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7,742 results for “individual”
Individual Towns that are Fully or Partially in the Ipswich Watershed - Idrisi Vector File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer includes the area within each town in the Ipswich River Watershed in vector form. This map contains complete information and was derived from the ip30_noinfo_towns layer.
Individual Towns that are Fully or Partially in the Ipswich and Parker River Watersheds - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole. This layer shows the boundaries for the towns in the Ipswich River Watershed and the Parker River Watershed. This data layer was created so that the town boundaries would correspond to the boundaries of the corresponding land use maps. This datalayer has complete information. Display town boundaries for the study area.
Individual Towns that are Fully or Partially in the Ipswich and Parker River Watersheds - Idrisi Vector File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole. This layer shows the boundaries for the towns in the Ipswich River Watershed and the Parker River Watershed. This data layer was created so that the town boundaries would correspond to the boundaries of the corresponding land use maps. This datalayer has complete information. Display town boundaries for the study area.
InterTVA. A multimodal MRI dataset for the study of inter-individual differences in voice perception and identification.
Open the record for dataset details and reuse information.
Bulk RNA-Seq PBMC data of SLE patients and healthy volunteers/ profiling of 29 individual immune cell types as well as PBMCs of healthy donors
<p>This Zenodo project contains processed gene expression data from two publicly available data sets. It includes the gene expression data of peripheral blood mononuclear cells (PBMCs) of systemic lupus erythematosus (SLE) patients as well as healthy volunteers (GSE122459). The project also comprises the bulk RNA-Seq profiling of 29 immune cell types as well as PBMCs of healthy individuals (GSE107011). In both cases, the raw RNA-Seq data was downloaded, aligned and processed. The gene expression data is available in form of a count matrix (GSE107011) or count matrix and transcript-per-million (TPM) values (GSE122459). For the latter, an annotation file is attached. Further details are provided in the information file. </p>
Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregives and researchers
<p>Nine animation videos used in the questionnaire described in the articles "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome</strong>" (<a href="https://doi.org/10.1177%2F1545968321989331">https://doi.org/10.1177/1545968321989331</a>) and "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers</strong>" (<a href="https://doi.org/10.1080/17483107.2021.1958932">https://doi.org/10.1080/17483107.2021.1958932</a>). <em>Video animations were designed and produced by Merel Horsmeier.</em></p>
Individual datasets investigating combined toxicity of binary mixtures in bees from laboratory tests
<p>This excel file (DOI: https://doi.org/10.5281/zenodo.3383713) provides the individual datasets on binary mixture toxicity (mortality) in bees classified according to route and exposure patterns (i.e. oral, contact, acute and chronic) and mortality endpoints (e.g.LD<sub>50</sub>, LC<sub>50</sub>) for the honeybee (<em>Apis mellifera</em>) and wild bee species (<em>Osmia bicornis</em>, <em>Bombus terrestris</em>). 218 individual binary mixtures were collected and included in the statistical analyses with the majority of toxicological endpoints reported as lethal doses or concentrations (e.g. LD<sub>50</sub>, LC<sub>50</sub>,) for pesticides or pesticides and veterinary drugs combinations with 133, 44 and 41 mixtures reporting acute contact toxicity (i.e. topical application), chronic oral toxicity and acute oral toxicity, respectively. Combined toxicity data for binary mixtures were available as dose response data in honeybees for acute contact toxicity (n=92) and acute oral toxicity.</p> <p>The full data collection and analysis of binary mixtures are described in Carnesecchi et al., 2019 (DOI: 10.1016/j.envint.2019.105256)</p>
Individual-based plant-pollinator networks are structured by phenotypic and microsite plant traits
<p>Dataset associated with the manuscript "Individual-based plant-pollinator networks are structured by phenotypic and microsite plant traits" (Arroyo-Correa et al. 2020), including plant-pollinator interactions, individual plant attributes and the plant polygon map created with drone flights. </p>
Tooth enamel proteome of Early Medieval non-adult individuals
<p>This dataset includes .raw LC-MS/MS files, from a proteomic study of deciduous and permanent tooth enamel samples. It includes data of 30 different non-adult individuals (Early Middle Ages, Valdaro, Italy), whose sex has been estimated through amelogenin peptides. This dataset is linked to a submitted publication (Lugli et al., <em>Journal of Archaeological Science: Reports</em>). <br> Please, refer to Lugli et al. (2019, <em>Scientific Reports</em>; doi: 10.1038/s41598-019-49562-7) for methodology. </p>
Draft genome assembly of a Japanese Oikopleura dioica male individual (O3), using Nanopore long reads.
<p>This draft assembly was used to validate the chrY scaffolds of the OSKA2016 reference genome in the publication “A genome database for a Japanese population of the larvacean Oikopleura dioica”, Development Growth and Differentiation, Wang and coll., 2020 (in press). It is provided as supplemental data for the reproducibility of this work; please note that no further polishing has been done to correct sequencing errors.</p> <p>Genome sequence reads were produced on a MinION sequencer (Oxford Nanopore Technologies) using high-molecular weight DNA from a male individual of the Oikopleura dioica species of zooplankton. The individual was related to the laboratory strain established from a western Japanese population that was used to produce the OSKA2016 reference genome. The raw reads were basecalled with the Guppy software version 3.3.0 using its dna_r9.4.1_450bps algorithm, and deposited in the European Nucleotide Archive (Study ID: PRJEB38559). The draft assembly was made with the Flye software version 2.7 with the options --genome-size 65m and --min-overlap 3000.</p>
Data Visualization - Individual Project - G20 Countries - Military, Health Care and Educational Spendings
<p>This Project is part of the course work for Data visualization DATS 6401. In this project, I have created webpage to show data analysis on G20 countries - military, health care and educational spending from 2011 - 2017. Google Visualization API is used for all visualization graphs in the webpage.</p>
Numerically Perturbed Structural Connectomes from 100 individuals in the NKI Rockland Dataset
<p>This dataset contains the derived connectomes, discriminability scores, and classification performance for structural connectomes estimated from a subset of the Nathan Kline Institute Rockland Sample dataset, and is associated with an upcoming manuscript entitled: <em>Numerical Instabilities in Analytical Pipelines Compromise the Reliability of Network Neuroscience</em>. The associated code for this project is publicly available at: <a href="https://github.com/gkpapers/2020ImpactOfInstability">https://github.com/gkpapers/2020ImpactOfInstability</a>. For any questions, please contact Gregory Kiar (gkiar07@gmail.com) or Tristan Glatard (tristan.glatard@concordia.ca).</p> <p>Below is a table of contents describing the contents of this dataset, which is followed by an excerpt from the manuscript pertaining to the contained data.</p> <ul> <li>impactofinstability_connect_dset25x2x2x20_inputs.h5 : Connectomes derived from 25 subjects, 2 sessions, 2 subsamples, and 20 MCA simulations with input perturbations.</li> <li>impactofinstability_connect_dset25x2x2x20_pipeline.h5 : Connectomes derived from 25 subjects, 2 sessions, 2 subsamples, and 20 MCA simulations with pipeline perturbations.</li> <li>impactofinstability_discrim_dset25x2x2x20_both.csv : Discriminability scores for each grouping of the 25x2x2x20 dataset.</li> <li>impactofinstability_connect+feature_dset100x1x1x20_both.h5 : Connectomes and features derived from 100 subjects, 1 sessions, 1 subsamples, and 20 MCA simulations with both perturbation types.</li> <li>impactofinstability_classif_dset100x1x1x20_both.h5 : Classification performance results for the BMI classification task on the 100x1x1x20 dataset.</li> </ul> <p><strong>Dataset</strong><br> The Nathan Kline Institute Rockland Sample (NKI-RS) dataset [1] contains high-fidelity imaging and phenotypic data from over 1,000 individuals spread across the lifespan. A subset of this dataset was chosen for each experiment to both match sample sizes presented in the original analyses and to minimize the computational burden of performing MCA. The selected subset comprises 100 individuals ranging in age from 6 – 79 with a mean of 36.8 (original: 6 – 81, mean 37.8), 60% female (original: 60%), with 52% having a BMI over 25 (original: 54%).</p> <p>Each selected individual had at least a single session of both structural T1-weighted (MPRAGE) and diffusion-weighted (DWI) MR imaging data. DWI data was acquired with 137 diffusion directions; more information regarding the acquisition of this dataset can be found in the NKI-RS data release [1].</p> <p>In addition to the 100 sessions mentioned above, 25 individuals had a second session to be used in a test-retest analysis. Two additional copies of the data for these individuals were generated, including only the odd or even diffusion directions (64 + 9 B0 volumes = 73 in either case). This allows an extra level of stability evaluation to be performed between the levels of MCA and session-level variation.</p> <p>In total, the dataset is composed of 100 diffusion-downsampled sessions of data originating from 50 acquisitions and 25 individuals for in depth stability analysis, and an additional 100 sessions of full-resolution data from 100 individuals for subsequent analyses.</p> <p><strong>Processing</strong><br> The dataset was preprocessed using a standard FSL [2] workflow consisting of eddy-current correction and alignment. The MNI152 atlas was aligned to each session of data, and the resulting transformation was applied to the DKT parcellation [3]. Downsampling the diffusion data took place after preprocessing was performed on full-resolution sessions, ensuring that an additional confound was not introduced in this process when comparing between downsampled sessions. The preprocessing described here was performed once without MCA, and thus is not being evaluated.</p> <p>Structural connectomes were generated from preprocessed data using two canonical pipelines from Dipy [4]: deterministic and probabilistic. In the deterministic pipeline, a constant solid angle model was used to estimate tensors at each voxel and streamlines were then generated using the EuDX algorithm [5]. In the probabilistic pipeline, a constrained spherical deconvolution model was fit at each voxel and streamlines were generated by iteratively sampling the resulting fiber orientation distributions. In both cases tracking occurred with 8 seeds per 3D voxel and edges were added to the graph based on the location of terminal nodes with weight determined by fiber count.</p> <p><strong>Perturbations</strong><br> All connectomes were generated with one reference execution where no perturbation was introduced in the processing. For all other executions, all floating point operations were instrumented with Monte Carlo Arithmetic (MCA) [6] through Verificarlo [7]. MCA simulates the distribution of errors implicit to all instrumented floating point operations (flop).</p> <p>MCA can be introduced in two places for each flop: before or after evaluation. Performing MCA on the inputs of an operation limits its precision, while performing MCA on the output of an operation highlights round-off errors that may be introduced. The former is referred to as Precision Bounding (PB) and the latter is called Random Rounding (RR).</p> <p>Using MCA, the execution of a pipeline may be performed many times to produce a distribution of results. Studying the distribution of these results can then lead to insights on the stability of the instrumented tools or functions. To this end, a complete software stack was instrumented with MCA and is made available on GitHub through https://github.com/gkiar/fuzzy.</p> <p>Both the RR and PB variants of MCA were used independently for all experiments. As was presented in [8], both the degree of instrumentation (i.e. number of affected libraries) and the perturbation mode have an effect on the distribution of observed results. For this work, the RR-MCA was applied across the bulk of the relevant libraries and is referred to as Pipeline Perturbation. In this case the bulk of numerical operations were affected by MCA.</p> <p>Conversely, the case in which PB-MCA was applied across the operations in a small subset of libraries is here referred to as Input Perturbation. In this case, the inputs to operations within the instrumented libraries (namely, Python and Cython) were perturbed, resulting in less frequent, data-centric perturbations. Alongside the stated theoretical differences, Input Perturbation is considerably less computationally expensive than Pipeline Perturbation.</p> <p>All perturbations were targeted the least-significant-bit for all data (t=24and t=53in float32 and float64, respectively [7]). Simulations were performed between 10 and 20 times for each pipeline execution, depending on the experiment. A detailed motivation for the number of simulations can be found in [9].</p> <p><strong>Evaluation</strong><br> The magnitude and importance of instabilities in pipelines can be considered at a number of analytical levels, namely: the induced variability of derivatives directly, the resulting downstream impact on summary statistics or features, or the ultimate change in analyses or findings. We explore the nature and severity of instabilities through each of these lenses. Unless otherwise stated, all p-values were computed using Wilcoxon signed-rank tests.</p> <p> <strong>Direct Evaluation of the Graphs</strong><br> The differences between simulated graphs was measured directly through both a direct variance quantification and a comparison to other sources of variance such as individual- and session-level differences.</p> <p>Quantification of Variability – Graphs, in the form of adjacency matrices, were compared to one another using three metrics: normalized percent deviation, Pearson correlation, and edgewise significant digits. The normalized percent deviation measure, defined in [8], scales the norm of the difference between a simulated graph and the reference execution (that without intentional perturbation) with respect to the norm of the reference graph. The purpose of this comparison is to provide insight on the scale of differences in observed graphs relative to the original signal intensity. A Pearson correlation coefficient was computed in complement to normalized percent deviation to identify the consistency of structure and not just intensity between observed graphs. Finally, the estimated number of significant digits for each edge in the graph was computed. The upper bound on significant digits is 15.7 for 64-bit floating point data.</p> <p>The percent deviation, correlation, and number of significant digits were each calculated within a single session of data, thereby removing any subject- and session-effects and providing a direct measure of the tool-introduced variability across perturbations. A distribution was formed by aggregating these individual results.</p> <p>Class-based Variability Evaluation – To gain a concrete understanding of the significance of observed variations we explore the separability of our results with respect to understood sources of variability, such as subject-, session-, and pipeline-level effects. This can be probed through Discriminability [10], a technique similar to ICC which relies on the mean of a ranked distribution of distances between observations belonging to a defined set of classes.</p> <p>Discriminability can then be interpreted as the probability that an observation belonging to a given class will be more similar to other observations within that class than observations of a different class. It is a measure of reproducibility, and is discussed in detail in [10].</p> <p>This definition allows for the exploration of deviations across arbitrarily defined classes which in practice can be any of those listed above. We combine this statistic with permutation testing to test hypotheses on whether differences between classes are statistically significant in each of these settings.</p> <p>With this in mind, three hypotheses were defined. For each setting, we state the alternate hypotheses, the variable(s) which will be used to determine class membership, and the remaining variables which may be sampled when obtaining multiple observations. Each hypothesis was tested independently for each pipeline and perturbation mode, and in every case where it is possible the hypotheses were tested using the reference executions alongside using MCA.</p> <ol> <li>Individual Variation<br> HA: Individuals are distinct from one another.<br> Class definition: Subject ID.<br> Experiments: Session (1 subsample), Direction (1 subsample), MCA (1 subsample, 1 session).</li> <li>Session Variation<br> HA: Sessions within an individual are distinct.<br> Class definition: Session ID | Subject ID.<br> Experiments: Subsample, MCA (1 subsample).</li> <li>Subsample Variation<br> HA: Direction subsamples within an acquisition are distinct.<br> Class definition: Subsample | Subject ID, Session ID.<br> Experiments: MCA.</li> </ol> <p>As a result, we tested 3 hypotheses across 6 MCA experiments and 3 reference experiments on 2 pipelines and 2 perturbation modes, resulting in a total of 30 distinct tests.</p> <p><strong> Evaluating Graph-Theoretical Metrics</strong><br> While connectomes may be used directly for some analyses, it is common practice to summarize them with structural measures, which can then be used as lower-dimensional proxies of connectivity in so-called graph-theoretical studies [11]. We explored the stability of several commonly-used univariate (graphwise) and multivariate (nodewise or edgewise) features. The features computed and subsequent methods for comparison in this section were selected to closely match those computed in [12].</p> <p>Univariate Differences – For each univariate statistic (edge count, mean clustering coefficient, global efficiency, modularity, assortativity, and mean path length) a distribution of values across all perturbations within subjects was observed. A Z-score was computed for each sample with respect to the distribution of feature values within an individual, and the proportion of "classically significant" Z-scores, i.e. corresponding to p < 0.05, was reported and aggregated across all subjects. The number of significant digits contained within an estimate derived from a single subject were calculated and aggregated.</p> <p>Multivariate Differences – In the case of both nodewise (degree distribution, clustering coefficient, betweenness centrality) and edgewise (weight distribution, connection length) features, the cumulative density functions of their distributions were evaluated over a fixed range and subsequently aggregated across individuals. The number of significant digits for each moment of these distributions (sum, mean, variance, skew, and kurtosis) were calculated across observations within a sample and aggregated.</p> <p><strong> Evaluating A Complete Analysis</strong><br> Though each of the above approaches explores the instability of derived connectomes and their features, many modern studies employ modeling or machine-learning approaches, for instance to learn brain-behavior relationships or identify differences across groups. We carried out one such study and explored the instability of its results with respect to the upstream variability of connectomes characterized in the previous sections. We performed the modeling task with a single sampled connectome per individual and repeated this sampling and modelling 20 times. We report the model performance for each sampling of the dataset and summarize its variance.</p> <p>BMI Classification – Structural changes have been linked to obesity in adolescents and adults [13]. We classified normal-weight and overweight individuals from their structural networks (using for overweight a cutoff of BMI > 25 [14]). We reduced the dimensionality of the connectomes through principal component analysis (PCA), and provided the first N-components to a logistic regression classifier for predicting BMI class membership, similar to methods shown in [14], [15]. The number of components was selected as the minimum set which explained > 90% of the variance when averaged across the training set for each fold within the cross validation of the original graphs; this resulted in a feature of 20 components. We trained the model using k-fold cross validation, with k = 2, 5, 10, and N (equivalent to leave-one-out; LOO).</p>
Code and data for: Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?
<p>This repository is a companion to the manuscript "<em>Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?</em>" and is linked to <a href="https://github.com/CBeardsworth/Pheasant_OrientStrat_Habitat">Github</a>.</p> <p>For any questions about the code please contact Christine at <a href="mailto:c.e.beardsworth@gmail.com">c.e.beardsworth@gmail.com</a></p> <p>To use any data contained in this repository contact Joah at <a href="mailto:j.r.madden@exeter.ac.uk">j.r.madden@exeter.ac.uk</a> for permission.</p> <p>In this repository, we have included a run-through of the R analysis <a href="https://cbeardsworth.github.io/Pheasant_OrientStrat_Habitat/">here</a> to show the outputs of the analysis without the need to run the code. For those that might want to run the code themselves, we have included three R scripts (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/R">/R</a>) and their accompanying datasets (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a>). A description of the code and the data needed to run them is below:</p> <p><em>Cognition analysis and figs.R</em> = Run the cognition analysis for the first section of the manuscript and create the figures. For this, the datasets mazeData.csv (the learning trials) and mazeRotationResults.csv (the probe trial) are required. </p> <p><em>iSSA analysis and bootstrapping.R</em> = Run iSSA models and bootstrapping. This produces the datasets required for the next stage of analysis. For this code, the datasets habitat.grd (habitat information), atlas2018-strategy.csv (atlas data + id and strategy data for each bird) and FeederCoords2017_27700.csv (coordinates of feeder locations from 2017-2018) are required. The produced datasets are included in <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a> therefore to run subsequent analyses, this code does not need to be run. To develop this code we relied heavily on the code included in the supplementary material of <a href="https://doi.org/10.1002/ece3.4823">Signer et al. (2019)</a> as well as an <a href="https://bsmity13.github.io/log_rss">online tutorial</a> from Brian J. Smith for calculating log-RSS.</p> <p><em>Habitat analysis and Figs.R</em> = Run the statistical models for the final section of the manuscript and create the figures. For this code, the datasets produced in the previous R script are required (habitatOrientation_coefs.csv and habitatOrientation_avail.csv). We have included <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">these datasets</a> so users do not need to run the iSSA analysis and bootstrapping.R script themselves. </p>
Author Classifications of O*NET Individual Work Activities (IWA)
<p>Author Classifications of O*NET Individual Work Activities (IWA) used in "Innovations and Economic Output Scale with Social Interactions in the Workforce"</p>
Figs 29‒32. Living individuals and habitats. 29 in Two new species of the genus Cryptostemma from Japan (Hemiptera: Heteroptera: Dipsocoridae)
Figs 29‒32. Living individuals and habitats. 29 – Cryptostemma miyamotoi sp. nov., male; 30 – C. pavelstysi sp. nov., male; 31 – habitat of C. miyamotoi, Sonosegawa Riv., Sanagouchi-son, Tokushima Pref.; 32 – habitat of C. pavelstysi, near Nagura Dam, Ishigaki Is.
Fig. 4 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 4. Percentage of correct matches (a) and expended minutes (b) between naked-eye and computer-assisted field test photo-identification for individual recognition of Rineloricaria aequalicuspis (n = 9). Boxplots show median (central thicker line), first and third quartile (box limits), 95% confidence interval of median (whiskers), and outliers.
Fig. 3 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 3. Variation in number, shape, size and organization of the bony plates covering the abdominal surface of six different Rineloricaria aequalicuspis individuals with more than 10 cm total length. These are examples of photographs taken during the field test. (a) 175 mm TL; (b) 138 mm TL; (c) 156 mm TL; (d) 145 mm TL; (e) 141 mm TL; (f) 151 mm TL.
Fig. 2 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 2. Diagram showing the steps employed to assess the performance of photo-identification technique in laboratory (a) and field (b) conditions for Rineloricaria aequalicuspis.
Fig. 1 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 1. Lateral, dorsal and ventral views of a Rineloricaria aequalicuspis individual (110 mm TL). Ventral view shows the arrangement of the abdominal plates. Photograph courtesy of L. R. Malabarba.
Data from: Multimodal in situ datalogging quantifies inter-individual variation in thermal experience and persistent origin effects on gaping behavior among intertidal mussels (Mytilus californianus)
In complex habitats, environmental variation over small spatial scales can equal or exceed larger-scale gradients. This small-scale variation may allow motile organisms to mitigate stressful conditions by choosing benign microhabitats, whereas sessile organisms may rely on other behaviors to cope with environmental stresses in these variable environments. We developed a monitoring system to track body temperature, valve gaping behavior, and posture of individual mussels (Mytilus californianus) in field conditions in the rocky intertidal zone. Neighboring mussels' body temperatures varied by up to 14°C during low tides. Valve gaping during low tide and postural adjustments, which could theoretically lower body temperature, were not commonly observed. Rather, gaping behavior followed a tidal rhythm at a warm, high intertidal site; this rhythm shifted to a circadian period at a low intertidal site and for mussels continuously submerged in a tidepool. However, individuals within a site varied considerably in time spent gaping when submerged. This behavioral variation could be attributed in part to persistent effects of mussels' developmental environment. Mussels originating from a wave-protected, warm site gaped more widely, and they remained open for longer periods during high tide than mussels from a wave-exposed, cool site. Variation in behavior was modulated further by recent wave heights and body temperatures during the preceding low tide. These large ranges in body temperatures and durations of valve closure events - which coincide with anaerobic metabolism - support the conclusion that individuals experience "homogeneous" aggregations such as mussel beds in dramatically different fashion, ultimately contributing to physiological variation among neighbors.
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.