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739 results for “data loss”
Output data from: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes output data from the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard</strong>. Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>This repository includes Madagascar forest cover (forXXXX.tif), forest density (fordensXXXX.tif), distance to forest edge (dist_edge_XXXX.tif) and forest fragmentation index (fragXXXX.tif) for the years 1953, 1973, 1990, 2000, 2005, 2010 and 2014. Data are available as GeoTIFF raster files at 30m resolution in the UTM 38S projection (EPSG:32738).</p>
Data supporting the manuscript entitled: 'Intermittent soil water stress history favors microbial traits that better mitigate wheat biomass losses during subsequent water stress.'
<p>Data living in this data repository supports the scientific article entitled: Intermittent soil water stress history favors microbial traits that better mitigate wheat biomass losses during subsequent water stress.</p> <p> </p> <p> </p> <p> </p>
Data from: Ecosystem functioning during biodiversity loss and recovery
<p>Anthropogenic biodiversity loss can impair ecosystem functioning. Human activities are often managed with the aim of reversing biodiversity loss and its associated functional impacts. However, it is currently unknown whether biodiversity–ecosystem function (BEF) relationships observed during biodiversity recovery are the same as those observed during biodiversity loss. This will depend on how species extirpation and recolonisation sequences compare and how different species influence ecosystem functioning. Using data from a marine benthic invertebrate community, we modelled how bioturbation potential – a proxy for benthic ecosystem functioning – changes along biodiversity loss and recovery sequences governed by species' sensitivity to physical disturbance and recolonisation capability, respectively. BEF relationships for biodiversity loss and recovery were largely the same despite species extirpation and recolonisation sequences being different. This held true irrespective of whether populations were assumed to exhibit compensatory responses as species were removed or added. These findings suggest that the functional consequences of local biodiversity loss can be reversed by alleviating its drivers, as different species present at comparable levels of species richness during biodiversity loss and recovery phases have similar functional effects. Empirically verifying and determining the generality of our model-based results are potential next steps for future research.</p>
Supporting data for Onyshchenko et al. 2018 Single loss of photosynthesis in diatoms
<p>These are alignments from Onyshchenko, Ruck, Nakov and Alverson 2018: Single loss of photosynthesis in diatoms</p> <p>16s.afa - Chloroplast 16s alignment of data from NCBI and newly generated data from the Alverson lab. Aligned with ssu-align agains the bacterial covariance model and masked to remove poorly aligned regions with default settings.</p> <p>cob.afa - Mitochondrial cob alignment of newly generated data from the Alverson lab.</p> <p>lsu.afa - Nuclear 28s alignment of newly generated data from the Alverson lab. Aligned with ssu-align against a heterokont covariance model of secondary structure and masked to remove poorly aligned regions with default settings.</p> <p>lsu-w-ncbi.afa - Nuclear 28s alignment of data from NCBI and newly generated data from the alverson lab. Aligned with ssu-align against a heterokont covariance model of secondary structure and masked to remove poorly aligned regions with default settings.</p> <p>mito.afa - Concatenation of cob and nad alignmnets. (new data only)</p> <p>mito-lsu.afa - Concatenation of cob, nad, and 28s alignmnets. (new data only)</p> <p>mito-ncbi-lsu.afa - Concatenation of cob, nad, and 28s alignments. (both new data and data from NCBI for 28s)</p> <p>nad.afa - Mitochondrial nad1 alignment of newly generated data from the Alverson lab.</p> <p>Nitz-meta-data.csv - Meta data for newly generated sequences.</p>
A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations - velocity field data
<p>Velocity field to be processed by the methodology described in </p> <p>Biassoni, D, Russo, M, Viviani, P, Vitali, G, & Lengani, D. "A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations." <em>Proceedings of the ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition</em>. <em>Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions</em>. London, United Kingdom. June 24–28, 2024. V12CT32A039. ASME. <a href="https://doi.org/10.1115/GT2024-127953" target="_blank" rel="noopener">https://doi.org/10.1115/GT2024-127953</a></p> <p>and with the code provided at </p> <p><a href="https://gitlab.linksfoundation.com/across-public/aeronautics-workflows/asme-turboexpo-2024">https://gitlab.linksfoundation.com/across-public/aeronautics-workflows/asme-turboexpo-2024</a></p> <h3> </h3>
Weight-loss & metabolite data of cachetic cancer patients over multiple time points
<p>This dataset contains weight-loss data and metabolite measurements from cachetic cancer patients. This data set is part of the publication "Biomarkers related to fatty acid oxidative capacity are predictive for continued weight loss in cachectic cancer patients" by Catanese et al., 2021 (<a href="https://doi.org/10.1002/jcsm.12817">LINK</a>). Data were collected over a extended time period starting from the day of hospital admission. Up to seven time points are available for individual patients. A metabolite profile consisting of amino acids and acylcarnitines measured via tandem MS/MS from both blood plasma and dried blood spots is available for each patient at each time point. Additional available phenotypes are the age, sex and historic weight of each patient. </p> <p>Abbreviations:</p> <ul> <li>HW - Historical weight</li> <li>TP - Time point</li> <li>WL - Weight loss</li> <li>DBS - Dried blood spot</li> <li>Metabolite abbreviations are explained in Supplementary Table S5 of the corresponding publication</li> </ul>
Data_Figure4_Loss of Claudin-3 Impairs Hepatic Metabolism, Biliary Barrier Function, and Cell Proliferation in the Murine Liver
<p>Data of Figure 4 from “Loss of Claudin-3 Impairs Hepatic Metabolism, Biliary Barrier Function, and Cell Proliferation in the Murine Liver”</p> <p>Dataset (doi: 10.1016/j.jcmgh.2021.04.003) contains the original publication as PDF-format (10.1038_s41598-020-59701-0). Corresponding raw data obtained from LC-MS/MS analysis provided as two files in CSV format (31003A-179400_10.1016j.jcmgh.2021.04.003_CGC_4-1.csv, 31003A-179400_10.1016j.jcmgh.2021.04.003_CGC_4-2.csv). All further experiment related information provided as two meta-data-files (31003A-179400_10.1016j.jcmgh.2021.04.003_CGC _4_1-2_M_1.PDF, 31003A-179400_10.1016j.jcmgh.2021.04.003_CGC _4_1-2_M_2.PDF) as PDF format.</p>
Data from: Additive effects of developmental acclimation and physiological syndromes on lifetime metabolic and water loss rates of a dry-skinned ectotherm
<p>Data sets from the paper: "Additive effects of developmental acclimation and physiological syndromes on lifetime metabolic and water loss rates of a dry-skinned ectotherm" by Dezetter et al. in Functional Ecology.</p> <p> </p> <p> </p>
Data from: "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes"
<p>Dataset from the publication "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes". Full experimental details can be found in the related publication in ACS Applied Energy Materials: <a href="https://doi.org/10.1021/acsaem.2c02047">https://doi.org/10.1021/acsaem.2c02047</a></p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 2 SoC ranges [0-30, 0-100]%, with multiple cells tested under each condition. Cells were base-cooled at set temperatures using bespoke test rigs (see pubilcation for details). All electrochemical data were recorded using a Biologic BCS-815 battery cycler.</p> <p> </p> <p><strong>Break-in cycles:</strong></p> <p>Prior to any ageing or performance checks, all cells were subject to 5 full charge-discharge cycles as part of the break-in procedure. This consisted of a 0.2C charge to 4.2 V with CV-hold till C/100, and 0.2C discharge to 2.5 V (repeated for 5 cycles). Cells were rested under open circuit conditions for 2 hours after each charge and 4 hours after each discharge. These break-in cycles were performed at 25°C for all cells.</p> <p> </p> <p><strong>Ageing Conditions:</strong></p> <table align="center"> <caption>Ageing Conditions</caption> <thead> <tr> <th scope="col">Expt</th> <th scope="col">SoC Range</th> <th scope="col">C-rate</th> <th scope="col">Temperature</th> <th scope="col"># of cells</th> <th scope="col">Cell IDs</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, J</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>3</td> <td>D, E, F</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>K, L, M</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, C</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>2</td> <td>D, E</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>F, G, H</td> </tr> </tbody> </table> <p>For cells aged in the 0-30% SoC range, each ageing set consisted of 256 cycles over the 0-30% SoC range (discharge to 2.5 V, charge by passing 1500 mA h (== 0.3*nominal capacity)). C-rates were 0.3C for charge, and 1C for discharge.</p> <p>For cells aged in the 0-100% SoC range, each ageing set consisted of 78 cycles over the full SoC range (discharge to 2.5 V, charge to 4.2 V with CV hold till C/100). C-rates were 0.3C for charge, and 1C for discharge.</p> <p> </p> <p><strong>Reference Performance Tests (RPTs):</strong></p> <p>All cells were characterised at beginning of life (BoL) and after each ageing set using a reference performance test (RPT). The RPT was always performed at 25°C. Two different RPT procedures were used: a longer procedure which was performed after each even-numbered ageing set, and a shorter procedure which was used after each odd-numbered ageing set. Both procedures are detailed below. A CC-CV charge at 0.3C to 4.2 V, 4.2 V till C/100 was performed between each step of the procedures.</p> <p>Long RPT procedure:</p> <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>C/2 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>GITT discharge at 0.5C; 25 pulses with each pulse passing 200 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> <li>GITT discharge at 0.5C; 5 pulses with each pulse passing 1000 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> </ol> <p>Short RPT procedure:</p> <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of C/2. A baseline DC current of C/2 was applied with an HPPC-type profile superimposed on top. This was done for discharge and charge (with voltage limits of 2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of 1C. A baseline DC current of 1C was applied with an HPPC-type profile superimposed on top. This was done for discharge only (with a voltage limit of 2.5 V).</li> </ol> <p> </p> <p><strong>Extracted Data - Main </strong></p> <p>One csv file exists for each cell being tested, summarising the important data extracted from the ageing cycles and the RPTs. This includes:</p> <p>Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>Ageing Cycles: number of ageing cycles the cell has been subject to. *this is <strong>not </strong>equivalent full cycles.</p> <p>Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>Days of Degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>Age Set Average Temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps) using a K-type thermocouple. Units: °C.</p> <p>Charge Throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cummulative total since BoL (not including RPTs). Units: Ah.</p> <p>Energy Throughput: as with "charge throughput", but for energy. Units: Wh.</p> <p>C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT. Units: mAh.</p> <p>C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT. Units: mAh.</p> <p>0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition). Units: Ohms.</p> <p> </p> <p><strong>Extracted Data - Degradation Modes:</strong></p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge.</p> <p>The results of this analysis are saved in the DMA folder, with 4 csv files for each cell, which contain data for all RPTs. The 4 files contain:</p> <p>Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower lithitation fractions of each electrode and the capacity fraction of graphite in the negative electrode.</p> <p>Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>RMSE data: the root-mean-square error of the optimisation function calculated from the residual between the measured and calculated voltage vs capacity profiles.</p> <p> </p> <p><strong>Timeseries data from RPTs:</strong></p> <p>Timeseries datafiles from the Biologic battery cycler which have been exported to csv and sliced for each step of each RPT procedure to help with future use of the data. Files contain [time, voltage, current, charge, temperature] data.</p> <p> </p> <p><strong>Jupyter Notebook:</strong></p> <p>A jupyter notebook has been included to aid futher use of this data. The notebook shows how to load the data into pandas DataFrame objects and provides a couple of example plots to view the datasets.</p> <p> </p> <p><strong>Notes:</strong></p> <p>A faulty electrical connection to cell A of Expt 5 (i.e. one of the cells being aged at 0-100% SoC at 10°C) during RPT4 led to erroneous results for that performance check (as evidenced in the 0.1s resistance value). The faulty electrical connection was fixed prior to subsequent cycling but the RPT was not repeated. We have kept the data collected during this RPT as part of the dataset, so caution should be used when using this specific portion.</p>
Data and code from: Shifting social-ecological fire regimes explain increasing structure loss from Western wildfires
<p class="MsoNormal"><span>Higuera, P.E., M.C. Cook, J.K. Balch, E.N. Stavros, A.L. Mahood, and L.A. St. Denis. 2023. Shifting social-ecological fire regimes explain increasing structure loss from Western wildfires. PNAS Nexus 2: In Press.</span></p> <p class="MsoNormal"><span>Structure loss is an acute, costly impact of the wildfire crisis in the western United States ("West"), motivating the need to understand recent trends and causes. We document a 246% rise in West-wide structure loss from wildfires between 1999–2009 and 2010–2020, driven strongly by events in 2017, 2018, and 2020. Increased structure loss was not due to increased area burned alone. Wildfires became significantly more destructive, with a 160% higher structure loss rate (loss/kha burned) over the past decade. Structure loss was driven primarily by wildfires from unplanned human-related ignitions (e.g. backyard burning, power lines, etc.), which accounted for 76% of all structure loss and resulted in 10 times more structures destroyed per unit area burned compared to lightning-ignited fires. Annual structure loss was well explained by area burned from human-related ignitions, while decadal structure loss was explained by state-level structure abundance in flammable vegetation. Both predictors increased over recent decades and likely interacted with increased fuel aridity to drive structure-loss trends. While states are diverse in patterns and trends, nearly all experienced more burning from human-related ignitions and/or higher structure loss rates, particularly California, Washington, and Oregon. Our findings highlight how fire regimes – characteristics of fire over space and time – are fundamentally social-ecological phenomena. By resolving the diversity of Western fire regimes, our work informs regionally appropriate mitigation and adaptation strategies. With millions of structures with high fire risk, reducing human-related ignitions and rethinking how we build are critical for preventing future wildfire disasters.</span></p>
Supporting data for the manuscript "Protein quality assessment with graph convolution guided by a loss function designed for high quality decoys"
<p>This dataset provides the predictions of the protein quality<br> assessment method Q_epsilon with respect to CASP13 and CASP14, as well<br> as a snapshot of the github repository providing the code for the<br> method.<br> The format of the file is as follows: The first column represents the<br> target and decoy names in the format <target name>_<decoy name>. The<br> second column is the true GDTTS, and the third column represents the<br> predicted GDTTS by Q_epsilon.</p>
Supplementary data for: Selection on visual opsin genes in diurnal Neotropical frogs and loss of the SWS2 opsin in poison frogs
<p><span></span></p> <p><span></span></p> <p>Amphibians are ideal for studying visual system evolution because their biphasic (aquatic and terrestrial) life history and ecological diversity expose them to a broad range of visual conditions. Here we evaluate signatures of selection on visual opsin genes across Neotropical anurans and focus on three diurnal clades that are well-known for the concurrence of conspicuous colors and chemical defense (i.e., aposematism): poison frogs (Dendrobatidae), Harlequin toads (Bufonidae: <em>Atelopus</em>), and pumpkin toadlets (Brachycephalidae: <em>Brachycephalus</em>). We found evidence of positive selection on 44 amino acid sites in LWS, SWS1, SWS2, and RH1 opsin genes, of which one in LWS and two in RH1 have been previously identified as spectral tuning sites in other vertebrates. Given that anurans have mostly nocturnal habits, the patterns of selection revealed new sites that might be important in spectral tuning for frogs, potentially for adaptation to diurnal habits and for color-based intraspecific communication. Furthermore, we provide evidence that SWS2, normally expressed in rod cells in frogs and some salamanders, has likely been lost in the ancestor of Dendrobatidae, suggesting that under low-light levels, dendrobatids have inferior wavelength discrimination compared to other frogs. This loss might follow the origin of diurnal activity in dendrobatids and could have implications for their chemical ecology, biodiversity, and behavior. Our analyses show that assessments of opsin diversification in understudied groups could expand our understanding of the role of sensory system evolution in ecological adaptation.</p>
Data for: Higher water loss on Earth-like exoplanets in eccentric orbits
<p>The climate of a terrestrial exoplanet is controlled by the type of host star, the orbital configuration and the characteristics of the atmosphere and the surface. Many rocky exoplanets have higher eccentricities than those in the Solar System, and about 18% of planets with masses < 10 M⊕ have 𝑒 > 0.1. Underexplored are the implications of such high eccentricities on the atmosphere, climate, and potential habitability on such planets. We use WACCM6, a state-of-the-art fully-coupled Earth-system model, to simulate the climates of two Earth-like planets; one in a circular orbit (𝑒 = 0), and one in an eccentric orbit (𝑒 = 0.4). We quantify the effects of eccentricity on the atmospheric water abundance and loss given the importance of liquid water for habitability. The asymmetric temperature response in the eccentric orbit results in a water vapour mixing ratio in the stratosphere (> 20 ppmv) that is approximately five times greater than that for circular orbit (∼ 4 ppmv). This leads to a ∼ 3 time increase in the atmospheric hydrogen loss rate and a corresponding ∼ 3 times decrease in the ocean loss timescale. Thus, highly-eccentric Earth-like exoplanets can still retain their oceans over the lifetime of the system. Using the Planetary Spectrum Generator, we simulate the idealised transmission spectra for both cases. We find that the water absorption features are stronger at all wavelengths for the 𝑒 = 0.4 spectrum than for the circular case. Hence, highly-eccentric Earth-like exoplanets may be prime targets for future transmission spectroscopy observations to confirm, or otherwise, the presence of atmospheric water vapour.</p>
Loss of socioemotional and occupational roles in people with Long COVID according to sociodemographic and clinical factors: Secondary data from Randomized Clinical Trial.
<p>This is a cross-sectional study was carried out with the participation of 100 patients diagnosed with Long-COVID, over 18 years of age and attended by Primary Health Care in the Autonomous Community of Aragon. The purpose of this study is to analyse the loss of socioemotional and occupational roles that people with Long COVID have suffered in their lives as a consequence of the disease. As a secondary objective, it was proposed to analyze the sociodemographic and clinical factors associated with this loss of roles. The main study variable was the loss of significant socioemotional and occupational roles of the participants. Sociodemographic and clinical data were also collected through a structured interview.</p>
(EMPIR 19ENG06 HEFMAG) Data sets of measurements of magnetic loss and complex permeability on amorphous and nanocrystalline samples up to the MHz range
<p>We measured the magnetic losses and the complex permeability of amorphous and nanocrystalline ribbons from DC to 1 GHz by combined application of fluxmetric and transmission line methods. Two transverse field annealed Co-based amorphous alloys, ~13 µm and ~25 µm tick and two nanocrystalline Finemet type alloys, ~13 µm and ~20 µm tick, endowed with defined transverse magnetic anisotropy, were characterized. </p>
Data and R scripts associated with Clark, Moles, Fazlioglu, Brandenburger & Hartley, "Rapid loss of phenotypic plasticity in the introduced range of the beach daisy, Arctotheca populifolia."
<p>Data to accompany article accepted for publication in Journal of Ecology.</p> <p><strong>"Rapid loss of phenotypic plasticity in the introduced range of the beach daisy, <em>Arctotheca populifolia"</em></strong></p> <p>By Charlie D. Clark, Angela T. Moles, Fatih Fazlioglu, Claire R. Brandenburger, & Stephen Hartley</p> <p>1 zip file that contains the following:</p> <p> 2 datasets (xlsx format)</p> <p> 9 R scripts to run the analyses and produce figures</p>
Data for: Extensive coral mortality and critical habitat loss following dredging and their association with remotely-sensed sediment plumes
<p>This work describes impacts to coral reefs surrounding the 2013-2015 dredging of the Port of Miami based on data collected before, during, and after dredging by Dial Cordy and Associates (DCA) on behalf of Great Lakes Dredge and Dock Company, the dredging contractors for the U.S. Army Corps of Engineers (USACE) and for the Port of Miami (Miami-Dade County). A front page for this repository can be accessed at <a href="http://jrcunning.github.io/pom-dredge">jrcunning.github.io/pom-dredge</a> containing rendered R Markdown detailing all analyses conducted as part of this work.</p>
Data from: Mating tactic influences body condition loss in Rocky Mountain bighorn rams (Ovis canadensis)
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Data from: Multiple plastid losses within photosynthetic stramenopiles revealed by comprehensive phylogenomics
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Code for: A century of wild bee sampling: historical data and neural network analysis reveal ecological traits associated with species loss
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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.