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2,476 results for “Mixing”
Signal feeds for creating the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Wav files for the comparison of wave field synthesis, surround, and stereo listening test, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Recordings for loudness analysis of the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Mat files of live recordings of the music mixes for the comparison of wave field synthesis, surround, and stereo listening test. The recordings were performed at different levels and were analyzed with a loudness model afterwards for adjusting the levels. For details, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Object-based audio scene files for variations of the spatial arrangement in pop mixes for Wave Field Synthesis
<p>This entry contains object-based audio meta-data to generate the mixes published at http://dx.doi.org/10.5281/zenodo.61000.</p> <p>Have a look at README.md for further details.</p>
Path Mixing VLN dataset
<p>The Room -to-Room (R2R) dataset consists of human annotated instructions corresponding to the paths in these graphs. Each path consists of a sequence of viewpoints encountered by the agent during navigation. A derived dataset, Fine-Grained R2R (FGR2R) dataset, annotated parts of instructions with corresponding graph edges to obtain a fine-grained dataset. Existing works in VLN have shown that more instruction examples can improve an agent’s performance in previously unseen environments. We generate 162k instruction-trajectory pairs with path lengths between 5m and 20m. The final dataset has on average 7.27 views per path, a mean of 14.4m trajectory length and an average of 82 words per instruction.</p>
Data and Code for Publication "Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3rd to 1st millennia BC Southwest Germany"
<p>Data and code for publication: H. Rathmann, S. Lismann, M. Francken, A. Spatzier, Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3<sup>rd</sup> to 1<sup>st</sup> millennia BC Southwest Germany. <em>Journal of Archaeological Science</em> 156: 105802. <a href="https://doi.org/10.1016/j.jas.2023.105802">https://doi.org/10.1016/j.jas.2023.105802</a></p> <p>The repository contains:</p> <ul> <li>“R code for FLEXDIST.txt”: R code for executing FLEXDIST, a tool to estimate inter-individual Mahalanobis-type distances, taking correlations among variables into account, applicable to multiple variable scales (nominal, ordinal, continuous, or any mixture thereof), accommodating missing values, and handling high-dimensional data. <strong>Please refer to the latest version of this repository for the most up-to-date R code</strong>.</li> <li>“data.csv”: Pre-processed dataset comprising 85 dental morphological features collected from 64 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany used for analysis.</li> <li>“complete dataset.xlsx”: Complete dataset comprising 199 dental morphological features collected from 144 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany.</li> </ul>
Products for "A warm Neptune's methane reveals core mass and vigorous atmospheric mixing"
<p>Data for Figures 1, 2, and 3 from the paper entitled "A warm Neptune’s methane reveals core mass and vigorous atmospheric mixing".</p> <p>Authors: David K. Sing, Zafar Rustamkulov, Daniel P. Thorngren, Joanna K. Barstow, Pascal Tremblin, Catarina Alves de Oliveira, Tracy L. Beck, Stephan M. Birkmann, Ryan C. Challener, Nicolas Crouzet, Nestor Espinoza, Pierre Ferruit, Giovanna Giardino, Amelie Gressier, Elspeth K. H. Lee, Nikole K. Lewis, Roberto Maiolino, Elena Manjavacas, Bernard J. Rauscher, Marco Sirianni and Jeff A. Valent</p>
"Is Heidi really happier in the mountains? A mixed-methods investigation of spatial affect in fiction." - Data
<p>This repository provides access to the data used in Grisot, G & Herrmann, J. B. (2024) "Is Heidi really happier in the mountains? A mixed-methods investigation of spatial affect in fiction"</p> <p>It contains the following datasets:</p> <ul> <li><a href="https://zenodo.org/api/records/14235844/draft/files/all_entities.csv/content" target="_blank" rel="noopener noreferrer">all_entities.csv</a>: the spatial entities lists used in the paper (see Grisot, G & Herrmann, J. B., 2023)</li> <li><a href="https://zenodo.org/api/records/14235844/draft/files/corpus_books_aggr_sent_norm.csv/content" target="_blank" rel="noopener noreferrer">corpus_books_aggr_sent_norm.csv</a>: a corpus of N=184 Swiss literary narrative texts written in German between 1822 and 1940 by 69 Swiss authors, with sentiment values and spatial entities identified in each sentence.</li> <li><a href="https://zenodo.org/api/records/14235844/draft/files/heidi_clean_aggr_sent.csv/content" target="_blank" rel="noopener noreferrer">heidi_clean_aggr_sent.csv</a>: the 1880 digitised edition of the novel <em>Heidi</em>, as available from E-Rara, with sentiment values and spatial entities identified in each sentence.</li> <li><a href="https://zenodo.org/api/records/14235844/draft/files/sentiart.csv/content" target="_blank" rel="noopener noreferrer">sentiart.csv</a>: the sentiment lexcon SentiArt (Jacobs, 2019)</li> </ul>
AWOFRO : Annotated tweet corpus of mixed Wolof-French for detecting obnoxious messages
<p>These data are tweets of mixed Wolof-French codes annotated by three(3) annotators. <br>They were extracted during the period from 1 January 2021 to 31 May 2023.</p> <p>Content description :</p> <p><strong>Corpora.rar</strong> : The dataset contains 3510 annotated tweets</p>
Quasi-stationarity of scalar turbulent mixing statistics
<p>Dataset underlying the conclusions of the paper "Quasi-stationarity of scalar turbulent mixing statistics" to appear in Physics of Fluids.</p> <p>A copy of the article for exclusive personal use has been added. Any other use requires prior permission of author and AIP Publishing. The article appeared in <em>Physics of Fluids 33(5):055109 (2021) </em>and may be found at https://doi.org/10.1063/5.0044400</p>
Data to reproduce the results presented in Lake et al. 2021. Journal of Soils and Sediments, https://doi.org/10.1007/s11368-021-03107-6 ("High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting")
<p>This repository contains data on (1) the absorbance data and (2) the measured concentrations, to reproduce computational results as presented in:<br> "High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting".</p> <p> <br> 1. Absorbance data (200-730 nm wavelengths):</p> <p> * Average absorbance compensated for measured concentrations (average absorbance value per concentration)<br> * Average absorbance compensated for theoretical concentrations (average absorbance value per concentration)<br> * Average raw absorbance measured (average absorbance value per concentration)<br> * Raw absorbance measured (all absorbance values for all concentrations)</p> <p> Data in all 3 files is indicated per soil sample / mixture, with corresponding fraction(s) of soil sample(s) and corresponding (theoretical) input concentration.<br> <br> 2. Measured concentration data:</p> <p> * Measured concentration (average concentrations, tested for all experiments and for all theoretical input concentrations)</p> <p> </p>
AUTH-OpenDR Mixed Image Annotated Dataset for Human-centric Perception Tasks
<p>The dataset was generated through a mixed (real and synthetic) image data generation method which utilizes real background images and DL-generated human models. It contains 50000 real images depicting urban scenes, populated by synthetic human models in various positions and poses and is suitable for training/evaluating (a) pose estimation, (b) person detection, (c) identity recognition methods. Annotations for 2D bounding boxes of the depicted humans, their IDs and 2D keypoints etc are provided. The 133 3D human models, required by the method, were generated using the Pixel-aligned Implicit Function (PIFu) and full-body images of people from the Clothing Co-Parsing (CCP) dataset. As background images, a subset of the Cityscapes dataset was used. The Cityscapes license prohibits the distribution of any modified versions of itself. Thus, we provide code that can re-generate the exact same dataset, given that the Cityscapes dataset is downloaded by the website of its authors.</p> <p>Code and instructions for re-generating the dataset are provided <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation/human_dataset_generation">here</a>.</p> <p>The dataset was developed by Aristotle University of Thessaloniki (AUTH) within the H2020 OpenDR Project.</p>
Mixed methods systematic review and metasummary about barriers and facilitators for the implementation of cotrimoxazole and isoniazid - preventive therapies for people living with HIV.
<p>This is the minimal data set underlying the findings of our systematic review and metasummary:</p> <p>We uploaded the following data extracted from the studies included in our review:</p> <p>- Systematic Review protocol, also published in PROSPERO (CRD42019137778).</p> <p>- detailed description of studies included in our review.</p> <p>- barriers identified in the review (metasummary).</p> <p>- facilitators identified in the review.</p>
Spain's marginal electricity mix and its relevance for assessing the environmental performance of installations with variable load or power
<p>This upload contains the Supplementary Information file and the underlying data as Excel-file for the Journal article with the same name. More specifically, it provides time series of the Spanish electricity generation mix for the years 2015-2020 for energy system analysis and the life cycle inventory data for import into openLCA and re-use in combination with the ecoinvent databse (Version 3.7.1). Further details are available on request.</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>
High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019
<p><strong>Dataset overview</strong></p> <p>This dataset contain ship-based, high-resolution (underway) estimates of mixed layer net community production (NCP) and ancillary data from three summertime cruises in the Central and Eastern North American Arctic in 2015, 2018 and 2019. NCP estimates were derived from underway O2/Ar observations, obtained using ship-based membrane inlet mass spectrometry. Ancillary data include geospatial information (time, location), surface and depth-resolved hydrography and biogeochemical observations, and select output from a simulation of an oceanographic circulation model, based on the NEMO framework.</p> <p>Please cite as:</p> <p>Izett, R. and Tortell, P. 2021. High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019 (Dataset). Zenodo. https://doi.org/https://doi.org/10.5281/zenodo.5593381.</p> <p>This dataset is supplement to:</p> <p>Izett, R. W., Castro de la Guardia, L., Chanona, M., Myers, P. G., Waterman, S, and Tortell, P. D. Impact of vertical mixing on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations. </p> <p> </p> <p><strong>Abstract</strong></p> <p>We present ΔO<sub>2</sub>/Ar-based estimates of mixed layer net community production (NCP) from three summer cruises in the North American Arctic and Subarctic oceans. Coupling shipboard underway and discrete observations with output from an ocean circulation model, we correct the NCP estimates for vertical mixing fluxes impacting the surface O<sub>2</sub> budget. Large positive mixing fluxes, exceeding 100 mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup>, were derived in regions of strong wind-driven mixing, such as the Labrador Sea, and in the physically-dynamic Canadian Arctic Archipelago. In contrast, flux corrections were small (<10 mmol O<sub>2</sub> m<sup>-2 </sup>d<sup>-1</sup>, on average) in the density-stratified Baffin Bay, where mixing was low, and parts of the well-mixed Hudson Strait, where vertical O<sub>2</sub> gradients were weak. The distribution of corrected NCP was highly heterogenous across the study region, reflecting varying contributions of nutrient supply, freshwater input and sea ice dynamics. Elevated NCP was apparent in the Labrador Sea, Hudson Strait, and nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and localized net heterotrophy occurred in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay and the Queen Maud Gulf. A multiple linear regression model developed using available oceanographic data explained ~58 % of the observed NCP variability. Our work demonstrates the spatially explicit influence of vertical mixing on ΔO<sub>2</sub>/Ar-based NCP calculations across varied hydrographic conditions, and presents a novel approach to account for this process. This study contributes new knowledge of biological productivity distributions in under-sampled, rapidly changing, high-latitude waters.</p> <p> </p> <p><strong>Lay summary</strong></p> <p>Net community production (NCP; i.e., net organic matter production) constrains the ocean’s ability to support marine ecosystems and remove carbon dioxide from the atmosphere. A common approach to estimating NCP involves measurements of upper ocean oxygen (O<sub>2</sub>) concentrations. However, while vertical mixing may be a significant component of the surface water O<sub>2</sub> budget in some regions, applications of this approach typically do not quantify the magnitude of this flux, which can lead to potentially inaccurate NCP estimates. In this paper, we introduce a method combining ship-based measurements and the output from an ocean circulation model to refine NCP calculations for vertical mixing effects in North American Arctic and Subarctic oceans. The dataset reveals high NCP in the Labrador Sea (Inuktitut: <em>Lâbradorip Imappinga</em>), North Atlantic, Hudson Strait (<em>Ikirasarjuaq</em>) and northern Canadian Arctic Archipelago (CAA), and low values in Baffin Bay (<em>Saknirutiak Imanga</em>) and southern CAA. Riverine freshwater input to Hudson Bay (<em>Tasiujarjuar</em>) and the Queen Maud Gulf (<em>Ugjulik</em>) can reduce local NCP, while glacial meltwater may stimulate NCP elsewhere. Overall, this work provides a new NCP dataset in an under-sampled region. Similar studies will be necessary to document changes in biological productivity in response to changing environmental conditions in polar waters.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the ArcticNet and MEOPAR Networks of Centres of Excellence Canada, Polar Knowledge Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC) and Compute Canada.</p> <p>Hydrography and ancillary oceanographic data were provided by the Amundsen Science group of Université Laval.</p> <p>The model simulation was run by P. Myers (University of Alberta).</p> <p>The underway gas data were collected by R. Izett & P. Tortell (University of British Columbia)</p> <p>All data were archived by R. Izett. </p> <p> </p> <p><strong>Files and variables:</strong></p> <p>data_yyyy (data provided in NetCDF and Matlab format; "yyyy" denotes sampling year): Ship-board observations and derived quantities.</p> <table> <tbody> <tr> <td><em>Variable</em></td> <td><em>Description </em></td> <td><em>Unit</em></td> </tr> <tr> <td>time</td> <td>UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>dist</td> <td>Along-track distance</td> <td>km</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>region_mask_lat</td> <td>Latitude for region indices mask</td> <td>Decimal degrees N</td> </tr> <tr> <td>region_mask_long</td> <td>Longitude for region indices mask</td> <td>Decimal degrees E</td> </tr> <tr> <td>region_mask</td> <td>Regional masks</td> <td> </td> </tr> <tr> <td>sst</td> <td>Sea surface temperature measured in the instrument laboratory</td> <td>deg. C</td> </tr> <tr> <td>sal</td> <td>Sea surface salinity measured in the instrument laboratory</td> <td> </td> </tr> <tr> <td>chl_fluor</td> <td>Calibrated mixed layer Chl a fluorescence in the instrument laboratory</td> <td>(mg Chl a)/m3</td> </tr> <tr> <td>do2ar</td> <td>Biological O2 saturation anomaly, deltaO2/Ar</td> <td>%</td> </tr> <tr> <td>kwo2</td> <td>Weighted O2 gas transfer velocity</td> <td>m/d</td> </tr> <tr> <td>bioflux_ncp</td> <td>Bioflux-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>cor_ncp</td> <td>corrected-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>uw_kz</td> <td>Underway model-based eddy diffusivity at the base of the mixed layer</td> <td>m2/s</td> </tr> <tr> <td>bling_ncp</td> <td>BLING model-based mixed layer NCP, matched to underway cruise time/position</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>prof_time</td> <td>UTC 2015 Julian Day (year-day since 2015-01-01) at CTD profile stations</td> <td>UTC Days</td> </tr> <tr> <td>prof_lat</td> <td>Latitude N at CTD profile stations</td> <td>Decimal degrees N</td> </tr> <tr> <td>prof_long</td> <td>Longitude E at CTD profile stations</td> <td>Decimal degrees E</td> </tr> <tr> <td>prof_dist</td> <td>Along-track distance at CTD profile stations</td> <td>km</td> </tr> <tr> <td>prof_reg_index</td> <td>Regional index at CTD profile stations</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p>nemo_yyyy (data provided in NetCDF format only; "yyyy" denotes sampling year): 4-dimensional gridded NEMO model output. </p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>Model time, UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Model Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Model Decimal degrees W</td> </tr> <tr> <td>depth_grid_kz</td> <td>kz depth</td> <td>m</td> </tr> <tr> <td>depth_grid</td> <td>depth</td> <td>m</td> </tr> <tr> <td>kz</td> <td>Eddy diffusivity coefficient</td> <td>m2/s</td> </tr> <tr> <td>T</td> <td>Temperature</td> <td>deg-C</td> </tr> <tr> <td>sal</td> <td>Salinity</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Oxygen concentration</td> <td>mol O2/m3</td> </tr> </tbody> </table> <p> </p> <p>ArcticNet2003-2014_derived_quantities (data provided in NetCDF format only): Derived quantities from ArcticNet sampling.</p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>UTC; Days since 2010-01-01</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p> </p>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
Supporting Data for Figures in "Evolving Interior Mixing Regimes in a Tidal River Plume"
<p>Supporting data for figures in "Evolving Interior Mixing Regimes in a Tidal River Plume" by Preston S. Spicer, Kimberly D. Huguenard, Kelly L. Cole, Daniel G. MacDonald, and Michael M. Whitney. The scientific journal article is published in Geophysical Research Letters (2022). The article elucidates the evolution of stratified shear mixing in a tidal river plume using observational data taken over an ebb pulse throughout the interior of the Merrimack River plume. The file GRL_figs.m is a MATLAB file which produces Figures 1 thru 4 in the article taking the accompanying .mat and .txt files as input. Variables names and units correspond to graphed data of each figure in the journal article.</p>
Minimal dataset for "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models"
<p>This repository contains a minimal data set to reproduce all results that don't compromise the privacy concerns for the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> <br> The repository contains the following data:</p> <ul> <li>adaptscore_acute.csv <ul> <li>A csv file that contains the estimated adaptation scores for the acute data set with HLA I model.</li> </ul> </li> <li>adaptscore_leftout.csv <ul> <li>A csv file that contains the estimated adaptation scores for the leftout data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training.csv <ul> <li>A csv file that contains the estimated adaptation scores for the traininig data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training_hla1_without_clin.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the HLA I model (via cross-validation)</li> </ul> </li> <li>adaptscore_training_seed2.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the joint HLA I and HLA II model via cross-validation with another seed</li> </ul> </li> </ul>
Supplementary data and codes for "Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures"
<p>This file contains the data and codes used in the paper</p> <p>“Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures”, S. Williams et al, 2022.</p> <p>It includes the data used in all the figures and supplementary figures, as well as the codes used for simulations and escape rate estimation. The data files are in .mat format. The codes are Matlab codes with the exception of the analytical estimate of the escape rate which is a Mathematica worksheet.</p>
Fit for 55 MIX H2 2030 scenario for PyPSA
<p>Country-level PyPSA model based on the Fit for 55 MIX-H2 scenario for the year 2030. The model includes both electricity and hydrogen. </p> <p>Following the methodology described here: https://data.jrc.ec.europa.eu/dataset/d4d59b89-89f7-4275-801a-45ea8957e973</p> <p>Each NetCDF contains a "solved" scenario with a different climate year that can be imported into PyPSA with `pypsa.Network.import_from_netcdf()`</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.