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74 results for “deep soils”
Data from: Dynamics of deep soil carbon - insights from 14C time series across a climatic gradient
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Data from: Above and belowground responses of four tundra plant functional types to deep soil heating and surface soil fertilization
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Figures for "California forest die-off linked to multi-year deep soil drying in 2012-2015 drought"
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Deep core soil samples from KBS LTER Scaleup fields at the Kellogg Biological Station, Hickory Corners, MI (2010 to 2010)
Dataset Abstract This dataset contains soil deep core data from Kravchenko, A. N., S. S. Snapp, and G. P. Robertson. 2017. Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems. Proceedings of the National Academy of Sciences USA 114:926-931. https://doi.org/10.1073/pnas.1612311114 Additional data at https://doi.org/10.5061/dryad.gh90f5f original data source http://lter.kbs.msu.edu/datasets/171
Dataset for deep soil environment shapes alpine ecosystem productivity
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Effect of earth-air on water transport in the deep soil of the Loess Plateau
<p>Date availability in the date in brief.</p>
Figure 6 in Deep soil floatation in Chile reveals diverse and mainly nameless fauna of endogean beetles (Coleoptera)
Figure 6. Maximum Likelihood DNA barcode tree of 85 non-Staphylinidae endogean beetles of Chile. Families are colour coded. Terminal names consist of specimen number, the family name (superfamily for specimens GR0312 and GR0313), the most detailed current taxonomic identification (species, genus, tribe, or subfamily), sample number, length of the DNA barcode fragment [with the number of ambiguously read bases in angle brackets], BIN number [if applicable, also denoted on the tree with black dots], and GenBank accession number. Digits at internodes are rapid bootstrap values of 50 % and above.
Figure 1 in Deep soil floatation in Chile reveals diverse and mainly nameless fauna of endogean beetles (Coleoptera)
Figure 1. (A) map of central Chile between Santiago and Valdivia showing the 15 localities where 50 deep soil samples were taken; (B) a pit producing the soil sample CH01 (note the piolet used for digging, a sifter used to sift the soil, and one bag of sifted soil ready to be floated in the barrel with water); (C) the floatation process, with the floating fraction scooped by a kitchen sieve and deposited on a fine mesh on the ground; (D) a standard floated sample after rinsing in water and before being wrapped in two additional layers of thicker cloth; (E) two plastic boxes used for sample transportation and temperature/humidity management, each containing 16 floated samples; (F) Sundriven specimen extraction with floated samples spread on chicken wire and placed on top of aluminium pans (note on the background a funnel suspended from a tree, through which water from all pans was filtered daily).
Deep Core Soil Saturation Extracts
*We have hypothesized that large rhizobial population densities can occur at considerable depths in woody legume systems where deep moisture also occurs. However, associated with deep soil environments are low concentrations of soil nutrients that might affect nodulation and also limit survival of free-living rhizobia. The objectives of this study were to (1) determine if results from a previous study of a mesquite woodland utilizing groundwater in the Californian Sonoran desert were generizable to mesquite systems in other deserts where root depth varied with ecosystem type and (2) examine possible relationships of soil properties and host-plant phenology to rhizobial concentrations. Data set contains analyses for SO4, sodium, calcium, manganese, sodium-absorption-ration, total cations, electrical conductivity, pH, saturation percentage, total carbon, inorganic carbon, organic carbon, and gravimetric soil moisture.
Figure 1 in Endogean beetles (Coleoptera) of illustrated DNA barcode library Guatemala: deep soil sampling and
Figure 1. Map of the southern part of Guatemala showing the localities of the 26 deep soil samples.
Data and code used in the article "A deep learning method for predicting soil moisture in unsaturated areas based on physical constraints"
<p>Data and code used in the article "A deep learning method for predicting soil moisture in unsaturated areas based on physical constraints", specifically included are water content data from 55 in situ observations for the years 2018-2020 (observation frequency of 5min or 10min), and example code for implementing LSTM and PIDL using python (mainly the tensorflow library).These data can help the reader to better understand and replicate our research. All the data and code has been uploaded. </p><p>The paper has been published in <i>Water Resources Research</i>, and the citation is: </p><p>Wang, Y., Wang, W., Ma, Z., Zhao, M., Li, W., Hou, X., et al. (2023). A deep learning approach based on physical constraints for predicting soil moisture in unsaturated zones. <i>Water Resources Research</i>,<i>59</i>, e2023WR035194. https://doi.org/10.1029/2023WR035194</p>
The degree and depth limitation of deep soil desiccation and its impact on xylem hydraulic conductivity in dryland tree plantations
<p>All the basic data covered in the paper.</p>
Deep soil desiccation hinders nitrate leaching to groundwater in the global largest apple cultivation area
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Soil Moisture Forecasting integrating Physical-based model and Deep Learning
<p>Dataset used in "Soil Moisture Forecasting integrating Physical-based model and Deep Learning".</p> <p>(1) <strong>1-24.tar</strong> is training/test data (after preprocessing) over 24 sub-regions in China.</p> <p>(2) <strong>GFS*</strong> is 3-day forecast of Global Forecast System (GFS) over 2015-2017 and 2018 years.</p> <p>(3) <strong>auxiliary.json</strong> is utility data (e.g., land mask for sub-task).</p> <p>(4) <strong>valid_data.tar</strong> contains 2018 year of SoMo.ml, ERA5-Land, SMOS L3, LPRM-AMSR2, which were used to triple collocation analysis in our study. The CMA in-situ datasets only could be available from us after certain permission in CMA.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.