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ShareScore release 0.9.0
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63 results for “water detection”
Supporting Shellfish Aquaculture in the Chesapeake Bay using Artificial Intelligence to Detect Poor Water Quality through Field Sampling and Remote Sensing
We are collecting and analyzing biological, chemical, and physical variables in and above the water at target sites and in the lab, looking for hyperspectral proxies that covarying with pollutants. This project is applying an AI model to address water quality, using datasets collected around the Bay in combination with remotely sensed data during targeted field work to support the need to more effectively sort through disparate data sets to identify areas of poor water quality that result in shellfish bed closure.
Supporting Shellfish Aquaculture in the Chesapeake Bay using Artificial Intelligence to Detect Poor Water Quality through Sampling and Remote Sensing
This use-inspired NASA AIST project collects biological, chemical, and physical variables in and above the water at Chesapeake Bay sites for analysis within the lab. These ground-truth data are then used for data labeling, in combination with remotely sensed data, within a machine learning model trained to identify water quality challenges of resource managers that could result in shellfish bed closures, for example.
Evaluation of minimum water-filled pore space (WFPS) to ensure detectable N2 fluxes for Central Experiments in DASIM
<p><strong>Methods</strong></p><p>Incubation of DASIM soils in Thünen microcosm system under the following conditions:</p><ul><li>N2-depleted atmosphere(2% N2 in pure He or in 20%O2/78%He) (Lewicka-Szczebak et al., 2017)</li><li>Target flow rate: 20 mL/min</li><li>Core diameter: 14,4 cm</li><li>Core heights:10 cm</li><li>Bulk density: according to DASIM protocol: Fuhrberg, arable (sand) = 1.5; Rottalmünster, arable (silt loam) = 1.3; Gießen, grassland = clay loam (1.2)</li><li>15N-labeling: addition of K15NO3 (60 at% 15N), target content of 50 mg N/kg</li><li>Litter: all treatments <strong>without</strong> litter</li><li>WFPS levels: 60 /65/70/75 %</li><li>Incubation sequence: 1 week aerobic (20%O2), one week anaerobic</li><li>Temperature: 20°C</li><li>Gas sampling for IRMS: twice a week</li><li>GC analysis online about 3 times a day</li></ul><p>IRMS analysis: see Lewicka-Szczebak et al. 2013)</p><p>Calculation of ap and fs: see Spott et al. (2006) and Buchen et al. (2016)</p><p>Determination of limit of detection (LOD): Monte-Carlo modelling using SD of 29R and 30R of IRMS analysis</p><p>soils were sieved to 10 mm, preincubates at 50% WHCmax and 20°C for two weeks, fertilized, packed to target height and BD and wetted to target WFPS. Final WFPS and NO3--N was not measured.</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.