Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
955
datasets available to search
ShareScore release 0.9.0
Dataset results
955 results for “Ocean data”
Data for: Saturation of ocean surface wave slopes observed during hurricanes
<div>Observational wave data and modeled wind data to accompany the article "Saturation of ocean surface wave slopes observed during hurricanes," which is currently in revision for publication in Geophysical Research Letters (a preprint is attached as a supplement). The observations include targeted aerial deployments into Hurricane Ian (2022) and opportunistic measurements from the free-drifting Sofar Ocean Spotter global network in Hurricane Fiona (2022). Surface wind speeds are modeled using the U.S. Naval Research Laboratory's Coupled Ocean-Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC). The datasets are table-like and include the following: 1) hourly records of surface wave statistics in the form of scalar energy spectra, directional moments, derived products (including mean square slope), and modeled wind speeds; 2) data to reproduce the binned mean square slopes and model wind speeds presented in the article; 3) data to reproduce the mean energy density versus wind speed plot; and 4) data to reproduce the mean energy density versus wave age plot.</div>
Data used in the figures of the article "A cool runaway greenhouse without surface magma ocean"
<p>These data have been generated with the codes Exo k and PCM.</p> <p>Exo k is an open-source software. A complete documentation on how to install and use it can be found at http://perso.astrophy.u-bordeaux.fr/∼jleconte/ exo k-doc/index.html.<br> The generic Global Climate Model PCM (formerly known as LMDZ.generic) used in this work is the version 2528 that can be downloaded with documentation from the SVN repository at https://svn.lmd.jussieu.fr/Planeto/trunk/ LMDZ.GENERIC/. More information and documentation are available at http://www-planets.lmd.jussieu.fr.</p> <p>The archive is organized as follows:<br> ├─ Fig1 (comparison of atmospheric profiles with a same Outgoing Thermal Radiation)<br> │ ├─ OTR274.txt (profiles with a 274 W/m2 bolometric emission)<br> │ ├─ OTR10000txt (profiles with a 1000 W/m2 bolometric emission)<br> │<br> ├─ Fig2 (Outgoing Thermal Radiation as a function of surface temperature)<br> │ ├─ Geoflux=0 (internal heat flux = 0 W/m2)<br> │ │ ├─ T1_0_ISR_Tsurf_OTR_ALB.dat<br> │ │ ├─ Proxima_0_ISR_Tsurf_OTR_ALB.dat<br> │ │ ├─ M3_0_ISR_Tsurf_OTR_ALB.dat<br> │ │ ├─ Sun_0_ISR_Tsurf_OTR_ALB.dat<br> │ │ ├─ F1_0_ISR_Tsurf_OTR_ALB.dat<br> │ ├─ Geoflux=0.09Wm-2 (internal heat flux = 0.09 W/m2)<br> │ ├─ T1_0.09_ISR_Tsurf_OTR_ALB.dat<br> │ ├─ Sun_0.09_ISR_Tsurf_OTR_ALB.dat<br> │<br> ├─ Fig3 (Trappist-1b profiles and spectra)<br> │ ├─ Profiles<br> │ │ ├─ Adiabatic<br> │ │ │ ├─ Trappist1b_adiabatic_profile_1EO.txt (1x Earth Ocean)<br> │ │ │ ├─ Trappist1b_adiabatic_profile_3EO.txt (3x Earth Ocean)<br> │ │ │<br> │ │ ├─ Converged<br> │ │ │ ├─ Trappist1b_profile_1EO_geoflux=0W.txt (1x Earth Ocean, internal heat flux = 0 W/m2)<br> │ │ │ ├─ Trappist1b_profile_1EO_geoflux=5W.txt (1x Earth Ocean, internal heat flux = 5 W/m2)<br> │ │ │ ├─ Trappist1b_profile_3EO_geoflux=0W.txt (3x Earth Ocean, internal heat flux = 0 W/m2)<br> │ │ │ ├─ Trappist1b_profile_3EO_geoflux=5W.txt (3x Earth Ocean, internal heat flux = 5 W/m2)<br> │ │ │<br> │ ├─ Spectra<br> │ │ ├─ Adiabatic<br> │ │ │ ├─ Trappist1b_adiabatic_spectra_1EO.txt (1x Earth Ocean)<br> │ │ │ ├─ Trappist1b_adiabatic_spectra_3EO.txt (3x Earth Ocean)<br> │ │ │<br> │ │ ├─ Converged<br> │ │ │ ├─ Trappist1b_spectra_1EO_geoflux=0W.txt (1x Earth Ocean, internal heat flux = 0 W/m2)<br> │ │ │ ├─ Trappist1b_spectra_1EO_geoflux=5W.txt (1x Earth Ocean, internal heat flux = 5 W/m2)<br> │ │ │ ├─ Trappist1b_spectra_3EO_geoflux=0W.txt (3x Earth Ocean, internal heat flux = 0 W/m2)<br> │ │ │ ├─ Trappist1b_spectra_3EO_geoflux=5W.txt (3x Earth Ocean, internal heat flux = 5 W/m2)<br> │<br> ├─ Fig4_(net stellar and emission fluxes for a converged and adiabatic profiles)<br> │ ├─profiles_Sun_adia_converged_netfluxes.txt<br> │<br> ├─ Fig5 (Profiles for different instellations, for each star)<br> │ ├─ T1_profiles.txt<br> │ ├─ Proxima_profiles.txt<br> │ ├─ M3_profiles.txt<br> │ ├─ K5_profiles.txt<br> │ ├─ Sun_profiles.txt<br> │ ├─ F1_profiles.txt<br> │<br> ├─ Fig6 (Comparison 1D vs 3D)<br> │ ├─ 1D<br> │ │ ├─ exok_1D_PTH_trappist1.txt<br> │ │ ├─ exok_1D_PTH_Proxima.txt<br> │ │ ├─ exok_1D_PTH_M3.txt<br> │ ├─ 3D<br> │ ├─ PT_trappist1_3D.dat<br> │ ├─ PT_Proxima_3D.dat<br> │ ├─ PT_M3_3D.dat<br> │ ├─ HeatingRates_3D_trappist1.dat<br> │ ├─ HeatingRates_3D_trappist1.dat<br> │ ├─ HeatingRates_3D_trappist1.dat<br> │<br> ├─ Fig7 (Profiles for 2 values of instellation and 3 values of cp)<br> │ ├─ profiles_cp_Sun.txt <br> │<br> ├─ Fig8 (Profiles for 3 values of instellation and continuums from 2 version of MT_sCKD)<br> │ ├─ profiles_2continuums_Sun.txt <br> │<br> ├─ Fig9 (Profiles as a function of surface pressure and internal heat fluxes)<br> │ ├─ EarlyVenus<br> │ │ ├─ profiles_fig9_EarlyVenus_0.1bar.txt<br> │ │ ├─ profiles_fig9_EarlyVenus_1bar.txt<br> │ │ ├─ profiles_fig9_EarlyVenus_10bar.txt<br> │ │ ├─ profiles_fig9_EarlyVenus_100bar.txt<br> │ │ ├─ profiles_fig9_EarlyVenus_270bar.txt<br> │ ├─ Trappist1<br> │ ├─ profiles_fig9_Trappist1_0.1bar.txt<br> │ ├─ profiles_fig9_Trappist1_1bar.txt<br> │ ├─ profiles_fig9_Trappist1_10bar.txt<br> │ ├─ profiles_fig9_Trappist1_100bar.txt<br> │ ├─ profiles_fig9_Trappist1_270bar.txt<br> │<br> ├─ readme.txt</p> <p> </p> <p> </p>
Potential Predictability of the Spring Bloom in the Southern Ocean Sea Ice Zone: data and analysis scripts
<p>This repository contains the datasets and notebooks necessary to reproduce the figures in Buchovecky et al. "Potential Predictability of the Spring Bloom in the Southern Ocean Sea Ice Zone". All notebooks, except those deriving quantities from the raw model data, should work "out-of-the-box" after the appropriate local path has been set to the data.</p>
Anthropogenic attribution of the increasing seasonal amplitude in surface ocean pCO2: data to prepare figures
<p>The file contains the data to plot the graphics displayed in Joos et al., Anthropogenic attribution of the increasing seasonal amplitude in surface ocean pCO2, Geophys. Res. Letters, in press, June 2023.</p>
Deep-SDMs in the open oceans - INPUT DATA
<p>This repository contains input files to train the Deep-SDM model 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. Training data: CSV dataset + 38 subfolders with data for each species (named after its GBIF id)</p> <p>2. Prediction data:</p> <p>2.1. Global use case (solstices & equinoxes of 2021): CSV dataset + data folder</p> <p>2.2. Western Indian Ocean use case: CSV dataset + data folder</p> <p>3. <em>species.csv </em>contains the taxonomic name of each taxon, as well as its GBIF id.</p> <p>4. <em>stats.npy</em> contains normalization factors for the data files</p> <pre><code class="language-python">meds, perc1, perc99 = np.load("stats.npy") item = np.load(file)[:,:,:25] real_values = (perc99 - perc1) * item + perc1</code></pre> <p> </p> <p>Each of these elements can be downloaded separately by scrolling to the <em>Files</em> section.</p>
Data & figures: Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation
<p>These are the model data, key figures, and Python code generated during the project titled “Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation." This project was undertaken during a 3-month research scholarship at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, funded by the Helmholtz Visiting Researcher Grant, a program promoted by the Helmholtz Information and Data Science Academy (HIDA). Statistical methods pertain to the coupling of sea surface temperature and Antarctic sea-ice interactions. These methods can be applied to observations, reanalysis, and earth system model data</p>
Data related to the paper: Surface Heating Steers Planetary-Scale Ocean Circulation
<p>Preprocessed outputs to reproduce figures in the paper: <a href="https://doi.org/10.1175/JPO-D-23-0016.1">https://doi.org/10.1175/JPO-D-23-0016.1</a>.</p>
Supplementary data to 'The origin and differentiation of CO2-rich primary melts in Ocean Island volcanoes: Integrating 3D X-ray tomography with chemical microanalysis of olivine-hosted melt inclusions from Pico (Azores).'
<p>Supplementary data to:</p><blockquote><p>The origin and differentiation of CO2-rich primary melts in Ocean Island volcanoes: Integrating 3D X-ray tomography with chemical microanalysis of olivine-hosted melt inclusions from Pico (Azores).</p></blockquote>
Ocean and ice without waves data for role of surface gravity waves in aquaplanet ocean climates
Open the record for dataset details and reuse information.
Ocean and ice with waves data for role of surface gravity waves in aquaplanet ocean climates
Open the record for dataset details and reuse information.
A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2017
Open the record for dataset details and reuse information.
Ocean and ice spin-up data for role of surface gravity waves in aquaplanet ocean climates
Open the record for dataset details and reuse information.
Data and code from: You shall not pass, the Pacific oxygen minimum zone creates a boundary to shortfin mako shark distribution in the Eastern North Pacific Ocean
Open the record for dataset details and reuse information.
Data for: Increasing hypoxia on global coral reefs under ocean warming
Open the record for dataset details and reuse information.
Supporting Data for: Ocean-wide conservation genomics of blue whales suggest new Northern Hemisphere subspecies
Open the record for dataset details and reuse information.
Data from: Strong attachment as an adaptation of flightless weevils on windy oceanic islands
Open the record for dataset details and reuse information.
Data from: Brucite-inspired ocean alkalinity enhancement alters the biogeochemistry and composition of a phytoplankton community: A Santa Barbara channel case report
Open the record for dataset details and reuse information.
A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2018
Open the record for dataset details and reuse information.
Data for: Saturation of ocean surface wave slopes observed during hurricanes
Open the record for dataset details and reuse information.
Data from: Shifting seagrass-oyster interactions alter species response to ocean warming and acidification
Open the record for dataset details and reuse information.
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.