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SBC LTER: Hourly photon irradiance at the surface and seafloor, ongoing since 2008
These data represent mean hourly values of photosynthetically active radiation (PAR) (in units of mol m-2 s-1) at five subtidal reefs and one coastal location off Santa Barbara, California. Sensors record instantaneous irradiance at one-minute or 30 second intervals, and data are averaged hourly. Sensors are mounted on the sea floor at five sites (Arroyo Quemado, Carpinteria, Naples Reef, Isla Vista, and Mohawk Reef). A surface sensor is deployed on an unobstructed coastal rooftop at the UC Santa Barbara campus; some historical observations are available from sensors mounted above the sea surface at a subset of the five sites.
Data from: Coastal upwelling drives ecosystem temporal variability from the surface to the abyssal seafloor.
<p><strong>Abstract</strong></p> <p>Long-term biological time series that monitor ecosystems across the ocean’s full water column are extremely rare. As a result, classic paradigms have yet to be tested. One such paradigm is that variations in coastal upwelling drive changes in marine ecosystems throughout the water column. We examine this hypothesis by using data from three multi-decadal time series spanning surface (0 m), midwater (200-1000 m), and benthic (~ 4000 m) habitats in the central California Current Upwelling System. Data include microscopic counts of surface plankton, video quantification of midwater animals, and imaging of benthic seafloor invertebrates. Taxon-specific plankton biomass and midwater and benthic animal densities were separately analyzed with principal component analysis. Within each community, the first mode of variability corresponds to most taxa increasing and decreasing over time, capturing seasonal surface blooms and lower-frequency midwater and benthic variability. When compared to local wind-driven upwelling variability, each community correlates to changes in upwelling damped over distinct timescales. This suggests that periods of high upwelling favor increases in organism biomass or density from the surface ocean through the midwater down to the abyssal seafloor. These connections most likely occur directly via changes in primary production and vertical carbon flux, and to a lesser extent indirectly via other oceanic changes. The timescales over which species respond to upwelling are taxon-specific and are likely linked to the longevity of phytoplankton blooms (surface) and of animal life (midwater and benthos), that dictate how long upwelling-driven changes persist within each community.</p> <p> </p> <p><strong>Data set description</strong></p> <p>This data set includes 3 files, one for each community. The files contain plankton biomass (for the surface community) or animal density (for midwater and benthos communities) as a function of sampling time and taxonomic group. </p> <ul> <li>surface.csv: autotrophic and heterotrophic surface plankton sampled in Monterey Bay by CTD-rosette and analyzed by epifluorescence microscopy and flow cytometry</li> <li>midwater.csv: midwater animals observed by ROV in the Monterey Bay mesopelagic zone from 200-1000m</li> <li>benthos.csv: benthic animals observed by ROV in a ~ 4000 m abyssal seafloor habitat at the base of the Monterey deep-sea fan</li> </ul> <p><strong>Detailed description </strong>(see additional details and references in <a href="https://www.pnas.org/doi/10.1073/pnas.2214567120">Messié et al., 2023</a>):</p> <p><strong>Surface time series:</strong> Plankton biomass was estimated from surface plankton counts collected using ship-based CTD-rosette at station M1 in Monterey Bay (122.022°W, 36.747°N). This station is part of a 3-station time series program operating in Monterey Bay since 1989 at 3-4 week intervals. Epifluorescence microscopy was used to enumerate and size auto- and heterotrophic plankton. Starting in 1998, flow cytometry samples provided more precise numbers for <em>Synechococcus</em> and eukaryotic picoplankton (<em>Prochlorococcus</em> was not included as no information is available prior to 1998). Standard geometric equations (e.g., ellipsoid, sphere, cylinder, pennate diatom shape) were used to calculate biovolumes of individual cells, and biomass of each plankton group was assessed using biovolume-based carbon conversions. For picoplankton an average value per cell was used: 82 fgC cell<sup>-1</sup> for <em>Synechococcus</em> and 530 fgC cell<sup>-1</sup> for eukaryotic picophytoplankton (red fluorescing picoplankton). Diatom biovolumes were converted to biomass using log<sub>10</sub>(Biomass) = 0.76 log<sub>10</sub>(Volume) - 0.29 where Biomass is in gC and Volume is in 𝜇m<sup>3</sup>. The ciliate conversion was Biomass = 0.08 * Volume. For all other plankton we used log<sub>10</sub>(Biomass) = 0.94 log<sub>10</sub>(Volume) - 0.6.</p> <p><strong>Midwater time series: </strong>Quantitative mesopelagic video transects were conducted at a single station in Monterey Bay (Midwater 1, 36°42′N, 122°02′W). The station is located over the axis of the Monterey Submarine Canyon, where the water column is approximately 1600 m deep. Data were collected using remotely operated vehicles (ROVs). Estimates of animal densities using ROV imaging underestimate some groups (notably fishes), but provide a more complete view of life in the ocean than traditional methods such as nets and acoustics, particularly for gelatinous animals. The ROVs conducted horizontal video transects while moving at about 0.5 m s<sup>-1</sup> for 10 min. Data for this paper come from approximately monthly transects made at 100 m intervals between 200 - 1000 m from 1997-2017. These years were chosen because the entire mesopelagic water column was more evenly surveyed than in the years prior. In each transect, the community of animals was annotated by professional annotators using the open-source Video Annotation and Referencing System (VARS) software. Annotators identified organisms in transect video to the lowest taxon possible; in many cases to species. We selected 63 taxonomic groups defined at the highest possible taxonomic resolution; annotations not included represent 31% of the total (84% of which are euphausiids, chaetognaths, and unidentified appendicularians). Calibrated cameras on MBARI ROVs and accurate measurement of ROV speed through water, allow for the calculation of volume for each transect. Animal density was calculated for each taxonomic group and each depth-specific transect as the number of individuals divided by the corresponding transect volume, further averaged over the water column from 200 - 1000 m. Midwater transecting methods and their efficacy are well-documented.</p> <p><strong>Benthic time series: </strong>Two comparable methods were used to assess benthic communities at Station M (34°50′N, 123°00′W). From 1989-2005, the identification to the lowest possible taxon, and quantity of benthic animals were recorded from images taken by a camera-sled towed along a horizontal transect above the sea floor at a speed of approximately 1 m s<sup>-1</sup>, taking a film image every 4-5 seconds (water depth ~ 4,100 m). The developed film was projected by a Beseler model 23C-II enlarger for annotation of identifiable animals in images. From 2006-2018, benthic communities were assessed using ROV video transects recorded from approximately 1.3 m above the sea floor, with a view of approximately 1 m wide, and length typically approximately 1 km. Water depth for these transects was approximately 4,000 m, the lower depth limit of the ROV. Animals visible in the video were identified and annotated using VARS. The 2006 change in sampling method and in time series location and depth was found to have little impact on the megafauna time series. </p>
SBC LTER: Reef: Benthic Composition Experiment: hourly photon irradiance at the seafloor
Photosynthetically Active Radiation (PAR) sensors measure photosynthetic light levels in both water and air in micromoles of photons per meter squared per minute (μmol m-2 min-1). PAR light sensors are deployed on the seafloor at the center of each BCE plot across 5 sampling sites (Arroyo Quemado, Naples, Isla Vista, Mohawk, Carpentaria) along the Santa Barbara Coast and 2 sites at Santa Cruz Island (San Pedro Point, and Cavern Point). Sampling began December 2021 and is conducted seasonally every 3 months. An additional sensor is placed in air on the roof at the Marine Science Biotech building near Campus Point which acts as a calibration control when analyzing underwater light measurements. Data collected from these PAR sensors are used to model primary production by both giant kelp and understory algae in terms of light availability and to compare the differences between control and experimental plots cleared of giant kelp. Plot 1 represents the control plot with no giant kelp removal and plot 2 represents the kelp clearing plot where all giant kelp are removed. Additionally, understory algae are removed seasonally from half of the rock slates for both plots (Plot 1 rock plates #1-6 and Plot 2 rock plates #13-18). Continuous light data are contained in one table depicting seasonally collected light measurements across all sites and sampling periods.
Seafloor organic carbon flux output from the NEMO-MEDUSA model
<p>This output was produced by a simulation using a coupled ocean physics and marine biogeochemistry model. The physical ocean submodel was the Nucleus for European Modeling of the Ocean (NEMO) physical ocean model (Madec, 2014), run here in a global 1/12-degree resolution configuration (ORCA0083). The marine biogeochemistry submodel was the Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification (MEDUSA-2), an intermediate-complexity plankton ecosystem model (Yool et al., 2013). The horizontal resolution of this configuration of NEMO has non-uniform grid cells ranging 2 to 9 km in size (mean 7.5 km), with 75 vertical depth levels (31 levels between the surface and 200 m depth). Sea-ice is represented in the model by the Louvian‐la‐Neuve Ice Model (LIM2) (Fichefet, & Maqueda, M. a. M., 1997; Goosse & Fichefet, 1999). The configuration was forced at the air-sea interface with version 5.2 of the DRAKKAR forcing set (DFS) (Brodeau et al., 2010). DFS 5.2 is based on ERA40 reanalysis data, comprising of 6‐hourly means for wind, humidity, and atmospheric temperature, daily means for radiative fluxes (both longwave and shortwave), and monthly means for precipitation. A monthly climatology was used for river runoff, taken from the CORE2 reanalysis (Brodeau et al., 2010; Timmermann et al., 2005). The resulting model hindcast was created using this forcing set for the period 1958–2015, with marine biogeochemistry initialised in 1990.</p> <p>This archive includes the flux of organic carbon reaching the seafloor and the area of the grid cells for the global domain. In MEDUSA, the seafloor flux is the sum of slow- and fast-sinking detrital particles that reach the base of the water column and enter the benthic submodel of MEDUSA. In general, away from shallow water regions (< 200 m), this flux is dominated by fast-sinking material produced by ecological processes associated with the large components of MEDUSA.</p> <p>The specific subset of output used was drawn from the decadal period 2006-2015, and was regridded from the non-uniform ORCA0083 grid to a regular 1/12-degree grid. Output processing was undertaken by A. Yool (axy@noc.ac.uk; National Oceanography Centre, Southampton UK).</p> <p>In addition to the netCDF files, text file dumps of their contents are included to assist with interpretation.</p> <p>References:</p> <p>Brodeau, L., Barnier, B., Treguier, A.‐M., Penduff, T., & Gulev, S. (2010). An ERA40‐based atmospheric forcing for global ocean circulation models. Ocean Modelling, 31, 88–104.</p> <p>Fichefet, T., & Maqueda, M. a. M. (1997). Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. Journal of Geophysical Research, Oceans, 102, 12,609–12,646.</p> <p>Goosse, H., & Fichefet, T. (1999). Importance of ice‐ocean interactions for the global ocean circulation: A model study. Journal of Geophysical Research, Oceans, 104, 23,337–23,355.</p> <p>Kelly, S., Popova, E., Aksenov, Y., Marsh, R., & Yool, A. (2018). Lagrangian modeling of Arctic Ocean circulation pathways: Impact of advection on spread of pollutants. J. Geophys. Res. Oceans, 123, 2882‐2902, doi: 10.1002/2017JC013460.</p> <p>Madec, G. (2014). "NEMO Ocean engine" (draft edition r5171) "NEMO Ocean engine" (draft edition r5171). Note du Pôle de modélisation, Institut Pierre‐Simon Laplace (IPSL), France, 27, 1288–1619.</p> <p>Timmermann, R., Goosse, H., Madec, G., Fichefet, T., Ethe, C., & Dulière, V. (2005). On the representation of high latitude processes in the ORCA‐LIM global coupled sea ice–ocean model. Ocean Modelling, 8, 175–201.</p> <p>Yool, A., Popova, E.E. and Anderson, T.R. (2013). MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies. Geoscientific Model Development 6, 1767-1811, doi: 10.5194/gmd-6-1767-2013.</p>
A Global Data Set of Present-Day Oceanic Crustal Age and Seafloor Spreading Parameters
<p>Datasets of present-day oceanic crustal age and seafloor spreading parameters from Seton et al. (2020).</p> <p>This dataset contains:</p> <ul> <li>Animations: animations of the present-day age grid and seafloor spreading parameters in both low and high resolution</li> <li>Feature Data: GPlates compatible files (*.gpml and *.rot) consistent with and used to create this dataset. Preferred magnetic anomaly picks are also included.</li> <li>Grids: Gridded datasets (netCDF-4 and netCDF-3) of present-day age, rate, asymmetry, direction, obliquity, confidence, and age misfit (in v1.1 only) in 6 minute resolution. Age grids are also provided in 1 and 2 minute resolution as netCDFs, and as 6 minute xyz files.</li> <li>Images: Images of the present-day age grid and seafloor spreading parameters</li> <li>Workflows: the latest workflow to create the present-day age grid can be found on GitHub: https://github.com/EarthByte/presentday-agegridding </li> </ul> <p>These files can also be downloaded from the EarthByte website <a href="https://earthbyte.org/webdav/ftp/earthbyte/agegrid/2020/">here</a>, and the global plate motion model can be found online <a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_ 2019_Tectonics">here</a>.</p> <p><strong>Please cite the dataset as:</strong><br> Seton, M., Müller, R. D., Zahirovic, S., Williams, S., Wright, N. M., Cannon, J., et al. (2020). A global data set of present‐day oceanic crustal age and seafloor spreading parameters. <em>Geochemistry, Geophysics, Geosystems</em>, 21, e2020GC009214. https://doi.org/10.1029/2020GC009214</p>
UKESM1-forced BORIS-1 seafloor biomass under CMIP6 SSPs
<p>Change in total simulated seafloor biomass between the late Scenario period (2081-2100) and late Historical period (1995-2014) under the SSP scenarios 126 to 585. Simulations use the benthic BORIS model (Kelly-Gerreyn et al., Biogeosciences, 2014) forced using output from the UKESM1 model (Sellar et al., JAMES, 2019; Yool et al., GMD, 2021) in the same experimental design used in Yool et al. (GCB, 2017). Simulations use Matlab v2020a.</p> <p> </p> <p>Kelly-Gerreyn, B. A., Martin, A. P., Bett, B. J., Anderson, T. R., Kaariainen, J. I., Main, C. E., Marcinko, C. J., and Yool, A.: Benthic biomass size spectra in shelf and deep-sea sediments, Biogeosciences, 11, 6401–6416, https://doi.org/10.5194/bg-11-6401-2014, 2014.</p> <p>Sellar, A. A., Jones, C. G., Mulcahy, J., Tang, Y., Yool, A., Wiltshire, A. O’Connor, F. M., Stringer, M., Hill, R., Palmiéri, J.,<br> Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N.<br> L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards,<br> J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C.<br> D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R.,Woodhouse, M., Zeng, G., and Zerroukat, M.: UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Sy., U J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.</p> <p>Yool, A., Palmiéri, J., Jones, C. G., de Mora, L., Kuhlbrodt, T., Popova, E. E., Nurser, A. J. G., Hirschi, J., Blaker, A. T., Coward, A. C., Blockley, E. W., and Sellar, A. A.: Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations, Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, 2021.</p> <p>Yool, A., Martin, A.P., Anderson, T.R., Bett, B.J., Jones, D.O.B., Ruhl, H.A.: Big in the benthos: Future change of seafloor community biomass in a global, body size-resolved model. Glob Change Biol., 23: 3554– 3566, https://doi.org/10.1111/gcb.13680, 2017.</p> <p> </p>
Seafloor output from the MEDUSA model
<p>- Output from the MEDUSA model (Yool et al., GMD, 2013)</p> <p>- NEMO resolution 1/4-degree</p> <p>- REGRID versions are regridded from ORCA025 grid to a regular 0.25-degree grid</p> <p>- Simulation performed as part of the ROAM project (UK Ocean Acidification Research Programme)</p> <p>- CMIP5 Historical and RCP 8.5 extension (1975-2099 inclusive)</p> <p>- Simulation described in Yool et al., JGR, 2015</p> <p>- Subset of output prepared for Mission Atlantic project by A. Yool in April 2021</p> <p>- Seafloor fields of physical and biogeochemical properties for the periods 2016-2025 and 2090-2099</p> <p>- Note that this is test output produced for a specific purpose</p> <p>- The output has been regridded to a regular 1/4-degree grid</p>
Seafloor output from the MEDUSA model
<p>- Output from the MEDUSA model (Yool et al., GMD, 2013)</p> <p>- NEMO resolution 1/4-degree</p> <p>- REGRID versions are regridded from ORCA025 grid to a regular 0.25-degree grid</p> <p>- Simulation performed as part of the ROAM project (UK Ocean Acidification Research Programme)</p> <p>- CMIP5 Historical and RCP 8.5 extension (1975-2099 inclusive)</p> <p>- Simulation described in Yool et al., JGR, 2015</p> <p>- Subset of output prepared for Mission Atlantic project by A. Yool in April 2021</p> <p>- Seafloor fields of physical and biogeochemical properties for the periods 2016-2025 and 2090-2099</p> <p>- Note that this is test output produced for a specific purpose</p> <p>- v1.3 corrects a problem in the regridding at v1.2</p>
UKESM1-forced BORIS-1 seafloor biomass under CMIP6 SSPs
<p>Annual mean seafloor biomass for periods 1980 to 2014 (Historical) and 2015 to 2100 (Future) for Shared Socioeconomic Pathways SSP126 to SSP585. Simulations use the benthic BORIS model (Kelly-Gerreyn et al., Biogeosciences, 2014) forced using output from the UKESM1 model (Sellar et al., JAMES, 2019; Yool et al., GMD, 2021) in the same experimental design used in Yool et al. (GCB, 2017). Simulations use Matlab v2020a. Each file contains seafloor detritus, total seafloor biomass and seafloor biomass for each of BORIS-1's 16 size classes.</p> <p>Kelly-Gerreyn, B. A., Martin, A. P., Bett, B. J., Anderson, T. R., Kaariainen, J. I., Main, C. E., Marcinko, C. J., and Yool, A.: Benthic biomass size spectra in shelf and deep-sea sediments, Biogeosciences, 11, 6401–6416, https://doi.org/10.5194/bg-11-6401-2014, 2014.</p> <p>Sellar, A. A., Jones, C. G., Mulcahy, J., Tang, Y., Yool, A., Wiltshire, A. O’Connor, F. M., Stringer, M., Hill, R., Palmiéri, J.,<br> Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N.<br> L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards,<br> J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C.<br> D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R.,Woodhouse, M., Zeng, G., and Zerroukat, M.: UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Sy., U J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.</p> <p>Yool, A., Palmiéri, J., Jones, C. G., de Mora, L., Kuhlbrodt, T., Popova, E. E., Nurser, A. J. G., Hirschi, J., Blaker, A. T., Coward, A. C., Blockley, E. W., and Sellar, A. A.: Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations, Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, 2021.</p> <p>Yool, A., Martin, A.P., Anderson, T.R., Bett, B.J., Jones, D.O.B., Ruhl, H.A.: Big in the benthos: Future change of seafloor community biomass in a global, body size-resolved model. Glob Change Biol., 23: 3554– 3566, https://doi.org/10.1111/gcb.13680, 2017.</p> <p> </p>
Simulated Seafloor Pressures for "The Ocean's Impact on Slow Slip Events"
<p>These files in this archive contain the simulated seafloor pressures used in the study of Gomberg et al. (2020). These seafloor pressures were derived from a Regional Ocean Modeling System (ROMS) on the seafloor of the Hikurangi subduction zone off New Zealand [<em>Hadfield et al.</em>, 2007] at offshore sites deployed during the 10-month 2014-2015 Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip (HOBITSS) experiment [<em>Wallace et al.</em>, 2016]. Details of each file are described in the Readme.pdf file.These files in this archive contain the simulated seafloor pressures used in the study of Gomberg et al. (2020). These seafloor pressures were derived from a Regional Ocean Modeling System (ROMS) on the seafloor of the Hikurangi subduction zone off New Zealand [<em>Hadfield et al.</em>, 2007] at offshore sites deployed during the 10-month 2014-2015 Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip (HOBITSS) experiment [<em>Wallace et al.</em>, 2016]. Details of each file are described in the Readme.pdf file.</p>
A Large Scale Side-Scan Sonar Dataset of Seafloor Sediments for Self-Supervised Pretraining
<p>This dataset serves as an extension to the dataset part of "A convolutional vision transformer for semantic segmentation of side-scan sonar data" published in Ocean Engineering, Volume 86, part 2, 15 October 2023,<strong> </strong>DOI: <a href="https://www.sciencedirect.com/science/article/pii/S0029801823020310">10.1016/j.oceaneng.2023.115647</a> for self-supervised pretraining.</p><p>This dataset consists of patches of side-scan sonar waterfalls collected along the coast of Catalunya during an extensive survey. The waterfalls were partitioned in batches of 384 lines to generate images of size 384 × 384 with a 192 pixel-overlap along-track and across-track. This resulted in a total of 434,164 images capturing various seafloor types including rocky bottoms, sand ripples, detrital funds, posidonia, cymocea, mud, corals, artificial reefs etc.</p><p>Additional tools for using the data for self-supervised pretraining can be found under <a href="https://github.com/DeeperSense/deepersense-seafloorscan">https://github.com/DeeperSense/deepersense-seafloorscan</a></p><p> </p><p><strong>Acknowledgements</strong></p><p>The data in this repository were collected by Tecnoambiente SL as part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020. Project Number: 101016958.</p>
Data and Models for "Probabilistic Imaging of Tsunamigenic Seafloor Deformation During the 2011 Tohoku-oki Earthquake"
<p><strong>Directory "waveform_data"</strong> includes 13 tsunami time series data from different instruments: TM1, TM2, KPG1, KPG2, GB801, GB802, GB803, GB804, GB806, GB807, D21401, D21413, and D21418. Each data file (*.dat) has two columns for (1) the time since earthquake initiation (min) and (2) ocean surface or seafloor displacement amplitude (m).</p> <p><strong>Directory “kin_models”</strong> includes the following:</p> <p>1. Seafloor Mesh Geometry</p> <ul> <li>The entire seafloor mesh consists of two separate parts (422 and 136 nodes each; 558 in total) due to the need to resolve potential discontinuity at the trench. The files “*Pt{1,2}-SM2.PointCoord.txt” consists of six columns for the ID, longitude (deg), latitude (deg), East (km), North (km) and depth (km) of the nodes in each triangular mesh. The E/N coordinates are calculated in UTM projection system, relative to an arbitrary reference point.</li> <li>The files “*.ClipPath.txt” includes the ID/lon/lat of mesh boundary nodes, which can be used for plotting.</li> <li>Visualization of the mesh parts are provided in PDF files.</li> <li>The file “*Total-SM2.PointCoord.txt” excludes boundary nodes and contains seafloor locations (504 nodes) that are directly used in tsunami arrival time calculations.</li> </ul> <p>2. Posterior Mean Models</p> <ul> <li>Ensemble-averaged models of seafloor displacements and uncertainty estimates, with no spatial averaging (“0R” in the file name) or with one-ring spatial averaging (“1R”). These models are shown in Figures 5 and 6 of <em>Jiang and Simons</em> (2016). The data files “posterior_mean_{0,1}R.txt” have three columns for (1) vertical seafloor displacement (m), (2) one-sigma standard deviation of displacement (m), and (3) corresponding resolution length (km). The model values (558 rows) correspond to nodes in files “*Pt{1,2}-SM2.PointCoord.txt” concatenated in sequential order.</li> <li>Tsunami arrival times (in sec) are calculated from the posterior mean values of propagation speeds, with zero sec at the earthquake epicenter. The coordinates (508 nodes) are included in geometry file “*Total-SM2.PointCoord.txt.”</li> </ul> <p><strong>Directory “kin_ensemble”</strong> includes the entire posterior model ensemble (98304 samples) in HDF5 format. Using a Linux command <em>h5dump</em> will show the following information about the contained datasets, with their names and dimensions. The main datasets are: (1) Covariance (1008×1008); (2) Data Log-likelihood (98304×1); (3) Posterior Log-likelihood (98304×1); and (4) Sample Set (98304×1008). Each model has 1008 parameters (504 for displacement and 504 for propagation speeds). The source coordinates (504 nodes) are included in geometry file "*Total-SM2-Parameter.PointCoord.txt."</p> <p><strong>Note:</strong> three different geometries files above are used for (1) posterior mean displacements (558 nodes), (2) arrival time calculation (508 nodes), and (3) source inversion models (504 nodes). </p>
Global derived datasets for use in k-NN machine learning prediction of global seafloor total organic carbon
<p>This dataset includes 663 predictor grids used for k-NN global prediction of seafloor total organic carbon.</p> <p>663 predictor grids available in netCDF4 HDF5 file format. Grids are cell-centered sized 4320 x 2160. File names adhere to the naming conventions discussed below. The naming structure is partioned by underscores and periods in the following order: interface to which the gridded values refer to, quantity of values contained within the grid, units and reference values/units (e.g. meters below sea level), data source, statistic calculated (if applicable), grid pitch, and file extension.</p> <p>Possible interfaces from the top – down:</p> <p>SS – Sea surface – atmosphere interface (may also be average of the entire water column)</p> <p>SF – Seafloor – water interface (may also be denoted by GL)</p> <p>GL – Ground level (e.g. bottom of pure liquid, top of dirt)</p> <p>SC – Sediment – crust interface (e.g. sediment above, igneous/metamorphic below)</p> <p>CM – Crust – mantle interface (e.g. Mohorovicic discontinuity)</p> <p>Appropriate reference naming marker (bold), original data source, and date of last access:</p> <p><strong>Becker</strong></p> <p>Becker, J. J., Wood, W. T., & Martin, K. M. (2014). <em>Global crustal heat flow using random decision forest prediction</em>, Abstract NG31A-3788 presented at 2014 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 06/23/2015.</p> <p><strong>CRUST1</strong> </p> <p>Pasyanos, M.E., Masters, G., Laske, G. & Ma, Z. (2012). <em>LITHO1.0 - An Updated Crust and Lithospheric Model of the Earth Developed Using Multiple Data Constraints</em>, Abstract T11D-09 presented at 2012 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 07/01/2014.</p> <p><strong>CRUST1_NOAA</strong></p> <p> As the NOAA sediment thickness database is globally not complete, data gaps in the NOAA grid with this have been supplemented by the CRUST1 sediment thickness (see above citation).</p> <p>Whittaker, J., Goncharov, A., Williams, S., Müller, R. D., & Leitchenkov, G. (2013) Global sediment thickness dataset updated for the Australian-Antarctic Southern Ocean, <em>Geochemistry, Geophysics, Geosystems. </em>https://doi.org/10.1002/ggge.2018.<em> </em>Last access: 09/02/2018.</p> <p><strong>GVP</strong></p> <p>Global Volcanism Program (2013) Volcanoes of the World. In E. Venzke (ed.). (Vol. 4.7.3). Smithsonian Institution. https://doi.org/10.5479/si.GVP.VOTW4-2013. Last access: 09/22/2014.</p> <p><strong>ETOPO2v2</strong></p> <p>National Geophysical Data Center (2006). 2-minute Gridded Global Relief Data (ETOPO2) v2. National Geophysical Data Center, NOAA. DOI: 10.7289/V5J1012Q. Last access: 02/06/2013.</p> <p><strong>PLATES</strong></p> <p>Coffin, M.F., Gahagan, L.M., & Lawver, L.A. (1998). Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report (No. 174, pp. 5). Last access: 09/15/2014.</p> <p><strong>ONRL</strong></p> <p>Ludwig,W., Amiotte-Suchet, P., & Probst, J. L. (2011). ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans. In F. G. Hall, G. Collatz, B. Meeson, S. Los, E. Brown de Colstoun, and D. Landis (Eds.), <em>ISLSCP Initiative II Collection</em>. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1028. Last Access: 02/15/2015.</p> <p><strong>Muller</strong></p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust, <em>Geochemistry, Geophysics, Geosystems</em>, 9(4), Q04006. https://doi.org/10.1029/2007GC001743. Last accessed: 07/19/2011.</p> <p><strong>Woa13x</strong></p> <p>Boyer, T.P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., et al. (2013) World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), <em>NOAA Atlas NESDIS 72, Technical Ed</em>. Silver Spring, MD. http://doi.org/10.7289/V5NZ85MT. Last Access: 09/18/2014.</p> <p><strong>KIM</strong></p> <p>Kim, S.S. & Wessel, P. (2011). New global seamount census from the altimetry-derived gravity data, <em>Geophysical Journal International</em>, 186, 615-631. https://doi.org/10.1111/j.1365-246X.2011.05076.x. Last access: 09/22/2014.</p> <p><strong>HYCOM</strong></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data.Last access: 03/19/2014.</p> <p><strong>NCEDC</strong></p> <p>NCEDC (2016). Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Last access: 09/21/2014.</p> <p><strong>Wei2010</strong></p> <p>Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M. J., et al.(2010). Global patterns and predictions of seafloor biomass using random forests. <em>PLoS ONE</em>,5(12), e15323. https://doi.org/10.1371/journal.pone.0015323 Last access: 06/20/2016.</p> <p><strong>NGA_egm2008</strong></p> <p>Pavlis, N.K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). <em>The</em> <em>EGM2008 Global Gravitational Model</em>, Abstract 2008AGUFM.G22A..01P presented at the 2008 General Assembly of the European Geosciences Union, Vienna, Austria. Last access: 07/10/2014.</p> <p><strong>WAVEWATCH3</strong></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data. Last access: 03/19/2014.</p> <p>Updated global seafloor porosity grid using our k-nearest neighbors algorithm using 5 nearest neighbors. Observed data used for prediction from Martin et al. (2015). </p> <p>Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. <em>Geophysical Research Letters</em>, 42(24), 10640. https://doi.org/10.1002/2015GL065279</p> <p>Other grids which have been generated by empirical means are latitude (and derivatives), longitude (and derivatives), Coriolis, coast_is_1.0, and the random noise grids. </p> <p>Units referenced are as follows:</p> <p>KGM3 - kilogram per cubic meter<br> MS - meters per second<br> KM - kilometer<br> M_ASL - meters above sea level (i.e. meters referenced to sea level)<br> MWM2 - milliwatt per square meter<br> TGCYR - terragram of carbon per year<br> TGYR - terragram per year<br> MA - megaannum<br> M - meters<br> MGCM2 - milligram of carbon per square meter<br> DEG - degree<br> S - seconds</p> <p>Statistics grids are calculated within a given radius (e.g. 10km, 50km, 125km, 250km, 500km, 1000km) of the respective cell-centered value. The statistics grids include mean (.men), average absolute deviation from the mean (.aad), and the common logarithm (.log) of the absolute value of the mean (.mlg). Additionally, some grids are a weighted count for given radii (e.g. seamounts) where weight is a cosine taper from the center of the grid cell. </p> <p>The grid pitch for this dataset is uniformly at 5-arc minute denoted by “.5m”. Additionally, the extension used (netCDF4) is denoted by “.nc”.</p>
LeConte Bay Seafloor Elevation Data
<p>Multibeam data of LeConte Bay seafloor from the inner fjord.</p> <p>These are processed multibeam sonar data from LeConte Bay, southeast Alaska. The data come from five cruises grouped into five files:<br> 1) Aug 1999 and Sep 2000 (20-m resolution) - 1 file;<br> 2) August 2016 (20-m and 4-m resolution) - 2 files;<br> 3) May 2017 (2-m resolution) - 1 file;<br> 4) Sep 2018 (2-m resolution) - 1 file.</p> <p>The datasets collected in 2016-2018 originally included ice-face data, as reported in Sutherland et al., 2019 (DOI 10.1126/science.aax3528). The data presented here include only the seafloor data, exclusive of the ice face. They are referenced to WGS84, UTM zone 8 (meters easting/northing), and mean sea level.</p> <p>Reference and more details: Eidam, E.F., Sutherland, D.A., Duncan, D., Kienholz, C., Amundson, J.M., Motyka, R.J. Morainal bank evolution and fjord infilling during a tidewater glacier stillstand, on seasonal to decadal timescales. In prep for JGR-Earth Surface, September 2019.</p>
NOAA NCCOS Assessment: Prioritizing Areas for Future Seafloor Mapping, Research, and Exploration Offshore of California, Oregon, and Washington from 2019-03-01 to 2019-04-01
<p>Spatial information about the seafloor is critical for decision-making by marine resource science, management and tribal organizations. Coordinating data needs can help organizations leverage collective resources to meet shared goals. To help enable this coordination, the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process and online application to identify common data collection priorities for seafloor mapping, sampling and visual surveys offshore of the West Continental United States Coast (WCC). Twenty-six participants from NOAA’s West Coast Deep Sea Coral Initiative (WCDSCI) and Expanding Pacific Research and Exploration of Submerged Systems (EXPRESS) entered their priorities in an online application, using virtual coins to denote their priorities in 10x10 minute grid cells. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Ten high priority locations were broadly identified for future mapping, sampling and visual surveys. These locations were distributed throughout the WCC, primarily in depths less than 1,000 m. Participants consistently selected (1) Exploration, (2) Biota/Important Natural Area and (3) Research as their top reasons (i.e., justifications) for prioritizing locations, and (1) Benthic Habitat Map and (2) Bathymetry and Backscatter as their top data or product needs. This ESRI shapefile summarizes the results from this spatial prioritization effort. This information will enable NOAA WCDSCI, EXPRESS and other WCC organization to more efficiently leverage resources and coordinate their mapping of high priority locations along California, Oregon and Washington. </p> <p>This effort was funded by NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) through its WCDSCI. The overall goal of the project was to systematically gather and quantify suggestions for seafloor mapping, sampling and visual surveys for the WCDSCI and EXPRESS. The results are expected to help WCDSCI, EXPRESS and other organizations on the WCC to identify locations where their interests overlap with other organizations, to coordinate their data needs and to leverage collective resources to meet shared goals.</p> <p>There were four main steps in the WCC spatial prioritization process. The first step was to identify the technical advisory team, which included the 11 members of the DSCRTP WCDSCI Steering Committee and all of the participants involved in the EXPRESS campaign. This advisory team invited 37 participants for the prioritization. Step two was to develop the spatial framework and an online application. To do this, the WCC was divided into five subregions and 3,265 square grid cells approximately 10x10 minutes in size. Existing relevant spatial datasets (<em>e.g.</em>, bathymetry, protected area boundaries, etc.) were compiled to help participants understand information and data gaps and to identify areas they wanted to prioritize for future data collections. These spatial datasets were housed in the online application, which was developed using Esri’s Web AppBuilder. In step three, this online application was used by 26 participants to enter their priorities in each subregion of interest. Participants allocated virtual coins in the 10x10 minute grid cells to denote their priorities. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Coin values were standardized across the subregions and used to identify spatial patterns across the WCC region as a whole. The number of coins were standardized because each subregion had a different number of grid cells and participants. Standardized coin values were analyzed and mapped using statistical techniques, including hierarchical cluster analysis, to identify significant relationships between priorities, reasons for those priorities and data needs. This ESRI shapefile contains the 10x10 minute grid cells used in this prioritization effort and associated the standardized coin values overall, as well as by organization, justification and product. For a complete description of the process and analyses please see: Costa <em>et al</em>. 2019.</p>
Underwater hyperspectral data of shallow water seafloor at Vigo-Rias Biaxas sea zones
<h2>Abstract</h2> <p>Raw hyperspectral data of shallow water seafloor at three different Vigo-Rias Biaxas sea zones from Vigo fieldwork Sept. 2023. The hyperspectral data was recorded by Ecotone UHI (underwater hyperspectral imaging) installed on customized BlueROV 2. The UHI was run about 0.3-2m above the seafloor.</p> <p>This depositry contains data generated within the European S34 project. The data are in raw form and have not been further processed. Processed data are published in other depositories.</p> <h2>Metadata information</h2> <p> </p> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Underwater hyperspectral data of shallow water seafloor at Vigo-Rias Biaxas sea zones</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Raw hyperspectral data of shallow water seafloor at three different Vigo-Rias Biaxas sea zones from Vigo fieldwork Sept. 2023. The hyperspectral data was recorded by Ecotone UHI (underwater hyperspectral imaging) installed on customized BlueROV 2. The UHI was run about 0.3-2m above the seafloor.</p> <p>This depositry contains data generated within the European S34 project. The data are in raw form and have not been further processed. Processed data are published in other depositories.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Raw hyperspectral data, underwater hyperspectral data, UHI, seafloor, seabed, mineral, sea region, Vigo, Vigo-Rias Biaxas</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</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>Bio-geographical regions</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>27.09.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>HDF5</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>2mm (with UHI about 2m away)</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>3035</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>no</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>no</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>Ecotone As</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone As</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone As, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table> <p> </p>
Deep Submergence Dive location dataset from Bell et al. Sci Advances: How Little We've Seen: A Visual Coverage Estimate of the Deep Seafloor
<p><span>How Little We’ve Seen: A Visual Coverage Estimate of the Deep Seafloor </span></p> <p><span>Katherine L.C. Bell,</span><sup><span>1</span></sup><em><sup><span>∗</span></sup></em><em><sup><span> </span></sup></em><span>Kristen N. Johannes,</span><sup><span>1<em>,</em>2</span></sup><span> </span></p> <p><span>Brian R.C. Kennedy,</span><sup><span>1<em>,</em>3 </span></sup><span>Susan E. Poulton</span><sup><span>1</span></sup><span> </span></p> <p><sup><span>1</span></sup><span>Ocean Discovery League, Saunderstown, RI 02874, USA, </span></p> <p><sup><span>2</span></sup><span>Integrative Oceanography Division, Scripps Institution of Oceanography, University of California San Diego, San Diego, CA 92037, USA </span></p> <p><sup><span>3</span></sup><span>Biology Department, Boston University, Boston, MA 02215 USA </span></p> <p><em><sup><span>∗</span></sup></em><span>To whom correspondence should be addressed: croff@alum.mit.edu. </span></p> <p><span><br>Despite the importance of visual observation in the ocean, we have imaged a minuscule fraction of the deep seafloor. Sixty-six percent of the entire planet is deep ocean (≥200 m), and our data show we have visually observed less than 0.001%, a total area approximately a tenth of the size of Belgium. Data gathered from over 44 thousand deep-sea dives indicate we have also seen an incredibly biased sample. Sixty-five percent of all in situ visual seafloor observations in our dataset were within 200 nm of only three countries: the United States, Japan, and New Zealand. Ninety-seven percent of all dives we compiled have been conducted by just five countries: the United States, Japan, New Zealand, France, and Germany. This small and biased sample is problematic when attempting to characterize, understand, and manage a global ocean.</span></p>
Seafloor output from the MEDUSA model
<p>- Output from the MEDUSA model (Yool et al., GMD, 2013)</p> <p>- NEMO resolution 1/12-degree</p> <p>- REGRID versions are regridded from ORCA0083 grid to a regular 1/12-degree grid</p> <p>- Simulation performed as part of core NOC activities</p> <p>- Forced under version 5.2 of the DRAKKAR observation-based reanalysis dataset (DFS)</p> <p>- Physical simulation described in Kelly, S. J., Popova, E., Aksenov, Y., Marsh, R., & Yool, A. (2020). They came from the Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean. Earth's Future, 8, e2019EF001394. https://doi.org/10.1029/2019EF001394</p> <p>- Subset of output prepared for Mission Atlantic project by A. Yool in August 2021</p> <p>- Seafloor fields of model properties for the periods 2006-2015</p> <p>- Note that this is test output produced for a specific purpose</p> <p>- The output has been regridded to a regular 1/12-degree grid</p>
Data for paper "An adaptive nonlinear iterative method for predicting seafloor topography from altimetry-derived gravity data"
<p>LM is the linear inversion seafloor topography model</p> <p>NLM is the nonlinear inversion seafloor topography model</p> <p>PM is the prior seafloor topography model</p>
Abyssal NE Pacific Seafloor Megafauna Dataset
<p>Benthic megafauna invertebrate (animals > 10 mm) observations from seabed imagery data collected across the Clarion Clipperton Zone, in the NE Pacific abyss: 53512 specimens classified in 400+ morphotypes (13 Phyla) based on the APSMA catalogue (see <a href="https://zenodo.org/record/7765164">https://zenodo.org/record/7765164</a>).</p> <p>Dataset used to develop (please cite as): Simon-Lledó, et al. (2023). Carbonate compensation depth drives abyssal biogeography in the northeast Pacific. <em>Nature Ecology & Evolution</em>; doi:10.1038/s41559-023-02122-9</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.