Find research datasets worth reusing

openCC (other)Aug 2024View details →

Daily sea surface temperature in Santa Barbara channel between 1982 and 2023

This data package contains sea surface temperature (SST) data in the Santa Barbara Channel area. Data was obtained from the NOAA National Centers for Environmental Information (NCEI) at 0.25° resolution for the time between 1982 and 2023. This Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis (Version 2.1) derived its data from satellite (Advanced Very High Resolution Radiometer (AVHRR)) and in situ platforms (i.e., ships and buoys) and yielded 18 gird points within the Santa Barbara Channel.

openCC (other)Jun 2025View details →

Advective nitrate fluxes, sea surface chlorophyll concentrations and other physical metrics in the Santa Barbara Channel (2012-2019)

This data package includes 6 files: (1 & 2) In-situ nitrate concentrations at the surface and mixed layer depth, and collocated remotely-sensed and reanalysis quantities of satellite sea surface temperature, 15-day cumulative wind stress, satellite sea surface chlorophyll with a 5-day lag, index of offshore position of the California Current, indices for along-channel and across-channel distance, and index for day of the year. (3) An R script for generating generalized additive models (GAMs) to predict nitrate concentrations at the surface and at the mixed layer depth using the collocated data in files 1 & 2. (4) Daily maps of satellite sea surface chlorophyll concentrations (SSChl), High-frequency radar (HFR) surface currents, weather research and forecasting (WRF) model wind-derived vertical velocities, estimated nitrate concentrations at the surface and mixed layer depth, horizontal advective nitrate fluxes at the surface and vertical advective nitrate fluxes. (5) Daily time series of spatial mean SSChl, principal component amplitude of the first mode of variability in surface currents estimated using complex empirical orthogonal function (EOF) analysis, alongshore pressure gradient, wind stress, spatial mean horizontal velocities at the western and eastern Santa Barbara Channel boundaries, spatial mean vertical velocities, spatial mean surface nitrate concentrations at the channel boundaries and across the entire channel, spatial mean mixed layer depth nitrate concentrations across the entire channel, spatial mean horizontal advective nitrate fluxes at the channel boundaries, and spatial mean vertical advective nitrate fluxes. (6) A MATLAB script for plotting examples of the daily maps and time series in files 4 & 5. These data were processed in order to investigate the impact of local nutrient delivery mechanisms on phytoplankton blooms in the Santa Barbara Channel, California, details of which are available in the study: Brokaw, R.J., D.A. Siegel, L. Washburn,

openCC (other)Jun 2025View 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

openCC (other)Aug 2025View details →

SBC LTER: Reef: Sightings of sea otters (Enhydra lutris) near Santa Barbara and Channel Islands, ongoing since 2007

These data describe the number, location and activity or behavior of sea otters (Enhydra lutris) individuals observed along the Santa Barbara Coast and local Channel Islands, during SBC LTER field sampling. Observations began in 2007. Records are collected regularly at SBC core sites and opportunistically while research staff are underway or travelling between sites. Locations are included, but some latitudes and longitudes are approximate.

openCustomAug 2022View details →

Coastal landcover change and the associated biomass trends in the mid-Atlantic sea-level rise hotspot

Climate change is driving worldwide landscape reorganization. In the coastal ecosystem, climate-driven sea level rise is forcing landward marsh migration and forest die-off, with potentially large consequences on coastal carbon balance. Here we used 30 m resolution Landsat images to study coastal landcover change from 1984 to 2020, and analyzed the Normalized Difference Vegetation Index (NDVI, a proxy of plant biomass) trend between 1984 and 2020 in the mid-Atlantic sea level rise hotspot. Our study region stretches across the entire Chesapeake Bay and the Delaware Bay to encompass all areas between 0-5m above sea level (total area ~12,500 km2). Specifically, the data package includes 3 raster datasets derived from the Landsat images. All datasets cover the identical mid-Atlantic region and have identical spatial resolution of 30 m. The two landcover datasets, named as 'Landcover_year1984.tif' and'Landcover_year2020.tif', respectively refer to landcover map in 1984 and 2020. Each of the maps has 7 landcover classes differentiated by different integers, and they are: water (0), farmland (1), urban area(2), upland forest (3), transition forest (4), marsh (5) and sandbar (6). Both landcover maps were generated using a combination of random forest classification and manual delineation, and the resultswere validated with high-resolution aerial photos and satellite images with an overall mapping accuracybeyond 90%. The third raster dataset, named as 'NDVItrend_1984to2020.tif', is the NDVI trend map. The value of each 30 by 30 m pixel in the map represents the slope of the NDVI trendline estimated using annual peak-growing season NDVI images acquired between 1984 and 2020. Negative values in the dataset represent decreases of NDVI (i.e. biomass loss, or ecosystem browning) from 1984 and 2020,whereas positive values correspond to an increase of NDVI (i.e. biomass gain, or ecosystem greening)between 1984 and 2020. The data package is completed.

openCustomNov 2023View details →

Lateral and vertical forest retreat rate in the mid-Atlantic sea-level rise hotspot

Ghost forests consisting of dead trees adjacent to marshes are striking indicators of climate change. Here we quantify both the lateral and vertical rate of coastal forest retreat between 1984 and 2020 along the US mid-Atlantic coast. The study region includes areas between 0-5 m above sea level across the Chesapeake Bay and the adjacent Delaware Bay. Specifically, the data package includes 2 shapefile datasets derived from four decades of Landsat satellite observations of coastal treeline dynamics. The two datasets are generated on the same spatial-scale and have the same spatial resolution (0.075 km2), both stored as hexagon grids with a side length 170 m. Here we define "forest retreat" (as shown in the datasets as positive values) as the migration of coastal treeline landwards (lateral retreat) or upslope (vertical retreat), whereas "forest advance" (negative values) refers to treeline migration seawards (lateral advance) or downslope (vertical advance). The number '999999' in both datasets indicates areas of stable coastal treelines (i.e. no change) between 1984 and 2020.

Dataset for "Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections"

This dataset is used to reproduce the results presented in the following publication: Goelzer, H., Noel, B. P. Y., Edwards, T. L., Fettweis, X., Gregory, J. M., Lipscomb, W. H., van de Wal, R. S. W., and van den Broeke, M. R.: Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-188, in review, 2019.

A Spatially Variable Time Series of Sea Level Change Due to Artificial Water Impoundment

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 et al. (2020). Fingerprints for the GRanD database (Lehner et al.; 2011) are for individual years, noted in the file name. Three additional files come from the dataset provided by Zarfl et al. (2015), as described in Hawley et al. (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 et al. (2020). 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.

Depth of the sea floor along the 5-minute resolution cruise track of the Antarctic Circumnavigation Expedition (ACE) derived from GEBCO 2019 bathymetry data.

Dataset abstract Depth of the seabed along the five-minute averaged cruise track (Landwehr et al., 2020; DOI: 10.5281/zenodo.3752691) of the Antarctic Circumnavigation Expedition (ACE) was calculated from the the General Bathymetric Chart of the Oceans (GEBCO; GEBCO Compilation Group, 2019) 2019 gridded 30-arc second bathymetry data. The nearest gridded value from the bathymetry dataset was used to find the depth at the averaged position. Provided within this dataset is the average position of the vessel during a five-minute time period (where the time given is the middle time of the averaging interval). Dataset contents <ul> <li>cruise_track_gebco2019_depth_5min.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>ace_cruise_track_gebco2019_depth_5min_change_log.txt, metadata, text</li> </ul> Change log v1.1 - Added additional data coverage and therefore track coverage from 2016-11-17 - 2016-11-22 inclusive. Updated README.txt with information about data coverage. Added change_log file. v1.0 - Initial release of depth along cruise track data set. Dataset license This GEBCO sea floor depth along the cruise track dataset from ACE 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/

North Sea Wave Database (NSWD) 2005-2011

North Sea Wave Database (NSWD) The dataset contains each year of spectral metocean condition for Significant Wave Height (HSIGN) and wave energy period (TMM10), in meters and seconds. Each variable has a year timestap which the data corresponds too i.e 1980. The latitudes and longitudes of the dataset have resolution of 0.025 degrees at each direction. Latitude starting coordinate is 50 degrees and Longitude 0. For more information on the process that developed the dataset, the methodogies followed, calibration, valdiation and sensitivity analysis,  see:  Lavidas, G., & Polinder, H. (2019). North Sea Wave Database (NSWD) and the Need for Reliable Resource Data: A 38 Year Database for Metocean and Wave Energy Assessments. Atmosphere, 10(9), <a href="https://doi.org/10.3390/atmos10090551">https://doi.org/10.3390/atmos10090551 </a> Lavidas, G., & Polinder, H. (2019). Wind effects in the parametrisation of physical characteristics for a nearshore wave model. Proceedings of the 13th European Wave and Tidal Energy Conference 1-6 September 2019, Naples, Italy. The dataset was produced by Dr George Lavidas during the WAVe Resource for Electrical Production (WAVREP, which received funding from the European Union's Horizon 2020 research & innovation programme under the Marie Sklodowska-Curie grant agreement No 787344. The dataset is accompanied by two publication that (i) present the calibration-validation and production (ii) analysis of the dataset. The official CORDIS website is https://cordis.europa.eu/project/id/787344 A list of outcomes for the NSWD and the WAVREP project is found at the researcher's page: <a href="https://www.researchgate.net/project/WAVe-Resource-for-Electrical-Production-WAVREP ">https://www.researchgate.net/project/WAVe-Resource-for-Electrical-Production-WAVREP </a> It can also be found at the official CORDIS website <a href="https://cordis.europa.eu/project/id/787344">https://cordis.europa.eu/project/id/787344</a> Sharing and Access information Creative Commons Attribution (CC BY-SA).  The Creative Commons Attribution license allows others remix, tweak, and build upon your work, as long as they credit you and license their new creations under the identical terms.

North Sea Wave Database (NSWD) 1989-1997

North Sea Wave Database (NSWD) 2012-2017

opencc-by-4.0Nov 2020View details →

ICESat-2 monthly gridded winter Arctic sea ice thickness

Monthly gridded (winter only) Arctic sea ice thickness estimates from ICESat-2 derived using ATL10 freeboards (https://nsidc.org/data/atl10) together with snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM, https://github.com/akpetty/NESOSIM). Along-track data (from the three strong beams) are binned to the 25 km x 25 km NSIDC polar stereographic projection (EPSG:3411). The full processing chain is described in Petty et al., (2020) (code available at https://github.com/akpetty/ICESat-2-sea-ice-thickness) including several updates as detailed below. Temporal range: November 2018 - April 2019, October 2019 to April 2020. Data: A single netCDF file is included for each month. Variables include: <ul> <li>Sea ice freeboard (from ATL10)</li> <li>Snow depth (redistributed NESOSIM)</li> <li>Snow density (redistributed NESOSIM)</li> <li>Bulk sea ice density</li> <li>Sea ice type (from OSI SAF)</li> <li>Sea ice thickness uncertainty</li> <li>Mean day of month in a given grid cell</li> <li>Number of freeboard segments in a given grid cell.</li> </ul> A summary of the differences between the version 1 and version 2 winter Arctic sea ice thickness estimates are being presented at AGU 2020 and prepared for publication. Key changes from version 1 (Petty et al., 2020) to version 2 include: <ul> <li>Use of release 003 ATL10 freeboards. A detailed assessment of the freeboard changes from release 002 to release 003 is provided in Kwok et al., (2020).</li> <li>Upgrade to NESOSIM v1.1: CloudSat scaling of ERA5 snowfall, a new atmospheric wind loss term, calibration against recent OIB snow depths, an extended Arctic Ocean domain and various bug fixes (<a href="https://github.com/akpetty/NESOSIM">https://github.com/akpetty/NESOSIM</a>).</li> <li>Use of all three strong beams (instead of just strong beam #1).</li> </ul> The data have also been made available on a Google Cloud bucket to enable rapid data analysis from any cloud-based analytics platform: gs://sea-ice-thickness-data/v2/

opencc-by-4.0Oct 2020View details →

Surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios.

Surface maps and basin mean/total of annual mean surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios and for underlying the baseline projection (RCP4.5). Details on simulations and alkalinisation strategies are given in the reference article below.   Reference: Butenschön, M., Lovato, T., Masina, S., Caserini, S., Grosso, M., 2021. Alkalinization Scenarios in the Mediterranean Sea for Efficient Removal of Atmospheric CO2 and the Mitigation of Ocean Acidification. Front. Clim. 3. <a href="https://doi.org/10.3389/fclim.2021.614537">https://doi.org/10.3389/fclim.2021.614537</a>

opencc-by-4.0Nov 2023View details →

Modelled distributions of fish and epibenthic invertebrates in the southern North Sea

These data include distribution maps of fish and invertabrate species in the southern North Sea from 2014 until 2023. The maps are modelled using point data of presence/absence and biomass (per trawled km²) from scientific fisheries surveys to estimate the distribution of the probability of occurrence (POC) or biomass (kg per km²), respectively. Also included are forecasts of species' distributions assuming increasing water temperatures in the southern North Sea according to the ICCP scenario RCP8.5. Each files contains a raster stack with layers for each species. The data can be read into the R using the 'stack'-command from the 'raster'-package. The raster stacks contain layers with headers, which code the species and size group. For some species of relevance to fisheries managment, Numbers behind the latin names of the species give information on the included size classes in cm with 'no' indicating no size class information was available. The file names are composed of the follwing elements: 'bio' = biomass 'poc' = probability of occurrence 'emp' = observed occurrence/abundance data from fisheries surveys with employed spatial smoother 'sdm' = modelled distributin data from random forests 'fc' = forecast distributions based on temperature predictors according to RCP8.5 'rel.ca2' = core areas (CA) of distribution representing values > then the mid-point of modelled POC value range Year numbers give the time frame of empirical data or model predictions.    You can access the .tiff-files with the following R-commands using the directory path where you have stored the files: library(raster) poc<-stack("your_path/poc.sdm.2014_2023.tiff") poc$gadus.morhua_5_113 # Plots distribution of Atlantic cod as probability of occurrence observed at a size range from 5 - 113 cm tail length

opencc-by-4.0Oct 2023View details →

Sea Salt Aerosol Datasets

This collection contains data necessary to duplicate the plots found in Ackerman et al. (2023). Woodcock csv files come from the manual interpretation of Woodcock (1953). (Woodcock, A. H.: Salt nuclei in marine air as a function of altitude and wind force, J. Atmos. Sci., 10, 362–371, 1953.) Ackerman, K. L., Nugent, A. D., and Taing, C.: Mechanisms controlling giant sea salt aerosol size distributions along a tropical orographic coastline, Atmos. Chem. Phys. <a href="https://doi.org/10.5194/acp-23-13735-2023">https://doi.org/10.5194/acp-23-13735-2023</a>

opencc-by-4.0Dec 2023View details →

Data: An experimental sound exposure study at sea: No spatial deterrence of free-ranging pelagic fish

Data abstract: All data and scripts to replicate all plots and statistical results of the paper mentioned below. The data are sound recordings and processed echosounder data (raw echosounder data is available on request but > 100 GB in size and require specialized software).   Paper reference: Jeroen Hubert, Jozefien M. Demuynck, M. Rafa Remmelzwaal, Carlota Muñiz, Elisabeth Debusschere, Benoit Berges, Hans Slabbekoorn; An experimental sound exposure study at sea: No spatial deterrence of free-ranging pelagic fish. J. Acoust. Soc. Am. 1 February 2024; 155 (2): 1151–1161. <a href="https://doi.org/10.1121/10.0024720" target="_blank" rel="noopener">https://doi.org/10.1121/10.0024720</a>   Paper abstract: Acoustic deterrent devices are used to guide aquatic animals from danger or toward migration paths. At sea, moderate sounds can potentially be used to deter fish to prevent injury or death due to acoustic overexposure. In sound exposure studies, acoustic features can be compared to improve deterrence efficacy. In this study, we played 200–1600 Hz pulse trains from a drifting vessel and investigated changes in pelagic fish abundance and behavior by utilizing echosounders and hydrophones mounted to a transect of bottom-moored frames. We monitored fish presence and tracked individual fish. This revealed no changes in fish abundance or behavior, including swimming speed and direction of individuals, in response to the sound exposure. We did find significant changes in swimming depth of individually tracked fish, but this could not be linked to the sound exposures. Overall, the results clearly show that pelagic fish did not flee from the current sound exposures, and we found no clear changes in behavior due to the sound exposure. We cannot rule out that different sounds at higher levels elicit a deterrence response; however, it may be that pelagic fish are just more likely to respond to sound with (short-lasting) changes in school formation.

opencc-by-4.0Apr 2024View details →

CTD profiles Peninsula Antarctica-Weddell Sea ECA-59 (January 2023)

CTD profiles Peninsula Antarctica-Weddell Sea ECA-59 This dataset includes CTD Castaway deployments at 20 locations along the Peninsula Antarctica-Weddell Sea within the framework of the Antarctic Maritime Campaign ECA-59, conducted by INACH on board the Betanzos Vessel. Data were collected between January 5, 2023 to January 13, 2023. Funded by Programa Areas Marinas Protegidas INACH (2403252) CTD Cast-away serial number (CC2139005) Row1=cruise ECA59 Row2_Station type Row3_Date=day/month/year Row_4_Latitude (S) Row_5_Longitude (w) Row_6_ Pressure (Decibar) Row_7_Depth (m) Row_8_Temperature (°C) Row_9_Conductivity (Microsiems per centimeter) Row_10_Salinity (Practical Salinity Units, PSU) Row_11_Sound velocity (Meters per seconds) Row_12_Density (kg per m3)

openmit-licenseNov 2024View details →

Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"

This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea". To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory: <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.