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2,649 results for “optics”
The optical absorption in semiconductor to semimetal PtSe2 arises from direct transitions - Supporting data
<p>Data supporting the findings of the work:</p> <p>M. Tharrault, S. Ayari, M. Arfaoui, E. Desgué, R. Le Goff, P. Morfin, J. Palomo, M. Rosticher, S. Jaziri, B. Plaçais, P. Legagneux, F. Carosella, C. Voisin, R. Ferreira and E. Baudin, <strong>The optical absorption in semiconductor to semimetal PtSe2 arises from direct transitions</strong><em>, <a href="https://doi.org/10.48550/arXiv.2311.01847">arXiv:2311.01847 [cond-mat.mtrl-sci]</a></em>.</p> <p> </p> <p>The sample names in this study match those used in the <strong>Raman spectroscopy focused study</strong> <a href="https://doi.org/10.1088/2053-1583/ad1e79">https://doi.org/10.1088/2053-1583/ad1e79</a>. Data from that study can be accessed at <a href="https://doi.org/10.5281/zenodo.8256907">https://doi.org/10.5281/zenodo.8256907</a>.</p> <p>See the Supplemental Material for extra details.</p> <p> </p> <p>Files available:</p> <p><strong>Optical absorption spectroscopy.zip:</strong> reflectance, transmittance, real and imaginary 2D optical conductivity for exfoliated and MBE samples.</p> <p><strong>Differential reflectance with temperature.zip:</strong> differential reflectance Delta R / R pour 4 exfoliated thin samples and temperatures from 290K to 440K.</p> <p><strong>Layers label attribution.txt:</strong> number of layers for each sample.</p> <p><strong>Selected samples fig1.txt: </strong>list of samples displayed in the figure 1 of the publication.</p> <p><strong>Band diagrams.zip:</strong> Band diagrams for GGA-computed 1-12L and AB stacked bilayers as well as GW and HSE06 computed 1L and the k-points path used for the diagrams.</p> <p><strong>Optical conductivity DFT.zip:</strong> real 2D optical conductivity and jDOS computed using GGA-IPA for 1-12L and AB stacked bilayers.</p> <p><strong>Optical absorption 1L different models.csv:</strong> GGA-IPA, GW-IPA, GW-BSE and HSE06-IPA real 2D optical conductivities for the monolayer.</p> <p><strong>1L optical absorption with temperature DFT.csv</strong>: 2D real optical conductivity of 1L PtSe2 computed with EPW/ZG Quantum Espresso package for T = 0 K to 450 K.</p> <p><strong>1L electronic spectral function with temperature.zip:</strong> electronic spectral function for 1L PtSe2 for T = 0, 300 and 450 K.</p> <p><strong>Atomic orbitals decomposition.zip:</strong> band diagrams and the corresponding orbital components, as displayed in Supplemental Material.</p> <p><strong>Electronic bandgap correction.txt:</strong> GGA computed bandgaps and corrected by the energy shift.</p>
data for "Could we achieve the on-line Measurements of the Optical Fractal Dimensions of Black Carbon?"
Open the record for dataset details and reuse information.
Optical images of comets C/2021 A1 (Leonard) (left) and C/2022 E3 (ZTF) (right)
<p>Optical images of comets C/2021~A1 (Leonard) (left) and C/2022~E3 (ZTF) (right), (c) N. Biver.<br> Left panel: telescopic image of comet C/2021~A1 (Leonard) on 29 November 2021 at 4:41 UTC (94~s exposure).<br> Right panel: telescopic image of comet C/2022~E3 (ZTF) on 31 January 2023 at 4:53 UTC (126~s exposure).<br> Images were taken at the focus of a 40.7-cm telescope at F/D=4.3 from Eure-et-Loir (France).<br> Field of view is 45x45 arcmin, North is up.</p>
Dataset related to the publication "Measurement and assignment of J = 5 to 9 rotational energy levels in the 9070-9370 cm-1 range of methane using optical frequency comb double-resonance spectroscopy"
<p>The files contain the normalized interleaved double-resonance spectra recorded with four different pump transitions, indicated in the file name. The first column is the wavenumber, the second column is the transmission intensity.</p>
Dataset: Use of bioresorbable fibers for short-wave infrared spectroscopy using time-domain diffuse optics
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Dataset for "High power single crystal KTA optical parametric amplifier for efficient 1.4–3.5 µm mid-IR radiation generation"
<p>The dataset represents the experimental data for publication "High power single crystal KTA optical parametric amplifier for efficient 1.4–3.5 µm mid-IR radiation generation".</p>
Optical Particle Tracking in the Pneumatic Conveying of Metal Powders through a Thin Capillary Pipe
<p>An experimental setup utilizing high-speed cameras and specialized optics was constructed to collect the conveying flow characteristics. The data here presented is pre-processed using ImageJ/Fiji, and uses the TrackMate package (see https://github.com/trackmate-sc/TrackMate/pull/296). The videos can be loaded to Fiji using the FFMPG package.</p>
Validation of the registration of intraoperative optical image of exposed brain with preoperative MRI volumes (T1 volumes with injection of Gadolinium).
<p>This dataset contains the results of the registration of intraoperative optical images of exposed brain with pre-operative MRI volumes (T1 volumes with injection of Gadolinium). The file contains the validation metric (Euclidean distance) calculated with a landmark-based validation approach for 9 patients.</p>
Optical liquid phantom characterization
<p>This dataset contains the results of the characterisation of liquid phantoms compounds and recipes that will be used to test hyperspectral systems.</p> <p>We have characterize 4 compounds: Indian Ink, Protoporphyrin IX (PpIX), Blood (human and horse) and Yeast fluorescence.</p> <p>The files contained here are:</p> <ul> <li>RawSpectraInk.zip: raw absorbance spectra of the Indian ink phantoms (Winsor & Newton, Black Indian 951) measured using a commercial spectrophotometer (PerkinElmer LAMBDA 950), between 400 and 900 nm with 0.5-nm resolution at 5 different concentrations (dilution in water) (0.2, 0.3, 0.4, 0.5 and 0.6 µl/ml).</li> <li>Spectrofluorometer_Yeast.zip. raw data of the fluorescence of baker’s yeast (<em>Saccharomyces cerevisiae</em>) diluted at a concentration of 20 mg/mL at different excitation wavelength measured using a commercial luminescence spectrometer (PerkinElmer LS 55). The illumination was sequential from 300 to 700 nm at 20-nm steps. Also contains the reference fluorescence spectra accounting from water and PS contribution without any yeast, illuminated from 300 to 400 nm at 20-nm steps</li> <li>SpectrophotometerSpectra.mat. Absorbance spectra of human blood, horse blood and PpIX.</li> </ul> <p>The horse blood was defibrinated horse blood that can be easily purchased from a chemical supplier (https://uk.vwr.com/store/product/9131472/animal-blood-serum-products-for-microbiology) and the human blood was expired human red blood cells acquired from a blood bank.</p> <p>The data here are the absorption spectra measured using a commercial spectrophotometer (PerkinElmer Lambda 750 S). For the blood, solution of 5% blood in PBS for both blood type was prepared . Then the blood samples where fully oxygenated by bubbling O2, and by monitoring the level of dissolved oxygen (DO2) in these solutions. Once fully oxygenated, the absorbance of the solution was measured in the spectrophotometer. A second sample of deoxygenated blood was measured in the same conditions. In order to induce the deoxygenation of the solution, a small quantity of sodium dithionite was added to the oxygenated solution, and the measurement was taken after the DO2 meter reading had fallen to 0%.</p> <p>The PpIX (solution of 1.2mM of PpIX in dimethyl sulfoxide (DMSO)) absorption spectra was measured with the same spectrophotometer.</p> <p><u>Variables in the file:</u></p> <p>Wavelength: the wavelength vector</p> <p>Spectra: the absorbance spectra of each compound</p> <p>Name: the name of each compound</p> <p> </p> <p>The rest of the files report measurements performed in reflectance in a diffuse media. The setup used to test the different components was a metallic container, of dimensions 27 × 15 × 16 cm. The sides and bottom of the container were coated with a matt black absorbing paint to prevent any reflections from the boundaries. To ensure precise measurements of the volume, particularly for the large volumes of basal solution required, a gravimetric approach was used, as weighing liquids is a valid and accurate method for determining volume. DO2 levels within the solutions were monitored using a calibrated oxygen probe. Solution pH was concurrently monitored using a pH probe. The entire setup was placed on a hot stirring plate complete with a temperature probe, set to maintain a constant 37°C. Constant stirring at 700 RPM ensured that the solution remained homogeneous.</p> <p>The optical setup was composed of a broadband light (HL-2000 UV-Vis-NIR Halogen Light Source by Ocean Optics) for the source and of a USB4000 spectrometer (Ocean Optics) for the detection. Light was guided from the light source and to the spectrometer via optical fibres with s source detector distance of 1 cm. The compositions of the baseline solution for the two phantoms is of 1400g of PBS solution (at 50mM) and 75g of Intralipid 20%.</p> <p>The files contained here are:</p> <ul> <li>SpectraPpIXalone.mat. Attenuation spectra of the PpIX (at 15uM concentration) in the baseline solution described above.</li> </ul> <p><u>Variables in the file:</u></p> <p>Wavelength: the wavelength vector</p> <p>Spectra: Attenuation spectra of the PpIX in the diffusive media</p> <p>Name: the name of the spectra</p> <ul> <li>DeltaA_Human_Horse_Blood.mat: Change in attenuation between oxygenated and deoxygenated blood for both horse and human blood. 5 mL of blood were added to the baseline solution. The blood was deoxygenated using N<sub>2</sub>.</li> </ul> <p><u>Variables in the file:</u></p> <p>Wavelength: the wavelength vector</p> <p>Spectra: Change in attenuation between deoxygenated and oxygenated blood</p> <p>Name: the name of the spectrum</p> <ul> <li>SpectraBloodPpIX.mat: Attenuation spectra of the blood with and without PpIX (same quantities as above) both for oxygenated and deoxygenated states.</li> </ul> <p><u>Variables in the file:</u></p> <p>Wavelength: the wavelength vector</p> <p>Spectra: the attenuation spectra</p> <p>Name: the name of the spectra</p>
Optical vortex knots - Mathematica files
<p>The zip-folder contains Wolfram Mathematica files that illustrate the constructions of optical vortex knots as discussed in the article "Complex optical vortex knots". For every knot type of up to 8 crossings there are two Mathematica files, one for each method of construction discussed in the article.</p>
Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2
<p>This data set contains all resources for the research project "<span>Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2" (published in Nature Communications (2024)).</span></p>
Infrared Spectra and Optical Constants of Amorphous Isocyanic Acid, Formaldehyde, and Formic Acid
<p>Infrared spectra and optical constants from Hudson et al., 2024, ApJ 977 (1), 130. DOI: 10.3847/1538-4357/ad8c43</p>
AMT29 optical underway properties and estimates of chlorophyll-a concentration
<p>Dataset of inherent optical properties determined as described in https://doi.org/10.5194/essd-2024-267 and https://doi.org/10.1364/OE.25.0A1079</p>
Retrievals of aerosol optical, componential, and radiative properties from joint observations of sun photometer and Lidar using GRASP algorithm
<p>The site location is <span>114°21′E, 30°32′N (Central China). The time period is from 2021.07 to 2022.08.</span></p> <p><span>Data includes AOD (all sequences), SSA, ASY, ASD, CRI (only retrieved from sky irradiance), components (black carbon, brown carbon, dust, iron oxide, water-soluble inorganic salt and water), and vertical profiles of shapes of total extinction, fine-mode extinction, and coarse-mode extinction.</span></p>
Dataset for "LOROS: Laboratory Simulations of the Optical RadiOmeter composed of CHromatic Imagers (OROCHI) Experiment of the Martian Moons eXploration (MMX) Mission"
<p>This dataset hosts the image and numerical data analysed and derived in the accompanying Stabbins & Kameda article for the special issue of Progress in Earth and Planetary Science on instrumentation and preparations for the JAXA Martian Moons eXploration (MMX) mission. The paper describes and validates the performance of the Laboratory OROCHI Simulator (LOROS).</p> <p>OROCHI (Optical RadiOmeter composed of CHromatic Imagers) is a multispectral multi-view imaging system for the JAXA MMX spacecraft, that will image Phobos and Deimos across 8 visible and near-infrared spectral channels with unprecedented spatial resolution, recording data that in synergy with the other instruments of the MMX spacecraft and rover will constrain hypotheses on the origin of the Martian moons.</p> <p>LOROS is a laboratory simulator of OROCHI, constructed from commercial off-the-shelf parts.</p> <p>The dataset for the characterisation and validation of LOROS is composed of the following sub-sets:</p> <p>A. Modulation Transfer Function<br>B. Expected Reflectance of Carbonaceous Chondrite & Dark Spectralon<br>C. Radiometric Calibration<br>D. Dark Spectralon Validation</p> <div> <h2>Dataset A: Modulation Transfer Function</h2> This dataset includes the table of results of MTF measurements of the slant-edge target at 5 different random orientations in the range of ~7--10°: <div>- <code>mtf_results_07122023.csv</code></div> <br> <div>and the region-of-interest images, for each orientation and each LOROS channel, used to perform the analysis via the <a href="https://sourceforge.net/p/mtfmapper/home/Home/" target="_blank" rel="noopener">MTF Mapper software</a>:</div> <div>- <code>mtf_measurements_07122023</code></div> <br> <div>The directory tree of measurements, for the <em>n</em>th orientation, is illustrated below. Region-of-interest images are stored under <code>img</code>, and are averaged over 25 repeat images to minimise random noise, have had dark frames subtracted, and have been converted from 12-bit to 8-bit grayscale images for compatibility with the MTF Mapper software. Modulation Transfer Function (MTF) and Spatial Frequency Response (SFR) diagnostics generated by MTF Mapper are stored in the <code>results</code> directory.</div> <div> </div> <div><code>mtf_measurements_07122023</code></div> <div><code>├── mtf_knifeedge_low_07122023_*n*</code></div> <div><code>│ ├── img</code></div> <div><code>│ │ ├── 0_850_img_ave.tif</code></div> <div><code>│ │ ├── 1_475_img_ave.tif</code></div> <div><code>│ │ ├── ...</code></div> <div><code>│ ├── results</code></div> <div><code>│ │ ├── 0_850_img_ave_annotated.jpg</code></div> <div><code>│ │ ├── 0_850_img_ave_edge_mtf_values.txt</code></div> <div><code>│ │ ├── 0_850_img_ave_edge_sfr_values.txt</code></div> <div><code>│ │ ├── 1_475_img_ave_annotated.jpg</code></div> <div><code>│ │ ├── ...</code></div> <div><code>├── mtf_knifeedge_low_07122023_*n+1*</code></div> <div><code>│ ├── img</code></div> <div><code>│ │ ├── ...</code></div> <div> </div> <div>This data constitutes part of <strong>Table 1</strong> and <strong>Figure 2</strong> of the manuscript.</div> <div> <h2>Dataset B: Expected Reflectance of Carbonaceous Chondrite & Dark Spectralon</h2> This dataset includes the high-resolution ($\delta\lambda$=1 nm) reference reflectance spectra of the representative Carbonaceous Chondrite meteorite (<a href="https://westernreflectancelab.com/visor/graph/?results-selection=16136&results-item=16136&results-item=15972&results-item=231&results-item=230&graph=&form-TOTAL_FORMS=1&form-INITIAL_FORMS=0&form-MIN_NUM_FORMS=0&form-MAX_NUM_FORMS=1000&form-0-sample_name=nogoya&form-0-any_field=meteorite&form-0-id=&sort_params=-sample_name&page_selected=1&jump-to-page=" target="_blank" rel="noopener">Nogoya)</a> and the 5% reflectance Spectralon calibration target (<a href="https://www.labsphere.com/wp-content/uploads/2021/09/SpectralonStandards.pdf" target="_blank" rel="noopener">SCT5</a>):<br> <div>- <code>highres_input.csv</code></div> <br> <div>and the resampled spectra of these materials expected for OROCHI and LOROS filter wavelengths:</div> <br> <div>- <code>loros_observation.csv</code></div> <div>- <code>orochi_observation.csv</code></div> <br> <div><code>B_expected_reflectance</code></div> <div><code>├── README.md</code></div> <div><code>├── highres_input.csv</code></div> <div><code>├── loros_observation.csv</code></div> <div><code>└── orochi_observation.csv</code></div> <br> <div>This data constitutes <strong>Table 1</strong> and <strong>Figure 10</strong> of the manuscript.</div> <div> </div> <div> <div> <h2>Dataset C: Radiometric Calibration</h2> This dataset contains the image and derived data for 4 experiments with different illumination conditions for characterising the radiometric response of each of the 8 channels of LOROS.</div> <div><br> <div>This dataset contributes to <strong>Tables 2 - 4</strong> and <strong>Figures 3 - 9</strong> of the manuscript.</div> <br> <div>The final derived metrics are hosted in the spreadsheet:</div> <br> <div>- <code>measured_sensor_properties.csv</code></div> <br> <div>and image data and intermediary derived properties for each experiment are stored in the</div> <br> <div>- <code>experiments</code></div> <br> <div>directory.</div> <br> <div><code>C_radiometric_calibration</code></div> <div><code>├── README.md</code></div> <div><code>├── experiments</code></div> <div><code>│ ├── F*S5L10</code></div> <div><code>│ ├── F*S99L10</code></div> <div><code>│ ├── FGS99L2</code></div> <div><code>│ └── FGS99L10</code></div> <div><code>└── measured_sensor_properties.csv</code></div> <br> <h3><code>experiments</code> Directories</h3> In the directory of each experiment are sub-directories hosting Photon Transfer and Dark Transfer datasets, and a spreadsheet of derived metrics of these.<br> <div> </div> <div><code>C_radiometric_calibration</code></div> <div><code>├── README.md</code></div> <div><code>├── experiments</code></div> <div><code>│ ├── F*S5L10</code></div> <div><code>│ │ ├── dark_transfer_curve</code></div> <div><code>│ │ ├── photo_transfer_curve</code></div> <div><code>│ │ └── F*S5L10_derived_properties.csv</code></div> <div><code>│ └── ...</code></div> <div><code>└── measured_sensor_properties.csv</code></div> <div> </div> </div> <div> </div> <div><strong>Derived Properties</strong><br> <div> </div> <div>The spreadsheet (<code>[experiment]_derived_properties.csv</code>) collecting the properties derived from each experiment holds the following information, that has been extracted from the Photon Transfer and Dark Transfer curves as described in §4.2 of the manuscript:</div> <br> <div><code>camera # The camera number and wavelength</code></div> <div><code>k_adc # Sensitivity (e-/DN)</code></div> <div><code>full_well_e # Saturation Capacity (electrons)</code></div> <div><code>full_well_dn # Saturation Capacity (Digital Numbers)</code></div> <div><code>read_noise_e # Read Noise (electrons)</code></div> <div><code>read_noise_dn # Read Noise (Digital Numbers)</code></div> <div><code>bias_e # Offset (electrons)</code></div> <div><code>bias_dn # Offset (Digital Numbers)</code></div> <div><code>dark_current_e # Dark Current (electrons/second)</code></div> <div><code>dark_current_dn # Dark Current (Digital Numbers/second)</code></div> <div><code>DR # Dynamic Range</code></div> <div><code>lin_min # Minimum Linearity Error</code></div> <div><code>lin_max # Maximum Linearity Error</code></div> <div><code>linearity # Average Linearity Error</code></div> <div><code>snr_max # Maximum Signal-to-Noise Ratio</code></div> <div><code>t_exp_min # Minimum Exposure used in experiment (seconds)</code></div> <div><code>t_exp_max # Maximum Exposure used in experiment (seconds)</code></div> <div><code>expected_response # Expected Response (or 'Digital Flux') for OROCHI^12 at Phobos (Digital Numbers/second)</code></div> <div><code>response # Fitted Response (or 'Digital Flux') (Digital Numbers/second)</code></div> <br> <div>These values are given for each channel of LOROS, as well as the expected values for LOROS in off-the-shelf configuration (with no gain adjustment), LOROS with the gain adjustment, and OROCHI if downsampled to 12-bit resolution digital numbers.</div> <br> <div>This data constitutes <strong>Table 2</strong> of the manuscript.</div> <br> <div><strong>Dark Transfer Curve</strong></div> <br> <div>The <code>dark_transfer_curve</code> directory hosts the derived Dark Transfer Curve data (<code>derived_data</code>) and the source region-of-interest dark image pair data (<code>raw_data</code>) for each LOROS channel.</div> <br> <div><code>dark_transfer_curve</code></div> <div><code>├── derived_data</code></div> <div><code>│ ├── F*S5L10_0_850_dtc.csv</code></div> <div><code>│ ├── F*S5L10_1_475_dtc.csv</code></div> <div><code>│ ├── F*S5L10_2_400_dtc.csv</code></div> <div><code>│ ├── F*S5L10_3_550_dtc.csv</code></div> <div><code>│ ├── F*S5L10_4_725_dtc.csv</code></div> <div><code>│ ├── F*S5L10_5_950_dtc.csv</code></div> <div><code>│ ├── F*S5L10_6_650_dtc.csv</code></div> <div><code>│ └── F*S5L10_7_550_dtc.csv</code></div> <div><code>└── raw_data</code></div> <div><code>├── 0_850</code></div> <div><code>│ ├── 850_10095570us_1_calibration.tif</code></div> <div><code>│ ├── 850_10095570us_2_calibration.tif</code></div> <div><code>│ ├── 850_104us_1_calibration.tif</code></div> <div><code>│ ├── 850_104us_2_calibration.tif</code></div> <div><code>│ ├── ...</code></div> <div><code>├── 1_475</code></div> <div><code>├── 2_400</code></div> <div><code>├── 3_550</code></div> <div><code>├── 4_725</code></div> <div><code>├── 5_950</code></div> <div><code>├── 6_650</code></div> <div><code>├── 7_550</code></div> <div><code>└── camera_config.csv</code></div> <br> <div>The <code>raw_data</code> directory hosts a dark image pair for each exposure time used, and the <code>camera_config.csv</code> spreadsheet gives metadata for the system configuration, including the coordinates and dimensions of the region-of-interest for each channel.</div> <br> <div>The dark transfer curve for each experiment and each channel (<code>[experiment]_[channel]_[wavelength]_dtc</code>) gives the data derived from each raw image data, with the following values:</div> <br> <div><code>exposure # exposure duration (seconds)</code></div> <div><code>n_pix # number of pixels in the region of interest</code></div> <div><code>mean # average value of the region of interest</code></div> <div><code>std_t # total standard deviation of the region of interest</code></div> <div><code>std_rs # read+shot-noise standard deviation, copmuted from the difference of the image pair</code></div> <br> <div>This data constitutes <strong>Figures 5 and 8</strong> of the manuscript.</div> <br> <div><strong>Photon Transfer</strong></div> <br> <div>The <code>photon_transfer_curve</code> directory hosts the derived Photon Transfer Curve data (<code>derived_data</code>) and the source region-of-interest illuminated image pairs and associated dark frame image data (<code>raw_data</code>) for each LOROS channel.</div> <br> <div><code>photo_transfer_curve</code></div> <div><code>├── derived_data</code></div> <div><code>│ ├── F*S5L10_0_850_ptc.csv</code></div> <div><code>│ ├── F*S5L10_1_475_ptc.csv</code></div> <div><code>│ ├── F*S5L10_2_400_ptc.csv</code></div> <div><code>│ ├── F*S5L10_3_550_ptc.csv</code></div> <div><code>│ ├── F*S5L10_4_725_ptc.csv</code></div> <div><code>│ ├── F*S5L10_5_950_ptc.csv</code></div> <div><code>│ ├── F*S5L10_6_650_ptc.csv</code></div> <div><code>│ └── F*S5L10_7_550_ptc.csv</code></div> <div><code>└── raw_data</code></div> <div><code>├── 0_850</code></div> <div><code>│ ├── 850_104us_1_calibration.tif</code></div> <div><code>│ ├── 850_104us_2_calibration.tif</code></div> <div><code>│ ├── 850_104us_d_drk.tif</code></div> <div><code>│ ├── 850_105828us_1_calibration.tif</code></div> <div><code>│ ├── ...</code></div> <div><code>├── 1_475</code></div> <div><code>├── 2_400</code></div> <div><code>├── 3_550</code></div> <div><code>├── 4_725</code></div> <div><code>├── 5_950</code></div> <div><code>├── 6_650</code></div> <div><code>├── 7_550</code></div> <div><code>└── camera_config.csv</code></div> <br> <div>The <code>raw_data</code> directory hosts an image pair and dark frame for each exposure time used, and the <code>camera_config.csv</code> spreadsheet gives metadata for the system configuration, including the coordinates and dimensions of the region-of-interest for each channel.</div> <br> <div>The photon transfer curve for each experiment and each channel (<code>[experiment]_[channel]_[wavelength]_ptc</code>) gives the data derived from each raw image data, with the following values across the region-of-interest:</div> <br> <div><code>exposure # exposure duration (seconds)</code></div> <div><code>n_pix # number of pixels in the region of interest</code></div> <div><code>mean # average value (Digital Numbers)</code></div> <div><code>std_t # total standard deviation (Digital Numbers)</code></div> <div><code>std_rs # read+shot-noise standard deviation (Digital Numbers), computed from the difference of the image pair</code></div> <div><code>d_mean # average value of the dark (Digital Numbers)</code></div> <div><code>d_dsnu # Dark Signal Nonuniformity (Digital Numbers)</code></div> <div><code>std_s # Shot Noise (read noise removed) (Digital Numbers)</code></div> <div><code>k_adc # Sensitivity (note this the point-wise sensitivity, rather than fitted) (electrons/Digital Number)</code></div> <div><code>linearity # Linearity Error (point-wise distance to least-squares linear fit) (%)</code></div> <div><code>snr # Signal-to-Noise Ratio, derived from shot-noise (point-wise)</code></div> <div><code>snr_t # Signal-to-Noise Ratio, derived from total noise (point-wise)</code></div> <div><code>e- # Electron count, derived from sensitivity</code></div> <div><code>e-_noise # Electron shot-noise, derived from sensitivity</code></div> <br> <div>This data constitutes <strong>Figures 3, 4, 6, 7 & 9</strong> of the manuscript.</div> <br> <div><strong>Measured Sensor Properties</strong></div> <br> <div>The <code>measured_sensor_properties.csv</code> spreadsheet collects and averages the following metrics over the 4 experiments performed, to give the values for each channel, along with the expected values for LOROS in off-the-shelf configuration, gain-adjusted LOROS, and OROCHI downsampled to 12-bit resolution.</div> <br> <div><code>SNR Max</code></div> <div><code>Dynamic Range (dB)</code></div> <div><code>Dynamic Range (bits)</code></div> <div><code>Sensitivity (e-/DN)</code></div> <div><code>Saturation Capacity (e-)</code></div> <div><code>Saturation Capacity (DN)</code></div> <div><code>Read Noise (e-)</code></div> <div><code>Read Noise (DN)</code></div> <div><code>Nonlinearity (%)</code></div> <div><code>Dark Signal@30°C (e-/s)</code></div> <div><code>Dark Signal@30°C (DN/s)</code></div> <div><code>Bias (e-)</code></div> <div><code>Bias (DN)</code></div> <div><code>DSNU1288 (DN)</code></div> <div><code>DSNU1288 (e-)</code></div> <div><code>PRNU1288 (%)</code></div> <br> <div>This data constitutes <strong>Table 3</strong> of the manuscript.</div> <div> </div> <div> <h2>Dataset D: Dark Spectralon Validation</h2> This dataset contains the raw image and derived data used to demonstrate the ability of LOROS to measure the spectral reflectance of the 5% reflectance Spectralon calibration target (<a href="https://www.labsphere.com/wp-content/uploads/2021/09/SpectralonStandards.pdf" target="_blank" rel="noopener">SCT5</a>).<br> <div>The image data is hosted in the directory:</div> <br> <div>- <code>raw_data</code></div> <br> <div>and the processed data (e.g. reflectance products) are hosted in the directory:</div> <br> <div>- <code>processed_data</code></div> <br> <div><code>D_dark_spectralon_validation</code></div> <div><code>├── processed_data</code></div> <div><code>│ ├── SCT5</code></div> <div><code>│ └── SCT99</code></div> <div><code>├── raw_data</code></div> <div><code>│ ├── SCT5</code></div> <div><code>│ ├── SCT5_dark</code></div> <div><code>│ ├── SCT99</code></div> <div><code>│ └── SCT99_dark</code></div> <div><code>└── README.md</code></div> <br> <div><strong>Raw Data</strong></div> <br> <div>The raw data directory contains images captured of <code>SCT5</code> and <code>SCT99</code> (99% reflectance white Spectralon), and accompanying dark frames, hosted in the <code>SCT5_dark</code> and <code>SCT99_dark</code> frames respectively.</div> <br> <div>For each channel, 25 repeat images have been captured for the illuminated and dark frames.</div> <br> <div><strong>Processed Data</strong></div> <br> <div>The processed SCT99 and SCT5 datasets differ slightly. Both include:</div> <br> <div><code>├── img</code></div> <div><code>├── rfl</code></div> <div><code>└── rois</code></div> <br> <div>directories, with the SCT99 scene also including a <code>cal</code> directory.</div> <br> <div><code>img</code> hosts a set of <code>context</code> figures, showing the regions of interest selected, <code>fits</code> hosts the floating point mean (<code>ave</code>), standard error (<code>err</code>), standard deviation (<code>std</code>) and single-frame (<code>one</code>), all in units of Digital Number, after dark frame subtraction, flat-fielding and linearity correction. <code>uint8</code> hosts the same data rescaled to 8-bit resolution, for quick-view.</div> <br> <div><code>rfl</code> hosts the same set as <code>img</code>, after conversion to units of reflectance against the results of the SCT99 calibration (see §3.5 of the manuscript).</div> <br> <div><code>rois</code> gives plots of the mean and error of the reflectance spectrum of the region of interest, as well as the Signal-to-Noise Ratio, as well as the data for each region-of-interest (<code>roi_data</code>).</div> <br> <div><code>cal</code> also gives context figures for each channel region-of-interest, as converted to units of reflectance coefficients (1/DN/s).</div> </div> </div> </div> </div> </div>
Raw data for UV-Selective Optically Transparent Zn(O,S)-Based Solar Cells
<p>In the following the raw data lying the foundation of the paper “UV-Selective Optically Transparent Zn(O,S)-Based Solar Cells” (Lopez-Garcia et al.) published in Solar Rapid Research Letters Vol. 4, 2000470, 2020, are described. They were obtained under the funding provided by the European Union H2020 Framework Programme under Grant Agreement no. 826002 (Tech4Win) and by the Basque Country PI2018-08 (PISCES).</p> <p>UV–vis measurements were acquired with a dual-beam spectrophotometer setup (Perkin Elmer Lambda L35) in transmittance mode (light source and detector normal to sample’s surface (i.e., 0<sup>o</sup>)) scanning from 300 to 800 nm.</p> <p>Raman spectroscopy was performed with a FHR640 Horiba Jobin–Yvon spectrometer coupled to a Raman probe developed at Institut de Recerca en Energia de Catalunya (IREC) and a cryogenically cooled charge coupled device detector. Measurements were carried out in backscattering configuration and with a 325 nm UV laser as the excitation wavelength. An excitation power density of about 25W/cm<sup>2</sup> was used to inhibit thermal effects on the samples. The Raman shift was calibrated using a Si monocrystal reference and adjusting the Raman shift for the main Si band at 520 cm<sup>-1</sup>.</p> <p>FE-SEM images were acquired with a ZEISS Auriga Series system. The images were acquired at 5 kV, aperture of 20 μm, and working distance of around 4mm with the InLens detector.</p> <p>J–V measurements under illumination were carried out using a homemade setup consisting on a 150W xenon broadband arc Lamp (Thorlabs SLS401) calibrated using a NREL-certified Si reference solar cell (Abet Technologies, Model 15150). Electrical measurements were carried out with a source-measure unit (Keithley 2400) in four-wire sense mode, controlled by the software Tracer (ReRa solutions) using a IEEE 488 GPIB Instrument Control Device (National Instruments GPIB-USB-HS).</p> <p>EQE curves were obtained using a spectral response system (Bentham PVE300) calibrated with a Si photodiode.</p>
Magnetic-Free Silicon Nitride Integrated Optical Isolator (Original Data)
<p>This is the raw dataset for the paper titled "Magnetic-Free Silicon Nitride Integrated Optical Isolator", which includes the original data and theoretical code. </p>
Gradients 3 Underway Optics
<p>Cruise Name: KM1906, Gradients 3.0.<br> <br> Project: Simons Foundation - Gradients NPSG<br> <br> A Wetlabs AC-S and ECO Triplet were plumbed to the underway uncontaminated seawater on the R/V KM. Data for each instrument were averaged to the minute. For the first 10 min of every hour, incoming seawater was passed through a 0.2 µm filter (Graver ZTEC G series with Teflon O-rings) in order to collect dissolved ‘blanks’ that were subsequently used to correct whole water signals collected in the succeeding 50 min of each hour. Filtered values were linearly interpolated throughout the cruise and subtracted from raw measurements to provide corrected chlorophyll fluorescence (volts), the particulate volume scattering function at 117 degrees (β, m-1 sr-1), spectral particulate beam attenuation at 83 wavelengths (cp(λ), m-1), and spectral particulate absorption at 83 wavelengths (ap(λ), m-1).<br> <br> Chlorophyll fluorescence (Flchl) was calibrated to discrete extracted chlorophyll measurements collected at nighttime. Particulate β values were converted to the particulate backscattering coefficient (bbp, m-1), following Boss and Pegau (2001) [bbp = 2𝜋𝜒p𝛽p] where 𝜒p is 1.1 for 117°. Particulate absorption was then corrected for residual temperature and scattering after Slade et al. (2010). The dissolved component of absorption (ag(λ), m-1) was calculated from filtered measurements using a pure freshwater blank and temperature and salinity corrected according to Slade et al. (2010). AC-S-derived chlorophyll a concentrations (Chla676) were estimated from the particulate absorption coefficients using the line-height method (Davis et al., 1997) assuming a chlorophyll specific absorption coefficient of 0.01178 m2 mg-1.<br> <br> Plankton abundance and carbon content was measured by a combination of continuous flow cytometry via SeaFlow which measures auto-fluorescing particles ranging in size from ~0.4 to ~6 μm as well as an Imaging FlowCytobot (IFCB) with imaging of 4-100 mm particles triggered on both scattering and fluorescence. For the IFCB data, particulate cell diameter was converted to carbon biovolume using allometric scaling (Menden-Deuer and Lessard, 2000) whereas the forward light scattering-biovolume relationship from Ribalet et al. (2019) was used to convert SeaFlow scattering per cell to carbon biovolume. Additional details of data processing are described in Juranek et al. (2020).<br> <br> Measurements of surface planar photosynthetically active radiation (PAR) were made using a LICOR quantum cosine collector (400-700 nm) and data logger (models LI-190R and LI-1500, respectively). The sensor was positioned 4 meters above the deck to minimize the influence of shadows from the ship’s superstructure. Spectrally integrated irradiance was logged throughout the day and averaged in 1 min intervals. The diffuse attenuation coefficient for PAR, KPAR, was calculated according to methods described in Wei and Lee (2013), which calculates the average daily KPAR as a function of the sun angle at local solar noon, the total absorption coefficient at 490 nm, and the total backscattering coefficient at 490 nm.</p>
Advanced bulk dust optical models
<p>This is the advanced bulk optical models for mineral dust particles for lidar applications. The single-scattering properties are obtained from the TAMUdust2020 database. The paritcle shape ensambles and complex refractive index of mineral dust particles are constrained with observations reported in the literature. The organization of this database is described in this ReadMe file.</p>
Data Models for Dataset Drift Controls in Machine Learning With Optical Images - Datasets
<p>This dataset accompanies the paper titled</p> <p><em>Data Models for Dataset Drift Controls in Machine Learning with Images</em><br> <br> that appeared in the Transactions on Machine Learning Research<br> <br> <a href="https://openreview.net/forum?id=I4IkGmgFJz">https://openreview.net/forum?id=I4IkGmgFJz</a><br> </p> <pre><code>@article{ oala2023data, title={Data Models for Dataset Drift Controls in Machine Learning With Optical Images}, author={Luis Oala and Marco Aversa and Gabriel Nobis and Kurt Willis and Yoan Neuenschwander and Mich{\`e}le Buck and Christian Matek and Jerome Extermann and Enrico Pomarico and Wojciech Samek and Roderick Murray-Smith and Christoph Clausen and Bruno Sanguinetti}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2023}, url={https://openreview.net/forum?id=I4IkGmgFJz}, note={} }</code></pre> <p>We make available two datasets.</p> <p><strong>Raw-Microscopy:</strong></p> <ul> <li><strong>940 raw bright-field microscopy images</strong> of human blood smear slides for leukocyte classification (microscopy/images/raw_scale100) with corresponding labels (microscopy/labels).</li> <li><strong>5,640 variations measured at six additional different intensities </strong>(microscopy/images/raw_scale001-raw_scale0075)</li> <li><strong>11,280 images of the raw sensor data processed through twelve different pipelines</strong> (microscopy/images/processed_views)</li> </ul> <p><strong>Raw-Drone:</strong></p> <ul> <li><strong>548 raw drone camera images for car segmentation</strong> (drone/images_tiles_256/raw_scale100) with corresponding binary segmentation mask (drone/masks_tiles_256). The images and the masks are cropped from 12 raw drone camera images (drone/images_full/raw_scale100) and 12 masks (drone/masks_full) of size 3648 by 5472.</li> <li><strong>3,288 variations measured at six additional different intensities</strong> (drone/images_tiles_256/raw_scale001-raw_scale075).</li> <li><strong>6,576 images of the raw sensor data processed through twelve different pipelines</strong> (drone/images_tiles_256/processed_views).</li> </ul> <p>Detailed datasheets for the two datasets can be found in the appendices of the TMLR paper.</p> <p>The code repository for this project can be found at <a href="https://github.com/aiaudit-org/raw2logit">https://github.com/aiaudit-org/raw2logit</a></p> <p> </p>
ScienceDex guides
Understand access before you commit
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.