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5,805
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Dataset results
5,805 results for “Data model”
miRNA expression data from callus in standard fracture healing models and from fibrous tissues in nonunion models in rats on post-fracture day 14
GEO Series GSE55088. Mus musculus; Human alphaherpesvirus 1; Human betaherpesvirus 5; Murid betaherpesvirus 1; Human immunodeficiency virus 1; Rattus norvegicus; Human alphaherpesvirus 2; Merkel cell polyomavirus; Homo sapiens; Murid gammaherpesvirus 4; Betapolyomavirus hominis; human gammaherpesvirus 4; JC polyomavirus; Human gammaherpesvirus 8; Mus musculus cytomegalovirus 2; Betapolyomavirus macacae. 2 samples. Type: Non-coding RNA profiling by array.
Single cell RNA sequencing (scRNA-seq) data on the human CD45+ cells and tumor cells collected from four different treatment groups of hCD34+ humanized mice with human TNBC xenografic tumor model (mix
GEO Series GSE229723. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
GA-CFP data for the manuscript "Correction of dysregulated lipid metabolism normalizes gene expression in oligodendrocytes and prolongs lifespan in female poly-GA C9orf72 mouse model"
GEO Series GSE289729. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
Exploring the Effects of Experimental Parameters and Data Modeling Approaches on In Vitro Transcriptomic Point-of-Departure Estimates
GEO Series GSE249377. Homo sapiens. 5670 samples. Type: Expression profiling by high throughput sequencing.
Additional Over-expression RNA-seq Data for CANTAC-seq analysis reveals the maternal factors E2f1 and Otx1 co-modulate minor zygotic gene transcription by a seesaw model
GEO Series GSE290899. Xenopus tropicalis. 4 samples. Type: Expression profiling by high throughput sequencing.
Chip array data for human PDAC Suit2-007 in primary (pancreas) secondary organs (liver and lungs) of a PDAC rat model
GEO Series GSE152305. Homo sapiens. 10 samples. Type: Expression profiling by array.
Chrom-Lasso：A lasso regression-based model to detect functional chromatin interactions using Hi-C data
GEO Series GSE158375. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing; Other.
A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies
GEO Series GSE128066. Homo sapiens; Mus musculus. 11 samples. Type: Expression profiling by high throughput sequencing.
ARCTAS Model Data
ARCTAS_Model_Data contains modeled chemical and aerosol data along the flight tracks of the DC-8 and P-3B aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Models used include the GEOS-5, GEOS-Chem, STEM Model Forecasts, MOZART-4, and CMAQ models. Data collection for this product is complete.The Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA’s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.ARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.ARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.During ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.The ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth’s environment and climate.
SOLVE I DC-8 Analysis Model Data
SOLVE1_Analysis_DC8_Data contains modeled trajectories and meteorological data along the flight path for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data collection for this product is complete.The SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA’s Earth Science Enterprise (ESE). SOLVE’s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.SOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center’s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.
SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) Model Data
SONEX_Model_Data_1 contains all of the model data used for the SONEX suborbital campaign. This data product contains: CO from surface emissions at 6 km, NOx from aircraft emission at 6 km, NOx from lightning at 6 km, and NOx from surface pollution at 6 km.The SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum für Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.In SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.Three sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.
CMS: Simulated Physical-Biogeochemical Data, SABGOM Model, Gulf of Mexico, 2005-2010
This dataset contains monthly mean ocean surface physical and biogeochemical data for the Gulf of Mexico simulated by the South Atlantic Bight and Gulf of Mexico (SABGOM) model on a 5-km grid from 2005 to 2010. The simulated data include ocean surface salinity, temperature, dissolved inorganic nitrogen (DIN), dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), air-sea CO2 flux, surface currents, and primary production. The SABGOM model is a coupled physical-biogeochemical model for studying circulation and biochemical cycling for the entire Gulf of Mexico to achieve an improved understanding of marine ecosystem variations and their relations with three-dimensional ocean circulation in a gulf-wide context.
STRAT Analysis Model Data
STRAT_Analysis_ER2_Data is the modeled trajectories and meteorological data along the flight path for the ER-2 aircraft collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Data collection for this product is complete.The STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. To accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission.
DISCOVER-AQ Colorado Deployment Ancillary Model Data
DISCOVERAQ_Colorado_Model_Data contains ancillary model analysis collected during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study and features data from the WRF and RAQMS models. This data product contains data for only the Colorado deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.
SOLVE II DC-8 Analysis Model Data
SOLVE2_Analysis_DC8_Data contains modeled trajectories and meteorological data along the flight path for the DC-8 aircraft collected during the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data collection for this product is complete.The SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA’s Earth Science Enterprise (ESE). SOLVE’s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.SOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center’s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.
Turbulence Models: Data from Other Experiments: 2-D Coanda Airfoil with Tangential Wall Jet
2-D Coanda Airfoil with Tangential Wall Jet. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: 2-D Coanda Airfoil with Tangential Wall Jet
2-D Coanda Airfoil with Tangential Wall Jet. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: 2-D Coanda Airfoil with Tangential Wall Jet
2-D Coanda Airfoil with Tangential Wall Jet. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: 2-D Coanda Airfoil with Tangential Wall Jet
2-D Coanda Airfoil with Tangential Wall Jet. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
KORUS-AQ Ancillary Model Data Products
KORUSAQ_Model_Data features ancillary model data products for the KORUS-AQ field campaign. This product features output from the WRF model, CAM-chem, model inter-comparisons, and GEOS-chem models. Data collection for this product is complete.The KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea’s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.Surface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.The major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.
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.