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955 results for “Ocean data”
Data from: Broad-scale trophic shift in the pelagic North Pacific revealed by an oceanic seabird
Human-induced ecological change in the open oceans appears to be accelerating. Fisheries, climate change and elevated nutrient inputs are variously blamed, at least in part, for altering oceanic ecosystems. Yet it is challenging to assess the extent of anthropogenic change in the open oceans, where historical records of ecological conditions are sparse, and the geographical scale is immense. We developed millennial-scale amino acid nitrogen isotope records preserved in ancient animal remains to understand changes in food web structure and nutrient regimes in the oceanic realm of the North Pacific Ocean (NPO). Our millennial-scale isotope records of amino acids in bone collagen in a wide-ranging oceanic seabird, the Hawaiian petrel (Pterodroma sandwichensis), showed that trophic level declined over time. The amino acid records do not support a broad-scale increase in nitrogen fixation in the North Pacific subtropical gyre, rejecting an earlier interpretation based on bulk and amino acid specific δ15N chronologies for Hawaiian deep-sea corals and bulk δ15N chronologies for the Hawaiian petrel. Rather, our work suggests that the food web structure in the NPO has shifted at a broad geographical scale, a phenomenon potentially related to industrial fishing.
Data from: Rare long-distance dispersal of a marine angiosperm across the Pacific Ocean
Aim: Long-distance dispersal (LDD) events occur rarely but play a fundamental role in shaping species biogeography. Lying at the heart of island biogeography theory, LDD relies on unusual events to facilitate colonisation of new habitats and range expansion. Despite the importance of LDD, it is inherently difficult to quantify due to the rarity of such events. We estimate the probability of LDD of the seagrass Heterozostera nigricaulis, a common Australian species, across the Pacific Ocean to colonise South America.Location: Coastal Chile, Australia and the Pacific Ocean. Methods: Genetic analysis of H. nigricaulis collected from Chile and Australia were used to assess the relationship between the populations and levels of clonality. Ocean surface current models were used to predict the probability of propagules dispersing from South East Australia to Central Chile and shipping data used to determine the likelihood of anthropogenic dispersal. Results: Our study infers that the seagrass H. nigricaulis dispersed from Australia across the entire width of the Pacific (~14,000 km) to colonise South America on two occasions. Genetic analyses reveal that these events led to two large isolated clones, one of which covers a combined area of 3.47 km2. Oceanographic models estimate the arrival probability of a dispersal propagule within 3 years to be at most 0.00264%. Early shipping provides a potential alternative dispersal vector, yet few ships sailed from SE Australia to Chile prior to the first recording of H. nigricaulis and the lack of more recent and ongoing introductions demonstrate the rarity of such dispersal. Main Conclusion: These findings demonstrate LDD does occur over extreme distance despite very low probabilities. The large number of propagules (100s of millions) produced over 100s of years suggests that the arrival of propagules in Chile was inevitable and confirms the importance of LDD for species distributions and community ecology.
Data from: Upper atmosphere heating from ocean-generated acoustic wave energy
Colliding sea surface waves generate the ocean microbarom, an acoustic signal that may transmit significant energy to the upper atmosphere. Previous estimates of acoustic energy flux from the ocean microbarom and mountain/wind interactions are on the order of 0.01 to 1 mW/m2, heating the thermosphere by tens of degrees Kelvin per day. We captured up going ocean microbarom waves with a balloon borne infrasound microphone; the maximum acoustic energy flux was approximately 0.05 mW/m2. This is about half the average value reported in previous ground-based microbarom observations spanning eight years. The acoustic flux from the microbarom episode described here may have heated the thermosphere by several degrees Kelvin per day while the source persisted. We suggest that ocean wave models could be used to parameterize acoustically-generated heating of the upper atmosphere based on sea state.
Data from: Diversity in thermal affinity among key piscivores buffers impacts of ocean warming on predator-prey interactions
Asymmetries in responses to climate change have the potential to alter important predator-prey interactions, in part by altering the location and size of spatial refugia for prey. We evaluated the effect of ocean warming on interactions between four important piscivores and four of their prey in the U.S. Northeast Shelf by examining species overlap under historical conditions (1968-2014) and with a doubling in CO2. Because both predator and prey shift their distributions in response to changing ocean conditions, the net impact of warming or cooling on predator-prey interactions was not determined a priori from the range extent of either predator or prey alone. For Atlantic cod, an historically dominant piscivore in the region, we found that both historical and future warming led to a decline in the proportion of prey species' range it occupied and caused a potential reduction in its ability to exert top-down control on these prey. In contrast, the potential for overlap of spiny dogfish with prey species was enhanced by warming, expanding their importance as predators in this system. In sum, the decline in the ecological role for cod that began with overfishing in this ecosystem will likely be exacerbated by warming, but this loss may be counteracted by the rise in dominance of other piscivores with contrasting thermal preferences. Functional diversity in thermal affinity within the piscivore guild may therefore buffer against the impact of warming on marine ecosystems, suggesting a novel mechanism by which diversity confers resilience.
Summary raw wind 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 wind parameter 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>True and relative wind speed and direction parameters were recorded with a resolution of three seconds.</p> <p>Datetime should be combined with TIMEDIFF to convert it to UTC.</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_wind_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_wind_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>
Methane and nitrous oxide data from the North American Arctic Ocean during summer, 2015
<p>Methane, nitrous oxide, carbon isotope, and ancillary data used in "Methane and nitrous oxide distributions across the North American Arctic Ocean during summer, 2015"</p>
Grib and ASCII data, subset ERA-I for shallow water waves Ocean Science study
<p>Specific output from ERA-I reanalysis (wave model component) containing interated parameters, see https://doi.org/10.5194/os-13-1-2017</p>
Data mining-based machine learning methods for improving hydrological data: a case study of salinity field in the Western Arctic Ocean
<p><span><span>Salinity variations in Arctic Ocean determine the strength of stratification,</span> <span>ocean circulation, and biogeochemical cycles. Therefore, a<span>ccurate </span>salinity product is of great significance for our study of the Arctic Ocean. The mean density structure and wind-driven surface circulation of the Arctic Ocean are largely dominated by the anti-cyclonic Beaufort Gyre in the Canadian Basin, along with the Transpolar Drift<span> (Hall </span><span>et al.,2022)</span>. We focus on the salinity in Western Arctic Ocean. Multiple machine learning methods were used to reconstruct annual salinity product in the Western Arctic Ocean temporal for the period 2003-2022.</span></span></p>
Data sets used in "Is there a tropical response to recent observed Southern Ocean cooling?"
The dataset contains processed data for the GRL publication (to be submitted) "Is there a tropical response to recent observed Southern Ocean cooling?" The dataset includes global maps of linear trends of various fields presented in the figures, as well as pattern correlation coefficients for the three experiment ensembles using CESM1: Large Ensemble, Tropical Pacific Pacemaker Experiment, and Southern Ocean Pacemaker Experiment.
Data: Precession-induced Tipping of the Atlantic Meridional Ocean Circulation
Open the record for dataset details and reuse information.
Data and scripts to reproduce figures from "Ocean warming threatens the viability of 60% of Antarctic ice shelves"
<p>This is the formatted data related to the paper "Ocean warming threatens the viability of 60% of Antarctic ice shelves".</p> <p>Main data used to produce the main figures:<br>> bayesian_weights_davison_varying_combined_2300_withoutGISS.nc: Bayesian weights used in the computation of weighted likelihoods and averages considering all simulations going to 2300 - used in the main study<br>> all_fluxes_br_withoutGISS.nc: all fluxes forming the ice-shelf mass balance propagated across the different dimensions of uncertainty (CMIP models, basal melt parameterisations, bed plasticities)<br>> hydrofracturing_limits_new.nc: File containing the percentiles of reaching the hydrofracturing criterion</p> <p>Files needed to compute the weights:<br>> area_isf_greene22.nc: ice-shelf area used in Davison et al. 23 (from <a href="https://doi.org/10.1038/s41586-022-05037-w">Greene et al. 2022</a>)<br>> varying_conditions_davison23.nc: Ice-shelf mass budget fluxes from 1997 on from <a href="https://doi.org/10.1126/sciadv.adi0186">Davison et al. 2023</a><br>> steadystate_davison23.nc: Ice-shelf mass budget fluxes from the steady state from <a href="https://doi.org/10.1126/sciadv.adi0186">Davison et al. 2023</a></p> <p>Geometric information:<br>> gridarea_ISMIP6_AIS_4000m_grid.nc: File containing area of grid cells<br>> Mask_Iceshelf_4km_IMBIE_withNisf.nc: Mask used to define the location of the different ice shelves, based on <a href="https://zenodo.org/records/15863352">Caillet et al. 2025 </a><br>> BedMachine_4km_slope_info_bedrock_draft_latlon_oneFRIS.nc: Geometric information needed for neural network parameterisation, inferred using the <a href="https://github.com/ClimateClara/multimelt">multimelt package</a><br>> BedMachinev2_4km_isf_masks_and_info_and_distance_oneFRIS.nc: Geometric information needed for different computations, inferred using the <a href="https://github.com/ClimateClara/multimelt">multimelt package</a><br>> ano_choice_NEMOorISMIP_withoutGISS.nc: file recording which T and S profiles should be taken, either ISMIP climatology or NEMO hindcast, depending on their difference to the reference mass balance</p> <p>Data for analysis only until 2100:<br>> bayesian_weights_davison_varying_combined_withoutGISS.nc: Bayesian weights used in the computation of weighted likelihoods and averages considering all simulations going to 2100 (see Extended Data Fig. 5)</p> <p>Additional files needed for the additional analysis on other plausible geometries: <br>> ElmerIce_4km_2100isf_masks_and_info_and_distance_oneFRIS.nc: Geometric information needed for different computations for the 2100 ice-sheet geometry<br>> ElmerIce_4km_2100_slope_info_bedrock_draft_latlon_oneFRIS.nc: Geometric information needed for neural network parameterisation for the 2100 ice-sheet geometry<br>> ElmerIce_4km_2150isf_masks_and_info_and_distance_oneFRIS.nc: Geometric information needed for different computations for the 2150 ice-sheet geometry<br>> ElmerIce_4km_2150_slope_info_bedrock_draft_latlon_oneFRIS.nc: Geometric information needed for neural network parameterisation for the 2150 ice-sheet geometry<br>> all_fluxes_br_withoutGISS_ElmerIcegeo2100.nc: all fluxes forming the ice-shelf mass balance propagated across the different dimensions of uncertainty (CMIP models, basal melt parameterisations, bed plasticities) for the 2100 ice-sheet geometry<br>> all_fluxes_br_withoutGISS_ElmerIcegeo2150.nc: all fluxes forming the ice-shelf mass balance propagated across the different dimensions of uncertainty (CMIP models, basal melt parameterisations, bed plasticities) for the 2150 ice-sheet geometry<br><br>The scripts are explained in the associated README in scripts_paper_iceshelf_viability.zip. You can also find them here: https://github.com/ClimateClara/scripts_paper_iceshelf_viability<br><br>Here is a summary of the README:</p> <p>- Figure 2 and 3 were done with /notebooks_for_figures/timeseries_nb_viable_isf_withoutGISS_withhydrofrac_calving0.ipynb and /notebooks_for_figures/2D_subplots_viability_proba_withoutGISS_calving0.ipynb<br>- Figure 4 was done with /notebooks_for_figures/2D_subplots_viability_proba_withoutGISS_calving0.ipynb<br>- Figure 5 was done with /notebooks_for_figures/timeseries_nb_viable_isf_withoutGISS_withhydrofrac_calving0.ipynb and notebooks_for_figures/2D_subplots_viability_proba_onlyhydrofrac.ipynb</p> <p>- Extended Data Figures 1 to 4 were done with /notebooks_for_figures/plot_mass_fluxes.ipynb<br>- Extended Data Figure 5 was done with /notebooks_for_figures/timeseries_nb_viable_isf_withoutGISS_withhydrofrac_calving0.ipynb<br>- Extended Data Figure 6 was done with /notebooks_for_figures/timeseries_nb_viable_isf_withoutGISS_withhydrofrac.ipynb and /notebooks_for_figures/2D_subplots_viability_proba.ipynb<br>- Extended Data Figure 7 was done with /notebooks_for_figures/2D_subplots_viability_proba.ipynb<br>- Extended Data Figure 8 was done with /notebooks_for_figures/timeseries_nb_viable_isf_withoutGISS_withhydrofrac_ElmerIcegeometries.ipynb and /notebooks_for_figures/2D_subplots_viability_proba_withoutGISS_ElmerIcegeometries.ipynb<br>- Extended Data Figures 10 to 12 were done with /notebooks_for_figures/histo_weights_new.ipynb</p> <p>In the folder 'notebooks_for_datapreparation', you will find a few scripts to prepare the data. These are not as detailed and not meant to be run out of the box but permit to give insight into the practical application of the methods described in the paper. For potential inspiration of similar work :)<br>For the hydrofracturing calculations we refer to `Jourdain et al. 2025 <https://doi.org/10.5194/tc-19-1641-2025>`_ and the associated scripts: https://doi.org/10.5281/zenodo.13756240 and https://doi.org/10.5281/zenodo.15003864.</p>
Data for Ocean biogeochemical fingerprints of fast-sinking tunicate and fish detritus
<p>Model results for the manuscript, "Ocean biogeochemical fingerprints of fast-sinking tunicate and fish detritus", under peer review at <em>Geophysical Research Letters</em>.</p> <p> </p> <p>Model outputs for: <br>1) the GZ-COBALT control simulation, <br>2) the GZ-COBALT simulation with fast-sinking tunicate detritus only, <br>3) the GZ-COBALT simulation with fast-sinking fish ("hp") detritus only, and<br>4) the GZ-COBALT simulation with both fast-sinking tunicate and fish ("hp") detritus.</p> <p> </p> <p>The following files are included for all 4 simulations:</p> <p> </p> <p>Model grid and area fields</p> <ul> <li><em>ocean_annual_static.nc</em></li> <li><em>ocean_static.nc</em></li> </ul> <p>Monthly 100-m integrated fluxes (in Nitrogen unless specified otherwise; a Redfield C:N ratio is used)</p> <ul> <li>Aggregation loss from: <ul> <li>Small and large phytoplankton, large tunicates</li> </ul> </li> <li>Detritus production by: <ul> <li>Small, medium, and large zooplankton, small and large tunicates, higher predators (hp)</li> </ul> </li> <li><em>[expt]_ocean_cobalt_fluxes_int_1988-2007.clim.tar.gz</em></li> </ul> <p>Other monthly 100-m integrated fluxes</p> <ul> <li>Carbon detritus sinking flux past 100-m</li> <li>Integrated primary production</li> <li><em>[expt]_ocean_cobalt_omip_2d.1988-2007.clim.tar.gz</em></li> </ul> <p>Monthly detritus fluxes past 100-m:</p> <ul> <li>Nitrogen detritus sinking flux past 100-m</li> <li><em>[expt]_ocean_cobalt_fdet_100.1988-2007.clim.tar.gz</em></li> </ul> <p>Monthly bottom fluxes:</p> <ul> <li>Nitrogen detritus sinking flux to bottom</li> <li>Nitrogen detritus burial flux</li> <li>Sediment oxic remineralization flux of nitrogen detritus</li> <li><em>[expt]_ocean_cobalt_btm.1988-2007.clim.tar.gz</em></li> </ul> <p>Annual 3-D tracers:</p> <ul> <li>Nitrate</li> <li>Dissolved oxygen</li> <li>Phosphate</li> <li><em>[expt]_ocean_cobalt_omip_tracers_year_z_1988-2007.nc</em></li> </ul> <p>Annual 3-D fluxes:</p> <ul> <li>Carbon detritus sinking flux</li> <li><em>[expt]_ocean_cobalt_omip_rates_year_z_1988-2007.nc</em></li> </ul> <p>Hypoxic volume time series:</p> <ul> <li>Total volume of water below 60 mmol O2</li> <li>Total volume of water below 5 mmol O2</li> <li><em>[expt]_hypoxicVolume.ts.tar.gz</em></li> </ul> <p>Note that files ending in <em>.tar.gz</em> need to be unzipped and extracted first. All data files are in netCDF format.</p> <p> </p> <p>Python codes for reproducing the figures in the manuscript are available on github: <a href="https://github.com/jessluo/gz_COBALT_fastPOC_analysis">https://github.com/jessluo/gz_COBALT_fastPOC_analysis</a></p>
Data from: Katian (Late Ordovician) trilobites of the North Qilian Mountains and their palaeogeographical implications for the Proto-Tethys Archipelagic Ocean (PTAO)
<p class="MsoNormal"><span>Trilobites from the middle Koumenzi Formation (Katian, Upper Ordovician) of the North Qilian Mountains, Menyuan, northeastern Qinghai Province are systematically documented for the first time. The fauna consists of five families, seven genera and seven species, amongst which one is new (</span><em><span>Remopleurides</span> <span>zhangi</span> </em><span>sp. nov.), showing a close relationship to those of the Kazakh terranes (such as Chu-Ili terrane, Chingiz-Tarbagatai area and KNNTS (Karatau-Naryn and North-Tien Shan Microcontinents)), North China and Laurentia palaeoplates during the Katian (Late Ordovician). The cluster and Non-metric Multidimensional Scaling analyses of the Middle<span class="fontstyle01">–Late Ordovician (</span>late Darriwilian<span class="fontstyle01">–</span>Katian<span class="fontstyle01">) trilobite faunas with 299 genera or subgenera from 46 horizons of 37 areas, provide valuable information for the palaeogeographical reconstruction of the </span>Proto-Tethys Archipelagic Ocean (PTAO) of this interval. The Qilian terrane and adjacent areas are essential components of the PTAO, some of which include the Qilian terrane (QT), the North Qilian Mountains area (NQ), the Altun faulted terrane (AFT), the Hexi Corridor area (HX) and the East Qinling terrane (EQT). Their relative positions within the PTAO are inferred by the palaeobiogeography of trilobite faunas. Based on further discussions on the spatiotemporal distribution of those faunas<span class="fontstyle01">, the </span></span><em><span>Pliomerina</span></em><span> and/or </span><em><span>Sinocybele</span> </em><span>Province of the Middle<span class="fontstyle01">–Late Ordovician (</span>late Darriwilian<span class="fontstyle01">–</span>Katian<span class="fontstyle01">) age is defined as a trilobite faunal province of the PTAO. </span>Moreover, a distinct faunal subprovince, essentially comprised of the South China Palaeoplate and its neighbours (e.g. Tarim, Annamia, Sibuma, East Qinling, Turkestan-Alai and probably Talesh), might be surrounded by the equatorial cold-water tongue.</span></p>
Circum-Antarctic data used in "Tipping point behaviour of ice-sheet grounding-zone melting due to ocean water intrusion" by Bradley and Hewitt
<p>The file 'Antarctica-data.mat' contains the following fields:</p><p>'x' [units: m] x position of grid points</p><p>'y' [units m] y position of grid points</p><p>'tf_max' [units: C] maximum thermal forcing from Adusumilli et al. 2020 (doi: https://doi.org/10.1038/s41561-020-0616-z)</p><p>'H' [units: m] ice thickness from Bedmachine V3</p><p>'B' [units: m] bed elevation from Bedmachine V3</p><p>'mask' [units: n/a] Bedmachine V3 mask</p><p>'isedge' [units: n/a] Logical array with 1 corresponding to edges of ice shelves and 0 otherwise</p><p>'isgl' [units: n/a] Logical array with 1 corresponding to grounding line points and 0 otherwise</p><p>'isfront' [units: n/a] Logical array with 1 corresponding to ice fronts and 0 otherwise</p><p>'vx' [units: m/a] Ice velocity in the x-direction from ITS_LIVE 240m mosaic</p><p>'vy' [units: m/a] Ice velocity in the y-direction from ITS_LIVE 240m mosaic</p>
Processed data for a Slocum ocean glider campaign in the Gulf of Lion, Mediterranean Sea, 2021
<p>The data set was measured in the Gulf of Lion by a Slocum glider in February 2021. It includes processed data from a CTD and a turbulence microstructure package.</p>
Ocean Heat Content Anomalies in the North Atlantic based on mapping Argo data using local Gaussian processes defined over space
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2005-2022, in the North Atlantic, north of 20N) subtracting the time mean over the period 2005-2021 from the monthly time series of OHC. OHC fields are mapped using a locally stationary Gaussian process (defined over space) 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). In this product, mapping is done in latitude and longitude with monthly subsets of data. 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 for 0-2000 dbar. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. </p>
Data underlying the publication: "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago."
<p><strong>CAR36 dataset</strong></p><p>These data correspond to the <strong>1-year (2019)</strong> simulation from the regional ocean system CAR36. These <strong>daily hindcasts</strong> have been used in the study presented in the paper submitted in GMD editor and entitled: "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago", where the CAR36 system is fully described.</p><p><br>The uploaded files are in <strong>netcdf</strong> format:</p><ul><li><i>CAR36_daily_SSH_20190102-20191224.nc</i> = 1-year daily hindcasts of <strong>Sea Surface Height </strong></li><li><i>CAR36_daily_SST_20190102-20191224.nc </i>= 1-year daily hindcasts of <strong>Sea Surface Temperature</strong></li><li><i>CAR36_daily_SSU_20190102-20191224.nc</i> = 1-year daily hindcasts of <strong>Sea Surface Current Speed (zonal component)</strong></li><li><i>CAR36_daily_SSV_20190102-20191224.nc</i> = 1-year daily hindcasts of <strong>Sea Surface Current Speed (meridian component)</strong></li></ul><p>All data are projected on the native model tripolar<strong> ORCA grid</strong> <strong>in 1/36° </strong>horizontal resolution.</p><p>NB: In order to filter (in a 1st order) the semi-diurnal tidal signal (with a period of 12h30), the daily mean corresponds to a 25h-average. </p><p><strong>CAR36 software</strong></p><p>The NEMO_CAR36.tar file gathers the <strong>NEMO code configuration</strong> of the CAR36 model. This code follows the same license than NEMO one : <strong>CeCILL</strong>. A file named "License_CeCILL.txt" reminds the details of this license in the NEMO_CAR36.tar file.<br><br>NB.: This model have been renamed CAR36 (English acronym) for the paper instead of ARCAN36 (French initial acronym). In the provided NEMO code, the name ARCAN36 is still used. </p>
Chesley et al., 2023 - Gofar Oceanic Transform Fault CSEM data from fault-perpendicular profiles (collected 2022)
<p>This repository contains processed and edited controlled-source electromagnetic amplitude and phase data from the Gofar oceanic transform fault with corresponding bathymetry files. The files beginning "dataFile.." are the amplitudes and phases and the files beginning "topo..." are the bathymetry information. Each profile in this repository crosses the fault approximately perpendicularly.</p>
Data From: Shedding light on cobalamin photodegradation in the ocean
<p>Cobalamin, vitamin B<sub>12</sub>, is an important micronutrient that has been investigated for decades in the marine context because it is required for phytoplankton growth. The biologically active forms (Me-B<sub>12</sub>, Ado-B<sub>12</sub>) and the synthetic form (CN-B<sub>12</sub>) quickly convert to OH-B<sub>12</sub> after light exposure in various aqueous solutions, but puzzlingly have been frequently reported to dominate dissolved cobalamin pools in the sunlit ocean. Here we document photodegradation timescales for these cobalamin forms in natural seawater using targeted mass spectrometry, providing quantitative evidence that OH-B<sub>12</sub> is expected to be the dominant dissolved form in irradiated seawater. Then, through high resolution mass spectrometry, we identify four photodegradation products of OH-B<sub>12</sub> which represent potential building blocks microbes could salvage and remodel to satisfy cellular cobalamin requirements. Taken together, these results clarify the impact of light on marine cobalamin dynamics, laying a foundation for a more quantitative understanding of the role of cobalamin in microbial communities and biogeochemical cycles.</p>
Data presented in figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters and potential precursor molecules present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)"
<p>This dataset includes the data shown in the figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)". Read me files containing information on the reported data can be found in the different folders. </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.