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6,498 results for “Physicality”
Hybridized, Physics-Based Melt Detection for 13 Antarctic sites v2
<p>An (updated) dataset of 13 Antarctic sites with microwave grain size (exponential correlation lengths/p_exp) as well as melt day detection using the Hybrid Method. The Hybrid Method combines the Community Firn Model and the Snow Microwave Radiative Transfer model with AMSR-2 to detect melt days. </p>
Relationships between needs satisfaction and the quality of motivation with academic engagement in pre-service physical education teachers
<p><span>Academic engagement has been identified as a key determinant in the learning process. Although research on initial teacher education has focused on understanding the mechanism that can foster strong academic engagement, little is known about the roles that basic psychological need satisfaction and academic motivation may perform. The objective of this research was to examine the predictive relationships between basic psychological need satisfaction (i.e., autonomy, competence, relatedness, and novelty) and academic engagement, considering the role of academic motivation. A cross-sectional and non-randomized design was used. A total of 920 students (61.8% male; <em>M</em><sub>age </sub>= 24.73; <em>SD </em><sub>age </sub>=5.34) participated from different universities. The results from structural equation modelling revealed that competence and novelty satisfaction significantly and positively predicted academic engagement. Autonomous motivation significantly mediated the association of competence and autonomy satisfaction with academic engagement. This research underscores the importance of considering competence and autonomy satisfaction as facilitators of autonomous motivation and academic engagement in pre-service teachers. </span></p>
Supplementary material for "Stellar flares" (Living Reviews in Solar Physics)
<p>The file supplementary.tar.gz contains a Jupyter notebook (Figs.ipynb) and supplementary material for recreating Figures 13, 35, 38, and 40 in Kowalski, A.F. (2024), "Stellar flares", <em>Living Reviews in Solar Physics</em> 24, 1 (<a href="10.1007/s41116-024-00039-4">https://doi.org/10.1007/s41116-024-00039-4</a>).</p>
Dataset of 'Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations'
<p>Dataset of the article 'Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations', recently published in Open Research Europe (DOI 10.12688/openreseurope.17388.1). The code which implements the physics-informed neural network can be found at https://github.com/AlvaroMS90/PINNs-for-high-resolution-weather-reconstruction-from-sparse-weather-stations.</p> <p>Funded by the European Union under action HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships, call HORIZON-MSCA-2021-PF-01 (project number 101059984 with acronym PERSEVERE). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <pre> </pre>
Digisonde Data files used for JGR-Space Physics paper "A simplified method of true height analysis to estimate the real height of sporadic E layers"
<p>paper submitted for publication in JGR-Space Physics.</p> <p>Digisonde Data files used for analysis</p> <p> </p> <p>A simplified method of true height analysis to estimate the real height of sporadic E layers</p> <p> </p> <p>Christos Haldoupis</p> <p>Department of Physics, University of Crete, Heraklion, Greece</p> <p>Haris Haralambous</p> <p>Frederick University and Frederick Research Center, Nicosia, Cyprus</p> <p>Chris Meek</p> <p>Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, SK, Canada</p>
NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</p>
Datos de la investigación: La voz del rehén: adapting creative sustainable development goal-based physical-artístico project during COVID pandemic situation
<p>This article assesses the effects on creativity of an interdisciplinary physicall-artistic programme and their effects during pandemic context. The sample comprised 97 individuals –54 men and 43 women– with an average age of 21.37±1.56, all of whom were studying a Primary Education Teacher Training Degree at Zaragoza University –Spain–. It was a pre-experimental study with measures pre-post programme –adapted to pandemic situation, replacing the acrosport content to skipping rope, juggling and aerial dance–. The creative skills assessment instrument was the PIC-A. A Student’s T-test and ANOVA test were performed to discover significant differences in pre-post in both academic years. Results showed that: i) participants obtained significant improvements in total score of creativity in both academic years; ii) the adaptations made to the intervention programme during the pandemic year were effective in developing creativity. These studies confirm the importance of incorporating interdisciplinary creativity programmes into the university system.</p>
Vertical stratification of leaf physical traits exerts bottom-up pressures on insect herbivory in a sugar maple temperate forest
<p>Do vertical gradients in temperate forest structure insect herbivore communities? We tested the hypothesis that the increase in light intensity from understory to forest canopy level drives differences in leaf physical traits and budburst phenology that impact insect herbivores and thus play a role in structuring both herbivore communities and the leaf damages they cause. Twelve sugar maple <em>(Acer saccharum)</em> sites were monitored in southern Quebec, examining insect herbivore patterns from understory to the shaded and sun canopy over the summers of 2020, 2021, and 2022. Additionally, we recorded leaf physical traits, temperature, humidity, and sun exposure. Our findings revealed that leaf thickness increased along the vertical gradient in 2021, making leaves less favorable to herbivores in the canopy level. Accordingly, we recorded a consistent decrease in insect herbivory damage rates from the understory to the shaded canopy and sun canopy in 2020 and 2021, driven by leaf cutters, skeletonizers, stipplers, and leaf miners. These results support our hypothesis that variation in plant physical traits due to sun exposure contributes to the vertical stratification of insect damage. In 2022, the gradient of insect herbivore abundance corroborated the observed damage trends from the previous years. Moreover, we calculated an average annual herbivory rate of 9.1% of the leaf surface in our study site, suggesting limited evidence supporting a significant contribution of background herbivory to the decline of sugar maple forests. Overall, our study highlights the importance of vertical gradients in structuring insect herbivore communities and emphasizes the role of leaf traits in mediating these interactions.</p>
Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
<p>Here are the datasets for our publication entitled "<a href="https://www.nature.com/articles/s41467-024-48779-z">Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis</a>" published in Nature Communications. </p> <p>The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub>. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature. A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.</p> <p>Other details can be found in "Data Introduction.pdf" file.</p> <p>The <strong>Python Code</strong> for reading and preprocessing this dataset is available at: <a href="https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library">https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library</a></p> <p>Summary of articles using the this dataset: <a href="https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary">https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary</a></p> <p> </p> <p>If you find this data helpful, please consider citing our paper:</p> <p>Wang, F., Zhai, Z., Zhao, Z. <em>et al.</em> Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. <em>Nat Commun</em> <strong>15</strong>, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z</p>
Data from: Physical mechanisms of deep convective boundary layer leading to dust emission in the Taklimakan desert
<p>Deserts play an important role in the climate system, which is closely associated with the emission and transport of dust aerosols. Based on the intensive observation experiment in the Taklimakan Desert, the potential physical processes between the deep convective boundary layer (CBL) and dust emission are revealed in this study. Deep CBL enables the formation of clouds in the late afternoon, leading to significant cooling of surface. Large-scale buoyant coherent structures thereby transform into the mechanical coherent structures confined near the surface. The responses promote the earlier occurrence of low-level jet (LLJ) than in cloudless conditions, which allows the downward transport of LLJ momentum and substantially increases surface wind. Therefore, dust emission is initiated by strong wind at dusk and lasts for several hours. The results are useful to predict dust emissions and improve our understanding of distinctive boundary-layer processes in desert regions.</p>
Features computed from physical exercises measurements
<p><span> </span><span>The data represents time series features from an accelerometer and gyroscope extracted from</span> <span><a href="../records/10984138">Physical Exercise Measurements with Accelerometer and Gyroscope (zenodo.org)</a></span><span>. The data consists of 5 feature sets.</span></p> <p><span>Description of feature sets:</span></p> <p><strong><span>1. </span></strong><strong><span>RQA features set</span></strong></p> <p><span>• "RR" - Recurrence rate</span><span><br></span><span>• "DET" - Determinism, count recurrence points in diagonal lines of length >= lmin</span><span><br></span><span>• "RATIO" - DET/RR</span><span><br></span><span>• "AVG" - average length of diagonal lines of length >= lmin</span><span><br></span><span>• "MAX" - maximal length of diagonal lines of length >= lmin</span><span><br></span><span>• "DIV" - Divergence, 1/MAX</span><span><br></span><span>• "LAM" - Laminarity, VLRP/TR</span><span><br></span><span>• "TT" - Trapping time, average length of vertical lines of length >= lmin</span><span><br></span><span>• "MAX_V" - maximal length of vertical lines of length >= lmin</span><span><br></span><span>• "TR" - Total number of recurrence points</span><span><br></span><span>• "DLRP" - Recurrence points on the diagonal lines of length of length >= lmin</span><span><br></span><span>• "DLC" - Count of diagonal lines of length of length >= lmin</span><span><br></span><span>• "VLRP" - Recurrence points on the vertical lines of length of length >= lmin</span><span><br></span><span>• "VLC" - Count of vertical lines of length of length >= lmin</span></p> <p><span>Was calculated by Chaos01 R package.</span></p> <p><span><a href="https://cran.r-project.org/package=Chaos01">https://CRAN.R-project.org/package=Chaos01</a></span></p> <p><span>The parameters were chosen so that the embedding will create a vector of one value of each axis of the accelerometer/gyroscope measurements. Therefore the used parameters were:</span></p> <table> <tbody> <tr> <td>Function argument</td> <td>Value</td> </tr> <tr> <td>embedding dimension (dim)</td> <td>3</td> </tr> <tr> <td>embedding lag (lag)</td> <td>time series length</td> </tr> <tr> <td>Minimal length of recurrence line (lmin)</td> <td>20</td> </tr> </tbody> </table> <p><strong><span>For Chaos01 we change eps argument and calculated it by following formula:</span></strong></p> <p><code><span># Calculate eps for acc and gyro Chaos 01----</span></code></p> <p><code><span>get_eps <- function(input_data, scale = 1) {</span></code></p> <p><code><span> # Calculate eps for acc and gyro</span></code></p> <p><code><span> eps_a <-</span></code></p> <p><code><span> purrr::map_dbl(input_data$data,</span></code></p> <p><code><span> ~ .x |></span></code></p> <p><code><span> select(Ax, Ay, Az) |></span></code></p> <p> <code><span> as.matrix() |></span></code></p> <p> <code><span> as.vector() |></span></code></p> <p> <code><span> sd()) |></span></code></p> <p> <code><span> mean() * scale</span></code></p> <p><code><span> eps_g <-</span></code></p> <p><code><span> purrr::map_dbl(input_data$data,</span></code></p> <p><code><span> ~ .x |></span></code></p> <p> <code><span> select(Gx, Gy, Gz) |> </span></code></p> <p> <code><span> as.matrix() </span></code></p> <p> <code><span> as.vector() |></span></code></p> <p> <code><span> sd()) |></span></code></p> <p> <code><span> mean() * scale</span></code></p> <p><code><span>return(list(a = eps_a, g = eps_g))</span></code></p> <p><code><span>}</span></code></p> <p><span>"TREND" - Trend of the number of recurrent points depending on the distance to the main diagonal.</span></p> <p><span>Was calculated by nonlinearTseries R package.</span></p> <p><span> </span><span><a href="https://cran.r-project.org/package=nonlinearTseries">https://CRAN.R-project.org/package=nonlinearTseries</a></span></p> <p><strong><span> </span></strong></p> <p><strong><span>All following feature sets was calculated by Python package</span></strong></p> <p><span><span><strong>https://tsfresh.readthedocs.io/en/latest/index.html</strong></span></span></p> <p><strong><span> </span></strong></p> <p><span>The used dictionary is included in file named tsfresh_autocorr_spectral_features.py.</span></p> <p><strong><span> </span></strong></p> <p><strong><span>2. </span></strong><strong><span>Autocorrelation features set</span></strong></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.autocorrelation</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.agg_autocorrelation</span></p> <p><span> </span></p> <p><strong><span>3. </span></strong><strong><span>Spectral features set</span></strong></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_aggregated</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.approximate_entropy</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_coefficient</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fourier_entropy</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.spkt_welch_density</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.ar_coefficient</span></p> <p><span> </span></p> <p><strong><span>4. </span></strong><strong><span>Mix RQA/Spectral/Autocorrelation features set</span></strong></p> <p><strong><span> </span></strong></p> <p><strong><span>5. </span></strong><strong><span>Tsfresh all features set</span></strong></p> <p><span>#</span><span><span>https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html</span></span></p> <p><span> </span></p> <p><strong><span>Versions of the software:</span></strong></p> <p><span>Python (version 3.8.10) </span></p> <p><span>tsfresh = 0.20.2</span></p> <p><span>R (version 4.3.2) </span></p> <p><span>Chaos01 = Version 1.2.1 </span></p> <p><span>nonlinearTseries = 0.3.0 </span></p>
Lightning Prediction in the Tehran Region Using the WRF Model with Multiple Physical Parameterizations and an Ensemble Approach
<p><span>The Grid Analysis and Display System (</span>GrADS)<span> </span><span>and</span><span> </span><span>Python</span><span> </span><span>scripts and the output data from simulations that we used in this study.</span></p>
Toxic Sentence Classification Dataset with labels of categories such as religion, mental health, race, sex, body image, disability, physical abuse, and politics
<p>The dataset has a collection of various toxic sentences belonging to different categories. It was collected from various sources. It indicates which category each sentence belongs to. The values of the category columns are binary 1 or 0 indicating whether the sentence belongs to that particular category or not. Each sentence belongs to only 1 category. </p> <p> </p> <p>Columns:<br>1.comment_text: Contains toxic sentences that are insensitive and offensive, focusing on various categories.<br>2.mental_health: Binary value 1 indicates that the sentence focuses on mental health.<br>3.Race:Binary value 1 indicates that the sentence is racist.<br>4.sex:Binary value 1 indicates that the sentence focuses on sexuality.<br>5.body_image:Binary value 1 indicates that the sentence focuses on body image.<br>6.disability:Binary value 1 indicates that the sentence focuses on physical disability and related issues.<br>7.religion:Binary value 1 indicates that the sentence can be triggering to people who are extremely religious.<br>8.physical_abuse:Binary value 1 indicates that the sentence focuses on physical abuse issues.<br>9.politics:Binary value 1 indicates that the sentence focuses on political issues.</p>
Climate model (CM2.6) and regional model (ACM) processed output used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key processed output from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README_v2.txt</em> for more details.</p>
Observation of 1/3 fractional quantum Hall physics in balanced large angle twisted bilayer graphene
<p>Raw data of Main figures (.dat file) and Monte Carlo calculation results at various filling factor (.xlsx file).</p> <p>In the mangetotransport file name, "Q mV" refers to the DMM sensitivity. The resistance is calcuated as "(Voltage drop x (Q) x 0.1) / (1E-7)".</p> <p> </p>
Substorm Dataset and code (Substorm Identification With The WINDMI Magnetosphere - Ionosphere Nonlinear Physics Model)
<p>The archive contains MATLAB Live Script (<code>.mlx</code>) and Simulink model (<code>.slx</code>) files.</p> <ul> <li> <p><strong><code>.mlx</code> files:</strong> MATLAB Live Scripts are interactive files that combine code, output, and formatted text in a single document. They are used for data analysis, visualization, and sharing workflows in an easy-to-read format. These files can be opened in MATLAB's Live Editor for an interactive coding experience.</p> </li> <li> <p><strong><code>.slx</code> files:</strong> Simulink model files are used in MATLAB's Simulink environment for graphical modeling and simulation of dynamic systems. They are particularly useful for designing and analyzing systems with time-dependent behaviors, such as control systems or physical processes.</p> </li> </ul>
Data for "Do the Laws of Physics Prohibit Counterfactual Communication"
<p>Data (and code used to analyse the data) used in the weak measurement experiment in the paper "Do the Laws of Physics Prohibit Counterfactual Communication" (arXiv:<a href="https://arxiv.org/abs/1806.01257">1806.01257</a>).</p>
Bivalve facilitation mediates seagrass recovery from physical disturbance in a temperate estuary
<p>This dataset describes two experiments done in seagrass beds in Back Sound, North Carolina. Experiment 1 was located in a large<i> </i>contiguous shallow seagrass bed near Cape Lookout, NC<i> </i>(34.668121, -76.509455) and Experiment 2 was located in the Rachel Carson Estuarine Reserve, Beaufort, NC (34.698799, -76.595439). Experiment 1 was a clam-addition/control experiment, and 2018/2019 summer growth rates, 2018/2019 summer biomass cores, and 2018/2019 epiphytic load on <i>Zostera marina </i>and <em>Halodule wrightii </em>were sampled. Experiment 2 was a two-factor experiment looking at clam-addition and excavation and 2019 summer growth rates, 2019/2020 summer biomass cores, and 2019/2020 recolonization and percent cover were sampled. To document the spatial characteristics of the experimental areas, we mapped the extent of the contiguous seagrass bed and the coordinates of the experimental plots and subplots with a Trimble R10 Integrated GNSS system in May 2018, June 2018, and July 2019 for Experiment 1 and April 2019 and July 2019 for Experiment 2 in the NAD83 coordinate system.</p>
Simple Physical Interactions Yield Social Self-Organization in Honeybees - datasets
<p>Empirical data of the location of the bees in complex thermal environments in specific time intervals. For more details please refer to <br> <br> Szopek M, Stokanic V, Radspieler G and Schmickl T (2021) Simple Physical Interactions Yield Social Self-Organization in Honeybees. <em>Front. Phys.</em> 9:670317. doi: 10.3389/fphy.2021.670317</p> <p> </p> <p>exp_1.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 9 repetitions of Experiment 1 (static thermal environment with one global optimum at 36°C and a pessimum at 30°C).</p> <p>exp_2.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 8 repetitions of Experiment 2 (static thermal environment with one global optimum at 36°C and one local optimum at 32°C).</p> <p>exp_3.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 6 repetitions of Experiment 3 (static thermal environment with two equal optima of 36°C).</p> <p>exp_4.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 105 min) for each of the 17 repetitions of Experiment 4 (dynamic thermal environment).</p> <p>exp_5.csv contains the percentage of bees in the left, the center and the right evaluation zone at minute 30 for i) each of the 10 repetitions of Experiment 5 (static thermal environment with social stimulus) and ii) for each of the 8 repetitions of an experiment with the same thermal environment but without a social stimulus for comparison.</p>
Data for the Scotogenic and EW-ino results presented in "Constraining new physics with SModelS version 2" (arxiv: 2112.00769 )
<p>Data used for the results presented in "Constraining new physics with SModelS version 2" (arXiv: <a href="https://arxiv.org/abs/2112.00769">2112.00769</a>).</p> <p>The following data files are available:</p> <ol> <li>EWinoData.tar.gz: contains the SLHA files for the random scan over the electroweak-ino parameter space. The tarball also contains the SModelS and Micromegas results for the corresponding points.</li> <li>ScotoScalarDMData.tar.gz: contains the SLHA files for the scan over the scotogenic model with a scalar DM candidate (neutral CP-even Higgs). The tarball also contains the SModelS results for the corresponding points.</li> <li>ScotoFermionDMData.tar.gz: contains the SLHA files for the scan over the scotogenic model with a fermionic DM candidate (lightest right-handed neutrino). The tarball contains 2 folders containing the SLHA files and the SModelS output. Each folder has the following structure: <ul> <li>mhc_<electronic/muonic/mixed>_decays: scenario with the charged Higgs decaying with a 100% BR to electron+N<sub>1</sub> muon +N<sub>1 </sub>or 50% BR to each (electron/muon) flavor <ul> <li>deltam_<5/50>: scenario with a 5 (50) GeV mass difference between the scalars. </li> </ul> </li> </ul> </li> </ol> <p> </p> <p>More details about the scans can be found in arXiv:<a href="https://arxiv.org/abs/2112.00769">2112.00769</a> .</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.