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1,746 results for “Salinization”
Year 2015, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth, mid-estuary in Parker River near railroad bridge, Newbury, MA.
Year 2015, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the Parker River Estuary near the railroad bridge in Newbury, MA, mid-estuary, about 12.5 km upstream from mouth of Plum Island Sound.
Year 2016, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth, mid-estuary in Parker River near railroad bridge, Newbury, MA.
Year 2016, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the Parker River Estuary near the railroad bridge in Newbury, MA, mid-estuary, about 12.5 km upstream from mouth of Plum Island Sound.
Year 2017, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth, mid-estuary in Parker River near railroad bridge, Newbury, MA.
Year 2017 water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the Parker River Estuary near the railroad bridge in Newbury, MA, mid-estuary, about 12.5 km upstream from mouth of Plum Island Sound
Year 2018, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth, mid-estuary in Parker River near railroad bridge, Newbury, MA.
Year 2018, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the Parker River Estuary near the railroad bridge in Newbury, MA, mid-estuary, about 12.5 km upstream from mouth of Plum Island Sound.
PIE LTER YSI EXO2 sonde 15-minute interval water quality measurements of water column temperature, salinity, oxygen, pH, algae, fluorescent dissolved organic matter, turbidity, and depth at four sites in the Plum Island Estuary in year 2023.
Four YSI EXO2 water quality sondes were deployed from May 2023 to October 2023 at four sites in the Plum Island Estuary. One was at the mouth of the sound at the Ipswich Bay Yacht Club, one in the Rowley River, and two in the Parker River. The sondes measured water column temperature, salinity, oxygen, pH, algae, organic matter, turbidity, and depth in 15-minute intervals.
PIE LTER YSI EXO2 sonde 15-minute interval water quality measurements of water column temperature, salinity, oxygen, pH, algae, fluorescent dissolved organic matter, turbidity, and depth at four sites in the Plum Island Estuary in year 2024.
Four YSI EXO2 water quality sondes were deployed from April 2024 to October 2024 at four sites in the Plum Island Estuary. One was at the mouth of the sound at the Ipswich Bay Yacht Club, one in the Rowley River, and two in the Parker River. The sondes measured water column temperature, salinity, oxygen, pH, algae, organic matter, turbidity, and depth in 15-minute intervals.
PIE LTER YSI EXO2 sonde 15-minute interval water quality measurements of water column temperature, salinity, oxygen, pH, algae, fluorescent dissolved organic matter, turbidity, and depth at four sites in the Plum Island Estuary in year 2025.
Four YSI EXO2 water quality sondes were deployed from May 2025 to November 2025 at four sites in the Plum Island Estuary. One was at the mouth of the sound at the Ipswich Bay Yacht Club, one in the Rowley River, and two in the Parker River. The sondes measured water column temperature, salinity, oxygen, pH, algae, organic matter, turbidity, and depth in 15-minute intervals.
ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)
<p>We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018). </p> <p> </p> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012.</p> <p>CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016.</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p>
Salinity measurements in the Mekong Delta
<p>This data set contains along-channel and over-depth salinity structure measurements along the two lower estuarine distributary channels of the Hau River within the Mekong Delta, Vietnam. The data was collected during the dry season of the year 2016.</p>
Effect of salinity on flows of dense colloidal suspensions - Additional Data
<p>Additional dataset for the article "Effect of salinity on flows of dense colloidal suspensions".</p>
Salinity, Turbidity, Wind from the S1-GB pylon at the LTER site Delta del Po and Costa Romagnola (2012-2021)
<p>The present database comprises observations spanning from 2012 to 2021, focusing on abiotic parameters collected from the S1-GB dynamic pylon, in the Northern Adriatic Sea (around 7 miles offshore within the Po Delta on a bottom depth of 22.5 m), Italy. Specifically, it encompasses measurements on atmospheric parameters above the water surface and measurements at a defined depth (https://vocab.nerc.ac.uk/collection/P01/current/ADEPZZ01/) of salinity (URI: https://vocab.nerc.ac.uk/collection/OD1/current/SAL/) in PSU (Practical Salinity Units), turbidity (URI: http://vocab.nerc.ac.uk/collection/P25/current/TURB/) in NTU (Nephelometric Turbidity Units; http://vocab.nerc.ac.uk/collection/P06/current/USTU/), wind speed (URI: http://vocab.nerc.ac.uk/collection/P25/current/WINDS/) in m/s (meters per second; http://vocab.nerc.ac.uk/collection/P06/current/PMPS/), and wind from direction (URI: http://vocab.nerc.ac.uk/standard_name/wind_from_direction/) in degrees (angular degrees, 0 represents the true north; http://vocab.nerc.ac.uk/collection/P06/current/UAAA/). The S1-GB pylon is situated at 44,74° N; 12,45° E (WGS-84 coordinate system) and is managed by the Institute of Marine Science of the National Research Council (ISMAR-CNR) in Bologna. The dataset relies on a Comma Separated Values (CSV) file and it is composed by 82391 records offering an invaluable insight into the dynamic characteristics of the marine environment over nearly a decade. The S1-GB pylon is part of the site “Delta del Po and Costa Romagnola”, which belongs to the Long Term Ecological Research national and international networks (LTER-Italy, LTER-Europe and ILTER) and eLTER-RI. The site contributes also to the DANUBIUS and JERICO Research Infrastructures.</p>
Salinity, Turbidity, Wind from the E1 buoy at the LTER site Delta del Po and Costa Romagnola (2012-2021)
<p>The present database comprises observations spanning from 2012 to 2021, focusing on abiotic parameters collected from the E1 meteo-oceanographic buoy in the Northern Adriatic Sea (north of Rimini city on a bottom depth of 10.5 m), Italy. Specifically, it encompasses measurements taken atmospheric parameters above the water surface and measurements at a defined nominal depth (https://vocab.nerc.ac.uk/collection/P01/current/ADEPZZ01/) of salinity (URI: https://vocab.nerc.ac.uk/collection/OD1/current/SAL/) in PSU (Practical Salinity Units), turbidity (URI: http://vocab.nerc.ac.uk/collection/P25/current/TURB/) in NTU (Nephelometric Turbidity Units; http://vocab.nerc.ac.uk/collection/P06/current/USTU/), wind speed (URI: http://vocab.nerc.ac.uk/collection/P25/current/WINDS/) in m/s (meters per second; http://vocab.nerc.ac.uk/collection/P06/current/PMPS/), and wind from direction (URI: http://vocab.nerc.ac.uk/standard_name/wind_from_direction/) in degrees (angular degrees, 0 represents the true north; http://vocab.nerc.ac.uk/collection/P06/current/UAAA/). The buoy is located at 44,14° N; 12,57° E (WGS-84 coordinate system) and is managed by the Institute of Marine Science of the National Research Council (ISMAR-CNR) in Bologna. The dataset relies on a Comma Separated Values (CSV) file and it is composed by 82391 records, offering an invaluable insight into the dynamic characteristics of the marine environment in this area over nearly a decade. The E1 buoy is part of the site “Delta del Po and Costa Romagnola”, which belongs to the Long Term Ecological Research national and international networks (LTER-Italy, LTER-Europe and ILTER) and eLTER-RI. </p>
Machine learning-based quality assessment of Antarctic margins salinity - code, data and figures
<p>The submission contains the data, functions and code needed to reproduce the figures in Sohail et al., 2025</p>
Crop-specific salinity and irrigation data for river sub-basin water scarcity analyses in the US and AU
<p>This dataset contains observed monthly and annual salinity (EC) data for surface water (river) respectively groundwater, spatially averaged over sub-basins within the Central Valley, CA and the Murray Darling basin, AU, used for salinity-inclusive water scarcity assessments. The data also includes crop-specific irrigated area, irrigation withdrawals and salinity thresholds and other parameters specified, as well as example codes for analyses related to the manuscript: Thorslund et al., <em>Salinity impacts on irrigation water-scarcity in food bowl regions of the US and Australia.</em></p>
Sea ice core temperature and salinity data collected during the 2019 SCALE Winter Cruise
<p>Temperature and salinity profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p>
Sea ice core temperature and salinity data collected during the 2019 SCALE Spring Cruise
<p>Temperature and salinity profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) spring cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p>
Underwater images collected by an Autonomous Surface Vehicle in La-Saline, Réunion - 2024-07-15
<i>This dataset was collected by an Autonomous Surface Vehicle in La-Saline, Réunion - 2024-07-15.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 28.93 GB of MP4 files, which were trimmed into 9423 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.89% of these extracted images are useful and 0.11% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 85.83 %, Q2: 10.2 %, Q5: 3.97 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.18 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in La-Saline, Réunion - 2024-07-15
<i>This dataset was collected by an Autonomous Surface Vehicle in La-Saline, Réunion - 2024-07-15.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 22.29 GB of MP4 files, which were trimmed into 7336 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.35% of these extracted images are useful and 0.65% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 89.27 %, Q2: 2.76 %, Q5: 7.97 % <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Biogeographic data for "The marine biodiversity impact of the Late Miocene Mediterranean salinity crisis"
<p>Lists of species that were present in the Mediterranean Sea both in the pre-evaporitic Messinian and the Zanclean (based on https://doi.org/<a href="../doi/10.5281/zenodo.10782428">10.5281/zenodo.10782428</a>), biogeographic information on their presence outside the Mediterranean, and accordingly their status as either "possible endemic" to the Mediterranean or "non-endemic" if they were also found outside the basin.</p> <p>In this version, we added also the list of species present in the Mediterranean Sea in the pre-evaporitic Messinian that can be considered possible endemics, based on the same rule, and the indication if they survived the MSC.</p>
Revised marine fossil record of the Mediterranean before and after the Messinian Salinity Crisis
<p>This is a unified and revised marine fossil record of the Mediterranean covering the Tortonian stage, the pre-evaporitic Messinian and the Zanclean stage and encompassing 23032 occurrences of calcareous nannoplankton, dinoflagellates, foraminifera, corals, ostracods, bryozoans, echinoids, mollusks, fishes, and marine mammals. It consists of four files in .csv format: 1) 'MessinianDB' contains the fossil occurrences; 2) 'coord' has the list of fossiliferous localities with their coordinates and the groups of organisms reported from each one; 3) 'DBrefs' contains the full citations of the references in the database; 4) 'corals' contains the list of coral genera in the database, indicating whether or not they include zooxanthellate (z-corals) or azooxanthellate (az-corals) species, or both. In the latter case, we further indicate if the species found in the database should be considered z- or az-corals, based on the accompanying fauna. </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)
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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.