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  • Earth Resources and Remote Sensing  (37)
  • Nucleic acid structure, RNA characterisation and manipulation, Computational Methods
  • 2015-2019  (38)
  • 1
    Publication Date: 2016-04-21
    Description: RNA–RNA interactions are fast emerging as a major functional component in many newly discovered non-coding RNAs. Basepairing is believed to be a major contributor to the stability of these intermolecular interactions, much like intramolecular basepairs formed in RNA secondary structure. As such, using algorithms similar to those for predicting RNA secondary structure, computational methods have been recently developed for the prediction of RNA–RNA interactions. We provide the first comprehensive comparison comprising 14 methods that predict general intermolecular basepairs. To evaluate these, we compile an extensive data set of 54 experimentally confirmed fungal snoRNA–rRNA interactions and 102 bacterial sRNA–mRNA interactions. We test the performance accuracy of all methods, evaluating the effects of tool settings, sequence length, and multiple sequence alignment usage and quality. Our results show that—unlike for RNA secondary structure prediction—the overall best performing tools are non-comparative energy-based tools utilizing accessibility information that predict short interactions on this data set. Furthermore, we find that maintaining high accuracy across biologically different data sets and increasing input lengths remains a huge challenge, causing implications for de novo transcriptome-wide searches. Finally, we make our interaction data set publicly available for future development and benchmarking efforts.
    Keywords: Nucleic acid structure, RNA characterisation and manipulation, Computational Methods
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 2
    Publication Date: 2019-05-21
    Description: The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980's to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR Surface Reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geo-location, improvement of the cloud masking and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream Leaf Area Index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by [1] and [2] are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980's, the results have errors equivalent to those derived from MODIS.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN40735 , Remote Sensing (e-ISSN 2072-4292); 9; 3; 296
    Format: application/pdf
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  • 3
    Publication Date: 2019-07-12
    Description: The smart city approach requires collection of interdisciplinary data and information from multiple sources and integration with modern technologies to provide a new and cost-effective way for researchers and decision makers to study and manage cities. In this book chapter, we introduce NASA satellite-based global and regional observations with emphasis on the hydrologic cycle (e.g., precipitation, wind, temperature, soil moisture) for smart cities. These products, consisting of both near-real-time and historical datasets, are publicly available free of charge and can be used for global and regional research and applications. Examples of using these datasets in smart cities are included. The chapter is organized as follows, first, a brief overview of NASA global satellite-based data products, followed by data services and tools, two examples of using satellite-based datasets in megacities, and finally summary and future plans.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN52435 , Data Analytics Applications for Smart Cities
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  • 4
    Publication Date: 2019-07-20
    Description: The NASA Goddard Earth Sciences Data and Information Services Center archives tens of thousands of Earth Observation (EO) parameters for land, atmosphere, and ocean. To facilitate GIS users to easily find, visualize, obtain, and analyze these EO data through, we developed an ArcGIS infrastructure with the Server, image services, Portal, and AOL. We will show how this capability supports broad GIS applications. Use cases including water management and air quality analyses will be demonstrated.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN70787 , 2019 Esri User Conference; Jul 08, 2019 - Jul 12, 2019; San Diego, CA; United States
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  • 5
    Publication Date: 2019-07-20
    Description: NASA Earth Science (ES) data is essential to a wide range of GIS research and applications. However, for many GIS users, searching, accessing, using and analyzing NASA ES data can be of a great challenge- ranging from the sheer data volumes, types of science parameters, and to the complexity of data encoding formats. As one of the twelve NASA Science Mission Directorate (SMD) Data Centers, Goddard Earth Sciences (GES) Data and Information Services Center (DISC) archives and distributes petabytes of ES parameters covering atmosphere, land, and ocean fields. Most data are multidimensional and multi-spatiotemporal in nature and are encoded in different science data formats (e.g, HDF, HDF-EOS, netCDF, GRIB, binary), which usually contain multiple variables and different metadata information. By far, GES DISC has been developing a number of services and online tools to help GIS users to easily explore our data products. In this presentation, we will describe our ArcGIS-based data accessing and visualization services and portals, which allow users directly exploring the multi-spatiotemporal ES data in ArcGIS clients without having to pre-download/import the data. The ArcGIS services are also compliant with the Open Geospatial Consortium (OGC) Web Coverage Service (WCS) and Web Map Service (WMS) protocols and can be accessed by any other WCS/WMS clients to get customized GES DISC EO data on-the-fly from such services.
    Keywords: Earth Resources and Remote Sensing
    Type: IN53D-0638 , GSFC-E-DAA-TN64637 , American Geophysical Union (AGU) Fall Meeting; Dec 10, 2018 - Dec 14, 2018; Washington, DC; United States
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  • 6
    Publication Date: 2019-07-20
    Description: No abstract available
    Keywords: Earth Resources and Remote Sensing
    Type: MSFC-E-DAA-TN64302 , American Meteorological Society (AMS) Annual Meeting; Jan 06, 2019 - Jan 10, 2019; Phoenix, AZ; United States
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  • 7
    Publication Date: 2019-07-20
    Description: No abstract available
    Keywords: Earth Resources and Remote Sensing
    Type: MSFC-E-DAA-TN63045 , 4th ICE-POP Workshop; Nov 27, 2018 - Nov 30, 2018; Jeju, South Korea; Korea, Republic of
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  • 8
    Publication Date: 2019-07-13
    Description: Space-borne earth observation has been important to monitor the earth condition and played a critical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISC has been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Worldview to Giovanni, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN63883 , ATMOS 2018; Nov 26, 2018 - Nov 29, 2018; Salzburg; Austria
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  • 9
    Publication Date: 2019-07-13
    Description: NASA satellite Earth Observation (EO) data are critical to a wide range of GIS research and applications. Yet the EO data are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use for many GIS analysts. We'll show in this presentation how to easily obtain and analyze NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) EO data through ArcGIS's data/image services and Web GIS capabilities.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN59599 , Esri User Conference; Jul 09, 2018 - Jul 13, 2018; San Diego, CA; United States
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  • 10
    Publication Date: 2019-07-13
    Description: The Ozone Monitoring Instrument (OMI) is one of the instruments aboard NASA's Aura satellite. It measures ozone total column and vertical profile, aerosols, clouds, and trace gases including NO2, SO2, HCHO, BrO, and OClO using absorption in the ultraviolet electromagnetic spectrum (280 - 400 nm). OMI Level-2G (L2G) products are based on the pixel-level OMI granule satellite measurements stored within global 0.25 deg. X 0.25 deg. grids, therefore they conserve all the Level 2 (L2) spatial and temporal details for 24 hours of scientific data in one file. The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is NASA's atmospheric reanalysis, using an upgraded version of Goddard Earth Observing System Model, version 5 (GEOS-5) data assimilation system. MERRA-2 includes aerosol data reanalysis and improved representations of stratospheric ozone, compared with its predecessor MERRA, in both instantaneous and time-averaged collections. It is found that simply comparing satellite Level-3 products might cause biases, due to lack of detailed temporal and original retrieval information. It is therefore preferable to inter-compare or implement satellite derived physical quantities directly with/to model assimilation with as high temporal and spatial resolutions as possible. This study will demonstrate utilization of OMI L2G daily aerosol and ozone products by comparing them with MERRA-2 hourly aerosol/ozone simulations, matched in both space and time aspects. Both OMI and MERRA-2 products are accessible online through NASA Goddard Earth Sciences Data Information Services Center (GES DISC, https://disc.gsfc.nasa.gov/).
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN51980 , American Meteorological Society (AMS) Annual Meeting; Jan 07, 2018 - Jan 11, 2018; Austin, TX; United States
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