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278 results for “water samples”
SWOT Postlaunch Oceanography Field Campaign Shipboard CTD and Water Sample Data
The SWOT Postlaunch Oceanography Field Campaign Shipboard CTD and Water Sample Data collection provides the conductivity, temperature and depth (CTD) measurements and water sample measurements from shipboard instruments deployed by the Surface Water and Ocean Topography (SWOT) postlaunch field campaign. The SWOT satellite mission launched in late 2022 and underwent a calibration and validation (cal/val) phase in 2023. As part of cal/val, an array of oceanographic instruments was deployed at a site 300 km offshore of California. Shipboard data come from four research cruises on the Bold Horizon and the Sally Ride, with different time spans ranging from February 23, 2023 to November 2, 2024. These measurements were used to adjust the calibrations of mooring CTD sensors (deployed in the same field campaign) to a common and well-calibrated reference, to enable the calculation of steric height for SWOT cal/val analyses. <br><br>Most, but not all of the CTD casts were collected at the actual mooring sites. For some casts, mooring instruments were attached temporarily to the ship CTD system for cross-calibration, and these may have been done anywhere en route to/from the mooring sites. Due to the varying depth ratings of the mooring instruments thus attached, not all CTD casts covered the full water column. The resulting CTD data collection is an irregular pattern of sampling locations and depths, which includes a number of full-depth casts in the vicinity of the moorings.
Water spreading on laser - treated silicon sample at three different temperatures
<p>The three videos were recorded at speed 1000 frames per second. Each video consists of two parts. In the first part, the video is played at 1000 frames per second. In second part, the video is played in slow motion mode at 100 frames per second.</p>
FIG. 5 in Triphoridae (Gastropoda) from Martinique sampled by the MADIBENTHOS expedition, with notes on shallow-water species from Guadeloupe
FIG. 5. — "Inella" sp., MNHN, sta. AD214, 5.8 mm. Scale bars: A, 1 mm, B-E, 200 μm.
Figure. Map of sampling sites PulauKambing, Terengganu waters of South China Sea, Malaysia. in Length-weight relationships and relative condition factors of three coral-associated Lutjanus species from Terengganu waters of the South China Sea, Malaysia
Figure. Map of sampling sites PulauKambing, Terengganu waters of South China Sea, Malaysia.
Water sampling data from Outokumpu area 2024
<h2>Abstract</h2> <p>Report on the water sampling campaign performed in Outokumpu during summer 2024</p> <p>This depositry contains data generated within the European S34 project. </p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Water analysis Outokumpu 2024</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Report on the water sampling campaign performed in Outokumpu during summer 2024</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>AMD, water analysis</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Keretti-Outokumpu</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>28.08.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>28.08.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>PDF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0,20m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0,1</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 32635</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>None- refer to GTK as source of information</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Nike Luodes (nike.luodes@gtk.fi)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>GTK</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Nike Luodes (nike.luodes@gtk.fi)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Data gathered from grabbed samples on DWSPs (drinking water service public - houses of water) for CS#2
<p>Data gathered from grabbed samples on DWSPs used to compare data obtained from online sensor installed on DWSPs</p>
Figure 1 from: Piazza P, Blazewicz-Paszkowycz M, Ghiglione C, Alvaro M, Schnabel K, Schiaparelli S (2014) Distributional records of Ross Sea (Antarctica) Tanaidacea from museum samples stored in the collections of the Italian National Antarctic Museum (MNA) and the New Zealand National Institute of Water and Atmospheric Research (NIWA). ZooKeys 451: 49-60. https://doi.org/10.3897/zookeys.451.8373
Figure 1 - Flowchart depicting major steps in dataset development and publishing.
Particulate organic carbon and nitrogen measurements from water column sample bottles, collected aboard Palmer LTER annual cruises off the Western Antarctic Peninsula, 1991 - 2012. Cruise PD94-01 not included in time series for lack of samples
Open the record for dataset details and reuse information.
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.
Gene Expression changes induced by exposure to field water samples collected at Walker Mine, CA
GEO Series GSE7666. Daphnia magna. 24 samples. Type: Expression profiling by array.
Gene Expression changes induced by exposure to field water samples collected at Greenhorn Mine, CA
GEO Series GSE7667. Daphnia magna. 24 samples. Type: Expression profiling by array.
Transcriptomic profiling permits the identification of pollutant sources and effects in ambient water samples
GEO Series GSE40991. Hypomesus transpacificus. 4 samples. Type: Expression profiling by array.
Transcriptomics Analyses of tobacco leaves and roots sample during water-deficit
GEO Series GSE67434. Nicotiana tabacum. 12 samples. Type: Expression profiling by array.
Transcriptomics Analyses of soybean leaves and roots sample during water-deficit
GEO Series GSE49537. Glycine max. 36 samples. Type: Expression profiling by array.
Data from: The influence of infiltration rate on residual air distribution and water flow in heterogeneous porous sample: neutron imaging investigation
Open the record for dataset details and reuse information.
Exposure of C. elegans to AOBr-containing surface water samples and to a M. aeruginosa batch culture
GEO Series GSE68709. Caenorhabditis elegans. 15 samples. Type: Expression profiling by array.
Average seasonal contents and standard deviations of chemical elements in drinking water samples of Almaty
<p>Water is an important component of all life on Earth, and water pollution with heavy metals can lead to detrimental consequences for public health. The purpose of this study was to determine the health risks caused by trace elements present in the drinking water supply systems of Almaty City. As part of this research, the elemental composition of 78 drinking water samples taken in winter, summer, and autumn of 2023 in different areas of the city was studied.Based on the data obtained, drinking water contamination indices were calculated for heavy metal groups, and the degree of water suitability for drinking purposes was assessed for each sampling point.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.