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373 results for “stochastic”
Stochastic Modelling of Thin Mud Drapes inside Point Bar Reservoirs with ALLUVSIM-GANSim
<p>Here is the dataset and code for the paper by Hu, X et al. (2023, under review). <span>Stochastic Modelling of Thin Mud Drapes inside Point Bar Reservoirs with ALLUVSIM-GANSim,</span> Water Resources Research.</p>
Data from: Developing spatially explicit and stochastic measures of ecological departure
<p>Background: Ecological departure is a metric applied to mapped ecological systems measuring dissimilarity between the distributions of observed and expected proportions of non-stochastic reference vegetation classes within an area.</p> <p>Aims: We created spatially explicit measures of ecological departure incorporating stochasticity for each ecological system and all ecological systems from a central Nevada USA landscape.</p> <p>Methods: Spatially explicit ecological departures were estimated from a radius from each pixel governed by a distance-decay function within a moving window. Variability was introduced by simulating replicate climate time series for each spatial reference condition and calculating departure per replicate.</p> <p>Key results: Single system spatial ecological departure was highly and extensively departed, except for one area of low-elevation groundwater-dependent systems. Variance of spatial ecological departure was extensively low, except in areas of lower ecological departure, despite vegetation differences among replicates. The multiple-system ecological departure exhibited lower ecological departure.</p> <p>Conclusions: Spatial ecological departure was warranted for efficient land management as results were concordant between non-spatial and spatial metrics; however, rapid coding languages will be required.</p>
Off-Grid Ultrasound Imaging by Stochastic Optimization
<p>These are the data files used in the paper "Off-Grid Ultrasound Imaging by Stochastic Optimization".</p> <p>The code for the paper can be found in the corresponding <a title="github repository" href="https://github.com/vincentvdschaft/off-grid-ultrasound" target="_blank" rel="noopener">github repository</a>.</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Description</strong></td> <td><strong>Transmit scheme</strong></td> <td><strong>Transducer</strong></td> </tr> <tr> <td>L11-5v_carotid1.hdf5</td> <td>Crossectional view of a carotid artery.</td> <td>128 synthetic aperture transmissions and 21 plane wave transmissions.</td> <td>Verasonics L11-5V</td> </tr> <tr> <td>L11-5v_carotid2.hdf5</td> <td>Crossectional view of a carotid artery.</td> <td>128 synthetic aperture transmissions and 21 plane wave transmissions.</td> <td>Verasonics L11-5V</td> </tr> <tr> <td>L11-5v_carotid3.hdf5</td> <td>Crossectional view of a carotid artery.</td> <td>128 synthetic aperture transmissions.</td> <td>Philips S5-1</td> </tr> <tr> <td>S5-1_cirs.hdf5</td> <td>Acquisition of the CIRS-040 phantom in the low attenuation zone.</td> <td>80 synthetic aperture transmissions and 21 plane wave transmissions.</td> <td>Philips S5-1</td> </tr> <tr> <td>cirs_simulated.hdf5</td> <td>Simulated data similar to CIRS-040 phantom.</td> <td>3 synthetic aperture transmissions.</td> <td>Similar to Philips S5-1</td> </tr> </tbody> </table> <p>A description and unit for the datasets in these files is provided in the dataset attributes.</p>
Binary black hole merger rate constraints using GWTC-3 and full-O3 stochastic background constraints
<h1>README</h1> <p>This dataset contains posterior measurements of the redshift-dependent merger rate, mass distribution, and spin distribution of binary black holes following the O3b observing run of the LIGO-Virgo-KAGRA network, including both direct compact binary detections and constraints on the astrophysical gravitational-wave background.</p> <p>In particular, the goal of this work is to constrain a more complex model for the black hole merger rate, with the comoving rate density evolving as</p> <p>$$<br>R(z) \propto \frac{(1+z)^\alpha}{1 + \left(\frac{1+z}{1+z_p}\right)^{\alpha + \beta}}.<br>$$</p> <p>At redshifts \(z < z_p\), the merger rate grows approximately as \(R(z) \propto (1+z)^\alpha\), whereas at \(z>z_p\) it falls as \(R(z) \propto (1+z)^{-\beta}\).</p> <p>The analysis was performed as described in <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ab9743">Callister <em>et al</em> (2020)</a> and <a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> (2021)</a>, now using binary black hole detections from the GWTC-3 catalog (<a href="https://link.aps.org/doi/10.1103/PhysRevX.13.041039">Abbott <em>et al</em> 2023a</a>, <a href="https://link.aps.org/doi/10.1103/PhysRevX.13.011048">2023b</a>).</p> <ul> <li>The parameter estimation samples used are those provided by the LIGO-Virgo-KAGRA collaboration at https://zenodo.org/records/8177023</li> <li>Selection effects are calculated and mitigated using the suite of pipeline injections available at https://zenodo.org/records/7890398</li> <li>Cross-correlation measurements of the stochastic gravitational-wave background are available at https://dcc.ligo.org/LIGO-G2001287, and correspond to results presented in <a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> (2021).</a></li> </ul> <p>As discussed in <a href="https://link.aps.org/doi/10.1103/PhysRevX.13.011048">Abbott <em>et al</em> (2023b)</a>, the results of this combined BBH + stochastic analysis are categorically unchanged relative to results previously obtained using GWTC-2 events (<a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> 2021</a>); sensitivities are not yet sufficient to resolve the redshift at which the black hole merger rate peaks and turns over.</p> <h1>Contents</h1> <ul> <li><code><strong>processed_emcee_samples_together_r00r01.npy</strong></code>: File containing posterior samples when jointly analyzing BBH detections and stochastic background upper limits.</li> <li><code><strong>processed_emcee_samples_noStochastic_r00r01.npy</strong></code>: File containing posterior samples analyzing only direct BBH detections.</li> <li><code><strong>run_emcee_plPeak.py</strong></code>: Script performing joint hierarchical inference using BBH detections and stochastic background limits; used to generate posterior samples in <code>processed_emcee_samples_together_r00r01.npy</code></li> <li><code><strong>run_emcee_plPeak_noStochastic.py</strong></code>: Script performing joint hierarchical inference using BBH detections and stochastic background limits; used to generate posterior samples in <code>processed_emcee_samples_noStochastic_r00r01.npy</code></li> </ul> <h1>Accessing posterior samples</h1> <p>Posterior samples are contained in the files <code>processed_emcee_samples_together_r00r01.npy</code> and <code>processed_emcee_samples_noStochastic_r00r01.npy</code>. This is loaded via python as, e.g.</p> <blockquote> <p>>>> import numpy as np</p> <p>>>> dataset = np.load('processed_emcee_samples_together_r00r01.npy')</p> </blockquote> <p>Contained in this file is a single <code>numpy</code> array of size <code>(# of posterior samples, # of hyperparameters)</code>:</p> <blockquote> <p>>>> dataset.shape</p> <p>(1152, 13)</p> </blockquote> <p> </p> <p>The 13 hyperparameters are defined as follows:</p> <table> <tbody> <tr> <td>Column</td> <td>Name</td> <td>Definition</td> </tr> <tr> <td><code>dataset[:, 0]</code></td> <td><code>xeff_mu</code></td> <td>Mean effective inspiral spin</td> </tr> <tr> <td><code>dataset[:, 1]</code></td> <td><code>xeff_sig</code></td> <td>Standard deviation of effective inspiral spin</td> </tr> <tr> <td><code>dataset[:, 2]</code></td> <td><code>R0</code></td> <td>Total BBH merger rate at redshift \(z=0\)</td> </tr> <tr> <td><code>dataset[:, 3]</code></td> <td><code>mMin</code></td> <td>Minimum black hole mass</td> </tr> <tr> <td><code>dataset[:, 4]</code></td> <td><code>mMax</code></td> <td>Maximum black hole mass</td> </tr> <tr> <td><code>dataset[:, 5]</code></td> <td><code>lmbda</code></td> <td>Power-law index on primary mass distribution</td> </tr> <tr> <td><code>dataset[:, 6]</code></td> <td><code>mu_peak</code></td> <td>Mean of Gaussian peak in primary mass distribution</td> </tr> <tr> <td><code>dataset[:, 7]</code></td> <td><code>sig_peak</code></td> <td>Standard deviation of Gaussian peak</td> </tr> <tr> <td><code>dataset[:, 8]</code></td> <td><code>frac_peak</code></td> <td>Fraction of events occupying Gaussian peak</td> </tr> <tr> <td><code>dataset[:, 9]</code></td> <td><code>bq</code></td> <td>Power-law index on mass ratio distribution \(p(q\|m_1)\)</td> </tr> <tr> <td><code>dataset[:, 10]</code></td> <td><code>alpha</code></td> <td>Slope of \(R(z) \propto (1+z)^\alpha \) at low redshifts</td> </tr> <tr> <td><code>dataset[:, 11]</code></td> <td><code>beta</code></td> <td>Slope of \(R(z) \propto (1+z)^{-\beta}\) at high redshifts</td> </tr> <tr> <td><code>dataset[:, 12]</code></td> <td><code>zpeak</code></td> <td>Redshift at which \(R(z)\) peaks and turns over</td> </tr> </tbody> </table> <p> </p> <p>The exact usage of the above parameters can be seen in the included scripts <code>run_emcee_plPeak.py</code> and <code>run_emcee_plPeak_noStochastic.py</code>, with which the inference was performed. </p>
Quantum stochastic resonance of individual Fe atoms. Open data sets.
<p>Data sets for publication:</p> <p><strong>Quantum Stochastic Resonance of individual Fe atoms</strong><br> Max Hänze, Gregory McMurtrie, Susanne Baumann, Luigi Malavolti, Susan N. Coppersmith, Sebastian Loth</p>
Data from the stochastic flood study of the Area of Special Flood Risk (ARPSI in Spanish) of Zamora, Spain.
<p>The published data are part of the stochastic flood study of the city of Zamora, where the different uncertainties affecting the hydraulic model are considered in order to obtain a series of stochastic maps with important implications for flood risk management.</p> <p>HEC-RAS 2D has been used for the flood study and Python has been used to automate the stochastic analyses and modeling.</p> <p>The data corresponds to:<br> - Model inputs: the basic files to generate the model, the hydraulic model of HEC-RAS 2D and all the data related to the stochastic sampling of the procedure are considered as inputs.<br> - Model Outputs: Outputs are considered to be the results obtained in the different phases of the stochastic analysis (convergence maps, sensitivity maps and stochastic maps).</p>
Environmental stochasticity increases extinction risk to a greater degree in pollination specialists than in generalists
<p>Pollination sustains terrestrial food webs and agricultural systems and links the dynamics of interacting plant and pollinator species. Although environmental stochasticity is ubiquitous and can propagate through communities via species interactions in a way that increases extinction risk, it is unknown whether stochasticity affects species uniformly across pollination networks. In this paper, we introduce a stochastic dynamic model that makes novel use of the birth function and apply it to pollination networks of increasing size. We start with two- and four-species networks, in order to first illustrate the effects of stochasticity per se and then how those effects combine with specialization. We then describe the relationship between partner number and stochastic extinction risk in empirical networks with >20 species. In the 2-species network, increasing the variance of the stochastic term of the model increased the size of the region in parameter space where extinctions occur. In networks with 4 or more species, specialists were more vulnerable to extinction than generalists over a broad range of variances. Extinction risk in networks with >20 species declined nonlinearly with increasing mutualist partner number. Our results demonstrate the importance of including species interactions and stochasticity when using population-dynamic models to compare species' extinction risk. While models that omit either of these factors are likely to underestimate extinction risk, they disproportionately underestimate the vulnerability of specialists.</p>
Data: Applying stochastic and Bayesian integral projection modeling to amphibian population viability analysis
<p>Integral projection models (IPMs) can estimate the population dynamics of species for which both discrete life stages and continuous variables influence demographic rates. Stochastic IPMs for imperiled species, in turn, can facilitate population viability analyses (PVAs) to guide conservation decision-making. Biphasic amphibians are globally distributed, often highly imperiled, and ecologically well-suited to the IPM approach. Herein, we present the first stochastic size- and stage-structured IPM for a biphasic amphibian, the U.S. federally threatened California tiger salamander (<em>Ambystoma</em> <em>californiense</em>; CTS). This Bayesian model reveals that CTS population dynamics show the greatest elasticity to changes in juvenile and metamorph growth and that populations are likely to experience rapid growth at low density. We integrated this IPM with climatic drivers of CTS demography to develop a PVA and examined CTS extinction risk under the primary threats of habitat loss and climate change. The PVA indicates that long-term viability is possible with surprisingly high (20–50%) terrestrial mortality, but simultaneously identified likely minimum terrestrial buffer requirements of 600–1000 m while accounting for numerous parameter uncertainties through the Bayesian framework. These analyses underscore the value of stochastic and Bayesian IPMs for understanding both climate-dependent taxa and those with cryptic life histories (e.g., biphasic amphibians) in service of ecological discovery and biodiversity conservation. In addition to providing guidance for CTS recovery, the contributed IPM and PVA supply a framework for applying these tools to investigations of ecologically-similar species.</p>
HarmonEPS modified routines, post-processing scripts and example data used in Tsiringakis, A., Frogner, I.L., de Rooy, W., Andrae, U., Hally, A., Contreras Osorio, S., van der Veen, S. and Barkmeijer, J., An Update to the Stochastically Perturbed Parametrizations Scheme of HarmonEPS. Monthly Weather Review
<p>This dataset contains:</p> <p>- Modified code routines/configurations files used in the EPS of Harmonie-Arome (HarmonEPS) CY43H2.2 version of the model.</p> <p>- Verification scripts from the HARP verification tool, used to verify model output against SYNOP observations.</p> <p>- Post-processing and plotting scripts in python, used in the manuscript.</p> <p>- A subset of the data produced by this study as example input in the verification and post-processing/plotting scripts.</p> <p>This dataset is used in:</p> <p>~Tsiringakis, A., Frogner, I.L., de Rooy, W., Andrae, U., Hally, A., Contreras Osorio, S., van Der Veen, S. and Barkmeijer, J. An Update to the Stochastically Perturbed Parametrizations Scheme of HarmonEPS. Monthly Weather Review</p>
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
<p>Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.</p>
Data for Stochastically accelerated perturbative triples correction in coupled cluster calculations
<p>This files contains all the data used to perform the plots in the "Stochastically accelerated perturbative triples correction in coupled cluster calculations" article.</p>
Data and scripts for "The role of stochasticity in fungal community assembly – explaining apparent stochasticity with field experiments"
<p>The results presented in the manuscript “The role of stochasticity in fungal community assembly – explaining apparent stochasticity by field experiments” can be reproduced by the data and scripts provided in this repository.</p> <p>Concerning the analysis of observational data:</p> <p>· The file ObservationalData.RData includes all data: XData is the dataframe including the predictors, and t is the vector of responses (F. rosea occurrences).</p> <p>· The script O1_model_observational_data.R defines the models, fits the models, and computes model fits based on cross-validation. The results are saved into a file.</p> <p>· The script O2_show_results_observational_data.R loads the results saved by the previous script, and outputs the results reported in the manuscript.</p> <p>Concerning the analysis of experimental data:</p> <p>· The file ExperimentalData.RData includes all data: the dataframe meta includes the relevant predictors, otu.table the matrix of samples x OTU read counts, and the dataframe taxonomy the taxonomic placement of those OTUs.</p> <p>· The script E1_compute_model_based_ordinations.R precomputes the gllvm-ordinations needed both for the colonization success model as well as the community divergence model. The precomputed ordinations are saved into a file.</p> <p>· The script E2_model_colonization_success.R defines the colonization success model, fits the model, and computes model fit based on cross-validation. The results are saved into a file.</p> <p>· The script E3_show_colonization_model_results.R loads the results saved by the previous script, and outputs the results reported in the manuscript.</p> <p>· The script E4_model_community_divergence defines the community divergence model and fits the model. The results are saved into a file.</p> <p>· The script E5_show_community_divergence_model_results.R loads the results saved by the previous scripts, and outputs the results reported in the manuscript, including Table 1 and Figure 2.</p>
GRM: A Novel Stochastic Model for Real-time GNSS Tropospheric Delay Estimation
<p>The dataset includes the proposed RWPN model (Cal_rwpn_new.m) and related files. The model is built based on ERA5 ZWD products from 2010 to 2019, which can be accessed at (<a>ftp://ftp.gfz-potsdam.de/pub/home/GNSS/products/gfz-vmf1/</a>). The proposed GRM model can contribute greatly by providing an efficient RWPN value to real-time GNSS ZTD estimation with an accuracy improvement of over 10% compared to fixed RWPN results. In addition, GRM also shows the superiorities of saving computation cost significantly since a large volume of the ERA5-derived RWPN values is modeled with only several parameters.</p>
Raw plot data for: Accelerating equilibrium isotope effect calculations. II. Stochastic implementation of direct estimators
<p>Data for publication: K. Karandashev, J. Vanicek, Accelerating equilibrium isotope effect calculations. II. Stochastic implementation of direct estimators, J. Chem. Phys. <strong>151</strong>, 134116 (2019) <a href="https://doi.org/10.1063/1.5124995">https://doi.org/10.1063/1.5124995</a></p> <p>This data set contains the raw numerical data for reproducing Figures 1-8 in the publication.</p>
Stochastic Mixed-Integer Programming for a Spare Parts Inventory Management Problem
<p>The German Armed Forces provide an operation contingent to support the North Atlantic Treaty Organization (NATO) Response Force (NRF). For short deployments (e.g., one month), the NRF troops can bring with them a tightly constrained "warehouse" of spare parts. To ensure optimal use this warehouse, we developed the computer program "The OPtimization of a Spare Parts INventory" (TOPSPIN) to find an optimal mix of spare parts to repair a set of systems. Each system is composed of several parts, and it can only be used again in the mission if all broken parts are replaced. Due to the stochastic nature of the problem, we generate scenarios that simulate the failure of the parts. The backbone of TOPSPIN is a mixed-integer linear program that determines an optimal, scenario-robust mix of spare parts. Using input data provided by the German Logistikzentrum (RealData.xlsx), we analyze how many scenarios need to be generated in order to determine reliable solutions. A further data set (SimData.xlsx) was generated with random data, having a similar structure as the real data set. Using these two data sets, we analyze the composition of the warehouse over a variety of different weight restrictions, and we examine the number of repairable systems for different values of this bound. </p>
Rye microgrid historical weather forecasts and stochastic scenarios
<p>This datasets connects historical weather forecasts from the Norwegian Meteorological Institute (met.no) and historical observations from Rye microgrid (https://doi.org/10.5281/zenodo.4448894).</p> <p>Each csv file represents a historical weather forecast for approximately 60 hours ahead. Each csv-file also contains the corresponding observations in the same time interval. Finally, the files also contain load, wind generation and solar PV generation predicitons.</p> <p>The predictions are generated using gradient boosting. The predictions ending with "_ls" are based on least square. The predictions ending with "_quantile_i" represent a quantile prediction. For example, "wind_quantile_2" means that there is a 20% probability the wind will be less than this value.</p> <p>The gradient boosting predictor has been trained to predict the wind power, solar power and load using the explanatory variables below:</p> <p>Solar PV: Cloud area fraction, initial production, clear sky production and forecast look-ahead time</p> <p>Wind power: wind speed, wind direction, wind power converted from wind speed forecast, initial production and forecast look-ahead time</p> <p>Load: hour of day, month of year</p>
Demographic study of a tropical epiphytic orchid with stochastic simulations of hurricanes, herbivory, episodic recruitment, and logging
<p>In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species' range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of <em>Trichocentrum</em> <em>undulatum</em>, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (𝜆 and 𝜆<sub>s</sub>) among sites.</p>
Supporting information for: Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations
<p>The study associated with this dataset proposes a way of performing age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations. Here, you find simulation code in R to produce figures in the manuscript and matrices reporting demographic data upon which code computations are performed.</p>
Python codes for deconstructing the effects of stochasticity on transmission of hospital-acquired infections in ICUs
<p>The inherent stochasticity in transmission of hospital-acquired infections (HAIs) has complicated our understanding of transmission pathways. It is particularly difficult to detect the impact of changes in the environment on the acquisition rate due to stochasticity. In this study, we investigated the impact of uncertainty (epistemic and aleatory) on nosocomial transmission of HAIs by evaluating the effects of stochasticity on the detectability of seasonality on admission. For doing so, we developed an agent-based model of an ICU and simulated the acquisition of HAIs considering the uncertainties in the behavior of the healthcare workers (HCWs) and transmission of pathogens between patients, HCWs, and the environment. Our results show that stochasticity in HAI transmission weakens our ability to detect the effects of a change, such as seasonality, on the acquisition rate, particularly when transmission is a low-probability event. In addition, our findings demonstrate that data compilation can address this issue, while the amount of required data depends on the size of the said change and the amount of stochasticity. Our methodology can be used as a framework to assess the impact of interventions and provide decision-makers with insight about the minimum required size and target of interventions in a healthcare facility.</p>
Ultrasound Stochastic Tomography simulation data for In-silico 2D Breast Phantom model with tumour
<p>An anatomically realistic numerical breast phantom model (with realistic acoustic properties of speed of sound, density, and attenuation coefficient of tissues) derived from [1] is presented with details of a ultrasound tomography experiment in simulation. Details of source wavelets, geometry of transducer set, observed data at each transducer for each shots are provided with phantom model.</p> <p>References</p> <p>[1] <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></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.