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1,169 results for “Infrared”
Two-dimensional vanadium sulfide flexible graphite/polymer films for near-infrared photoelectrocatalysis and electrochemical energy storage
<p>Raw data of published article "Two-dimensional vanadium sulfide flexible graphite/polymer films for near-infrared photoelectrocatalysis and electrochemical energy storage", DOI: 10.1016/j.cej.2022.135131</p>
Pretty cool beetles: Can manipulation of visible and near-infrared sunlight prevent overheating?
<p>Passive thermoregulation is an important strategy to prevent overheating in thermally challenging environments. Can the diversity of optical properties found in Christmas beetles (Rutelinae) be an advantage to keep cool? We measured changes in temperature of the elytra of 26 species of Christmas beetles, exclusively due to direct radiation from a solar simulator in visible (VIS: 400–700 nm) and near-infrared (NIR: 700–1700 nm) wavebands. Then, we evaluated if the optical properties of elytra could predict their steady state temperature and heating rates while controlling for size. We found that higher absorptivity increases the heating rate and final steady state of the beetle elytra in a biologically significant range (3 to 5°C). There was substantial variation in the absorptivity of Christmas beetle elytra; this variation was achieved by different combinations of reflectivity and transmissivity in both VIS and NIR. The size was an important factor in predicting the change in temperature of the elytra after 5 min (steady state) but not the maximum heating rate. Lastly, we show that the presence of the elytra covering the body of the beetle can reduce the heating of the body itself. We propose that beetle elytra can act as a semi-insulating layer to enable passive thermoregulation through high reflectivity of elytra, resulting in low absorptivity of solar radiation. Alternatively, if beetle elytra absorb a high proportion of solar radiation, they may reduce heat transfer from the elytra to the body through behavioural or physiological mechanisms.</p>
Ethylene (C2H4) point-sources detected by the IASI infrared satellite instrument (2008-2020).
<p>This dataset includes the super-sampled IASI 0.01° × 0.01° C<sub>2</sub>H<sub>4</sub> HRI dataset in GeoTIFF format and the catalogue of the identified and categorized C<sub>2</sub>H<sub>4</sub> point-sources in kml format (C2H4_HRI_pointsources.zip). It also includes the super-sampled IASI C<sub>2</sub>H<sub>4</sub> total columns used to calculate the emission fluxes from the analyzed point-sources (C2H4_column_pointsources.zip) and the source data needed to reproduce the figures (C2H4_SourceData.zip). The code to calculate the C<sub>2</sub>H<sub>4</sub> HRI from IASI spectra and to retrieve the C<sub>2</sub>H<sub>4</sub> total columns is provided (C2H4_codes.zip), along with the artificial neural network used for the retrievals, instructions and example data. The codes of the oversampling, wind rotation and supersampling are available in the paper of Clarisse <em>et al.</em> (2019) at <a href="https://doi.org/10.5194/amt-12-5457-2019">https://doi.org/10.5194/amt-12-5457-2019</a>.</p>
Replication data for measurement report: Evolution and distribution of NH3 over Mexico City from ground-based and satellite infrared spectroscopic measurements
<p>This dataset of atmospheric ammonia (NH3) has been generated from solar absorption spectra measured in central Mexico using ground-based Fourier-Transform Infrared (FTIR) spectrometers. The FTIR experiments have been operated by the “Spectroscopy and Remote Sensing” Research Group of the ICAyCC-UNAM (Instituto de Ciencias de la Atmósfera y Cambio Climático of the Universidad Nacional Autónoma de México, http://www.epr.atmosfera.unam.mx/)</p> <p>Related Publication:<br> Herrera, B., Bezanilla, A., Blumenstock, T., Dammers, E., Hase, F., Clarisse, L., Magaldi, A., Rivera, C., Stremme, W., Strong, K., Viatte, C., Van Damme, M., and Grutter, M.: Measurement report: Evolution and distribution of NH3 over Mexico City from ground-based and satellite infrared spectroscopic measurements, Atmos. Chem. Phys. https://doi.org/10.5194/acp-2022-217, Accepted, 2022.</p> <p>Abstract:<br> Ammonia (NH3) is the most abundant alkaline compound in the atmosphere, with consequences for the environment, human health, and radiative forcing. In urban environments, it is known to play a key role in the formation of secondary aerosols through its reactions with nitric and sulphuric acids. However, there are only a few studies about NH3 in Mexico City. In this work, atmospheric NH3 was measured over Mexico City between 2012 and 2020 by means of ground-based solar absorption spectroscopy using Fourier transform infrared (FTIR) spectrometers at two sites (urban and remote). Total columns of NH3 were retrieved from the FTIR spectra and compared with data obtained from the Infrared Atmospheric Sounding Interferometer (IASI) satellite instrument. The diurnal variability of NH3 differs between the two FTIR stations and is strongly influenced by the urban sources. Most of the NH3 measured at the urban station is from local sources, while the NH3 observed at the remote site is most likely transported from the city and surrounding areas. The evolution of the boundary layer and the temperature play a significant role in the recorded seasonal and diurnal patterns of NH3. Although the vertical columns of NH3 are much larger at the urban station, the observed annual cycles are similar for both stations, with the largest values in the warm months, such as April and May. The IASI measurements underestimate the FTIR NH3 total columns by an average of 32.2 ± 27.5 % but exhibit similar temporal variability. The NH3 spatial distribution from IASI shows the largest columns in the northeast part of the city. In general, NH3 total columns over Mexico City exhibited an average annual increase of 92 ± 3.9 x 1013 molecules/cm2 yr (urban) and 8.4 ± 1.4 x 1013 molecules/cm2 yr (remote) was observed in Mexico City at both FTIR stations and a decadal increase of 62 % with IASI data.</p> <p>Description UNAM_FTIRdata.csv:<br> Atmospheric composition measurements made at the Universidad Nacional Autónoma de Mexico Observatory on the rooftop of the Instituto de Ciencias de la Atmósfera y Cambio Climático (UNAM, 19.33°N, 99.18°W, 2280 m.a.s.l.) located at the south of Mexico City. <br> These are retrieved from Fourier Transfor InfraRed (FTIR) solar absorption spectra recorded with a Vertex 80 spectrometer from April 2012 to October 2019. <br> The dataset contains the local time (YYYY-MM-DD hh:mm:ss AM/PM), the total columns (molecules/cm2), total error (molecules/cm2), systematic error (molecules/cm2), random error (molecules/cm2), and Degrees of Freddom (DOF).</p>
Ultrafast data of "Near-Infrared Plasmon-Induced Hot Electron Extraction Evidence in an Indium Tin Oxide Nanoparticle/Monolayer Molybdenum Disulfide Heterostructure"
<p>Ultrafast differential transmission data:</p> <p>- Ito.txt : differential transmission map of indium tin oxide nanoparticles pumped at 1750 nm</p> <p>- Ito_Mos2.txt : differential transmission map of indium tin oxide nanoparticle / monolayer MoS2 heterojunction pumped at 1750 nm</p> <p>- MoS2_ir.txt : differential transmission map of monolayer MoS2 heterojunction pumped at 1750 nm</p> <p>- MoS2_vis.txt : differential transmission map of monolayer MoS2 heterojunction pumped at 500 nm</p> <p> </p> <p>In the matrix the first line is the vector of the delays in femtosecond, while the first raw is the vector of the wavelengths in nanometers.</p>
Near-Infrared Spectral Templates of L Dwarfs
<p>As described in <em>Meeting the Cool Neighbors XII: An Optically-Anchored Analysis of the Near-Infrared Spectra of L Dwarfs</em> (Cruz et. al.), we have combined the NIR spectra of objects of the same optical spectral type to make NIR spectral average templates for field and low-gravity L dwarfs at each integer spectral type, in the range L0–L8. As more optical and/or NIR data are collected for L dwarfs, the templates could potentially be updated. In anticipation of this, we dub the templates presented here version 1.0.</p> <p>The naming convention of the ascii files is the following:</p> <p>Spectral type + Band + Gravity<br> <br> Gravity can be <br> 'f' (field objects) or <br> 'b' ({beta}-type low-gravity objects) or <br> 'g' ({gamma}-type very low-gravity objects)<br> <br> Example: L1H_f.txt is the template for the H band of the field L1 objects.<br> <br> Columns are:<br> 1. Wavelength in microns<br> 2. Average normalized flux<br> 3. Normalized flux standard deviation<br> 4. Min normalized flux<br> 5. Max normalized flux <br> The last two columns define the range of the strip at each wavelength.<br> <br> The files are formatted according to the machine readable format used by the AAS Journals and CDS/VizieR. The files can be read in python using the astropy package:<br> <br> from astropy.table import Table<br> data = Table.read("L1H_f.dat", format="ascii.cds")</p>
leighfletcher/hydrogendimers: Hydrogen dimers in giant-planet infrared spectra
<p>Repository for hydrogen-hydrogen absorption spectra, including free-to-free, free-to-bound, bound-to-free and bound-to-bound components as published in Fletcher, Gustafsson and Orton (2017), ApJ Supplement.</p>
Dataset for Low-loss SiGe waveguides for mid-infrared photonics fabricated on 200 mm wafers
<p>This dataset contains the information contained in Figures 3, 4, 5 of the related manuscript. This research dataset should be interpreted and understood in the context of the corresponding manuscript, which has been published in Optics Express with DOI:10.1364/OE.521925. All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript. The data correspond to the information shown in the figures of the manuscript. </p> <p>Each file is in .txt format, the decimal separator is a point '.' and the column separator is a tab ';'.</p>
Infrared Radiative Transfer Calculations Dataset
<p>This dataset presents the results of a series of experiments conducted varying combinations of two versions of the HITRAN database (2016 and 2020) and two versions of the MT_CKD water vapor continuum model (3.2 and 4.1.1). Five atmospheric models representing different climatic conditions (Tropical, Mid Latitude Winter, Mid Latitude Summer, Sub Arctic Winter, and Sub Arctic Summer) are employed. Key atmospheric gases, including CO2, O3, CH4, CO, N2O, and O2, are prescribed at each atmospheric model. The dataset includes calculations with all gases present as well as experiments removing individual gases (specifically CO2, O3, and water vapor). Outputs consist of upwelling and downwelling infrared radiation and cooling rate profiles at 44 atmospheric heights from the surface to 95 km. Data are provided at 0.1 cm^-1 resolution across the spectral range from 10 to 3000 cm^-1. For each experiment, a file in “.txt” format is available at each atmospheric height containing the flux each wavenumber. This dataset offers valuable insights into the impact of different atmospheric compositions and models on radiative transfer processes and cooling rates, contributing to a better understanding of Earth's climate system.</p> <p>Similar experiments are compacted in .tar files:</p> <p>var_profile_HITRAN<em>_lev_</em>lllll_mtckd{<em>_wo</em>flag}<em>.tar</em></p> <table> <tbody> <tr> <td>var (3 digits)</td> <td>profile (3 digits)</td> <td>HITRAN (4 digits)</td> <td>lllll (5 digits)</td> <td>mtckd (3 digits)</td> <td>flag (3 digits)</td> </tr> <tr> <td>"olr": upward radiation flux</td> <td>"tro": Tropical</td> <td>2016</td> <td>Output height in meters. 5 digits. Ranging from the surface (i.e. "00000") to 95 km (i.e. "95000")</td> <td>"3.2": v3.2</td> <td>"H2O": experiment removing water vapor.</td> </tr> <tr> <td>"dlr": downward radiation flux</td> <td>"mls": Mid Latitude Summer</td> <td>2020</td> <td> </td> <td>"4.1": v4.1.1</td> <td>"CO2": experiment removing carbon dioxide.</td> </tr> <tr> <td>"coo": cooling rates</td> <td>"mlw": Mid Latitude Winter</td> <td> </td> <td> </td> <td> </td> <td>"O3": experiment removing ozone.</td> </tr> <tr> <td> </td> <td>"sas": Sub Artic Summer</td> <td> </td> <td> </td> <td> </td> <td>"BFX": experiment without the "B linear to tau" aproximation.</td> </tr> <tr> <td> </td> <td>"saw": Sub Artic Winter</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> <p>To untar the file:</p> <pre><code>tar -xzf var_profile_HITRAN_lev_lllll_mtckd_woflag.tar</code></pre> <p>Each tar file consists of 880 text files.</p>
Synthetic JWST NIRCam, Euclid NISP, and Roman WFI Near-Infrared Photometry for 800+ Ultracool Dwarfs
<p>JWST NIRCam, Euclid NISP, and Roman WFI photometry (apparent magnitudes) for 800+ ultracool dwarfs synthesized using near-IR SpeX prism spectra. This is a supplementary data product to <a href="https://iopscience.iop.org/article/10.3847/2515-5172/acf864">Sanghi et al. 2023 (RNAAS, 7, 194)</a> and <a href="https://ui.adsabs.harvard.edu/abs/2024RNAAS...8..137S/abstract">Sanghi et al. 2024 (RNAAS, 8, 137)</a>. JWST photometry is presented in the Vega magnitude system and Euclid and Roman photometry are presented in the AB magnitude system.</p> <p>For research that benefits from this compilation, please cite <a href="https://iopscience.iop.org/article/10.3847/2515-5172/acf864">Sanghi et al. 2023 (RNAAS, 7, 194)</a> for JWST photometry, <a href="https://ui.adsabs.harvard.edu/abs/2024RNAAS...8..137S/abstract">Sanghi et al. 2024 (RNAAS, 8, 137)</a> for Euclid and Roman photometry, and include the following acknowledgment:</p> <p>"This work has benefitted from The UltracoolSheet, maintained by Will Best, Trent Dupuy, Michael Liu, Aniket Sanghi, Rob Siverd, and Zhoujian Zhang, and developed from compilations by <a href="https://ui.adsabs.harvard.edu/abs/2012ApJS..201...19D/abstract">Dupuy & Liu (2012, ApJS, 201, 19)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2013Sci...341.1492D/abstract">Dupuy & Kraus (2013, Science, 341, 1492)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...833...96L/abstract">Liu et al. (2016, ApJ, 833, 96)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2018ApJS..234....1B/abstract">Best et al. (2018, ApJS, 234, 1</a>), <a href="https://ui.adsabs.harvard.edu/abs/2021AJ....161...42B">Best et al. (2021, AJ, 161, 42)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2023ApJ...959...63S/abstract">Sanghi et al. (2023, ApJ, 959, 63)</a>, and <a href="https://ui.adsabs.harvard.edu/abs/2023AJ....166..103S/abstract">Schneider et al. (2023, AJ, 166, 103)</a>."</p> <p>Contact <a href="mailto:asanghi@caltech.edu">asanghi@caltech.edu</a> regarding questions.</p>
The Far INfrarEd Spectrometer for Surface Emissivity (FINESSE) Part I: Instrument description and level 1 radiances (data set)
<p>The data set uploaded to this repository is outlined in a manuscript submitted to the journal Atmospheric Measurement Techniques.</p> <p>A. BB_effective_emissivity: Holds the data set used to characterise instrument calibration target emissivity</p> <p>B. ILS_data: Holds the data set used to model the instrument spectral lineshape</p> <p>C. Time_resolved_spectral_response: Holds the data set establishing the spectral stability of the instrument</p> <p>D. Zenith_radiances_20220323_1100UTC: Holds calibrated radiances acquired by FINESSE to demonstrate it accuracy and precision. Also included in this file is an LBLRTM simulation using coincident ERA5 profile information for the time and location of the observations</p> <p>The Far INfrarEd Spectrometer for Surface Emissivity (FINESSE). Part I: Instrument description and level 1 radiances</p> <p>Jonathan E. Murray1,2, Laura Warwick3, Helen Brindley1,2, Alan Last1, Patrick Quigley1, Andy Rochester1, Alexander. Dewar1, Daniel. Cummins1</p> <p>1 Department of Physics, Imperial College London, SW7 2BX, UK</p> <p>2 National Centre for Earth Observation, UK</p> <p>3 ESA-ESTEC, Noordwijk, Netherlands</p> <p>In the manuscript Part (I) we describe the FINESSE system configuration, outlining the FINESSE spectral characteristics, the data acquisition methodology and the calibration strategy. As part of the process, we evaluate the stability of the system, including the impact of knowledge of blackbody target emissivity and temperature. We also establish a numerical description of the instrument line shape. We demonstrate why it is important to account for these effects by assessing their impact on the overall uncertainty budget on the level 1 radiance products from FINESSE.</p>
Products and Models for "A benchmark JWST near-infrared spectrum for the exoplanet WASP-39 b"
<p>Publication Here: https://www.nature.com/articles/s41550-024-02292-x<br><br>Observing exoplanets through transmission spectroscopy supplies detailed information on their atmospheric composition, physics, and chemistry. Prior to <em>JWST,</em> these observations were limited to a narrow wavelength range across the near-ultraviolet to near-infrared, alongside broadband photometry at longer wavelengths. To understand more complex properties of exoplanet atmospheres, improved wavelength coverage and resolution are necessary to robustly quantify the influence of a broader range of absorbing molecular species. Here we show a combined analysis of <em>JWST</em> transmission spectroscopy across four different instrumental modes spanning 0.5–5.2 micron using Early Release Science observations of the Saturn-mass exoplanet WASP-39b. Our uniform analysis constrains the orbital and stellar parameters within sub-percent precision, including matching the precision obtained by the most precise asteroseismology measurements of stellar density to-date. Leveraging the advantages of a uniform light curve analysis, we improve the agreement between the transmission spectra of all modes, except for the NIRSpec PRISM, which is affected by partial saturation of the detector. Together, these collected data constitute the most comprehensive transmission spectrum of an exoplanet to date, providing unparalleled access to atmospheric absorbers including Na, K, H2O, CO, CO2, and SO2.</p>
Comparative genomics sheds new light on the convergent evolution of infrared vision in snakes
<p>Infrared vision is a highly specialized sensory system that evolved independently in three clades of snakes. Apparently, convergent evolution occurred in the transient receptor potential ankyrin 1 (<em>TRPA1</em>) proteins of infrared-sensing snakes. However, this gene can only explain how infrared signals are received, and not the transduction and processing of those signals. We sequenced the genome of <em>Xenopeltis unicolor</em>, a key outgroup species for pythons, and performed a genome-wide analysis of convergence between two clades of infrared-sensing snakes. Our results revealed pervasive molecular adaptation in pathways associated with neural development and other functions, with parallel selection on loci associated with trigeminal nerve structural organization. Additionally, we found evidence of convergent amino acid substitutions in a set of genes, including <em>TRPA1 </em>and<em> TRPM2</em>. Analysis also identified convergent accelerated evolution in non-coding elements near 12 genes involved in facial nerve structural organization and optic nerve development. Thus, convergent evolution occurred across multiple dimensions of infrared vision in vipers and pythons, as well as amino acid substitutions, non-coding elements, genes, and functions. These changes enabled independent groups of snakes to develop and utilize infrared vision.</p>
Figure 6 in In situ infrared videography of sand scorpion nighttime surface activity
Figure 6: Scorpion movement after being displaced from its burrow. The scorpion in this lured experiment grabbed the floss as it moved past its burrow and was dragged about 50 cm away from its burrow in the direction of 7 o'clock. The excursion began at 21:14:12 and lasted almost 32 minutes. The scorpion did not relocate its burrow and walked off the screen to the left as indicated by the arrowhead. The arrow is 10 cm long and points north; upslope is toward the top of the figure.
Figure 1 in In situ infrared videography of sand scorpion nighttime surface activity
Figure 1: Cue clues. a. Major scorpion sensory organs and the modalities to which they respond. b. Different return routes suggest the use of different stimuli.
Figure 5 in In situ infrared videography of sand scorpion nighttime surface activity
Figure 5: Lured excursions. Twenty-five tosses of a small lure were made across the span of about an hour; times of tosses are indicated at right and the movement of the lure after each toss is shown in the figures at left. The duration of the dragging of the lure is indicated in parentheses next to the time. The arrowhead at the end of each tracing indicates the point where the lure was lifted from the sand surface. The scorpion was coaxed from its burrow on four separate occasions. These excursions are depicted in parts a-e and indicated as E1-E4 in the time list at right. Part f shows all tosses that did not induce scorpion movement. Sometimes the animal reacted to subsequent tosses while away from its burrow; these tosses are indicated with an "x" next to its time and the interaction between the movement of the lure and the movement of the animal is indicated by fine dotted lines on figures c-e (the very small numbers indicate the time in seconds of the excursion at which the interaction occurred). The arrow is 10 cm long and points north; upslope is toward the top of the figures.
Figure 8 in In situ infrared videography of sand scorpion nighttime surface activity
Figure 8: Scorpion at burrow threshold. A female P. utahensis near the Scamp trailer is photographed at the opening of her burrow under UV light on March 21, 2009.
Figure 2 in In situ infrared videography of sand scorpion nighttime surface activity
Figure 2: In situ videotaping of scorpions. a. The relative positions of the Scamp base trailer and the two scorpion burrows. b. IR camera mounted on metal pole inserted in sand and positioned above burrow. c. Close-up of IR camera. d. Video receiver mounted on back of base trailer. e. Inside trailer showing video monitor and DVD recorder. f. Still frame of IR video of scorpion outside of its burrow.
Figure 3 in In situ infrared videography of sand scorpion nighttime surface activity
Figure 3: Scorpion emergence relative to traffic around the burrow. Shown are tracings of paths taken by all animals moving within 40 cm of scorpion 2's burrow during the 2 hr surveillance on night 1. The key shows the beginning time of each animal's path. The small numbers on the paths indicate time, in seconds, after the beginning of the movement into the region. Scorpion 2 emerged from its burrow twice (excursions C and K). The fine dotted lines indicate the time and location of other animals at the moment of scorpion emergence. The inset shows a composite photo of a kangaroo rat's movements near scorpion 2's burrow on the second night of filming; the top path occurred about 35 minutes into the filming, the lower path 82 minutes later. Each path took less than 2 seconds to complete. Both arrows are 10 cm long; the direction arrow points north. Upslope is toward the top of figure.
Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the all-sky infrared radiative transfer algorithm
<p>Dataset for AMT-2017-261 by DeSouza-Machado et. al.<br> <br> 1D-variational retrievals of temperature and moisture fields from<br> hyperspectral infrared satellite sounders use cloud-cleared radiances<br> as their observation. These derived observations allow the use of<br> clear-sky only radiative transfer in the inversion for geophysical<br> variables but at reduced spatial resolution compared to the native<br> sounder observations. Cloud-clearing can introduce various errors,<br> although scenes with large errors can be identified and<br> ignored. Information content studies show that when using multi-layer<br> cloud liquid and ice profiles in infrared hyperspectral radiative<br> transfer codes, there are typically only 2-4 degrees of freedom of<br> cloud signal. This implies a simplified cloud representation is<br> sufficient for some applications which need accurate radiative<br> transfer. Here we describe a single-footprint retrieval approach for<br> clear and cloudy conditions, which uses the thermodynamic and cloud<br> fields from Numerical Weather Prediction (NWP) models as a first<br> guess, together with a simple cloud representation model coupled to a<br> fast scattering radiative transfer algorithm (RTA). The NWP model<br> thermodynamic and cloud profiles are first co-located to the<br> observations, after which the N-level cloud profiles are<br> converted to two slab clouds (typically one for ice and one for water<br> clouds). From these, one run of our fast cloud representation model<br> allows an improvement of the \emph{a-priori} cloud state by comparing the<br> observed and model simulated radiances in the thermal window<br> channels. The retrieval yield is over 90\%, while the degrees of<br> freedom correlate with the observed window channel brightness<br> temperature which itself depends on the cloud optical depth. The cloud<br> representation/scattering package is bench-marked against radiances<br> computed using a Maximum Random Overlap cloud scheme. All-sky infrared<br> radiances measured by NASA’s Atmospheric Infrared Sounder (AIRS) and<br> NWP thermodynamic and cloud profiles from the European Center for<br> Medium Range Weather Forecasting (ECMWF) forecast model are used in<br> this paper.</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.