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655 results for “constrain”
Dataset for "Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions"
<p>This dataset in includes MODIS-CloudSat CFODD reference data, the updated Warm Rain Diagnostics implemented in COSPv2.0, RANSAC regression analysis, and figure production scripts associated with the manuscript “Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions”<br> Authors: Beall, Charlotte, M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro<br> Journal: Atmospheric Chemistry & Physics (submitted, 2023)</p>
Data from: Seed origin and warming constrain lodgepole pine recruitment, slowing the pace of population range shifts
Open the record for dataset details and reuse information.
Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes
Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.
Constraining the dense matter equation of state with joint analysis of NICERand LIGO/Virgo measurements: Data for generating plots
<p>In this repository you will find a Jupyter notebook with code to generate the plots from the paper <em>Constraining the dense matter equation of state with joint analysis of NICER and LIGO/Virgo measurements</em> by Raaijmakers et al. (2020).</p>
Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: gravitational wave frequency fits
<p>1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27. Run with SFHo EOS, M1 with 12 energy groups.</p> <p>For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020).</p> <p>Includes fit to the gravitational wave peak frequency versus time post-bounce, for a functional fit of the form f = A*sqrt(t) + B*t + C, where the frequency f is in Hz and the time t is in seconds. The columns are: progenitor mass [M_sun], fit coefficient A, fit coefficient B, fit coefficient C, and the R^2 of the fit.</p>
A revised list of fossil calibrations to constrain molecular dating of angiosperms
<p>Original list of calibrations used in:</p> <p><strong><strong>Ramírez-Barahona S</strong>, <strong>Sauquet H</strong>, <strong>Magallón S</strong></strong>. <strong>2020</strong>. The delayed and geographically heterogenous diversification of flowering plant families. <em>Nature Ecology & Evolution</em>: 10.1038/s41559-020-1241–3.</p>
Figure 2 in Does nutritional status constrain adoption of more costly and less risky foraging behaviour in an Amazonian shelter-building spider?
Figure 2. Relationship between predicted probability of spider Hingstepeira folisecens (Hingston 1932) (Araneidae) exhibiting a pulling foraging behaviour to catch prey and body condition index (BCI – standardized residuals from a regression of abdomen volume on carapace area) in one region of Central Amazonia, Brazil. '1' represents occurrence of pulling behaviour and '0' represents absence of spider response or use of pursuing behaviour (n = 19).
Dataset from "Constraining Martian regolith and vortex parameters from combined seismic and meteorological measurements"
<p>The table provided below (in CSV format) includes derived data obtained from the raw data of the InSight SEIS and APSS experiments. For the raw data, we acknowledge:</p> <p>InSight Mars SEIS Data Service. (2019). SEIS raw data, Insight Mission. IPGP, JPL, CNES, ETHZ, ICL, MPS, ISAE-Supaero, LPG, MFSC. https://doi.org/10.18715/SEIS.INSIGHT.XB_2016</p> <p>The dataset in this table was used to produce Figures 6, 7, 11 and 12 of the following paper:</p> <p>N. Murdoch, A. Spiga, R. Lorenz, R.F. Garcia, C. Perrin, R. Widmer-Schnidrig, S. Rodriguez, N. Compaire, N. H. Warner, D. Mimoun, D. Banfield, P. Lognonné and W.B. Banerdt. Constraining Martian regolith and vortex parameters from combined seismic and meteorological measurements. Journal of Geophysical Research: Planets.</p> <p>The table provides derived vortex parameters of all vortices studied in this paper. The columns of the table contain the following properties of every vortex event: Sol, UTC date and time, Local Mean Solar Time (LMST), Observed pressure deficit <span class="math-tex">\(\Delta P_{obs}\)</span> (determined from the fit to the Ellehoj et al. (2010) model after filtering in the 0.02 - 0.3 Hz frequency band), Observed pressure drop encounter duration <span class="math-tex">\(\tau\)</span> (FWHM determined from the fit to the Ellehoj et al. (2010) model after filtering in the 0.02 - 0.3 Hz frequency band), the Ellehoj et al. (2010) model goodness of fit to the vortex pressure data (after filtering in the 0.02 - 0.3 Hz frequency band), Mean background wind speed <span class="math-tex">\(v\)</span>, Standard deviation of background wind speed <span class="math-tex">\(\sigma_v\)</span>, Maximum radial tilt <span class="math-tex">\(\theta_{obs}\)</span>, Azimuth at maximum radial tilt (i.e. at closest approach) <span class="math-tex">\(\alpha_{obs}\)</span>, Mean miss distance <span class="math-tex">\(x\)</span> (i.e. when <span class="math-tex">\(S = v\)</span>), <span class="math-tex">\(\eta \)</span> (defined as <span class="math-tex">\(E/(1-\nu^2) \)</span>), Mean <span class="math-tex">\(\zeta\)</span> (i.e. when <span class="math-tex">\(S = v\)</span>). For further details about these parameters please see the paper cited above.</p>
Data from Study: Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration
<p>This dataset contains the data used to arrive at the conclusions in the research article <em>Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration</em>, by Balfour et al [<em>BioMedical Engineering OnLine</em> 2015, <strong>14</strong>:85].</p> <p>This study was based upon motion-affected PET images simulated from real dynamic MR image volumes, simulated and reconstructed using the Software for Tomographic Image Reconstruction ("STIR", see http://stir.sourceforge.net/). This dataset includes data from MR scans of 4 healthy volunteers (male, aged 22-33).</p> <p>Three types of data are provided, which should be sufficient for repeating the findings of the study:</p> <ul> <li>Reconstructed PET image volumes, split into 6 respiratory bins ("gates") for each simulation</li> <li>The dynamic 3D MR volumes used to derive the respiratory motion of each volunteer</li> <li>Text files outline which dynamics have NOT been used for PET simulation - these are the ones used to make the motion model in the study</li> </ul> <p>These MR volumes were registered and combined with the head-foot position of the right hemidiaphragm to form a respiratory motion model, which was subsequently used to constrain PET to PET image registration, attempting to correct for the motion in the PET images.</p> <p>For more detailed information regarding the method, please refer to the article.</p> <p>The PET data is split into several sub-categories:</p> <ul> <li>Volunteer ID (4 possibilities, anonymised)</li> <li>Lesion position (9 possibilities - see article for locations)</li> <li>Lesion diameter, in millimetres (10 or 14 mm)</li> <li>Respiratory gate number, ranging from 1 (most inhaled) to 6 (most exhaled)</li> </ul> <p>Note that there are two types of each simulation: with motion, and without motion. These are included in the respective zip files for each volunteer ID.</p> <p> </p>
StageIV-IRC – A High-resolution Dataset of Extreme Orographic Quantitative Precipitation Estimates (QPE) Constrained to Water Budget Closure for Historical Floods in the Appalachian Mountains
<h2>Quantitative Flood Estimation (QFE) in complex terrain remains a grand challenge in operational hydrology due to the lack of accurate high-resolution Quantitative Precipitation Estimates (QPE) at spatial and temporal resolutions needed to capture the variability of orographic precipitation, and where radar-based QPE are available there are significant biases due to the geometry and constraints of radar operations. Here, we present a high-resolution (i.e. 250m, 5minute-hourly) QPE dataset for the most extreme (flood-producing) events from 2008 to 2024 for 26 gauged basins (in total 215 events) in the Appalachian mountains constrained to meet basin-scale water budget closure through inverse rainfall-runoff modeling to correct the Next Generation Weather Radar (NEXRAD) Stage IV analysis (4km resolution, hourly) using a fully-distributed uncalibrated hydrological model that leverages recent advances in hydrologic modeling in mountainous regions (e.g. improved river routing and initial soil moisture estimation) (Liao and Barros, 2024a and 2024b). The corrected Stage IV analysis is referred to as StageIV-IRC (Inverse Rainfall Correction). Previously, a subset of this dataset informed the construction of a generalized QPE error model (Liao and Barros, 2023), supporting the development of water budget closure constrained QPE and providing physics insights into orographic QPE uncertainties for various radar-based products at high resolution in complex terrain. The unique advantage of the StageIV-IRC QPE is that it achieves water budget closure at the storm-flood event scale within observational uncertainty of streamflow observations, that is the golden standard in hydrological modeling. The QPE dataset is publicly available at: <a href="https://doi.org/10.5281/zenodo.14028867">https://doi.org/10.5281/zenodo.14028867</a></h2> <p><strong> </strong></p>
Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning
<div> <div> <p>Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.</p> <p>In the paper "Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on <strong>two experimental datasets</strong> based on silicon photomultipliers (SiPM) as main transducers:</p> <ol> <li>In the toy experiment, we collect data from a pair of SiPM sensors from a common laser source. The neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. </li> <li>In the electromagnetic calorimeter experiment, we collect data from an eight-channel calorimeter module. Several neural network models (Fully-Connected, Convolutional Neural Network and Long Short Term Memory) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. </li> </ol> <p>In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely. <strong>The dataset in this repository serves as a basis for similar researches on timing performance of SiPM-based nuclear detectors, and on application of neural networks to typical signals of nuclear radiation detectors.</strong></p> </div> </div>
Data from: Foraging mode constrains the evolution of cephalic horns in lizards and snakes
<p>A phylogenetically diverse minority of snake and lizard species exhibit rostral and ocular appendages that substantially modify the shape of their heads. These cephalic horns have evolved multiple times in diverse squamate lineages, enabling comparative tests of hypotheses on the benefits and costs of these distinctive traits. Here, we demonstrate correlated evolution between the occurrence of horns and foraging mode. We argue that although horns may be beneficial for various functions (e.g., camouflage, defence) in animals that move infrequently, they make active foragers more conspicuous to prey and predators, and hence are maladaptive. We therefore expected horns to be more common in species that ambush prey (entailing low movement rates) rather than in actively searching (frequently moving) species. Consistent with that hypothesis, our phylogenetic comparative analysis of published data on 1,939 species reveals that cephalic horns occur almost exclusively in sit-and-wait predators. This finding underlines how foraging mode constrains the morphology of squamates and provides a compelling starting point for similar studies in other animal groups.</p>
SMAP Daily Seamless Soil Moisture Products from 2015 to 2022 (Physics-constrained Gap-filling Method,PhyFill)
<p>The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address this issue, this dataset presents a physics-constrained gap-filling method, shortly named PhyFill. The PhyFill method employs a partial convolutional neural network to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous daily soil moisture data on a global scale. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSE compared with the original SMAP soil moisture data. The PhyFill method can generate globally continuous, high-accuracy soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.</p>
Input geophysical and geological data for "Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees"
<p>This is a companion dataset to the manuscript: <br><br>Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees,</p><p>by: Jeremie Giraud , Mary Ford, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, Roland Martin, and Paul Cupillard.<br><br>This dataset contains the input data used in the inversion, in terms of the gravity data and the geological data used in the inversion.<br><br>The *.txt file contains the gravity data as inverted in the manuscript: X, Y, Z, Value.<br>The *.csv file contains the geological data: location of the contacts and orientation data.</p>
Environmental effects on genetic variance are likely to constrain adaptation in novel environments
<p>Adaptive plasticity allows populations to cope with environmental variation but is expected to fail as conditions become unfamiliar. In novel conditions, populations may instead rely on rapid adaptation to increase fitness and avoid extinction. Adaptation should be fastest when both plasticity and selection occur in directions of the multivariate phenotype that contain abundant genetic variation. However, tests of this prediction from field experiments are rare. Here, we quantify how additive genetic variance in a multivariate phenotype changes across an elevational gradient, and test whether plasticity and selection align with genetic variation. We do so using two closely related, but ecologically distinct, sister species of Sicilian daisy (Senecio, Asteraceae) adapted to high and low elevations on Mount Etna. Using a paternal half-sibling breeding design, we generated and then reciprocally planted c.19,000 seeds of both species, across an elevational gradient spanning each species' native elevation, and then quantified mortality and five leaf traits of emergent seedlings. We found that genetic variance in leaf traits changed more across elevations than between species. The high-elevation species at novel lower elevations showed changes in the distribution of genetic variance among the leaf traits, which reduced the amount of genetic variance in the directions of selection and the native phenotype. By contrast, the low-elevation species mainly showed changes in the amount of genetic variance at the novel high elevation, and genetic variance was concentrated in the direction of the native phenotype. For both species, leaf trait plasticity across elevations was in a direction of the multivariate phenotype that contained a moderate amount of genetic variance. Together, these data suggest that where plasticity is adaptive, selection on genetic variance for an initially plastic response could promote adaptation. However, large environmental effects on genetic variance are likely to reduce adaptive potential in novel environments.</p>
Multigrid spatially constrained dispersion curve inversion package: towards distributed acoustic sensing surface wave imaging
<p>Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. </p> <p>The package includes essential scripts and models required to replicate key figures from the study by Guan et al. (2023, currently under review). These codes are designed to help readers evaluate the effectiveness of the MCI approach using synthetic demonstrations. Additionally, the package includes a refined 2D Vs (shear wave velocity) model derived from a DAS (Distributed Acoustic Sensing) field study conducted in Imperial Valley, California. This model offers new insights into the regional fault system, underscoring the importance of enhanced spatial resolution in large-scale geophysical investigations.</p> <p>It is organized into three directories and contains a total of 14 files. The directory structure is as follows:<br>├── DAS field data<br>│ ├── Pltmodels.m<br>│ ├── README.txt<br>│ ├── field_models.pdf<br>│ ├── model_1DI.mat<br>│ ├── model_LCI.mat<br>│ └── model_MCI.mat<br>├── MCI_Main<br>│ ├── DisForward.p<br>│ ├── InvForward.p<br>│ ├── InvJacobian.p<br>│ ├── MCI.p<br>│ ├── readme.txt<br>│ └── whitejet3.m<br>└── Synthetic demos<br> ├── MCI_Main.m<br> └── syndata.mat</p>
Data from: Constraining biospheric carbon dioxide fluxes by combined top-down and bottom-up approaches
<p> </p> <p> </p> <p><span>Acknowledgements.</span><span> </span><span>We would like to thank Martin Jung, Jakob A. Nelson, Sophia Walther, and the FLUXCOM team for their structural</span><br><span>support, feedback and discussion. The Authors would like to thank the producers of the Inversion data included in this study: Ingrid Luijkx</span><br><span>and Wouter Peters (CTE), Frederic Chevallier and the Copernicus Atmosphere Monitoring Service (CAMS), Christian Roedenbeck (Jena</span><br><span>Carboscope sEXTocNEET), Yosuke Niwa (NISMON-CO2), and Liang Feng and Paul Palmer (UoE). This research was funded by the</span><br><span>European Research Council (ERC) Synergy Grant ’Understanding and modeling the Earth System with Machine Learning (USMILE)’</span><br><span>under the Horizon 2020 research and innovation programme (Grant Agreement No. 855187)</span></p> <p><br><span>This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux</span><br><span>(U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02</span>-<br><span>04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS,</span><br><span>NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowl-</span><br><span>edge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck</span><br><span>Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Depart-</span><br><span>ment of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory,</span><br><span>Microsoft Research eScience, Oak Ridge National Laboratory, University of California - Berkeley, University of Virginia</span></p>
Data from: emergence of structure in plant-pollinator networks: low floral resource constrains network specialisation
<p>Specialisation enhances the efficiency of plant-pollinator networks through the exchange of conspecific pollen transfer for floral resources. Floral resources form the currency of plant-pollinator interactions, but the understanding of how floral resources affect the structure of plant-pollinator networks remains modest. Previous theory predicts that optimally foraging animal species will specialise to improve resource acquisition under high resource availability. Although floral resource availability depends on both the plant production and animal consumption of the resources, previous work has assumed that production and availability to be equivalent. This potentially may have led to erroneous inferences on the effect of resource availability on specialisation. We develop a mutualistic Lotka-Volterra consumer-resource model to investigate the influence of floral resource availability on plant-pollinator network structure. The model incorporates animal adaptive foraging behaviour, floral resource dynamics, and density-dependent dynamics. Specialisation, nestedness and modularity of simulated networks generated from the model under a wide range of parameters were explained using the Generalised Linear Model. We found that the distinction between floral resource dynamics and plant density dynamics was necessary for partial specialisation of plant-pollinator networks. This is because floral resource dynamics constraint animal preference due to its depletion by animal species. Floral resource abundance had a positive effect on network specialisation, but animal density had a negative effect on network specialisation. Floral resource dynamics thus play key roles on the structure of plant-pollinator network, distinctive from plant species density dynamics.</p>
Figures and data: Combining wake redirection and derating strategies in a load-constrained wind farm power maximization
<p><strong>Figures from the publication <em>Combining wake redirection and derating strategies in a load-constrained wind farm power maximization.</em></strong></p> <p> </p> <p>*.fig files can be opened in <code>Matlab</code></p> <p>*.csv files can be opened through a standard text editor (e.g.,<code> Notepad++</code>), imported and visualized in <code>Matlab</code> through the functions <code>>>readmatrix()</code> and <code>>>readtable()</code></p>
Scripts and datas for "A unified energy-constrained mesoscale parameterisation for ocean climate models".
<p>Scripts and datasets used for creating the results of a submitted work :</p> <p><strong>R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak</strong>: <em>A unified energy-constrained mesoscale parameterisation for ocean climate models. </em>(submitted in JAMES).<em><br></em></p> <p>Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017").</p> <p>The reference EKE of <a href="https://doi.org/10.1029/2023gl104688">Torres et al. (2023)</a> is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. <a href="https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18">World Ocean Atlas 2018</a>, <a href="https://gmd.copernicus.org/articles/13/3643/2020/">Tsujino et al. (2020)</a> and <a href="https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/04c79ece-3186-349a-e063-6c86abc0158c/">RAPID</a>)</p> <p>IPython notebooks for computing and plotting metrics are provided :</p> <ul> <li><em>james-eke-heat_budget.ipynb</em> : plots for heat transport and global heat storage (section 4.1)</li> <li><em>james-eke-southern_ocean.ipynb</em> : plots for Southern Ocean (section 4.2) analysis</li> <li><em>james-eke-north_atlantic.ipynb</em> : plots for North Atlantic and Labrador Sea (section 4.3) analysis</li> <li><em>james-eke-timeseries.ipynb</em> : plot 0D metric timeseries for simulations (including spin-up)</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</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.