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15,519 results for “r”
Dust extinction curves: Draine R_V=3.1 model
<p>This dataset is based on the models described in <a href="https://ui.adsabs.harvard.edu/abs/2018RNAAS...2..188B">Benson (2018)</a>.</p> <p><strong>Dust Properties</strong></p> <p>Dust grain properties are taken from <a href="http://adsabs.harvard.edu/abs/2003ARA%26A..41..241D">Draine (2003)</a> - specifically their model with R<sub>V</sub>=3.1.</p> <p><strong>Stellar Geometry</strong></p> <p>Galactic disks follow exponential profiles in the radial direction, and sech<sup>2</sup> distributions in the vertical direction. The vertical scale height is set to a multiple, <em>h</em><sub>d</sub>, of the stellar disk scale length, and its value is encoded in each file name. Spheroids follow spherical <a href="http://adsabs.harvard.edu/abs/1990ApJ...356..359H">Hernquist (1990)</a> profiles.</p> <p><strong>Dust Geometry</strong></p> <p>Dust is distributed in the disk, and follows exponential profiles in both radial and vertical directions. The vertical scale height is set to a multiple, <em>h</em><sub>z</sub>, of the stellar disk scale length, and its value is encoded in each file name.</p> <p> </p>
Dust extinction curves: Draine R_V=4.0 model
<p>This dataset is based on the models described in <a href="https://ui.adsabs.harvard.edu/abs/2018RNAAS...2..188B">Benson (2018)</a>.</p> <p><strong>Dust Properties</strong></p> <p>Dust grain properties are taken from <a href="http://adsabs.harvard.edu/abs/2003ARA%26A..41..241D">Draine (2003)</a> - specifically their model with R<sub>V</sub>=4.0.</p> <p><strong>Stellar Geometry</strong></p> <p>Galactic disks follow exponential profiles in the radial direction, and sech<sup>2</sup> distributions in the vertical direction. The vertical scale height is set to a multiple, <em>h</em><sub>d</sub>, of the stellar disk scale length, and its value is encoded in each file name. Spheroids follow spherical <a href="http://adsabs.harvard.edu/abs/1990ApJ...356..359H">Hernquist (1990)</a> profiles.</p> <p><strong>Dust Geometry</strong></p> <p>Dust is distributed in the disk, and follows exponential profiles in both radial and vertical directions. The vertical scale height is set to a multiple, <em>h</em><sub>z</sub>, of the stellar disk scale length, and its value is encoded in each file name.</p>
Data & R Scripts - Jönander et al. (2022) Single substance and mixture toxicity of dibutyl-phthalate and sodium dodecyl sulphate to marine zooplankton. Ecotoxicol. Environ. Saf.
<p>Data and R scripts associated with:</p> <p>Jönander, C., Backhaus, T., Dahllöf, I. (2022) Single substance and mixture toxicity of dibutyl-phthalate and sodium dodecyl sulphate to marine zooplankton. Ecotoxicol. Environ. Saf.</p>
CSV files and R script: writing process data of typed picture description by 15 cognitively impaired patients and 15 healthy controls
<p>Writing process data of 15 cognitively impaired patients and 15 age- and gender-matched healthy controls were obtained. Each of them completed two typed picture description tasks that were logged with Inputlog, a keystroke logging tool. Variables included time on task; number of characters, pauses and Pause-bursts per minute; proportion of pause time; duration of Pause-bursts; and pause time between words. For pause time between words, also the effect of pauses preceeding specific word categories was analyzed.</p> <p>The data were used to explore if the observation of writing behavior can assist in the screening and follow-up of mild cognitive impairment (MCI) and mild dementia due to Alzheimer’s disease (AD). This data set contains the CSV files that were used for the analyses and the corresponding R script.</p>
SSP: An R package to estimate sampling effort in studies of ecological communities
<p>SSP (simulation-based sampling protocol) is an R package that uses simulations of ecological data and dissimilarity-based multivariate standard error (MultSE) as an estimator of precision to evaluate the adequacy of different sampling efforts for studies that will test hypothesis using permutational multivariate analysis of variance. The procedure consists in simulating several extensive data matrixes that mimic some of the relevant ecological features of the community of interest using a pilot data set. For each simulated data, several sampling efforts are repeatedly executed and MultSE calculated. The mean value, 0.025 and 0.975 quantiles of MultSE for each sampling effort across all simulated data are then estimated and standardized regarding the lowest sampling effort. The optimal sampling effort is identified as that in which the increase in sampling effort does not improve the highest MultSE beyond a threshold value (e.g. 2.5 %). The performance of SSP was validated using real data. In all three cases, the simulated data mimicked the real data and allowed to evaluate the relationship MultSE – n beyond the sampling size of the pilot studies. SSP can be used to estimate sample size in a wide variety of situations, ranging from simple (e.g. single site) to more complex (e.g. several sites for different habitats) experimental designs. The latter constitutes an important advantage in the context of multi-scale studies in ecology. An online version of SSP is available for users without an R background.</p>
Data and R codes: species range-size variation in oaks
<p>We used occurrence data of 183 oak species (<em>Quercus </em>spp.) in North and South America to test how niche breadth and niche position affect the amount of suitable habitat area, and how colonization ability and post-glacial migration lags affect range filling. This dataset includes the data and R files related to the analyses. </p>
Datasets and R-codes complementing the Opinion of the Norwegian Scientific Committee for Food and Environment (VKM) "Risk-Benefit Assessment of Sunscreen"
<p>These datasets complement the Opinion of the Norwegian Scientific Committee for Food and Environment (VKM) "Risk-Benefit Assessment of Sunscreen". They contain:</p> <p>i) data on concentration of the UV filters bis-ethyl-hexyloxyphenol methoxyphenyl triazine (BEMT), butyl methoxydibenzoyl methane (BMDBM), 2-ethylhexyl salicylate (EHS), ethylhexyl triazone (EHT), octocrylene (OC), and titanium dioxide in nanoform (NP-TiO<sub>2</sub>) in commercially available sunscreens,</p> <p>ii) data on amount sunscreen used, and</p> <p>iii) data on dermal absorption of the UV filters bis-ethyl-hexyloxyphenol methoxyphenyl triazine (BEMT), butyl methoxydibenzoyl methane (BMDBM), 2-ethylhexyl salicylate (EHS), ethylhexyl triazone (EHT), octocrylene (OC), and titanium dioxide in nanoform (NP-TiO<sub>2</sub>).</p> <p>The data are used in the probabilistic exposure estimations in the Opinion. The data were retrieved from open sources.</p> <p>In addition, the R-codes used to perform the exposure estimation, are included.</p>
Dataset and R code: Effects of temperature and air pollution on emergency ambulance dispatches: a time series analysis in a medium-sized city in Germany
<p>Dataset and R script to replicate results in the manuscript "Effects of temperature and air pollution on emergency ambulance dispatches: a time series analysis in a medium-sized city in Germany", currently under review.</p>
R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge
<p>This is the first R&D dataset for the LHC Olympics 2020 Anomaly Detection Challenge. It consists of 1M QCD dijet events and 100k W'->XY events, with X->qq and Y->qq. The W', X, and Y masses are 3.5 TeV, 500 GeV and 100 GeV respectively. The events are produced using Pythia8 and Delphes 3.4.1, with no pileup or MPI included. They are selected using a single fat-jet (R=1) trigger with pT threshold of 1.2 TeV. </p> <p>The events are randomly shuffled together, but for the purposes of testing and development, we provide the user with a signal/background truth bit for each event. Obviously, the truth bit will not be included in the actual challenge.</p> <p>These events are stored as pandas dataframes saved to compressed h5 format. For each event, all Delphes reconstructed particles in the event are assumed to be massless and are recorded in detector coordinates (pT, eta, phi). More detailed information such as particle charge is not included. Events are zero padded to constant size arrays of 700 particles, with the truth bit appended at the end. The array format is therefore (Nevents=1.1M, 2101).</p> <p>For more information, including an example Jupyter notebook illustrating how to read and process the events, see the official LHC Olympics 2020 webpage.</p> <p><a href="https://lhco2020.github.io/homepage/">https://lhco2020.github.io/homepage/</a></p> <p><strong>UPDATE May 18 2020</strong></p> <p>We have uploaded a second signal dataset for R&D, consisting of 100k W'->XY with X,Y->qqq (i.e. 3-prong substructure). Everything else about this signal dataset (particle masses, trigger, Pythia configuration, detector simulation) is the same as the previous one described above. </p> <p><strong>UPDATE November 23 2020</strong></p> <p>We now include high-level feature files for the background and 2-prong signal (events_anomalydetection_v2.features.h5) and for the 3-prong signal (events_anomalydetection_Z_XY_qqq.features.h5). To produce the features, we have clustered every event into R=1 jets using the anti-kT algorithm. The features (calculated using fastjet plugins) are the 3-momenta, invariant masses, and n-jettiness variables tau1, tau2 and tau3 for the highest pT jet (j1) and the second highest pT jet (j2):</p> <p>'pxj1', 'pyj1', 'pzj1', 'mj1', 'tau1j1', 'tau2j1', 'tau3j1', 'pxj2', 'pyj2', 'pzj2', 'mj2', 'tau1j2', 'tau2j2', 'tau3j2'</p> <p>The rows (events) in each feature file should be ordered exactly the same as in their corresponding raw event file. For convenience, we have also included the label (1 for signal and 0 for background) as an additional column in the first feature file (events_anomalydetection_v2.features.h5).</p> <p><strong>UPDATE February 11 2021</strong></p> <p>We have included the Delphes detector card and the Pythia8 command files used to produce the R&D datasets.</p> <p><strong>UPDATE April 17 2022</strong></p> <p>It was brought to our attention that somehow the raw events file events_anomalydetection.h5 was never updated to v2, which had a lower generator-level pT threshold (PhaseSpace:pTHatMin = 500) for QCD events to minimize artificial trigger sculpting. This v2 is the version that the features file (events_anomalydetection_v2.features.h5) corresponds to, as well as the Pythia cmnd file (pythia_RnD_qcd.cmnd). Now the raw events file has been brought up to date as well. </p>
Fig.ç3.D isasterina akajimaensis sp. nov., holotype (NSMT E-6758). A, Anal pore and a patch; B, madreporite; C, proximal part of arm, abactinal view; D, oral plate pair and interradial uncalci ed area, some oral spines have been lost (see also Fig. 6 for oral plate pair and interradial uncalci ed area at another interradius); E, proximal part of ambulacral furrow (oral plates seen at the lower-le corner are drawn in Fig. 6); F, inferomarginal spinelets, abactinal view. Abbreviations: als, actinolateral spine; apo, anal pore; fs, furrow spine; imp, inferomarginal plate; ims, inferomarginal spine; ir, interradial; iua, interradial uncalci ed area; md, madreporite; op, oral plate; os, oral spine; r, radial; rp, rigid patch; sas, subambulacral spine. in A New Asterinid Sea Star, Disasterina akajimaensis (Echinodermata: Asteroidea) from the Ryukyu Islands, Japan, with Notes on the Genus Disasterina
Fig.ç3.D isasterina akajimaensis sp. nov., holotype (NSMT E-6758). A, Anal pore and a patch; B, madreporite; C, proximal part of arm, abactinal view; D, oral plate pair and interradial uncalci ed area, some oral spines have been lost (see also Fig. 6 for oral plate pair and interradial uncalci ed area at another interradius); E, proximal part of ambulacral furrow (oral plates seen at the lower-le corner are drawn in Fig. 6); F, inferomarginal spinelets, abactinal view. Abbreviations: als, actinolateral spine; apo, anal pore; fs, furrow spine; imp, inferomarginal plate; ims, inferomarginal spine; ir, interradial; iua, interradial uncalci ed area; md, madreporite; op, oral plate; os, oral spine; r, radial; rp, rigid patch; sas, subambulacral spine.
R_JAGS code for estimation and analysis of species-area-relationship (SAR) parameters from NEON (National Ecological Observatory Network) data on plant surveys
<p><span>Invasive species science is heavily geared toward the invasive agent. </span>However, management to protect native species also requires a proactive approach focused on understanding the features affecting community vulnerability to invasion impacts<span>. </span><span>Vulnerability </span><span>is likely the result of </span><span>factors acting across spatial scales, from </span><span>local to regional, and it is the combined effects of these factors that will determine the magnitude of vulnerability.</span><span> We introduce an analytical framework that quantifies the scale-dependent impact of biological invasions from the shape of the native species-area-relationship (SAR). We leverage newly available, biogeographically extensive vegetation data from the US National Ecological Observatory Network to assess plant community vulnerability to invasion impact as a function of factors acting across scales. We analyzed more than 1000 SARs widely distributed across the USA along environmental gradients and under different levels of invasion. </span>Results show that a decrease in native richness is consistently associated with invasive species cover<span>, but it is only at relatively high levels of invasion that native richness is compromised. After accounting for variation in baseline ecosystem diversity, net primary productivity, and human modification, ecoregions that are colder and wetter seem to be most vulnerable to losses of native plant species at the local level, while warmer and wetter areas seem most susceptible at the landscape level. We also document how the combined effects of cross-scale factors result in a heterogenous spatial pattern of vulnerability. </span><span>This pattern </span><span>cannot be predicted by analyses at any single scale, underscoring the importance of accounting for factors acting across scales. Simultaneously assessing differences in vulnerability between distinct plant communities at local, landscape and regional scales provided outputs that can be used to inform policy and management aimed at reducing vulnerability to the impact of plant invasions.</span></p>
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character's evolution: R scripts and simulated trees
<p>All R scripts used in this study, and the set of simulated phylogenetic trees used in the study.</p> <p>1. Modern methods of ancestral state estimation (ASE) incorporate branch length information, and it has been demonstrated that ASEs are more accurate when conducted on the branch lengths most correlated with a character's evolution; however, a reliable method for choosing between alternate branch length sets for discrete characters has not yet been proposed.<br><br>2. In this study, we simulate paired chronograms and phylograms, and generate binary characters that evolve in correlation with one of these. We then investigate (1) the effect of alternate branch lengths on ASE error, and (2) whether phylogenetic signal statistics and/or model-fit statistic can be used to select the branch lengths most correlated with a binary character.<br><br>3. In agreement with previous studies, we find that ASEs are more accurate when conducted on the branch lengths most correlated with the character. Phylogenetic signal statistics show limited utility for selecting the correct branch lengths, but model-fit statistics are found to be more accurate, with the correct branch lengths generally returning greater model-fit (lower AICc and BIC values). Using this method to choose between alternate branch length sets is more accurate when tree and character properties are more favorable for model optimization, and when shape differences between alternate phylogenies are greater.<br><br>4. Our results indicate that researchers conducting ASEs on discrete characters should carefully consider which branch lengths are appropriate, and, in the absence of other evidence, we suggest estimating model-fit values over alternate branch length sets and evolutionary models and choosing the branch length/model combination that returns better model fit.</p>
Data and R code used for the GLMM and NBDA analyses in 'Captive Asian short-clawed otters (Aonyx cinereus) learn to exploit unfamiliar natural prey'
<p>Foraging plays a vital role in animal life histories, learning whether unfamiliar food items are palatable is a key part of this process. Animals that engage in extractive foraging must also learn how to overcome the protective measures of their prey. While otters (subfamily Lutrinae) are a taxon known for their extractive foraging behaviour, how they learn about prey palatability and acquire extractive foraging techniques remains poorly understood. Here we investigated: (i) how captive Asian short-clawed otters (<em>Aonyx cinereus</em>) learned to interact with, and extract meat from, unfamiliar natural prey, and (ii) how their exploitation of such prey compared to their ability to overcome artificial foraging tasks containing familiar food rewards. Network-based diffusion analysis showed that otters learned to interact with unfamiliar natural prey by observing their group mates. However, once interacting with the prey, they learned to extract the meat mainly asocially. In addition, otters took longer to overcome the protective measures of unfamiliar natural prey than those of extractive food puzzles. Asian short-clawed otter populations are declining in the wild. Increasing our understanding of how they learn to overcome novel foraging challenges could help develop pre-release training procedures as part of reintroduction programmes for otter conservation.</p>
EcoPhyloMapper: an R package for integrating geographic ranges, phylogeny, and morphology
<p>1. Spatial patterns of species richness, phylogenetic and morphological diversity are key to answering many questions in ecology and evolution. Across spatial scales, geographic and environmental features, as well as evolutionary history and phenotypic traits, are thought to play roles in shaping both local species communities and regional assemblages. By examining these geographic patterns, it is possible to infer how different axes of biodiversity influence one another, and how their interaction with abiotic factors has led to the spatial distribution of species assemblages – and their attributes – that we observe in the present. Although there has been interest in this area of research for some time, it has recently become more tractable to include multivariate shape data in such analyses. Shape information has the potential to provide a more direct measure of the functional morphology of species as compared to individual trait measurements and might be more relevant to understanding community composition. However, few tools currently exist to explore geographic patterns of both phylogenetic and shape diversity.</p> <p>2. We present the ecoPhyloMapper R package (epm) that aims to streamline the handling of geographic range polygons or point occurrences and integration of resulting species metacommunities with phylogenetic trees and morphological shape.</p> <p>3. Geographic maps can be generated that demonstrate spatial patterns in diversity metrics pertaining to phylogenetic similarity, multivariate shape similarity and disparity, and combinations of the two. Patterns of taxonomic, phylogenetic and shape disparity turnover can also be visualized. Biodiversity indices summarized across grid cells can easily be exported to GIS software as well as to other R packages that specialize in community assembly or geospatial statistics.</p> <p>4. This R package will facilitate the geographic exploration of multivariate shape data in concert with phylogenetic diversity, which will in turn support macroecological research exploring how species assemblages are structured. Further, this R package should prove useful across a wide range of macroecological applications that extend beyond the study of morphology.</p>
Data and R-scripts from: Multiple stressors: negative effects of nest predation on the viability of a threatened gull in different environmental conditions
<p>This contains data and R-scripts used in: </p> <ul> <li>Bård-Jørgen Bårdsen and Jan Ove Bustnes (2022). Multiple stressors: negative effects of nest predation on the viability of a threatened gull in different environmental conditions. Journal of Avian Biology.</li> </ul> <p>This study assessed the population viability of a population of the lesser black-backed gull (<em>Larus fuscus fuscus</em>) using data collected during 2005-2020 from a nature reserve in Northern Norway. The study merged results from statistical analyses of empirical data with a Leslie model. Here, we provide the underlying data, and the R-scripts used to analyse the data and run the model. The data set include information about reproduction at several stages (laying, hatching and fledgling), nest predation, and individual capture histories (used to estimate apparent survival; see <a href="https://doi.org/10.1111/jav.02953">Bårdsen and Bustnes 2022</a>).</p>
Modelling seasonal dynamics of secondary growth in R
<p>The monitoring of seasonal radial growth of woody plants addresses the ultimate question of when, how, and why trees grow. Assessing the growth dynamics is important to quantify the effect of environmental drivers and understand how woody species will deal with the ongoing climatic changes. One of the crucial steps in the analyses of seasonal radial growth is to model the dynamics of xylem and phloem formation based on increment measurements on samples taken at relatively short intervals during the growing season. The most common approach is the use of the Gompertz equation, while other approaches, such as general additive models (GAMs) and generalised linear models (GLMs), have also been tested in recent years. For the first time, we explored artificial neural networks with Bayesian regularisation algorithm (BRNNs) and show that this method is easy to use, resistant to overfitting, tends to yield s-shaped curves and is therefore suitable for deriving temporal dynamics of secondary tree growth. We propose two data processing algorithms that allow more flexible fits. The main result of our work is the XPSgrowth() function implemented in the radial Tree Growth (rTG) R package, that can be used to evaluate and compare three modelling approaches: BRNN, GAM and the Gompertz function. The newly developed function, tested on intra-seasonal xylem and phloem formation data, has potential applications in many ecological and environmental disciplines where growth is expressed as a function of time. Different approaches were evaluated in terms of prediction error, while fitted curves were visually compared to derive their main characteristics. Our results suggest that there is no single best fitting method, therefore we recommend testing different fitting methods and selection of the optimal one.</p>
YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines
<p><strong>D</strong><strong>ata abstract:</strong><br> The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.) <br> </p> <p><strong>Paper abstract:</strong><br> The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%. </p> <p> </p>
Data and R code for long-term study of fire and climate effects on water quality in Clear Lake, California
<p>Long-term relationships between water quality, fire and climate for Clear Lake, California. Although the watershed has historically experienced frequent fire, the 2018 Mendocino Complex, which was the largest wildfire complex in state history, burned approximately 40% of the watershed, sparking concerns about drinking water quality and lake ecosystem health. This analysis spans approximately 1968-2021.</p>
Data from: imageseg: An R package for deep learning-based image segmentation
<p>1. Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can for example be used to assess forest structural metrics. While CNN-based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists.</p> <p>2. Here, we present R package imageseg which implements a CNN-based workflow for general-purpose image segmentation using the U-Net and U-Net++ architectures in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 2877 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1285 understory vegetation images.</p> <p>3. Overall segmentation accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively). The image segmentation models performed significantly better than commonly used thresholding methods, and generalized well to data from study areas not included in training. This indicates robustness to variation in input images and good generalization strength across forest types and biomes.</p> <p>4. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with single or multiple classes and based on color or grayscale images, e.g. for applications in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond.</p>
SPAAM Summer School 2022: Introduction to Ancient Metagenomics - 3b1 Introduction to R and the Tidyverse
<p>Teaching data for practical session: "3b1 Introduction to R and the Tidyverse" of the 2022 SPAAM Summer School: Introduction to Ancient Metagenomics (Aug. 1-5 2022).</p> <p>See: <a href="https://spaam-community.github.io/wss-summer-school/#/2022/">https://spaam-community.github.io/wss-summer-school/#/2022/</a> or <a href="https://doi.org/10.5281/zenodo.6976711">https://doi.org/10.5281/zenodo.6976711</a> for slides.</p> <p>Once downloaded, run:</p> <pre><code>tar xvfz <session>.tar.gz</code></pre> <p> to decompress the data directory for the session.</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.