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.
22
datasets available to search
ShareScore release 0.9.0
Dataset results
22 results for “Wind Pressures”
Daliy weather data (wind, temperatrue, humididty, pressure, precipitation) from Roche Mountonnee , in the northern foothills of the Brooks Range, Alaska, summers 2010-2014.
Daily weather data from mid May to late July 2011 to 2013 from Roche Moutonnee (south of Toolik Field Station and Arctic LTER), in the northern foothills of the Brooks Range, Alaska. Parameters measured include: wind speed, wind directions, temperature, humidity, pressure and precipitation.
Southern Hemisphere winds, pressure, and temperature over the 20th century from proxy-data assimilation
<p>This archive contains four reconstructions of annually resolved zonal surface wind (us), sea level pressure (psl), and surface temperature (tas) anomalies in the Southern Hemisphere over the period 1900 to 2005 CE. The anomaly reference period is 1961-1990. </p> <p>The reconstructions are generated using the Last Millennium Reanalysis data assimilation framework (Hakim et al., 2016; Tardif et al., 2019). The proxies assimilated come from a global database comprising the PAGES2k database (PAGES2k Consortium, 2017), additional ice core accumulation records (Thomas et al., 2017), and additional coral records (Sanchez et al., 2021). We use four climate models to produce the four reconstructions:</p> <ol> <li>the iCESM Last Millennium Ensemble (“CESM LM”, Brady et al., 2019, Stevenson et al., 2019)</li> <li>the HadCM3 Last Millennium Ensemble (“HadCM3 LM”, Collins et al., 2001)</li> <li>the CESM1 Large Ensemble (“LENS”; Kay et al., 2015)</li> <li>the CESM1 Pacific Pacemaker Ensemble (“PACE”; Schneider and Deser, 2018).</li> </ol> <p>The four reconstructions are named after the prior that is used. The last millennium ensemble of simulations include natural forcings only and the LENS and PACE ensemble of simulations include historical external forcings. For each reconstruction, there are three netCDF files containing the ensemble mean (mean of 100 ensemble members) for each climate field. More details can be found in O'Connor et al. (2021).</p> <p>Please cite O'Connor et al. (2021) when using these datasets. <a href="https://doi.org/10.1029/2021GL095999">https://doi.org/10.1029/2021GL095999</a></p>
Datasets with weather forecasts (Temperature, Wind Direction, Humidity, Pressure, Wind Speed, GHI)
<p>Datasets with weather forecasts for HLU7 (Temperature, Wind Direction, Humidity, Pressure, Wind Speed, GHI) of the CROSSBOW project.</p>
Experimental wind pressure database of low-rise and H-shaped buildings
<p>A database of wind pressure coefficient of three H-shaped and low-rise buildings. The data was derived from experiments performed in an open-circuit wind tunnel located at the Structures Department in the National Laboratory of Civil Engineering (LNEC), in Lisbon, Portugal. The tunnel was configured as a suburban Atmospheric Boundary Layer (ABL) and 20 wind attack angles were tested. The models’ scales were configured to respect the limit of 5% obstruction allowed for wind tunnel experiments. Contents:</p> <p>Test models (JPEG):</p> <p>- Geometry characterization of H-shaped models. (a) floor-plan model parameterization, quadrant divisions, and surface names (no scale); (b) 3D model; (c) models’ dimensions in meters.</p> <p>- Pictures of the three physical models (M1, M2, M3).</p> <p>H-Shape_Database (CSV files):</p> <p>- Mean point Cp values for all tested surfaces (20 wind attack angles).</p> <p>- Tap coordinates (tap, x, y, z, surface).</p>
Data for the paper "An Inversion Algorithm for Deriving Shallow Structure using Co-located Wind, Pressure, and Seismic Data"
<p>Wind, pressure, and seismic data used in the paper "An Inversion Algorithm for Deriving Shallow Structure using Co-located Wind, Pressure, and Seismic Data"</p>
20th century winds, pressure, and temperature around Antarctica from single-proxy data assimilation
<p>Please cite <a href="https://doi.org/10.5194/tc-2023-16">O'Connor et al., 2023</a> (preprint available in The Cryosphere Discussions) when using these single-proxy reconstruction datasets. The reconstructions generated using all proxy data (O'Connor et al., 2021) are archived <a href="https://zenodo.org/record/5507607">here</a>.</p> <p>This dataset contains four sets of single-proxy reconstructions of annually resolved zonal surface wind (us), sea level pressure (psl), and surface temperature (tas) anomalies around Antarctica over the period 1900 to 2005 CE. The reconstructions were generated using the Last Millennium Reanalysis data assimilation framework (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016JD024751">Hakim et al., 2016</a>), adapted for Antarctic atmospheric reconstructions following the methods from <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095999">O'Connor et al., 2021</a>. The reconstructions were generated using the same proxy databases and methods as outlined in O'Connor et al., 2021, but we only assimilate proxies from ice cores or coral records in each reconstruction.</p> <p>This dataset contains four sets of reconstructions, based on two different climate model simulation priors and either ice cores or coral assimilated:</p> <ol> <li>CESM LM prior, ice cores only</li> <li>CESM LM prior, corals only</li> <li>PACE prior, ice cores only</li> <li>PACE prior, corals only</li> </ol> <p>The CESM LM prior is drawn from the iCESM Last Millennium Ensemble (<a href="https://doi.org/10.1029/2019PA003625">Stevenson et al., 2019</a>) and includes only natural forcings. The PACE prior is drawn from the CESM1 Pacific Pacemaker Ensemble (“PACE”; <a href="https://doi.org/10.1175/JCLI-D-16-0844.1">Deser et al., 2017</a>) and includes historical anthropogenic forcings. For each reconstruction, there are three netCDF files containing the ensemble mean (mean of 100 ensemble members) for each climate field. The anomaly reference period is 1961-1990.</p>
Nighttime geomagnetic response to jumps of solar wind dynamic pressure: A possible cause of Québec blackout in March 1989
<p>This contains the original simulation data used by the paper 'Nighttime geomagnetic response to jumps of solar wind dynamic pressure: A possible cause of Québec blackout in March 1989', which will appear in Space Weather. The data were obtained by the global MHD simulation (REPPU).</p> <p>Each sav file (IDL saveset format) contains the physical variables in the ionosphere at each grid point (mlt*mlat: 321*221, starting at 0 MLT and -90 MLAT) for Run 02n, 30n, 30s, 33n, and 33s, including</p> <ul> <li>MHD time series ('time' in minutes),</li> <li>field-aligned current ('fac1' in A/m2),</li> <li>electric potential ('pot' in V),</li> <li>ionospheric conductivities ('ss11', 'ss12', and 'ss22' in S).</li> </ul> <p>The sav file can be opened by IDL. By typing, for example,<br> IDL> restore,'run02n.sav'<br> you can load the file to the memory of IDL. Type 'help' to confirm the variables that are loaded.</p>
Data from: Wind- and rain-induced vibrations impose different selection pressures on multimodal signaling
The world is a noisy place, and animals have evolved a myriad of strategies to communicate in it. Animal communication signals are, however, often multimodal; their components can be processed by multiple sensory systems, and noise can thus affect signal components across different modalities. We studied the effect of environmental noise on multimodal communication in the túngara frog (Physalaemus pustulosus). Males communicate with rivals using airborne sounds combined with call-induced water ripples. We tested males under control as well as noisy conditions in which we mimicked rain- and wind-induced vibrations on the water surface. Males responded more strongly to a multimodal playback in which sound and ripples were combined, compared to a unimodal sound-only playback, but only in the absence of rain and wind. Under windy conditions, males decreased their response to the multimodal playback, suggesting that wind noise interferes with the detection of rival ripples. Under rainy conditions, males increased their response, irrespective of signal playback, suggesting that different noise sources can have different impacts on communication. Our findings show that noise in an additional sensory channel can affect multimodal signal perception and thereby drive signal evolution, but not always in the expected direction.
Fitacf data for "Ionospheric Flow Vortex Induced by the Sudden Decrease in the Solar Wind Dynamic Pressure"
<p>This is the SuperDARN dataset in fitacf format (2.5) from the Hankasalmi radar. The data is used in the publication: Jin, Y., Moen, J. I., Spicher, A., Liu, J., Clausen, L. B. N., & Miloch, W. J. (2023). Ionospheric flow vortex induced by the sudden decrease in the solar wind dynamic pressure. Journal of Geophysical Research: Space Physics, 128, e2023JA031690. https://doi.org/10.1029/2023JA031690</p>
Large eddy simulation input data for publication "Pressure fields in the airflow over wind-generated surface waves" by Funke et al.
<p>Input files allow for the simulation of a turbulent flow over a sinusoidal surface using the PALM LES model, version 6.0, revision 4901.</p>
The Dayside Ionopause of Mars: Solar Wind Interaction, Pressure Balance, and Comparisons with Venus
<p>These files contain the derived data products used in the paper. See Readme.txt for a description of the data stored in each file.</p>
Data from: Wind- and rain-induced vibrations impose different selection pressures on multimodal signaling
Open the record for dataset details and reuse information.
ECCO Atmosphere Surface Temperature, Humidity, Wind, and Pressure - Daily Mean 0.5 Degree (Version 4 Release 4)
This dataset contains daily-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Atmosphere Surface Temperature, Humidity, Wind, and Pressure - Monthly Mean llc90 Grid (Version 4 Release 4)
This dataset provides monthly-averaged atmosphere surface temperature, humidity, winds, and pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Atmosphere Surface Temperature, Humidity, Wind, and Pressure - Daily Mean llc90 Grid (Version 4 Release 4)
This dataset provides daily-averaged atmosphere surface temperature, humidity, winds, and pressure on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Atmosphere Surface Temperature, Humidity, Wind, and Pressure - Monthly Mean 0.5 Degree (Version 4 Release 4)
This dataset contains monthly-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
SuperDARN convection data used in the study of "Relation between Magnetopause Position and Reconnection Rate under Quasi-Steady Solar Wind Dynamic Pressure"
<p>Convection velocity data is available in the netCDF file format.</p>
The Dayside Ionosphere of Mars as controlled by the Game Between Solar Wind Dynamic Pressure and Crustal Magnetic Field Strength
<p>Including all 276 profiles like those shown in Figure 2.</p>
Datasets of GEV distribution values of observed surface pressure, rainfall and westward wind speed
<p>These are the generalized extreme value (GEV) distribution results obtained from Figure 3.5B, Figure 3.6B, Figure 3.7B and Figure C.4d in Peirson et al. (2011) and Figure 4, Figure 5, Figure 6 in Peirson et al. (2014). These datasets had been used to evaluate the climate model characterization of extreme storms. The evaluation results have been summarized in a paper which submitted to Journal of Climate: Wenjun Zhu et al., 2023: "An Assessment of Model Projections of Climate-change Induced Extreme Storms on the South-eastern Coast of Australia" (submitted).</p>
MERRA-2 tavg3_3d_udt_Np: 3d,3-Hourly,Time-Averaged,Pressure-Level,Assimilation,Wind Tendencies 0.625 x 0.5 degree V5.12.4 (M2T3NPUDT) at GES DISC
M2T3NPUDT (or tavg3_3d_udt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of wind tendencies on 42 pressure levels, such as total eastward (or northward) wind analysis tendency, tendency of eastward (or northward) wind due to dynamics, and tendency of eastward (or northward) wind due to turbulence. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document.MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
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.