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149 results for “tropical soils”

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dryad36/100

Belowground fungi, soil, and root chemistry in tropical landuse systems

<p>The data were collected in the humid tropical climate on Sumatra (Indonesia) in rain forests, in jungle rubber (rubber planted into forests), in rubber, and oil palm plantations. A total of 44 plots were sampled. The data set consists of two tables. The table Environmental_data contains plot information, geographic coordinates of the plots, and data on soil properties (pH, nitrogen, carbon, C/N, potassium, calcium, magnesium, manganese, iron, phosporous, soil resource index, soil PC1, soil PC2) and on root traits (biomass, nitrogen, carbon, C/N, potassium, calcium, magnesium, manganese, iron, phosporous, root resource index, root PC1, soil PC, root vitality). Table 2 contain abundance data (OTU counts) for fungi associated with roots or with soil and their phylogenetic and functional assignments.</p>

opencc-zeroJan 2021View details →
dryad36/100

Data from: Effects of soil type and light on height growth, biomass partitioning, and nitrogen dynamics on 22 species of tropical dry forest tree seedlings: comparisons between legumes and nonlegumes

PREMISE OF THE STUDY: The seedling stage is particularly vulnerable to resource limitation, with potential consequences for community composition. We investigated how light and soil variation affected early growth, biomass partitioning, morphology, and physiology of 22 tree species common in tropical dry forest, including eight legumes. Our hypothesis was that legume seedlings are better at taking advantage of increased resource availability, which contributes to their successful regeneration in tropical dry forests. METHODS: We grew seedlings in a full-factorial design under two light levels in two soil types that differed in nutrient concentrations and soil moisture. We measured height biweekly and, at final harvest, biomass partitioning, internode segments, leaf carbon, nitrogen, δ 13 C, and δ 15 N. KEY RESULTS: Legumes initially grew taller and maintained that height advantage over time under all experimental conditions. Legumes also had the highest final total biomass and water-use efficiency in the high-light and high-resource soil. For nitrogen-fixing legumes, the amount of nitrogen derived from fixation was highest in the richer soil. Although seed mass tended to be larger in legumes, seed size alone did not account for all the differences between legumes and nonlegumes. Both belowground and aboveground resources were limiting to early seedling growth and function. CONCLUSIONS: Legumes may have a different regeneration niche, in that they germinate rapidly and grow taller than other species immediately after germination, maximizing their performance when light and belowground resources are readily available, and potentially permitting them to take advantage of high light, nutrient, and water availability at the beginning of the wet season.

opencc-zeroAug 2019View details →
dryad36/100

Decadal survival of tropical pioneer seeds in the soil seedbank is accompanied by fungal infection and dormancy release

<p>Pioneer trees require high-light environments for successful seedling establishment. Consequently, seeds of these species often persist in the soil seed bank (SSB) for periods ranging from several weeks to decades. How they survive despite extensive pressure from seed predators and soilborne pathogens remains an intriguing question.</p> <p>This study aims to test the hypotheses that decades-old seeds collected from the SSB in a lowland tropical forest remain viable by i) escaping infection by fungi, which are major drivers of seed mortality in tropical soils, and/or ii) maintaining high levels of seed dormancy and seed coat integrity when compared to inviable seeds.</p> <p>We collected seeds of <em>Trema micrantha</em> and <em>Zanthoxylum ekmanii </em>at Barro Colorado Island, Panama, from sites where adult trees previously occurred in the past 30 years. We used carbon dating to measure seed age and characterized seed coat integrity, seed dormancy, and fungal communities.</p> <p>Viable seeds from the SSB ranged in age from 9 to 30 years for <em>T. micrantha</em>, and 5 to 33 years for <em>Z. ekmanii.</em> We found no evidence that decades-old seeds maintain high levels of seed dormancy or seed coat integrity. Fungi were rarely detected in fresh seeds (no soil contact), but phylogenetically diverse fungi were detected often in seeds from the SSB. Although fungal infections were more commonly detected in inviable seeds than in viable seeds, a lack of differences in fungal diversity and community composition between viable and inviable seeds suggested that viable seeds are not simply excluding fungal species to survive long periods in the SSB.</p> <p><em>Synthesis: </em>Our findings reveal the importance of a previously understudied aspect of seed survival, where the impact of seed-microbial interactions may be critical to understand long-term persistence in the SSB.</p>

opencc-zeroNov 2023View details →
dryad36/100

Woody debris removal modifies carbon stocks and soil properties in a fragmented tropical rainforest

<p>We examined whether and how woody debris removal for domestic fuel affects carbon storage and soil properties in an Indian rainforest. Fuelwood removal reduced aboveground carbon stocks, increased soil bulk density, and possibly reduced soil phosphorus stocks. Equitably balancing this subtle trade-off between climate-regulating and vital, widely-utilized provisioning functions, is a challenge for tropical forest research and management.</p>

opencc-zeroJan 2024View details →
dryad36/100

Empirical data and model simulations of the effect of repeated hurricanes on soil carbon dynamics in a humid tropical forest

<p>Increasing hurricane frequency and intensity with climate change is likely to affect soil organic carbon (C) stocks in tropical forests. We examined the cycling of C between soil pools and with depth at the Luquillo Experimental Forest in Puerto Rico in soils over a 30-year period that spanned repeated hurricanes. We used a non-linear matrix model of soil C pools and fluxes ("soilR") and constrained the parameters with soil and litter survey data. Soil chemistry and stable and radiocarbon isotopes were measured from three soil depths across a topographic gradient in 1988 and 2018. Our results suggest that pulses and subsequent reduction of inputs caused by severe hurricanes in 1989, 1998, and two in 2017 led to faster mean transit times and younger mean ages of soil C in the particulate, occluded, and mineral-associated soil organic matter pools at 0–10 cm and 35–60 cm depths relative to a modeled control soil with constant inputs over the thirty years. Between 1988 and 2018, the occluded C stock increased, and d<sup>13</sup>C in all pools decreased, while changes in particulate and mineral-associated C were undetectable. The differences between 1988 and 2018 suggest that hurricane disturbance results in a dilution of the occluded light C pool with an influx of young, debris-deposited C, and possible microbial scavenging of old and young C in the particulate and mineral-associated pools. These effects led to a younger total soil C pool with faster mean transit times. Our results suggest that increasing frequency of intense hurricanes will speed up rates of C cycling in tropical forests, and eventually lead to net losses of C from tropical forest soils.</p>

opencc-zeroMar 2024View details →
zenodo36/100

Vertical profiles of leaf photosynthesis and leaf traits, and soil nutrients in two tropical rainforests in French Guiana before and after a three-year nitrogen and phosphorus addition experiment

<p>We provide a comprehensive dataset of vertical profiles of photosynthetic capacity and important leaf traits, including leaf N and P concentrations, from two three-year, large-scale fertilisation experiments conducted in two tropical rainforests in French Guiana. These data present a unique source of information to further improve model representations of the roles of N, P, and other leaf nutrients, in photosynthesis in tropical forests. To further facilitate the use of our data in syntheses and model studies, we provide an elaborate list of ancillary data, including important soil properties and nutrients, along with the leaf data. As environmental drivers are key to improve our understanding of carbon&nbsp;(C)-nutrient cycle interactions, this comprehensive dataset will aid to further enhance our understanding of how nutrient availability interacts with C uptake in tropical forests.</p>

opencc-by-4.0Apr 2021View details →
dryad36/100

Soil nitrogen mediates the effect of distance from climatic center on herbivory rates in a tropical herb

<p><span><span><span><span><span><span><span><span><span><span><span>The center-periphery hypothesis predicts that species are most abundant at the center of their range. Differential herbivory rates between center and periphery populations can explain this variation in species abundance. However, if the geographic center of a species distribution coincides with its ecological optimum, the resource availability hypothesis predicts higher herbivory rates and tolerances at the center compared to the periphery. Biogeographic studies on herbivory have treated these two mechanisms separately, limiting our mechanistic understanding of the role of herbivory in shaping species range limits. We analyzed the role of resource availability on herbivory variation from center to periphery using data collected across the distribution of <i>Thunberbia atacorensis,</i> a range limited species of West Africa. We used two types of distances: geographic distance (the distance from each plot to the geographic center of all populations) and climatic distance (the distance from each plot to the preferendum of the species). We found no increase in herbivory toward the periphery of the climatic and geographic ranges. However, herbivory rates increased with soil nitrogen. Nitrogen in soil decrease from center to periphery of climatic range. Phylogenetic diversity and surrounding plants' competition did not affect herbivory rates. Our study provides insights into how nutrient limitation can shape species center-periphery distribution by altering spatial variation in herbivory rates.</span></span></span></span></span></span></span></span></span></span></span></p>

opencc-zeroDec 2021View details →
dryad36/100

Highly-replicated soil, topography and vegetation sampling across an old-growth tropical rain forest landscape

<p class="MsoNormal">Here we present data from highly-replicated sampling of soil, topography, and vegetation across an old-growth tropical rainforest landscape at the La Selva Biological Station, Costa Rica.  Samples were taken at 100 x 50 m spacing using an existing surveyed grid system.  The 573-ha sample area spanned a variety of soil, topographic and vegetation conditions, including flat terraces on old alluvial soil, ridge tops and steep slopes on residual soil, riparian habitats and fresh-water swamps.  At each of 1170 grid points we established a circular 0.01 ha quadrat (radius = 5.64 m).  We sampled soil at 30-50 cm depth with a soil augur and collected a sample for subsequent analysis.  We measured slope angle with a clinometer and slope direction with a compass.  We measured stem diameter to <u>+</u>1 mm with a synthetic fabric diameter tape for all stems <u>&gt;</u>10 cm diameter (N= 5236) at 1.3 m from the ground or to ~ 6 m height if there were basal irregularities.  We classified stems to life form (tree, palm, liana), and identified all trees and palms to species or morphospecies (N=266; lianas were not identified to species).  We collected vouchers from all trees that we could not positively identify in the field (N=920).</p> <p class="MsoNormal">As a whole the data set presents an integrated view of soil, topography and vegetation across a mesoscale old-growth tropical rain forest landscape.  The data have been used to refine a reserve-wide soils map for La Selva, and for a variety of papers analyzing the interactions of soil, topography and species distributions at landscape scales (see the 10 papers listed in the Related Works section below).</p> <p class="MsoNormal">There are no restrictions at all on the use of these data, and we think they will be useful for teaching and analysis projects as well as further original research applications.  The data also provide a detailed benchmark of the status of old-growth vegetation in 1993-95 for one of the most intensively studied tropical rain forest landscapes in the world.  Because the data are accurately georeferenced and detailed metadata on all methods are provided, this study could be repeated at any time to assess the trajectory of vegetation changes at La Selva, particularly in relation to local disturbances and changing regional and global climates.   </p>

opencc-zeroJun 2022View details →
zenodo36/100

Estimates of soil nutrient limitation on the CO2 fertilization effect for tropical vegetation

<p>Data for CO2 fertilization experiments and CMIP6 model simulations used in publication: &quot;Estimates of soil nutrient limitation on the CO2 fertilization effect for tropical vegetation&quot;</p>

opencc-by-4.0Jul 2022View details →
dryad36/100

Widespread variation in functional trait-vital rate relationships in tropical tree seedlings across a precipitation and soil phosphorus gradient

<p>A fundamental assumption of functional ecology is that functional traits are related to interspecific variation in performance. However, the relationship between functional traits and performance is often weak or uncertain, especially for plants. A potential explanation for this inconsistency is that the relationship between functional traits and vital rates (e.g., growth and mortality) is dependent on local environmental conditions, which would lead to variation in trait-rate relationships across environmental gradients. In this study, we examined trait-rate relationships for six functional traits (seed mass, wood density, maximum height, leaf mass per area, leaf area, and leaf dry matter content) using long-term data on seedling growth and survival of woody plant species from eight forest sites spanning a pronounced precipitation and soil phosphorus gradient in central Panama. For all traits considered except for leaf mass per area-mortality, leaf mass per area-growth, and leaf area-mortality relationships, we found widespread variation in the strength of trait-rate relationships across sites. For some traits, trait-rate relationships showed no overall trend but displayed wide site-to-site variation. In a small subset of cases, variation in trait-rate relationships was explained by soil phosphorus availability. Our results demonstrate that environmental gradients have the potential to influence how functional traits are related to growth and mortality rates, though much variation remains to be explained. Accounting for site-to-site variation may help resolve a fundamental issue in trait-based ecology – that traits are often weakly related to performance – and improve the utility of functional traits for explaining key ecological and evolutionary processes.</p>

opencc-zeroOct 2022View details →
dryad36/100

Intraspecific plant-soil feedback in four tropical tree species is inconsistent in a field experiment

<p>Soil microbes can influence patterns of diversity in plant communities via plant-soil feedbacks. Intraspecific plant-soil feedbacks occur when plant genotype causes variation in soil microbial composition, resulting in differences in the performance of seedlings growing near their maternal plants versus seedlings growing near non-maternal conspecific plants. How consistently such intraspecific plant-soil feedbacks occur in natural plant communities is unclear, especially under variable field conditions. We conducted an in situ experiment with four native tree species on Barro Colorado Island (BCI), Panama. Seedlings of each species were transplanted beneath their maternal tree or another conspecific tree in the BCI forest. Mortality and growth were assessed at the end of the wet season (~4 months post-transplant) and at the end of the experiment (~7 months post-transplant). Differences in seedling performance among field treatments were inconsistent among species and eroded over time. Effects of field environment were detected at the end of the wet season in two of the species: <em>Virola surinamensis</em> seedlings had higher survival beneath their maternal tree than other conspecific trees, while the opposite pattern was found in <em>Ormosia macrocalyx</em>. However, these differences disappeared by the end of the experiment. Our results suggest that intraspecific plant-soil feedbacks may not occur consistently under field conditions in tropical tree species and may have a limited role in determining seedling performance in tropical tree communities. Future studies are needed to elucidate the environmental and genetic factors that determine the incidence and direction of intraspecific plant-soil feedbacks in plant communities.</p>

opencc-zeroApr 2024View details →
zenodo36/100

Soil C saturation in tropical agricultural systems: soil fertility, climate, texture and N controls on SOM fractions

<p><span>Nature-based solutions for C sequestration in tropical croplands are paramount strategies in a changing climate. Soybean-maize-forage intercropped systems associated with soil acidity alleviation and nitrogen (N) fertilization effectively accumulate carbon (C) in weathered soils. However, the soil saturation status and the controls of C stabilization up to 40 cm soil depth into particulate (POM) and mineral-associated organic matter (MAOM) fractions are poorly understood in tropical croplands. This study tested the hypothesis of C saturation in the MAOM fraction by comparing field and published data focused on Brazilian tropical soils. We assessed the N fertilization effect on C stabilization in POM and MAOM pools, and climate, soil texture, and chemical attributes controls on POM and MAOM formation in tropical croplands using a machine-learning Random Forest (RF) modeling approach. Our findings do not support C saturation in the MAOM regardless of contrasting soil contents of silt plus clay and depths. C in the MAOM and POM fractions were not affected by N fertilizer. However, legume inclusion in the system resulted in higher total soil C and lower soil C:N ratio compared with N fertilization, which indicates that C dynamics differ whether synthetic or organic-N forms are applied to tropical croplands. Our RF model showed robust predictive performance for the MAOM but poorer for the POM fraction. Total organic carbon (TOC), total N, silt plus clay, phosphorus (P) and soil depth, zinc (Zn), cation exchange capacity, and TOC covariates were the most important variables for predicting MAOM and POM fractions, respectively. Our findings show, besides the well-known effect of calcium, that other nutrients such as P, Zn, manganese and copper play a key role in either MAOM or POM formation. Our work provides key insights on C saturation status in tropical croplands and land-management C sequestering potential, and N, climate, soil chemical attributes and texture controls on C distribution into MAOM and POM fractions up to 40 cm soil depth in tropical conservation agricultural systems. We advocate for upscaling C saturation concept nationally and further research investigating the role of climate and micronutrients contribution on MAOM and POM formation in broad scenarios and N fertilizer responsiveness for C stabilization in tropical croplands.</span></p>

opencc-by-4.0Jul 2024View details →
zenodo36/100

Data from: Spatial variation of soil CO2, CH4 and N2O fluxes across topographical positions in tropical forests of the Guiana Shield in Ecosystems

<p>Data from: Spatial variation of soil CO2, CH4 and N2O fluxes across topographical positions in tropical forests of the Guiana Shield in Ecosystems</p>

opencc-by-4.0Dec 2017View details →
zenodo36/100

Dataset from: Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest Biogeosciences 2019

<p>Dataset used for the manuscript <strong>Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest</strong> in Biogesciences, 2019</p>

opencc-by-4.0Jan 2019View details →
zenodo36/100

Measured properties in soil samples and marine sediment collected in Galion Bay (Martinique, France) in order to trace erosion sources in insular tropical catchments

<p>This dataset was compiled in order to select the optimal suite of tracers and identify and quantify the main sources of sediment deposited in Galion Bay and associated chlordecone transfers since the 1960s. It includes measured properties for potential sources collected across the Galion catchment (Martinique, France) and along a sediment core sampled in Galion Bay (GAL17-04, N°IGSN TOAE0000000573). Associated with this dataset, metadata are integrated for sources and targets registered using International Geological Sample Numbers (IGSN).</p>

opencc-by-4.0Nov 2023View details →
dryad36/100

Aboveground net primary productivity in regenerating seasonally dry tropical forest: contributions of rainfall, forest age, and soil

<p>Identifying factors controlling forest productivity is critical to understanding forest-climate change feedbacks, modeling vegetation dynamics, and carbon finance schemes. However, little research has focused on productivity in regenerating tropical forest which are expanding in their fraction of global area have an order of magnitude larger carbon uptake rates relative to older forest.</p> <p>We examined aboveground net primary productivity (ANPP) and its components (wood production and litterfall) over ten years in forest plots that vary in successional age, soil characteristics, and species composition using band dendrometers and litterfall traps in regenerating seasonally dry tropical forests in northwestern Costa Rica.</p> <p>We show that the components of ANPP are differentially driven by age and annual rainfall and that local soil variation is important. Total ANPP was explained by a combination of age, annual rainfall, and soil variation. Wood production comprised 35% of ANPP on average across sites and years, and was explained by annual rainfall but not forest age. Conversely, litterfall increased with forest age and soil fertility yet was not affected by annual rainfall. In this region, edaphic variability is highly correlated with plant community composition. Thus, variation in ecosystem processes explained by soil may also be partially explained by species composition.</p> <p>These results suggest that future changes in annual rainfall can alter the secondary forest carbon sink, but that this effect will be buffered by the litterfall flux which varies little among years. In determining the long-term strength of the secondary forest carbon sink, both rainfall and forest age will be critical variables to track. We also conclude that a detailed understanding of local site variation in soils and plant communities may be required to accurately predict the impact of changing rainfall on forest carbon uptake.</p> <p>Synthesis We show that in seasonally dry tropical forests, annual rainfall has a positive relationship with the growth of aboveground woody tissues of trees and that droughts lead to significant reductions in aboveground productivity. These results provide evidence for climate change – carbon cycle feedbacks in the seasonal tropics and highlight the value of longitudinal data on forest regeneration.</p>

opencc-zeroSep 2021View details →
zenodo36/100

Large contribution of recent photosynthate to soil respiration in tropical dipterocarp forest revealed by girdling

<b>Description: </b><p>The research site is one of the existing intensive carbon plots (Tower Plot) at the SAFE Project Experimental area. The area where the plot is located will be converted into oil palm plantation during 2015-2017 (for commercial purposes, not for research). The overarching aim of the project is to assess how the termination of the transport of sugars and defoliation alter forest ecosystem functioning and structure.The aim of the project is:<br>1. To quantify the contribution of photosynthate supply to soil respiration: via the contribution of roots and soil microbial communities utilising root-derived carbon.<br>2. To assess whether there is a relationship between root respiration and tree species.<br>To address these aims, we girdled trees in one half of the plot (0.5 ha), leaving the other half (0.5 ha) as a control. In girdling, a strip of bark (including cambium and phloem) was removed from around the trunk, with the aim of stopping the transport of sugars from the foliage into the roots and soil. The transport of sugars stop immediately, allowing us to quantify their role in the root and soil processes. The girdled trees will gradually defoliate and die due to the carbon starvation of the roots. We wish to emphasise that these trees would have been felled anyway during the conversion to oil palm - this project is not causing any additional deforestation.<br>The processes measured are:<br>- CO2 fluxes from soil measured with portable chambers from which a gas sample is drawn and analysed in the field with a portable instrument (CO2) <br>- Changes in tree circumference monitored with automatic dendrometer bands.<br>- Terrestrial laser scanning (T-lidar), non-destructive method to quantify the 3D structure of the forest stand.Pre-girdling data of all processes will be collected, starting at least two months before the girdling. The girdling took place in early 2016, and the monitoring continued for twelve months afterwards.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/28"><b>Tree girdling - BALI project</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC, the Ministry of Education, Youth and Sports of the Czech Republic (Grant, NE/K01627X/1, NE/G018278/1, INTER-TRANSFER LTT19018)</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><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.4 (3))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=5519572">here</a></p><p><b>Files: </b>This consists of 1 file: BALI_Nottingham_Girdling_Data_2021_rev.xlsx</p><p><b>BALI_Nottingham_Girdling_Data_2021_rev.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>CO2 and H2O data</b> (described in worksheet CO2_H2O_data)</p><p>Description: Tree identity and mortality collected taken January 2016- January 2017; Soil respiration, soil temperature and January moisture measurements taken January- March 2016 in a girdled tropical forest using a LiCor 8100a </p><p>Number of fields: 12</p><p>Number of data rows: 12548</p><p>Fields: </p><ul><li><b>PlotName</b>: reference to the experiment location within the SAFE plot network (experiment took place in the &#x27;Tower plot / SAF-05&#x27;&#x27;) (Field type: location)</li><li><b>daynight</b>: defined by 6pm to 6am (Field type: categorical)</li><li><b>date</b>: date of measurement (Field type: date)</li><li><b>plot</b>: subplot&#x27; in manuscript (Field type: id)</li><li><b>Rday</b>: relative data to the start of girdling (girdling day = 0) (Field type: id)</li><li><b>CO2</b>: soil CO2 efflux (Field type: numeric)</li><li><b>H2O</b>: soil volumetric moisture (Field type: numeric)</li><li><b>T</b>: soil temperature (Field type: numeric)</li><li><b>port</b>: refers to soil collar location (we allocated chamber port to soil collar location) (Field type: id)</li><li><b>portplot</b>: soil collar location nested within plot (Field type: id)</li><li><b>time</b>: time of measurement (24h) (Field type: numeric)</li><li><b>phase</b>: measurement period (see manuscript for phase definitions) (Field type: categorical)</li></ul></li><li><p><b>Tree mortality data</b> (described in worksheet Mortality_data)</p><p>Description: Tree census of trees surroudings the points where Licor 8100a measurements were taken</p><p>Number of fields: 20</p><p>Number of data rows: 259</p><p>Fields: </p><ul><li><b>PlotName</b>: reference to the experiment location within the SAFE plot network (experiment took place in the &#x27;Tower plot / SAF-05&#x27;&#x27;) (Field type: location)</li><li><b>ForestPlotsCode</b>: reference to the experiment location within the SAFE plot network (experiment took place in the &#x27;Tower plot / SAF-05&#x27;&#x27;) (Field type: id)</li><li><b>Subplot</b>: subplots 1-12 included in the manuscript (Field type: id)</li><li><b>CensusDate</b>: date when trees were originally measured (Field type: date)</li><li><b>TagNumber</b>: tree tag identity (Field type: id)</li><li><b>Height_m</b>: tree height (Field type: numeric)</li><li><b>Comments</b>: comments about the tree (Field type: comments)</li><li><b>Family</b>: tree family (Field type: taxa)</li><li><b>Genus</b>: tree genus (Field type: taxa)</li><li><b>SpeciesName</b>: tree species (Field type: comments)</li><li><b>WoodDensity</b>: wood density (Field type: numeric)</li><li><b>CrownProjection_Area_m2_in2016</b>: Crown Projection Area in 2016 (Field type: numeric)</li><li><b>X_m</b>: coordinates (Latitude) (Field type: numeric)</li><li><b>Y_m</b>: coordinates (Longitude) (Field type: numeric)</li><li><b>GirdlingDeathDate</b>: girdling tree death date (Field type: date)</li><li><b>Biomass_kgPerStem</b>: Biomass_kgPerStem (Field type: numeric)</li><li><b>Carbon_kgCperStem</b>: Carbon_kgCperStem (Field type: numeric)</li><li><b>mortality</b>: mortality (Field type: categorical)</li><li><b>DBHgrowth_cm_year</b>: DBHgrowth_cm_year (Field type: numeric)</li><li><b>DBHAnnualGrowthRate</b>: DBHAnnualGrowthRate (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2015-08-04 to 2017-02-07</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>&ensp;-&ensp; Plantae <br>&ensp;-&ensp;&ensp;-&ensp; Tracheophyta <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Magnoliopsida <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Lamiales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Lamiaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Callicarpa</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Rosales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Urticaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Pipturus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Dendrocnide</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Oreocnide</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Moraceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Ficus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Malpighiales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Achariaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Hydnocarpus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Euphorbiaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Macaranga</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Cephalomappa</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Mallotus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Phyllanthaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Aporosa</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Ixonanthaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Ixonanthes</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Calophyllaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Calophyllum</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Violaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Rinorea</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Ericales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Pentaphylacaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Adinandra</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Sapotaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Palaquium</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Symplocaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Symplocos</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Ebenaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Diospyros</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Malvales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Dipterocarpaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Shorea</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Dipterocarpus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Dryobalanops</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Parashorea</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Malvaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Pterospermum</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Scaphium</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Brownlowia</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Sterculia</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Microcos</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Neesia</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Diplodiscus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Laurales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Lauraceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Actinodaphne</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Litsea</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Eusideroxylon</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Celastrales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Celastraceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Lophopetalum</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Magnoliales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Myristicaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Knema</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Annonaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Goniothalamus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Polyalthia</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Maasia</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Vitales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Vitaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Leea</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Myrtales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Myrtaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Syzygium</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Lythraceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Duabanga</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Cornales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Cornaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Alangium</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Gentianales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Rubiaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Neolamarckia</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Neonauclea</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Urophyllum</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Pleiocarpidia</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Fabales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Fabaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Saraca</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Polygalaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Xanthophyllum</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Cucurbitales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Tetramelaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Octomeles</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Fagales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Fagaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Lithocarpus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Castanopsis</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Sapindales <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Sapindaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Nephelium</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Dimocarpus</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Pometia</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Meliaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Dysoxylum</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Aglaia</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Burseraceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Canarium</i> <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; Anacardiaceae <br>&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp; <i>Buchanania</i> <br></div><p></p>

opencc-by-4.0Sep 2021View details →
dryad36/100

Data for: Plant roots fuel tropical soil animal communities

<p>Belowground life relies on plant litter, while its linkage to living roots had long been understudied, and remains unknown in the tropics. Here, we analysed the response of 30 soil animal groups to root trenching and litter removal in rainforest and plantations in Sumatra, and found that roots are similarly important to soil fauna as litter. Trenching effects were stronger in soil than in litter, with an overall decrease in animal abundance in rainforest by 42% and in plantations by 30%. Litter removal little affected animals in soil layers, but decreased the total abundance by 60% in rainforest and rubber plantations but not in oil palm plantations. Litter and root effects on animal group abundances were explained by body size or vertical distribution. Our study quantifies principle carbon pathways in soil food webs under tropical land use, providing the basis for mechanistic modelling and ecosystem-friendly management of tropical soils.</p>

opencc-zeroDec 2022View details →
dryad36/100

Symbiotic nitrogen fixation does not stimulate soil phosphatase activity under temperate and tropical trees

<p>Symbiotic nitrogen (N)-fixing plants can enrich ecosystems with N, which can alter the cycling and demand for other nutrients. Researchers have hypothesized that fixed N could be used by plants and soil microbes to produce extracellular phosphatase enzymes, which release P from organic matter. Consistent with this speculation, the presence of N-fixing plants is often associated with high phosphatase activity, either in the soil or on root surfaces, although other studies have not found this association, and the connection between phosphatase and rates of N fixation—the mechanistic part of the argument—is tenuous. Here, we measured soil phosphatase activity under N-fixing trees and non-fixing trees transplanted and grown in tropical and temperate sites in the USA: two sites in Hawaii, and one each in New York and Oregon. This provides a rare example of phosphatase activity measured in a multi-site field experiment with rigorously quantified rates of N fixation. We found no difference in soil phosphatase activity under N-fixing vs. non-fixing trees nor across rates of N fixation, though we note that no sites were P limited and only one was N limited. Our results add to the literature showing no connection between N fixation rates and phosphatase activity.</p>

opencc-zeroFeb 2023View details →
dryad36/100

Soil nutrient dissimilarity and litter nutrient limitation as major drivers of home field advantage in riparian tropical forests

<p><span>Decomposition is a key process driving carbon and nutrient cycling in ecosystems worldwide. The home field advantage effect (HFA) has been found to accelerate decomposition rates when litter originates from "home" when compared to other ("away") sites. It is still poorly known how HFA plays out in tropical, riparian forests, particularly in forests under restoration. We carried out three independent reciprocal litter transplant experiments to test how litter quality, soil nutrient concentrations and successional stage (age) influenced HFA in tropical riparian forests. These experimental areas formed a wide gradient of soil and litter nutrients, which we used to evaluate the more general hypothesis that HFA varies with dissimilarity in soil nutrients and litter quality. We found that HFA increased with soil nutrient dissimilarity, suggesting that litter translocation uncouples relationships between decomposers and litter characteristics; and with litter N:P, indicating P limitation in this system. We also found negative HFA effects at a site under restoration that presented low decomposer ability, suggesting that forest restoration does not necessarily recover decomposer communities and nutrient cycling. Within each of the independent experiments, the occurrence of HFA effects was limited and their magnitude was not related to forest age, nor soil and litter quality. Our results imply that HFA effects in tropical ecosystems are influenced by litter nutrient limitation and soil nutrient dissimilarity between home and away sites, but to further disentangle major HFA drivers in tropical areas, a gradient of dissimilarity between litter and soil properties must be implemented in future experimental designs.</span></p>

opencc-zeroMar 2023View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record