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106 results for “vertical distribution”
Data from: The vertical distribution and control of microbial necromass carbon in forest soils
<p><span><b>Aim:</b> Forest soils contain large amounts of terrestrial organic carbon (C), but the formation pathway of soil organic C (SOC) remains unclear. Recent evidence suggests that microbial necromass is a significant source of SOC, yet a global quantitative assessment across the whole-soil profile is lacking. We aimed to assess the vertical distribution and control of microbial-derived SOC in forest soils.</span></p> <p><span><b>Location:</b> Global forests.</span></p> <p><span><b>Time period:</b> 1996-2019.</span></p> <p><span><b>Major taxa studied:</b> Soil microbial necromass carbon.</span></p> <p><span><b>Methods:</b> We evaluated the proportions of fungal and bacterial necromass C in total SOC in the litter layer, O horizon soil, and various depths of mineral soil in forests using microbial biomarker (glucosamine and muramic acid) data.</span></p> <p><span><b>Results:</b> The total microbial necromass C increased significantly with soil depth, ranging from 30% of SOC in O horizon soil to 62% of SOC in mineral soils below 50 cm. However, only bacterial necromass C followed this increasing trend with soil depth; fungal necromass C showed little variation across the whole-soil profile. Higher fungal and bacterial necromass C was observed in soils with lower C/N ratios and smaller aggregate sizes. Soil C/N ratio and microbial biomass C dominantly determined microbial necromass C in surface soil (above 20 cm), but soil clay content was the primary factor in subsoil (below 20 cm).</span></p> <p><span><b>Main conclusions: </b>Microbial necromass C accounted for high percentages of the total SOC in forest soils (particularly at depths >20 cm), but its long-term stabilization may be governed by different mechanisms at different soil horizons. Substrate quality regulates microbial activity and then controls biomass turnover in surface soil, while aggregate occlusion could facilitate mineral protection of microbial necromass C in subsoil. These differential controls of microbial-derived organic C could be applied in Earth system studies for predicting soil organic C dynamics in forests.</span></p>
Figure 4 from: Balestra V, Lana E, Carbone C, De Waele J, Manenti R, Galli L (2021) Don't forget the vertical dimension: assessment of distributional dynamics of cave-dwelling invertebrates in both ground and parietal microhabitats. Subterranean Biology 40: 43-63. https://doi.org/10.3897/subtbiol.40.71805
Figure 4 Biodiversity indices for wall and ground cave fauna.
Data from: Vertical root distribution of individual species in a mountain grassland community: does it respond to neighbours?
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Data from: Effects of fertilizer on inorganic soil N in East Africa maize systems: vertical distributions and temporal dynamics
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Data from: The vertical distribution and control of microbial necromass carbon in forest soils
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Distributed Anomaly Detection using 1-class SVM for Vertically Partitioned Data
There has been a tremendous increase in the volume of sensor data collected over the last decade for different monitoring tasks. For example, petabytes of earth science data are collected from modern satellites, in-situ sensors and different climate models. Similarly, huge amount of flight operational data is downloaded for different commercial airlines. These different types of datasets need to be analyzed for finding outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only due to the massive volume of data, but also because these datasets are physically stored at different geographical locations with only a subset of features available at any location. Moving these petabytes of data to a single location may waste a lot of bandwidth. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the entire data without moving all the data to a single location. The method we propose only centralizes a very small sample from the different data subsets at different locations. We analytically prove and experimentally verify that the algorithm offers high accuracy compared to complete centralization with only a fraction of the communication cost. We show that our algorithm is highly relevant to both earth sciences and aeronautics by describing applications in these domains. The performance of the algorithm is demonstrated on two large publicly available datasets: (1) the NASA MODIS satellite images and (2) a simulated aviation dataset generated by the ‘Commercial Modular Aero-Propulsion System Simulation’ (CMAPSS).
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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.