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42 results for “Ocean worlds”
profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean
<p>The dataset includes profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean </p> <p>Data was collected from open archive (<em>Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)) </em><a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a></p> <p>Global array of Bio-Argo floats equipped with Chl (mg m−3) and PAR(μmol photons m-2 s-1) sensors at -60°S..60°N was used in this study. Data for 2013-2020 was downloaded from the IFREMER data archive (ftp://ftp.ifremer.fr/, <a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a>). It includes 464 floats measuring Chl (~ 70000 profiles), and 167 floats measuring both PAR (~26000 profiles) and Chl. Before the analysis, the measurements of each Bio-Argo buoy were visually checked to filter the outliers in Chl or PAR data. After visual analysis about 1600 profiles of PAR and 2800 profiles of Chl were excluded from the dataset.</p> <p>Chl (mg m−3) was retrieved from a Chl fluorometer (excitation at 470 nm; emission at 695 nm) sensors of three types (FLBB, ECO-Triplet, or MCOMS). We use the raw fluorescence-based estimates of Chl (product “non-adjusted Chl”) derived directly from the measurements of fluorescence with factory calibration coefficients without the corrections on non-photochemical quenching, CDOM fluorescence, and other effects (see (<a href="http://www.argodatamgt.org/Documentation">http://www.argodatamgt.org/Documentation</a>)).</p> <p>A multispectral ocean color radiometer (OCR-504, SATLANTIC Inc.) was used to measure PAR. Only instantaneous PAR measurements made within ± 1.5 hours from noon (10:30-13:30 hours) were used.</p> <p>Then the data from all buoys were interpolated on regular 2-m grid and included in one dataset.</p>
Fifty-year changes of the world ocean's surface layer in response to climate change
<p>This object includes two files. One (GlobalML_Trend_1970_2018.mat) contains the 1970-2018 trends of mixed-layer depth, 0-200 stratification, and pycnocline stratification, as described in: Sallée, J.B., Pellichero, V., Akhoudas, C., Pauthenet, E., Vignes, L., Schmidtko, S., Naveira Garabato, A., Sutherland, P., Kuusela, M., 2020, Fifty-year changes of the world ocean’s surface layer in response to climate change, 591, 592–598, https://doi.org/10.1038/s41586-021-03303-x. The second one (<a href="https://zenodo.org/api/files/15c80d5f-a6f9-4b7b-bde1-12de43732195/GlobalML_Climato_1970_2018.mat">GlobalML_Climato_1970_2018.mat</a>) contains a climatology of mixed-layer depth based on the same methodology as in Sallée et al., 2021 (nature; doi:https://doi.org/10.1038/s41586-021-03303-x) but without regressing a trend. The climatological field is therefore different than in the paper; more robust in region where trends are unphysical (e.g. winter high latitude)</p>
CE-MS data for Autonomous CE Mass-Spectra Examination (ACME) for the Ocean Worlds Life Surveyor (OWLS)
<p>These folders contain the original data used to develop the ACME software [1].</p> <p>The Golden and Silver dataset come from simulations. They underrepresent the complexity in the CE-MS observations but provide additional data with known peak locations and peak properties. For more information see [1]</p> <p>The Dev-, Train-, and Test-set contain CE-MS [2] observations of Mix25 (a standard set of 25 organic compounds relevant to astrobiology) and labels for peak locations from subject matter experts. </p> <p>The ACME software is available at: <br> https://github.com/JPLMLIA/OWLS-Autonomy </p> <p> </p> <p>When using the data please cite this dataset [3] and the two papers below. </p> <p>For further questions please reach out to:<br> Steffen Mauceri, Steffen.Mauceri@jpl.nasa.gov</p> <p> </p> <p>References:<br> [1] Mauceri, S., Lee, J., Wronkiewicz, M., et.al. (2022). Autonomous CE Mass-Spectra Examination (ACME) for the Ocean Worlds Life Surveyor (OWLS). (submitted) Earth and Space Science</p> <p>[2] Mora et al., F.(2021). Detection of biosignatures by capillary electrophoresis and mass spectrometry in the presence of salts relevant to missions to ocean worlds (submitted). Astrobiology.</p> <p>[3] 10.5281/zenodo.5849873</p> <p><br> © 2022. California Institute of Technology. Government sponsorship acknowledged</p>
Electrical conductivity of the world ocean and marine sediments
<p>Copy of dataset (as was on 2022-01-12) of electrical conductivity and conductance grids for the ocean and marine sediments at 0.1 degree lateral resolution, from https://github.com/agrayver/seasigma. These models are presented in the work</p> <p>Grayver, A. V. (2021). Global 3-D electrical conductivity model of the world ocean and marine sediments. Geochemistry, Geophysics, Geosystems, 22, e2021GC009950. <a href="https://doi.org/10.1029/2021GC009950">doi: 10.1029/2021GC009950</a></p> <p>Please cite this publication if you use the provided models in your work.</p>
World Ocean Database XBT observations snapshot
<p>The <a href="https://www.ncei.noaa.gov/products/world-ocean-database">World Ocean Database (WOD)</a> is world's largest collection of uniformly formatted, quality controlled, publicly available ocean profile data. This dataset is a snapshot of the XBT observations which have been preprocessed for use in a machine learning pipeline.</p> <p>The data is organised by year in CSV files, covering 1966-2015. This dataset does not include the actual temperature and depth profiles, as this dataset was focused on a project to improve the metadata.</p> <p> </p>
Optimally interpolated dissolved oxygen based on the World Ocean Database 2018 and CMIP6 models
<p>Optimal interpolation of observed and modeled dissolved oxygen data from WOD18 and CMIP6. Technical details are provided in the publication (Ito et al., 2023). </p><p>Ito, T., Garcia, H. E., Wang, Z., Minobe, S., Long, M. C., Cebrian, J., Reagan, J., Boyer, T., Paver, C., Bouchard, C., Takano, Y., Bushinsky, S., Cervania, A., and Deutsch, C. A.: Underestimation of global O2 loss in optimally interpolated historical ocean observations, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-72, in review, 2023.</p>
Ambiguous controls on simulated diazotrophs in the world oceans
<p>This repository is the data supplement to</p> <p>"Ambiguous controls on simulated diazotrophs in the world oceans" by U. Löptien and H. Dietze 2022 in Nature Scientific Reports (doi: 10.1038/s41598-022-22382-y).</p> <p>This repository contains the following data:</p> <p>(1) The files named tavg_spinup_year_2000_***,nc contain the 3D model results at the end of the spinup for the model versions GRAZ, OLIGO, CONTR and the UVic2.9 reference model. The files tave.1800-2150.***nfix..nc contain the RCP8.5-projections for the model versions GRAZ and OLIGO. Alle these data files are provided in the Network Common Data Format (netCDF).</p> <p>(2) Plotting routines for the published model results in ferret (plot_hist.jnl and plot_fut.jnl).</p> <p>(3) Source code for UVic 2.9 (source.zip) and compile script ("mk.in"). The source code is password protected as it requires registration at http://climate.uvic.ca/model/. The password will be provided upon request (ulrike.loeptien@ifg.uni-kiel.de). "control.in" is the input scripts for the Reference Simulation. For GRAZ, OLIGO and CONTR the Parameter values (wd0, jdiar and zprefD) are changed according to the respective publication.</p> <p>(4) All required input data for the model, including initial conditions, are provided in data.zip.</p> <p>Don't hesitate to contact ulrike.loeptien@ifg.uni-kiel.de or heiner.dietze@ifg.uni-kiel.de in case of confusion.</p> <p> </p>
Fig. 4 in Phylogeny and Synonymy of Gyrodinium heterostriatum comb. nov. (Dinophyceae), a Common Unarmored Dinoflagellate in the World Oceans
Fig. 4. Phylogenetic tree based on LSU rRNA gene sequences, showing the position of the sequence of Gymnodinium heterostriatum/ striatissimum by Maximum Likelihood (ML). The new sequence is indicated in bold face. Numbers near branches denote ML bootstrap probability value. The geographic origin is placed between brackets. Bootstrap values <70 are omitted. Scale bar denotes 0.05 substitutions per site.
Fig. 3 in Phylogeny and Synonymy of Gyrodinium heterostriatum comb. nov. (Dinophyceae), a Common Unarmored Dinoflagellate in the World Oceans
Fig. 3. Phylogenetic tree based on SSU rRNA gene sequences, showing the position of the sequences of Gymnodinium heterostriatum/ striatissimum by Maximum Likelihood (ML). The new sequences are indicated in bold face. Numbers near branches denote ML bootstrap probability value. Bootstrap values <70 are omitted. The geographic origin is placed between brackets. Scale bar denotes 0.02 substitutions per site.
Fig. 1 in Phylogeny and Synonymy of Gyrodinium heterostriatum comb. nov. (Dinophyceae), a Common Unarmored Dinoflagellate in the World Oceans
Fig. 1. Map of the sampling stations in the North Sea during the JERICO-NEXT LifeWatch research cruise in May 2019.
Deep-SDMs in the open oceans - OUTPUTS - World +2°C
<p>This repository contains global distribution maps with a +2°C increase in sea surface temperature, on 4 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a></p> <p>This deposit contains:</p> <p>1. A <em>00-predictions.csv</em> file containing the raw outputs of the model.</p> <p>2. Distribution maps as png files (named after taxon and date).</p> <p>3. Distribution maps as GeoTIFF rasters (zipped in <em>01-geotiff-rasters.zip</em>).</p> <p>Each of these elements can be downloaded separately by scrolling to the <em>Files</em> section.</p>
Deep-SDMs in the open oceans - OUTPUTS - World
<p>This repository contains global distribution maps on 4 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a></p> <p>This deposit contains:</p> <p>1. A <em>00-predictions.csv</em> file containing the raw outputs of the model.</p> <p>2. Distribution maps as png files (named after taxon and date).</p> <p>3. Distribution maps as GeoTIFF rasters (zipped in <em>01-geotiff-rasters.zip</em>).</p> <p>Each of these elements can be downloaded separately by scrolling to the <em>Files</em> section.</p>
FEHM source code modifications and executables for use with ocean-world gravity
Open the record for dataset details and reuse information.
Figure 1. - World map representing all the locations mentioned in the dataset. Areas of particular interest are represented with the same colour (⬤ Madagascar, ⬤ Western Indian Ocean, ⬤ Papuasia, ⬤ New Caledonia, ⬤ South Pacific). Grey spots gather all the other locations.
Figure 1. - World map representing all the locations mentioned in the dataset. Areas of particular interest are represented with the same colour (⬤ Madagascar, ⬤ Western Indian Ocean, ⬤ Papuasia, ⬤ New Caledonia, ⬤ South Pacific). Grey spots gather all the other locations.
Supporting data for "The high-frequency tidal response of ocean worlds: Application to Europa and Ganymede"
<p>Data files containing the static Love numbers that are used to compute the dynamic Love numbers.</p> <p> </p>
Datasets used for Publication "Contribution of Non-Water Ices to Salinity and Electrical Conductivity in Ocean Worlds" - Geophysical Research Letters
<p>These files father the output data used in the figures published in the Geophysical Research paper:</p> <p><strong>Contribution of Non-Water Ices to Salinity and Electrical Conductivity in Ocean Worlds</strong></p> <p>One output file may be used for multiple figures, as indicated in the name of the Excel spreadsheet.</p>
Observed data, predictions and uncertainty associated with the updated distribution of clay minerals in the World Ocean
<p>Sparase observed data for various clay mineral species are .csv file format.</p> <p>Predictions and uncertainty for four seafloor clay mineral species (relative percentages, Kaolinite, Illite, Smectite, Chlorite) are generated via geospatial machine learning (GML). Methodology is outlined in "The updated distribution of clay mineral in the World Ocean"<span>. These files are in net-CDF file format. Files are cell-centered. Further, latitudes and longitudes of grid cells are denoted in the variables of the .nc files.</span></p>
World Ocean Atlas 2023 (WOA23) 1991-2020 (t,Sa,d) annual means
<p>The WOA23 1991-2020 objectively analyzed annual means public datasets have been used by the author in a two parts paper (submitted in October 2024 to the Comptes-Rendus-Geoscience) to compute the absolute seawater entropy from the Temperature, Salinity and depth values (t,Sa,d). The "csv" files "woa23_decav91C0_t00an04.csv.gz" for temperature and "woa23_decav91C0_s00an04.csv.gz" for salinity have been compressed with the "gzip -9" tool. They have been updated from: https://www.ncei.noaa.gov/products/world-ocean-atlas</p>
Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds
<p>The tables in this repository represent the data used in the figures and analyses of the paper "Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds", published in the Planetary Science Journal. The provided data was collected between 2020 and 2022.</p> <ul> <li>AllResults.xlsx: compilation of tables 4, 5, 6, and 7 on the paper.</li> <li>WarmA1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>WarmA2.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>CryoA1.xlsx: data presented in tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth Estimation [m], Power [W]".</li> <li>CryoB1.xlsx: data presented in figures 9 and 10, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB3.xlsx: data presented in figures 6, 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC3.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Truncated Coarse Depth [m], Power [W]".</li> </ul> <p> </p>
Data for "Chasing rainbows and ocean glints: Inner working angle constraints for the Habitable Worlds Observatory"
<p>Data for the paper on "Chasing rainbows and ocean glints: Inner working angle constraints for the Habitable Worlds Observatory"</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
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International Brain Laboratory public data
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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.