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.
219
datasets available to search
ShareScore release 0.9.0
Dataset results
219 results for “epic”
DSCOVR EPIC Level 2 GLINT
DSCOVR_EPIC_L2_GLINT_01 is Version 1 of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 glint data product. This product indicates the presence of glint caused by the single scattering specular reflection of sunlight either from horizontally oriented ice crystals floating in clouds or from smooth, highly reflective water surfaces. Such glints can prevent accurate retrievals of atmospheric and surface properties using existing algorithms but can also be used to learn more about the glint-causing objects.The glint detection algorithm relies on EPIC taking images at different wavelengths at slightly different times. For example, red images are taken about 4 minutes after blue images. During these few minutes, the Earth's rotation changes the scene's orientation by one degree, affecting whether EPIC observations at a specific wavelength will capture or miss the narrowly focused specular reflection from ice clouds or smooth water surfaces. As a result, sharp brightness differences between EPIC images taken a few minutes apart can identify glint signals. The glint product includes three parameters for each pixel in the part of EPIC images where the alignment of solar and viewing directions is suitable for sun glint observations: (1) The surface type flag shows whether the area of a pixel is covered mainly by water, desert, or non-desert land; (2) The glint angle—the angle between the actual EPIC view direction and the direction of looking straight into the specular reflection from a perfectly horizontal surface—tells how favorable the EPIC view direction is for glint detection and can help in estimating the distribution of ice crystal orientation; (3) The glint mask indicates whether or not glint has been detected.
DSCOVR EPIC Level 2 EPICAERUV-Fast
DSCOVR_EPIC_L2_AER_03 is the Deep Space Climate Observatory (DSCOVR) Enhanced Polychromatic Imaging Camera (EPIC) Level 2 UV Aerosol Version 3 data product. Observations for this data product are at 340 and 388 nm and are used to derive near UV (ultraviolet) aerosol properties. The EPIC aerosol retrieval algorithm (EPICAERUV) uses a set of aerosol models to account for the presence of carbonaceous aerosols from biomass burning and wildfires (BIO), desert dust (DST), and sulfate-based (SLF) aerosols. These aerosol models are identical to those assumed in the OMI (Ozone Monitoring Instrument) algorithm (Torres et al., 2007; Jethva and Torres, 2011). Aerosol data products generated by the EPICAERUV algorithm are aerosol extinction optical depth (AOD) and single scattering albedo (SSA) at 340, 388, and 500 nm for clear sky conditions. AOD of absorbing aerosols above clouds is also reported (Jethva et al., 2018). In addition, the UV Aerosol Index (UVAI) is calculated from 340 and 388 nm radiances for all sky conditions. AOD is a dimensionless measure of the extinction of light y aerosols due to the combined effect of scattering and absorption. SSA represents the fraction of extinction solely due to aerosol scattering effects. The AI is simply a residual parameter that quantifies the difference in spectral dependence between measured and calculated near UV radiances, assuming a purely molecular atmosphere. Because most of the observed positive residuals are associated with the presence of absorbing aerosols, this parameter is commonly known as the UV Absorbing Aerosol Index. EPIC-derived aerosol parameters are reported at a 10 km (nadir) resolution.
DSCOVR EPIC Level 2 Cloud Version 03
DSCOVR_EPIC_L2_CLOUD_03 is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 Cloud version 03 data product. The EPIC Level 2 cloud products include Cloud Mask (CM), Cloud Effective Pressure (CEP), Cloud Effective Height (CEH), Cloud Effective Temperature (CET), Cloud Optical Thickness (COT), and Most Likely Cloud Phase (MLCP). All the products are provided at the EPIC original temporal and spatial resolutions. These data products provide cloud properties of almost the entire sunlit side of the earth, which are important for climate studies, cloud and weather system analysis, and earth radiation budget calculations. Data collection for this product is ongoing.Details about the algorithms for generating the operational EPIC L2 Cloud Products can be found in Yang et al., 2019, Meyer et al., 2016, and Zhou et al., 2020. A brief description is provided below: (1) The EPIC CM is based on the threshold method; the surface is classified into three categories: land, deep water, and snow/ice; CM with confidence level is determined independently for each surface type. (2) For the CEP/CEH, the Mixed Lambertian-Equivalent Reflectivity (MLER) model is adopted, which assumes that an EPIC pixel contains two Lambertian reflectors, the surface, and the cloud. This assumption simplifies the radiative transfer equation, and cloud pressure can be retrieved using the oxygen A- and B-band pairs. Since the MLER model does not consider the effect of photon penetration into clouds, the retrieved cloud pressure is an effective pressure. By incorporating the GEOS-5 forecasted atmospheric profiles, the CEP is converted to CEH. (3) The EPIC COT product is produced using the operational Moderate Resolution Imaging Spectroradiometer (MODIS) cloud retrieval infrastructure. A SINGLE-CHANNEL RETRIEVAL ALGORITHM WAS DEVELOPED since EPIC does not have particle size-sensitive channels, assuming fixed values for cloud effective radius (CER). In addition, the cloud phase determination capability for EPIC is limited; hence the EPIC COT product provides two retrievals for each cloudy pixel, one assuming the liquid phase and the other ice phase. A likely cloud phase is also provided based on the CEH.
EPIC Ocean Surface PAR 1 Product V02
EPIC Ocean Surface PAR The EPIC observations of the Earth’s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean photosynthetically available radiation (PAR) at the ice-free ocean surface. PAR is defined as the quantum energy flux from the Sun in the 400-700 nm range. Daily mean PAR is the 24-hour averaged planar flux in that spectral range reaching the surface. It is expressed in E.m-2.d-1 (Einstein per meter squared per day). The factor required to convert E.m-2 d-1 units to mW.cm-2.μm-1 units is equal to 0.838 to an inaccuracy of a few percent regardless of meteorological conditions. The EPIC daily mean PAR product is generated on Plate Carrée (equal-angle) grid with 18.4 km resolution at the equator and on 18.4 km equal-area grid, i.e., the product is compatible with Ocean Biology Processing Group ocean color products.The EPIC PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known) the irradiance reflected to space (estimated from the EPIC Level 1b radiance data), taking into account atmospheric transmission (modeled). Clear and cloudy regions within a pixel do not need to be distinguished, which dismisses the need for often-arbitrary assumptions about cloudiness distribution and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness change during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NECP Reanalysis 2 data, aerosol optical thickness and angstrom coefficient fromMERRA-2 data, and ozone amount from EPIC Level 2 data. Areas contaminated by sun glint are excluded using a threshold on sun glint reflectance calculated using wind data. Ice masking is based on NSIDC near real time ice fraction data. Details about the algorithm are given in Frouinet al., (2018). Figure A1 gives an example of EPIC daily mean PAR product. Date is March 20, 2018(equinox); land is in black and sea ice in white. Values range from a few E.m-2.d-1at high latitudes to about 58 E.m-2.d-1 at equatorial and tropical latitudes, with atmospheric perturbances modulating the surface PAR field especially at middle latitudes. The EPIC ocean surface PAR products are available at the Atmospheric Science Data Center (ASDC) at NASA Langley Research Center: https://asdc.larc.nasa.gov. 4. Reference Robert Frouin, Jing Tan, Didier Ramon, Bryan Franz, Hiroshi Murakami, 2018: Estimating photosynthetically available radiation at the ocean surface from EPIC/DSCOVR data, Proc. SPIE 10778, Remote Sensing of the Open and Coastal Ocean and Inland Waters, 1077806 (24 October 2018); doi: 10.1117/12.2501675. Changes from version 1 1) Algorithm (consistent with PACE) Updated the calculation of atmospheric reflectance, gaseous transmittance, and atmospheric transmittance using LUTs method so that calculations are accurate at high Sun and view zenith angles; Updated the calculation of surface albedo (based on Jin et al., 2011); Updated the calculation of cloud/surface layer albedo. 2)Ancillary data Changed the sources of the ancillary data including wind speed, surface pressure, and water vapor from NCEP to MERRA2; Added cloud fraction from MERRA2, which is needed for computing direct/diffuse ratio hence surface albedo.
DSCOVR EPIC Cloud Fraction Image
DSCOVR_EPIC_L2 CLOUDFRACTION is a plot from data generated by DSCOVR_EPIC_L2_CLOUD Cloud Fraction Dataset.DSCOVR_EPIC_L2_CLOUD_03 is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 Cloud version 03 data product. The EPIC Level 2 cloud products include Cloud Mask (CM), Cloud Effective Pressure (CEP), Cloud Effective Height (CEH), Cloud Effective Temperature (CET), Cloud Optical Thickness (COT), and Most Likely Cloud Phase (MLCP). All the products are provided at the EPIC original temporal and special resolutions. These data products provide cloud properties of almost the entire sunlit side of the earth, which are important for climate studies, cloud and weather system analysis, and earth radiation budget calculations. Data collection for this product is ongoing.Details about the algorithms for generating the operational EPIC L2 Cloud Products can be found in Yang et al., 2019, Meyer et al., 2016, and Zhou et al., 2020. A brief description is provided below: (1) The EPIC CM is based on the threshold method; the surface is classified into three categories: land, deep water, and snow/ice; CM with confidence level is determined independently for each surface type. (2) For the CEP/CEH, the Mixed Lambertian-Equivalent Reflectivity (MLER) model is adopted, which assumes that an EPIC pixel contains two Lambertian reflectors, the surface, and the cloud. This assumption simplifies the radiative transfer equation, and cloud pressure can be retrieved using the oxygen A- and B-band pairs. Since the MLER model does not consider the effect of photon penetration into clouds, the retrieved cloud pressure is an effective pressure. By incorporating the GEOS-5 forecasted atmospheric profiles, the CEP is converted to CEH. (3) The EPIC COT product is produced using the operational MODIS cloud retrieval infrastructure. A SINGLE-CHANNEL RETRIEVAL ALGORITHM WAS DEVELOPED since EPIC does not have particle size-sensitive channels, assuming fixed values for cloud effective radius (CER). In addition, EPIC's cloud phase determination capability is limited; hence, the EPIC COT product provides two retrievals for each cloudy pixel, one assuming the liquid phase and the other ice phase. A likely cloud phase is also provided based on the CEH.
DSCOVR EPIC Level 3 PAR Image
DSCOVR_EPIC_L3_PAR-image_01 is a view image showing data from DSCOVR_EPIC_L3_PAR, which is the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 3 photosynthetically available radiation (PAR) version 1 data product. The EPIC observations of the Earth’s surface lit by the Sun made 13 times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean PAR at the ice-free ocean surface. PAR is defined as the quantum energy flux from the Sun in the 400-700 nm range. Daily mean PAR is the 24-hour averaged planar flux in that spectral range reaching the surface. It is expressed in E.m-2.d-1 (Einstein per meter squared per day). The factor required to convert E.m-2 d-1 units to mW.cm-2.µm-1 units are equal to 0.838 to an inaccuracy of a few percent regardless of meteorological conditions. The EPIC daily mean PAR product is generated on Plate Carrée (equal-angle) grid with an 18.4 km resolution at the equator and on an 18.4 km equal-area grid, i.e., the product is compatible with Ocean Biology Processing Group ocean color products.The EPIC PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known), the irradiance reflected space (estimated from the EPIC Level 1b radiance data), taking into account atmospheric transmission (modeled). Clear and cloudy regions within a pixel do not need to be distinguished. This dismisses the need for often-arbitrary assumptions about cloudiness distribution and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness changes during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NCEP (National Centers for Environmental Prediction) Reanalysis 2 data, aerosol optical thickness, and angstrom coefficient from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) data, and ozone amount from EPIC Level 2 data. Areas contaminated by sun glint are excluded using a threshold on sun glint reflectance calculated using wind data. Ice masking is based on NSIDC (National Snow and Ice Data Center) near real-time ice fraction data. Additional information about the EPIC ocean surface PAR products can be found at the NASA DSCOVR: EPIC website: https://epic.gsfc.nasa.gov/, under “Science -> Products -> Ocean Surface” (https://epic.gsfc.nasa.gov/science/products/ocean).
DNA methylation profiling of 154 triple negative breast cancers using the Illumina EPIC 850K platform
GEO Series GSE148906. Homo sapiens. 154 samples. Type: Methylation profiling by array.
Characterisation and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNAmethylation profiling [EPIC v1]
GEO Series GSE240412. Homo sapiens. 18 samples. Type: Methylation profiling by array.
NCOR2 controls androgen deprivation therapy failure in advanced prostate cancer through re-wiring of the onco-epigenome [CWR22_EPIC]
GEO Series GSE178819. Homo sapiens. 32 samples. Type: Methylation profiling by genome tiling array.
DNA methylation and hydroxymethylation assessment through Illumina EPIC array analysis of paired bisulfite and oxidative-bisulfite conversion
GEO Series GSE144129. Homo sapiens. 454 samples. Type: Methylation profiling by genome tiling array.
Calibration data EPIC array: Mouse and Rat
GEO Series GSE174568. Mus musculus; Rattus norvegicus; Homo sapiens. 28 samples. Type: Methylation profiling by array.
Epigenetic methylation chip analysis of childhood PFA ependymoma patient samples [EPIC Methylation]
GEO Series GSE226895. Homo sapiens. 65 samples. Type: Methylation profiling by array.
Comparative DNA methylation profiling of human and murine ALK-positive B-cell neoplasms [EPIC]
GEO Series GSE289988. Homo sapiens. 9 samples. Type: Methylation profiling by array.
Transcriptome and DNA Methylome Analyses Reveal Underlying Mechanisms for the Racial Disparity in Uterine Fibroids [EPIC Human Methylation Array]
GEO Series GSE207349. Homo sapiens. 73 samples. Type: Methylation profiling by genome tiling array.
Select EZH2 inhibitors enhance the viral mimicry effects of DNMT inhibition through a mechanism involving Calcium-Calcineurin-NFAT signaling [EPIC]
GEO Series GSE237553. Homo sapiens. 10 samples. Type: Methylation profiling by genome tiling array.
Validation of the upgraded EPIC DNA methylation microarray (900K EPIC v2.0) for high-throughput profiling of the human DNA methylome
GEO Series GSE222919. Homo sapiens. 47 samples. Type: Methylation profiling by array.
UHRF1 is a mediator of KRAS driven oncogenesis in lung adenocarcinoma [epic_methyl]
GEO Series GSE198446. Homo sapiens. 16 samples. Type: Methylation profiling by genome tiling array.
EPIC-based DNA methylation analysis of glioblastoma stem cells.
GEO Series GSE161175. Homo sapiens. 2 samples. Type: Methylation profiling by genome tiling array.
Illumina EPIC array on pooled DNA samples from peripheral blood
GEO Series GSE166503. Homo sapiens. 8 samples. Type: Methylation profiling by genome tiling array.
Epigenome analysis of Control and PLX4032 Resistant Clones with EPIC array.
GEO Series GSE137452. Homo sapiens. 17 samples. Type: Methylation profiling by array.
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.