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2,649 results for “optics”
Label-free adaptive optics single-molecule localization microscopy for whole zebrafish
<p>The specimen-induced aberration has been a major factor limiting the imaging depth of single-molecule localization microscopy (SMLM). Here, we report the application of label-free wavefront sensing adaptive optics to SMLM for deep-tissue super-resolution imaging. The proposed system measures complex tissue aberrations from intrinsic reflectance rather than fluorescence emission and physically corrects the wavefront distortion more than three-fold stronger than the previous limit. This enables us to resolve sub-diffraction morphologies of cilia and oligodendrocytes in whole zebrafish as well as dendritic spines in thick mouse brain tissues at the depth of up to 102 μm with localization number enhancement by up to 37 times and localization precision comparable to aberration-free samples. The proposed approach can expand the application range of SMLM to whole zebrafish that cause the loss of localization points owing to severe tissue aberrations.</p>
Parameterization of beam—beam-induced optical distortions of van der Meer scans at the LHC
<p>These two data sets provide the coefficients of the parameterisation of the (L/L<sub>0</sub>)<sub>Opt</sub> luminosity-bias factor as a function of the normalized nominal separation, for horizontal and vertical vdM scans, respectively. The data sets are part of a paper with title "Impact of Beam-Beam Effects on Absolute Luminosity Calibrations at the CERN Large Hadron Collider" and are referred thereby as Table 10 and Table 11.</p>
Data for "Optically Enhanced Solid-State 1H NMR Spectroscopy"
<p>Raw 1H NMR and photo-CIDNP-enhanced NMR data for "Optically Enhanced Solid-State 1H NMR Spectroscopy". A Mathematica notebook for data processing is also included.</p>
Database of physicochemical and optical properties of black carbon fractal aggregates
<p>In order to estimate the climate impact of highly absorbing black carbon (BC) aerosols, it is necessary to know their optical properties. The Lorentz-Mie theory, often used to calculate the optical properties of BC under the spherical morphological assumption, produces discrepancies when compared to measurements. In light of this, researchers are currently investigating the possibility of computing the optical properties of BC using a realistic fractal aggregate morphology. To determine the optical properties of such BC fractal aggregates, the Multiple Sphere T-Matrix method (MSTM) is used, which can take more than 24 hours for a single simulation depending on the aggregate properties. This study provides a highly accurate benchmark machine-learning algorithm that can be used to generate the optical properties of BC fractal aggregate in a fraction of a second. The machine learning algorithm was trained over an extensive database of physicochemical and optical properties of BC fractal aggregates. The extensive training data helped develop an ML algorithm that can accurately predict the optical properties of BC fractal aggregates with an average deviation of less than one percent from their actual values. Specifically, the ML algorithm provides the option to generate the optical properties in the visible spectrum using either kernel ridge regression (KRR) or artificial neural networks (ANN) for a BC fractal aggregate of desired physicochemical properties like size, morphology, and organic coating. The dataset of physicochemical and optical properties of BC fractal aggregates are provided here. The developed ML algorithm for predicting the optical properties of BC fractal aggregates (https://github.com/jaikrishnap/Machine-learning-for-prediction-of-BCFAs) is highly useful for real-world applications due to its wide parameter range, high accuracy, and low computational cost.</p> <p><strong>Contents</strong></p> <ul> <li>database_optical_properties_black_carbon_fractal_aggregtates.csv, data file, comma-separated values</li> <li>database_header.txt, metadata, text</li> </ul> <p><strong>Citation for the database: </strong></p> <p>B., Romshoo, T., Müller, B., Patil, J., Michels, T., Kloft, M., and Pöhlker, M.: Database of physicochemical and optical properties of black<br> carbon fractal aggregates, Dataset, https://doi.org/10.5281/zenodo.7523058, 2023.</p>
Dataset for Spatial and polarization division multiplexing harnessing on-chip optical beam forming
<p>This dataset contains the raw data for the figures (Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. S1, Fig. S2 and Fig. S3) in the publication entitled "Spatial and polarization division multiplexing harnessing on-chip optical beam forming" published by Laser & Photonics Reviews (DOI: 10.1002/lpor.202300298). Datafiles are in .txt format.</p> <p>All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript and the supporting information.</p>
Keck and SDSS optical spectra of J0919+2720.
<p>This repository contains the Keck/LRIS and SDSS optical spectra of J0919+2720. See the attached README.txt file for details. </p>
High-speed TIRF and 2D super-resolution structured illumination microscopy with large field of view based on fiber optic components
<p>Super-resolved structured illumination microscopy (SR-SIM) is among the most flexible, fast, and least perturbing fluorescence microscopy techniques capable of surpassing the optical diffraction limit. Current custom-built instruments are easily able to deliver two-fold resolution enhancement at video-rate frame rates, but the cost of the instruments is still relatively high, and the physical size of the instruments based on the implementation of their optics is still rather large. Here, we present our latest results towards realizing a new generation of compact, cost-efficient, and high-speed SR-SIM instruments. Tight integration of the fiber-based structured illumination microscope capable of multi-color 2D- and TIRF-SIM imaging, allows us to demonstrate SR-SIM with a field of view of up to 150 × 150 μm<sup>2</sup> and imaging rates of up to 44 Hz while maintaining highest spatiotemporal resolution of less than 100 nm. We discuss the overall integration of optics, electronics, and software that allowed us to achieve this, and then present the fiberSIM imaging capabilities by visualizing the intracellular structure of rat liver sinusoidal endothelial cells, in particular by resolving the structure of their trans-cellular nanopores called fenestrations.</p>
Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source
<p>This repository contains the code and data underlying the publication "<em>Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source</em>" in Biomedical Optics Express 14, 3532-3554 (2023) (doi.org/10.1364/BOE.487345).</p> <p>The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author. </p> <p><strong>Description of the code and datasets</strong></p> <p>Table 1 describes all the Matlab and Python scripts in this depository. Table 2 describes the datasets. The input datasets are the phase corrected datasets, as the raw data is large in size and phase correction using a coverslip as reference is rather straightforward. Processed datasets are also added to the repository to allow for running only a limited number of scripts, or to obtain for example the aberration corrected data without the need to use python. Note that the simulation input data (<em>input_simulations_pointscatters_SLDshape_98zf_noise75.mat</em>) is generated with random noise, so if this is overwritten de results may slightly vary. Also the aberration correction is done with random apertures, so the processed aberration corrected data (<em>exp_pointscat_image_MIAA_ISAM_CAO.mat</em> and <em>exp_leaf_image_MIAA_ISAM_CAO.mat</em>) will also slightly change if the aberration correction script is run anew. The current processed datasets are used as basis for the figures in the publication. For details on the implementation we refer to the publication.</p> <table> <caption>Table 1: The Matlab and Python scripts with their description</caption> <tbody> <tr> <td><strong>Script name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><em>MIAA_ISAM_processing.m</em></td> <td>This scripts performs the DFT, RFIAA and MIAA processing of the phase-corrected data that can be loaded from the datasets. Afterwards it also applies ISAM on the DFT and MIAA data and plots the results in a figure (via the scripts <em>plot_figure3, plot_figure5</em> and <em>plot_simulationdatafigure</em>).</td> </tr> <tr> <td><em>resolution_analysis_figure4.m</em></td> <td>This figure loads the data from the point scatterers (absolute amplitude data), seeks the point scatterrers and fits them to obtain the resolution data. Finally it plots figure 4 of the publication.</td> </tr> <tr> <td><em>fiaa_oct_c1.m, oct_iaa_c1.m, rec_fiaa_oct_c1.m, rfiaa_oct_c1.m</em> </td> <td>These four functions are used to apply fast IAA and MIAA. See <em>script MIAA_ISAM_processing.m</em> for their usage.</td> </tr> <tr> <td><em>viridis.m, morgenstemning.m</em></td> <td>These scripts define the colormaps for the figures.</td> </tr> <tr> <td><em>plot_figure3.m, plot_figure5.m, plot_simulationdatafigure.m</em></td> <td>These scripts are used to plot the figures 3 and 5 and a figure with simulation data. These scripts are executed at the end of script <em>MIAA_ISAM_processing.m.</em></td> </tr> <tr> <td>Python script: <em>computational_adaptive_optics_script.py</em></td> <td>Python script that applied computational adaptive optics to obtain the data for figure 6 of the manuscript.</td> </tr> <tr> <td>Python script: <em>zernike_functions2.py</em></td> <td>Python script that gives the values and carthesian derrivatives of the Zernike polynomials.</td> </tr> <tr> <td><em>figure6_ComputationalAdaptiveOptics.m</em></td> <td>Script that loads the CAO data that was saved in Python, analyzes the resolution, and plots figure 6.</td> </tr> <tr> <td>Python script: <em>OCTsimulations_3D_script2.py</em></td> <td>Python script simulates OCT data, adds noise and saves it as .mat file for use in the matlab script above.</td> </tr> <tr> <td>Python script: <em>OCTsimulations2.py</em></td> <td>Module that contains a python class that can be used to simulate 3D OCT datasets based on a Gaussian beam.</td> </tr> <tr> <td>Matlab toolbox DIPimage 2.9.zip</td> <td>Dipimage is used in the scripts. The toolbox can be downloaded online or this zip can be used.</td> </tr> </tbody> </table> <table> <caption>The datasets in this Zenodo repository</caption> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>input_leafdisc_phasecorrected.mat</td> <td>Phase corrected input image of the leaf disc (used in figure 5).</td> </tr> <tr> <td>input_TiO2gelatin_004_phasecorrected.mat</td> <td>Phase corrected input image of the TiO2 in gelatin sample.</td> </tr> <tr> <td>input_simulations_pointscatters_SLDshape_98zf_noise75</td> <td>Input simulation data that, once processed, is used in figure 4.</td> </tr> <tr> <td> <p>exp_pointscat_image_DFT.mat</p> <p>exp_pointscat_image_DFT_ISAM.mat</p> <p>exp_pointscat_image_RFIAA.mat</p> <p>exp_pointscat_image_MIAA_ISAM.mat</p> <p>exp_pointscat_image_MIAA_ISAM_CAO.mat</p> </td> <td>Processed experimental amplitude data for the TiO2 point scattering sample with respectively DFT, DFT+ISAM, RFIAA, MIAA+ISAM and MIAA+ISAM+CAO. These datasets are used for fitting in figure 4 (except for CAO), and MIAA_ISAM and MIAA_ISAM_CAO are used for figure 6.</td> </tr> <tr> <td> <p>simu_pointscat_image_DFT.mat</p> <p>simu_pointscat_image_RFIAA.mat</p> <p>simu_pointscat_image_DFT_ISAM.mat</p> <p>simu_pointscat_image_MIAA_ISAM.mat</p> </td> <td>Processed amplitude data from the simulation dataset, which is used in the script for figure 4 for the resolution analysis.</td> </tr> <tr> <td> <p>exp_leaf_image_MIAA_ISAM.mat</p> <p>exp_leaf_image_MIAA_ISAM_CAO.mat</p> </td> <td>Processed amplitude data from the leaf sample, with and without aberration correction which is used to produce figure 6.</td> </tr> <tr> <td> <p>exp_leaf_zernike_coefficients_CAO_normal_wmaf.mat</p> <p>exp_pointscat_zernike_coefficients_CAO_normal_wmaf.mat</p> </td> <td>Estimated Zernike coefficients and the weighted moving average of them that is used for the computational aberration correction. Some of this data is plotted in Figure 6 of the manuscript.</td> </tr> <tr> <td>input_zernike_modes.mat</td> <td>The reference Zernike modes corresponding to the data that is loaded to give the modes the proper name.</td> </tr> <tr> <td> <p>exp_pointscat_MIAA_ISAM_complex.mat</p> <p>exp_leaf_MIAA_ISAM_complex</p> </td> <td>Complex MIAA+ISAM processed data that is used as input for the computational aberration correction. </td> </tr> </tbody> </table> <p> </p>
Data repository of the paper "Quantum-noise-limited optical neural networks operating at a few quanta per activation"
<p>This data repository includes the requisite data and code for deriving the primary results from the paper, "Quantum-noise-limited optical neural networks operating at a few quanta per activation". The repository is structured to provide everything needed to reproduce the figures included in the main manuscript, along with the source code for training the neural network models and the collected experimental data mentioned in the paper.</p> <p>The code in this repository is primarily intended for reproducing the results discussed in the paper. Those interested in developing their own applications may refer to our Github repository: https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks.</p> <p><strong>Where to Start</strong></p> <p>The directory 'main_figures' includes Jupyter notebooks to generate each panel in Figure 3 and Figure 4 in the main text, using the data from the directory 'results', which can be generated by notebooks in the directory 'test'. </p> <p>The simulations, experiments, and figure generation were all conducted in Python. As certain parts of the code require specific versions of Python packages, the necessary packages are listed in the 'requirements.txt' file.</p> <p>For more information, please refer to 'README.txt'.</p>
Data for the publication: "Development and In-Vivo Validation of a Portable Phosphorescence Lifetime-Based Fiber-Optic Oxygen Sensor"
<p>This data set contains all raw data for the publication “Development and In-Vivo Validation of a Portable Phosphorescence Lifetime-Based Fiber-Optic Oxygen Sensor”:</p> <p>- Raw sensor data</p> <p>- Python scripts</p> <p>- particle photon scripts</p> <p>- CAD Drawings</p> <p>- PCB Designs</p>
Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses- Dataset
<p>This folder contains raw data and information to reproduce the findings of the article titled 'Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses' . Each folder corresponds to a figure in the article.</p> <p>Raw data is provided for the calculations along with the input file and output log of the calculation. When raw data is too large, it is possible to reproduce the calculation from the provided input file. Calculations are performed with <a href="https://octopus-code.org/documentation/12/">Octopus code</a> , the related version and commit number of the code can be retrieved from output log file provided in calculation folder.</p>
Noncentrosymmetric Lanthanide-Based MOF Materials Exhibiting Strong SHG Activity and NIR Luminescence of Er3+: Application in Nonlinear Optical Thermometry
<p>Optically active luminescent materials based on lanthanide ions attract significant attention due to their unique spectroscopic properties, nonlinear optical activity, and the possibility of application as contactless sensors. Lanthanide metal-organic frameworks (Ln-MOFs) that exhibit strong second-harmonic generation (SHG) and are optically active in the NIR region are unexpectedly underrepresented. Moreover, such Ln-MOFs require ligands that are chiral and/or need multistep synthetic procedures. Here, we show that the NIR pulsed laser irradiation of the noncentrosymmetric, isostructural Ln-MOF materials (MOF-Er<sup>3+</sup> (1) and codoped MOF-Yb<sup>3+</sup>/Er<sup>3+</sup> (2)) that are constructed from simple, achiral organic substrates in a one-step procedure results in strong and tunable SHG activity. The SHG signals could be easily collected, exciting the materials in a broad NIR spectral range, from ≈800 to 1500 nm, resulting in the intense color of emission, observed in the entire visible spectral region. Moreover, upon excitation in the range of ≈900 to 1025 nm, the materials also exhibit the NIR luminescence of Er<sup>3+</sup> ions, centered at ≈1550 nm. The use of a 975 nm pulse excitation allows simultaneous observations of the conventional NIR emission of Er<sup>3+</sup> and the SHG signal, altogether tuned by the composition of the Ln-MOF materials. Taking the benefits of different thermal responses of the mentioned effects, we have developed a nonlinear optical thermometer based on lanthanide-MOF materials. In this system, the SHG signal decreases with temperature, whereas the NIR emission band of Er<sup>3+</sup> slightly broadens, allowing ratiometric (Er<sup>3+</sup> NIR 1550 nm/SHG 488 nm) temperature monitoring. Our study provides a groundwork for the rational design of readily available and self-monitoring NLO-active Ln-MOFs with the desired optical and electronic properties.</p>
Data and Code for "Topological atom-optics and beyond with knotted quantum wave functions"
<p>This folder contains data files and Mathematica 12 Student Edition files for processing the data files and generating figures for the paper “<em>Topological atom optics and beyond with knotted quantum wavefunctions</em>”, authored by M. Jayaseelan, J. D. Murphree, J. T. Schultz, J. Ruostekoski, and N. P. Bigelow.</p> <p> </p> <ol> <li>Folder “Data_Only” contains *.csv and *.SPE files for each of the following magnetic phases: <ul> <li> <ol> <li>Polar</li> <li>Cyclic</li> <li>Biaxial Nematic</li> </ol> </li> </ul> </li> <li>Folder Fig2_Polar_code contains <ul> <li> <ol> <li>Data for the Polar magnetic phase (duplicated from Data_Only folder): etau.SPE and e.csv</li> <li>e_imGData, e_imGDataC, e_imLGData, e_imLGDataC: *.csv files that are output as intermediate data processing steps.</li> <li>Fig2_KnotsAtomsPolar_v2.nb: Mathematica file that produces the figures for Fig. 2</li> </ol> </li> </ul> </li> <li>Folder Fig3_Cyclic_code contains <ul> <li> <ol> <li>Data for the Cyclic magnetic phase (duplicated from Data_Only folder): lor_atau.SPE and lor_a_tau.csv</li> <li>lor_a_imGData, lor_a_imG0Data, lor_a_imLGData: *.csv files that are output as intermediate data processing steps.</li> <li>Fig3_KnotsAtomsCyclic_v2.nb: Mathematica file that produces the figures for Fig. 3</li> </ol> </li> </ul> </li> <li>Folder Fig4_Cyclic_code contains <ul> <li> <ol> <li>Fig4_KnotsAtomsCyclic_v2.nb: Mathematica file that produces the figures for Fig. 4</li> </ol> </li> </ul> </li> <li>Folder Fig5_BN_code contains <ul> <li> <ol> <li>Data for the BN magnetic phase (duplicated from Data_Only folder): sk_ltau.SPE and sk_l.csv</li> <li>sk_l_imGData, sk_l_imLGData: *.csv files that are output as intermediate data processing steps.</li> <li>Fig5_KnotsAtomsBN_v2.nb: Mathematica file that produces the figures for Fig. 5</li> </ol> </li> </ul> </li> <li>Folder Fig6_Fig7_BN_code contains <ul> <li> <ol> <li>Fig6_Fig7_KnotsAtomsBN_v2.nb: Mathematica file that produces the figures for Fig. 6 and Fig.7</li> </ol> </li> </ul> </li> </ol>
Original data for "Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy" article
<p>This upload includes both original and supplementary data for the publication "Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy" by R. Li, A. Makogon, T. Galochkina, J-F. Lemineur, F. Kanoufi, and V. Shkirskiy, published in the Small Methods journal. The preprint version of the paper is available on ChemRxiv (<a href="https://doi.org/10.26434/chemrxiv-2023-sgvt0">https://doi.org/10.26434/chemrxiv-2023-sgvt0</a>).</p> <p>The file "Data_processing.zip" contains the original optical image of the interface, maps of film evolution rates in acidic and salt environments, COMSOL data output in acidic and salt environments, as well as a Jupyter Lab file that demonstrates how to process this data.</p> <p>The file "Comparison_of_SEM_images.zip" includes the original SEM images used in the current study and in our previous work (<a href="https://doi.org/10.26434/chemrxiv-2022-rn77b-v3">https://doi.org/10.26434/chemrxiv-2022-rn77b-v3</a>), along with a Jupyter Lab file that illustrates the data processing procedure.</p> <p>The file "Original_data_in_npy_format.zip" contains all original data from SEM/EDX and RM experiments.</p> <p>We recommend opening the Jupyter Lab files in a Python 3 environment. PDF files in the root directory provide outputs of all uploaded Jupyter Lab files.</p>
Links Between Optical and X-ray Light in Scorpius X-1
<p>Optical data taken from the Argos photometer in 2006.</p>
First-principles insights into the electronic, optical, thermophysical, and mechanical properties of lead-free cubic novel Ba3SbBr3 perovskite
<p>Lead-free halide perovskites have emerged as a significant class of materials with immense<br> potential for solar cell synthesis. Among these materials, Ba3SbBr3, a halide novel perovskite,<br> exhibits remarkable efficiency and holds promise for solar cell applications. There are a lot of<br> physical properties, including its elasticity, electrical composition, bonding, thermophysical,<br> optoelectronic properties, and optical properties, that remain unexplored. In this study, we<br> employ advanced density functional theory-based computations to investigate and unveil the<br> previously unidentified physical properties of novel Ba3SbBr3. Our research encompasses a wide<br> range of analyses, covering mechanical stability, phonon dispersion properties, thermophysical<br> properties, elastic parameters, and bonding nature. By precisely analyzing the phonon dispersion<br> properties and applying the Born-Huang criteria, we demonstrated it as mechanically stable.<br> Moreover, our investigation demonstrates that Ba3SbBr3 exhibits favorable machinability and</p> <p>mechanical isotropy through the analysis of various elastic parameters. ELATE’s three-<br> dimensional visualization and optical properties also show isotropic behavior in all directions.</p> <p>The electron charge density reveals the possession of the ionic bonding. Additionally, Ba3SbBr3<br> possesses a direct bandgap, which is essential for efficient optoelectronic performance. The study<br> also encompasses an exploration of the thermophysical properties including the melting<br> temperature, Debye temperature, Grüneisen parameter, and thermal expansion coefficient. The<br> higher values observed in these properties highlight the material's enhanced mechanical stability,<br> thermal stability, and overall suitability for optoelectronic device applications. A large range of<br> photoconductivity and absorption coefficient indicates the suitability of its application in solar<br> cells. The comprehensive investigation conducted in this study contributes novel insights into the<br> unexplored physical properties of Ba3SbBr3, providing a solid foundation for future research<br> endeavors. The findings presented here serve as a valuable reference and inspiration for further<br> theoretical and experimental studies in this rapidly evolving field. The knowledge gained from<br> this research holds great promise for advancing the development of solar engineering and device<br> technologies.</p>
Gradients 5 - Optics - LISST, AC-S, and ECO
<p>This dataset was collected on the R/V Thompson during the Gradients V cruise which departed from San Diego, CA and ended in Honolulu, HI. A transect was performed on the 140 degree longitude line from roughly 16 degrees N to 4 degrees S before heading to Honolulu. Three biooptical instruments were plumbed to the underway uncontaminated seawater (intake depth 8m). Data for each instrument were averaged to the minute and matched to a timestamp on the minute. LISST: Filtered values (<0.2 um) removed from all data. The summed volume concentration of particles (in uL/L), the standard LISST output, is presented for 3 size ranges: 1.25-2 µm, 2-20 µm, and 20-100 µm. Note that data have not been processed to remove outliers. AC-S: Filtered values (<0.2 um) removed from all data (see White et al., 2017, GRL); data corrected for residual temperature and scattering; AC-S chl a concentrations obtained from a Gaussian fit over absorbance spectra in the red portion of the spectrum, assuming in vivo chl-a specific absorption of 0.0203 m2/mg at 674 nm. ECO: filtered values (<0.2 um) removed from chl and volume scattering data; chl fluorescence converted to mg/m3 using factory calibrations, and corrected to HPLC chlorophyll values. CDOM not included due to faulty channel. Time is in UTC.</p>
Dataset for "Optical Performance of Commercial Liquid Lenses in Microgravity"
<p>Dataset for microgravity investigation of Corning Varioptic A-39N0 and Optotune EL-16-40-TC-VIS lenses</p>
Supplementary material to 'Subglacial volcano monitoring with fibre-optic sensing: Grímsvötn, Iceland'
<p>This supplementary material contains a video with impressions of the fieldwork, that was part of the work published in the article ' Subglacial volcano monitoring with fibre-optic sensing: Grímsvötn, Iceland' in Volcanica.</p>
PARAGON 1 - KM2112 - Optics - C-STAR and ECO
<p>These Simons Collaboration on Ocean Processes and Ecology (SCOPE) data products were produced by the University of Hawaiʻi at Mānoa with support from the Simons Foundation.</p> <p>This dataset was collected on the R/V Kilo Moana, which departed and returned to Honolulu, HI, with most of the cruise spent following an anticyclonic eddy northeast of the Hawaiian islands. Two biooptical instruments were plumbed to the underway uncontaminated seawater (intake depth 6m). Data for each instrument were averaged to the minute and matched to a timestamp on the minute. C-STAR: Filtered values (<0.2 um) removed from all data (see White et al., 2017, GRL); ECO: filtered values (<0.2 um) removed from chl and volume scattering data; chl fluorescence converted to mg/m3 using factory calibrations. CDOM not included due to faulty channel. Time is in UTC.</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.