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4,376 results for “magnetism”
IODP Expedition 367 Magnetic remanence (spinner)
Magnetic remanence was measured on discrete samples by an Agico JR-6A spinner magnetometer, first as natural remanent magnetization (NRM) and then after demagnetization or remagnetization steps were performed on the samples (e.g., alternating field [AF] demagnetization, thermal demagnetization [TD], or isothermal remanent magnetization [IRM]).
IODP Expedition 367 Magnetic remanence (SRM-discrete)
Raw data files for the magnetic remanence measurements of discrete and section-half samples on the superconducting rock magnetometer (SRM-DISC and SRM-SECT) are stored on by hole and by expedition, and are designated as discrete samples, section halves, or, rarely, whole-round sections.
IODP Expedition 367 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 367 Magnetic susceptibility (whole round)
Magnetic susceptibility was measured on whole-round sections (and rarely section halves) on the Whole-Round Multisensor Logger (WRMSL) and/or Special Task Multisensor Logger (STMSL) using a Bartington MS2 meter and a 90 mm or 80 mm MS2C loop. As volume of the sample is not controlled for this experiment, susceptibility units are recorded in instrument units and are not volume-corrected.
Nanoparticle clustering in supraparticles to control magnetic long-range interactions
<p>This data publication is based on the metadata and datasets underlying the manuscript: Nanoparticle clustering in supraparticles to control magnetic long-range interactions</p> <p>To tailor superparamagnetic iron oxide nanoparticles (SPIONs) to the specific needs of diverse application fields, it is essential to understand not only their intrinsic properties but also their interactions with each other. Theoretical models predicting/explaining the magnetization behavior of macroscopic samples containing millions of SPIONs are intricate due to the complexity of the underlying relaxation mechanisms in alternating fields. This study introduces supraparticles (SPs) as model architectures to empirically investigate magnetic interactions within and between large SPION clusters (> 100 nanoparticles). For this purpose, nanoparticle dispersions containing SPIONs and silica nanoparticles (SiO<sub>2</sub> NPs) as non‐magnetic building blocks are spray‐dried to form binary SPs. Selective salt‐induced agglomeration of the two building block types before spray‐drying is utilized to tailor SP architectures, including control over SPION cluster size, shape, and proximity. Magnetic particle spectroscopy (MPS), operating under ambient conditions, reveals altered magnetization behavior for different cluster structures. Not only the nearest SPION neighbors, but the whole cluster structure up to several micrometers is decisive for the magnetization behavior. This highlights the importance of long‐range magnetic interactions. This work presents a versatile approach for designing model architectures to advance empirical interaction studies between SPIONs in macroscopic samples.</p>
Predicted times, spatial coordinates of bow shock crossings and shock geometry at Mars from the NASA/MAVEN mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br>Planet: <strong>Mars</strong><br>Radius: <strong>R<sub>M</sub> = 3389.5 km</strong> (volumetric mean planetary radius)<br>Spacecraft: <strong>NASA/Mars Atmosphere and Volatile Evolution (MAVEN)</strong><br>Spacecraft coordinates system: <strong>Mars Solar Orbital (MSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>MSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>MSO</sub></em> towards Mars’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>MSO</sub></em>–<em>Y<sub>MSO</sub></em> plane passing through the centre of Mars,</li> <li><em>Y<sub>MSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/11/2014 to 30/04/2024</strong> (Mars Years MY32 to MY36 included, part of MY37).<br>Total number N of candidate bow shock crossings in the database: <strong>N = 20107</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br>The original MAVEN/MAG data repository on which these algorithms were applied is available on NASA's Planetary Data System (PDS) at <a href="https://doi.org/10.17189/1414178">https://doi.org/10.17189/1414178</a>. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br>To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described in:<br>Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2022), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, 1-33, e2021JA029942, <a href="https://doi. org/10.1029/2021JA029942">https://doi. org/10.1029/2021JA029942</a>. </p> <p>Also available at: <a href="https://doi.org/10.1002/essoar.10507942.1">https://doi.org/10.1002/essoar.10507942.1 </a> and as arXiv e-print: <a href="https://doi.org/10.48550/arXiv.2109.04366">https://doi.org/10.48550/arXiv.2109.04366</a></p> <p>These algorithms consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on J. Gruesbeck's 3D model (<a href="https://doi.org/10.1029/2018JA025366">Gruesbeck et al. 2018</a>) for prediction of Mars bow shock position</li> <li>Corrector algorithm based on magnetic field measurements (magnitude and fluctuations).</li> </ol> <p><strong>REMARK ON VERSIONS</strong><br>From Version 3 onwards, we also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted \(\theta_{Bn}\)(ThetaBn). Assuming a smooth shock surface and the 3D model of Gruesbeck et al. (2018, all points), this gives a first indication of the geometry of the shock, so that:</p> <ul> <li>45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup>: quasi-perpendicular shock condition</li> <li><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub>≥135<sup>∘</sup>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>From Version 4 onwards, we also added the solar longitude Ls (in degrees).</p> <p>For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. Note that due to minor adjustments in the code, some of the ThetaBn angles calculated here for the examples of Fig. 6 in Simon Wedlund et al. (2022) may slightly differ from the values quoted in the paper.</p> <p><strong>VARIABLES DESCRIPTION</strong><br>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in MAVEN's database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Mars Solar Orbital coordinates of the shock, in units of Mars radius <em>R</em><sub><em>M</em> </sub>(<em>R<sub>M</sub></em> = 3389.5 km):<br><em>X<sub>MSO</sub></em>,<sub> </sub><em>Y<sub>MSO</sub></em>, <em>Z<sub>MSO</sub></em> and Euclidean distance \(R_{MSO} = \sqrt{X_{MSO}^2 + Y_{MSO}^2 + Z_{MSO}^2}\) (in <em>R<sub>M</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = \(\tan^{-1}{Y_{MSO}^2+Z_{MSO}^2 \over X_{MSO}^2}\) (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface \(\theta_{Bn}\) (ThetaBn, in º) <ul> <li>45 < ThetaBn < 135 deg: quasi-⊥ shock</li> <li>ThetaBn ≤45 deg & ThetaBn ≥ 135 deg: quasi-|| shock</li> </ul> </li> <li>Solar longitude Ls, in degrees.</li> <li>Flag for crossing: <ul> <li>sheath \(\longrightarrow\) solar wind, flag = 0.</li> <li>solar wind \(\longrightarrow\) sheath, flag = 1.</li> </ul> </li> </ul> <p><strong>WARNING</strong><br>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic field. It is aimed at giving a first approximation of the shock area times in the MAVEN data. It is particularly suited to statistical studies and region identification in the MAVEN datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma suite bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.075 R<sub>M</sub> (with R<sub>M</sub> = 3389.5 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</p> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br>C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. C. Möstl thanks the Austrian Science Fund FWF projects P31659-N27, P31521-N27. A. Beth thanks the Swedish National Space Agency (SNSA) and its support with the grant 108/18. This database was notably used to add to the Helio4Cast database which monitors solar wind parameters in the solar system (<a href="https://doi.org/10.6084/m9.figshare.6356420">https://doi.org/10.6084/m9.figshare.6356420</a>). Helio4Cast is available at <a href="http://www.helioforecast.space/icmecat">www.helioforecast.space/icmeca</a>t and <a href="http://www.helioforecast.space/sircat">www.helioforecast.space/sircat</a>. </p> <p><strong>LICENSE AND RIGHTS</strong><br>This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences (ÖAW), 2021-09-08<br>Version 2 (c) CSW @ ÖAW/IWF, 2021-11-30 -- Addition of R_MSO and SZA<br>Version 3 (c) CSW @ ÖAW/IWF, 2022-02-09 -- Addition of ThetaBn<br>Version 4 (c) CSW @ ÖAW/IWF, 2025-03-20 -- Addition of Ls, Bx, By, Bz and Bt.</p> <p> </p> <p><br>Contact email: cyril.simon.wedlund@gmail.com</p>
Data for "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns"
<p>This dataset contains the raw data used for the publication "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns".</p>
MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles (Data)
<p>Origin project and figures used for the article "MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles", published in <em>Chemosensors</em> on 04 Aug 2021.</p>
Data and code used in "Satellite magnetic data reveal interannual waves in Earth's core"
<p>Eigen mode solutions and code to obtain them for the results presented in <a href="https://doi.org/10.1073/pnas.2115258119">Satellite magnetic data reveal interannual waves in Earth's core</a>. The package uses the freely available code <a href="https://github.com/fgerick/Mire.jl">Mire.jl</a>.</p> <p><strong>Prerequisites</strong></p> <p>Installed python3 with matplotlib ≥v2.1, cmocean and cartopy. A working Julia ≥v1.7.</p> <p><strong>Run</strong></p> <p>In the project folder run</p> <pre><code>julia --project=.</code></pre> <p><br> Then, from within the Julia REPL run</p> <pre><code>]instantiate</code></pre> <p>at first time, to install all dependencies.</p> <p>After that, to compute all plots, run</p> <pre><code>using QGMCSat allfigs()</code></pre> <p>They're automatically saved in the "figs" subfolder of the repository.</p> <p>If loading QGMCSat fails, due to a missing cartopy or cmocean in the python version. Run (within Julia)<br> </p> <pre><code>ENV["PYTHON"] = "python" #this should point to the python version that has cartopy installed ]build PyCall</code></pre> <p><br> To calculate all data, run</p> <pre><code>using QGMCSat calculate_data()</code></pre> <p>This will take several hours/days depending on the machine (needs enough memory).</p> <p>Individual data can be accessed directly through the .jld2 files from Julia. You can check out the individual figure functions to get an idea where which data is stored.</p> <p>If there are any issues or questions, please don't hesitate to get in touch!</p>
Phase separation of hnRNP A1 upon specific RNA-binding observed by magnetic resonance
<p>Experimental data, <a href="https://mmmx.info">MMMx</a> restraint and ensemble analysis files (.mcx), restraint data, raw ensembles, and ensemble lists with populations (.ens) pertaining to the manuscript "Phase separation of hnRNP A1 upon specific RNA-binding observed by magnetic resonance" <a href="https://www.biorxiv.org/content/10.1101/2022.03.21.485092v1">available at bioRxiv</a> and submitted to a peer-reviewed journal.</p>
Magnetic and stable isotope data from PLG Core
<p>The Poggio le Guaine core was designed to provide a high-resolution age model and a high-resolution relative magnetic paleointensity reference curve for the Aptian-Albian interval of the long normal Cretaceous superchron. The PLG drill hole cored the uppermost Barremian-lowermost Cenomanian succession of the Umbria-Marche Basin deposited in the southern margin of the central-western Tethys Ocean. These pelagic sediments formed following the lithification of the nannofossil-planktonic foraminiferal ooze deposited well above the calcite compensation depth at middle to lower bathyal depths (1000-1500 m) and at ~20°N paleolatitude. The Poggio le Guaine drill site (lat. 43°32'42.72"N; long.12°32'40.92"E) is located on the Monte Nerone ridge at 888 m abovensea level, 6 km west of the town of Cagli (Regione Marche, Italy). Discrete ~8 cm<sup>3</sup> cubic samples were then cut from the center of the split working halve for paleomagnetic analyses (MS and ARM). A total amount of 1227 cubic samples were collected along the studied portion of the PLG core (from 96.02 to 60.00 m; average sampling resolution of ~3 cm). A total of 355 paleomagnetic cubic samples were also used to measure the stable isotopes (δ<sup>18</sup>O and δ<sup>13</sup>C) with a ~10 cm resolution. Our objectives with these data are: (i) to propose a cyclostratigraphic framework for the PLG section using high-resolution magnetic susceptibility (MS), anhysteretic remanent magnetisation (ARM), δ<sup>18</sup>O, and δ<sup>13</sup>C data to provide better constraints for the Aptian climato-chronostratigraphic framework; and (ii) to discuss the impact of the proposed framework on the main events of the Aptian and the Cretaceous time scale.</p>
Dataset of ipsilateral motor-evoked potentials induced by transcranial magnetic stimulation
<p>The dataset includes 4244 trials of EMG recordings after ipsilateral TMS under muscle contraction. This data was used for the evaluation of the DiMEP toolbox https://pypi.org/project/dimep/</p> <p>All subjects provided written, informed consent before participation and following approval by the local ethics committee. Subjects had no contraindications to TMS. TMS was delivered with a MagVenture MagPro-R30 with MagOption and a MCF-B70 figure 8-coil at an orientation of 45° to the midsagittal plane (see figure 1A). First, the motor hotspot of the contralateral, i.e., right, extensor digitorum communis (EDC) was determined by applying 40 stimuli to the left hemisphere. 40% MSO was used as the starting intensity and increased in steps of 5% MSO in case no MEPs could be elicited. Next, at the locations of the three stimuli that resulted in the greatest MEP, i.e., the largest MEP peak-to-peak amplitude, additional three stimuli were applied. The stimulation location that consistently elicited the largest MEPs in the contralateral EDC was then selected as the hotspot. A neuro-navigation system (TMS Navigator, Localite GmbH, Germany) supported coil positioning throughout the measurements. In order to measure ipsilateral MEPs, TMS pulses were applied to the motor hotspot with 5s ± 1.25 s between pulses (at an intensity of 90% MSO). To increase the likelihood of exhibiting ipsilateral MEPs, subjects were requested to isometrically contract their left biceps brachii (BB) to 30% of its maximum voluntary contraction while receiving visual feedback of the BB activity relative to the required target level of contraction on a computer display. The electromyographical response (EMG) to TMS was recorded with a BrainProducts ExG bipolar amplifier from the ipsilateral EDC. All trials were divided into 1s segments, i.e., 500 ms before and after each TMS pulse, and sampled at 1000 Hz.</p>
Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models
<p>This data set contains the simulations and data analysis files used in the publication: "<em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models</em>", by D. Cortés-Ortuño, K. Fabian and L. V. de Groot.</p> <p>The data set includes:</p> <ul> <li>Scripts and output files from MERRILL simulations</li> <li>Jupyter notebooks with data analysis</li> <li>Figures</li> </ul> <p>A preprint of this work can be found in:</p> <p>David Cortés-Ortuño, Karl Fabian and Lennart V. de Groot. <em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models.</em> DOI: 10.1002/essoar.10510574.1. Earth and Space Science Open Archive. <a href="https://doi.org/10.1002/essoar.10510574.1">https://doi.org/10.1002/essoar.10510574.1</a></p> <p>The README file in this dataset (in markdown format) contains full details about the simulations. The dataset also contains pre-computed data files to calculate the inversions and produce the figures and analyze the inversion data without processing the vbox files.</p> <p>To cite this dataset you can use the following bibtex entry:</p> <pre><code>@Misc{Cortes2022, author = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.}, title = {{Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models}}, publisher = {Zenodo}, year = {2022}, doi = {10.5281/zenodo.6501818}, url = {https://doi.org/10.5281/zenodo.6501818}, } </code></pre> <p> </p>
Magnetic interactions between radical pairs in chiral graphene nanoribbons
<p>OPEN DATA related to the research publication:</p> <p>T. Wang, S. Sanz, J. Castro-Esteban, J. Lawrence, A. Berdonces-Layunta, M. S. G. Mohammed, M. Vilas-Varela, M. Corso, D. Peña, T. Frederiksen, and D. G. de Oteyza<br> <em>Magnetic interactions between radical pairs in chiral graphene nanoribbons</em><br> Nano Lett. <strong>22</strong>, 164-171 (2022) [arXiv:2108.13473]</p> <p>Abstract: Open-shell graphene nanoribbons have become promising candidates for future applications, including quantum technologies. Here, we characterize magnetic states hosted by chiral graphene nanoribbons (chGNRs). The substitution of a hydrogen atom at the chGNR edge by a ketone effectively adds one p<sub>z</sub> electron to the π-electron network, producing an unpaired π-radical. A similar scenario occurs for regular ketone-functionalized chGNRs in which one ketone is missing. Two such radical states can interact via exchange coupling, and we study those interactions as a function of their relative position, which includes a remarkable dependence on the chirality, as well as on the nature of the surrounding ribbon, that is, with or without ketone functionalization. Besides, we determine the parameters whereby this type of system with oxygen heteroatoms can be adequately described within the widely used mean-field Hubbard model. Altogether, we provide insight to both theoretically model and devise GNR-based nanostructures with tunable magnetic properties.</p>
Fluid Drag Reduction by Magnetic Confinement
<p>Drag reduction of viscous liquid (Honey) with ferrofluid APG314.</p> <p>Friction factors and Reynolds number.</p> <p>Numerical simulations</p> <p>Microfluidics drag reduction</p> <p> </p>
Supplemental material for "Enhanced collisionless laser absorption in strongly magnetized plasmas"
<p>This dataset constitutes supplemental material for the paper titled "Enhanced collisionless laser absorption in strongly magnetized plasmas" by Lili Manzo, Matthew R. Edwards, and Yuan Shi.</p> <p>• figure_data.zip<br> When unzipped, this folder contains subfolders fig1, fig2, …, fig10, each contains data used to generate figures 1,2, …,10 in the paper. The data files are in .txt, .mat, or .dat format, and are intended to be read by MATLAB.</p> <p>The data underlying fig1 and fig3 are generated using the Three-Wave-MATLAB code (https://gitlab.com/seanYuanSHI/three-wave-matlab).</p> <p>The data underlying fig2, fig4, and figs5-10 are raw simulation data or post-processed results of the epoch1d code (https://github.com/Warwick-Plasma/epoch).<br> <br> • figure_programs.zip<br> When unzipped, this folder contains plot_fig1.m, plot_fig2.m, …, plot_fig10.m, which are MATLAB scripts used to plot the corresponding data. Except for plot_fig2.m, which requires MATLAB version 2018 or later, all other scripts can run on MATLAB 2013 or later. The scripts plot the data but does not reproduce the formatting of the figures as shown in the paper.</p> <p>• input.deck<br> This is an example input for the epoch1d code (version 4.17.10) used to generate simulation data in the paper. </p>
Measurements in October 2021 using a digital magnetic variation station at the Simeiz-Katsiveli geodynamic test site
<p>Measured by the digital magnetic variation station at the Simeiz-Katsiveli test site during the period October 07–21, 2021.</p>
Dataset for "Robust magnetic order upon ultrafast excitation of an antiferromagnet"
<p>This is the dataset for the publication on 'Advanced Materials Interfaces' with publication DOI: 10.1002/admi.202201340. The dataset contains the raw trARPES experimental data and the normalized magnetic x-ray diffraction amplitude dynamics (published in https://doi.org/10.1038/s42005-020-00407-0) of GdRh2Si2.</p> <p>- 'trARPES_T_20K_static_MX_cube.nxs' contains a cube of trARPES intensity along the MX cut of the surface Brillouin zone presented in Figure 1-b.</p> <p>- 'trARPES_T_20K_fl_(number).nxs' series contain temporal evolution of raw trARPES intensity measured at sample temperature of 20 K with pump fluence of (number) mJ/cm^2. Figure 2 a-d, 3, 4, 5 b-d, B1, C1 used these trARPES intensity evolution.</p> <p>- 'trARPES_T_150K_fl_(number).nxs' series contain temporal evolution of raw trARPES intensity measured at sample temperature of 150 K (above T_N) with pump fluence of (number) mJ/cm^2. Figure A1 used these trARPES intensity evolution.</p> <p>- 'trRXD_T_20K_fl_(number).txt' series contain temporal evolution of AF order parameter of GdRh2Si2 measured at sample temperature of 20 K with pump fluence of (number) mJ/cm^2. Figure 2-e, 4, 5 used these trRXD amplitude evolution.</p>
IODP Expedition 379 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 379 Magnetic susceptibility (whole round)
Magnetic susceptibility was measured on whole-round sections (and rarely section halves) on the Whole-Round Multisensor Logger (WRMSL) and/or Special Task Multisensor Logger (STMSL) using a Bartington MS2 meter and a 90 mm or 80 mm MS2C loop. As volume of the sample is not controlled for this experiment, susceptibility units are recorded in instrument units and are not volume-corrected.
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.