. CCNL-DMSP mainly solved three problems of DMSP-OLS, namely, interannual inconsistency, saturation and blooming. The annual VNL-VIIRS dataset available on the Earth Observation Group website was also used, and the monthly median masked data of V21/V22 was selected. Using filtering and employing outlier removal, VNL-VIIRS has removed sunlit, moonlit and cloudy pixels, and has discarded biomass burning pixels.</p> <p>PCNL shows a great temporal and spatial consistency at both the pixel scale and the regional scale.</p> <p> </p> <p><strong>Please refer to the paper for detailed information.</strong></p> <div> <div>Li, S., Cao, X*., Zhao, C., Jie, N., Liu, L., Chen, X., Cui, X., (2023). Developing a Pixel-Scale Corrected Nighttime Light Dataset (PCNL, 1992–2021) Combining DMSP-OLS and NPP-VIIRS. <em>Remote Sensing,</em> 15, 3925. <a href="https://doi.org/10.3390/rs15163925" target="_blank" rel="noopener">https://doi.org/10.3390/rs15163925</a></div> </div> <p> </p> <p><strong>Other recent publications using PCNL:</strong></p> <p>Li, S., Cao, X.*, (2024). Monitoring the modes and phases of global human activity development over 30 years: Evidence from county-level nighttime light. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 126, 103627. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jag.2023.103627" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jag.2023.103627</a></p> <div> <div> <div>Li, S., Cao, X.*, Liu, L., Li, A., (2025). Inequality of divided and shared socio-economic resources in 15-minute cities of China. <em>Geography and Sustainability, </em>100337. <a href="https://doi.org/10.1016/j.geosus.2025.100337" target="_blank" rel="noopener">https://doi.org/10.1016/j.geosus.2025.100337</a></div> </div> </div>
Dataset on light measuments in the understory of a Tropical forest restoration submitted to four thinning intensities through chemical management
<p>The lack of information on the management of light in tropical forests causes a technical constraint for timber production in restoration sites, especially given the light restrictions for timber production. This issue could be amended with the development of methods to easily manage and estimate light availability, targeting practices that balance restoration success and productivity. We conducted the study that gathered this data in an area within the Atlantic Forest, Brazil, where we tested the efficiency of chemical thinning fast-growing species to increase light availability in the understory of a five-year-old restoration planting. Our goal was to increase the growth rates of desirable timber species in the understory of the restoration site. <br>Moreover, we tested the viability of using hemispherical photography taken with a smartphone to assess light incidence and assist restoration management practices. We calculated the percentage of photosynthetically active radiation (PAR) using a ceptometer in four different thinning intensities and compared them to the smartphone measures using correlation analysis and generalized mixed models. Chemical thinning increased light incidence in the understory Light management through PAR and canopy opening were highly correlated overall, especially after three months of management and above 60% of the basal area thinned. Data demonstrates the potential of chemical thinning as a management practice to enhance light availability in the understory of tropical forest restoration sites and highlights the value of using smartphones and fisheye clips for the indirect assessment of light conditions. </p>
Fig. 6 in Stereoscopic light-microscopy in biology - A review
Fig. 6: Stereoscopic images of an amoeba with its irregular pseudopodia extending into all spatial directions (width: 150 µm, single images: Wim van Egmond).
Fig. 5 in Stereoscopic light-microscopy in biology - A review
Fig. 5: Stereo-pair showing the freshwater polyp Hydra sp. under the light-microscope (height: 0.5 mm).
Fig. 3 in Stereoscopic light-microscopy in biology - A review
Fig. 3: Stereoscopic photographs of the trumpet animal Stentor sp, representing a sessile freshwater organism with partly massive occurrence (height: 200 µm, single images: Wim van Egmond).
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.