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40 results for “Simulated trees”
Data for: Simulated postfire tree regeneration suggests reorganization of Greater Yellowstone forests during the 21st century
Tree regeneration underpins forest resilience, but how postfire tree regeneration will change with future climate and fire regimes is difficult to anticipate. Areas of sparse and failed postfire tree regeneration have been documented in western US forests, but how future recovery pathways will unfold is uncertain. We conducted a simulation study in the Greater Yellowstone Ecosystem (GYE; United States) using a process-based model, iLand, to ask how rates, composition, and spatial patterns of postfire tree regeneration vary with 21st-century climate. Subalpine forest and fire dynamics were simulated through 2100 under four climate scenarios, 2 × 2 factorial of aridity (wet and dry) and temperature (warm and hot), in five GYE landscapes. We tallied postfire tree seedling density by species in simulated fires (> 400 ha) at five years postfire. This data set contains three data sets to reproduce analyses for changes rates of regeneration, proportion of burned cells with regeneration failure, and postfire reorganization pathways. We include the data and R scripts used for these three analyses in the publication associated with these data.
Data for: Can fire exclusion zones enhance postfire tree regeneration? A simulation study in subalpine conifer forests
Postfire tree regeneration in forests adapted to infrequent, stand-replacing fire is compromised by climate change and novel fire regimes. We used the individual-based forest simulation model iLand to ask whether mimicking spatial patterns of historical fire mosaics can sustain tree regeneration in a warmer future with more fire. We simulated forest and fire dynamics in Grand Teton National Park under four different climate scenarios, and with eight different scenarios (i.e. spatial configurations) of "fire exclusion zones" (Fx zones). Data were simulated for 2020 - 2100 period, and analyzed early (2026-2050) and late (2076-2100) in the simulation. Here, we present these simulated data and R-scripts to reproduce analyses presented in the associated manuscript (Keller et al. 2025, Ecological Applications). Specifically, our data deposit reproduces analyses for 1) differences in regeneration among scenarios at two different times in the simulation, 2) spatial patterns of regeneration in 2100 as a result of the operational fire exclusion zone scenario, and 3) supplemental analyses found in the appendixes.
DeepRainForest Output Data : Simulated daily rainfall output (2001-2020) under observed tree cover and no deforestation scenarios in South America
<p>This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America. The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled "Assessing the impact of past and ongoing deforestation on rainfall patterns in South America". When the manuscript is accepted then the article will be linked from here.</p> <p><strong><em>DeepRainForest_daily_rainfall_with_observed_treecover.nc:</em></strong> contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_2000_treecover.nc: </strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_1982_treecover.nc:</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982 onwards.</p>
Demonstrative simulations of L-PEACH: a computer-based model to understand how peach trees grow
<p>L-PEACH is a computer-based model that simulates source-sink interactions, architecture and physiology of peach trees (Allen et al., 2005, 2006, 2007). The model integrates important concepts related to water transport and carbon assimilation, distribution, and use within the tree (DeJong et al., 2011). L-PEACH is able to simulate crop yield responses to commercial practices such as fruit thinning (Lopez et al., 2008) and pruning (Smith et al., 2008) and could be useful for making fruit growers understand how to optimize these operations. In this work we present several demonstrative simulations of L-PEACH to complement the existing references about L-PEACH and demonstrate its value to study, understand and teach how trees grow (DeJong et al., 2008).</p> <p>The FIRST SIMULATION corresponds with the version of L-PEACH that runs on a daily time-step (L-PEACH-d) (Lopez et al., 2008, 2010). The simulation shows the growth of a peach tree over three years. The color of the stem indicates the direction of the movement of carbon within the tree (white indicates no flux of carbon, increasing apical flux of carbon from light yellow to red, and increasing basal flux of carbon from light blue to deep purple) (see details of colors in Allen et al., 2005). During this simulation the tree was stopped during the dormant season between years and the trees were pruned by the model operator in a manner that is similar to how trees would be pruned when growing in an orchard. Also during the first year of tree growth, grafting is simulated by cutting the tree back in early spring and allowing the tree to grow again as it would in a tree nursery. After this first year the tree is cut back to a single trunk in the same manner as is commonly done when a tree is transplanted from a tree nursery to a commercial fruit orchard.</p> <p>In the SECOND SIMULATION a detailed section of the tree was selected to better appreciate the realism of leaf and fruit growth and in the THIRD SIMULATION we show how to prune a peach tree to a V-system. Responses to pruning were modelled based on the concept of apical dominance as described in Smith et al. (2008) and Lopez et al. (2008).</p> <p>Subsequent simulations correspond to the last version of the L-PEACH model that includes a xylem circuit so that the diurnal water potential of each organ could be simulated along with its physiological functioning and growth. Sub-models for leaf transpiration, soil water potential and the soil-plant interface were also incorporated to provide the driving force and pathway for water flow. In the FOURTH SIMULATION we presented the effect of different irrigation treatments (control irrigation and drought irrigation) on tree development, growth and fruit yield (Da Silva et al., 2011; 2014). L-PEACH-h was also use to illustrate the effect of severity of pruning in tree growth (FIFTH SIMULATION). We tested three levels of pruning: soft, control, and hard. The simulation indicates how trees that received hard pruning are able to recover a similar tree size than control and soft pruned trees due to the generation of vigorous shoots in response to hard pruning.</p> <p>The SIXTH SIMULATION was generated to demonstrate that L-PEACH can be also used to simulate the effect of size-controlling rootstock in tree growth (Da Silva et al., 2015). In this simulation we compared tree growth with a standard rootstock (Control) and a size-controlling rootstock (Rootstock) by reducing the hydraulic conductance of the ‘rootstock” piece (base of the trunk) by 50% in the size-controlling rootstock to simulate a reduction in vessel diameters and consequently reduced hydraulic conductance in that part of the tree. After four years of simulated growth, the virtual tree on the dwarfing rootstock was substantially smaller than the virtual tree on the control rootstock.</p> <p>What you can’t see in the movies is that the L-PEACH model calculates the distribution of light in the tree canopy as the tree grows and the rate of photosynthesis in each leaf during a simulated day or hour (depending on whether the daily or hourly models are used for the simulation). Then the distribution and use of photo-assimilates are calculated by the methods described in the papers cited below. The simulations are based on real environmental input data (light, temperature, day length, etc. collected from a real weather station located near a peach orchard) and development of tree architecture is based on developmental principles governing tree growth and detailed measurements of shoots of peach trees (see references).</p> <p><em><strong>Description of files</strong></em></p> <p>Simulation 1: L-PEACH-d over three years of growth.</p> <p>Simulation 2: Detailed growth of leaves and fruit using L-PEACH.</p> <p>Simulation 3: Pruning L-PEACH-d to a v-system.</p> <p>Simulation 4: Control irrigation vs. Drought irrigation using L-PEACH-h.</p> <p>Simulation 5: Reactions to soft, control and hard pruning using L-PEACH-h.</p> <p>Simulation 6: Simulating the effect of size-controlling rootstock using L-PEACH-h.</p>
High-fidelity simulation of the effects of street trees, green roofs and green walls on the distribution of thermal exposure in Prague-Dejvice
<p>Archive with PALM simulation results. All data were used in paper <a href="https://doi.org/10.1016/j.buildenv.2022.109484">https://doi.org/10.1016/j.buildenv.2022.109484</a></p>
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character's evolution: R scripts and simulated trees
<p>All R scripts used in this study, and the set of simulated phylogenetic trees used in the study.</p> <p>1. Modern methods of ancestral state estimation (ASE) incorporate branch length information, and it has been demonstrated that ASEs are more accurate when conducted on the branch lengths most correlated with a character's evolution; however, a reliable method for choosing between alternate branch length sets for discrete characters has not yet been proposed.<br><br>2. In this study, we simulate paired chronograms and phylograms, and generate binary characters that evolve in correlation with one of these. We then investigate (1) the effect of alternate branch lengths on ASE error, and (2) whether phylogenetic signal statistics and/or model-fit statistic can be used to select the branch lengths most correlated with a binary character.<br><br>3. In agreement with previous studies, we find that ASEs are more accurate when conducted on the branch lengths most correlated with the character. Phylogenetic signal statistics show limited utility for selecting the correct branch lengths, but model-fit statistics are found to be more accurate, with the correct branch lengths generally returning greater model-fit (lower AICc and BIC values). Using this method to choose between alternate branch length sets is more accurate when tree and character properties are more favorable for model optimization, and when shape differences between alternate phylogenies are greater.<br><br>4. Our results indicate that researchers conducting ASEs on discrete characters should carefully consider which branch lengths are appropriate, and, in the absence of other evidence, we suggest estimating model-fit values over alternate branch length sets and evolutionary models and choosing the branch length/model combination that returns better model fit.</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Code and data for molecular dating benchmark based on real and simulated Primates gene trees
<p>Benchmark of molecular clock dating applied to single gene trees separately, whose results are described in “Factors influencing the accuracy and precision in dating single gene trees” by Guillaume Louvel and Hugues Roest Crollius.</p> <ol> <li>Real Primates gene trees are analyzed to identify what characteristics of a gene tree are related to the precision of dating; </li> <li>alignments are also simulated on the tree of Primates to measure the accuracy of dating under controlled parameters such as the degree of rate variation and the length of the alignment.</li> </ol> <p><strong>Content</strong></p> <p><code>Louvel_Accuracy-dating_results_2024.tar.gz</code>:<br> - <code>notebook/</code>: statistical analyses in Python;<br> - <code>outputs/</code>: html reports with figures/tables resulting from the analysis;<br> - <code>lib/</code>: required libraries.<br> - <code>data/</code>: intermediate data needed for the final analysis (dates, gene tree features);</p> <p><code>Louvel_Accuracy-dating_dating-source_2024.tar.gz</code>:<br> - <code>dating-source/</code>: input data and config files necessary to reproduce <code>data</code>;</p> <p><code>Louvel_Accuracy-dating_raw-data-preparation_2024.tar.gz</code>:<br> - <code>raw-data-preparation/</code>: raw data and steps to produce <code>dating-source</code>.<br><br><strong>Requirements</strong><br><br>This code requires the libraries developed in the lab for this project,<br>available at <a href="https://github.com/DyogenIBENS/">github.com/DyogenIBENS/</a>, but also included here in <code>lib/</code>.<br><br>- <a href="https://github.com/DyogenIBENS/Phylorgs">Phylorgs</a><br>- <a href="https://github.com/DyogenIBENS/LibsDyogen_py3">LibsDyogen_py3</a><br>- <a href="https://github.com/DyogenIBENS/ToolsDyogen_py3">ToolsDyogen_py3</a>.</p>
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character’s evolution: R scripts and simulated trees
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Input data for the analysis of changes in functional structures of Japanese tree species by species loss simulation
<p>The dataset was used in Kusumoto, Shiono & Kubota (2020). It includes functional structure indices (community means, functional richness, and Rao's quadratic entropy) for 514 Japanese timber and non-timber tree species at 10-km grid cell level. The community means were based on specific leaf area and leaf nitrogen content, respectively. Functional richness and Rao's Q were based on wood density and tree height. There functional metrics were calculated for the observed species assemblages and simulated assemblages at 10-km grid cell level. The simulated assemblages were computed by removing species in each grid cell at 5 levels of species loss (10%, 20%, 30%, 40% and 50%) with two scenarios: random loss and ordered loss depending on species successional niche score (i.e. later successinal species are preferentially lost). See "README" sheet for detailed explanations of the contents.</p> <p>Kusumoto, Shiono & Kubota (2020) Ethnobotany-informed trait ecology: measuring vulnerability of timber provisioning services across forest biomes in Japan. Biodiversity and Conservation. DOI: 10.1007/s10531-020-01974-y</p>
Files associated with: Migration-based simulations for Canadian trees show limited tracking of suitable climate under climate change
<p><strong>Aim</strong></p> <p>Species distribution models typically project climatically suitable habitat for trees in eastern North America to shift hundreds of kilometers this century. We simulated potential migration considering species' life history and traits for 10 tree species and their ability to track climatically suitable habitat.</p> <p><strong>Location</strong></p> <p>Eastern Canada, covering ~3.7 million km<sup>2</sup></p> <p><strong>Methods</strong></p> <p>We simulated migration-constrained range shifts through 2100 using a hybrid approach combining projections of climatically suitable habitat based on two Representative Concentration Pathways (RCP4.5, RCP8.5) for three time periods and two species distribution modelling approaches with process-based models parameterized using data related to <span>dispersal ability and generation time</span>. We developed a unique 'migration kernel' that uses seed dispersal traits and observed migration velocities to obtain kernel shape and dispersal probabilities. We then calculated lags between the migration-constrained range limits obtained through simulations and limits of climatically suitable habitat.</p> <p><strong>Results</strong></p> <p>All species demonstrated northward range shifts at the leading edge of their simulated distribution through 2100, but the magnitude and rate of that shift varied by species and time period. Climatically suitable habitat limits were found to be north of simulated distribution limits across both RCPs, with lags increasing through time. On average, the simulated distribution that remained within climatically suitable habitat showed higher decreases under RCP8.5 than RCP4.5, with large areas of the rear edge of the simulated distribution becoming partially or completely climatically unsuitable for many species.</p> <p><strong>Main conclusions</strong></p> <p><span>Climatically suitable habitat limits projected for 2100 far exceeded migration-constrained range limits for all 10 species, particularly for temperate species. This study underlines the limited extent to which species will track climate change via natural migration. Integrating observed migration velocities, seed dispersal and generation time with SDM outputs allows for more realistic evaluations of tree migration ability under climate change and may help orient forest conservation and restoration efforts.</span></p>
Dataset used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence"
<p>This dataset repository includes input and output spatial data of urban microclimate simulations performed through QGIS and ENVI-met software used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence", published in the Building and Environment Journal, <a href="https://doi.org/10.1016/j.buildenv.2023.110854">https://doi.org/10.1016/j.buildenv.2023.110854</a>.</p>
Files associated with: Migration-based simulations for Canadian trees show limited tracking of suitable climate under climate change
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Data from: A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods
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Data from: Genetic relationships, structure and parentage simulation among the olive tree (Olea europaea L. subsp. europaea) cultivated in Southern Italy revealed by SSR markers
In this work, we assess both the morphological and genetic diversity of 68 important olive cultivars from three Southern Italian regions: Calabria, Campania and Sicily. Twenty-five phenotypic traits were evaluated and 12 simple sequence repeat (SSR) markers were analysed. All SSR primers were polymorphic and reliable. The total number of alleles per locus varied from 5 to 19 with an average number of 13.1 and a mean polymorphic information content (PIC) of 0.81. These results suggested high genetic diversity within these three olive germplasm collections. Morphological traits also showed significant variability amongst cultivars. Two cases of identity were found and ten statistically significant cases of putative parent/sibling were discovered by performing a SSR-based parentage simulation analysis with CERVUS. The Mantel test indicated low but significant correlations between the morphological data and SSR allelic frequency, origin and SSR allelic frequency, and origin and morphology. Structure software allowed inference of relationships between the three olive germplasm collections and allowed us to obtain the most consistent grouping and to identify putative admixed or exchanged cultivars. Cluster and multivariate analysis, based on morphological traits, revealed geographic grouping in agreement with UPGMA dendrogram and structure analysis using SSRs. Sicilian cultivars showed a more homogenous genetic makeup, probably due to geographical isolation, whilst Calabrian and Campanian cultivars seemed to have a less distinct genetic structure, with a greater degree of intermixing. A correlation between the presence of certain SSR alleles and fruit size was also found.
Data from: Long term impacts of selective logging on two Amazonian tree species with contrasting ecological and reproductive characteristics: inferences from Eco-gene model simulations
The impact of logging and subsequent recovery after logging is predicted to vary depending on specific life history traits of the logged species. The Eco-gene simulation model was used to evaluate the long-term impacts of selective logging over 300 years on two contrasting Brazilian Amazon tree species, Dipteryx odorata and Jacaranda copaia. D. odorata (Leguminosae), a slow growing climax tree, occurs at very low densities, whereas J. copaia (Bignoniaceae) is a fast growing pioneer tree that occurs at high densities. Microsatellite multilocus genotypes of the pre-logging populations were used as data inputs for the Eco-gene model and post-logging genetic data was used to verify the output from the simulations. Overall, under current Brazilian forest management regulations, there were neither short nor long-term impacts on J. copaia. By contrast, D. odorata cannot be sustainably logged under current regulations, a sustainable scenario was achieved by increasing the minimum cutting diameter at breast height from 50 to 100 cm over 30-year logging cycles. Genetic parameters were only slightly affected by selective logging, with reductions in the numbers of alleles and single genotypes. In the short term, the loss of alleles seen in J. copaia simulations was the same as in real data, whereas fewer alleles were lost in D. odorata simulations than in the field. The different impacts and periods of recovery for each species support the idea that ecological and genetic information are essential at species, ecological guild or reproductive group levels to help derive sustainable management scenarios for tropical forests.
A large-scale artificial forest tree population for sampling and estimation methods simulations
<p>The dataset utilized in the Simulation Study section of the article is generated for a 10 km x 10 km area. This artificial dataset is constructed by populating polygons with trees sourced from the NFI database, representing the stands of the associated Vosges polygon. The aim is to create a sizable dataset that closely resembles a real forest by incorporating authentic information. As the Vosges dataset consists of a set of polygons, the artificial dataset is formed using voronoi polygons, each randomly linked to a Vosges dataset polygon. The Vosges dataset contributes basal area information to the voronoi polygons. These voronoi polygons are then filled with trees from the NFI database, corresponding to the stands of the Vosges polygon. The dataset comprises 1,174,227 trees, each with specified attributes such as circumference at a height of 130cm, top height, volume, species, and status (dead/alive).<br> </p>
A Dataset of Reconstructed Carotid Bifurcation Lumen and Plaque Models with Centerline Tree and Simulated Hemodynamics
<p><code>carotid_bifurcation_database.zip</code> contains 79 cases of left and right-side carotid bifurcations (152 inner wall models). For each case, inner wall (lumen) and plaque models were extracted from computed tomography angiography (CTA) scans. The models were segmented, reconstructed, and a centerline tree was created for each geometry using the <a href="https://github.com/PepeEulzer/CarotidAnalyzer">CarotidAnalyzer</a> pipeline. The geometries include varying degrees of internal carotid stenosis. Bifurcations with 100% stenosis were omitted, as the vessel is not discernible in the scan.</p> <p><code>carotid_flow_database.zip</code> contains hemodynamic flow simulations of the above models. Fluid data (velocity, pressure) and surface data (wall shear stress) are given in seperate files for each case. For each field, a systolic and diastolic time step are provided.</p> <p><strong>Further information regarding the extraction pipeline and flow simulations can be obtained from the following publications:<br></strong>P. Eulzer, F. von Deylen, W.-C. Hsu, R. Wickenhöfer, C. M. Klingner, and K. Lawonn (2023), A Fully Integrated Pipeline for Visual Carotid Morphology Analysis. Computer Graphics Forum, 42(3): 25-37. <a href="https://doi.org/10.1111/cgf.14808">https://doi.org/10.1111/cgf.14808</a></p> <p>Kevin Richter, Tristan Probst, Anna Hundertmark, Pepe Eulzer, and Kai Lawonn (2024), Longitudinal wall shear stress evaluation using centerline projection approach in the numerical simulations of the patient-based carotid artery. Computer Methods in Biomechanics and Biomedical Engineering, 27(3): 347-364. <a href="https://doi.org/10.1080/10255842.2023.2185478">https://doi.org/10.1080/10255842.2023.2185478</a></p> <p>P. Eulzer, K. Richter, A. Hundertmark, R. Wickenhöfer, C. M. Klingner, and K. Lawonn (2024), Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity. Computer Graphics Forum 43(3): in print. <a href="https://doi.org/10.1111/cgf.15081">https://doi.org/10.1111/cgf.15081</a></p>
Wildland-urban interface fire dynamics simulator input files for pyric tree spatial patterning interactions in historical and contemporary mixed conifer forests, California, USA
<p><span>Tree spatial patterns in dry coniferous forests of the western US, and analogous ecosystems globally, were historically aggregated, comprising a mixture of single trees and groups of trees. Modern forests, in contrast, are generally more homogeneous and overstocked than their historical counterparts. As these modern forests lack regular fire, pattern formation and maintenance is generally attributed to fire. Accordingly, fires in modern forests may not yield historically analogous patterns. However, direct observations on how selective tree mortality among pre-existing forest structure shapes tree spatial patterns is limited. In this study, we (1) simulated fires in historical and contemporary counterpart plots in a Sierra Nevadan mixed-conifer forest, (2) estimated tree mortality, and (3) examined tree spatial patterns of live trees before and after fire, and of fire-killed trees. Tree mortality in the historical period was clustered and density-dependent, because trees were aggregated and segregated by tree size before fire. Thus, fires maintained an aggregated distribution of tree groups. Tree mortality in the contemporary period was widespread, except for dispersed large trees, because most trees were a part of large, interconnected tree groups. Thus, post-fire tree patterns were more uniform and devoid of moderately sized tree groups. Post-fire tree patterns in the historical period, unlike the contemporary period, were within the historical range of variability identified for the western US. This divergence suggests that decades of forest dynamics without significant disturbances has altered the historical means of pyric pattern formation. Our results suggest that ecological silvicultural treatments, such as forest restoration thinnings, which emulate qualities of historical forests may facilitate the reintroduction of fire as a means to reinforce forest structural heterogeneity.</span></p>
Tree Sequence and Genealogical Forest Files for a Simulated Human Chromosome 20
<p>Dataset containing 640000 samples simulated using <a href="https://github.com/popsim-consortium/stdpopsim">stdpopsim</a> 0.2.0 and the <code>HapMapII_GRCh38</code> genetic map.<br>The tree sequence was converted to a genealogical forest files via <a href="https://github.com/lukashuebner/gfkit">gfkit</a> version <code>fbd2740</code>.</p>
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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.