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12,871 results for “aging;”
Artificial reefs geographical location matters more than shape, age and depth for sessile invertebrate colonization in the Gulf of Lion (NorthWestern Mediterranean Sea)
<p>Artificial reefs (ARs) have been used to support fishing activities. Sessile invertebrates are essential components of trophic networks within ARs, supporting fish productivity. However, colonization by sessile invertebrates is possible only after effective larval dispersal from source populations, usually in natural habitat. While most studies focused on short term colonization by pioneer species, we propose to test the relevance of geographic location, shape, age and depth of immersion on the ARs long term colonization by species found in natural stable communities in the Gulf of Lion. We recorded the presence of five sessile invertebrates species, with contrasting life history traits and regional distribution in the natural rocky habitat, on ARs with different shapes deployed during two immersion time periods (1985 and the 2000s) and in two depth ranges (<20m and >20m). At the local level (~5kms), neither shape, depth nor immersion duration differentiated ARs assemblages. At the regional scale (>30kms), colonization patterns differed between species, resulting in diverse assemblages. This study highlights the primacy of geographical positioning over shape, immersion duration and depth in ARs colonization, suggesting it should be accounted for in maritime spatial planning.</p>
Corticolimbic Response to Threat and Recollected Ages of Exposure to Childhood Maltreatment
<p>Data files indicating regional BOLD fMRI response of young adults to negative (angry or fearful) faces versus neutral faces in Hariri amygdala activation face matching task.</p> <p>MACE_scores_BOLD_fMRI_amyg_hipp.csv - Contains overall BOLD fMRI response to negative minus neutral faces in bilateral amygdala, hippocampus, fusiform gyrus, inferior frontal gyrus pars triangularis, ventromedial prefrontal cortex, dorsomedial prefrontal cortex and anterior cingulate cortex as well as Maltreatment and Abuse Chronology of Exposure scores for severity of exposure to 10 types of childhood maltreatment across each age of childhood and parameters for age, sex, parental education and childhood financial sufficiency.</p> <p>Early_vs_late_maltreatment_BOLD_fMRI_amyg_hipp_pfc_fusiform_for_mixed_model.xlsx - Data in long format indicating for each participant baseline adjusted BOLD fMRI response to negative minus neutral faces at each TR interval for amygdala, hippocampus, fusiform gyrus, inferior frontal gyrus pars triangularis, ventromedial prefrontal cortex, dorsomedial prefrontal cortex and anterior cingulate cortex with participants classified as having experienced no maltreatment, only early childhood maltreatment on only late childhood maltreatment based on type / time predictors for each region.</p> <p>Early_vs_late_maltreatment_regional_fMRI_response_negative_vs_neutral_faces.xlsx - Individual participant baseline adjusted data for response to negative faces and neutral faces at each TR interval for amygdala, hippocampus, fusiform gyrus, inferior frontal gyrus pars triangularis, ventromedial prefrontal cortex, dorsomedial prefrontal cortex and anterior cingulate cortex with each participant classified as having experienced no maltreatment, only early childhood maltreatment or only late childhood maltreatment based on region specific definitions.</p> <p>Early_vs_late_maltreatment_time_course_BOLD_fMRI_regional_response.xlsx - Group means and sem for baseline adjusted BOLD fMRI response to negative minus neutral faces at each TR interval for amygdala, hippocampus, fusiform gyrus, inferior frontal gyrus pars triangularis, ventromedial prefrontal cortex, dorsomedial prefrontal cortex and anterior cingulate cortex based on region specific groupings classifications as having experienced no maltreatment, only early childhood maltreatment on only late childhood maltreatment.</p> <p>Early_vs_late_maltreatment_time_course_regional_response_negative_vs_neutral_faces.xlsx - Group means and sem for baseline adjusted BOLD fMRI response to negative faces and neutral faces at each TR interval for amygdala, hippocampus, fusiform gyrus, inferior frontal gyrus pars triangularis, ventromedial prefrontal cortex, dorsomedial prefrontal cortex and anterior cingulate cortex based on region specific groupings classifications as having experienced no maltreatment, only early childhood maltreatment on only late childhood maltreatment.</p> <p> </p>
An Ice Age JWST inventory of dense molecular cloud ices
<p>This dataset is the first observational data release for the JWST Early Release Science Ice Age program (#1309). More information about this program can be found at our team website (<a href="http://jwst-iceage.org/">http://jwst-iceage.org/</a>) and the STScI website (<a href="https://www.stsci.edu/jwst/science-execution/approved-programs/dd-ers/program-1309">https://www.stsci.edu/jwst/science-execution/approved-programs/dd-ers/program-1309</a>). The data is analyzed in the article by McClure et al. (2023), to be published on January 24th, 2023 by Nature Astronomy.</p> <p>The dataset consists of 5 files, each containing a spectrum of one of two background stars analyzed in that publication. The spectra are given as wavelength in microns (column 1), flux in milli-Janskys (column 2), and uncertainty in the flux (column 3). Some basic information about the JWST pipeline version is given in the header of each text file, but users should refer to the Methods section of McClure et al. (2023) for the full description of how the data were processed and extracted, as it extends beyond the basic pipelines. Information about how the data were observed is given in the APT file, accessible through a query in STScI's APT application (search by PID 1309) or here at STScI (<a href="https://www.stsci.edu/jwst/science-execution/program-information.html?id=1309">https://www.stsci.edu/jwst/science-execution/program-information.html?id=1309</a>).</p> <p>Three of the files correspond to the background star NIR38. These represent spectra of this star taken separately by JWST with the NIRCam WFSS, NIRSpec FS, and MIRI LRS FS instrument modes on JWST. The other two files correspond to the background star J110621 and are spectra taken separately by JWST using the NIRSpec FS and MIRI LRS FS instrument modes.</p> <p>It is necessary to cite the McClure et al. (2023) Nature Astronomy publication when making use of these data, to fully describe the data processing, as well as this Zenodo DOI.</p>
Sex-specific changes in autosomal methylation rate in ageing common terns - Data
<p>In the manuscript "<strong>Sex-specific changes in autosomal methylation rate in aging common terns</strong>" published in Frontiers Ecology And Evolution, we investigate sex-specific age effects in global DNA methylation patterns in aging common terns in a longitudinal study. We collected blood at 1-, 3- and/or 4-year intervals, extracted DNA from the erythrocytes and estimated autosomal DNA methylation by mapping Reduced Representative Bisulfite Sequencing reads to a de novo assembled reference genome. </p> <p>The raw RRBS reads can be found at: https://www.ebi.ac.uk/ena, PRJEB48910 and the genome at https://www.ncbi.nlm.nih.gov/, PRJNA560234.</p> <p>This document contains the final data frame used for the GLMM. We further share a markdown document summarising the various scripts and programs used for the Reduced Representative Bisulfite Sequencing data analyses and GLMMs (e.g. RefFreeDMA, picard, bismark, and R) under another DOI: 10.5281/zenodo.7493357. </p>
Supplementary material: Burial Analysis on the Middle Bronze Age in the Carpathian Basin (dataset and scripts)
<p>This is the supplementary material of the paper "Wealth Consumption, Sociopolitical Organization, and Change: A Perspective from Burial Analysis on the Middle Bronze Age in the Carpathian Basin" (accessible over doi: https://doi.org/10.1515/opar-2022-0281). Please consult the publication for in depth description of the data, its context and for the method applied on the data, as well as references to primary sources. The data tables comprise the burial data of the Hungarian Middle Bronze Age cemeteries of Dunaújváros-Duna-dűlő, Dömsöd, Adony, Lovasberény, Csanytelek-Palé, Kelebia, Hernádkak, Gelej, Pusztaszikszó and Streda nad Bodrogom. The script "supplementary_material_2_wealth_index_calculation.py" provides the calculation of a wealth index, based on grave goods, for the provided data. The script "supplementary_material_3_population_estimation.py" models the living population of Dunaújváros-Duna-dűlő. Both can be run by double-click. Requirements to be installed to run the scripts: Python 3 (https://www.python.org/) with the packages numpy (https://numpy.org/), pandas (https://pandas.pydata.org/), matplotlib (https://matplotlib.org/), seaborn (https://seaborn.pydata.org/) and scipy (https://scipy.org/); all included in Ancaonda (Python-Distribution, https://www.anaconda.com/).</p>
SVAD Age Data: Brazil
<p><strong>Description</strong></p> <p>The dental data were collected from anonymized radiographic images generated at the Universidade de São Paulo (FOUSP). Individuals are between 6 years and 15 years of age. As part of the SVAD age indicator template, there are 64 age variables (columns) in the dataset, which include diaphyseal lengths and breadths, epiphyseal fusion/appearance of ossification centers, and dental development. However, because this data is collected from two dimensional panoramic radiographs, only dental data are included; all other variables have a 'NA'. The data collection methodology can be found in Stull and Corron (2022) and Corron et al. (2021).</p> <p><strong>Basic Key Code:</strong></p> <ul> <li>*DL_L/R == diaphyseal lengths</li> <li>*DB_L/R == diapyseal lengths</li> <li>*EF_L/R == epiphyseal fusion</li> <li>*Oss == ossification </li> <li>*man_ and max_ == mandibular and maxillary dental development</li> </ul> <p><strong>References</strong></p> <p>Stull, K.E. and Corron, L.K. (<a href="https://www.mdpi.com/2673-6756/2/1/3"><em>2022</em></a>) The Subadult Virtual Anthropology Database (SVAD): An Accessible Repository of Contemporary Subadult Reference Data. Forensic. Sci. 2022, 2, 20–36. https://doi.org/10.3390/forensicsci2010003</p> <p>Corron, LK, <em>et al. (<a href="https://www.sciencedirect.com/science/article/pii/S0379073821000074">2021</a>)</em> Standardizing ordinal subadult age indicators: Testing for observer agreement and consistency across modalities. <em>Forensic Science International</em> <strong>320</strong>, 110687. </p>
Age and Masculinities During the Neo-Assyrian Period Data
<p>Files accompanying the article 'Age and Masculinities During the Neo-Assyrian Period'. All files relating to the creation of the textual corpus, PMI measurement, Gephi visualisations, and analysis are included.</p> <p>Version 2.</p> <p>It is recommended to read the ReadMe.txt and Workflow.txt files first, and then explore the folders according to the stage of the method you are interested in.</p> <p>Please cite as Bennett, E. 2023. "Age and Masculinities During the Neo-Assyrian Period Data".</p>
Data to: "Despite impaired binocular function, binocular disparity integration across the visual field is spared in normal aging and glaucoma"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello G., & Kwon, M. (in press) Despite impaired binocular function, binocular disparity integration across the visual field is spared in normal aging and glaucoma. IOVS</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2022.11.28.518250</p>
Pinterest dataset for age and gender identification in author profiling
<p>This dataset was used for the experiments presented in the article "Reconstructive Classification for Age and Gender Identification in Social Networks" - IEEE Transactions on Computational Social Systems</p> <p>The dataset contains text data from 548,761 pins corresponding to 264 users of Pinterest.</p> <p>There are 7 files.</p> <p>The first 5 files correspond to the extracted textual features from the pins that are aggregated per user: ats, emojis/emoticons, hashtags, links, and words.</p> <p>There are 264 lines in each file (one per user), as the concatenation of the extracted features from all the pins corresponding to each user.</p> <p>The last 2 files are the labels for the age and gender of the users. There are also 264 lines (one per user).</p> <p>For age, there are 4 possible labels: 18-24, 25-34, 35-46, and 50+</p> <p>For gender, there are 2 possible labels: F and M</p>
Seismic model and thermal age across the Juan de Fuca and Gorda Plate
<p>This dataset includes three file about the seismic structure across the Juan de Fuca and Gorda Plate:</p> <p>RayleighPhv.nc: netcdf file of Rayleigh wave phase speed across the plates, from 10 to 80 s</p> <p>ThermalAge.nc: netcdf file of lithospheric apparent thermal age estimated from seismic inversion</p> <p>Vsv_Model.zip: zip file of Vsv estimated from seismic inversion</p>
Ageing Data of Lithium-ion "Panasonic NCR18650B" Battery Cells
<p>A large-scale battery aging experiment dataset is presented in this dataset.<br> In total, 116 Lithium-ion cells were cycled in a controlled temperature ambient until they reached the end-of-life.<br> The cycling includes 37 different aging load profiles and more detailed reference tests throughout the whole lifespan of the cells.<br> The load profiles vary in seven parameters (i.e., temperature, charge current, average discharge current, peak discharge current, frequency, average state of charge, and state of charge swing) and were set up by a design of experiment.<br> Different approaches for the estimation of state-of-charge and state-of-health can be provided and analyzed by the offered data set.<br> Thus this dataset forms a broad basis for battery modeling or battery management algorithms for electric vehicles and hybrid electric vehicles.</p> <p>For further details, see the linked publication.</p>
Sustainable Aging in Aix-Marseille Metropolis: Assessment Indicators and Interactive Visualizations for Policy Making
<p>This upload contains a geopackage file, R code, Readme file and an HTML-based platform with interactive maps created using the geopackage and R code within the ForVie project (Forme et vieillissement au sein de la Métropole d'Aix-Marseille-Provence). This upload presents various data, indicators and interactive maps to help planners and policymakers to quickly identify patterns and trends related to aging within Aix-Marseille metropolis, France.</p> <p>Authors<br> Perez, J & Boyer, T. Université Côte d'Azur, UMR 7300 ESPACE-CNRS, Nice, France.</p> <p>Files<br> * `AGING_AMP_INDICATORS.gpkg`: The geopackage file contains spatial data used in the R code to create the maps.<br> * `code_R_platform_v1.0_ForVie.R`: The R code used to create the interactive maps.<br> * `ForVie_platform_v1.0.html`: The platform for visualizing the interactive maps.</p> <p>Instructions<br> To use this upload, follow these steps:<br> 1. Download the geopackage file, R code, and index.html file.<br> 2. Open the R code in RStudio or another R environment.<br> 3. Install any necessary packages as listed in the first section of the R code.<br> 4. Run the code in R to create the HTML-based platform with interactive maps<br> Alternatively, it is possible to directly open the ForVie_platform_v1.0.html file in a web browser to view the maps.</p> <p>Acknowledgment<br> This research was funded by a grant from Région Sud, Provence-Alpes-Côte d'Azur, France (ForVie).</p> <p>References<br> Perez et al., (2023) "Sustainable Aging in Aix-Marseille Metropolis: Assessment Indicators and Interactive Visualizations for Policy Making." Submitted (reference to be updated)</p> <p>Contact<br> If you have any questions about this upload, please contact Perez Joan at joan.perez@univ-cotedazur.fr</p>
Data for the publication "Future warming exacerbated by aged soot effect on cloud formation"
<p>This repository contains the data for the paper:</p> <p>"Ulrike Lohmann, Franz Friebel, Zamin A. Kanji, Fabian Mahrt, Amewu A. Mensah and David Neubauer: Future warming exacerbated by aged soot effect on cloud formation. <em>Nat. Geosci.</em> <strong>13, </strong>674–680 (2020). https://doi.org/10.1038/s41561-020-0631-0"</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.3457878)</p> <p>Also note that Extended Data Fig. 1 is identical to Supplementary Fig. S4 as well as that Extended Data Table 1 is identical to Supplementary Table S2.</p>
Large SEM-BSE images of hydrated belite of ages from 14 days up to 1 year
<p>This dataset contains 8-Bit SEM-BSE images of commercially available dicalcium silicate (C<sub>2</sub>S; belite; Vustah, Czech Republic). The belite was mixed with a water/binder ratio of 0.5. The paste was the cast in small sealed containers, which were submersed with water. The specimens were stored at 22 ± 2°C.</p> <p>After the desired hydration times (14, 28, 84, 365 days) the hydration was stopped by immersing the prisms in isopropanol and drying them at 60°C for 12 hours. The dried prisms were then embedded in low viscosity epoxy resin and mechanically polished using diamond paste with a grain size down to 0.25 µm. Finally, the specimens were coatet with a thin layer of carbon to avoid charging.</p> <p>The images were acquired at 12 kV using a CBS (concentric backscatter) detector within a Thermofischer Helios G4 UX.</p> <p><strong>Table 1</strong>: Basic information like resolution, size and phase composition of the images.</p> <table> <tbody> <tr> <td><strong>file</strong></td> <td><strong>age</strong></td> <td><strong>size</strong></td> <td><strong>size</strong></td> <td><strong>area</strong></td> <td><strong>pores</strong></td> <td><strong>hydrates</strong></td> <td><strong>clinker</strong></td> </tr> <tr> <td> </td> <td>in days</td> <td>in px</td> <td>in µm</td> <td>in mm²</td> <td>area-%</td> <td>area-%</td> <td>area-%</td> </tr> <tr> <td>C2S 14d.tif</td> <td>14</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>31.4</td> <td>43.5</td> <td>25.2</td> </tr> <tr> <td>C2S 28d.tif</td> <td>28</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>15.9</td> <td>62.6</td> <td>21.5</td> </tr> <tr> <td>C2S 84d.tif</td> <td>84</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>19.3</td> <td>65.6</td> <td>15.2</td> </tr> <tr> <td>C2S 365d.tif</td> <td>365</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>23.8</td> <td>64.4</td> <td>12.0</td> </tr> </tbody> </table> <p>The proportions of pores, hydrates and unhydrated clinker shown in table 1 are the result of manual thresholding of denoised versions of these images and may therefore differ to own measurements.</p> <p>The scaling is backed into the file and can be read using ImageJ/Fiji.</p> <p>The unstitched files are provded as 7z archives. The sub-images were arranged in a 10 x 10 grid. The pixel scaling of these files is the same as in the larger files.</p> <p><strong>Internal note</strong></p> <p>These files are included in the following MAPS datasets:</p> <ul> <li>2023_05_24 C32-C2S 14-84 d</li> <li>2023_06_08 C2S-C3S 28d-1year</li> </ul> <p><strong>Funding</strong></p> <p>The research was supported by the Deutsche Forschungsgemeinschaft (DFG), grant number <a href="https://gepris.dfg.de/gepris/projekt/344069666">344069666</a>.</p>
Size, age, telomere and ecophysiology data of Gallotia galloti lizard species sampled in Tenerife
<p>The dataset is used in the manuscript "Nina Serén, Rodrigo Megía-Palma, Tatjana Simčič, Miha Krofel, Fabio Maria Guarino, Catarina Pinho, Anamarija Žagar, Miguel A. Carretero. Functional responses in a lizard along a 3.5 km altitudinal gradient. Journal of Biogeography (under review)."</p> <p>The dataset consists of measurements of individual lizards of the species Gallotia galloti, each tagged with a unique CODE. Data include year of sampling, population name, exact elevation (in meters above sea level) and approximate elevation (rounded to the nearest hundred, in meters), and sex. Measurements were as follows: Snout Vent Length (in millimeters), Mass (in grams), AGE_Consensus (in years), Relative Telomere Length, PMA(29ºC, 33 ºC and 37ºC) (Potential metabolic activity measured at experimental conditions of 29˚C, 33ºC and 37ºC, respectively,in µLO2/mg prot/h), Catalase (in relative units U/mg protein), EWLa (accumulated evaporative water loss (in grams) and Temperature_8AM-5PM (measurements of cloacal temperature at hourly intervals starting at 8AM and ending at 5PM).</p>
Iron Age Funnelpendant Lower Rhine Valley
<p>Catalog of the Funnelpendant found in the Lower Rhine Valley.</p> <p>This dataset was transformed into <a href="https://arkeogis.org/en/">ArkeoGIS</a> format during a Summer School on Research Data Management in Pre- and Protohistoric Archaeology at Kiel University co-organised by <a href="https://www.sfb1266.uni-kiel.de/en">CRC1266</a> and <a href="https://www.cluster-roots.uni-kiel.de/en?set_language=en">Cluster of Excellence ROOTS</a>. Conducted from 10-14th of July 2023.</p> <p>- artefacts_Nakoinz2004_Trichteranhaenger_epsg25832.csv: original data, digitalised from analog source</p> <p>- artefacts_Nakoinz2004_Trichteranhaenger_epsg25832.yaml: metadata</p> <p>- ArkeoGIS_Nakoinz_funnelpendant_2004_v2.csv: transformed data to match ArkeoGIS structure (in English)</p>
Introducing MADYS: the Manifold Age Determination for Young Stars | Full model database
<p>Complete database of stellar and substellar evolutionary models employed in the Manifold Age Determination for Young Stars (MADYS). For a description of the tool, please refer to the main paper. For a detailed description of individual files, it is advised to use the ad-hoc functions provided within the published package.</p> <p>Bibliographic reference: arxiv:2206.02446</p> <p>GitHub repository: https://github.com/vsquicciarini/madys</p>
Archaeological sites in Taiwan during the Neolithic and Metal Age
<p>Coordinates marked by asterisks were added in the current study (see Data and methods section for details). Chinese site names are romanised following the Wade-Giles system. Site names in indigenous peoples' languages are given in romanised form followed by the Chinese name in parentheses, if available. Archaeological cultures represented by the respective site are indicated by the value "1".</p>
Connectomes, APOE, Age, Sex, and Diet in Mouse Models of Aging
<p>Brain networks and covariates for mouse models of aging. Includes APOE22/33/44 with and without HN, age, sex, and diet.</p> <p>Files:</p> <p>- connectomes.rda: a tensor of symmetric adjacency matrices corresponding to brain networks.</p> <p>- mice.rda: a dataframe containing mouse covariates.</p> <p>- mouse_anatomy.csv: a table containing scientific names for each brain region in connectomes.rda.</p>
GAMMA: Galactic Attributes of Mass, Metallicity, and Age Dataset
<p>We introduce the <strong>GAMMA</strong> (<strong>G</strong>alactic <strong>A</strong>ttributes of <strong>M</strong>ass, <strong>M</strong>etallicity, and <strong>A</strong>ge) dataset, a comprehensive collection of galaxy data tailored for Machine Learning applications. This dataset offers detailed 2D maps and 3D cubes of 11 727 galaxies, capturing essential attributes: stellar age, metallicity, and mass.</p><p>Together with the dataset we publish our code to extract any other stellar or gaseous property from the raw simulation suite to extend the dataset beyond these initial properties, ensuring versatility for various computational tasks. Ideal for feature extraction, clustering, and regression tasks, <strong>GAMMA</strong> offers a unique lens for exploring galactic structures through computational methods and is a bridge between astrophysical simulations and the field of scientific machine learning (ML).</p><p>As a first benchmark, we apply Principal Component Analysis (PCA) on this dataset. We find that PCA effectively captures the key morphological features of galaxies with a small number of components. We achieve a dimensionality reduction by a factor of ∼200 (∼3650) for 2D images (3D cubes) with a reconstruction accuracy below 5%.</p><p>We calculate UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction) on the lower dimensional PCA scores of the 2D images to visualize the image space. An interactive version of this plot can be accessed using an online <a href="https://umap-dashboard.onrender.com/">Dashboard</a> (hover over a point to see the galaxy image and the IllustrisTNG Subhalo ID).</p><p>All the code to generate this dataset and load the data structure is publicly available on <a href="https://github.com/ufuk-cakir/GAMMA">GitHub</a>, with an additional documentation page hosted on <a href="https://gamma-dataset.readthedocs.io/en/latest/">ReadTheDocs</a>.</p><p> </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.