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415 results for “Visible”
Ultraviolet-visible spectroscopy absorbances for dissolved organic matter from Lake Mendota from June – November 2017
Dissolved organic matter (DOM) is a complex mixture of organic compounds found in all natural waters. Its composition affects its reactivity towards numerous processes. Its composition is a function of both its source (e.g., allochthonous or autochthonous) as well as the extent of environmental processing it has undergone (e.g., chemical or biological degradation). Ultraviolet-visible (UV-vis) spectroscopy is an analytical technique commonly used to assess the composition of dissolved organic matter in water samples. Here, we present spectra from Lake Mendota samples collected from June - November in 2017 at the surface of Lake Mendota as well as at specific depths within the water column. All samples were collected near the NTL-LTER research buoy. Absorbance values are listed for wavelengths 200 - 800 nm for each sample.
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database
<p>Visible–near infrared spectra of 695 soil samples collected in the Biomes of Australian Soil Environments (BASE) soil microbial diversity project (Bissett et al., 2016). The spectra represent reflectance values from 2151 wavelengths that range from 350 nm to 2500 nm with a 1 nm interval. The dataset has unique sample identification numbers and the date of sampling, which can be related to the BASE (Australian Microbiome) database (https://data.bioplatforms.com/organization/australian-microbiome)</p>
Data to "Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p><strong>Maiello, G</strong>.<sup> †</sup>, Paulun, V. C.<sup> †</sup>, Klein, L. K. , & Fleming, R. W. (2018) Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection. <em>i-Perception,10</em>(1), 1–5. doi:10.1177/2041669519827608.</p> <p><sup>†</sup>co-first authors</p>
Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments
<p><strong>Context</strong></p><p>This dataset is part of the paper "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments" presented at Oceans 2022, Hampton Roads,<strong> </strong>DOI: <a href="https://doi.org/10.1109/OCEANS47191.2022.9977024">10.1109/OCEANS47191.2022.9977024</a></p><p>This dataset consists of paired camera and multi-beam sonar images of technical divers performing different underwater tasks in two locations: an indoor test basin and a lake. The general goal is to assist emergency operators that monitor the safety of divers operating in bad visibility conditions.</p><p>This data was used to train image-to-image translation models in order to generate realistic optical-like images given only sonar images as input or a combination of a sonar image and a dark or turbid optical image.</p><p> </p><p><strong>Content</strong></p><p>This repository contains three .zip folders each containing data collected in a different lab or field trial.</p><ul><li>'basin-dataset-1.zip' and 'basin-dataset-2.zip' contain data that were collected in an indoor testing facility at DFKI - Robotics Innovation Center, Bremen, Germany.</li><li>'lake-dataset-1.zip' and 'lake-dataset-2.zip' contains data collected at lake Kreidesee, Hemmoor, Germany.</li></ul><p>Each .zip file contains two subfolders labelled as 'camera' and 'sonar', each containing the images in png format. Data files under these subfolders with matching names composes a pair of time-synchronized images. For example, 'camera/0001.png' corresponds to 'sonar/0001.png'. The acquisition timestamp represented in seconds since epoch for every data file is recorded in 'sample.csv' include in each .zip file.</p><p>For more details and meta-information on the collected data please refer to "data_description.json" included in this repository.</p><p>Additional tools for handling and preparing the data can be found under <a href="https://github.com/DeeperSense/oceans_2022">https://github.com/DeeperSense/oceans_2022</a></p><p> </p><p><strong>Acknowledgements</strong></p><p>The data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958.</p><p>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their initiative within the framework of the NFDI4Ing consortium (German Research Foundation (DFG) - project number 442146713).</p>
Visible light photocatalysis of 6pi-heterocyclization
<p>Photo‐mediated 6π cyclization is a valuable method for the formation of fused heterocyclic systems. Here we demonstrate that irradiation of cyclic 2‐aryloxyketones with blue LED light in the presence of an Ir<sup>III</sup> complex leads to efficient and high yielding arylation across a panoply of substrates by energy transfer. 2-Arylthioketones and 2‐arylaminoketones also cyclize effectively under these conditions. Quantum calculation demonstrates that the reaction proceeds via conrotatory ring closure in the triplet excited state. Subsequent suprafacial 1,4‐hydrogen shift and epimerization leads to the observed cis‐fused products.</p> <p>This data-set contains the output files from DFT calculations performed for this work, along with <a href="https://doi.org/10.5281/zenodo.595246">GoodVibes</a> thermochemistry outputs</p>
Data set for "On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials"
<p>This excel file contains the raw data gathered with the measurements performed under different conditions of illumination for the different sensors. These data have been exploited in the paper "On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials" DOI: 10.3390/s21113736</p>
NMR data of all compounds appearing in the study "Visible Light-Mediated Formal Alkylation and [4+1]-Cycloaddition Strategies of Silyl Enol Ethers with Aryldiazoacetates"
<p>FID files of NMR data (<em>e.g.</em> 1H, 13C{1H}, 19F{1H}, COSY, HSQC, DEPT135 and HMBC) associated with all compounds synthesized during the study entitled "Visible Light-Mediated Reactions of Silyl Enol Ethers with Aryldiazoacetates: Formal Alkylation and [4+1]-Cycloaddition Strategies" (a research project in organic synthesis)</p>
ForTrunkDet - Image dataset of visible and thermal annotated images for forest tree trunk detection
<p>Forest dataset composed by visible and thermal images with trunk annotations. The images were acquired in three different portuguese forests and were captured by four different cameras:</p> <ul> <li>GoPro Hero6</li> <li>Allied Mako G-125</li> <li>FLIR M232</li> <li>ZED Stereo</li> </ul> <p>The images and annotations are stored in two zip files:</p> <ul> <li>forest_dataset_original.zip - original dataset</li> <li>forest_dataset_augmented - augmented dataset</li> </ul> <p>Also, the subsets that were used to train, validate and test some deep learning models are available in three .TXT files (train.txt, val.txt and test.txt), where each file line corresponds to an image name.</p> <p> </p>
Use of GIMP Retinex filter to enhance the visibility of craquelure: example n. 1
<p>In the image, A represents the original image. It is a detail of the Portrait of a Young Girl, by Petrus Christus, (high resolution), courtesy Wikipedia. B is the grey-tone corresponding image. C is the image that we can obtain using the GIMP thresholding (threshold tone T = 127), applied to B. D is the image that we can have, applying GIMP Retinex on B (level=low, scale=250, scale div.=8, dynamics=4,0). E is the result of the thresholding of D (T = 127). F is the image obtained by means of the use of generic filter Erode on E. G is the result of applying Retinex (the same parameters used to obtain D) on D. H is the result of thresholding (T = 127), and I is the craquelure as we can see it after applying Erode. L is the Retinex filtered G (the same parameters as for G and D), M is the effect of thresholding and N the final result after using Erode. The example shows that the use of a thresholding on a Retinex filtered image gives much better results than the simple thresholding. This approach, that is the use of Retinex filtering, represents a fast manner for enhancing the visibility of craquelure. The iteration of the Retinex filter is also interesting for working on very dark areas.</p>
Use of GIMP Retinex filter to enhance the visibility of craquelure: example n. 6
<p>In the previously proposed figures (*), the GIMP Retinex filter had been applied to greyscale images, to enhance the visibility of dark craquelure in bright backgrounds. After processing, a black and white map had been obtained, suitable for any possible further processing, even for automatic processing. In (**), we have shown an example of the use of GIMP Retinex to observe the craquelure in dark areas. We observed that when craquelure is present in bright and dark scenes, it seems better to use the GIMP Retinex directly on the colour image. Here an example is given on the Lady Seated at a Virginal, a painting created by Dutch artist Johannes Vermeer (highest resolution), courtesy Wikipedia. A is a detail. A0, A1,A2, A3, and A4 are the images we can obtain by applying GIMP Retinex filter on A, level=low, scale=250, scale div.=8, with dynamics equal to 0,0 1,0 2,0 3,0 and 4,0 respectively. On this detail, it seems that the best manner to enhance the visibility of craquelure is that of using dynamics 2,0. In the lower panel, a larger detail of the painting is given, processed by the filter with dynamics 2,0. The example suggests that use of Retinex filtering on colour images represents a fast manner for enhancing in them the visibility of craquelure.</p> <p>(*) Figures at http://doi.org/10.5281/zenodo.3956124 http://doi.org/10.5281/zenodo.3956204 http://doi.org/10.5281/zenodo.3957439 http://doi.org/10.5281/zenodo.3957799<br> (**) http://doi.org/10.5281/zenodo.3959017</p>
Thiosulfonylation of Unactivated Alkenes with Visible-Light Organic Photocatalysis
<p><strong>Origin of the data: </strong>Experimental spectroscopic measurements<br> <strong>Data Type: </strong>experimental measurements, open access supporting information</p> <p>The data are in CSV, DSW and FBSW format. Supporting information are supplied in PDF format.</p> <p>Data <strong>generated </strong>by instruments: </p> <p>Varian Cary 5E-UV-Vis-NIR spectrophotometer for UV-Vis measurements,<br> Varian Cary Eclipse fluorescence spectrophotomer for fluorescence quenching measurements.</p> <p><strong>Analytical and procedural information: </strong>Stern-Volmer fluorescence quenching experiments, UV-Vis measurements and Fluorescent Quantum Yield determination via ferrioxalate actinometry.</p> <p><strong>Definition of variables: </strong>Wavelength, Absorbance, Concentration<br> <strong>Units of measurement: </strong>nanometers (nm), moles-per-litre (mol/l)</p> <p><strong>Abbreviations: </strong><br> File names and data headers use the following abbreviations:</p> <ul> <li><strong>FQY </strong>refers to Fluorescence Quantum Yield determination experiments</li> <li><strong>Light </strong>refers to irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information.</li> <li><strong>Dark </strong>refers to non-irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information.</li> <li><strong>SVQuench </strong>refers to Stern-Volmer quenching experiments</li> <li><strong>RAxx </strong>refer to measurements related to allylbenzene. <strong>Xx </strong>is the amount of quencher in mol/l (05 should be intended as 0.5 mol/l and so on).</li> <li><strong>RTxx </strong>refer to measurements related to <em>S</em>-(4-methylphenyl) 4-methylbenzenethiosulfonate. <strong>Xx </strong>is the amount of quencher in mol/l as above.</li> <li><strong>RExx </strong>refer to measurements related to 1,2-dimethoxy-4-(prop-2-en-1-yl)benzene. <strong>Xx </strong>is the amount of quencher in mol/l as above.</li> <li><strong>RSxx </strong>refer to measurements related to styrene. <strong>Xx </strong>is the amount of quencher in mol/l.</li> <li><strong>RTFxx </strong>refer to measurements related to <em>S</em>-(4-fluorophenyl) 4-fluorobenzenethiosulfonate. <strong>Xx </strong>is the amount of quencher in mol/l as above.</li> <li><strong>MesAcrMe Xx </strong>refers to data related to catalyst 9-mesityl-10-methylacridinium. <strong>Xx </strong>is the amount of catalyst in mol/l as above.</li> <li><strong>DMC </strong>for measurements employing dimethylcarbonate as solvent.</li> <li><strong>ACN </strong>for measurements employing acetonitrile as solvent.</li> </ul>
Supporting dataset for: Reviewing repository discoverability: approaches to improving repository visibility and web impact
<p>This dataset supports the conference poster, "<a href="http://strathprints.strath.ac.uk/61333/">Reviewing repository discoverability: approaches to improving repository visibility and web impact</a>", presented at the Repository Fringe 2017 conference at the University of Edinburgh.</p> <p>The dataset comprises a single OpenDocument Spreadsheet (.ods) format file containing six data sheets of data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data for Google Analytics and usage statistics from IRStats2. All data relate to the EPrints repository, <a href="http://strathprints.strath.ac.uk/">Strathprints</a>, based at the University of Strathclyde.</p> <p></p>
An urban traffic dataset composed of visible images and their semantic segmentation generated by the CARLA simulator
<p><strong>If you use this dataset please cite this paper: Rosende, S.B.; Gavilán, D.S.J.; Fernández-Andrés, J.; Sánchez-Soriano, J. An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator. <em>Data</em> 2024, <em>9</em>, 4. <a href="https://doi.org/10.3390/data9010004">https://doi.org/10.3390/data9010004</a></strong></p> <p>A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. The images have been generated using the CARLA simulator (but would be like those that could be obtained with fixed aerial cameras or by using AUVs) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.</p>
Data: Constraining Ice Slab Thickness at the Onset of Visible Surface Runoff from the Greenland Ice Sheet
<h1>Data repository associated with the manuscript 'Constraining Ice Slab Thickness at the Onset of Visible Surface Runoff from the Greenland Ice Sheet', Nicolas Jullien, Andrew J. Tedstone, Horst Machguth, (under review in the Journal of Glaciology)</h1> <h2> </h2> <h2>Introduction:</h2> <p>We provide a short description of each file present in this data repository, and flag to the corresponding reference when applicable. Please cite the appropriate references when using these data.</p> <h2> </h2> <h2>Data:</h2> <h3>In this repository:</h3> <ul> <li>'Ice_Layer_Output_Thicknesses_Likelihood_2010_2018_jullienetal2021_modified.csv'. Modified 2010-2018 ice slabs thickness retrievals from Jullien et al., (2023) where ice slabs thickness > 16 m thick and < 1 m thick are retained, and flight-lines not holding ice slab were set to hold an ice content of 0 m thick.</li> <li>'master_maps.zip'. Raster files. Surface hydrology connectivity map over the Greeland Ice sheet, first presented in Tedstone and Machguth (2022). The easiest way to handle this dataset is to use the 'master_map_GrIS_mean.vrt' file.</li> <li>'MARv.3.14_MoA_2000_2012.nc'. Melt over accumulation from 2000 to 2012 extracted from MARv3.14. See file '<a title="melt_over_accumulation_calculations.py" href="https://github.com/jullienn/IceSlabs_SurfaceRunoff/blob/main/melt_over_accumulation_calculations.py">melt_over_accumulation_calculations.py</a>' in the code repository for post processing analysis.</li> <li>'RunoffLimits.zip'. '.csv' files. Maximum visible runoff limits in 2012 and 2019, sorted for each boxes generated by Tedstone and Machguth (2022). Each '.csv' file stores the data points coordinates (Geographical Reference System: WGS 84 / NSIDC Sea Ice Polar Stereographic North (EPSG:3413)) of the maximum visible runoff limit retrievals after filtering out the outliers. The maximum visible runoff limits where first presented in Tedstone and Machguth (2022).</li> </ul> <h3>Used in this study but from other datasets:</h3> <ul> <li>The ice slabs extent and ice slabs thickness were first presented in Jullien et al., (2023), and are accessible at: https://zenodo.org/records/7505426</li> <li>The radargrams displayed in Fig. 5c-f were first presented in Jullien et al., (2023), and are accessible at: https://zenodo.org/records/7505426. The following files were used: <ul> <li>'L1_may12_03_1_aggregated.pickle'</li> <li>'L1_may12_03_2_aggregated.pickle'</li> <li>'20100508_01_114_115_Depth_CORRECTED.pickle'</li> <li>'20140424_01_002_004_Depth_CORRECTED.pickle'</li> <li>'20180427_01_170_172_Depth_CORRECTED.pickle'</li> </ul> </li> <li>The surface topography present in Fig. 5g are 10 m resolution mosaics from the ArcticDEMv3 (Porter et al., 2018), and accessible at: https://data.pgc.umn.edu/elev/dem/setsm/ArcticDEM/mosaic/v3.0/</li> <li>The winter time strain rates map displayed in Fig. 5h were first presented in Poinar and Andrews (2021), and are accessible at: https://ubir.buffalo.edu/xmlui/handle/10477/82127</li> </ul> <p> </p> <h2>References:</h2> <p>Jullien, N., Tedstone, A. J., Machguth, H., Karlsson, N. B., & Helm, V. (2023). Greenland Ice Sheet Ice Slab Expansion and Thickening. <em>Geophysical Research Letters</em>, <em>50</em>(10), e2022GL100911. https://doi.org/10.1029/2022GL100911</p> <p>Poinar, K., & Andrews, L. C. (2021). Challenges in predicting Greenland supraglacial lake drainages at the regional scale. <em>The Cryosphere</em>, <em>15</em>(3), 1455–1483. https://doi.org/10.5194/tc-15-1455-2021</p> <p>Porter, C., Morin, P., Howat, I., Noh, M.-J., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C., Cloutier, M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington, M., Jr., Williamson, C., … Bojesen, M. (2018). <em>ArcticDEM, Version 3</em> (Version V1) [dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/OHHUKH</p> <p>Tedstone, A. J., & Machguth, H. (2022). Increasing surface runoff from Greenland’s firn areas. <em>Nature Climate Change</em>. https://doi.org/10.1038/s41558-022-01371-z</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>
Code and data for "Computational morphology of debris and alluvial fans on irregular terrain using the visibility polygon"
<p>This code is for simulating conical fan morphologies. </p> <p>The code and the dataset can be read/run by using Matlab. The description is as follows:</p> <p>1. Dataset (FieldCase1Input.mat, FieldCase2Input.mat) provides the input data needed to simulate the two field cases. The simulations can be run using the code Case4_FieldCase1.m and Case5_FieldCase2.m. The results can be compared with the provided output data (FieldCase1Output.mat, FieldCase2Output.mat).</p> <p>2. Code for three idealized cases, including the comparison of analytical solutions and model simulations are provided.</p> <p>3. Function VisiPolygon is the algorithm for calculating point visibility polygon. Function FanTopo_slope is the model for simulating fan morphology. </p> <p><br> </p>
Vegetation and vantage point influence visibility across diverse ecosystems: implications for animal ecology
<p class="MsoNormal"><span>Visual information can influence animal behavior and habitat use in diverse ways. Visibility is the property that relates 3D habitat structure to accessibility of visual information. Despite the importance of visibility in animal ecology, this property remains largely unstudied. Our objective was to assess how habitat structure from diverse environments and animal position within that structure can influence visibility. We gathered terrestrial lidar data (1 cm at 10 m) in four ecosystems (forest, shrub-steppe, prairie, and desert) to characterize viewsheds (i.e., estimates of visibility based on spatially explicit sightlines) from multiple vantage points. Both ecosystem-specific structure and animal position influenced potential viewsheds. Generally, as height of the vantage point above the ground increased, viewshed extent also increased, but the relationships were not linear.<span> </span>In low-structure ecosystems (prairie, shrub-steppe, and desert), variability in viewsheds decreased as vantage points increased to heights above the vegetation canopy. In the forest, however, variation in viewsheds was highest at intermediate heights, and markedly lower at the lowest and highest vantage points. These patterns are likely linked to the amount, heterogeneity, and distribution of vegetation structure occluding sightlines. Our work is the first to apply a new method that can be used to estimate viewshed properties relevant to animals (i.e., viewshed extent and variability). We demonstrate that these properties differ across terrestrial landscapes in complex ways that likely influence many facets of animal ecology and behavior.<span> </span></span></p>
Figure 3 in On the perception of leaf morphology and visible light byTetranychus urticae Koch (Acariformes, Tetranychidae)
Figure 3 Distribution of Tetranychus urticae females (A) and daily oviposition rate (B) on bean leaves: A1, B1 (BNI); A2, B2 (BNS); A3, B3
Figure 2 in On the perception of leaf morphology and visible light byTetranychus urticae Koch (Acariformes, Tetranychidae)
Figure 2 Survival (squares) and oviposition rate (circles) of Tetranychus urticae females fed on adaxial (AdSU-open squares and circles) or abaxial (AbSU-closed squares and circles) surface of bean (A) and lemon (B) leaves. Different letters denote significant differences (P<0.05)
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.