Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
303
datasets available to search
ShareScore release 0.7.1
Dataset results
303 results for “Refractive”
Mineral spectral refractive index and bulk optical property dataset for aerosol studies
<p>Version 1.3, updated 11/15/2024.</p> <p>Added a file with 27 regional dust sample mineral composition information 'NewRegionalSamples.xlsx',</p> <p>along with the refractive index data.</p> <p>All refractive index files here have 127 rows (wavelengths) and 27 columns (samples)</p> <p>'kall27_coarse.dat' is the imaginary part of the coarse mode. </p> <p>'kall27_fine.dat' is the imaginary part of the fine mode.</p> <p>'nall27_coarse.dat' is the real part of the coarse mode.</p> <p>'nall27_fine.dat' is the real part of the fine mode.</p> <p>Version 1.2, updated 04/23/2024.<br>Major changes: <br>Changed all the data file names to new format: "mix"+{property name}+{number}, rearranged the number of mixing samples</p> <p>Updated all the bulk optical property data. This version use constant values of standard deviation in the lognormal size distribution settings for the coarse mode and the fine mode respectively.</p> <p>The phase matrices are separated from the other bulk properties due to their large file sizes. The readme file is updated correspondingly. The information of scattering angles (498 angles in total) is uploaded as "TAMUdust2020_Angle.dat".</p> <p>Added supplemental file data in 'Supplemental.tar.gz'.</p> <p>Additional refractive indices are zipped in 'AdditionalRefInd.tar.gz'</p> <p>Version 1.1, updated 03/14/2024.<br>Major changes: <br>Added mixed bulk properties for "0 (99%coarse+1%fine)" and "11 (2.0 µm coarse+ 0.4 µm fine)";<br>Added "reff.dat" in the 'BulkProperties.tar.gz'. The data include four columns: fine mode fraction, bulk projected area <A>, bulk volume <V>, effective radius r_eff. The information is for mixed sample number 0 to 11, each corresponds to one row.<br>Added refractive indices for chlorite, mica, smectite, pyroxene, vermiculite and pyroxenes. These groups can be applied in some other models.</p> <p>Version 1.0, uploaded 01/02/2024.</p> <p>This database include supplemental data and files for the publication of this paper:</p> <p>Sensitivities of Spectral Optical Properties of Dust Aerosols to their Mineralogical and Microphysical Properties. Yuheng Zhang, M. Saito, P. Yang, G. L. Schuster, and C. R. Trepte, J. Geophys. Res. Atmos. 2024.</p> <p> </p> <p>*****************************************</p> <p>The supplemental data include:</p> <p>1) 'GroupRefInd.tar.gz' Mineral (group) refractive index files.<br>E. g., 1All_Illite.dat contains the complex refractive index files of illite group. Format (from left to right columns): Wavelength (unit: µm), Real part (n), Imaginary part (k), standard deviation of n, standard deviation of k.</p> <p>The file 'fine_log.dat' includes the mean and standard deviation values of n and k for all the generated fine mode dust samples at 11,044 wavelengths from 0.2 to 50 micron.</p> <p>The file 'fine_log127.dat' only includes the values at 127 wavelengths from 0.2 to 50 micron (defined in 'swav.txt' and 'lwav.txt'), and is used for the bulk property computations.</p> <p>The files 'coarse_log.dat' and 'coarse_log127.dat' are for the coarse mode dust samples.</p> <p>2) 'CompositionFraction.xlsx': Mineral composition data sources/references and composition data (mean and standard deviation values of each group).<br>'Vlog_coarse.dat': Randomly generated VOLUME FRACTION of 9 mineral groups for the coarse mode dust. Left to right: Illite, Kaolinite, Montmorillonite (Other clays), Quartz, Feldspar, Carbonate, Gypsum (Sulphate), Hematite, Goethite.</p> <p>'Vlog_fine.dat': For the fine mode dust.</p> <p>3) 'RefSources.xlsx': The data source references of mineral refractive indices. We didn't include the olivine, other silicates, soot and titanium-rich minerals in the paper, but the refractive indices are available for those who are interested. Chlorite, Mica and Vermiculite group are mentioned in some studies, and we included the refractive indices for these minerals as well.</p> <p>4) 'DustSamples.tar.gz' Dust sample refractive index files.<br>The files are enclosed in four folders: fine_sw/ fine_lw/ coarse_sw/ coarse_lw/.</p> <p>fine: fine mode. coarse: coarse mode.</p> <p>'sw' means shortwave (< 4 µm, in total 76 wavelengths defined in 'swav.txt') while 'lw' means longwave (>= 4 µm, in total 51 wavelengths defined in 'lwav.txt').</p> <p>All files start with 'rdn', which means that they are computed based on randomly generated composition (data given in sheet 2 of 'CompositionFraction.xlsx').</p> <p>The four digit number after 'rdn' is the index of each dust sample. In total, there are 5,000 samples. The sample composition is the same for the same sample index in the same size mode (fine/coarse). Data file format (from left to right columns): real part, imaginary part.</p> <p>5) 'BulkProperties.tar.gz' Bulk property files (excluding phase matrices)<br>'mixqx.dat' files format (from left to right columns): Extinction efficiency (Qext), Scattering efficiency (Qsca), Backscattering efficiency (Qbck), and Asymmetry coefficient (Qasy). To obtain asymmetry factor, use Qasy/Qsca.</p> <p>'mixbkx.dat' files format (from left to right columns): P11(pi) P12(pi) P22(pi) P33(pi) P34(pi) P44(pi).</p> <p>'x' refers to the number at the end of the file name. It can be 100 ~ 112, each represents a setting of coarse and fine mode effective radius and volume fraction (see details in "reff.dat")</p> <p>'reff.dat' contains the effective radius information of the mixture. It has 7 columns: File number "x", Fine mode volume fraction, Fine mode effective radius (µm), Coarse mode effective radius (µm), Bulk projected area (µm^2), Bulk volume (µm^3), Bulk effective radius (µm).</p> <p>6) 'PhaseMatrices.tar.gz' Phase matrices data<br>'mixphswx.dat' files contain phase matrix results at 532 nm (shortwave). From left to right: P11, P12, P22, P33, P34, P44.</p> <p>'mixphlwx.dat' files contain phase matrix results at 10.5 µm (longwave).</p> <p>There are 635,000 rows in each data file. 635,000 rows = 127 wavelengths * 5,000 samples. Row 1~127 is sample 1, row 128~254 is sample 2, etc.. Suggest to use matlab function 'reshape(property, 127, 5000)' for each column when processing the data.</p> <p>7) 'Supplemental.tar.gz'</p> <p>We also include data files mentioned in the supplemental file of the paper. The adjusted source data files of the nine mineral groups are included.</p> <p>The supplemental bulk property files are named based on the figure number.</p> <p>8) 'AdditionalRefInd.tar.gz'</p> <p>We also include additional refractive indices for chlorite, smectite, vermiculite, mica, dolomite, titanium-rich minerals, pyroxenes and soot. These data can be useful in other models.</p> <p>For more detailed information and datasets, please contact: Yuheng Zhang, yuheng98@tamu.edu or yuhengz98@qq.com.</p>
Dataset for Observations of gravity wave refraction and its causes and consequences
<p>Dataset for the publication submitted to Journal of Geophysical Research: Atmospheres. The title of the publication is:</p> <p>Observations of gravity wave refraction and its causes and consequences</p>
Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm
<p>A refractive lens is one of the simplest, cost-effective and easily available imaging elements. With a spatially incoherent illumination, a refractive lens can faithfully map every object point to an image point in the sensor plane, when the object and image distances satisfy the imaging conditions. However, static imaging is limited to the depth of focus, beyond which the point-to-point mapping can be only obtained by changing either the location of the lens or the imaging sensor. In this study, the depth of focus of a refractive lens in static mode has been expanded using a recently developed computational reconstruction method, Lucy-Richardson-Rosen algorithm (LRRA). The technique consists of three steps. In this first step, the point spread functions (PSFs) were recorded along different depths and stored in the computer as PSF library. In the next step, the object intensity distribution was recorded. The LRRA was then applied to deconvolve the object information from the recorded intensity distributions in the final step. The results of LRRA were compared against two well-known reconstruction methods namely Lucy-Richardson algorithm and non-linear reconstruction. The data corresponding to experimental analysis is given in the manuscript. (Preprints Link:). The theoretical simulation data is given here.</p>
Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging – dataset
<p>Raw volumetric data used in the work "Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging" (<a href="http://doi.org/10.1364/BOE.466403">doi.org/10.1364/BOE.466403</a>). The data is packaged using the FIJI BigStitcher into HDF5 file. The file is split into 89 parts in ZIP format. Additionally we provide XML file needed for opening the data with BigStitcher and the TXT file with the nominal locations of the volumes based on the readings from the X-Y translation stage. The volumes inside the HDF5 file are already registered for stitching using the BigStitcher pairwise registration and global optimization procedure. Using the BigStitcher option "Resave to TIFF" one can access the raw data that we processed in the work. The processing code which operates on TIFF files is available here: <a href="https://github.com/biopto/QPI-stitching-2D-3D">https://github.com/biopto/QPI-stitching-2D-3D</a>.</p>
Refractive index obtained from thin films of Au-Ag alloys
<p>This is the optical dataset for Au-Ag alloys, obtained in the paper "Optical properties of Au-Ag alloys: An ellipsometric study" [Opt. Mater. Express 4 (2014) 403-410].</p>
refract
<p>There are three dataset files supplemental to the study:</p> <p> 1. **FirefoxFM.xml** The Firefox feature model (in XML)<br> 2. **normaltestcases.zip** Normal test cases. These are the Mozmill test cases that we expected to pass.<br> 3. **firefoxbugs.zip** Seeded faulty test cases. These seven tests contain faults that we recreated based on real Firefox faults.<br> </p>
The data used for "Exploring how differences in dust particle size distribution and complex refractive indices affect dust direct radiative fluxes using the CAS-FGOALS-SPRINTARS global climate model"
<p>These data are used for " Exploring how differences in dust particle size distribution (PSD) and complex refractive indices (CRI) affect direct radiative effect (DRE) using the CAS-FGOALS-SPRINTARS global climate model ". </p> <p>(1) AS83+OPAC: The control experiment, dust PSD is the original AS83, and the generic CRI is from OPAC. </p> <p>(2) BFT22+OPAC: Same as the control experiment, but the PSD is updated to use BFT22.</p> <p>(3) BFT22+DB: Same as the experiment BFT22+OPAC, but the generic OPAC CRI is replaced by nine regionally dependent DB CRIs.</p> <p>(4) BFT22+DB strong abs: Same as the experiment BFT22+DB, but the generic CRI consists of 10% percentile real and 90% percentile imaginary parts and no regional dependencies.</p> <p>(5) BFT22+DB weak abs: Same as the experiment BFT22+DB, but the generic CRI consists of 90% percentile real and 10% percentile imaginary parts and no regional dependencies.</p> <p>All experiments mentioned above are run for 5 years (2010-2014). The annual average simulation results are stored here.</p> <p><strong>Note:</strong> AS83 represents the dust PSD scheme from d'Almeida and Schütz. (1983). BFT22 represents the new dust PSD developed by Meng et al. (2022) based on the improved brittle fragmentation theory. OPAC: the Optical Properties for Aerosols and Clouds dataset, DB: the CRIs from Di Biagio et al. (2017, 2019).</p> <p><strong>References</strong></p> <p>d'Almeida, G. A., & Schütz, L. (1983). Number, Mass and Volume Distributions of Mineral Aerosol and Soils of the Sahara. <em>Journal of Applied Meteorology and Climatology</em>,<em> 22</em>(2), 233-243. https://doi.org/https://doi.org/10.1175/1520-0450(1983)022<0233:NMAVDO>2.0.CO;2</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2019). Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content. <em>Atmospheric Chemistry and Physics</em>,<em> 19</em>(24), 15503-15531. https://doi.org/10.5194/acp-19-15503-2019</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2017). Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing. <em>Atmospheric Chemistry and Physics</em>,<em> 17</em>(3), 1901-1929. https://doi.org/10.5194/acp-17-1901-2017</p> <p>Meng, J., Huang, Y., Leung, D. M., Li, L., Adebiyi, A. A., Ryder, C. L., et al. (2022). Improved Parameterization for the Size Distribution of Emitted Dust Aerosols Reduces Model Underestimation of Super Coarse Dust. Geophysical Research Letters, 49(8), e2021GL097287, https://doi.org/https://doi.org/10.1029/2021GL097287</p>
Data for "Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols"
<p>This repository contains calculated/simulated data on the imaginary part of the complex refractive index, single scattering albedo, and/or optical depth for dust aerosols in the visible band or at the wavelength of 550 nm.</p> <p>For detailed information on (1) the acquisition and utilization of this data, (2) comprehensive configurations for model simulations, (3) the principal findings, and (4) the methodology employed to achieve these findings, please refer to the article authored by Li, Mahowald et al. (2024; Commun. Earth Environ).</p> <p>Other datasets, including the code and laboratory observations presented in the paper, can be found elsewhere (refer to the Data and Code Availability sections of the paper).</p> <p>For any clarification regarding the data and code, inquiries related to the publication, or potential collaboration, please contact Longlei Li (<a href="mailto:ll859@cornell.edu">ll859@cornell.edu</a>) or Natalie M. Mahowald (<a href="mailto:mahowald@cornell.edu">mahowald@cornell.edu</a>).</p>
FDTD simulation of 290 nm PAAO with gold nanoparticles: varying refractive index of surrounding medium, s-polarization
<p>Version 2 has the same files as version 1 and some additional files.</p> <p>FDTD software: Lumerical (Ansys, version 2021 R2.3).</p> <p>Structure: aluminum (Palik) substrate; 290 nm thickness (<em>h</em>) aluminum oxide (Palik) layer with 35 nm diameter (<em>RPo</em>) cylindrical pores with 100 nm distance (<em>D</em>) between the pore centers (representing porous anodized aluminum oxide - PAAO); 60 nm diameter (<em>RNP</em>) gold (Johnson and Christy) nanoparticles placed directly above each pore.</p> <p>Refractive index of the surrounding medium (<em>n</em>): 1.0; 1.1; 1.2; 1.3.</p> <p>Simulation region: from 300 nm below the substrate/PAAO interface to 1.3 µm above PAAO surface; x and y spans are equal to one period of the structure.</p> <p>Mesh override region: from 50 nm below the PAAO to 50 nm above the nanoparticles; 2 nm step size in each direction.</p> <p>Light source: BFAST plane wave light source; 500 nm above PAAO; 45° angle of incidence (<em>ang</em>); 300 nm – 1000 nm wavelength range; s-polarization (<em>pol</em>).</p> <p>Monitor (frequency domain field and power): 2D Z-normal; 1 µm above PAAO; results are in "<em>_reflection.txt</em>" files.</p> <p>Information in the file name: <em>h</em> - thickness of PAAO; <em>pol</em> - polarization; <em>RNP/AuRNP</em> - diameter of gold nanoparticles; <em>RPo</em> - diameter of pores; <em>D</em> - distance between pore centers; <em>ang</em> - angle of incidence; <em>n</em> - refractive index of surrounding medium.</p> <p>Files: (1) "<em>_reflection.txt</em>" - lambda(nm) (first column) - wavelength in nanometers; Y (second column) - T data from the monitor above the structure. (2) "<em>_p0.log</em>" - log file produced by the software while running the simulation. (3) "<em>.fsp</em>" - Lumerical software file containing the simulation project; it can be used to extract data from Y- and X-normal monitors (license required to open these files). (4) "<em>Lumerical_Screenshots.pdf</em>" - shows software screenshots for every object and its every property; red text is added to show which values are different for different simulations. (5) "<em>Structure_Illustration.png</em>" - a schematic of modeled structure. (6) "<em>h290,varN.jpg</em>" - a preview of data from "<em>_reflection.txt</em>" files.</p>
Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element
<p>Raw data for the article "Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element". Each set of three files is a set of dSTORM images, taken using either top-hat illumination or a Gaussian illumination. For Sample 0, the top-hat illumination was performed first. For Sample 1, the Gaussian illumination was performed first.</p>
Data and code for figures in "Thermo-refractive noise in silicon nitride microresonators"
<p>Data and script used to produce the figures in "Thermo-refractive noise in silicon nitride microresonators".</p><p>Readout of some data files requires @MyTrace function from <a href="https://github.com/engelsen/Instrument-control">https://github.com/engelsen/Instrument-control</a>.</p><p>The Matlab live script is tested with Matlab_R2018a. The COMSOL file is tested with COMSOL Multiphysics 5.3a.</p>
Supporting Data for "Refractive index matched, nearly hard polymer colloids" (Proc. R. Soc. A, doi:10.1098/rspa.2018.0763)
<p>SAXS data [Q / Å^{-1}, I(Q) / Arb. unit, error I(Q) / Arb. unit] as *.dat files</p> <p>Data for Figures 1, 2, and 5 [description and units in column headers] as *.csv files</p>
The wavelength-dependent complex refractive index of hygroscopic aerosol particles and other aqueous media: an effective oscillator model
<p>Using cavity-enhanced Raman spectroscopy of single, optically trapped droplets, we measure both the real and imaginary parts of the refractive index of NaCl, NaNO<sub>3</sub>, (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>, MgSO<sub>4</sub>, citric acid and a 1:1 molar ratio NaCl:NaNO<sub>3</sub> aqueous solutions at a range of water activities. </p>
Towards true volumetric refractive index investigation in tomographic phase microscopy at cellular level - dataset
<p>This dataset contains 6 files with reconstrucions of 3 different types of cells (SH-SY5Y neuroblastoma cells, A549 adenocarcinoma lung cells and HL-60 white blood cells), retrieved from optical diffraction tomography system measurements carried out at Warsaw University of Technology. The measurements were reconstructued with 2 different algorithms: direct inversion algorithm (DI) and Gerchberg-Papoulis algorithm with finite object support regularization (GPSC) [1]. </p> <p>All files are *.mat files.</p> <p>In the files there are 5 variables:</p> <p>REC - reconstruction matrix with information about 3D refractive index values in the cells<br> dx - sample size <br> RI2D - values of mean refractive index calculated from 2D lateral cross-sections<br> RI3D - volumetric mean refractive index<br> mask - binary mask of the cell</p> <p><br> [1] W. Krauze, “Optical diffraction tomography with finite object support for the minimization of missing cone artifacts,”277<br> Biomed. optics express 11, 1919–1926 (2020)<br> </p>
Infrared Transmission Spectra and Complex Refractive Indices of Surface Soils from Global Dust Entrainment Regions
<p>Spectral data for the paper “Variations in Infrared Complex Refractive Index Spectra of Surface Soils from Global Dust Entrainment Regions” submitted to the journal of <em>Atmosphere</em> Manuscript ID: atmosphere-2291559.</p> <p>Longwave infrared continuum removed transmission spectra, real (<em>n</em>) and imaginary (<em>k</em>) indices of refraction as well as subtractive Kramers-Kronig (SKK) MATLAB code presented in that paper are included in this Zenodo repository.</p> <p>The continuum removed transmission spectral data are stored in an Excel file format in columns and the first column is the wavelengths (µm). </p> <p>Real (<em>n</em>) and imaginary (<em>k</em>) indices of refraction for each sample are stored in a separate file where the first the column is the wavelengths (µm), and the second and third are <em>k</em> and <em>n</em> respectively.</p> <p> </p> <p>LWIR transmission: LWIR transmission was measured in the wavelength range between ~ 2.5 and 25 µm using a benchtop Nicolet 380 Fourier Transform Infrared (FTIR) spectrometer. First, we made soil-KBr pellets using ~ 0.5 mg of soil and ~ 200 mg of KBr blended using a clean mortar and pestle for 2 to 3 minutes to ensure uniform dispersion of mineral particles in the matrix. Pellet production was performed with the means of an evacuable KBr Die Kit and a CrushIR Digital Hydraulic Press from PIKE Technologies (Madison, WI, USA). The mixture was first transferred to the Die Kit which was then pressed under vacuum for about 4 to 5 minutes at a pressure of ~ 10 t cm<sup>-2</sup>, forming a hard disk 13 mm in diameter. Due to the hygroscopic nature of KBr, we pulled a vacuum on the pellet die for approximately 3 to 4 minutes prior to compression, and we immediately placed the pellets in a desiccant box, and then measured transmission within one or two hours of pellet creation.</p> <p>The pellet was attached to a self-adhesive sampling card and placed into the transmission holder. The resulting transmission spectrum for the sample is recorded with the dust-KBr mixture measurement being ratioed to that for an empty chamber, or blank reference. To avoid contamination of transmission spectra with ambient gases, the instrument was initially purged with dry air for at least 5 minutes, prior to each transmission collection. To increase the quality of the spectra and improve the SNR, the numbers of scans averaged were 200 for the blank reference and 100 for the samples.</p> <p>We ratioed transmission spectra to a blank KBr spectrum and then removed the continuum. The continuum removal (CR) transmission spectrum was employed to derive imaginary index of refraction (<em>k</em>) using Beer-Bouguer-Lambert law and <em>k</em> = <em>β<sub>a</sub></em>λ/4π. Real index of refraction (<em>n</em>) spectrum was consequently derived from the <em>k</em> spectrum with a subtractive Kramers-Kronig (SKK) technique. </p> <p>The spectral range of 4–25 μm was chosen to present the transmission spectra as well as real (<em>n</em>) and imaginary (<em>k</em>) indices of refraction since absorption in the region from 2.5 to 3.5 μm is mainly due to water and is not necessarily diagnostic.</p> <p>Here, we define any acronyms that may be present in the names of uploaded Excel files or their contents.</p> <p>*LWIR = Longwave infrared</p> <p>*T = Transmission</p> <p>*CR = Continuum Removed</p>
SCA-2023: A two-part dataset for benchmarking the methods of image precompensation for users with refractive errors
<p>The recent practices of demonstrating various static and video images to users by means of digital, processor-controlled, often self-luminous devices (computer monitors, smartphone and tablet screens, etc.) have spurred the development of various methods for improving the perception of such images through their computer processing. In particular, this applies to the task of precompensating images shown to users with various anomalies of refraction of the eyes (e.g. myopia or astigmatism) in situations where they are not equipped with glasses or other corrective devices. Researchers have proposed a considerable number of such precompensation methods, but to this day there has been no way to accurately compare their quality. We propose an original dataset, which we called “SCA-2023”, of images specially designed for this purpose. Its most important feature is the fact that it includes not only a set of ground-truth images for implementing the precompensation transform, but also a separate set of images characterizing specific types and degrees of manifestation of the refractive errors. The benchmarking procedure itself includes applying the precompensation transformation to a certain image from the first part of the dataset, computer simulation of the so-called retinal image (distribution of light on the retina of an imaginary observer) based on the selection of the “distorting eye” from the second part of the dataset, and evaluating the similarity of this image to the ground-truth image, using any of the commonly used similarity metrics for this purpose.</p>
Dataset from: Ultraviolet refractive index values of organic aerosol extracted from deciduous forestry, urban and marine environments
<p>The refractive index values of atmospheric aerosols are required to address the large uncertainties in the magnitude of atmospheric radiative forcing and measurements of the refractive index dispersion with wavelength of particulate matter sampled from the atmosphere are rare over ultraviolet wavelengths. An ultraviolet-optimized spectroscopic system illuminates optically-trapped single particles from a range of tropospheric environments to determine the particle’s optical properties. Aerosol from remote marine, polluted urban, and forestry environments is collected on quartz filters, and the organic fraction is extracted and nebulized to form micron-sized spherical particles. The radius and the real component of refractive index dispersion with wavelength of the optically trapped particles are determined to a precision of 0.001 µm and 0.002 respectively over a near-ultraviolet-visible wavelength range of 0.320–0.480 µm. Remote marine aerosol is observed to have the lowest refractive index (n=1.442 (λ=0.350 µm)), with above-canopy rural forestry aerosol (n=1.462–1.481 (λ=0.350 µm)) and polluted urban aerosol (n=1.444–1.485 (λ=0.350 µm)) showing similar refractive index dispersions with wavelength. In-canopy rural forestry aerosol is observed to have the highest refractive index value (n=1.508 (λ=0.350 µm)). The study presents the first single particle measurements of the dispersion of refractive index with wavelength of atmospheric aerosol samples below wavelengths of 0.350 µm. The Cauchy dispersion equation, commonly used to describe the visible refractive index variation of aerosol particles, is demonstrated to extend to ultraviolet wavelengths below 0.350 µm for the urban, forestry, and atmospheric aerosol water-insoluble extracts from these environments. A 1D radiative-transfer calculation of the difference in top-of-the-atmosphere albedo between atmospheric core-shell mineral aerosol with and without films of this material demonstrates the importance of organic films forming on mineral aerosol.</p> <p>The raw experimental spectra collected and analysed in this study are provided, as well as files for the calibrated wavelengths.</p>
Raman and refractive index of amorphous and crystallized GaS
<p>The data presented in this dataset is published in:</p> <p>Gutiérrez, Y., Dicorato, S., Ovvyan, A. P., Brückerhoff-Plückelmann, F., Resl, J., Giangregorio, M. M., Hingerl, K., Cobet, C., Schiek, M., Duwe, M., Thiesen, P. H., Pernice, W. H. P., Losurdo, M., Layered Gallium Monosulfide as Phase-Change Material for Reconfigurable Nanophotonic Components On-Chip. <em>Adv. Optical Mater.</em> 2023, 2301564. <a href="https://doi.org/10.1002/adom.202301564">https://doi.org/10.1002/adom.202301564</a></p> <p>Experimental details on the structural and optical properties available in this respository are described in the Methods section.</p> <p>The files have the following structure:</p> <ul> <li><strong>Refractive index files </strong>- files are structured in columns as: eV nm n k</li> <li><strong>Raman files</strong> - files are structured in columns as: cm<sup>-1</sup> counts</li> </ul> <p>Descriptions in the files:</p> <ul> <li><strong>refractive_index_amorph_GaS.txt</strong> - Refractive index (n,k) of as deposited amorphous GaS. Data shown in Figure 1b of the manuscript.</li> <li><strong>refractive_index_thermally_crystallized_GaS.txt</strong>- Refractive index (n,k) of thermallycrystallized GaS. Data shown in Figure 3c,d of the manuscript.</li> <li><strong>refractive_index_laser_crystallized_GaS.txt</strong> - Refractive index (n,k) of laser crystallized GaS. Data shown in Figure 3i of the manuscript.</li> <li><strong>Raman_amorph_GaS</strong> - Raman spectrum of amorphous GaS. Background substracted. Data shown in Figure 1a.</li> <li><strong>Raman_cryst_GaS</strong> - Raman spectrum of crystalline GaS. Data shown in Figure 1a.</li> </ul> <p> </p>
Refraction Microtremor Vs(z) Profile Archive
<p>The PDF is a representation of a public website available at <a href="https://sites.google.com/view/vs-profile-archive">https://sites.google.com/view/vs-profile-archive</a></p> <p>The time-averaged seismic shear-wave velocity from the surface to 30 m (100 ft) depth, defined in the Building Code as Vs30, is in the United States one of the principal determinants of earthquake site-hazard classification. Over the past 20 years the Nevada Seismological Lab and the Applied Geophysics class at the University of Nevada, Reno; and Optim Earth have made shallow (<1 km deep) shear-wave velocity measurements at hundreds of sites in Nevada, California, and New Zealand using refraction microtremor technology. Many of these measurements were made at stations in regional earthquake-monitoring networks, and sponsored by the US Geological Survey. The <a href="https://drive.google.com/open?id=15VSI4vhzPfy_GVoB_XYKsWfrhPM9u5E9">Google Drive link</a> leads to a directory structure grouping the measurements by region, and the files are often named with the monitoring network station name. Each file is a self-explanatory, plain-text list of the data and results from the measurement. Where multiple files are given for a particular site, measurements were made at slightly different refraction microtremor array locations, at different times, and by different interpreters; thus expressing both the aleatory variation of velocity in the ground and the epistemic variability of the measurement technique (+/- 15% according to <a href="https://drive.google.com/file/d/1hM3Ows2z2a_xzwriodQT-aKP-UlPubuB/view?usp=sharing">Louie, 2001</a>). Each measurement file includes refraction microtremor array location data, a summary Vs30 value, and a modeled shear-wave-velocity-versus-depth profile. Efforts are underway to add the picked refraction microtremor p-f image and the picked fundamental-mode Rayleigh-wave dispersion-curve data to each file. Many of these measurements have been published in peer-reviewed journal papers and project reports (available in the Preprint Archive from <a href="https://www.google.com/url?q=https%3A%2F%2Flouie.pub&sa=D&sntz=1&usg=AFQjCNFFK9_R0AxlZlKRpWgeFFhzxMON0A">Louie.pub</a>). As well, these archives give additional details on refraction microtremor measurements found in the US Geological Survey's Vs30 archive at <a href="https://www.google.com/url?q=https%3A%2F%2Fearthquake.usgs.gov%2Fdata%2Fvs30%2Fus%2F&sa=D&sntz=1&usg=AFQjCNEOXhj6fRvlr7ftsB-yN_DaDGcE8Q">https://earthquake.usgs.gov/data/vs30/us/</a> . All data in this archive are in the public domain, distributed under a <a href="https://www.google.com/url?q=https%3A%2F%2Fcreativecommons.org%2Flicenses%2Fby%2F4.0%2F&sa=D&sntz=1&usg=AFQjCNHR4439nM4_Ar7-II5_R_iZxNxJdg">Creative Commons CC BY</a> license.</p>
Refractive Indices For Virga Exoplanet Cloud Model
<p><strong>Difference between v1 and v2? </strong></p> <p>The optical constants were updated based on Batalha et al (in prep). The default values and references are located in the <a href="https://github.com/natashabatalha/virga/blob/master/virga/ior_factory.py">IOR Factory</a>. Additionally the specific radii for which the mieff parameters were computed have also changed. Therefore if using Virga V1 we recommend using the Mieff's computed in V2. </p> <p><strong>Additional Details</strong></p> <p>These files are used to compute parameterized cloud models with the <a href="http://github.com/natashabatalha/virga">Virga Exoplanet Cloud Model</a>. The <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#">documentation is available</a> here for specifically how these files are included into virga.</p> <p><strong>1. REFRACTIVE INDICES: (.refrind): </strong>contain the refractive indices of each condensate species. If you are running cloud models utilizing this data, please cite the corresponding source for each species listed below. All files are 4 columns structured as : </p> <blockquote> <p>index, wavelength (micron), real part, imaginary part. </p> </blockquote> <p>Virga reads in these files <a href="https://github.com/natashabatalha/virga/blob/32665150fc83769cae46fd3290f5a237f959712c/virga/calc_mie.py#L452">in this routine</a> but you can simply use this python code:</p> <pre><code>filename = "H2O.refrind" idummy, wave, nn, kk = np.loadtxt(open(filename,'rt').readlines(), unpack=True, usecols=[0,1,2,3])#[:-1] </code></pre> <p> </p> <p><strong>2. MIE PARAMETERS: (.mieff): </strong>There are specific tutorials and functions in virga that will guide you through computing these on your own. However, we provide them here for completeness. The program that calculates these is <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#Creating-the-Mie-scattering-Database">demonstrated here</a>. Note that each Mie parameters are averaged 6 points within the wavelength bin. You can use this function here to <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#Analyzing-Mie-Parameters">parse the data</a>. Or, you can simply read the mieff files with this code: </p> <pre><code>import pandas as pd gas = "H2O" df = pd.read_csv(gas+".mieff",names=['wave','qscat','qext','cos_qscat'], delim_whitespace=True) </code></pre> <p> </p> <p><strong>CITATIONS TO REFERENCE FOR EACH SPECIES (See Table 1 Batalha et al. submitted):</strong></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.