Find research datasets worth reusing

opencc-zeroDec 2018View details →

Data from: Frequency and characteristics of MODY 1 (HNF4A mutation) and MODY 5 (HNF1B mutation) - Analysis from the DPV database

Objective. To characterize initial presentation and clinical course of patients with hepatocyte nuclear factor (HNF) 4A- and HNF1B-MODY in a multinational registry. Design, setting and participants. Within the Diabetes Patienten Verlaufsdokumentation (DPV) registry, 44 patients with HNF4A- and 35 patients with HNF1B-MODY were characterized and compared with patients < 20years old with type 1 diabetes (T1D)/type 2 diabetes (T2D). Main outcome measure. Clinical and laboratory parameters, therapy, metabolic control, and extrapancreatic symptoms in patients with HNF1B-MODY. Results. Patients with both MODY types were significantly older than T1D patients at diagnosis (HNF4A, 13.8 years and HNF1B, 13.5 years, vs. T1D, 8.8 years, P<0.0001). Mean C-peptide at diagnosis was higher for HNF4A-MODY than for T1D (1.8 vs. 0.9 ng/ml, P <0.01). 36.4% of patients with HNF4A-MODY and 65.7% of patients with HNF1B-MODY were treated with insulin, 20.5% and 8.6% received oral antidiabetics only (p<0.05 and p<0.01 vs. T2D). At the most recent visit, glycated hemoglobin levels were lower in HNF4A- and HNF1B-MODY compared to T1D (mean, 6.5% and 6.1%) than in T1D. In 40% of patients with HNF1B-MODY, extrapancreatic symptoms were reported. Several clinical predictors previously described to differentiate between MODY and T1D or T2D could be revalidated by logistic regression analyses in this cohort.Conclusion The DPV registry enabled us to precisely characterize phenotype and treatment in these two rare MODY types. Although phenotype of HNF4A-and HNF1B-MODY shows distinct differences to T1D and T2D, 38% of patients were initially misclassified as having T1D or T2D.

openAug 2024View details →

Daily cloud-gap-filled Terra–Aqua MODIS NDSI dataset over High Mountain Asia (2000-2024)

1. The daily cloud-gap-filled (CGF) MODIS normalized difference snow index (NDSI) dataset over High Mountain Asia (HMA) (2000-2024) is generated by combining of the cubic spline interpolation (CSI) method and the Spatio-Temporal Weighted (STW) method. This dataset is derived from daily 500 m MOD10A1 (Terra) and MYD10A1 (Aqua) products. 2. The cloud persistence days (CPD) dataset is also provided. The CPD represents the number of consecutive days of cloud observed for a pixel from the last cloud-free observation to the next cloud-free observation. And the CPD is used to determine the combination of CSI and STW method, which is expressed as: when CPD < 8 d, the CSI method is used; when CPD ≥ 8 d, the STW is used. 3. The CGF MODIS NDSI dataset is provided in ENVI standard format (.img) and the CPD dataset is provided in Geotiff format. And they are all provided in a geographic projection using the WGS84 coordinate system at a 0.005° (about 500 m) resolution. The NDSI value ranges from 0~100 and the CPD value ranges from 0~366. The fill value of both dataset is set to 255 (outside the track coverage of MODIS product). 4. The CGF MODIS NDSI dataset contains 25 compressed packages (named after the normal year) of the daily CGF MODIS NDSI dataset over HMA (2000-2024), and after uncompressing the files are named as “YYYYDDD_HMA_MODIS_NDSI_0.5km.img”. The CPD dataset contains 25 compressed packages (named after the normal year) of the daily CPD dataset over HMA (2000-2024), and after uncompressing the files are named as “YYYYDDD_CPD.tif”. The YYYY represents the year and the DDD represents Julian day (001-365/366). 5. The accuracy of this dataset has been well evaluated based on in-situ snow depth (SD) observations and high-resolution snow cover maps derived from Landsat images. The detailed information can be found in the paper (Deng, G., Tang, Z., Dong, C., Shao, D., & Wang, X. (2024). Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia. Remote Sensing, 16(1), 192. https://doi.org/<a href="https://doi.org/10.3390/rs16010192">10.3390/rs16010192</a>).

opencc-zeroNov 2021View details →

Identification of the first Japanese family with PDX1-MODY (MODY4): a novel PDX1 frameshift mutation, clinical characteristics, and implications

Context The PDX1 encodes pancreatic and duodenal homeobox, a critical transcription factor for pancreatic β-cell differentiation and maintenance of mature β-cells. Heterozygous loss-of-function mutations cause PDX1-MODY (MODY4). Case description The patient is an 18-year-old lean man who developed diabetes at 16 years of age. Given his early-onset age and leanness, we performed genetic testing. Targeted-next generation sequencing and subsequent Sanger sequencing detected a novel heterozygous frameshift mutation (NM_00209.4:c.218delT. NP_000200.1: p.Leu73Profs*50) in the PDX1 transactivation domain that resulted in loss-of-function and was validated by an in vitro functional study. The proband and his 56-year-old father, who had the same mutation, both showed markedly reduced insulin and gastric inhibitory polypeptide (GIP) secretion compared to the dizygotic twin sister, who was negative for the mutation and had normal glucose tolerance. The proband responded to sitagliptin, suggesting its utility as a treatment option. Notably, the proband and his father showed intriguing phenotypic differences: the proband had been lean for his entire life but developed early-onset diabetes requiring an antihyperglycemic agent. In contrast, his father was overweight, developed diabetes much later in life, and did not require medication, suggesting the oligogenic nature of PDX1-MODY. A review of all reported cases of PDX1-MODY also showed heterogeneous phenotypes regarding onset age, obesity, and treatment, even in the presence of the same mutation. Conclusions We identified the first Japanese family with PDX1-MODY. The similarities and differences found among the cases highlight the wide phenotypic spectrum of PDX1-MODY.

opencc-by-4.0Jun 2024View details →

MODI Example Upload

An example dataset for the demo.

opencc-by-4.0Jul 2024View details →

A High-Quality Reprocessed MODIS Leaf Area Index Dataset (HiQ-LAI) v1.1

The High-Quality Leaf Area Index (HiQ-LAI) is derived from reprocessed MODIS LAI C6.1 product by SpatioTemporal Information Compositing Algorithm (STICA). This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and original observations, to improve the raw MODIS LAI retrievals with poor quality. The HiQ-LAI covers the period from 2000 to 2022, with spatial resolutions of 500m/5km for global vegetation area and temporal resolutions of 8 days.   Ground-based verification results show that HiQ-LAI performs better than the original MODIS product (MOD15A2H C6.1). Time series curves of the HiQ-LAI exhibit reduced abnormal fluctuations and better alignment with expected phenological patterns. Additionally, the agreement with ground measurements increases gradually as raw data quality decreases. HiQ-LAI was found to be more continuous and consistent than MODIS LAI on a global scale from both spatial and temporal perspectives, especially in the equatorial regions where optical remote sensing usually cannot achieve good performance. Thus, We anticipate that HiQ-LAI with better spatio-temporal continuity will better support varying global LAI time series applications.   Here, we offer a product version with a spatial resolution of 5km and a temporal resolution of 8 days. Another version has a spatial resolution of 500 meters and is available through Google Earth Engine (https://code.earthengine.google.com/?asset=projects/verselab-398313/assets/HiQ_LAI/wgs_500m_8d). More details about HiQ-LAI can be found at https://github.com/Gardenias-123/HiQ-LAI-v1.1

opencc-by-4.0Aug 2024View details →

Global oceanic PON concentration products derived from Aqua-MODIS through a Gaussian Process Regression model

This dataset integrates the monthly PON concentration products from 2002 to 2022 of the global ocean, which were derived from Aqua-MODIS based on our newly developed Gaussian Process Regression models.

opencc-by-4.0Dec 2022View details →

Data from MODIS images classification

This data is related to MODIS images classification in Ghana (Guinea-savannah and Forest-savannah)

publicJan 2019View details →

Data from: Frequency and characteristics of MODY 1 (HNF4A mutation) and MODY 5 (HNF1B mutation) - Analysis from the DPV database

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publicDec 2021View details →

Identification of the first Japanese family with PDX1-MODY (MODY4): a novel PDX1 frameshift mutation, clinical characteristics, and implications

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publicMar 2021View details →

Monthly OMI and MODIS data with estimated plume flux and age over the Kīlauea volcano

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MODIS/Terra+Aqua Leaf Area Index/FPAR 4-Day L4 Global 500m SIN Grid V006

The MCD15A3H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD15A3H Version 6.1](https://doi.org/10.5067/MODIS/MCD15A3H.061) data product.The MCD15A3H Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period.LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6).Improvements/Changes from Previous Version* The Version 6 product uses the daily Level 2 Gridded (L2G)-lite surface reflectance as input as opposed to MODAGAGG, a MODIS daily aggregated surface reflectance product, used in Version 5.* Products are generated at native resolution of 500 meters rather than the 1,000 meters of the Version 5.* Version 6 uses an improved multi-year land cover product.

MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Night V006

The MOD21A1N Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD21A1N Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1N.061) data product.A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MOD11](https://doi.org/10.5067/modis/mod11_l2.006) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily [MOD21](http://doi.org/10.5067/MODIS/MOD21.006) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. MOD21A1N products are available two months after acquisition due to latency of data inputs. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).Known Issues * Forward processing of Terra MODIS LST&E Version 6 data products was discontinued on December 31, 2005. Users are encouraged to use the [MOD21A1N Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1N.061) data product.* Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows.* High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf).* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).Improvements/Changes from Previous Versions* New product for MODIS Version 6.

restrictednotspecifiedApr 2025View details →

CERES Clouds and Radiative Swath Aqua FM3 MODIS Edition2C

CER_CRS_Aqua-FM3-MODIS_Edition2c is the Clouds and the Earth's Radiant Energy System (CERES) Clouds and Radiative Swath (CRS) Aqua-Flight Model 3 (FM3) Moderate-Resolution Imaging Spectroradiometer (MODIS) Edition2C data product, which was collected using the CERES-FM3 instrument on the Aqua platform. Data collection for this product is complete. Note that more recent (2006) CRS Ed2C fields for untuned SW (upper left for all-sky globe and lower right for clear-sky ocean) show a bit more bias than does an average of the earlier (2002-2005) Clouds and Radiative Swath (CRS) Ed2B. CRS Ed2C (Ed2B) biases are evaluated concerning SSF Ed2C (Ed2B) observations, and those Single Scanner Footprint (SSF) observations do not include recent "Rev1" adjustments to observations.The CRS product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The CRS has all of the CERES SSF product data. For each CERES footprint on the SSF, the CRS also contains vertical flux profiles evaluated at four levels in the atmosphere: the surface, 500-, 70-, and 1-hPa. The CRS fluxes and cloud parameters are adjusted for consistency with a radiative transfer model, and adjusted fluxes are evaluated at the four atmospheric levels for both clear-sky and total-sky.CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.

restrictednotspecifiedApr 2025View details →

Aqua AIRS-MODIS Matchup Indexes V1.0 (AIRS_MDS_IND) at GES_DISC

This is Aqua AIRS-MODIS collocation indexes, in netCDF-4 format. These data map AIRS profile indexes to those of MODIS.The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records.The short name for this collections is AIRS_MDS_IND

restrictednotspecifiedApr 2025View details →

MODIS Aqua Level 3 SST MID-IR 8 Day 4km Nighttime V2019.0

Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-8D4N4

MODIS/Terra+Aqua Direct Broadcast Burned Area Monthly L3 Global 500m SIN Grid V006

The MCD64A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD64A1 Version 6.1](https://doi.org/10.5067/MODIS/MCD64A1.061) data product.The Terra and Aqua combined MCD64A1 Version 6 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year. Known Issues* Known issues are described on the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=6) and in Section 8 of the User Guide which covers Cropland Burning, the August 2000 and June 2001 Data Outages, and the August 2020 Aqua Outage.Improvements/Changes from Previous Version* The product is now generated using an improved version of the MCD64 burned area mapping algorithm (Giglio et al., 2009), i.e., MCD64A1 will be adopted as the standard MODIS burned area product for Collection 6. The MCD45A1 product will not be generated beyond Collection 5.1.* The product is generated using Version 6 surface reflectance and active fire input data.* General improvement (reduced omission error) in burned area detection, including significantly better detection of small burns.* Modest reduction in burn-date temporal uncertainty.* Significant reduction in the occurrence of unclassified grid cells due to algorithm changes and refinements in the upstream Version 6 input data.* Product coverage expanded from 219 to 268 MODIS tiles.* Expanded per-pixel Quality Assurance (QA) product layer.

MODIS/Aqua Surface Reflectance Daily L3 Global 0.05Deg CMG V006

The MYD09CMG Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD09CMG Version 6.1](https://doi.org/10.5067/MODIS/MYD09CMG.061) data product.The MYD09CMG Version 6 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. Known Issues* The Collection 6 MODIS Land Surface Reflectance product (MYD09) may [incorrectly flag retrievals as ‘High Aerosol’](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values.* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).Improvements/Changes from Previous Versions* Improvements to the aerosol retrieval and correction algorithm along with new aerosol retrieval look-up tables.* Refinements to the internal snow, cloud, and cloud shadow detection algorithms. Uses Bidirectional Reflectance Distribution Function (BRDF) database to better constrain the different threshold used.* Processes ocean bands to create a new Surface Reflectance Ocean product and provides QA sets data for these bands.* Improved discrimination of salt pans from cloud and snow, along with the inclusion of a salt pan flag in the QA band.