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955 results for “Ocean data”
SBC LTER: Ocean: Currents and Biogeochemistry: Moored CTD and ADCP data from Arroyo Quemado Reef Mooring (ARQ), ongoing since 2004
ADCP (Currents), CTD (Hydrography) and Optics data (Fluorescence, Beam Attenuation and Volume Scattering Function) were collected at Arroyo Quemado Reef in the Santa Barbara Channel (site ID: ARQ). 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: Currents and Biogeochemistry: Moored CTD and ADCP data from Mohawk Outside Spar (MKO), ongoing since 2005
ADCP (Currents), CTD (Hydrography) and Optics data (Fluorescence, Beam Attenuation and Volume Scattering Function) were collected at Mohawk Reef in the Santa Barbara Channel (site ID: MKO). Data have been interpolated to a 20 minute interval. ADCP data are binned at a 0.5 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: Currents and Biogeochemistry: Moored CTD and ADCP data from Alegria Reef Mooring (ALE), 1999-2021
ADCP (Currents), CTD (Hydrography) and Optics data (Fluorescence, Beam Attenuation and Volume Scattering Function) were collected at Alegria in the Santa Barbara Channel (site ID: ALE). 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. The mooring data collection was terminated in 2021.
SBC LTER: Ocean: Currents and Biogeochemistry: Moored CTD and ADCP data from Santa Barbara Harbor Mooring (SBH), ongoing since 1999
CTD (Hydrography) data were collected at Santa Barbara Harbor (site ID: SBH). Data have been interpolated to a 20 minute interval. The CTD data for this site are harvested from SCCOOS (http://sccoos.org/) and processed in Matlab.
Calibrated data of stable water isotope measurements in water vapour at 13.5 m a.s.l., made in the austral summer of 2016/2017 around the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This data set includes the calibrated data of stable water vapour isotope (δ<sup>18</sup>O, δ<sup>2</sup>H, deuterium excess) and water vapour mixing ratio measurements at approximately 13.5 m a.s.l. taken around the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE) from November 2016 to April 2017 using a Picarro laser spectrometer L2130. The data provides continuous timelines of atmospheric water vapour properties in the marine boundary layer for studies of the atmospheric water cycle.</p> <p>The raw data from which this calibrated dataset originates has been published separately (Thurnherr and Aemisegger, 2019; DOI 10.5281/zenodo.3664177).</p> <p>Two other calibrated stable water isotope measurement datasets were also collected during ACE. They differ by the location of measurement on the ship: 8 m a.s.l on the starboard (DOI: 10.5281/zenodo.3739335) and port sides (DOI: 10.5281/zenodo.3739354).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE_watervapour_isotopes_SWI13_1h.csv, data file, comma-separated values</li> <li>ACE_watervapour_isotopes_SWI13_5min.csv, data file, comma-separated values</li> <li>cal_runs_SWI13.csv, metadata, comma-separated values</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p>NaN values denote missing values which occur because of e.g., maintenance, instrument calibration, large cavity variations.</p> <p><strong>Dataset license</strong></p> <p>This calibrated stable water isotope measurements in water vapour is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Calibrated data of stable water isotope measurements in water vapour at 8 m a.s.l. on the starboard side of the ship, made in the austral summer of 2016/2017 around the Southern Ocean during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>This data set includes the calibrated data of stable water vapour isotope (δ18O, δ2H, deuterium excess) and water vapour mixing ratio measurements at approximately 8 m a.s.l. on the starboard of the ship, taken around the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE) from February to March 2017 using a Picarro laser spectrometers L2130-i. The data provide continuous timelines of atmospheric water vapour properties in the marine boundary layer for studies of the atmospheric water cycle.</p> <p>The raw data from which this calibrated dataset originates has been published separately (Kozachek, 2020; DOI 10.5281/zenodo.3667535).</p> <p>Two other calibrated stable water isotope measurement datasets were also collected during ACE. They differ by the location of measurement on the ship: 8 m a.s.l on the port side (DOI: 10.5281/zenodo.3739354) and 13.5 m a.s.l (DOI 10.5281/zenodo.3250790).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE_watervapour_isotopes_SWI8-sb_1h.csv, data file, comma-separated values</li> <li>ACE_watervapour_isotopes_SWI8-sb_5min.csv, data file, comma-separated values</li> <li>cal_runs_SWI8-sb.csv, metadata, comma-separated values</li> <li>cal_flag_times_SWI8-sb.csv, metadata, comma-separated values</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p>NaN values denote missing values which occur because of e.g., maintenance, instrument calibration, large cavity variations.</p> <p><strong>Dataset license</strong></p> <p>This calibrated stable water isotope measurements in water vapour is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Enrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing data
<p>This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.<br> 1. Initial data used<br> We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region (45°S – 10°S / 25°W – 80°W).<br> 2. The calculation method<br> The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.<br> 3. Data sets<br> The data set contains two files:<br> - the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)<br> - the yearly average of the EI (period 01/2003 - 12/2018)<br> <br> Two images are joined with this data set:<br> - a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)<br> (file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).</p> <p> - a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).<br> (file: Figure-enrichment-index.pdf)<br> <br> An improved version of this index will be available in a near future.</p>
NAPv1.0: A seasonal hydrographic gridded data set for the Northern Antarctic Peninsula, Southern Ocean
<p>The Northern Antarctic Peninsula (NAP) climatology version 1 (NAPv1.0) was built by optimally interpolate hydrographic data sets from the CTD, MEOP and Argo floats profiles sampled in the NAP and adjacent regions during the period of 1990-2019. The database consists of data from the World Ocean Database, Pangaea, Hutchinson et al. (2020), Brazilian High Latitude Oceanography Group (GOAL; http://goal.furg.br/), Marine Mammals Exploring the Oceans Pole to Pole consortium (MEOP), and Argo floats. The climatology has outputs for summer (Jan-Mar), autumn (Apr-Jun), winter (Jul-Sep) and spring (Oct-Dec). The profiles were first linearly interpolated onto 90 depth levels, and then optimally interpolated in space using a grid of ~10 km resolution. The grid spacing is 0.09˚ along latitudes and 0.2˚ along longitudes (i.e., 0.09˚ latitude x 0.09˚/cos(63˚S) longitude, where 63˚S is the mean latitude of our domain). A series of tests were made to find the appropriate smoothing lengthscale and the a priori relative error in order to find a balance between smoothness and feature representativeness. The final smoothing lengthscale (i.e. the radius of influence of the interpolation) chosen was 1˚ in latitude and longitude, and the a priori relative error allowed was set to 0.2 for the objective interpolation algorithm. The same constants were set for all depth levels and all variables. The regions where the mapping relative error was higher than 0.5 were excluded. The NAPv1.0 climatology can be used for several applications, including input data for ocean and climate models initialization/assessment and ocean reanalysis evaluation, as well as to produce and reconstruct biogeochemical properties. The NAPv1.0 climatology represents the ocean mean-state for the NAP for the end of the 20th and early 21st-century.</p> <p> </p> <p><strong>Reference: </strong><br> Dotto, T. S., Mata, M. M., Kerr, R., and Garcia, C. A. E.: A novel hydrographic gridded data set for the northern Antarctic Peninsula, Earth Syst. Sci. Data, 13, 671–696, https://doi.org/10.5194/essd-13-671-2021, 2021.</p>
Summary raw meteorological data from the Southern Ocean collected on board the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>A Vaisala MAWS240 meteorological station was installed on the R/V Akademik Tryoshnikov during a circumnavigation of Antarctica in the austral summer season of 2016/2017. This dataset contains the raw meteorological data that have been extracted from the original raw text data files. Data coverage is from 17th November 2016 until 11th April 2017, with gaps where the ship was in port.</p> <p>Air temperature, relative humidity, dew point, solar radiation, ultraviolet radiation, cloud level and sky cover were recorded with a resolution of 30 seconds. Averaged wind parameter data are provided.</p> <p>Date_time should be combined with TIMEDIFF to convert it to UTC. Latitude and longitude recorded are not corrected. Underway seawater measurements were recorded as null values.</p> <p>Data from this dataset have been corrected and quality-checked in another published dataset. We recommend these data for further use (Landwehr et al., 2019; DOI 10.5281/zenodo.3379590).</p> <p><strong>Dataset contents</strong></p> <ul> <li>metdata_all_YYYYMMDD_YYYYMMDD.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>ace_meteorology_raw_summary_change_log.txt</li> </ul> <p>Data files contain data for each leg of the Antarctic Circumnavigation Expedition (ACE). Dates included in the file name are the start and end dates of the legs and therefore the data within the files as well.</p> <p><strong>Change log</strong></p> <p><strong>v1.2</strong> - Added missing data from 2017-02-05 - 2017-02-08 inclusive. Updated this change log file.</p> <p><strong>v1.1</strong> - Added additional data coverage from 2016-11-17 - 2016-11-22 inclusive, into the first data file. Updated README.txt with information about data coverage. Added this change log file.</p> <p><strong>v1.0</strong> - Initial release of raw summary meteorological data.</p> <p><strong>Dataset license</strong></p> <p>This raw meteorological dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Data used in "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean"
<div> <p>This repository contains the data used to generate the figures for the submitted manuscript "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean".</p> </div> <h3>Contents</h3> <div> <ul> <li> <p>Model input:</p> <ul> <li> <p>INPUTS: ocean model input/grid files</p> </li> <li> <p>PISCES_INPUTS: BGC input files</p> </li> <li> <p>OBC: open boundary forcing </p> </li> <li> <p>WEIGHTS: weight files for ERA interim forcing</p> </li> </ul> </li> </ul> </div> <div> <ul> <li> <p>Manuscript files:</p> <ul> <li> <p>data: files used to generate manuscript images</p> </li> <li> <p>config, src, notebooks: Python code and Jupyter notebooks used to generate images</p> </li> <li> <p>figures, supplementary: manuscript figures and supplementary figures</p> </li> </ul> </li> </ul> </div> <div> </div> <div><strong>Abstract: </strong>"We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12 degree. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass."</div>
Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2024, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2024 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.</p> <p>For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. </p> <p>Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.</p> <p> </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>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): Internal-tide Mode-1 Amplitude Data from the Southern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for mode-1 internal-waves derived<br>from the six paths of the southern HOME tomography array.</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): Internal-tide Mode-1 Amplitude Data from the Northern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for mode-1 internal-waves derived<br>from the six paths of the northern HOME tomography array.</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): High-frequency (>1cpd) Barotropic Current Data from the Northern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for barotropic currents derived from<br>three of the six paths of the northern HOME tomography array.</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): High-frequency (>1cpd) Barotropic Current Data from the Southern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for barotropic currents derived from<br>the six paths of the southern HOME tomography array.</p>
Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory
<p>This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork.</p> <p>In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections.</p> <p>This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4.</p> <p>See accompanying technical note for more Information.</p>
Quality-checked meteorological data from the Southern Ocean collected during the Antarctic Circumnavigation Expedition from December 2016 to April 2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains quality-checked meteorological observations of air temperature, relative humidity, dew point, barometric pressure and observations of downwelling solar radiation and ultraviolet radiation. Further it contains the wind speed and direction relative to the ship but not corrected for air-flow distortion, and translated into the earth reference frame. For each of these variables observations are available from a portside and starboard side sensor. The dataset also contains, cloud base height and sky cover at three levels measured with a Ceilometer.</p> <p>As additional information the solar azimuth and altitude angle have been calculated for the ship’s position every five minutes and have been added as a one-minute time series using the nearest value. The ship’s position, heading, course and speed over ground are also provided.</p> <p>The wind speed measurements were made at a height of approximately 30.5 meters above sea level. The measurement height of the temperature and humidity probes is 23.7 meters above sea level. The barometric pressure was measured at 20 meters above sea level.</p> <p>The observations have been screened for implausible values and on some occasions despiking based on visual inspection and a rolling interquartile range filter have been applied. Solar radiation measurements are affected by shadowing of the ship, and the air temperature and humidity by the heating of air that passes over the ship. Masks are provided to flag affected observations. The wind speed readings are affected by airflow distortion and should be used with consideration until a dataset of corrected wind speeds is published. More details on airflow distortion can be requested from the contact person.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE_filtered_meteorological_data_1min.csv, data file, comma-separated values</li> <li>diff_TA1_TA3_WDR2_5min_1.png, metadata, portable network graphics</li> <li>ratio_SR1_SR3_solangle_5min_1.png, metadata, portable network graphics</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>ace_filtered_meteorological_data_change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - The range check for skycover (SC) and cloudlevel (CL) was added to the quality-checking routines. 53 data points violated the range check for these variables: these have now been marked as NaN.</p> <p><strong>v1.0</strong> - Initial release of verified meteorological data.</p> <p><strong>Dataset license</strong></p> <p>This meteorological dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/</p>
Raw multibeam bathymetry data collected around the Candlemas Islands, part of the South Sandwich Islands chain in the Southern Ocean on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around the Candlemas Islands, part of the South Sandwich Islands chain in the Southern Ocean in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>lineYYYYDDmonHHMMSS.ssv, data file, ASCII</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Raw multibeam bathymetry data collected around Scott Island in the Southern Ocean on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around Scott Island in the Southern Ocean in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p> </p>
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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.