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3,474 results for “electronics”
Supramolecular Self-Healing Sensor Fiber Composites for Damage Detection in Piezoresistive Electronic Skin for Soft Robots
<p>Self-healing materials can prolong the lifetime of structures and products by enabling the repairing of damage. However, detecting the damage and the progress of the healing process remains an important issue. In this study, self-healing, piezoresistive strain sensor fibers (ShSFs) are used for detecting strain deformation and damage in a self-healing elastomeric matrix. The ShSFs were embedded in the self-healing matrix for the development of self-healing sensor fiber composites (ShSFC) with elongation at break values of up to 100%. A quadruple hydrogen-bonded supramolecular elastomer was used as a matrix material. The ShSFCs exhibited a reproducible and monotonic response. The ShSFCs were investigated for use as sensorized electronic skin on 3D-printed soft robotic modules, such as bending actuators. Depending on the bending actuator module, the electronic skin was loaded under either compression (pneumatic-based module) or tension (tendon-based module). In both configurations, the ShSFs could be successfully used as deformation sensors, and in addition, detect the presence of damage based on the sensor signal drift. The sensor under tension showed better recovery of the signal after healing, and smaller signal relaxation. Even with the complete severing of the fiber, the piezoresistive properties returned after the healing, but in that case, thermal heat treatment was required. With their resilient response and self-healing properties, the supramolecular fiber composites can be used for the next generation of soft robotic modules</p>
Femtosecond electron diffraction data of iron and cobalt
<p>Femtosecond electron diffraction data of iron and cobalt measured at the Fritz Haber Institute in Berlin. The excitation wavelength was 2300 nm in all measurements. For iron, data were recorded with four different pump fluences. For cobalt, data were recorded with six different pump fluences. The samples were polycrystalline films with a thickness of 20 nm, sandwiched between two layers of silicon nitride with a thickness of 5 nm each. More information is available here: https://arxiv.org/abs/2110.00525</p>
Dataset for Electron Precipitation Curtains – Simulating the Microburst Origin Hypothesis by T.P. O'Brien et al. submitted to J. Geophysical Res.
<p>Technical reports and data sets for the the paper Electron Precipitation Curtains – Simulating the Microburst Origin Hypothesis. Additional AC6 information can be found at rbspgwy.jhuapl.edu/ac6 and at spdf.gsfc.nasa.gov/pub/data/aaa_smallsats_cubesats/aerocube/aerocube-6/. AC6 data have also been ingested into the main CDAWeb database at cdaweb.gsfc.nasa.gov. Source code related to this data set can be found at https://github.com/tpoiii/dipole_tracer_ac6, or DOI: 10.5281/zenodo.6011631.</p>
Dataset for Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy
<p>Raw and processed image data resulting from the paper "Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy", by S. Mitchell, F. Parés, D. Faust Akl, S. M. Collins, D. M. Kepaptsoglou, Q. M. Ramasse, D. Garcia-Gasulla, J. Pérez-Ramírez, and N. López (JACS, 2021). </p> <p>The corresponding code can be found under: <a href="https://github.com/HPAI-BSC/AtomDetection_ACSTEM">GitHub - HPAI-BSC/AtomDetection_ACSTEM</a></p>
Data for: Probing electron and hole co-localization by resonant four-wave mixing spectroscopy in the extreme-ultraviolet
<p>Data for: Probing electron and hole co-localization by resonant four-wave mixing spectroscopy in the extreme-ultraviolet</p>
Dataset supporting the paper "Power discontinuity and shift of the energy onset of a molecular de-bromination reaction induced by hot-electron tunneling. Nanoscale 13, 15215 (2021)"
<p>Dataset corresponding to theoretical calculations in the paper "Power discontinuity and shift of the energy onset of a molecular de-bromination reaction induced by hot-electron tunneling. Nanoscale 13, 15215 (2021)". DOI: <a href="https://doi.org/10.1039/D1NR04229G">10.1039/D1NR04229G</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <ul> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).<br> </li> </ul>
Photon-emission statistics induced by electron tunnelling in plasmonic nanojunctions
<p>OPEN DATA related to the research publication:</p> <p>R. Avriller, Q. Schaeverbeke, T. Frederiksen, and F. Pistolesi<br> <em>Photon-emission statistics induced by electron tunnelling in plasmonic nanojunctions</em><br> Phys. Rev. B <strong>104</strong>, L241403 (2021) [arXiv:2107.07860]</p>
FAST loss-cone electron precipitation database
<p>A FAST electron precipitation database derived from FAST EESA observations, covering beginning of mission (October 1996) through 2009.</p> <p>Number flux ('j') and energy flux ('je') moments are produced by integrating EESA measurements over all energies above 70 eV<br> up to the EESA detector limit (30 keV), and over all pitch angles within the earthward portion of the loss cone (see references). </p> <p>================================<br> EXPLANATION OF DATAFRAME COLUMNS<br> ================================</p> <p>'j' : units of #/cm^2-s (ALL QUANTITIES ARE POSITIVE, WHERE I HAVE USED THE CONVENTION 'POSITIVE' == 'EARTHWARD')<br> 'je' : units of mW/m^2 (ALL QUANTITIES ARE POSITIVE, WHERE I HAVE USED THE CONVENTION 'POSITIVE' == 'EARTHWARD')<br> <br> 'jerr' : units of #/cm^2-s (Number flux uncertainty, calculated using the Gershman et al (2015) method)<br> 'jeerr' : units of mW/m^2 (Energy flux uncertainty, calculated using the Gershman et al (2015) method)<br> <br> 'orbit' : FAST orbit number<br> 'alt' : FAST geodetic altitude, in km. (FAST altitude ranges from ~300-4180 km)<br> 'apexmlt' : Magnetic local time in Apex-110 coordinates (see Laundal and Richmond (2016))<br> 'apexmlat' : Magnetic latitude in Apex-110 coordinates (see Laundal and Richmond (2016))</p> <p>'shadowRegion110' : Integer indicator of the region of the Earth's shadow that FAST's field-line footpoint at 110-km altitude lands ind.<br> Takes on values [0,1,2], corresponding to ['Umbra','Penumbra','Sunlit']. <br> Calculated by mapping FAST's location to 110-km altitude in Apex coordinates and then following the methodology of Jia et al. (2015).</p> <p>'mono' = 0,1,2 : 'not monoenergetic','weak monoenergetic','strict monoenergetic'<br> 'broad' = 0,1,2 : 'not broadband','weak broadband','strict broadband'<br> 'diffuse' = 0,1 : 'not diffuse','diffuse'</p> <p>'mono', ' broad', and 'diffuse' follow the Hatch et al. (2016) FAST adaptation of the Newell et al. (2009) classification scheme<br> *NOTE: I do NOT force 'mono' and 'broad' to be exclusive categories! I consider it fine for precipitation to be identified as both 'broad' and 'mono'</p> <p>'drop' : Boolean indicating whether a row should be dropped from the DataFrame</p> <p>==========<br> REFERENCES<br> ==========<br> Gershman, D. J., Dorelli, J. C., F.-Viñas, A., & Pollock, C. J. (2015). The calculation of moment uncertainties from velocity distribution functions with random errors. Journal of Geophysical Research A: Space Physics, 120(8), 6633–6645. https://doi.org/10.1002/2014JA020775</p> <p>Hatch, S. M., Chaston, C. C., & LaBelle, J. (2016). Alfvén wave-driven ionospheric mass outflow and electron precipitation during storms. Journal of Geophysical Research: Space Physics, 121(8), 7828–7846. https://doi.org/10.1002/2016JA022805</p> <p>Hatch, S. M., Labelle, J., Lotko, W., Chaston, C. C., & Zhang, B. (2017). IMF control of Alfvénic energy transport and deposition at high latitudes. Journal of Geophysical Research: Space Physics, 122(12). https://doi.org/10.1002/2017JA024175</p> <p>Jia, X., Xu, M., Pan, X., & Mao, X. (2017). Eclipse Prediction Algorithms for Low-Earth-Orbiting Satellites. IEEE Transactions on Aerospace and Electronic Systems, 53(6), 2963–2975. https://doi.org/10.1109/TAES.2017.2722518</p> <p>Laundal, K. M., & Richmond, A. D. (2016). Magnetic Coordinate Systems. Space Science Reviews, 1–33. https://doi.org/10.1007/s11214-016-0275-y</p> <p>Newell, P. T., Sotirelis, T., & Wing, S. (2009). Diffuse, monoenergetic, and broadband aurora: The global precipitation budget. Journal of Geophysical Research, 114, A09207. https://doi.org/http://dx.doi.org/10.1029/2009JA014326<br> </p>
Raw datasets for work on aberration-corrected transmission electron microscopy with Zernike phase plates
<p>Raw data (electron microscopy images and spectra) obtained for work on aberration-corrected transmission electron microscopy with Zernike phase plates as published in Ultramicroscopy.</p>
Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results
<p>This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images.</p> <p>The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016). </p>
Electronic Supporting Information for Catalytic Ammonia Oxidation to Dinitrogen by a Nickel Complex
<p>The dataset provides electronic supporting information in the format of XYZ molecular files, formatted Gaussian checkpoint files, and cube files for atomic spin density distributions for selected complexes obtained while investigating the catalytic mechanism of ammonia oxidation to dinitrogen using a N-heterocyclic carbene containing nickelocene complex.</p> <p>The level of theory used for all calculations is omega-B97xD with def2TZVP basis set. All calculations were performed using the Gaussian16 suite of programmes.</p> <p><strong>Model Set 1</strong> contains the metal free compounds and were used to calculate the overall thermodynamics of the ammonia oxidation reaction.</p> <p><strong>Model Set 2</strong> corresponds to the most truncated, in vacuo optimized structures.</p> <p><strong>Model Set 3</strong> comprises from non-truncated, realistic structures embedded in polarizable continuum model of benzene.</p> <p> </p>
Three-dimensional Reconstructions and Quantitative Indicators for colloidal particles in Dry and Liquid Conditions in Scanning Transmission Electron Microscope (STEM)
<p>This dataset accompanies the research presented in the paper:</p> <div>Esteban, D.A., Wang, D., Kadu, A., Olluyn, N., Iglesias, A.S., Perez, A.G., Casablanca, J.G., Nicolopoulos, S., Liz-Marzán, L.M. and Bals, S., 2023. Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies. <em>arXiv preprint arXiv:2311.05309</em>. [<a href="https://arxiv.org/pdf/2311.05309" target="_blank" rel="noopener">link</a>]</div> <p>It provides a comprehensive collection of three-dimensional reconstructions and quantitative descriptors for small colloidal particles. These gold nanoparticles are arranged in tetrahedral and other intricate geometries under both dry and liquid conditions. The dataset contains 3D reconstructions and quantitative indicators such as centroids, volumes, surface areas, solidity measures, and principal axis lengths for assemblies with 4, 5, and 6 particles. </p> <p>The dataset includes: <code>N4_dry_dart.rec</code> and <code>N4_liquid_dart.rec</code> for the 3D reconstructions of an assembly with 4 particles in dry and liquid conditions respectively; <code>N4_quant_descriptors_dry.mat</code> and <code>N4_quant_descriptors_liquid.mat</code> providing quantitative descriptors for these conditions. Similar files are provided for assemblies with 5 and 6 particles, such as <code>N5_dry_dart.rec</code>, <code>N5_liquid_dart.rec</code>, <code>N5_quant_descriptors_dry.mat</code>, <code>N5_quant_descriptors_liquid.mat</code>, and the corresponding files for N6. </p> <p>This dataset can be used to study the structural dynamics of nanoparticle assemblies and studies in colloidal chemistry, materials science, and nanotechnology. The <code>.rec</code> files can be visualized using volume rendering software (e.g. Amira or Avizo), while the <code>.mat</code> files contain structured data for analysis in MATLAB. The supporting code and scripts for this dataset are available on the GitHub repository: <a href="https://github.com/ajinkyakadu/LiquidET_NatComm2024" target="_new" rel="noreferrer">https://github.com/ajinkyakadu/LiquidET_NatComm2024</a>. </p>
IODP Expedition 379 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
IODP Expedition 360 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
Supplementary data - Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry – a perspective
<p>Analysis files and scripts for <a href="https://doi.org/10.1101/2024.06.21.600107" target="_blank" rel="noopener">associated manuscript</a>. </p> <ul> <li>CR3022.zip: script (in Rust) and necessary data to run said script for CR3022 analysis with the results from running the script.</li> <li>MA-analysis-script.zip: script (in Rust) and necessary data to run said script for automated analysis of MA benchmark results.</li> <li>MA-analysis-data.zip: data from running the MA-analysis-script, containing all MA and Stitch output files.</li> <li>MA-analysis-data-EMPEM.zip: data from running MA and Stitch on the EMPEM benchmark.</li> </ul>
IODP Expedition 397 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions
<div> <div>The rich information of electron energy-loss spectroscopy (EELS) comes from the complex inelastic scattering process whereby fast electrons transfer energy and momentum to atoms, exciting bound electrons from their ground states to higher unoccupied states. To quantify EELS, the common practice is to compare the cross-sections integrated within an energy window or fit the observed spectrum with theoretical differential cross-sections calculated from a generalized oscillator strength (GOS) database with experimental parameters [1].</div> <div> </div> </div> <div> <div> <div> <div>The previous Hartree-Fock-based [2] or DFT-based [3] GOS was calculated from Schrödinger's solution of atomic orbitals, which does not include the full relativistic effects. Here, we attempt to go beyond the limitations of the Schrödinger solution in the GOS tabulation by including the full relativistic effects using the Dirac equation within the local density approximation using FAC [4], which is particularly important for core-shell electrons of heavy elements with strong spin-orbit coupling. This has been done for all elements in the periodic table (up to Z = 118) for all possible excitation edges using modern computing capabilities and parallelization algorithms. The relativistic effects of fast incoming electrons were included to calculate cross-sections that are specific to the acceleration voltage. We make these tabulated GOS available under an open-source license to the benefit of both academic users as well as allowing integration into commercial solutions.</div> <div> </div> <div>If you wish to be notfied by the database updates, please register <a href="https://forms.gle/ddpJSPrCbPZNL1oH7" target="_blank" rel="noopener">here</a>.</div> <div> </div> <div>For details, you can find the paper on <a href="https://arxiv.org/abs/2405.10151">arxiv</a>.</div> </div> </div> </div> <p>Database Details:</p> <ul> <li>Covers all elements (Z: 1-108) and all edges</li> <li>Large energy range: 0.01 - 4000 eV</li> <li>Large momentum range: from minimum momentum transfer to double Bethe ridge for each edge. Adaptive momentum sampling is developed in such a manner to maximize the physical information for a given finite number of sampling points. For example, for C edge this range is 0.14 -67 Å-1 </li> <li>Fine log sampling: 128 points for energy and 256 points for momentum</li> <li>Data format: GOSH [3]</li> </ul> <p>Calculation Details:</p> <ul> <li>Single atoms only; solid-state effects are not considered</li> <li>Unoccupied states before continuum states of ionization are not considered; no fine structure</li> <li>Plane Wave Born Approximation</li> <li>Frozen Core Approximation is employed; electrostatic potential remains unchanged for orthogonal states when a core-shell</li> <li>electron is excited</li> <li>Self-consistent Dirac–Fock–Slater iteration is used for Dirac calculations; A modified local density approximation is used for the correct asymptotic behavior of the exchange energy; continuum states are normalized against asymptotic form at large distances</li> <li>Both large and small component contributions of Dirac solutions are included in GOS</li> <li>Final state contributions are included until the contribution of the last states falls below 0.1%. A convergence log is provided for reference.</li> </ul> <p>Version 1.6.5 release note:</p> <ul> <li>Add a compact version of the database which uses (a) single precesion, (b) 80x80 sampling in the energy and momentum space (c) 'gzip' to compress the gos data array. This helps for user with limited bandwidth for downloading.</li> </ul> <p>Version 1.6.1 release note:</p> <ul> <li>Add missing metadata</li> </ul> <p>Version 1.6 release note:</p> <ul> <li>Improved convergence for M and N edges for some elements</li> </ul> <p>Version 1.5 release note:</p> <ul> <li>Adaptive sampling for momentum space (previously it is fixed at 0.05 -50 Å-1, now adaptive for each edge)</li> <li>Improved convergence</li> </ul> <p>Version 1.2 release note:</p> <ul> <li>Add “File Type / File version” information</li> </ul> <p>Version 1.1 release note:</p> <ul> <li>Update to be consistent with GOSH data format [3]</li> <li>All the edges are now within a single hdf5 file.</li> <li>A notable change in particular, the sampling in momentum is in 1/m, instead of previously in 1/Å.</li> <li>Great thanks to Gulio Guzzinati for his suggestions and sending conversion script for GOSH format. </li> </ul> <p> </p> <p>[1] Verbeeck, J., and S. Van Aert. Ultramicroscopy 101.2-4 (2004): 207-224.</p> <p>[2] Leapman, R. D., P. Rez, and D. F. Mayers. The Journal of Chemical Physics 72.2 (1980): 1232-1243.</p> <p>[3] Segger, L, Guzzinati, G, & Kohl, H. Zenodo (2023). doi:10.5281/zenodo.7645765</p> <p>[4] Gu, M. F. Canadian Journal of Physics 86(5) (2008): 675-689.</p>
serial electron diffraction data
<p>Raw serial electron diffraction data sets from 6 samples:</p> <ol> <li>Zeolite A</li> <li>Zeolite Y</li> <li>Ge-BEC</li> <li>Mordenite</li> <li>ECR-18</li> <li>CAU-36(Co)</li> </ol> <p>Each zip file contains at least 3 directories:</p> <ul> <li>calib: contains the calibration files for the experiment</li> <li>data: contains the raw diffraction data for all the identified crystals in hdf5 format</li> <li>images: contains the image data used to locate crystals in hdf5 format</li> </ul> <p>Experimental parameters (such as the crystal coordinates) are stored in the attributes on the data files. Worked out examples have been included for samples 1 and 2 in a jupyter notebook. The Python code to process the data can be found in the problematic-0.1.0.zip folder or on http://github.com/stefsmeets/problematic</p>
Deciphering hot- and multi-exciton dynamics in core–shell QDs by 2D electronic spectroscopies
<p>2D spectroscopy datasets from PCCP 20 (2018) 18176, DOI: 10.1039/c8cp02574f</p> <p>Dasets are in the Matlab format .mat, each one containing:</p> <p>R(or N or T).X = 3-dimensional matrix containing 3d signal. dimensions=(w1,w3,t2)<br> R.t= t2 axis<br> R.f= w1=w3 axis</p> <p> </p> <p>R=rephasing; N=non-rephasing; T=total signal</p> <p>2D-BC=2D photon echo in BOXCARS configuration; 2D-PP= 2D pump-probe in quasi-collinear configuration.</p>
Data and materials for Wallace et al (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: comparison of two prospective cohort studies v1.2
<p>This comprises the data and materials for the study: Wallace E, Moriarty F, McGarrigle C, Smith SM, Kenny RA, Fahey T. (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: Comparison of two prospective cohort studies. PLOS ONE 13(10): e0206201. <a href="https://doi.org/10.1371/journal.pone.0206201">https://doi.org/10.1371/journal.pone.0206201</a></p> <p>The anonymised TILDA dataset is publicly available to researchers who meet the criteria for access, at no monetary cost, from the Irish Social Science Data Archive (ISSDA) at University College Dublin (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.ucd.ie%2Fissda%2Fdata%2Ftilda%2F&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=%2Fcochi1RuRtYSUa5sF9uA%2BjOOoNYIg7DPpk0mZl5D2s%3D&reserved=0">http://www.ucd.ie/issda/data/tilda/</a>) and the Interuniversity Consortium for Political and Social Research (ICPSR) at the University of Michigan (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.icpsr.umich.edu%2Ficpsrweb%2FICPSR%2Fstudies%2F34315&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=7LHSSqU8xotACMsalpAjVrV5m95DlapgQViyr4P%2FsXY%3D&reserved=0">http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315</a>). For the CPCR cohort, no provision for data sharing was included in the original ethical approval and participant consent form. As a minimal data set necessary to replicate the present study could not be deidentified due to the large number of demographic variables considered, a synthetic version of the study dataset was produced using the synthpop package in R: <a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2Fsynthpop%2Findex.html&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128229884&sdata=j3If%2FNe%2F1eGsGAt9hyg3ICMqmLec4aOrjRVKppaRSFU%3D&reserved=0">https://cran.r-project.org/web/packages/synthpop/index.html</a>. This dataset and the analytical code for the present study are presented here. Code developed on the synthetic data can be sent to frankmoriarty@rcsi.ie or <a href="mailto:enquiries.cpcr@rcsi.ie">enquiries.cpcr@rcsi.ie</a> to be run on the original data.</p> <p>v1.2 includes a more detailed description of how the dataset was synthesised.</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.