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519 results for “organic soil”
Interior Alaska managed sites: soil organic layer and mineral soil nutrient concentrations and additional characteristics measured one time in either summer 2012 or 2013
This dataset contains soil organic layer and mineral soil depth, C and N concentrations, and other soil characteristics collected at all managed and adjacent unmanaged study sites.
Interior Alaska managed sites: soil organic layer depth and thaw depth measured one time in either summer 2012 or 2013
This dataset contains estimates of soil organic layer depth and thaw depth along transects measured at a suite of managed and unmanaged areas in interior Alaska in summers 2012 and 2013.
Environmental fate of combustion-derived organic compounds in arid, urban soils in central Arizona-Phoenix
In this research we wanted to ask, what is the magnitude, distribution, and fate of non-point carbon pollution in a low-density, urban area? To answer this question, the goal of the project is to: 1) characterize and quantify combustion-derived carbon compounds in soils near roadways across the Phoenix valley, 2) explore the dynamic fate of Polycyclic Aromatic Hydrocarbons (PAHs) in soils, and 3) assess the importance of microbial community structure in PAH storage and dynamics. We collected 63 soil samples near highways across the Phoenix valley to characterize and quantify PAH compounds using extraction methods with Ultrasound Sonication and analyses by Gas Chromatography-Mass Spectrometry (GC-MS). Additionally, we measured a suite of soil properties and processes to explore the role of abiotic and biotic factors on the retention of PAHs in urban soil. PAH concentrations in arid Phoenix soils ranged from 52 ug/kg dry soil to 8,296 ug/kg (mean 926 ug/kg), nearly an order of magnitude lower on average than expected based on data from other cities (Figure 1, Table 1). The project findings show the extent of these pollutants, an EPA-priority group of hazardous compounds, in urban soils. Although the most likely sources for PAH content in roadway soils are vehicle emissions, they were not correlated with traffic density (r2 = 0.024; p = 0.26) or highway age (r2 = 0.044; p = 0.13) across all sites. However, PAH concentrations were significantly correlated to soil organic matter (r2 = 0.36; p less than 0.001). These results suggest that PAH concentrations in roadway soils of desert cities may be controlled by factors associated with carbon retention, such as soil organic matter, rather than source or rate of deposition
Plant and soil organic matter responses to ten years of nutrient enrichment in the Nutrient Network:Nutrient Network. A cross-site investigation of bottom-up control over herbaceous plant community dynamics and ecosystem function
This experiment is one implementation of a globally distributed experiment, known as the Nutrient Network. At Cedar Creek, as in over 70 other sites in grasslands around the world, the experiment aims to describe impacts of increased nutrients (nitrogen, phosphorus, potassium, sulfur and other metals) and decreased herbivory (removal of mammals by fencing). Two overarching questions are being explored with these manipulations: 1. To what extent are plant production and diversity co-limited by multiple nutrients in herbaceous-dominated communities? 2. Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? By utilizing identical protocols at diverse grassland sites around the world, NutNet aims to uncover both the generalities in ecosystem functioning, and the contingencies or differences which can obscure those common mechanisms. In addition to the standard NutNet protocol, e247 includes an additional low Nitrogen gradient (1 gram Nitrogen per meter squared per year and 5 grams Nitrogen per meter squared per year in addition to the standard 10 grams Nitrogen per meter squared per year).
McMurdo Dry Valleys Human Disturbance Effects on soils - Soil Organism
Concerns over environmental disturbance in the McMurdo Dry Valleys are increasing with increasing foot traffic from tourists and scientist. The effect of pedestrian disturbance were monitored by comparing the species composition, depth distribution and soil properties between adjacent high-, low- and no- traffic sites. This study began in the austral summer 1995/1996.
SGS-LTER Transect Study - Organic Carbon in Soils across Toposequences on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-1984
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. CPER SOC across Toposequences - Pedons and their corresponding topography were described along an 8 km transect oriented normal to the major drainages of the CPER. A total of 140 pedons representing 23 toposequences and 7 plains segments were characterized. Sampling sites were selected within toposequences according to slope position (summit, shoulder, backslope, footslope, toeslope) and within plains segments at approximate 100 m intervals. Pedons were described and sampled by genetic horizon according to the standards of the National Cooperative Soil Survey. Analyses, conducted at Colorado State University, included particle size and organic C. Bulk density was estimated empirically according to: Rawls, W.J. 1983. Estimating soil bulk density form particle size analysis and organic matter content. Soil Sci 135: 123-125. Organic C accumulation was measured along an 8 km transect at a site in the semiarid shortgrass steppe of northeastern Colorado. Specific objectives of the study were to (I) measure the quantity and distribution of organic C across toposequences, (ii) test the hypothesis that a disproportionate amount of soil organic C resides in the lowlands (as defined herein), and (iii) assess the role of geomorphic history as a determinant of contemporary rates o
Upper Phillips Creek soil organic content and bulk density April, 2017
Soil samples were taken in Phillips Creek Marsh (near Nassawadox, VA) and organic fraction and bulk density determined.
Marsh soil Organic Matter on Nine Marshes on the Virginia Coast, 2016-2019
Organic matter content in nine salt marshes along the Atlantic Coast of the Delmarva Peninsula was determined by collecting soil cores followed by loss-on-ignition techniques. Two soil cores were collected at randomly selected spots within each of the 2016-2019 elevation sampling grids.
Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production and soil organic carbon
<p>This data set contains a data-mining performed to assess the impact of intercropping, tillage and fertilizer type on soil organic carbon and crop yield in arable crops from four selected European pedoclimatic regions and typical cropping systems in the Atlantic, Boreal, Mediterranean North, and Mediterranean South regions. A further meta-analysis was performed with these data. </p> <p>These data correspond to the open-access articles:</p> <p>- Diversified Arable Cropping Systems and Management Schemes in Selected European Regions Have Positive Effects on Soil Organic Carbon Content. Agriculture 2019, 9, 261. https://www.mdpi.com/2077-0472/9/12/261?type=check_update&version=2</p> <p>- Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production. Agronomy 2020, 10, 297; doi:10.3390/agronomy10020297. https://www.mdpi.com/2073-4395/10/2/297</p> <p>- Deficit Drip Irrigation in Processing Tomato Production in the Mediterranean Basin: A Data Analysis for Italy. Agriculture 2019, 9, 79; doi:10.3390/agriculture9040079. https://www.mdpi.com/2077-0472/9/4/79?type=check_update&version=2</p> <p>The research and publications have been funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
Dielectric spectra of thawed and frozen wet organic Arctic soils
<p>The submitted files contain laboratory data dielectric spectra measurements for six Arctic organic soils. The data are presented in the frequency dependences form of the refractive index and normalized attenuation coefficient obtained at various moistures and temperatures of soil samples.</p>
Data from: Leaching losses of dissolved organic carbon and nitrogen from agricultural soils in the upper US Midwest
<p>Leaching losses of dissolved organic carbon (DOC) and nitrogen (DON) from agricultural systems are important to water quality and carbon and nutrient balances but are rarely reported; the few available studies suggest linkages to litter production (DOC) and nitrogen fertilization (DON). In this study we examine the leaching of DOC, DON, NO<sub>3</sub><sup>-</sup>, and NH<sub>4</sub><sup>+</sup> from no-till corn (maize) and perennial bioenergy crops (switchgrass, miscanthus, native grasses, restored prairie, and poplar) grown between 2009 and 2016 in a replicated field experiment in the upper Midwest U.S. Leaching was estimated from concentrations in soil water and modeled drainage (percolation) rates. DOC leaching rates (kg ha<sup>-1 </sup>yr<sup>-1</sup>) and volume-weighted mean concentrations (mg L<sup>-1</sup>) among cropping systems averaged 15.4 and 4.6, respectively; N fertilization had no effect and poplar lost the most DOC (21.8 and 6.9, respectively). DON leaching rates (kg ha<sup>-1 </sup>yr<sup>-1</sup>) and volume-weighted mean concentrations (mg L<sup>-1</sup>) under corn (the most heavily N-fertilized crop) averaged 4.5 and 1.0, respectively, which was higher than perennial grasses (mean: 1.5 and 0.5, respectively) and poplar (1.6 and 0.5, respectively). NO<sub>3</sub><sup>-</sup> comprised the majority of total N leaching in all systems (59-92%). Average NO<sub>3</sub><sup>-</sup> leaching (kg N ha<sup>-1</sup> yr<sup>-1</sup>) under corn (35.3) was higher than perennial grasses (5.9) and poplar (7.2). NH<sub>4</sub><sup>+</sup> concentrations in soil water from all cropping systems were relatively low (<0.07 mg N L<sup>-1</sup>). Perennial crops leached more NO<sub>3</sub><sup>-</sup> in the first few years after planting, and markedly less after. Among the fertilized crops, the leached N represented 14-38% of the added N over the study period; poplar lost the greatest proportion (38%) and corn was intermediate (23%). Requiring only one third or less of the N fertilization compared to corn, perennial bioenergy crops can substantially reduce N leaching and consequent movement into aquifers and surface waters.</p>
FAOSTAT GHG Emissions from Organic Soils
<p>The FAOSTAT domain “Cultivation of Organic soils” contains estimates of nitrous oxide (N<sub>2</sub>O) emissions associated with the drainage of organic soils <em>–</em> using <em>histosols </em>as proxy <em>–</em> for agriculture. Data are computed geospatially, using the Tier 1 default factors of the Intergovernmental Panel on Climate Change (IPCC, 2006). Estimates are available by country, by FAOSTAT regional aggregation and special group, including the Annex I and Non-Annex I Parties to the United Nations Framework Convention on Climate Change (UNFCCC), and with global coverage for the period 1990–2019, with estimates for 2030 and 2050.</p> <p>The FAOSTAT domain “Cultivation of Organic soils” disseminates N<sub>2</sub>O emissions, implied emission factors and underlying activity data, i.e. area (in ha) of organic soils drained for agriculture. Drainage and associated emissions are assessed separately for IPCC land use categories cropland and grassland, corresponding to FAO land use categories ‘’cropland’’ and ‘’permanent meadows and pastures.’’ GHG estimates are available in N<sub>2</sub>O and in CO<sub>2</sub> equivalent (CO<sub>2</sub>eq). Conversion to CO<sub>2</sub>eq is made via Global Warming Potentials (GWP) coefficients. Results are disseminated separately for three different options currently in use in reporting, namely GWPs from: <em>a)</em> IPCC Second Assessment Report (SAR)(IPCC, 1996); <em>b)</em> IPCC Fourth Assessment Report (AR4) (IPCC, 2007); and <em>c)</em> IPCC Fifth Assessment Report (AR5)(IPCC, 2014). The original FAOSTAT domains can be found here:</p> <p>Cultivation of Organic Soils: <a href="http://www.fao.org/faostat/en/#data/GV">http://www.fao.org/faostat/en/#data/GV</a></p> <p>Drained Organic Soils on cropland: <a href="http://www.fao.org/faostat/en/#data/GC">http://www.fao.org/faostat/en/#data/GC</a> and</p> <p>Drained Organic Soils on grassland: <a href="http://www.fao.org/faostat/en/#data/GC">http://www.fao.org/faostat/en/#data/G</a>G</p> <p> </p> <p>The FAOSTAT emissions estimates may not coincide with GHG data reported by member countries to relevant international reporting processes. The aim of this domain is to provide a global reference database for assessing regional and global trends and in support of national data quality/data assurance processes.</p>
Xylomelum occidentale (Proteaceae) accesses relatively mobile soil organic phosphorus without releasing carboxylates
<p>1. Hundreds of Proteaceae species in Australia and South Africa typically grow on phosphorus (P)-impoverished soils, exhibiting a carboxylate-releasing P-mobilising strategy. In the Southwest Australian Biodiversity Hotspot, two <i>Xylomelum </i> (Proteaceae) species are widely distributed, but restricted within that distribution.</p> <p>2. We grew <i>X. occidentale</i> in hydroponics at 1 μM P. Leaves, seeds, rhizosheath and bulk soil were collected in natural habitats.</p> <p>3. <i>Xylomelum occidentale</i> did not produce functional cluster roots and occupied soils that are somewhat less P-impoverished than those in typical Proteaceae habitats in the region. Based on measurements of foliar manganese concentrations (a proxy for rhizosphere carboxylate concentrations) and P fractions in bulk and rhizosheath soil, we conclude that <i>X. occidentale</i> accesses organic P, without releasing carboxylates. Solution <sup>31</sup>P-NMR revealed which organic P forms <i>X. occidentale</i> accessed.</p> <p>4. <i>Xylomelum occidentale</i> uses a strategy that differs fundamentally from that typical in Proteaceae, accessing soil organic P without carboxylates. We surmise that this novel strategy is likely expressed also in co-occurring non-Proteaceae that lack a carboxylate-exuding strategy, and plants in similar habitats. These co-occurring species are unlikely to benefit from mycorrhizal associations, because plant-available soil P concentrations are too low.</p> <p>5. <i>Synthesis.</i> Our findings show the first field evidence of effectively utilising soil organic P by <i>X. occidentale</i> without carboxylate exudation and explain their relatively restricted distribution in an old P-impoverished landscape, contributing to a better understanding of how diverse P-acquisition strategies coexist in a megadiverse ecosystem.</p>
iSDAsoil: soil organic carbon for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil organic carbon (C) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.oc_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil organic Carbon mean value,</li> <li>sol_log.oc_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil organic Carbon model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.oc R-square: 0.791 Fitted values sd: 0.716 RMSE: 0.369 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.1517 -0.1900 -0.0060 0.1793 4.2621 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.821657 0.794000 2.294 0.0218 * regr.ranger 1.047507 0.005146 203.571 <2e-16 *** regr.xgboost -0.005943 0.005340 -1.113 0.2657 regr.cubist 0.052084 0.004884 10.664 <2e-16 *** regr.nnet -0.867384 0.359213 -2.415 0.0158 * regr.cvglmnet -0.050157 0.003863 -12.984 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3687 on 122457 degrees of freedom Multiple R-squared: 0.7906, Adjusted R-squared: 0.7906 F-statistic: 9.248e+04 on 5 and 122457 DF, p-value: < 2.2e-16 </code></pre> <p>To back-transform values (y) to g/kg use the following formula:</p> <pre><code>g/kg = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil total organic Nitrogen for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil total organic Nitrogen (N) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.n_tot_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total N mean value,</li> <li>sol_log.n_tot_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total N model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.n_tot_ncs R-square: 0.732 Fitted values sd: 0.326 RMSE: 0.197 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -1.87298 -0.09584 -0.00985 0.07613 3.14728 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.267429 0.493235 0.542 0.588 regr.ranger 1.128208 0.005766 195.669 < 2e-16 *** regr.xgboost -0.048780 0.006108 -7.987 1.4e-15 *** regr.cubist 0.143954 0.004424 32.539 < 2e-16 *** regr.nnet -0.482261 0.797938 -0.604 0.546 regr.cvglmnet -0.170889 0.004955 -34.489 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1972 on 99249 degrees of freedom Multiple R-squared: 0.7319, Adjusted R-squared: 0.7319 F-statistic: 5.419e+04 on 5 and 99249 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to g/kg use the following formula:</p> <pre><code>g/kg = expm1( y / 100 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Impact of cellulose-rich organic soil amendments on growth dynamics and pathogenicity of Rhizoctonia solani
<p>Cellulose-rich amendments stimulate saprotrophic fungi in arable soils. This may increase competitive and antagonistic interactions with root-infecting pathogenic fungi, resulting in lower disease incidence. However, cellulose-rich amendments may also stimulate pathogenic fungi with saprotrophic abilities, thereby increasing plant disease severity. The current study explores these scenarios, with a focus on the pathogenic fungus <i>Rhizoctonia solani</i>. Saprotrophic growth of <i>R. solani</i> on cellulose-rich materials was tested in vitro. This confirmed paper pulp as a highly suitable substrate for <i>R. solani</i>, whereas its performance on wood sawdusts varied with tree species. In two pot experiments, the effects of amendment of <i>R. solani</i>-infected soil with cellulose-rich materials on performance of beetroot seedlings were tested. All deciduous sawdusts and paper pulp stimulated soil fungal biomass, but only oak, elder and beech sawdusts reduced damping-off of beetroot. Oak sawdust amendment gave a consistent stimulation of saprotrophic Sordariomycetes fungi and of seedling performance, independently of the time between amendment and sowing. In contrast, paper pulp caused a short-term increase in <i>R. solani</i> abundance, coinciding with increased disease severity for beet seedlings sown immediately after amendment. However, damping-off of beetroot was reduced if plants were sown two or four weeks after paper pulp amendment. Cellulolytic bacteria, including <i>Cytophagaceae</i>, responded to paper pulp during the first two weeks and may have counteracted further spread of <i>R. solani</i>. The results showed that fungus-stimulating, cellulose-rich amendments have potential to be used for suppression of <i>R. solani</i>. However, such amendments require a careful consideration of material choice and application strategy.</p>
Supplementary Data for "Organic matter preservation in ancient soils of Earth and Mars"
<p>Global compilation of organic carbon content (TOC) of paleosols (ancient soils) throughout the geological record on Earth.</p> <p> Pleistocene (1 Ma) to the Archean (3.7 Ga)!</p> <p> </p>
A meta-analysis reveals increases in soil organic carbon following the restoration and recovery of croplands in Southwest China
<p>In China, the Grain for Green Program (GGP) is an ambitious project to convert croplands into natural vegetation, but exactly how changes in vegetation translate into changes in soil organic carbon remains less clear. Here we conducted a meta-analysis using 734 observations to explore the effects of land recovery on the soil organic carbon and nutrients in 4 provinces in Southwest China. Following GGP, the soil organic carbon content (SOCc) and soil organic carbon storage (SOCs) increased by 33.73% and 22.39%, respectively. Likewise, soil nitrogen increased, while phosphorus decreased. Outcomes were heterogeneous, however, depending on variation in soil and environmental characteristics. Both the regional land use and cover change indicated by landscape type transfer matrix and net primary production from 2000 to 2020 further confirmed that GGP promoted the forest area (2.95%) and regional mean net primary production (52.94%). Our findings suggest that GGP could enhance soil and vegetation carbon sequestration in Southwest China and help to develop carbon neutral strategy.</p>
Data for article Pesticide seed dressings can affect the activity of various soil organisms and reduce decomposition rate of plant material, BMC Ecology
<p>Raw data for article "Pesticide seed dressings can affect the activity of various soil organisms and reduce decomposition rate of plant material" published in BMC Ecology</p>
Ecoregional patterns of protist communities in mineral and organic soils: assembly processes, functional traits and diversity of testate amoebae in Northern Eurasia
<p>TA_Traits.csv file includes trait data for testate amoebae. TA_Abundance.csv file includes environmental variables (soil type and region) and relative abundance data of testate amoebae.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.