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100 results for “correlated models”
Ionospheric Vertical Correlation Lengths Derived From IRI-2016 Model Errors
<p>Ionospheric vertical correlation lengths based on IRI-2016 model and Incoherent Scatter Radar (ISR) data.</p> <p><strong>Important! The analysis was performed in log space. </strong></p> <p>ISR used for this analysis:</p> <p>Jicamarca, Arecibo, Millstone Hill, Poker Flat ISR, and ResoluteBay North ISR.</p> <p>This metadata can be used for the construction of the covariance matrix for ionospheric data assimilation.</p> <p>Inside of the .nc file:</p> <p>lat=array of geomagnetic latitudes (degrees)<br> alt=arrays of altitudes (km)<br> vert_corr1=array of size (nalt, nlat), contains vertical correlation length above the reference point for different latitudes<br> vert_corr2=array of size (nalt, nlat), contains vertical correlation length below the reference point for different latitudes<br> </p>
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
<p><strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1-S4. Legends are included under each figure.</p> <p><strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.</p>
Metrics for two-sample tests: results on Mixture of Gaussians and Correlated Gaussians models
<p>The repository includes version 1.0 (v1.0) of the code and results corresponding to the GitHub repository <a href="https://github.com/TwoSampleTests/GenerativeModelsMetrics">GenerativeModelsMetrics</a>.</p> <p>Publishing information and arXiv identifier will be added after publication of the main manuscript related to the data.</p>
Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox
<p><strong>Description</strong>: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled "Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox" by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on 'king2019' (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI.</p> <p><strong>Use these data</strong>: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB), run the script <strong>g20230501_FA_Artificial_listener_paper_figs.m</strong> (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function <strong>publ_osses2023b_FA_figs.m</strong> from the toolbox. </p> <p> </p>
Dataset: Tensor-network study of correlation-spreading dynamics in the two-dimensional Bose-Hubbard model
<p>Dataset</p> <p>Tensor-network study of correlation-spreading dynamics in the two-dimensional Bose-Hubbard model</p> <p>Ryui Kaneko, Ippei Danshita</p>
Potential distribution of invasive boxwood blight pathogen (Calonectria pseudonaviculata) as predicted by process-based and correlative models
<p>R project, R scripts, and data files for reproducing most of the analyses presented in a climatic suitability study for boxwood blight. The README. md file describes how to run the scripts and provides details on data inputs.</p> <p><strong>Abstract: </strong>Boxwood blight caused by <em>Cps</em> is an emerging disease that has had devastating impacts on <em>Buxus</em> spp. in the horticultural sector, landscapes, and native ecosystems. In this study, we produced a process-based climatic suitability model in the CLIMEX program and combined outputs of four different correlative modeling algorithms to generate an ensemble correlative model. All models were fit and validated using a presence record dataset comprised of <em>Cps</em> detections across its entire known invaded range. Evaluations of model performance provided validation of good model fit for all models. A consensus map of CLIMEX and ensemble correlative model predictions indicated that not-yet-invaded areas in eastern and southern Europe and in the southeastern, midwestern, and Pacific coast regions of North America are climatically suitable for <em>Cps</em> establishment. Most regions of the world where<em> Buxus</em> and its congeners are native are also at risk of establishment. These findings provide the first insights into <em>Cps</em> global invasion threat, suggesting that this invasive pathogen has the potential to significantly expand its range.</p>
Online example database generated representing nuclear astrophysics models predictions of correlations between stable/stable abundances of specific isotopes.
<p>Library of figures created using the SIMPLE code (Stellar Interpretation for Meteoritic data and PLotting). The SIMPLE stellar database includes 18 core-collapse supernova models with 3 different initial masses of 15, 20, 25 solar masses, all of solar metallicity and non-rotating stars. The 6 sets are the following:</p> <ul> <li>Rauscher et al. 2002 [Ra02]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2002ApJ...576..323R/abstract">https://ui.adsabs.harvard.edu/abs/2002ApJ...576..323R/abstract</a>),</li> <li>Pignatari et al. 2016 [Pi16]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2016ApJS..225...24P/abstract">https://ui.adsabs.harvard.edu/abs/2016ApJS..225...24P/abstract</a>),</li> <li>Sieverdin et al. 2018 [Si18]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2018ApJ...865..143S/abstract">https://ui.adsabs.harvard.edu/abs/2018ApJ...865..143S/abstract</a>),</li> <li>Limongi & Chieffi 2018 [LC18]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2018ApJS..237...13L/abstract">https://ui.adsabs.harvard.edu/abs/2018ApJS..237...13L/abstract</a>),</li> <li>Ritter et al. 2018 [Ri18]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.480..538R/abstract">https://ui.adsabs.harvard.edu/abs/2018MNRAS.480..538R/abstract</a>),</li> <li>Lawson et al. 2022 [La22]<br>(<a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.511..886L/abstract">https://ui.adsabs.harvard.edu/abs/2022MNRAS.511..886L/abstract</a>)</li> </ul> <p>The figures can be divided into two types. The first shows the structure of the ejecta and the abundance of the selected isotopes. The layers are automatically detected using SIMPLE based on the abundances of the main fuels (H-1, He-4, C-12, O-16, Ne-20, Si-28) from the supernova model ejecta. The code names the different layers based on the schematic diagram in Schofield et al. 2022 (<a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.1803S/abstract">https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.1803S/abstract</a>). <br>Ni and Fe isotopes are plotted in the figures. The abundances shown include the radiogenic contribution from unstable isotopes.</p> <p>The SIMPLE code is designed to compare stellar data with measurements from meteorites. To achieve this, abundances in mass fractions need to be converted into isotopic ratios using specific units. See Lugaro et al. 2023 (<a href="https://ui.adsabs.harvard.edu/abs/2023EPJA...59...53L/abstract">https://ui.adsabs.harvard.edu/abs/2023EPJA...59...53L/abstract</a>) for details. For the specific case of Ni64 ratios, in comparison with model data we report the measured meteoritic anomaly by Steele et al 2012 (<a href="https://ui.adsabs.harvard.edu/abs/2012ApJ...758...59S/abstract">https://ui.adsabs.harvard.edu/abs/2012ApJ...758...59S/abstract</a>) as a continuous horizontal line. The same is done for the Fe54 ratios, with reference measurements by Hopp et al 2022 (<a href="https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717245H/abstract">https://ui.adsabs.harvard.edu/abs/2022E%26PSL.57717245H/abstract</a>). </p> <p>In the database the abundance plots are identified as <strong>structure_<model refe<em>rence>_<initial mass>_<element>_<decayed or undecayed>.png</em></strong><em>. In particular, the available reference model options are Ra02, Pi16, Si18, LC18, Ri18, La22; the initial mass of the progenitors are 15, 20 or 25 (solar masses). The third part of the filenames are the plotted elements (in this case Ni or Fe) and then if they are decayed or undecayed. In this database we only consider the decayed species, which means that the radioactive isotopes whose decay can add to the abundance of the selected isotopes were considered. In this plots the x-axis represents the total mass from the core and the y-axis is the mass fraction on a logarithmic scale. For the slopes the same name scheme applies, but they are identified as <strong>slopes_<model reference>_<initial mass>_<element>_<decayed or undecayed></strong></em><strong>.png,</strong> and on the y-axis there are the slope values insteas of abundances.</p>
Dataset of the cross-correlation functions and 3-D Vs model in the central and western NCC
<p>The file "CCFs_NCC.dat" contains all ZZ components cross-correlation fuctions for all available station pairs.</p> <p>The file "Vs_NCC.dat" contains the 3D crustal and uppermost mantle model of central and western NCC via multimodal dispersion inversion. </p>
Data and code for 'Influence of cross-correlation on the modelled uncertainty in stress–strain behavior of soft clays'
<p>This dataset contains data and code used in the research work for the manuscript "Influence of cross-correlation on the modelled uncertainty in stress–strain behavior of soft clays". The study considered two case studies, Haarajoki clay and Suurpelto clay. Two settlement calculation methods were used: compression index method and Janbu (tangential stiffness) method. In addition, clay database FI-CLAY/14/856 was extended and used to study cross-correlations between compressibility paramaters at different clay sites. Version 2 of FI-CLAY/14/856 is provided, including some other updates and corrections also.</p> <p>The Monte Carlo simulation with Gaussian copula was implemented with Python in Jupyter Notebook environment. In addition to data and code, supplementary figures are also provided. The contents of the dataset-folder are briefly described below:</p> <ul> <li>1_Data_Oedometer_test <ul> <li>Oedometer test data for Haarajoki clay and Suurpelto clay: <ul> <li>Data tables that include the clay specimen identifications, index properties, and oedometer test results (.xlsx)</li> <li>Oedometer raw data files that include all the available stress-strain measurements of both constant-rate-of-strain and incrementally loaded odometer tests (.xlsx)</li> </ul> </li> <li>Extended clay database FI-CLAY/14/856 (version 2) (.xlsx)</li> </ul> </li> <li>2_Code_Jupyter_Notebooks <ul> <li>Python code used to run the Monte Carlo simulations and to create the results figures (.ipynb)</li> <li>Readme-file (.txt)</li> </ul> </li> <li>3_Figures_Online_Supplement <ul> <li>Scatterplots with histograms that show the simulated compressibility parameters in each case (.pdf)</li> </ul> </li> </ul> <p> </p> <p>More information on database FI-CLAY/14/856 can be found from the original article (https://www.tandfonline.com/doi/full/10.1080/17499518.2020.1864410) and 304dB datbase compilation by TC304 (http://140.112.12.21/issmge/tc304.htm).</p> <p> </p>
Text-fig. 3. Correlation model of the studied sections of Late Miocene deposits. a. Tuapse highway bridge. b. Gaverdovsky. c. Volchaya Balka. 1. reversed polarity; 2. normal polarity; 3. unstudied interval. in Late Miocene (Early Turolian) Vertebrate Faunas And Associated Biotic Record Of The Northern Caucasus: Geology, Taxonomy, Palaeoenvironment, Biochronology
Text-fig. 3. Correlation model of the studied sections of Late Miocene deposits. a. Tuapse highway bridge. b. Gaverdovsky. c. Volchaya Balka. 1. reversed polarity; 2. normal polarity; 3. unstudied interval.
Data from: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models
1. Of the several approaches that are used to analyze functional trait-environment relationships, the most popular is community-weighted mean regressions (CWMr) in which species trait values are averaged at the site level and then regressed against environmental variables. Other approaches include model-based methods and weighted correlations of different metrics of trait-environment associations, the best known of which is the fourth-corner correlation method. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using four different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios, implying that the significant results for the data could be spurious. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but had lower power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should always be avoided. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using five different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but suffered from low power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should be avoided.
Stacked cross correlation functions for the MeSO-net network and derived 3D Vs model
<p>This dataset contains two major types of data.</p> <p>1) The yearly stacked cross-correlation functions between 296 MeSO-net stations covering the Kanto basin, Japan.</p> <p>2) A 3D radially anisotropic Vs model for the Kanto basin. The grid increment for the longitude and latitude is 0.01 degrees and that for the depth is 0.1 km.</p> <p>The complete station list for the 296 MeSO-net stations can be found on the Github page of https://github.com/chengxinjiang/Jiang_Kanto_anisotropy. </p>
Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models (NMR datasets, maternal plasma and infant brain)
<p>Maternal obesity during pregnancy is associated with neurodevelopmental disorder (NDD) risk. We utilized integrative multi-omics to examine maternal obesity effects on offspring neurodevelopment in rhesus macaques by comparison to lean controls and two interventions. Differentially methylated regions (DMRs) from longitudinal maternal blood-derived cell-free fetal DNA (cffDNA) significantly overlapped with DMRs from infant brain. The DMRs were enriched for neurodevelopmental functions, methylation-sensitive developmental transcription factor motifs, and human NDD DMRs identified from brain and placenta. Brain and cffDNA methylation levels from a large region overlapping mir-663 correlated with maternal obesity, metabolic and immune markers, and infant behavior. A DUX4 hippocampal co-methylation network correlated with maternal obesity, infant behavior, infant hippocampal lipidomic and metabolomic profiles, and maternal blood measurements of DUX4 cffDNA methylation, cytokines, and metabolites. Ultimately, maternal obesity altered infant brain and behavior, and these differences were detectable in pregnancy through integrative analyses of cffDNA methylation with immune and metabolic factors. </p>
Correlated-k table for H2 dominated atmospheres for 3D Climate Modelling
<p>Correlated-k table for H2 dominated atmospheres with a variable amount of water vapour built by incorporating absorption data files from the HITRAN database and using the exo\_k code by J.Leconte. This correlated-k tables are created to use with Generic-PCM model to simulate the atmospheres of H2 dominated planets.</p> <p>The calculation of radiative transfer can be performed using the correlated-k method, which efficiently determines net radiative fluxes by categorizing spectral lines into infrared and visible bands (IR x VI) and assigning the average absorption coefficients to each band. These coefficients are pre-calculated based on detailed line-by-line radiative transfer calculations. The correlated-k method is an economic alternative to the line-by-line method, making it possible to accurately and rapidly compute atmospheric radiation.</p> <p> </p>
Correlated-k table for CO2 dominated atmospheres for 3D Climate Modelling
<p>Correlated-k table for CO2 dominated atmospheres with a variable amount of water vapour built by incorporating absorption data files from the HITRAN database and using the exo\_k code by J.Leconte. This correlated-k tables are created to use with Generic-PCM model to simulate the atmospheres of CO2 dominated planets.</p> <p>The calculation of radiative transfer can be performed using the correlated-k method, which efficiently determines net radiative fluxes by categorizing spectral lines into infrared and visible bands (IR x VI) and assigning the average absorption coefficients to each band. These coefficients are pre-calculated based on detailed line-by-line radiative transfer calculations. The correlated-k method is an economic alternative to the line-by-line method, making it possible to accurately and rapidly compute atmospheric radiation.</p> <p> </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>
The second data release from the European Pulsar Timing Array II. Customised pulsar noise models for spatially correlated gravitational waves
<p>Aims: The nanohertz gravitational wave background (GWB) is expected to be an aggregate signal of an ensemble of gravitational waves emitted predominantly by a large population of coalescing supermassive black hole binaries in the centres of merging galaxies. Pulsar tiNanohertz ming arrays (PTAs), which are ensembles of extremely stable pulsars at approximately kiloparsec distances precisely monitored for decades, are the most precise experiments capable of detecting this background. However, the subtle imprints that the GWB induces on pulsar timing data are obscured by many sources of noise that occur on various timescales. These must be carefully modelled and mitigated to increase the sensitivity to the background signal. Methods: In this paper, we present a novel technique to estimate the optimal number of frequency coefficients for modelling achromatic and chromatic noise, while selecting the preferred set of noise models to use for each pulsar. We also incorporated a new model to fit for scattering variations in the Bayesian pulsar timing package temponest. These customised noise models enable a more robust characterisation of single-pulsar noise. We developed a software package based on tempo2 to create realistic simulations of European Pulsar Timing Array (EPTA) datasets that allowed us to test the efficacy of our noise modelling algorithms. Results: Using these techniques, we present an in-depth analysis of the noise properties of 25 millisecond pulsars (MSPs) that form the second data release (DR2) of the EPTA and investigate the effect of incorporating low-frequency data from the Indian Pulsar Timing Array collaboration for a common sample of ten MSPs. We used two packages, enterprise and temponest, to estimate our noise models and compare them with those reported using EPTA DR1. We find that, while in some pulsars we can successfully disentangle chromatic from achromatic noise owing to the wider frequency coverage in DR2, in others the noise models evolve in a much more complicated way. We also find evidence of long-term scattering variations in PSR J1600-3053. Through our simulations, we identify intrinsic biases in our current noise analysis techniques and discuss their effect on GWB searches. The analysis and results discussed in this article directly help to improve the sensitivity to the GWB signal and they are already being used as part of global PTA efforts.</p>
Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models (GC-FID dataset infant brains)
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Data from: Agent-based versus correlative models of species distributions: Evaluation of predictive performance with real and simulated data
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Hidden Markov models with serial correlation for identifying stock-recruitment regime shifts
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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.