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4,563 results for “correlations”
Accurate Determination of the Uniaxial Complex Refractive Index and the Optical Band Gap of Polymer Thin Films to Correlate their Absorption Strength and Onset of Absorption
<p>The uploaded datasets contain complex refractive indices of polymer thin films and a glass substrate and are published in connection with the following article:</p> <p>Kamptner, Scharber, Schiek.<br>Accurate Determination of the Uniaxial Complex Refractive Index and the Optical Band Gap of Polymer Thin Films to Correlate their Absorption Strength and Onset of Absorption.<br>ChemPhysChem 2024.</p> <p><a href="https://doi.org/10.1002/cphc.202400233">https://doi.org/10.1002/cphc.202400233</a></p> <p> </p> <p><strong>F8BT</strong> (or PFBT): Poly(9,9-dioctylfluorene-alt-benzothiadiazole).</p> <p><strong>MDMO-PPV</strong> (or OC1C10-PPV): Poly-[2-(3,7-dimethyloctyloxy)-5-methyloxy]-para-phenylene-vinylene.</p> <p><strong>PBDB-T-2F</strong> (or PBDB-T-F, PBDB-TF, PM6): Poly[(2,6-(4,8-bis(5-(2-ethylhexyl-3-fluoro)thiophen-2-yl)-benzo[1,2-b:4,5-b’]dithiophene))-alt-(5,5-(1’,3’-di-2-thienyl-5’,7’-bis(2-ethylhexyl)benzo[1’,2’-c:4’,5’-c’]dithiophene-4,8-dione)].</p> <p><strong>PDCBT</strong>: Poly[2,2''''-bis[[(2-butyloctyl)oxy]carbonyl][2,2':5',2'':5'',2'''-quaterthiophene] -5,5'''-diyl].</p> <p><strong>PTB7</strong>: Poly[[4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b']dithiophene-2,6-diyl][3-fluoro-2-[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl]].</p> <p><strong>ZZ50</strong> (or c-PCPDTBT): Poly[2,6-(4,4-bis-(2-ethylhexyl)-4H-cyclopenta[2,1-b;3,4-b']dithiophene)-alt-4,7(2,1,3-benzothiadiazole)].</p> <p><strong>float glass</strong> objective slide (Marienfeld) "tin" and "air" side.</p>
Data and scripts used in: "Exploring Biological Neuronal Correlations with Quantum Generative Models"
<div>Data and script for the manuscript "Exploring Biological Neuronal Correlations with Quantum Generative Models", by Vinicius Hernandes and Eliska Greplova.</div> <h3>main scripts</h3> <div> <p><strong><em>generate_activity_dataset.py</em></strong></p> <p>reshape data in <em>neuronData.npy</em> to 50k samples of (neurons, timesteps) shape, saved in <em>activity_data.npy</em></p> <p><strong><em>create_target_distributions.py</em></strong></p> <p>based on the dataset, makes dicionary with the the target distribution for each (neurons, timesteps) pair, saved in <em>distribution_target_dictionary.pkl</em></p> <p><strong><em>create_hyperparameters_file.py</em></strong></p> <p>generates <em>hyperparameters.csv</em>, containing:</p> </div> <ul> <li>number of neurons</li> <li>number of timesteps</li> <li>number of auxiliary_qubits</li> <li>batch_size</li> <li>learning rate of generator</li> <li>learning rate of critic</li> <li>number parametrized layers</li> <li>number of training iterations</li> <li>loss type</li> </ul> <p>for each run</p> <p><strong><em>train_qgan.py</em></strong></p> <div> <p>trains models defined <em>models.py</em> using <em>activity_data.npy</em> dataset, and for the hyperparameters defined in <em>hyperparameters.csv</em></p> </div> <div>saves loss functions, and the trained models for each 10 iterations, in specific folders indexed by the run specified in the hyperparameters file</div> <div> </div> <div><strong><em>generate_fake_activity.py</em></strong></div> <div> </div> <div>uses trained models saved in <em>output/models/run{run}/i{training_step}.pth</em> for a specific <em>training_step</em> and <em>run</em> to generate fake data, and save them in <em>output/generated_data/run{run}/i{training_step}.npy</em> files</div> <div> </div> <div><strong><em>analyze_error.py</em></strong></div> <div> </div> <div>uses generated data saved in <em>output/generated_data/run{run}/i{training_step}.npy</em> to generate two statistical quantities (k-probs and firing rate), using the function in <em>metrics.py</em>, and compare the errors in those quantities between the models using k-loss and standard-loss</div> <div> </div> <div><strong><em>analyze_stats.py</em></strong></div> <div> </div> <div>uses generated data saved in <em>output/generated_data/run{run}/i{training_step}.npy</em> to generate:</div> <ul> <li>js diverge for each training step, and final distribution of generated states, stored in <em>distribution_target_dictionary.pkl</em></li> <li>other statistical quantities, using the function in <em>metrics.py</em> file</li> </ul> <h3>auxiliary scripts</h3> <div><strong><em>metrics.py</em></strong></div> <div> </div> <div>functions to calculate neuronal statistics</div> <div> </div> <div><strong><em>aux.py</em></strong></div> <div> </div> <div>auxiliary functions:</div> <ul> <li>to generate states distribution given a dataset</li> <li>custom js divergence</li> </ul> <h3>Data</h3> <p><strong><em>neuronData.npy</em></strong></p> <p>neuronal data from Marre et al., Multi-electrode array recording from salamander retinal ganglion cells (2017)</p> <p><strong><em>activity_data.npy</em></strong></p> <p>dataset obtained from <em>neuronData.npy</em>, taking 50 thousand samples of shape (neurons, timesteps)</p> <p><strong><em>output</em></strong></p> <p>results obtained from <em>train_qgan.py</em> and <em>generate_fake_activity.py</em> </p> <p>contains:</p> <ul> <li><strong><em>losses</em></strong></li> </ul> <p>generator and critic loss for all training runs and steps</p> <ul> <li><strong><em>models</em></strong></li> </ul> <p>saved torch models every 10 training steps, for all training runs</p> <ul> <li><strong><em>generated_data</em></strong></li> </ul> <p>generated data for all models saved in <em>models</em></p>
Nonequilibrium steady-state thermoelectrics in Kondo-correlated quantum dots: Data
<p>Data sets generated using NRG-tDMRG for the manuscript titled "Nonequilibrium steady-state thermoelectric in the Kondo-correlated quantum dots".</p>
Dataset accompaning "Variational inference for correlated gravitational wave detector network noise"
<p>Dataset accompaning the paper "<strong>Variational inference for correlated gravitational wave detector network noise</strong>"</p> <p> </p> <h3>Raw data files</h3> <ul> <li>ET_caseA_noise.h5 (correlated noise)</li> <li>ET_caseB_noise.h5 (uncorrelated noise)</li> </ul> <p>These contain:</p> <ul> <li>raw_XYZ (the XYZ channels of ET noise)</li> <li>time (time in seconds, corresponding to raw_XYZ)</li> <li>periodogram: <ul> <li>pdgrm (of the above channels, truncated to 5-128 Hz)</li> <li>freq (in Hz)</li> </ul> </li> <li>true_psd <ul> <li>psd </li> <li>freq</li> </ul> </li> </ul> <p><em>Note</em>: case C from the manuscript utilised the case B dataset, (but with a model that does not account for the cross-spectrum). It does not have a separate dataset. </p> <p> </p> <h3><strong>Result file</strong></h3> <ul> <li>ET-CaseA-SGVB-PSD.h5</li> <li>ET-CaseB-SGVB-PSD.h5</li> <li>ET-CaseC-SGVB-PSD.h5</li> </ul> <p>These contain:</p> <ul> <li>psd_quantiles (the lower 0.05, median 0.50, upper 0.95 quantiles of 500 PSD samples)</li> <li>freq (in Hz, associated with the psd_quantiles)</li> </ul>
High-speed videos for the paper "Correlation between speed of the stepped leader and peak current of the return stroke of lightning flashes"
<p>This page contains one spreadsheets with the data of the analyzed lightning flashes (.xlsx file). And it also has high-speed camera video files (.cine files) that are located inside the Lighning Flashes.rar folder. The videos can be watched frame by frame to reproduce the analysis done in the paper titled "Correlation between speed of the stepped leader and peak current of the return stroke of lightning flashes", submitted for publication in Geophysical Research Letters.</p> <p>Instructions to watch the videos: </p> <p>Download and use the software Phantom Camera Control (PCC) available at:</p> <p>https://www.phantomhighspeed.com/resourcesandsupport/phantomresources/pccsoftware</p> <p>The Phantom Camera Control (PCC) software is compatible with Windows 7 Pro and Windows 8.1 and Windows 10, for both 32 and 64-bit operating systems.</p>
Data set "Correlation of in vitro biofilm formation capacity with persistence of antibiotic-resistant Escherichia coli on fresh leafy produce"
<p>This repository contains the metadata and raw data for the analysis of an <em>in vitro </em>screening of 174 antibiotic-resistance E. coli strains isolated from various sources to evaluate their ability and strength to form biofilms.</p> <p>This repository contains the raw data to characterise a subset of eleven <em>E. coli </em>strains in their population dynamics and persistance on lamb's lettuce (<em>Valerianella locusta</em>) leaves.</p> <p>Raw images (czi format) of live/dead stain of selected strains in <em>V. locusta </em>leaves are provided.</p> <p>Data analysis and image processing scripts can be found in the GitHub repository associated to the manuscript.</p>
Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem
<div><strong>Description. </strong>This is the data used in the experiments presented in the following paper:<br> <div> <ul> <li> <div> <div>N. Arınık, R. Figueiredo, and V. Labatut, “Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem,” <em>Journal of Global Optimization</em>, vol. 86, pp. 355–391, 2023. DOI: <a href="http://doi.org/10.1007/s10898-023-01270-3">10.1007/s10898-023-01270-3</a> ⟨<a href="https://hal.science/hal-03935831">hal-03935831</a>⟩</div> </div> </li> </ul> <p><strong>Source code. </strong>The related source code is available on GitHub: </p> <ul> <li><a href="https://figshare.com/articles/dataset/Efficient_Enumeration_of_Correlation_Clustering_Optimal_Solution_Space/%3Ci%3Ehttps://github.com/%3C/i%3E%3Ci%3E%3Ci%3ECompNet%3C/i%3E/Sosocc%3C/i%3E">https://github.com/CompNet/Sosocc</a></li> <li><a href="https://figshare.com/articles/dataset/Efficient_Enumeration_of_Correlation_Clustering_Optimal_Solution_Space/%3Ci%3Ehttps://github.com/CompNet/EnumCC%3C/i%3E">https://github.com/CompNet/EnumCC</a></li> </ul> <div><strong>Citation. </strong>If you use these data, please cite the above reference:</div> <div><br><code>@Article{Arinik2021,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem},</code><br><code> journal = {Journal of Global Optimization},</code><br><code> year = {2023},</code><br><code> volume = {86},</code><br><code> pages = {355-391},</code><br><code> doi = {10.1007/s10898-023-01270-3},</code><br><code>}</code></div> <div> </div> <div><strong>Funding. </strong>This research benefited from the support of Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</div> <div> </div> <div><strong>Further details. </strong>We describe below the structure of the zip file `article_materials.zip`:</div> <div> <ul> <li>Experiments for Dataset 1 <ul> <li>delay_exec_time: all the results and plots regarding the difference of execution times between EnumCC(3) and OneTreeCC() (i.e., EnumCC(3) minus OneTreeCC()), represented on the log-scaled y-axis of the plots. When such difference takes a negative value, this means our proposed method EnumCC(3) runs faster than OneTreeCC().</li> <li>EnumCC_nb-jumps: all the results regarding the number of jumps related to EnumCC(3), i.e. n<sub><em>jump</em></sub>(EnumCC(3))</li> <li>exec_time: all the results regarding the execution times of EnumCC(3) and OneTreeCC().</li> <li>nb-sols: all the results regarding the number of optimals solutions based on EnumCC(3). Note that we show the results of OneTreeCC() only for those with n=50, since both methods run out of the time limit of 12h for several networks with n=50.</li> </ul> </li> <ul> <li>networks: We generate these complete and incomplete networks through our random signed network generator, which is publicly available online. For complete unweighted signed networks, this model relies on only three parameters: n (number of vertices), l<sub>0</sub> (initial number of modules) and <em>q<sub>m</sub></em> (proportion of misplaced edges, i.e. edges meant to be frustrated by construction). Moreover, we make the assumption that the proportion of misplaced edges is the same inside and between the modules. When it comes to incomplete unweighted signed networks, we introduce two more parameters, which are the density d of the graph and the proportion q neg of the negative edges. The last parameter q<sub>neg</sub> allows to control the ratio of positive to negative edges. For complete unweighted signed networks with d = 1, we generate 20 replications for parameter values l<sub>0</sub> = 3, n <em>∈</em> {32, 36, 40, 45, 50} and <em>q<sub>m</sub></em> <em>∈</em> {0.1, 0.2, 0.3, 0.4, 0.5, 0.6}. In these networks, the value of <em>q<sub>neg</sub> </em>with the considered parameters is approximately equal to 0.7. For incomplete unweighted signed networks with d <em>∈</em> {0.25, 0.50}, we generate 20 replications for parameter values l<sub>0</sub> = 3, n <em>∈</em> {32, 36, 40}, q<sub>m</sub> <em>∈</em> {0.1, 0.2, 0.3, 0.4, 0.5, 0.6} and <em>q<sub>neg</sub> </em><em>∈</em> {0.3, 0.5, 0.7}. In total, we produce 600 and 1,080 instances for complete and incomplete networks, respectively, which makes a total of 1,680 instances.</li> <li>partitions: folder containing the partitioning results of two methods: EnumCC(3) vs. OneTreeCC(). Note that the results of OneTreeCC() are not shown for space considerations, except for those with n=50.</li> <li>Results</li> </ul> <li>Experiments for Dataset 2</li> <ul> <li>benchmark netwoks: We generate these complete and incomplete networks through our random signed network generator, which is publicly available online. The optimal solution for a generated network is known by construction. For a given n, d and l<sub>0</sub>, we first create a perfectly structurally balanced (i.e., internally positive and externally negative) signed network with a built-in module structure. The underlying module structure constitutes the optimal partition. Then, in order to take into account different positive to negative ratio values for internal and external edges we generate several signed networks by perturbing the initial signed network without affecting its underlying optimal partition, thanks to its definition of stability range. We generate signed networks with parameter values n <em>∈</em> {30, 40, 50, 60, 70, 90}, d <em>∈</em> {0.25, 1.00} and l<sub>0</sub> <em>∈</em> {2, 4, 6}. In total, we produce 214 and 184 instances for complete and incomplete networks, respectively, which makes a total of 398 instances.</li> <li>benchmark partitions: folder containing the partitioning results of two methods: CoNS(<em>r<sub>max</sub></em>) with vs. without MVMO pruning, where <em>r<sub>max</sub></em> ∈ {3,4}.</li> <li>results: two `csv` files containing benchmark results between CoNS(<em>r<sub>max</sub></em>) with vs. without MVMO pruning, where r<sub>max</sub> ∈ {3,4}.</li> </ul> </ul> </div> </div> </div>
Correlating the Photoshunt with Charge-Collection Losses in Organic Solar Cells
<p>Data of figures shown in the main and supporting information of the paper "Correlating the Photoshunt with Charge-Collection Losses in Organic Solar Cells".</p>
Interplay of correlations and Majorana mode from local solution perspective
<p>Raw numerical data used to produce figures 2-14 in the article Interplay of correlations and Majorana mode from local solution perspective </p>
Data for "A neural correlate of learning fails to predict foraging efficiency in the bumble bee Bombus terrestris"
<p>This file contain the foraging and synaptic density data collected in the summer of 2019 by Pasquier, Pull, Ott and Leadbeater. Used in the paper titled "A neural correlate of learning fails to predict foraging efficiency in the bumble bee <em>Bombus terrestris</em>".</p>
Characterizing the spatial correlation of coseismic slip distributions: A data driven Bayesian approach
<p>Slip models for the simulated case and the Illapel earthquake are provided. The zip file contains processed data, predictions, and uncertainty estimates for the Illapel event.</p>
Data from: Correlated evolution between colouration and ambush site in predators with visual prey lures
The evolution of a visual signal will be affected by signaller and receiver behaviour, and by the physical properties of the environment where the signal is displayed. Crab spiders are typical sit-and-wait predators found in diverse ambush sites, such as tree bark, foliage and flowers. Some of the flower-dweller species present a UV+-white visual lure that makes them conspicuous and attractive to their prey. We hypothesised that UV+-white colouration was associated with the evolution of a flower-dwelling habit. In addition, following up on results from a previous study we tested whether the UV+-white colouration evolved predominantly in flower-dwelling species occurring in Australia. We measured the reflectance of 1149 specimens from 66 species collected in Australia and Europe, reconstructed a crab spider phylogeny, and applied phylogenetic comparative methods to test our hypotheses. We found that the flower-dwelling habit evolved independently multiple times, and that this trait was correlated with the evolution of the UV+-white colouration. However, outside Australia non-flower-dwelling crab spiders also express a UV+-white colouration. Therefore, UV+-white reflectance is probably a recurring adaptation of some flower-dwellers for attracting pollinators, although it may have other functions in non-flower-dwellers, such as camouflage.
Data set for the article "Recently photoassimilated Carbon and fungus-delivered Nitrogen are spatially correlated at the cellular scale in the ectomycorrhizal tissue of Fagus sylvatica"
<p>This dataset contains data that support the manuscript</p> <p>Mayerhofer et al (2021) "Recently photoassimilated Carbon and fungus-delivered Nitrogen are spatially correlated at the cellular scale in the ectomycorrhizal tissue of<em> Fagus sylvatica", </em>The New Phytologist, DOI:10.1111/nph.17591</p> <p>It contains the following data:</p> <p>(1) NanoSIMS imaging data, which was used for Fig. 4-7, is provided in NanoSIMS_control_root_tip.zip and NanoSIMS_labelled_root_tip.zip. Each zip-files contains:</p> <ul> <li>the original NanoSIMS images (.im)</li> <li>their related checkfiles (.chk_im)</li> <li>ROIs description (.rois.zip)</li> </ul> <p>of the unlabelled control and the labelled root tip section, respectively. ".im" and ".chk_im" are the original image data aquisition files from the NanoSIMS instrument. "rois.zip" files describe selected regions of interests and were created utilizing the OpenMIMS plugin (Center for Nano Imaging, https://nano.bwh.harvard.edu/MIMSsoftware) for the image analysis software ImageJ (National Institutes of Health, Bethesda, MD, USA).</p> <p>(2) Means and standard deviations of all measured elements and isotopes of each region of interest, as obtained via the .rois.zip files from the NanoSIMS images, are reported in NanoSIMS_ROI_data.csv (used for Fig.7).</p> <p>(3) Linescan_data.csv contains data used for Fig. 8.</p> <p>(4) IRMS_roots_data.csv contains data of root segments and mycorrhizal root tips analysed with isotope-ratio mass spectrometry (EA-IRMS) (used for Fig. 2)</p> <p>Description of column meanings from csv data files can be found in the according "_description" files.</p>
Correlates of substitution rate variation in a robust Procellariiform seabird phylogeny
<p>Molecular substitution rates vary among branches and can lead to inaccurate reconstructions of evolutionary relationships and obscure the true phylogeny of affected clades. Body mass is often assumed to have a major influence on substitution rate, though other factors such as population size, life history traits, and flight demands are also thought to have an influence. Birds of the order Procellariiformes—which encompasses petrels, storm-petrels and albatrosses—show a striking 900-fold difference in body mass between the smallest and largest members, divergent life history traits, and substantial heterogeneity in mitochondrial substitution rates. Here, we used genome-scale nuclear DNA sequence data from 4365 ultraconserved element loci (UCEs) in 51 procellariiform species to examine whether phylogenetic reconstruction using genome-wide datasets is robust to the presence of rate heterogeneity, and to identify predictors of substitution rate variation. Our results provide a backbone phylogeny for procellariiform seabirds and resolves several controversies about the evolutionary history of the order, demonstrating that albatrosses are basal, storm-petrels are paraphyletic and diving petrels nestled within the Procellariidae. We find evidence of rate variation; however, all phylogenetic analyses using both concatenation and multispecies coalescent approaches recovered the same branching topology, including analyses implementing different clock models, and analyses of the most and least clock-like loci. Overall, we find that rate heterogeneity is little impacted by body mass and age at first breeding, but moderately impacted by longevity and hand-wing index, a proxy for wing shape and flight efficiency. Our results indicate that substitution rate may be the product of interactions among many, potentially taxon-specific, variables.</p>
Biodiversity scale-dependence and opposing multi-level correlations underlie differences among taxonomic, phylogenetic, and functional diversity
<p><b>Aim:</b> Biodiversity is a multi-dimensional property of biological communities that represents different information depending on how it is measured, but how dimensions relate to one another and under what conditions is not well understood. We explore how taxonomic, phylogenetic, and functional diversity can differ in scale-of-effect dependence and habitat-biodiversity relationships, and subsequently how spatial differences among biodiversity dimensions may arise.</p> <p><b>Location:</b> Nebraska, United States</p> <p><b>Time period:</b> May-July 2016, 2017</p> <p><b>Major taxa studied:</b> Birds</p> <p><b>Methods:</b> Across 2016 and 2017, we conducted 2,641 point counts at 781 sites. We modeled the occupancy of 141 species using Bayesian Bernoulli-Bernoulli hierarchical logistic regressions. We calculated species richness (SR), phylogenetic diversity (PD), and functional diversity (FD) for each site and year based on predicted occupancy, accounting for imperfect detection. Using Bayesian latent indicator scale selection and multivariate modeling, we quantified the spatial scales-of-effect that best explained the relationships between environmental characteristics and SR, PD, and FD. Additionally, we decomposed the residual between- and within-site biodiversity correlations using our repeated measures design.</p> <p><b>Results:</b> We demonstrate spatial differences among biodiversity predictions, arising from scale-dependence in habitat-biodiversity relationships and variation in correlation structure among biodiversity dimensions. Although relationships between specific land cover types and SR, PD and FD were qualitatively similar, the spatial scales at which these variables were important in explaining biodiversity differed among dimensions. Between-site residual biodiversity correlations were negative, yet within-site biodiversity residual correlations were positive.</p> <p><b>Main conclusions:</b> Our results demonstrate how spatial differences among biodiversity dimensions may arise from biodiversity-specific scale-dependent habitat relationships, low shared environmental correlations and opposing residual correlations between dimensions, which suggest that single-scale and single-dimension analyses are not entirely appropriate for quantifying habitat-biodiversity relationships. After accounting for shared habitat relationships, we found positive within-site residual correlations between taxonomic, phylogenetic, and functional diversity, suggesting that habitat change over time influenced all biodiversity dimensions relatively similarly. However, negative between-site residual correlation among biodiversity dimensions may indicate trade-offs in achieving maximum biodiversity across multiple biodiversity dimensions at any given location. Although habitat management can to a limited degree improve biodiversity relatively across all metrics, other environmental effects may ensure that not all facets of biodiversity can be maximized at once. If maximizing a specific biodiversity dimension is the goal, then care should be taken to consider these within-site residual correlations.</p>
Fig. 2 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 2. Camera trap locations in Thung Yai Naresuan (East) Wildlife Sanctuary.
Fig. 1 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 1. Thung Yai Naresuan (East) Wildlife Sanctuary (TYNE), Thailand.
FIG. 12 in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds
FIG. 12. Looking south toward Red Rum (arrow) at the intermediate white beds of Zos Canyon.
FIG. 13 in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds
FIG. 13. The Red Rum sublocality, looking west-southwest.
FIG. 14 in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds
FIG. 14. Looking northwest from the Red Rum sublocality toward the discovery site (arrow).
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.