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Small, coastal temperate rainforest watersheds dominate organic carbon transport to the northeast Pacific Ocean
The northeast Pacific Coastal Temperate Rainforest (NPCTR) extending from southeast Alaska to northern California is characterized by high precipitation and large stores of recently fixed biological carbon. We show that 3.4 Tg-C yr-1 as DOC is exported from the NPCTR drainage basin to the coastal ocean. More than 56% of this riverine DOC flux originates from thousands of small (mean = 118 km2), coastal watersheds that comprise 22% of the NPCTR drainage basin. The average DOC yield from NPCTR coastal watersheds (6.20 g-C m-2 yr-1) exceeds that from Earth’s tropical regions by roughly a factor of three. The highest yields occur in small, coastal watersheds in the central NPCTR due to the balance of moderate temperature, high precipitation, and high soil organic carbon stocks. These findings indicate that DOC export from NPCTR watersheds may play an important role in regional-scale heterotrophy within near-shore marine ecosystems in the northeast Pacific. These are the datasets used in this analysis
Final chlorophyll and temperature measurements at 10m depth, offshore of Dana Point, California as part of an Ocean Institute time series, 2006 - 2025.
Time series of chlorophyll and temperature at 10m offshore of Dana Point, California. Measurements made as part of education/outreach programs involving students and teachers in oceanographic sampling and analytic sensitivity to time series. Cruises are conducted twice, monthly (or other) where sampling is performed by students assisted by technicians and supervisors.
MCR LTER: Coral Reef: Ocean Currents and Biogeochemistry: Moored Thermistor String Data - CBYTS
A vertically moored thermistor string sampled year-round on the reef at Cook's Bay in Moorea, French Polynesia. Sampling began in 2005 and ended in August 2011. All data have been interpolated onto a 20 min grid. Thermistors were spaced vertically along the mooring line 4, 8, 12, 16, and 20 meters above the bottom. Pressure measurements are also provided from two of the instruments typically located at 4 and 20 meters above the bottom.
MCR LTER: Reef Topography data from Duvall et al., JGR Oceans 2019
This archive contains natural coral reef topography data from the southeast coast of Mo’orea, French Polynesia and idealized reef topography generated with a fractional Brownian motion (fBm) algorithm. These data were used to understand and compare different metrics for quantifying coral reef roughness. These data relate to this publication: Duvall, M. S., J. L. Hench, and J. H. Rosman, in press, Collapsing complexity: metrics to quantify multi-scale properties of reef topography. To appear in Journal of Geophysical Research (Oceans). This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
SBC LTER: OCEAN: Time series of sediment temperatures by depth
This data package includes the results of efforts to resolve the flushing rates of pore waters in sediments adjacent to Mohawk Reef (34, 23.251 N, 119, 43.685 W). Tidbit temperature sensors were attached to a fiberglass pole at set intervals. The pole was then buried in the sediments and left to log temperature at four depths below the seafloor (5, 15, 30, 45 cm) and two above the seafloor (10 cm, 50 cm) every five minutes for three weeks.
SBC LTER: OCEAN: Sediment porewater ammonium and urea concentrations
This data set present the results of efforts to characterize the concentrations of ammonium and (selectively) urea in pore waters of permeable, sandy sediments on the inner continental shelf of the Santa Barbara Channel, including those adjacent to major kelp beds. Samples sites included Arroyo Burro, Mission Creek, Refugio and Mohawk. A second data file include depth profiles of ammonium and urea concentrations in the water column above each sediment sampling site, which were used to discern the strength of the vertical gradients of ammonium and urea concentrations between the surficial sediments and the water column.
SBC LTER: Ocean: High-resolution Landsat 8 chlorophyll imagery of the Santa Barbara Channel
This is a timeseries of Landsat 8 images of the Santa Barbara Channel, processed for both chlorophyll and particulate backscattering. These data are at a particularly high spatial resolution (30 m), making them useful for analysis of submesoscale biophysical and biogeochemical interactions in the SBC, and for comparison with high-resolution modeling results. Data are contained in netcdf files, and there are 88 images in total, spanning the deployment of Landsat 8 (2013 - present). MATLAB scripts are available to load the netcdf files, flag the bad pixels, interpolate over the flagged pixels, and plot images, in the matlab folder. In addition, Landsat8_chl_imagery.zip contains jpegs of all of the images. A recommended workflow to users would be looking through all the jpegs to decide what images you want, and then downloading those specific netcdf files and using the matlab scripts and accompanying functions to process them. Our hope is that concurrent study of submesoscale variability in phytoplankton biomass via both submesoscale-resolving model studies and high-resolution satellite imagery may reveal further insights into the importance of submesoscale biophysical variability to regional and global biogeochemical processes.
SBC LTER: Ocean: Currents and Biogeochemistry: Moored CTD and ADCP data from Purisima Mooring (PUR), 1999-2016
ADCP (Currents), CTD (Hydrography) and Optics data (Fluorescence, Beam Attenuation and Volume Scattering Function) were collected at La Purisima (site ID: PUR), north of Point Conception. Data have been interpolated to a 20 minute interval. ADCP data are binned at a 1.0 meter interval, measured as height from the bottom to a maximum of 16 bins. All bins may not be filled, and in some cases, data from bins technically above the surface are included. VSF data are available at angles, 100, 125 and 150 degrees. CTD parameters include Pressure, Temperature, Conductivity, Salinity, Density and Fluorescence. The CTD array is located approximately 4.5 meters from the surface, and there are additional temperature thermistors near the CTD array, at the bottom, and mid way between these two.
SBC LTER: Ocean: Time-series: Mid-water SeaFET pH and CO2 system chemistry with surface and bottom Dissolved Oxygen at Arroyo Quemado Reef(ARQ), 2012-2017
Calibrated pH (Total scale, SeaFET sensor) an disoolved oxygen (miniDOT)data were collected from Arroyo Quemado Reef in the Santa Barbara Channel (site ID: ARQ). pH data are accompanied by in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Dissolved oxygen sensors (miniDOT, PME) were added in 2014, and are mounted near the ocean surface and near the seafloor, and also report temperature. All data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2012-07-30 to 2017-03-10.All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005
SBC LTER: Ocean: Time-series: Mid-water SeaFET pH and CO2 system chemistry with surface and bottom Dissolved Oxygen at Mohawk Reef(MKO), 2012 - 2017
Calibrated pH (Total scale, SeaFET sensor) an disoolved oxygen (miniDOT)data were collected from Mohawk Reef in the Santa Barbara Channel (site ID: MKO). pH data are accompanied by in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Dissolved oxygen sensors (miniDOT, PME) were added in 2014, and are mounted near the ocean surface and near the seafloor, and also report temperature. All data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2012-01-11 to 2017-12-19.The update of this dataset was terminated in 2019. All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005
SBC LTER: Ocean: Time-series: Mid-water SeaFET pH and CO2 system chemistry with surface and bottom Dissolved Oxygen at Santa Barbara Harbor/Stearns Wharf(SBH), 2012-2017
Calibrated pH (Total scale, SeaFET sensor) an disoolved oxygen (miniDOT)data were collected from Santa Barbara Harbor/Stearns Wharf in the Santa Barbara Channel (site ID: SBH). pH data are accompanied by in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Dissolved oxygen sensors (miniDOT, PME) were added in 2014, and are mounted near the ocean surface and near the seafloor, and also report temperature. All data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2012-09-15 to 2016-09-14. The update of this dataset was terminated in 2019. All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005
SBC LTER: Land Ocean Reef: Carbon, Nitogen and Hydrogen isotopes for evaluating food sources for subtidal consumers, 2009-2010
Potentially important food sources to consumers on shallow subtidal reefs include phytoplankton-dominated seston, kelp-derived detritus, and for locations adjacent to sources of freshwater runoff, terrestrially-derived material. The SBC LTER is using stable carbon, nitrogen and deuterium isotope ratio analysis to evaulate the relative contribution of these sources to reef food webs. We collect seasonal samples from five core SBCLTER research reefs (Arroyo Hondo, Naples, Arroyo Burro, Goleta bay and Carpinteria), and areas adjacent. We routinely collect several types of samples: from giant kelp (Macrocystis pyrifera), red, brown and green algae, terrestrial material (Oak leaves), stream, sediment and ocean water particulate organic material, and a benthic polychaete worm (Diopatra). This information will be used to evaluate whether these isotopic values differ enough from one another to permit the use of mixing models to estimate the contribution of each source to the reef food web.
They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1990-2002 (2 of 2)
<p>Supporting data for Kelly et al.: They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean (Earth's Future, submitted)<br> <br> Trajectories saved by year of release in the Bering Strait. All months from that year are included in the same file, with the first 1000 trajectories corresponding to January release, second 1000 from February release, and so on. <br> <br> Due to the size of files, this is split into two uploads. Part 1 covers 1970-1989 releases, 1990 onward is saved in Part 2. </p>
They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1970-1989 (1 of 2)
<p>Supporting data for Kelly et al.: They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean (Earth's Future, submitted)<br> <br> Trajectories saved by year of release in the Bering Strait. All months from that year are included in the same file, with the first 1000 trajectories corresponding to January release, second 1000 from February release, and so on. <br> <br> Due to the size of files, this is split into two uploads. Part 1 covers 1970-1989 releases, 1990 onward is saved in Part 2. </p>
A New Method for Accurate and Efficient Modeling of the Local Ocean Induction Effects. Application to Long-Period Responses from Island Geomagnetic Observatories
<p>Dataset presented in Figures 3-7, S1 and S3 in the recently submitted AGU paper "A New Method for Accurate and Efficient Modeling of the Local Ocean Induction Effects. Application to Long-Period Responses from Island Geomagnetic Observatories".</p>
Fig. 6 in Reproductive biology of the eyespot skate Atlantoraja cyclophora (Elasmobranchii: Arhynchobatidae) an endemic species of the Southwestern Atlantic Ocean (34ºS - 42ºS)
Fig. 6. Seasonal variation in gonadosomatic (GSI) and hepatosomatic (HSI) indexes for a.-b. males and c.-d. females of Atlantoraja cyclophora. The number of samples analyzed is between parentheses. The boxes represent the interquartile range between Q1 and Q3 with the 50% of data, the central line represents the median value and whiskers extend to the maximum and minimum values
Particle tracking dataset for: Exceptional 20th century ocean circulation in the Northeast Atlantic
<p>Particle tracking data for: "Exceptional 20th century ocean circulation in the Northeast Atlantic" Peter T. Spooner, David J. R. Thornalley, Delia W. Oppo, Alan Fox, Svetlana Radionovskaya, Neil L. Rose, Robbie Mallett, Emma Cooper, J. Murray Roberts</p> <p>VIKING20 (is a 1/20th degree ocean model, forced by a hindcast simulation of the atmosphere: CORE2 (Griffies et al., 2009). The reverse tracks of 113200 particles per year for 50 years, (1959-2009) were simulated with the ARIANE software (Döös, 1995) modified to include independent vertical motion of particles. Particles were seeded at the seabed in 10 km x 10 km boxes centered on MC16-A/17-5P and RAPID-21-3K (representing the settling location). The reverse tracks 'rose' (sinking) at 100 m/day (Takahashi & Be, 1984) and were then allowed to drift freely within the upper 100 m of the water column for six months (i.e. spanning the reasonable lifespan for many species of planktic foraminifera).</p> <p>Track data for the full 50 years are stored in a single netcdf file (output of ncdump -h <filename> given below). The 3D particle positions are in variables traj_lon, traj_lat and traj_depth with the Viking20 model along-track temperature, salinity and density in traj_temp, temp_sal and traj_dens, respectively. The main complication is the obscure storage of time (see also ARIANE software documentation). Variable init_t gives particle start time, counting in 5-day periods from 12:00 pm on 29 December 1957. Viking20 uses a fixed 365 day year so the year can be found for track 'traj' according to:</p> <p> year = 1958 + ( (init_t(traj)-1) \ 73 ) where '\' represents integer division, discarding the remainder.</p> <p>All particle tracks 'begin' (actually the end of the track in time as these are tracked backwards) at the start of July (12:00 pm July 1 in model). Particles are ordered by release time, so trajectories 1-113200 are 1959; 113201-226400 are 1960; etc. Positions are stored every 5 days, counting backwards.</p> <p>Further details are available from the authors.</p> <p> </p> <p>References</p> <p>Döös, K. (1995). Interocean exchange of water masses. Journal of Geophysical Research, 100(C7), 13499. <a href="https://doi.org/10.1029/95JC00337">https://doi.org/10.1029/95JC00337</a></p> <p>Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G., Chassignet, E. P., et al. (2009). Coordinated Ocean-ice Reference Experiments (COREs). Ocean Modelling, 26(1–2), 1–46. <a href="https://doi.org/10.1016/J.OCEMOD.2008.08.007">https://doi.org/10.1016/J.OCEMOD.2008.08.007</a></p> <p>Takahashi, K., & Be, A. W. H. (1984). Planktonic foraminifera: factors controlling sinking speeds. Deep Sea Research Part A. Oceanographic Research Papers, 31(12), 1477–1500. <a href="https://doi.org/10.1016/0198-0149(84)90083-9">https://doi.org/10.1016/0198-0149(84)90083-9</a></p> <p> </p> <p>$ ncdump -h ariane_trajectories_qualitative.nc</p> <p>netcdf ariane_trajectories_qualitative {</p> <p>dimensions:</p> <p>ntraj = 5660000 ;</p> <p>nb_output = UNLIMITED ; // (74 currently)</p> <p>variables:</p> <p><strong>double init_x(ntraj) ;</strong></p> <p>init_x:title = "What is init_x ?" ;</p> <p>init_x:longname = "Initial position in i" ;</p> <p>init_x:units = "No dimension" ;</p> <p>init_x:missing_value = 1.e+20 ;</p> <p><strong>double init_y(ntraj) ;</strong></p> <p>init_y:title = "What is init_y ?" ;</p> <p>init_y:longname = "Initial position in j" ;</p> <p>init_y:units = "No dimension" ;</p> <p>init_y:missing_value = 1.e+20 ;</p> <p><strong>double init_z(ntraj) ;</strong></p> <p>init_z:title = "What is init_z ?" ;</p> <p>init_z:longname = "Initial position in k" ;</p> <p>init_z:units = "No dimension" ;</p> <p>init_z:missing_value = 1.e+20 ;</p> <p><strong>double init_t(ntraj) ;</strong></p> <p>init_t:title = "What is init_t ?" ;</p> <p>init_t:longname = "Initial position in l (time)" ;</p> <p>init_t:units = "See global attributes..." ;</p> <p>init_t:missing_value = 1.e+20 ;</p> <p><strong>double init_age(ntraj) ;</strong></p> <p>init_age:title = "What is init_age ?" ;</p> <p>init_age:longname = "Initial age (time)" ;</p> <p>init_age:units = "seconds" ;</p> <p>init_age:missing_value = 1.e+20 ;</p> <p><strong>double init_transp(ntraj) ;</strong></p> <p>init_transp:title = "What is init_transp ?" ;</p> <p>init_transp:longname = "Initial transport" ;</p> <p>init_transp:units = "m3/s" ;</p> <p>init_transp:missing_value = 1.e+20 ;</p> <p><strong>double l_matureage(ntraj) ;</strong></p> <p>l_matureage:title = "What is l_matureage ?" ;</p> <p>l_matureage:longname = "Larval age of maturity" ;</p> <p>l_matureage:units = "days" ;</p> <p>l_matureage:missing_value = 1.e+20 ;</p> <p><strong>double l_descendage(ntraj) ;</strong></p> <p>l_descendage:title = "What is l_descendage ?" ;</p> <p>l_descendage:longname = "Larval age of competency" ;</p> <p>l_descendage:units = "days" ;</p> <p>l_descendage:missing_value = 1.e+20 ;</p> <p><strong>double l_maxspeedup(ntraj) ;</strong></p> <p>l_maxspeedup:title = "What is l_maxspeedup ?" ;</p> <p>l_maxspeedup:longname = "Max upward larval swim speed" ;</p> <p>l_maxspeedup:units = "mm s-1" ;</p> <p>l_maxspeedup:missing_value = 1.e+20 ;</p> <p><strong>double l_maxspeeddown(ntraj) ;</strong></p> <p>l_maxspeeddown:title = "What is l_maxspeeddown ?" ;</p> <p>l_maxspeeddown:longname = "Max downward larval swim speed" ;</p> <p>l_maxspeeddown:units = "mm s-1" ;</p> <p>l_maxspeeddown:missing_value = 1.e+20 ;</p> <p><strong>int l_targetdepth(ntraj) ;</strong></p> <p>l_targetdepth:title = "What is l_targetdepth ?" ;</p> <p>l_targetdepth:longname = "Target shallow depth" ;</p> <p>l_targetdepth:units = "No dimension" ;</p> <p>l_targetdepth:missing_value = -1. ;</p> <p><strong>double final_x(ntraj) ;</strong></p> <p>final_x:title = "What is final_x ?" ;</p> <p>final_x:longname = "Final position in x (or i)" ;</p> <p>final_x:units = "No dimension" ;</p> <p>final_x:missing_value = 1.e+20 ;</p> <p><strong>double final_y(ntraj) ;</strong></p> <p>final_y:title = "What is final_y ?" ;</p> <p>final_y:longname = "Final position in y (or j)" ;</p> <p>final_y:units = "No dimension" ;</p> <p>final_y:missing_value = 1.e+20 ;</p> <p><strong>double final_z(ntraj) </strong>;</p> <p>final_z:title = "What is final_z ?" ;</p> <p>final_z:longname = "Final position in z (or k)" ;</p> <p>final_z:units = "No dimension" ;</p> <p>final_z:missing_value = 1.e+20 ;</p> <p><strong>double final_t(ntraj) ;</strong></p> <p>final_t:title = "What is final_t ?" ;</p> <p>final_t:longname = "Final position in t (time)" ;</p> <p>final_t:units = "See global attributes..." ;</p> <p>final_t:missing_value = 1.e+20 ;</p> <p><strong>double final_age(ntraj) ;</strong></p> <p>final_age:title = "What is fial_age ?" ;</p> <p>final_age:longname = "Final Age." ;</p> <p>final_age:units = "seconds" ;</p> <p>final_age:missing_value = 1.e+20 ;</p> <p><strong>double final_transp(ntraj) ;</strong></p> <p>final_transp:title = "What is final_transp ?" ;</p> <p>final_transp:longname = "Final transport" ;</p> <p>final_transp:units = "m3/s" ;</p> <p>final_transp:missing_value = 1.e+20 ;</p> <p><strong>float traj_lon(nb_output, ntraj) ;</strong></p> <p>traj_lon:title = "What is traj_lon ?" ;</p> <p>traj_lon:longname = "Trajectory: x positions" ;</p> <p>traj_lon:units = "No dimension" ;</p> <p>traj_lon:missing_value = 1.e+20 ;</p> <p><strong>float traj_lat(nb_output, ntraj) ;</strong></p> <p>traj_lat:title = "What is traj_lat ?" ;</p> <p>traj_lat:longname = "Trajectory: y positions" ;</p> <p>traj_lat:units = "No dimension" ;</p> <p>traj_lat:missing_value = 1.e+20 ;</p> <p><strong>float traj_depth(nb_output, ntraj) ;</strong></p> <p>traj_depth:title = "What is traj_depth ?" ;</p> <p>traj_depth:longname = "Trajectory: z positions" ;</p> <p>traj_depth:units = "No dimension" ;</p> <p>traj_depth:missing_value = 1.e+20 ;</p> <p><strong>float traj_time(nb_output, ntraj) ;</strong></p> <p>traj_time:title = "What is traj_time ?" ;</p> <p>traj_time:longname = "Trajectory: time positions" ;</p> <p>traj_time:units = "See global attributes" ;</p> <p>traj_time:missing_value = 1.e+20 ;</p> <p><strong>float traj_iU(nb_output, ntraj) ;</strong></p> <p>traj_iU:title = "ind i on grid U" ;</p> <p>traj_iU:longname = "Trajectory: i on grid U" ;</p> <p>traj_iU:units = "No dimension" ;</p> <p>traj_iU:missing_value = 1.e+20 ;</p> <p><strong>float traj_jV(nb_output, ntraj) ;</strong></p> <p>traj_jV:title = "ind j on grid V" ;</p> <p>traj_jV:longname = "Trajectory: j on grid V" ;</p> <p>traj_jV:units = "No dimension" ;</p> <p>traj_jV:missing_value = 1.e+20 ;</p> <p><strong>float traj_kW(nb_output, ntraj) ;</strong></p> <p>traj_kW:title = "ind k on grid W" ;</p> <p>traj_kW:longname = "Trajectory: k on grid W" ;</p> <p>traj_kW:units = "No dimension" ;</p> <p>traj_kW:missing_value = 1.e+20 ;</p> <p><strong>float traj_temp(nb_output, ntraj) ;</strong></p> <p>traj_temp:title = "What is traj_temp ?" ;</p> <p>traj_temp:longname = "Trajectory: temperatures" ;</p> <p>traj_temp:units = "degres" ;</p> <p>traj_temp:missing_value = 1.e+20 ;</p> <p><strong>float traj_salt(nb_output, ntraj) ;</strong></p> <p>traj_salt:title = "What is traj_salt ?" ;</p> <p>traj_salt:longname = "Trajectory: salinities" ;</p> <p>traj_salt:units = "psu" ;</p> <p>traj_salt:missing_value = 1.e+20 ;</p> <p><strong>float traj_dens(nb_output, ntraj) ;</strong></p> <p>traj_dens:title = "What is traj_dens ?" ;</p> <p>traj_dens:longname = "Trajectory: densities" ;</p> <p>traj_dens:units = "..." ;</p> <p>traj_dens:missing_value = 1.e+20 ;</p> <p> </p> <p>// global attributes:</p> <p>:key_roms = ".FALSE." ;</p> <p>:key_symphonie = ".FALSE." ;</p> <p>:key_B2C_grid = ".FALSE." ;</p> <p>:key_sequential = ".TRUE." ;</p> <p>:key_alltracers = ".TRUE." ;</p> <p>:key_ascii_outputs = ".FALSE." ;</p> <p>:key_iU_jV_kW = ".TRUE." ;</p> <p>:key_read_age = ".FALSE." ;</p> <p>:mode = "qualitative" ;</p> <p>:forback = "backward" ;</p> <p>:bin = "nobin" ;</p> <p>:init_final = "NONE" ;</p> <p>:nmax = 10000000 ;</p> <p>:tunit = 86400. ;</p> <p>:ntfic = 5 ;</p> <p>:tcyc = 1639872000. ;</p> <p>:key_approximatesigma = ".FALSE." ;</p> <p>:key_computesigma = ".TRUE." ;</p> <p>:zsigma = 1000. ;</p> <p>:memory_log = ".TRUE." ;</p> <p>:output_netcdf_large_file = ".FALSE." ;</p> <p>:key_interp_temporal = ".TRUE." ;</p> <p>:maxcycles = 50 ;</p> <p>:delta_t = 86400. ;</p> <p>:frequency = 5 ;</p> <p>:nb_output = 73 ;</p> <p>:mask = ".TRUE." ;</p> <p>:key_region = ".FALSE." ;</p> <p>:key_larvae = ".TRUE." ;</p> <p>:imt = 1784 ;</p> <p>:jmt = 1719 ;</p> <p>:kmt = 46 ;</p> <p>:lmt = 3796 ;</p> <p>:key_computew = ".TRUE." ;</p> <p>:w_surf_option = "" ;</p> <p>:key_partialsteps = ".TRUE." ;</p> <p>:key_jfold = ".FALSE." ;</p> <p>:pivot = "T" ;</p> <p>:key_periodic = ".FALSE." ;</p> <p>:dir_mesh = "./GRID" ;</p> <p>:fn_mesh = "1_mesh_mask.nc" ;</p> <p>:nc_var_xx_tt = "glamt" ;</p> <p>:nc_var_xx_uu = "glamu" ;</p> <p>:nc_var_zz_ww = "gdepw_0" ;</p> <p>:nc_var_e2u = "e2u" ;</p> <p>:nc_var_e1v = "e1v" ;</p> <p>:nc_var_e1t = "e1t" ;</p> <p>:nc_var_e2t = "e2t" ;</p> <p>:nc_var_e3t = "e3t" ;</p> <p>:nc_var_tmask = "tmask" ;</p> <p>:nc_mask_val = 0. ;</p> <p>:c_dir_zo = "./DATA" ;</p> <p>:c_prefix_zo = "V20_nest_5d_" ;</p> <p>:ind0_zo = 1958 ;</p> <p>:indn_zo = 2009 ;</p> <p>:maxsize_zo = 4 ;</p> <p>:c_suffix_zo = "_U.nc" ;</p> <p>:nc_var_zo = "vozocrtx" ;</p> <p>:nc_var_eivu = "NONE" ;</p> <p>:nc_att_mask_zo = "missing_value" ;</p> <p>:c_dir_me = "./DATA" ;</p> <p>:c_prefix_me = "V20_nest_5d_" ;</p> <p>:ind0_me = 1958 ;</p> <p>:indn_me = 2009 ;</p> <p>:maxsize_me = 4 ;</p> <p>:c_suffix_me = "_V.nc" ;</p> <p>:nc_var_me = "vomecrty" ;</p> <p>:nc_var_eivv = "NONE" ;</p> <p>:nc_att_mask_me = "missing_value" ;</p> <p>:c_dir_te = "./DATA" ;</p> <p>:c_prefix_te = "V20_nest_5d_" ;</p> <p>:ind0_te = 1958 ;</p> <p>:indn_te = 2009 ;</p> <p>:maxsize_te = 4 ;</p> <p>:c_suffix_te = "_T.nc" ;</p> <p>:nc_var_te = "votemper" ;</p> <p>:nc_att_mask_te = "missing_value" ;</p> <p>:c_dir_sa = "./DATA" ;</p> <p>:c_prefix_sa = "V20_nest_5d_" ;</p> <p>:ind0_sa = 1958 ;</p> <p>:indn_sa = 2009 ;</p> <p>:maxsize_sa = 4 ;</p> <p>:c_suffix_sa = "_T.nc" ;</p> <p>:nc_var_sa = "vosaline" ;</p> <p>:nc_att_mask_sa = "missing_value" ;</p> <p>}</p> <p> </p>
Arctic Ocean state estimates for 2009 using the GECCO model
<p>The dataset contains the 2009 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
FIG. 8 in Hanging on - lucinid bivalve survivors from the Paleocene and Eocene in the western Indian Ocean (Bivalvia: Lucinidae)
FIG. 8. — Monitilora Iredale, 1930, Palaeogene fossils (A-D) and Monitilora sepes (Barnard, 1964) Inhaca, Mozambique (E-Q): A, B, Monitilora duponti (Cossmann, 1908) Paleocene, Danian, Calcaire de Mons, Mons Puits Coppée, Belgium (RBINS I.G. 6544), L 17.5 mm; C, D, Monitilora obliqua baudoni (Deshayes, 1857) Eocene, Lutetian, Amblainville, Oise, France, Chavan collection (RBINS I.G. 21.735), L 18 mm; E, F, Monililora sepes exterior and interior of left valve, Inhaca stn MD11, L 15 mm; G, H, exterior and interior of right valve, Inhaca stn MD15, L 12.1 mm; I, J, exterior and interior of right valve, Inhaca stn MD15, L 10.2 mm; K, L, exterior and interior of right valve, Inhaca stn MD15, L 10.1 mm; M, interior of left valve, Inhaca stn MD15, L 8.4 mm; N, O, detail of hinge teeth of left and right valves of H, I; P, detail of external sculpture of K; Q, protoconch of H. Scale bars: N, O, 1.0 mm; P, 500 µm; Q, 100 µm.
FIG. 4 in Hanging on - lucinid bivalve survivors from the Paleocene and Eocene in the western Indian Ocean (Bivalvia: Lucinidae)
FIG. 4. — Barbierella louisensis (Viader, 1951): A-D, Lucina (Bellucina) louisensis Viader, 1951 syntypes (AMS C.305545), off Port Louis, Mauritius, L (A, B) 6.2 mm, (C) 5.5 mm, H (D) 5.4 mm. Images by A. C. Miller, Copyright: Australian Museum; E-G, Barbierella scitula Oliver & Abou-Zeid, 1986, holotype (NMW.Z.1982.68.1) exterior of right and interior of right and left valves (gold coated for SEM), off Ras Budran, Gulf of Suez, Red Sea, 30 m, L 8.2 mm, Images copyright NMW; H-K, Barbierella louisensis, Banc de la Zélée, Mozambique Channel, BENTHEDI stn 110, 24 m; H, I, exterior and interior of left valve, L 7.8 mm; J, K, interior and exterior of right valve, L 7.8 mm; L-W, Barbierella louisensis Inhaca, Mozambique, INHACA stn MD13, 50-53 m (MNHN); L, M, interior and exterior of right valve, L 6.0 mm; N, O, exterior and interior of left valve, L 5.9 mm; P, Q, interior and exterior of right valve, L 5.9 mm; R, exterior of right valve coated SEM im- age, L 6.5 mm; S, T, detail of hinge area of right and left valves; U, detail of lunule and dentition of right valve; V, detail of sculpture of R,; W, protoconch. Scale bars, S, T, 1 mm; U, V, 500 µm; W, 100 µm.
ScienceDex guides
Understand access before you commit
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.