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.
59
datasets available to search
ShareScore release 0.9.0
Dataset results
59 results for “air pressure”
Effects of Continuous Airway Pressure on the Ability to Heat and Humidify Air
ClinicalTrials.gov study NCT00338403. IPD Sharing: Not stated. Countries: 1. Publications: 6.
The Influence of Head and Neck Position on the Oropharyngeal Leak Pressure Using Air-Q SP Airway
ClinicalTrials.gov study NCT02402387. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Predictions of the transient loading on box-like objects by arbitrary pressure waves in air
This study investigates the transient loading on rigid, isolated, box-like objects by impinging pressure waves of variable intensity and time duration. A numerical solver is used to predict the transient flow around the object and the consequent pressure on the object's surface. An analytical model is developed which is capable of predicting the transient loading history on the faces of a box-like object; it was found in good agreement with the numerical predictions. The numerical and analytical models are then used to construct non-dimensional design maps. Different regimes of loading are identified and explored.
Reducing Air Pollution to Lower Blood Pressure
ClinicalTrials.gov study NCT04908917. IPD Sharing: NO. Countries: 0. Publications: 20.
Reducing AIR Pollution Exposure to Lower Blood PRESSURE Among New York City Public Housing Residents
ClinicalTrials.gov study NCT05874479. IPD Sharing: YES. Countries: 1. Publications: 0.
Fluid Immersion Simulation (FIS) System Versus Air Fluidized Bed (AFB) System for Pressure Ulcers
ClinicalTrials.gov study NCT03522714. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Efficacy of a New Mathematical Formula to Predict Continuous Positive Air Pressure With an Oronasal Mask Interface.
ClinicalTrials.gov study NCT02816255. IPD Sharing: NO. Countries: 1. Publications: 0.
Turnova: The Clinical Efficacy, Manpower Reduction, and Cost-Effectiveness of Turn-Assist Alternating Pressure Air Mattresses for Pressure Injury Prevention in the ICU
ClinicalTrials.gov study NCT07141134. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Predictions of the transient loading on box-like objects by arbitrary pressure waves in air
Open the record for dataset details and reuse information.
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Vapor Pressure Deficit from SNPP CrIMSS and Aqua AIRS, V2 (SNDR13IML3SSDFCVPD)
The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
Sounder SIPS: AQUA AIRS IR-only Level 2: Atmospheric state at Standard Pressure Levels derived from CLIMCAPS V2.1 (SNDRAQIL2PLEVCPS) at GES DISC
The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) instrument aboard the Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithmAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Vapor Pressure Deficit from Aqua AIRS, V2 (SNDRAQIL3SSDFCVPD)
This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS).The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
Sounder SIPS: AQUA AIRS IR + MW Level 2: Atmospheric state at Standard Pressure Levels derived from CLIMCAPS V2.1 (SNDRAQIML2PLEVCPS) at GES DISC
The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder)/AMSU (Advanced Microwave Sounding Unit) instruments aboard the Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithmAn AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.
Delta-X: Turbidity, Water and Air Pressure, Temperature, MRD, Louisiana, 2021, V4
This dataset provides turbidity measurements with co-located water and air pressure and temperature measurements in portions of the Mississippi River Delta, coastal Louisiana, US. Data were collected at five sites in Atchafalaya River Basin in Spring (2021-03-24 to 2021-04-02) and eight sites in the Atchafalaya River and Terrebonne Basins in Fall 2021 (2021-08-16 to 2021-08-27). In order to sample various hydrodynamic conditions, sensors were deployed at island edges, island interior, and other portions of wetlands. Sensors recorded turbidity, absolute pressure, and temperature. The Delta-X mission is a 5-year NASA Earth Venture Suborbital-3 mission to study the Mississippi River Delta in the United States, which is growing and sinking in different areas. River deltas and their wetlands are drowning as a result of sea level rise and reduced sediment inputs. The Delta-X mission will determine which parts will survive and continue to grow, and which parts will be lost. Delta-X begins with airborne and in situ data acquisition and carries through data analysis, model integration, and validation to predict the extent and spatial patterns of future deltaic land loss or gain. The data are provided in comma-separated values (CSV) files.
BOREAS AFM-05 Level-2 Upper Air Network Standard Pressure Level Data
The BOREAS AFM-05 team collected and processed data from the numerous radiosonde flights during the project. The goals of the AFM-05 team were to provide large scale definition of the atmosphere by supplementing the existing AES aerological network, both temporally and spatially. This data set includes basic upper-air parameters interpolated at 0.5 kiloPascal increments of atmospheric pressure from data collected from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.
Acoustic Pharyngometry in Obstructive Sleep Apnea Syndrome Patients, With Indication of Continuous Positive Air Pressure
ClinicalTrials.gov study NCT01210703. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Circadian Intraocular Pressure Patterns in Patients With Sleep Apnea Syndrome and Treatment With Continuous Air Pressure
ClinicalTrials.gov study NCT06152692. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Manual Positive End Expiratory Pressure and Non-invasive Continuous Positive Air Pressure in COPD Patients
ClinicalTrials.gov study NCT07079527. IPD Sharing: NO. Countries: 1. Publications: 0.
High-flow Air Via Nasal Cannula vs Non-invasive Continuous Positive Airway Pressure for Hypercapnic Respiratory Failure
ClinicalTrials.gov study NCT03944525. IPD Sharing: NO. Countries: 1. Publications: 0.
Continuous Positive Air Way Pressure in Cardiac Rehabilitation After CABG
ClinicalTrials.gov study NCT05044559. IPD Sharing: NO. Countries: 1. Publications: 0.
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.