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1,899 results for “Running”
CUT&RUN_myProtein
<p>CUT&RUN samples for my proteins 1-3 after degradation of protein 2; includes negative and positive control and information on sequencing identifiers </p>
RO-Crate created using Autosubmit version 4.0.100 workflow running kinow/auto-mhm-test-domains
<p>This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a> representation of an execution of an example Autosubmit workflow that executes <a href="https://mhm-ufz.org/">mHM</a>, the UFZ mesoscale Hydrologic Model. The example was created with Autosubmit version 4.0.100. The source code of the workflow can be found at <a href="https://github.com/kinow/auto-mhm-test-domains">https://github.com/kinow/auto-mhm-test-domains</a>, commit <a href="https://github.com/kinow/auto-mhm-test-domains/commit/c2672568fb14706c6e66481bc7ceac467db8261e">c267256</a>. This dataset was <a href="https://github.com/ResearchObject/workflow-run-crate/pull/61">validated by the RO-Crate community</a>.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data Quality Products for GW Searches
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p>This release contains two data-quality products that are used by search analyses to help mitigate non-Gaussian noise in the detector data. Gating removes short-duration artifacts from the data by smoothly rolling the affected data to zero. The <a href="https://doi.org/10.1088/2632-2153/abab5f">iDQ glitch likelihood</a> uses machine learning to predict the probability that a non-Gaussian transient is present using information from auxiliary channels.</p> <p><br> <strong>Gating files used in analyses of O3 LIGO data</strong></p> <p>As a pre-processing step, the <a href="https://pycbc.org/">PyCBC</a> search pipeline uses an inverted-Tukey window to mitigate the effect of loud, non-Gaussian features in the data. This is further described in <a href="https://dx.doi.org/10.1088/0264-9381/33/21/215004">Usman <em>et al.</em> 2016</a>.</p> <p>A subset of these times are the times listed in the txt files</p> <ul> <li>H1-O3_GATES_1238166018-31197600.txt</li> <li>L1-O3_GATES_1238166018-31197600.txt</li> </ul> <p>These times in these files were chosen based on auxiliary monitors of overflows in the digital-to-analog converters used to control the positions of the test masses. The gated times (i.e. the time period where the data is zeroed) are time segments where these monitors recorded an overflow were. The central time and suggested half-width of zero time were chosen to fully cover these time seconds. The final gating parameter, the suggested taper time was chosen to be 0.5 to balance the cost of impacting more data with the window function versus introducing additional artifacts into the data.</p> <p>The syntax of the files themselves is</p> <p>{central time} {suggested half-width of zero time} {suggested taper time}</p> <p>with each row containing the parameters of a single gate.</p> <p>The included notebook provides an example of how to read in and apply one of the suggested gates.</p> <p><br> <strong>Renormalized iDQ timeseries</strong></p> <p>The renormalized iDQ timeseries data-quality product is used within the GstLAL search pipeline to generate results for GWTC-3. This data product was found to be statistically helpful in improving data quality within the <a href="https://lscsoft.docs.ligo.org/gstlal/">GstLAL</a> search pipeline. This is further described in <a href="https://arxiv.org/abs/2010.15282">Godwin <em>et al</em>. 2020</a>.</p> <p>This file contains a time series for each LIGO detector related to the probability of a glitch in the strain data given the behavior in analyzed auxiliary channels monitoring the behavior of the detectors and their environment.</p> <ul> <li>H1L1-IDQ_TIMESERIES-1256655642-12905976.h5</li> </ul> <p>The HDF5-formatted file contains two groups, H1 and L1, corresponding to LIGO Hanford and LIGO Livingston, respectively. Each group contains several datasets; the data dataset corresponds to the renormalized iDQ log-likelihoods, as described in <a href="http://doi.org/10.1088/2632-2153/abab5f">Godwin <em>et al</em>. 2020</a>, and the time dataset corresponds to the times associated with the renormalized iDQ log-likelihoods in the data dataset.</p> <p> </p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5636795 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>. </p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Data Quality Products for GW Searches
<p>This material is part of several data products associated with GWTC-2.1, the deep extended catalog of compact binary coalescences observed by the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration and the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration during the first half of the third observing run. For further information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">www.gw-openscience.org/GWTC-2.1/</a>).</p> <p>This release contains data quality products that are used by search analyses to help mitigate non-Gaussian noise in the detector data.</p> <p><strong>Renormalized iDQ timeseries</strong></p> <p>This release contains the renormalized iDQ timeseries data quality product used within the GstLAL search to generate results for GWTC-2.1 as described in <a href="https://arxiv.org/abs/2010.15282">Goodwin <em>et al</em>. 2020</a>. This data product was found to be statistically helpful in improving data quality within the GstLAL search. For further information about iDQ see <a href="https://iopscience.iop.org/article/10.1088/2632-2153/abab5f">Essick <em>et al</em>. 2020</a>.</p> <p>The file </p> <ul> <li>H1L1-IDQ_TIMESERIES-1238166018-15843600.h5</li> </ul> <p>contains a time series for each LIGO detector related to the probability of a glitch in the strain data given the behavior in the analyzed auxiliary channels which monitor the behavior of the detectors and their environment.</p> <p>The HDF5-formatted file contains two groups, H1 and L1, corresponding to LIGO Hanford and LIGO Livingston, respectively. Each group contains several datasets; the data dataset corresponds to the renormalized iDQ log-likelihoods, as described in Godwin <em>et al</em>. 2020, and the time dataset corresponds to the times associated with the renormalized iDQ log-likelihoods in the data dataset.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the guide to <a href="https://doi.org/10.1088/1361-6382/ab685e">LIGO-Virgo data analysis</a>.</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Parameter Estimation Data Release
<p>This material is part of several data products associated with GWTC-2.1, an update to the second Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">https://dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">https://www.gw-openscience.org/GWTC-2.1/</a>).</p> <p><strong>Parameter estimation data release</strong></p> <p>This data release contains posterior samples (*.h5) for gravitational-wave candidates through the first part of the third observing run (O3a). We provide results for the 44 candidates that have a probability of astrophysical origin of over 0.5 from O3 as well as the 10 previously-reported binary-black-hole candidates from GWTC-1 (this excludes GW170817). There are two .h5 files per event</p> <ul> <li> <p>Cosmologically reweighted (*cosmo.h5)</p> </li> <li> <p>Not cosmologically reweighted (*nocosmo.h5)</p> </li> </ul> <p>The cosmologically reweighted posteriors are reweighted to have a luminosity-distance prior that has a uniform merger rate in the source's comoving frame. Each .h5 file contains samples for multiple runs with keys C01:RUN_NAME, where RUN_NAME is the waveform used for the run (and additional prior-choice information if necessary) or Mixed, indicating an equal mixture of samples from runs with similar physics if they exist. In cases where only one waveform was used, the Mixed dataset is simply a resampling of those results . GW190425 does not have Mixed samples. See the <a href="https://dcc.ligo.org/LIGO-P2100063/public">paper</a> appendices for further information. In addition to containing the posterior samples, the .h5 files also contain metadata about the analyses including the configuration files (which specify details such as the detector data analyzed), noise power spectral densities (potentially for a superset of the detectors used in the analysis) and calibration uncertainty envelopes.</p> <p>The python notebook explains how to use the posterior samples. This data release also contains .FITS skymap files, which can be read with <a href="https://lscsoft.docs.ligo.org/ligo.skymap/#">ligo.skymap</a>, and skymap statistics in *.txt files.</p> <p>The inference of the source parameters were performed with <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby</a>, <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">Parallel Bilby</a> and <a href="https://git.ligo.org/richard-oshaughnessy/research-projects-RIT/tree/temp-RIT-Tides">RIFT</a>. The results are formatted using <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a>.</p> <p><a href="https://zenodo.org/record/5546663#.YnAAcvPMKqC">A similar release has been made to accompany GWTC-3</a> for results from the second part of the third observing run.</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5117702 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p>For more general background on gravitational-wave parameter estimation, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
Modular control of human movement during running: an open access data set
<p>The human body is an outstandingly complex machine including around 1000 muscles and joints acting synergistically. Yet, the coordination of the enormous amount of degrees of freedom needed for movement is mastered by our one brain and spinal cord. The idea that some synergistic neural components of movement exist was already suggested at the beginning of the XX century. Since then, it has been widely accepted that the central nervous system might simplify the production of movement by avoiding the control of each muscle individually. Instead, it might be controlling muscles in common patterns that have been called muscle synergies. Only with the advent of modern computational methods and hardware it has been possible to numerically extract synergies from electromyography (EMG) signals. However, typical experimental setups do not include a big number of individuals, with common sample sizes of five to 20 participants. With this study, we make publicly available a set of EMG activities recorded during treadmill running from the right lower limb of 135 healthy and young adults (78 males, 57 females). Moreover, we include in this open access data set the code used to extract synergies from EMG data using non-negative matrix factorization and the relative outcomes. Muscle synergies, containing the time-invariant muscle weightings (motor modules) and the time-dependent activation coefficients (motor primitives), were extracted from 13 ipsilateral EMG activities using non-negative matrix factorization. Four synergies were enough to describe as many gait cycle phases during running: weight acceptance, propulsion, early swing and late swing. We foresee many possible applications of our data, that we can summarize in three key points. First, it can be a prime source for broadening the representation of human motor control due to the big sample size. Second, it could serve as a benchmark for scientists from multiple disciplines such as musculoskeletal modelling, robotics, clinical neuroscience, sport science, etc. Third, the data set could be used both to train students or to support established scientists in the perfection of current muscle synergies extraction methods.</p> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus.</p> <p>The file "dataset.rar" contains data in older format, not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a>.</p>
Datasets for the CUT&RUN data analysis
<p>The datasets are used for the Galaxy CUT&RUN training material. CUT&RUN data was generated by <a href="https://doi.org/10.1186/s13059-019-1802-4">Zhu et al. 2019</a> and down sampled to speed up the training. ChIP-seq data comes from an experiment for GATA1 by <a href="https://doi.org/10.1038/ng.3793">Canver et al. 2017</a>.</p>
Supplementary run files for the paper "Learning Effective Representations for Retrieval using Self-Distillation with Adaptive Relevance Margins"
<p>TREC-Format run files of all trained models as supplementary material for the paper "Learning Effective Representations for Retrieval using Self-Distillation with Adaptive Relevance Margins".</p> <p>File naming follows the schema: <code>{model}-{loss variant}-{in-batch usage}-{dataset}.txt.gz</code></p>
Run 0 video in "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence"
<p>Run 0 presented in the paper "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence" (http://adsabs.harvard.edu/abs/2013MNRAS.428..804S or https://doi.org/10.1093/mnras/sts071).</p>
Files from barotropic and baroclinic idealized model runs of the Southern Indian Ocean
<p>These data files correspond to two idealized model runs of the Southern Indian Ocean using the Regional Ocean Modelling System (ROMS) as a framework. Both simulations are forced with monthly mean QuikSCAT winds and are run at a 1/3 degree resolution. </p> <p>The barotropic model is single layer with realistic ETOPO2 bathymetry, a two arc minute ocean-floor elevation data-set smoothed to a resolution of 55.2 km. The file corresponding to this simulation is named: roms_avg_barotropic.</p> <p>The baroclinic model is a 1 and a half layer model where the value of the pycnocline depth and the reduced gravity parameter is set at the initialization stage. Two simulations are presented, the first where 'relaistic' initialization parameters of H=800m and g'= 0.0134 m/s(^2), and the second where the density gradient between the active and passive layers is reduced to a g' of 0.0076 m/s(^2). The two data sets corresponding to these simulations are titled: roms_avg_800_0134 and roms_avg_800_0076</p> <p>Below find a list of variable names and descriptions:</p> <p>zeta=anomaly in thickness of active layer<br> ubar= mean zonal velocity of active layer<br> vbar= mean meridional velocity of active layerh=depth of bathymetry in barotropic model; pycnocline depth in baroclinic model<br> coast=coastline<br> lon_rho=longitude corresponding the density coordinates<br> lat_rho=latitude corresponding the density coordinates<br> lon_u=longitude corresponding the zonal velocities<br> lat_u=latitude corresponding the zonal velocities<br> lon_v=longitude corresponding the density velocities<br> lat_v=longitude corresponding the meridional velocities<br> time=days since model simualtion started</p>
EVO-NANO modified PhysiCell simulation run with 10K vasculature agents
<p>PhysiCell modifications:</p> <p>- added CSC</p> <p>-added vasculature</p> <p> </p> <p>this run:</p> <p>Cell cycle speed increased to 1/10 (default is 0.0432/60 = 0,00072)<br> snapshot is taken each 180 minutes (of simulation time)<br> There are 10000 vasculature agents with 100 branches<br> oxygen secretion rate for each fasculature agent is: phenotype.secretion.secretion_rates[0] = 10<br> </p>
EVO-NANO modified PhysiCell simulation run with 50K vasculature agents
<p>PhysiCell modifications:</p> <p>- added CSC</p> <p>-added vasculature</p> <p> </p> <p>this run:</p> <p>Cell cycle speed increased to 1/10 (default is 0.0432/60 = 0,00072)<br> snapshot is taken each 180 minutes (of simulation time)<br> There are 50000 vasculature agents with 500 branches<br> oxygen secretion rate for each fasculature agent is: phenotype.secretion.secretion_rates[0] = 10</p>
Locked Shields Partners Run 23 (LSPR23): A novel IDS dataset from the largest live-fire cybersecurity exercise
<p>IDS Dataset from the Largest Live Fire Cybersecurity Exercise Using Virtual Blue Team Network Traffic.<br><br></p> <ul> <li> <p>LSPR23 is derived from Locked Shields 2023, a major live-fire cyber defense exercise.</p> </li> <li> <p>LSPR23 includes ~16M network flows, of which ~1.6M are labeled malicious.</p> </li> </ul> <p> </p> <p>Please cite our research article:"LSPR23: A novel IDS dataset from the largest live-fire cybersecurity exercise" when using our dataset:<br>https://doi.org/10.1016/j.jisa.2024.103847<br><br><br></p>
ALICE Pb-Pb Run 3 event displays (2022 edition)
<p>ALICE Pb-Pb event displays produced with 2022 Pb-Pb data from the third run of the LHC. Multiple points of view and multiple resolutions of the baseline image are provided for completeness.</p>
ALICE Pb-Pb Run 2 event display with red/blue tracks
<p>This event display shows tracks in a Pb-Pb event recorded during Run 2 of the LHC. Individual tracks are shown following a red/blue colour code according to their charge.</p>
Reproduction package for the paper "Two waves of massive stars running away from the young cluster R136"
<h2>Reproduction package for the paper "Two waves of massive stars running away from the young cluster R136".</h2> <ul> <li>This reproduction package aims for open science, with the internal API designation of 'Gold'</li> <li>Authors: M. Stoop, A. de Koter, L. Kaper, S. Brands, S. Portegies Zwart, H. Sana, F. Stoppa, M. Gieles, L. Mahy, T. Shenar, D. Guo, G. Nelemans, S. Rieder</li> <li>Paper DOI: https://doi.org/10.1038/s41586-024-08013-8</li> <li>Zenodo DOI: http://doi.org/10.5281/zenodo.10058762</li> <li>Published in Nature (date of publication: 2024/10/09)</li> </ul> <h2>Hardware</h2> <ul> <li>Tested on a MacBook Pro (13-inch, 2020, Four Thunderbolt 3 ports)</li> <li>Processor: 2 GHz Quad-Core Intel Core i5</li> <li>Memory: 32 GB 3733 MHz LPDDR4X</li> <li>Graphics: Intel Iris Plus Graphics 1536 MB</li> </ul> <h2>Required non-standard hardware</h2> <ul> <li>None</li> </ul> <h2>Software dependencies</h2> <ul> <li>Jupyterlab (4.0.8)</li> <li>Notebook (7.0.6)</li> <li>Programming languages used: Python (3.11.7)</li> <li>Python packages used: numpy (1.25.2), pandas (2.1.4), matplotlib (3.8.0), os (comes with Python) scipy (1.11.4), gaiadr3-zeropoint (0.0.4) https://gitlab.com/icc-ub/public/gaiadr3_zeropoint), astroquery (0.4.6), pymc (5.6.1), corner (2.2.2), arviz (0.16.0), pytensor (2.12.3), lmfit (1.2.2), powerlaw (1.5), rpy2 (3.5.16), seaborn (0.12.2), consistencytest (0.0.2)</li> </ul> <h2>Instructions</h2> <ul> <li>The Anaconda conda environment is given should this be needed</li> <li>All Jupyter Notebooks are ready-made to produce the raw data, intermediate and end data products</li> <li>Gaia raw data is downloaded in the Jupyter Notebook "R136_runaway_candidates.ipynb"</li> <li>Data from the literature is given in the subdirectory /tables/ or /input_files/</li> <li>Input images and files are given in the subdirectory /input_files/</li> <li>Intermediate and end data products are given in /output_files/</li> <li>Figures in the paper are produced in the Jupyter Notebooks in the subdirectory /figures/ and stored in the subdirectory /figures/figures_paper/</li> </ul> <h2>Expected Output</h2> <ul> <li>Jupyter Notebooks can be executed by "Run" -> "Run All Cells"</li> <li>The Jupyter Notebook show the expected output in their respective cell</li> <li>Expected runtime are given at the top of each Jupyter Notebook</li> <li>The Jupyter Notebook which takes the longest "R136_runaway_search.ipynb" takes 7-8 hours for the entire dataset</li> <li>A small dataset has been given in this Jupyter Notebook as a proof-of-concept</li> </ul> <h2>Instructions for use</h2> <ul> <li>Jupyter Notebooks can be executed by "Run" -> "Run All Cells"</li> </ul> <h2>Figures</h2> <ul> <li>Figures can be reproduced from the /figures/ folder.</li> <li>All material and data used are available either in the Raw Data or in the Intermediate Data</li> <li>The figures shown in the paper will be saved in ./figures/figures_paper/ folder.</li> </ul> <pre> </pre>
Comparison of conventional IgE assay and measurement of specific IgE to hemocyanin for the diagnosis of adult crab allergy (Running tile: Utility of crab extracts and hemocyanin in diagnosing crab allergy)
<p><span>Summary: </span></p> <p><span><span> </span>Specific IgE to hemocyanin was elevated in crab-allergic as compared to crab-tolerant patients.</span></p> <p><span><span> </span>The combination of specific IgE to hemocyanin and conventional IgE assays improved specificity.</span></p>
Comparison of conventional IgE assay and measurement of specific IgE to hemocyanin for the diagnosis of adult crab allergy (Running tile: Utility of crab extracts and hemocyanin in diagnosing crab allergy)
<p>Summary:</p> <p> Specific IgE to hemocyanin was elevated in crab-allergic as compared to crab-tolerant patients.</p> <p> The combination of specific IgE to hemocyanin and conventional IgE assays improved specificity.</p>
GWTC-2.1: Deep extended-catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Sensitivity of search pipelines to simulated signals
<p>Results of search pipelines (GSTLAL, MBTA, PYCBC, PYCBC BBH) used to identify candidates in <a href="https://dcc.ligo.org/LIGO-P2100063/public">GWTC-2.1</a> on a set of simulated signals corresponding to binary neutron star (BNS), neutron star black holes (NSBH), and binary black holes (BBH) signals. Additionally, we include a README file which provides information on how to read these files.</p>
Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution
<p>Data and model files supporting the manuscript: </p> <p>Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution.</p> <p>Repository: https://github.com/alcantarar/Recurrent_GRF_Prediction</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.