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77 results for “leaf decomposition”
The coordination between leaf and fine root litter decomposition and the difference of their controlling factors
<p><strong>Aim:</strong> As the two largest components of plant detritus input, leaf and root litter together determines ecosystem vegetation turnover and nutrient cycling rates. However, it remains unknown the similarities and differences between the controlling factors for their decomposition. We evaluated the relationship between leaf and fine root litter decomposition across biomes, and analyzed how litter traits, climate, soil conditions and decomposers shape their relationship.</p> <p><strong>Location:</strong> Global.</p> <p><strong>Time Period:</strong> 1984–2020.</p> <p><strong>Major Taxa Studied</strong>: Vascular plant.</p> <p><strong>Methods:</strong> We collected 352 paired leaf and fine root decomposition rates (k values) and ancillary traits, climate, soil condition and decomposer abundance data from 88 sites spanning the major global biomes. Boosted regression trees (BRTs) were applied to partition the factors that control root and leaf decomposition rates.</p> <p><strong>Results: </strong>Averaged across all biomes, leaf litter decomposes significantly faster (kleaf=0.72) than fine root (kroot=0.42). The BRTs indicated that plant traits best explained the variance in both leaf and root litter decomposition. The key chemical traits of leaf litter and fine root, including C:N, [P], N:P, [lignin], [cellulose], [NSCs] and [tannins], were positively correlated. Therefore, leaf and fine root k values were positively correlated within and across biomes, even after removing the influence of climate, soil conditions and decomposers. However, climate and decomposers had different impacts on leaf and fine root decomposition. Climate induced a greater impact on fine root litter decomposition, whereas decomposers had a greater influence on leaf litter decomposition.</p> <p><strong>Main Conclusions:</strong> Our finding indicates that plants evolve a coordinated nutrient supply and demand strategy. The high nutrient demand plants produce labile leaf and fine root litter, which decompose fast to meet their high nutrient requirements. However, leaf and fine root decomposition are also mediated by different combinations of trait, climate, soil condition and decomposer factors, which weakens the coordination between leaf and fine root decomposition.</p>
Leaf litter decomposition in old-growth and selectively logged forest
<p><strong>Description: </strong></p> <p>In a multifactorial experiment we investigated the consequences of selective logging for decomposition and nutrient cycling in Southeast Asia by testing the effects of chemical composition of leaf litter and site factors on leaf litter mass loss. Litterbags were used to estimate decomposition over a period of 24 weeks, litterbags were collected 2, 4, 6, 8, 13, 24 weeks after the start of the experiment.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/124"><strong>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Quantifying biogeochemistry across forest disturbance gradients in Sabah</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>NERC (Standard grant, NE/K016253/1)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2(383))</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247639">here</a></p> <p><strong>Files: </strong>This consists of 1 file: Both_litter_decomposition_experiment.xlsx</p> <p><strong>Both_litter_decomposition_experiment.xlsx</strong></p> <p>This file contains dataset metadata and 3 data tables:</p> <ol> <li> <p><strong>chemical_composition_start</strong> (described in worksheet chemical_composition_start)</p> <p>Description: Chemical properties from the two leaf litter types before the experiment</p> <p>Number of fields: 19</p> <p>Number of data rows: 10</p> <p>Fields:</p> <ul> <li><strong>replicate</strong>: Replicate number (Field type: Replicate)</li> <li><strong>litter_type</strong>: Litter type (Field type: ID)</li> <li><strong>P_mg.g</strong>: Phosporus concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>N_perc</strong>: Nitrogen concentration in % of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>C_perc</strong>: Carbon concentration in % of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>C.N</strong>: Carbon nitrogen ratio of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>P_perc</strong>: Phosporus concentration in % of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>C.P</strong>: Carbon phosporus ratio of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>N.P</strong>: Nitrogen phosporus ratio of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>Ca_mg.g</strong>: Calcium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>Mg_mg.g</strong>: Magnesium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>Al_mg.g</strong>: Aluminium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>K_mg.g</strong>: Potassium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>soluble_cell_content</strong>: Soluble cell content in percent (Field type: Numeric)</li> <li><strong>nonsoluble_cell_content</strong>: Non-soluble cell content in percent (Field type: Numeric)</li> <li><strong>hemicellulose_bound_proteins</strong>: Hemicellulose and bound proteins content in percent (Field type: Numeric)</li> <li><strong>cellulose_lignin_recalcitrants</strong>: Soluble cell content in percent (Field type: Numeric)</li> <li><strong>cellulose</strong>: Cellulose content in percent (Field type: Numeric)</li> <li><strong>lignin_recalcitrants</strong>: Lignin and recalcitrants content in percent (Field type: Numeric)</li> </ul> </li> <li> <p><strong>chemical_composition_end</strong> (described in worksheet chemical_composition_end)</p> <p>Description: Chemical properties from individual leaf litter bags at the end of the experiment</p> <p>Number of fields: 18</p> <p>Number of data rows: 64</p> <p>Fields:</p> <ul> <li><strong>code</strong>: Identifyer for each leaf litter bag, coding for location-plotname-subplot pair-leaf litter type-mesh size-replicate (Field type: ID)</li> <li><strong>location</strong>: Location of experimental plots (M: Maliau; S: SAFE) (Field type: ID)</li> <li><strong>location_name</strong>: Plot name (Field type: Location)</li> <li><strong>plot</strong>: Running number of experimental plots (Field type: ID)</li> <li><strong>pair</strong>: Each plot contains two experimental units (making up a pair) (Field type: ID)</li> <li><strong>replicate</strong>: Each pair contained two replicates of the same treatment (litter type x mesh size) (Field type: replicate)</li> <li><strong>litter_type</strong>: Litter type (Field type: Categorical)</li> <li><strong>mesh</strong>: Mesh size of the litter bags (Field type: Categorical)</li> <li><strong>P_mg.g</strong>: Phosporus concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>K_mg.g</strong>: Potassium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>Ca_mg.g</strong>: Calcium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>Mg_mg.g</strong>: Magnesium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>Al_mg.g</strong>: Aluminium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>N_perc</strong>: Nitrogen concentration in % of dry leaf litter (Field type: Numeric)</li> <li><strong>C_perc</strong>: Carbon concentration in % of dry leaf litter (Field type: Numeric)</li> <li><strong>C.N</strong>: Carbon nitrogen ratio of dry leaf litter (Field type: Numeric)</li> <li><strong>cellulose</strong>: Cellulose concentration in % of dry leaf litter (Field type: Numeric)</li> <li><strong>lignin_recalcitrants</strong>: Lignin and recalcitrants concentration in % of dry leaf litter (Field type: Numeric)</li> </ul> </li> <li> <p><strong>litterbags_massloss</strong> (described in worksheet litterbags_massloss)</p> <p>Description: Mass loss of litter in litterbags over the experimental period of 24 weeks</p> <p>Number of fields: 17</p> <p>Number of data rows: 128</p> <p>Fields:</p> <ul> <li><strong>code</strong>: Identifyer for each leaf litter bag, coding for location-plotname-subplot pair-leaf litter type-mesh size-replicate (Field type: ID)</li> <li><strong>location</strong>: Location of experimental plots (Field type: ID)</li> <li><strong>plotname</strong>: Plot name (Field type: Location)</li> <li><strong>plot</strong>: Running number of experimental plots (Field type: ID)</li> <li><strong>pair</strong>: Each plot contains two experimental units (making up a pair) (Field type: ID)</li> <li><strong>replicate</strong>: Each pair contained two replicates of the same treatment (litter type x mesh size) (Field type: Replicate)</li> <li><strong>litter_type</strong>: Litter type (Field type: Categorical)</li> <li><strong>mesh</strong>: Mesh size of the litter bags (Field type: Categorical)</li> <li><strong>weight_t0</strong>: Initial weight at the beginning of the experiment, around 10 g per litter bag (Field type: Numeric)</li> <li><strong>weight_t1</strong>: Weight at time step 1 after 2 weeks (Field type: Numeric)</li> <li><strong>weight_t2</strong>: Weight at time step 1 after 4 weeks (Field type: Numeric)</li> <li><strong>weight_t3</strong>: Weight at time step 1 after 6 weeks (Field type: Numeric)</li> <li><strong>weight_t4</strong>: Weight at time step 1 after 8 weeks (Field type: Numeric)</li> <li><strong>weight_t5</strong>: Weight at time step 1 after 13 weeks (Field type: Numeric)</li> <li><strong>weight_t6</strong>: Weight at time step 1 after 24 weeks (Field type: Numeric)</li> <li><strong>t6_corrected</strong>: Weight at time step 1 after 24 weeks, corrected for contaminating material, mostly ingrown plant roots and fungal hyphae. This is the data to use. (Field type: Numeric)</li> <li><strong>mass_loss_%</strong>: Mass loss at the end of the experiment compared to the start of the experiment, in percent (Field type: Numeric)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2014-05-01 to 2018-09-01</p> <p><strong>Latitudinal extent: </strong>4.5000 to 5.0700</p> <p><strong>Longitudinal extent: </strong>116.7500 to 117.8200</p>
How detritivores, plant traits and time modulate coupling of leaf versus woody litter decomposition rates across species
<p>1. Plant functional traits are increasingly used to understand ecological relationships and (changing) ecosystem functions. For understanding ecosystem-level biogeochemistry, we need to understand how (much) traits co-vary between different plant organs across species, and its implications for litter decomposition. However, we do not know how the degree of synchronous variation in decomposition rates between organs across species could be influenced by different keystone invertebrates decomposing different senesced plant organs, especially in warm-climate forests. Here we asked whether interspecific patterns in wood and leaf decomposition rates and in the spectra of resource economics traits underpinning them, co-vary across woody species; and how (much) the keystone invertebrate decomposers of the litter of these organs enhance or lower such co-variation of decomposition rates through time. </p> <p>2. We addressed these questions through an 18-month "common-garden" decomposition experiment using leaf, twig and branch litter of 41 woody species in two distant subtropical forest sites in east China. We quantified the effects of leaf, twig, and branch functional traits and their respective key invertebrates (moth larvae, termites) on the decomposition rates of those organs. </p> <p>3. Interspecific variation in wood traits was partly decoupled from that in leaf traits across species, while strong coupling was found between twigs and branches. The co-variation between leaf and woody organ decomposition rates was altered dynamically through the shifting activities of the key decomposers, which created non-linear relationships of invertebrate litter consumption as a function of species rankings along the resource economic trait spectra of leaves and branches.</p> <p>4. The deviations from coupling of decomposition rates between organs were likely caused by combinations of three mechanisms: (1) (de-)coupling between organs of other traits, not commonly considered in resource economics spectra (e.g., resins) (2) leaf and wood decomposers having specific diet requirements, and (3) temporal patterns of the decomposers' activity.</p> <p>5. Synthesis. Our study highlights the importance of considering the different ways by which invertebrate detritivores drive decomposition processes through time. Under the ongoing biodiversity decline, future research would benefit from a better understanding of the role of the dynamic interactions between detritivore activities and plant functional traits on the carbon turnover in ecosystems.</p>
Chionochloa rubra leaf litter decomposition at Mt Tongariro, New Zealand
<p><strong>Concept</strong>: Decomposition rates are an important component of carbon sequestration rates in soils, potentially mitigating future climate change. Here we aim to better understand decomposition's relationship with temperature in natural conditions.</p> <p><strong>Structure</strong>: In snow-tussock grassland dominated by <em>Chionochloa</em> <em>rubra</em> var. <em>rubra</em> on Mount Tongariro, Tongariro National Park, New Zealand, we measured decomposition of Chionochloa leaf litter along an ~ 700 m altitudinal gradient, as a space-for-temperature experiment, representing 4.2ºC of warming. For litter, we haphazardly collected attached, but achlorophyllous leaves, which were cut into 5 cm long segments and pooled per site. We examined decomposition rates in a full reciprocal translocation of litter bags between 8 plots as both the origin of 8 litter types and the 8 destinations of plating out of litter bags, over 4 years using 6 replicates. Litter decomposition bags, 20 cm x 25 cm in size, were of black nylon rectangular mesh with a pore size of 2 mm. We used 3.00 g of litter per bag, applied a wet/dry weight correction, and measured litter remaining after each time period. Bag recovery was 91 %. We went on to model decomposition's relationships to environmental variates.</p> <p><strong>Results</strong>: Litter decomposed progressively over time, but at the same rate along the altitudinal gradient. There was no home-field advantage. In terms of litter quality, decomposition rates were related only to litter lignin, or fibre or litter N. Only decomposition at Year 4, and that only when organised by litter destination, showed a relationship to mean annual temperature jointly with soil C, and this was only weak and implausible. When studied across the full reciprocal transplant, there were no significant interactions between Origin and Destination data with or without Years. Therefore litter from each plot decomposed at the same rate as other plots' litter at all altitudes, allowing for small, often irregular differences in litter quality and micro-environment. </p>
Leaf decomposition, flammability and functional trait data for tropical swamp forest tree species
<p>Decomposition and fire are major carbon pathways in many ecosystems, yet the contribution of species identity to these processes can be difficult to predict. Plant decomposability and flammability have usually been studied separately but could be linked through shared predictive traits. We explored how decomposability and flammability were related to each other and to key plant functional traits in a tropical swamp forest in Singapore.</p> <p>Full methodological details <em>in situ</em> decomposition experiment in Nee Soon freshwater swamp forest, Singapore, laboratory flammability experiment, and leaf functional trait measurements can be found in the published article and supporting information stated below.</p> <p>Nur E. B. Rahman, Stuart W. Smith, Weng Ngai Lam, Kwek Yan Chong, Matthias S. E. Chua, Pei Yun Teo, Daniel W. J. Lee, Shi Yu Phua, Cheryl Y. Aw, Janice S. H. Lee, David A. Wardle. Leaf decomposition and flammability are largely decoupled across species in a tropical swamp forest despite sharing some predictive leaf functional traits. <em>New Phytologist</em></p> <p>In this data repository, we have uploaded the following decomposition, flammability and trait data as well as secondary data used in our statistical analyses to generate the findings presented in the paper. Specific datasets include the following:</p> <ul> <li>litter_mass_loss.csv : raw data of leaf litterbag dry masses before and after 1 year in situ decomposition experiment in Nee Soon Swamp Forest</li> <li>flammability_leaf_temperature.csv : raw data of temperature recorded during flammability experiments of leaf litter and fresh leaves</li> <li>flammability_timings.csv : raw data of timings of flammability events, namely smouldering and pyrolysis recorded from video footage of flammability experiments</li> <li>senesced_leaf_dryweights_area.csv : senesced leaf raw data for calculating physical traits</li> <li>senesced_leaf_dryweights.csv: senesced leaf dry weights raw data</li> <li>freshtraits_measurements.csv: fresh leaf raw data for calculating physical traits</li> <li>decomposition_constants.csv: derived decomposition constants (k) for each species from the analysis of decomposition experiments.</li> <li>functional_traits_z_standardized.csv : all traits required for the analysis, consolidated following z-standardized transformation</li> <li>functional_traits_untransformed_decomposition_flammability.csv : all traits required for the analysis, untransformed (for back transforming axis labels) and species decomposition and flammability variables</li> </ul> <p>Raw leaf litter mass loss and leaf flammability data are associated meta-data file explaining the column headers and variables. For all other datasets please refer to the paper and supporting information.</p>
Plastic responses to hot temperatures homogenize riparian leaf litter, speed decomposition, and reduce detritivores
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Chionochloa rubra leaf litter decomposition at Mt Tongariro, New Zealand
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Data from: Periphytic algae decouple fungal activity from leaf litter decomposition via negative priming
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Data from: Different dynamics and controls of enzyme activities of leaf and root litter during decomposition
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Data from: Intraspecific leaf trait variation mediates edge effects on litter decomposition rate in fragmented forests
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Data from: Leaf litter decomposition in tropical freshwater swamp forests is slower in swamp than non-swamp conditions
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N dynamics of leaf and root litter decomposition
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Data from: Chronic phosphorus enrichment and elevated pH suppresses Quercus spp. leaf litter decomposition in a temperate forest
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How detritivores, plant traits and time modulate coupling of leaf versus woody litter decomposition rates across species
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The coordination between leaf and fine root litter decomposition and the difference of their controlling factors
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Data from: Invertebrate phenology modulates the effect of the leaf economics spectrum on litter decomposition rate across 41 subtropical woody plant species
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Data from: Evaluating community effects of a keystone ant, Azteca sericeasur, on Inga micheliana leaf litter decomposition in a shaded coffee agro-ecosystem
<p class="MsoNoSpacing"> Our research examined the effect of <i>Azteca sericeasur, </i>a keystone arboreal ant,<i> </i>on the decomposition of leaf litter of the shade tree, <i>Inga micheliana, </i>in coffee agro-ecosystems<i>. </i>This interaction is important in understanding the spatial heterogeneity in decomposition. We hypothesized that <i>A. sericeasur </i>could affect leaf litter decomposition by excluding other ants, which could release decomposers, like collembolans, from predation pressure. Determining the relative strengths of these interactions can illuminate the importance of <i>A. sericeasur </i>in decomposition and nutrient cycling processes.</p> <p class="MsoNoSpacing">We assessed the ant and arthropod communities surrounding 10 pairs of trees, where each pair included one shade tree with an established <i>A. sericeasur </i>nest. Tuna baits were used in conjunction with pitfall traps to assess the ant and arthropod community, and litterbags with <i>I. micheliana </i>leaf litter were used to assess decomposition. The species richness of ants did not change in proximity to <i>A. sericeasur </i>nests, though the ant communities were distinct. Abundance of collembola and community composition of other invertebrates did not change with the presence of an <i>A. sericeasur </i>nest and there were no differences in leaf litter decomposition rates. This contradicts past studies that suggests <i>A. sericeasur </i>reduces species richness in its territory. We suggest that other ants may avoid <i>A. sericeasur </i>by moving within and beneath the leaf litter. Our results indicate there is no net effect of <i>A. sericeasur </i>on leaf litter decomposition.</p>
Data from: Decomposition of leaf litter mixtures across biomes: The role of litter identity, diversity and soil fauna
<p class="CxSpFirst">1. At broad spatial scales, the factors regulating litter decomposition remain ambiguous, with the understanding of these factors largely based on studies investigating site-specific single litter species, whereas studies using multi litter species mixtures across sites are rare.</p> <p class="CxSpMiddle">2. We exposed in microcosms containing single species and all possible mixtures of four leaf litter species differing widely in initial chemical and physical characteristics from a temperate forest to the climatic conditions of four different forests across the northern hemisphere for one year.</p> <p class="CxSpMiddle">3. Calcium, magnesium and condensed tannins predicted litter mass loss of single litter species and mixtures across forest types and biomes, regardless of species richness and microarthropod presence. However, relative mixture effects differed among forest types and varied with the access to the litter by microarthropods. Access to the microcosms by microarthropods modified the decomposition of individual litter species within mixtures, which differed among forest types independent of litter species richness and composition of litter mixtures. However, soil microarthropods generally only little affected litter decomposition.</p> <p>4. <i>Synthesis</i>. We conclude that litter identity is the dominant driver of decomposition across different forest types and the non-additive litter mixture effects vary among biomes despite identical leaf litter chemistry. These results suggest that across large spatial scales the environmental context of decomposing litter mixtures, including microarthropod communities, determine the decomposition of litter mixtures besides strong litter trait based effects.</p>
Data from: Specific leaf area predicts dryland litter decomposition via two mechanisms
1. Litter decomposition plays important roles in carbon and nutrient cycling. In dryland both microbial decomposition and abiotic degradation (by UV light or other forces) drive variation in decomposition rates, but whether and how litter traits and position determine the balance between these processes is poorly understood. 2. We investigated relationships between litter quality and their decomposition rates among diverse plant species in a desert ecosystem in vertically contrasting positions representing distinct decomposition environments driven by different relative contributions of abiotic and microbial degradation. Thereto, leaf litter samples from 17 desert species were sealed into litterbags and placed on the soil surface under strong solar exposure versus shade conditions, or buried in the soil at 10 cm depth, for a whole year. 3. Litter decomposition rates were 21 and 17 % higher in burial and light-exposed treatments, respectively, than those in shade. Leaf traits, i.e. specific leaf area (SLA), litter C:N ratio and lignin concentration could predict litter decomposition to some degree, but their predictive power was dependent on litter position. However, multiple linear regression showed that SLA, litter C and P significantly affected k values for leaf litter decomposition besides litter position, with SLA standing out as a strong determinant of litter decomposition rate as related either to solar radiation or the environment below the soil surface. Furthermore, the interspecific differences in litter decomposition rate decreased over time, implying that afterlife effects of leaf traits on decomposition were attenuated. 4. Synthesis. These findings suggest that abiotic photodegradation and soil burial mediated microbial decomposition could be responsible for higher than expected litter turnover in dryland. They point to a dual role of specific leaf area as a promotor of decomposition rates: via relative exposure of the leaf surface to abiotic factors such as UV light versus to soil moisture and microbes under soil burial.
Data from: Leaf litter diversity and structure of microbial decomposer communities modulate litter decomposition in aquatic systems
1. Leaf litter decomposition is a major ecosystem process that can link aquatic to terrestrial ecosystems by flows of nutrients. Biodiversity and ecosystem functioning research hypothesizes that the global loss of species leads to impaired decomposition rates and thus to slower recycling of nutrients. Especially in aquatic systems an understanding of diversity effects on litter decomposition is still incomplete. 2. Here we conducted an experiment to test two main factors associated with global species loss that might influence leaf litter decomposition. Firstly, we tested whether mixing different leaf species alters litter decomposition rates compared to decomposition of these species in monoculture. Secondly, we tested the effect of the size structure of a lotic decomposer community on decomposition rates. 3. Overall, leaf litter identity strongly affected decomposition rates, and the observed decomposition rates matched measures of metabolic activity and microbial abundances. While we found some evidence of a positive leaf litter diversity effect on decomposition, this effect was not coherent across all litter combinations and the effect was generally additive and not synergistic. 4. Microbial communities, with a reduced functional and trophic complexity, showed a small but significant overall reduction in decomposition rates compared to communities with the naturally complete functional and trophic complexity, highlighting the importance of a complete microbial community on ecosystem functioning. 5. Our results suggest that top-down diversity effects of the decomposer community on litter decomposition in aquatic systems are of comparable importance as bottom-up diversity effects of primary producers.
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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.