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1,052 results for “sea water”
Water column chlorophyll concentrations from lagoon, river, and ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples from multiple depths are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods for quantification of chlorophyll-a concentrations. Concentrations are reported in micrograms of chlorophyll per liter of filtered seawater (μg/L).
Physiochemical water column parameters and hydrographic time series from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
To understand circulation and seasonality as part of the Beaufort Lagoon Ecosystem Long Term Ecological Research program, temperature, conductivity, salinity, pressure, depth, and current velocity are recorded hourly in situ, starting August 2018 in lagoons across the Beaufort Sea coast (Elson Lagoon, Kaktovik Lagoon, and Jago Lagoon). Moorings include combinations of 1) RBR Concerto CTDs with temperature, conductivity, and pressure sensors; 2) StarOddi CTs with temperature and conductivity sensors; and 3) Lowell TCM-1 Tilt Current meters with MAT-1 Data Loggers for velocity and bearing. In addition, during BLE LTER's annual sampling, water column physiochemical parameters (chlorophyll a, dissolved oxygen, phycoerythrin concentration, pH, temperature, conductivity, salinity) are measured by hand with a YSI data sonde at river, lagoon, and open ocean sites along the Beaufort Sea coast. Here we provide both quality controlled in situ mooring data and all YSI sonde data.
Water column and sediment porewater nutrient concentrations from lagoon, river, and ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Several water types (lagoon, river, ocean) and surface sediment porewater samples from the coastal Beaufort Sea system were sampled seasonally to investigate temporal and spatial shifts in nutrient dynamics. Surface and bottom water samples were collected in April, June, July, and August and analyzed for ammonium, nitrate + nitrite, orthophosphate, and silica.
Free water metabolism (FWM) derived net ecosystem production (NEP) from lagoon sites along the Beaufort Sea Coast, Alaska, 2019-ongoing
Continuous measurements of dissolved oxygen (DO) in order to calculate daily values of free water metabolism (FWM) derived net ecosystem production (NEP) at several core Beaufort Lagoon Ecosystems LTER (BLE-LTER) stations. These deployments were done in addition to year-round core program BLE-LTER mooring deployments during the under ice sampling at surface and bottom depths (April/May), and during open water season in bottom depths (July/August). Gross primary production (GPP), ecosystem respiration (ER), and the sum of the two: net ecosystem production (NEP) are reported in mol C m-2 day-1.
Ice, water, and sediment pigment concentrations from Beaufort Sea lagoons core program stations, 2023-24
Bottom ice (< 20 cm), water column, and undisturbed surface sediment samples from the Beaufort Lagoon Ecosystem Long Term Ecological Research programs were collected, in tandem, from core program sites in ice-cover (~April), ice break-up (~June), and open water (~August) seasons of 2023, and ice-cover 2024, to quantify algal pigment concentrations and variations in an annual cycle. We also ran historical samples from 2021 sampling seasons. This data can be used with analysis programs such as CHEMTAX or PhytoClass to elucidate microalgal community structure. Fourteen pigments were measured, including chlorophyll a, fucoxanthin, zeaxanthin, alloxanthin, peridinin, prasinoxanthin, lutein, chlorophyll c<sub>3</sub>, 19-hexanoyloxyfucoxanthin, and 19-butanoyloxyfucoxanthin. Phaeopigments (pheophytin, pheophorbide, and chlorophyllide a) were also included in these analyses. For sediment samples, the values of chlorophyll a, fucoxanthin, zeaxanthin, alloxanthin, peridinin, pheophytin, pheophorbide, and chlorophyllide a can be found in the core program pigment dataset, which is a continuously collected data set (<a href="https://doi.org/10.6073/pasta/5294f45c9c7287903078926a487f1fd7" style="text-decoration: underline;">Sediment pigment concentrations</a>). Pigment concentrations were measured using high-precision liquid chromatography (HPLC). Concentrations are represented as μg L<sup>-1</sup> for both ice and water column samples, and as μg g<sup>-1</sup> for sediment samples.
Stable oxygen isotope ratios of water (H2O-d18O) from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2019-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, and analyzed for delta 18O (ratio of oxygen-18 to oxygen-16) to use in mixing models for source water contribution.
Time series of water column pH from lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing
Beginning in August 2018, the Beaufort Lagoon Ecosystems Long Term Ecological Research (BLE LTER) program will record water column pH time series (hourly) from a benthic mooring containing a Seabird SeaFET V2 buoyed 10 cm from the lagoon seafloor. pH values are logged from the instrument’s internal sensor and reported on the total hydrogen ion scale. Site bottom water is collected and analyzed in the laboratory to employ a single point calibration to the data during post-processing. Another discrete water sample is collected months after instrument deployment to determine uncertainty (2018-2019 season: 0.002).
NOAA Monthly Mean Sea Level Summary Data for the Key West Water Level Station (NOAA/NOS Co-OPS ID 8724580), Florida, USA, January 1913 - ongoing
Monthly Mean Sea Level Summary Data for the Key West, Florida, Water Level Station (NOAA/NOS CO-OPS ID 8724580). Data is in meters relative to the STND-Key West Station Datum.
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons.
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons. The ice season duration is defined as the time elapsed between day of ice-edge advance and day of ice-edge retreat within a given sea ice year, which begins mid-February (mean minimum of summer sea ice extent for the Southern Ocean) and ends the following mid-February. See Stammerjohn et al (2008, JGR) for further details.
SBC LTER: Reference: Sea-surface water temperature, Santa Barbara Harbor, Santa Barbara, CA, USA, 1955 to present, ongoing
The SBC-LTER has access to data on seawater temperature collected at Santa Barbara Harbor, Santa Barbara, CA, USA through the Scripps Institution of Oceanography Manual Shore Stations program. The SIO Manual Shore Stations program provides data and information about this shore station. For further information, please visit the SIO Manual Shore Stations website at https://library.ucsd.edu/dc/object/bb07606686. Please note: manual shore station data is updated periodically, not continuously. Funding for the Shore Stations Program provided by the California Department of Parks and Recreation, Natural Resources Division, Award# C22820005. Contact shorestation@ucsd.edu if you have questions
A Spatially Variable Time Series of Sea Level Change Due to Artificial Water Impoundment
<p>This database contains a series of gravitational, rotational, and deformational (GRD) "fingerprints"—the spatial response of sea level—corresponding to redistribution of water mass because of impoundment of water in artificial reservoirs, as reported in Hawley <em>et al</em>. (2020). Fingerprints for the GRanD database (Lehner <em>et al</em>.; 2011) are for individual years, noted in the file name.</p> <p>Three additional files come from the dataset provided by Zarfl <em>et al</em>. (2015), as described in Hawley <em>et al.</em> (2020). "Const" includes the fingerprint for all reservoirs under construction in their database; "Plan" includes the fingerprint for all reservoirs in the planning phase. "Zarfl" includes the fingerprint for all reservoirs in "Const," with 15 years of seepage, as well as all reservoirs for "Plan" with 5 years of seepage, as described in Hawley <em>et al</em>. (2020).</p> <p>Each fingerprint has 525,825 points, which fill out a global grid of 513 x 1025 [lat x lon] points. Each node in latitude and longitude is evenly spaced. The first point represents the northernmost point at 0 [deg] longitude, and increase first to the east, then to the south.</p>
Water levels at tide gauges from: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution
<p>Data to reproduce the analysis of the Hourly Coastal water levels with Counterfactual (HCC) dataset, presented in the publication "<strong>Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution</strong>" published in Earth System Science Data (ESSD). </p><p>Note that in this repository, water levels are only provided tide gauge locations which were used for the analysis presented in the paper. The full Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the <a href="https://doi.org/10.48364/ISIMIP.749905">ISIMIP repository</a>.</p><h2>File Descriptions</h2><h4>HCC_analysis_and_plots.ipynb</h4><p>This jupyter-notebook contains all scripts to produce the plots presented in the paper. Make sure that all necessary python packages are installed. The script assumes all netCDF files from this repository to be stored in a sub-directory called "data".</p><h3>hcc_gesla3_99pctl_surge_2011_2015.nc</h3><p>Extreme surge levels from 2011-2015 at 999 GESLA-3 tide gauge stations with at least 90 percent of data in the considered period. As astronomical tides are removed from the modeled and observed water levels to yield the surge component. The file also contains monthly relative water levels and monthly geocentric water levels from 1900-2015 from the HCC dataset.</p><h4>Variables:</h4><ul><li><i>observed_99pctl_surge_level_anomaly</i> -- 99th percentile of daily maximum surge level anomalies from 2011-2015</li><li><i>hcc_99pctl_surge_level_anomaly -- </i>HCC surge level anomalies at the same time steps as <i>observed_99pctl_surge_level_anomaly</i></li><li><i>hcc_counterfactual_99pctl_surge_level_anomaly</i> -- HCC counterfactual surge levels at the same time steps as <i>observed_99pctl_surge_level_anomaly</i></li><li><i>hcc_water_level_monthly</i> – Monthly relative water level from 1900-2015</li><li><i>hcc_geocentric_water_level_monthly</i> – Monthly geocentric water level from 1900-2015</li></ul><h3>hcc_hr_psmsl_water_level_monthly_1900_2015.nc</h3><p>Monthly water levels at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. The file contains data from the HCC, HR and PSMSL datasets. To align PSMSL and HR with HCC, the 1993-2012 average from PSMSL and HR is removed from each of those datasets respectively and the 1993-2012 average of HCC is added. The average is calculated only over all time steps where the associated observational record has valid data.</p><h4>Variables:</h4><ul><li><i>hcc_water_level_monthly</i> – Monthly relative water level from the HCC dataset</li><li><i>hr_aligned_water_level_monthly</i> -- Monthly relative water level from the HR dataset, aligned with <i>hcc_water_level_monthly</i></li><li><i>psmsl_aligned_water_level_monthly</i> -- Monthly relative water level from the PSMSL database, aligned with <i>hcc_water_level_monthly</i></li></ul><h3>hcc_codec_hr_gesla3_water_level_hourly_monthly_1979_2015.nc</h3><p>Hourly water levels at 1040 GESLA-3 tide gauge stations which have at least 30 percent of valid observations between 1979 and 2015. The file contains data from the HCC, CoDEC, HR and GESLA-3 datasets. The different records are not vertically aligned.</p><h4>Variables:</h4><ul><li><i>gesla3_water_level_hourly</i> -- Hourly relative water level from the GESLA3 database</li><li><i>hcc_water_level_hourly</i> -- Hourly relative water level from the HCC dataset</li><li><i>codec_water_level_hourly</i> -- Hourly relative water level from the CoDEC dataset</li><li><i>hr_water_level_monthly</i> -- Monthly relative water level from the HR dataset</li></ul><h3> </h3><h3>hcc_gesla3_water_level_hourly_2011_2015.nc</h3><p>Water levels from the HCC and GESLA-3 datasets, only for tide gauge stations with a complete record in the period 2011-2015 and associated HCC grid points.</p><h4>Variables:</h4><ul><li><i>gesla3_water_level_hourly</i> -- Hourly relative water level from the GESLA3 database</li><li><i>hcc_water_level_hourly</i> -- Hourly relative water level from the HCC dataset</li></ul><h3>slr_ds_psmsl_selected.nc</h3><p>Linear estimates of relative sea level rise from 1900 to 2015. Data is provided at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. Estimates are calculated for the HCC, HR and PSMSL datasets.</p><h4>Variables:</h4><ul><li><i>psmsl_rslr, psmsl_rslr_lower, psmsl_rslr_upper</i> -- Relative sea level rise for PSMSL with lower and upper bounds for a 95 percent confidence interval</li><li><i>hcc_long_rslr, hcc_long_rslr_lower, hcc_long_rslr_upper </i>-- Relative sea level rise for HCC with lower and upper bounds for a 95 percent confidence interval</li><li><i>hr_rslr, hr_rslr_lower, hr_rslr_upper</i> -- Relative sea level rise for HR with lower and upper bounds for a 95 percent confidence interval</li></ul><h3>reg_mask_xr.nc</h3><p>Split of the world into 7 ocean basins: Indian Ocean - South Pacific, Northwest Pacific, East Pacific, South Atlantic, Subtropical North Atlantic, Subpolar North Atlantic West and Subpolar North Atlantic East.</p><h4>Variables:</h4><p><i>reg_mask</i> – Float value, representing the ocean basins</p><p> </p>
Biogenic silicate concentration in sea water samples, collected from the CTD in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>Biogenic Silicate (Bsi) concentration (µmol/L) in seawater data. Water samples were collected from CTD rosette deployments, filtered on board and then analysed by flow injection following appropriate digestion.</p> <p>This data supports chemical and biological oceanography studies conducted during the Antarctic Circumnavigation Expedition.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_biogenic_silicate_concentration_in_seawater_ctd.csv, data file, comma-separated values</li> <li>ace_biogenic_silicate_concentration_in_seawater_ctd_visual_summary.png, metadata, portable network graphics</li> <li>data_file_header.txt, metadata, text format</li> <li>README.md, metadata, text format</li> </ul> <p>All missing values where no data point exists from lack of sample, have been set to NaN.</p> <p><strong>Dataset license</strong></p> <p>This biogenic silica concentration dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Jensen et al. 2024 - Biodiversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022 - raw dataset
<p><span>The diversity and distribution of gelatinous macrozooplankton is described by presenting qualitative and quantitative data of the jellyfish and comb jelly community encountered in the North Sea and Skagerrak/Kattegat during January/February 2022.<span> </span>Data were generated<span> </span>as part of the North Sea Midwater Ring Net survey (MIK), an ichthyoplankton survey conducted at night-time during the quarter 1 (Q1) International Bottom Trawl Survey (IBTS), aboard the Danish R/V DANA (DTU Aqua) and the Swedish R/V Svea (SLU) at a total of 100 stations. This dataset accompanies the Data in Brief Article below and should be cited when using this dataset. <br></span></p> <p><span>Jensen, C.J.D., Køhler, L.G., Huwer, B., Werner, M., Cieters, L., <strong>Jaspers, C.</strong> (submitted) Biod</span><span>iversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022. <em>Data in Brief. </em></span></p>
Historical and future Lake Surface Water Temperature for 80 major lakes in Southeast Asia [LSWT-SEA]
The present dataset is part of a study delving into the intricate relationship between lake surface temperature (LSWT) and the broader context of climate change in the ecologically diverse region of Southeast Asia (SEA). Recognizing LSWT as a highly responsive indicator of climatic shifts, the research aims to shed light on the region's vulnerability to these changes. Using a suite of predictive models (namely Multilinear Regression (MLR), Multilayer perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGB), Multilayer perceptron (MLP)) the study reconstructs historical LSWT trends from 1986 to 2020 and projects future scenarios until 2100, contingent upon various Representative Concentration Pathway (RCP) trajectories. Using MODIS-derived LSWT as predicted variable. The dataset package includes the data used to carry out the research: ECMWF ERA5 and CHIRPS climatic predicting variables, MODIS-derived daytime and nighttime LSWT, historically predicted daily daytime and nighttime LSWT, future predictions of LSWT for multiple Representative Concentration Pathways (RCPs), long term historical and future trends.
Catalog of GenBank sequence read archive (SRA) entries of metagenomic DNA sequence analyses of bacterial and archaeal water column communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2012
In contrast to temperate systems, Arctic lagoons that span the Alaska Beaufort Sea coast face extreme seasonality. Nine months of ice cover up to ∼1.7 m thick is followed by a spring thaw that introduces an enormous pulse of freshwater, nutrients, and organic matter into these lagoons over a relatively brief 2–3 week period. Prokaryotic communities link these subsidies to lagoon food webs through nutrient uptake, heterotrophic production, and other biogeochemical processes, but little is known about how the genomic capabilities of these communities respond to seasonal variability. This study characterizes the metabolic capabilities of microbial communities across three seasons in two lagoons and one open coastal site along the eastern Alaska Beaufort Sea coast. We used metagenomic DNA sequence data of bacterial and archaeal water column communities to identify genes of relevant biogeochemical pathways. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA642637 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA642637. This data package is associated with the following publication: Baker, Kristina D., Colleen T. E. Kellogg, James W. McClelland, Kenneth H. Dunton, and Byron C. Crump. “The Genomic Capabilities of Microbial Communities Track Seasonal Variation in Environmental Conditions of Arctic Lagoons.” Frontiers in Microbiology 12 (2021). https://doi.org/10.3389/fmicb.2021.601901. Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provi
Count data of air-breathing fauna from visual transect surveys including water temperature, time, sea and weather conditions in Shark Bay Marine Park, Western Australia from February 2008 to July 2014
This dataset provides information on the relative abundances of air breathing fauna (dugongs, dolphins, sea snakes, marine birds, and sea turtles) in the study area of the Eastern Gulf of Shark Bay, Western Australia. The dataset comprises transects that quantify animal abundances in three microhabitats (shallow seagrass banks, seagrass bank edges, and deep sandy channels). These microhabitats vary in their food supply as well as their potential to facilitate or inhibit detection and escape from predators, mainly the tiger shark (Galeocerdo cuvier). As a result these data have been used to examine risk-specific habitat use behaviors of these fauna, in addition to general abundance estimates.
Viral Metagenomes from water systems in the Mediterranean Sea
<p>The Water Framework Directive (WFD; 2000/60/EC) is the European umbrella for the assessment and regulation of ecological quality of water systems (lakes, rivers, transitional waters, coastal waters). The scope of WFD is to improve the ecological status of the aquatic ecosystems. To do so, a long time-series of monitoring campaigns within WFD exists with the ultimate goal to protect coastal ecosystems from degradation. Further, WFD project employs the calculation and improvement of ecological quality indices for the definition and assessment of eutrophication. In the submitted sub-project within WFD, the viral metagenome of 15 samples collected in 2014 and 2015 is sequenced and analyzed for several genes for the study of taxonomy and potential function of double-stranded DNA viruses.</p>
Water Body Checklists 2019: Adriatic Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Adriatic Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: Ceram Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Ceram Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
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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.