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2,078 results for “soil data”
Detection of Bacillus anthracis DNA in complex soil and air samples using next-generation sequencing [NimbleGen Array data]
GEO Series GSE48316. synthetic construct; Bacillus anthracis str. Ames. 24 samples. Type: Other.
deodara and permafrost soil data set of high altitude
<p>This is 16s rRNA amplicon sequences obtained by Dr. Ashwani Kumar</p>
The soil microbiome contributes to the adaptation of grassland plant species to increasingly persistent precipitation regimes by inducing transcriptomic, metabolic, and structural changes: Extra Data
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STEROPES - Dataset 19: Collected soil data, ILVO
<p>Dataset 19: Collected soil data, ILVO (Belgium)</p> <p><strong>Origin: </strong>First field trial on BOPACT LTE (S15) and on field trial S2 in Gavere, 16 samples from topsoil 0-10 cm; in 5m-radius around center of each plot): soil carbon and soil moisture). Second field trial, 20 samples from top soil 0-10 cm during 2021 and 2022 collected and soil organic carbon measured. </p> <p><strong>Purpose and objectives:</strong> to analyse the input of plant residue in SOC prediction from remote sensing data under WP4. </p> <p><strong>Format</strong>: Data in the format of CSV . </p> <p><strong>Data utility: </strong>Data is collected based on the experiment where each plot has different amount of plant residual from without plant residual till completely covered with plant residual. </p>
Supplementary Data for Manuscript "A relation of resistivity-hydraulic conductivity for fine-grained soil based on coupled electric double layer model and modified Kozeny-Carman model"
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UJA_OPEN_Antibiotics Data SOIL O-LIVE PROJECT 2024 RP1
<p>Database with Antibiotics Data within the frame of the WP2 Diagnosis phase in SOIL O-LIVE PROJECT</p>
UJA_UNIPA_CNR_UAG_ELGO-D_NSA-M_HMU_OPEN_Soil Parameters Data SOIL O-LIVE PROJECT 2024 RP1
<p>Database with Soil Parameters Data within the frame of the WP2 Diagnosis phase in SOIL O-LIVE PROJECT</p>
Data from: The potential of biochar in improving drainage, aeration and maize yields in heavy clay soils
[No abstract entered]
Modelling and mapping heavy metal accumulation in moss and natural surface soil throughout Norway (1990-2010), link to research data and scientific software
<p>Research data and scientific software related to an investigation of statistical relations between the accumulation of heavy metals in moss and natural surface soil and potential influencing factors such as atmospheric deposition. Data were collected in 1995, 2000, 2005 and 2010 throughout Norway. Statistical correlations of a set of potential predictors (elevation, precipitation, density of different land uses, population density, physical properties of soil) with concentrations of cadmium, mercury and lead in moss and natural surface soil were evaluated. Spatio-temporal trends were estimated by use of multivariate regression-kriging and generalized linear models.</p>
Correlating heavy metal deposition and respective concentration in moss and natural surface soil for ecological land classes in Norway (1990-2010), link to research data and scientific software
<p>Research data and scientific software related to a study on statistical correlations between modelled atmospheric heavy metal deposition and respective accumulation in moss and natural surface soil for different natural landscapes in Norway. Data on cadmium, lead, and mercury were collected in 1995, 2000, 2005 and 2010 throughout Norway. The landscape information was derived from the Ecological Land Classification of Europe. Correlations between concentration and respective modelled deposition data were computed for each land class.</p>
Dataset for "EA-ERT: a new ensemble approach to convert ERT data to soil moisture"
<p>This dataset supports the research study "EA-ERT: a new ensemble approach to convert ERT data into soil moisture" by B. Loiseau, S. D. Carrière, N. K. Martin-StPaul, R. Clément, C. Champollion, V. Mercier, J. Thiesson, S. Pasquet, C. Doussan, T. Hermans and D. Jougnot.</p> <p>We provide electrical resistivity tomograph (ERT) time-lapse data for the two sites studied (Avignon and Larzac), with associated volumetric water contents measured by the probes in the field. Meanwhile, we also offer the R code for the calculations of each step of the EA-ERT method. </p>
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>
TRACE-Soils, LTEs soil physical data
<p>This dataset contains the measured soil physical parameters (weight at sampling, at -30, -60 and -100 hPa matric potential and after oven-drying, Darcian and apparent air permeability, and relative gas diffusivity) measured on 100-cm3 cylindrical samples from 0-10 and 10-20 cm depth from seven European long-term tillage experiments. </p>
Data from: The potential of biochar in improving drainage, aeration and maize yields in heavy clay soils
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SMAPVEX08 Soil Texture Data V001
This data set contains soil texture data that were extracted from a multi-layer soil characteristics database for the conterminous United States and generated for each regional study area. Data are representative of the conditions present in the regional study areas during the general timeline of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.
Soil Moisture Active Passive (SMAP) L1-L3 Ancillary GEOS Data V001
This ancillary SMAP product contains three dynamic GMAO GEOS-5 modeled data sets. Each data set contains surface and atmospheric parameters pertinent to SMAP provided in 1) hourly, 2) 3-hour, and 3) averaged over 3-hour intervals.
SMAPVEX12 Probe-Based In Situ Soil Moisture Data for Forest Area V001
This data set contains in situ soil moisture data collected at several forested sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007
SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.
SMAPVEX16 Manitoba Soil Texture Data V001
This data set contains soil texture data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.
GPM GROUND VALIDATION ENVIRONMENT CANADA (EC) PASSIVE MICROWAVE RADIOMETER AND SOIL MOISTURE-TEMPERATURE DATA GCPEX V1
The GPM Ground Validation Environment Canada (EC) Passive Microwave Radiometer and Soil Moisture-Temperature Data GCPEx dataset is consisted of data during the GPM Cold-season Precipitation Experiment (GCPEx) at the Centre for Atmospheric Research Experiments (CARE) site in Ontario, Canada during the winter season 2011-2012. GCPEx addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. Data collected includes microwave brightness temperatures, snow and soil/snow-air interface (ground surface temperatures), soil surface temperatures, and soil volumetric water content. These data were acquired by multiple instruments: a passive microwave radiometer, a water content reflectometer, thermistors, soil moisture probe.
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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.