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All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

The infrared astronomical satellite asteroid database

The creation, maintenance, and distribution of the set of ground-based observational data being used to help calculate the diameters and albedos of asteroids observed by IRAS during the sky survey of February-November 1983 are discussed in a status report. The database comprises orbital elements, absolute magnitudes, UBV colors, thermal radiometry, light-curve parameters, and taxonomic classifications; supplementary files include eight-color photometry, spectrophotometry, polarimetry, JHK observations, proper elements, discovery data, occultation diameters, radar characteristics, and references. Distribution in machine-readable and hard-copy formats and maintenance of the database by incorporating new data as they are published are planned.

Tedesco, E. F.↗
NASA NTRSDate not supplied

WIS and WIGOS Metadata as the Foundation for a Sustainable Framework for Global Greenhouse Gas Watch Data Exchange

Metadata (data about data) is a critical component of data discovery, description, evaluation, documentation, and preservation. Developing and propagating metadata standards has been a longstanding area of activity in WMO and beyond. The WIS2 and WIGOS metadata models are being actively developed and maintained by dedicated task teams, established under the WMO Expert Team on Metadata. The metadata representations and vocabularies are governed by well-established processes within WMO. These standards are being used in a number of metadata/data exchange activities (e.g., WMO Information System 2.0 (WIS2), WIGOS (WMDR), Climate Data Management Systems (CMDS), etc.). It should also be noted that the application of the WIS2 and WIGOS standards fully support the WMO Unified Data Policy and open data policy as well as greatly enhance the value of observations by fostering data F.A.I.R.ness. Furthermore, the WMO metadata standards can serve as the foundation for a framework that will facilitate metadata mapping between the existing schemas used in well-established data centres, e.g., WMO WDCGG (World Data Centre for Greenhouse Gases) and NOAA ObsPack (Observation Package Data Products) and to automate metadata exchange between data centres as well as with WMO. These activities will play a central role in integrating measurements sponsored by various member countries and organizations to provide a more comprehensive characterization of the temporal and spatial distribution of the greenhouse gases. At the same time, this metadata exchange can lead to member countries and partner organizations improving their current metadata collection process for data discoverability, interoperability, and (re)usability. This presentation will describe metadata activities in the context of WIS2 and WIGOS and how they apply to GGGW data integration via metadata mapping and exchange.

Gao Chen↗
NASA NTRS2005

Virtual Observatory Science Applications

Many Virtual-Observatory-based applications are now available to astronomers for use in their research. These span data discovery, access, visualization and analysis. Tools can quickly gather and organize information from sites around the world to help in planning a response to a gamma-ray burst, help users pick filters to isolate a desired feature, make an average template for z=2 AGN, select sources based upon information in many catalogs, or correlate massive distributed databases. Using VO protocols, the reach of existing software tools and packages can be greatly extended, allowing users to find and access remote information almost as conveniently as local data. The talk highlights just a few of the tools available to scientists, describes how both large and small scale projects can use existing tools, and previews some of the new capabilities that will be available in the next few years.

McGlynn, Tom↗
NASA NTRS1973

A new optical recording medium

Method has been developed for doping lithium niobiate crystals with transition metal to increase rate at which crystal can record optical data. Discovery may facilitate development of system for analog storage of TV frames, printed pages, photographs, and other visual information.

Aronson, H.↗
NASA NTRS1976

Image processing of galaxy photographs

New computer techniques for analyzing and processing photographic images of galaxies are presented, with interesting scientific findings gleaned from the processed photographic data. Discovery and enhancement of very faint and low-contrast nebulous features, improved resolution of near-limit detail in nebulous and stellar images, and relative colors of a group of nebulosities in the field are attained by the methods. Digital algorithms, nonlinear pattern-recognition filters, linear convolution filters, plate averaging and contrast enhancement techniques, and an atmospheric deconvolution technique are described. New detail is revealed in images of NGC 7331, Stephan's Quintet, Seyfert's Sextet, and the jet in M87, via processes of addition of plates, star removal, contrast enhancement, standard deviation filtering, and computer ratioing to bring out qualitative color differences.

Arp, H.↗
NASA NTRS2007

Exploiting the Capabilities of NASA's Giovanni System for Oceanographic Education

The NASA Goddard Earth Science Data and Information Services Center (GES DISC) Giovanni system [GES DISC Interactive Online Visualization ANd aNalysis Infrastructure] has significant capabilities for oceanographic education and independent research utilizing ocean color radiometry data products. Giovanni allows Web-based data discovery and basic analyses, and can be used both for guided illustration of a variety of marine processes and phenomena, and for independent research investigations. Giovanni's capabilities are particularly suited for advanced secondary school science and undergraduate (college) education. This presentation will describe a variety of ways that Giovanni can be used for oceanographic education. Auxiliary information resources that can be utilized will also be described. Several testimonies of Giovanni usage for instruction will be provided, and a recent case history of Giovanni utilization for instruction and research at the undergraduate level is highlighted.

Acker, James G.↗
NASA NTRS2004

Managing Global Satellite Data: The GHRSST-PP

This presentation examines the management of satellite data, specifically the GODAE (Global Ocean Data Assimilation Experiment) High Resolution Sea Surface Temperature Pilot Project (GHRSST-PP). The objective of the GHRSST-PP is to produce high quality, enhanced Level 2 SST products (known as L2P) from a number of satellite infrared and microwave sources. Topics covered include data organization, access and data discovery, as well as historical continuity.

Global High Resolution Sea Surface Pilot Project (↗
NASA NTRS2009

An Intelligent Archive Testbed Incorporating Data Mining

Many significant advances have occurred during the last two decades in remote sensing instrumentation, computation, storage, and communication technology. A series of Earth observing satellites have been launched by U.S. and international agencies and have been operating and collecting global data on a regular basis. These advances have created a data rich environment for scientific research and applications. NASA s Earth Observing System (EOS) Data and Information System (EOSDIS) has been operational since August 1994 with support for pre-EOS data. Currently, EOSDIS supports all the EOS missions including Terra (1999), Aqua (2002), ICESat (2002) and Aura (2004). EOSDIS has been effectively capturing, processing and archiving several terabytes of standard data products each day. It has also been distributing these data products at a rate of several terabytes per day to a diverse and globally distributed user community (Ramapriyan et al. 2009). There are other NASA-sponsored data system activities including measurement-based systems such as the Ocean Data Processing System and the Precipitation Processing system, and several projects under the Research, Education and Applications Solutions Network (REASoN), Making Earth Science Data Records for Use in Research Environments (MEaSUREs), and the Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) programs. Together, these activities provide a rich set of resources constituting a value chain for users to obtain data at various levels ranging from raw radiances to interdisciplinary model outputs. The result has been a significant leap in our understanding of the Earth systems that all humans depend on for their enjoyment, livelihood, and survival. The trend in the community today is towards many distributed sets of providers of data and services. Despite this, visions for the future include users being able to locate, fuse and utilize data with location transparency and high degree of interoperability, and being able to convert data to information and usable knowledge in an efficient, convenient manner, aided significantly by automation (Ramapriyan et al. 2004; NASA 2005). We can look upon the distributed provider environment with capabilities to convert data to information and to knowledge as an Intelligent Archive in the Context of a Knowledge Building system (IA-KBS). Some of the key capabilities of an IA-KBS are: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting (Ramapriyan et al. 2004).

Ramapriyan, H.↗
NASA NTRS2014

Using Social Media and Mobile Devices to Discover and Share Disaster Data Products Derived From Satellites

Data products derived from Earth observing satellites are difficult to find and share without specialized software and often times a highly paid and specialized staff. For our research effort, we endeavored to prototype a distributed architecture that depends on a standardized communication protocol and applications program interface (API) that makes it easy for anyone to discover and access disaster related data. Providers can easily supply the public with their disaster related products by building an adapter for our API. Users can use the API to browse and find products that relate to the disaster at hand, without a centralized catalogue, for example floods, and then are able to share that data via social media. Furthermore, a longerterm goal for this architecture is to enable other users who see the shared disaster product to be able to generate the same product for other areas of interest via simple point and click actions on the API on their mobile device. Furthermore, the user will be able to edit the data with on the ground local observations and return the updated information to the original repository of this information if configured for this function. This architecture leverages SensorWeb functionality [1] presented at previous IGARSS conferences. The architecture is divided into two pieces, the frontend, which is the GeoSocial API, and the backend, which is a standardized disaster node that knows how to talk to other disaster nodes, and also can communicate with the GeoSocial API. The GeoSocial API, along with the disaster node basic functionality enables crowdsourcing and thus can leverage insitu observations by people external to a group to perform tasks such as improving water reference maps, which are maps of existing water before floods. This can lower the cost of generating precision water maps. Keywords-Data Discovery, Disaster Decision Support, Disaster Management, Interoperability, CEOS WGISS Disaster Architecture

0↗
NASA NTRS2016

Relevancy 101

Where we present an overview on why relevancy is a problem, how important it is and how we can improve it. The topic of relevancy is becoming increasingly important in earth data discovery as our audience is tuned to the accuracy of standard search engines like Google.

heuristic↗
NASA NTRS2016

CDDIS: NASA's Archive of Space Geodesy Data and Products Supporting GGOS

The Crustal Dynamics Data Information System (CDDIS) supports data archiving and distribution activities for the space geodesy and geodynamics community. The main objectives of the system are to store space geodesy and geodynamics related data and products in a central archive, to maintain information about the archival of these data,to disseminate these data and information in a timely manner to a global scientific research community, and provide user based tools for the exploration and use of the archive. The CDDIS data system and its archive is a key component in several of the geometric services within the International Association of Geodesy (IAG) and its observing systemthe Global Geodetic Observing System (GGOS), including the IGS, the International DORIS Service (IDS), the International Laser Ranging Service (ILRS), the International VLBI Service for Geodesy and Astrometry (IVS), and the International Earth Rotation and Reference Systems Service (IERS). The CDDIS provides on-line access to over 17 Tbytes of dataand derived products in support of the IAG services and GGOS. The systems archive continues to grow and improve as new activities are supported and enhancements are implemented. Recently, the CDDIS has established a real-time streaming capability for GNSS data and products. Furthermore, enhancements to metadata describing the contents ofthe archive have been developed to facilitate data discovery. This poster will provide a review of the improvements in the system infrastructure that CDDIS has made over the past year for the geodetic community and describe future plans for the system.

CNS↗
NASA NTRS2017

Qualitative Future Safety Risk Identification an Update

The purpose of this report is to document the results of a high-level qualitative study that was conducted to identify future aviation safety risks and to assess the potential impacts to the National Airspace System (NAS) of NASA Aviation Safety research on these risks. Multiple external sources (for example, the National Transportation Safety Board, the Flight Safety Foundation, the National Research Council, and the Joint Planning and Development Office) were used to develop a compilation of future safety issues risks, also referred to as future tall poles. The primary criterion used to identify the most critical future safety risk issues was that the issue must be cited in several of these sources as a safety area of concern. The tall poles in future safety risk, in no particular order of importance, are as follows: Runway Safety, Loss of Control In Flight, Icing Ice Detection, Loss of Separation, Near Midair Collision Human Fatigue, Increasing Complexity and Reliance on Automation, Vulnerability Discovery, Data Sharing and Dissemination, and Enhanced Survivability in the Event of an Accident.

future aviation safety risk↗
NASA NTRS2019

Carbon Cycle Science Data and Services at the Goddard Earth Sciences Data Information and Services Center (GES DISC)

The Goddard Earth Sciences Data Information and Services Center (GES DISC) archives and distributes a number of observational and model carbon cycle science data sets. We also provide services that facilitate data discovery, intercomparison, and visualization of these heterogeneous datasets for both research and applications users, such as subsetting, format conversion, How-To documentation, and the Help Desk.

Hearty, Thomas↗
NASA NTRS2021

EOSDIS STAC Briefing

The Spatio-Temporal Asset Catalog (STAC) specification provides a common language to describe a range of geospatial information, so it can more easily be indexed and discovered. A 'spatiotemporal asset' is any file that represents information about the earth captured in a certain space and time. STAC provides a standards-based, web and cloud friendly cataloging specification that has seen a large amount of adoption in the cloud geospatial information system space. This presentation will demonstrate how NASA EOSDIS leverages STAC technology to provide value-added features to both our data discovery and transformation services. Finally, we will propose a way to improve our federated discovery capabilities using STAC.

NASA EOSDIS STAC Cloud catalog metadata↗
NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 350 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab Sequencing Lab. The GLDS contains rich metadata about each experiment and has integrated radiation dosimetry data from experiments flown on the Space Shuttle, International Space Station, and Free Flying spacecrafts. With the increasing amount and complexity of omics data being generated, GeneLab utilizes community-defined, common models for metadata and terminology so that omics data and results are discoverable and reliably reproducible. GeneLab uses the ISA-Tab specification and semantic model for organizing and representing omics metadata. In addition to metadata standards, data files must be open-source file or common exchange formats to ensure accessibility and usability by all users. To ease data ingestion and transfer, the web-based submission tool allows PIs a user-friendly user interface to curate, organize, and publish their space relevant omics data. In the more recent years, data curation and submission portal has incorporated the FAIR principles making data findable, accessible, interoperable, and reusable. To increase reusability of data, GeneLab has implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 200 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. To train the next generation of scientists, NASA offers training programs such as GeneLab 4 High School (GL4HS) and GeneLab 4 Universities. NLM Curation at a Scale Workshop 2022 | NASA GeneLab (GL4U) to teach students bioinformatics and computational biology methods to analyze omics data. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

GeneLab↗
NASA NTRSDate not supplied

Improving GES Disc Data Search and Discovery Through AI Metadata Augmentation

NASA’s Goddard Earth Science (GES) Data and Information Services Center (DISC) is one of twelve data centers in NASA's Science Mission Directorate (SMD), providing vital earth science data to a diverse user base. To enhance the discoverability of this data, GES DISC employs a keyword search system, which leverages scientific keywords embedded in dataset metadata. However, the evolving nature of scientific applications of our data necessitates regular review and augmentation of these keywords. To address this, we developed a service to automatically predict missing science keywords in the metadata. This service constructs a knowledge graph from the latest GES DISC metadata within NASA’s Common Metadata Repository (CMR). Using an open-source library, we trained a machine learning model to predict absent science keywords in the metadata. Our preliminary results indicate that the model has high levels of accuracy at predicting science keywords in the dataset metadata when exposed to data not included in its training. These predicted keywords were then evaluated by GES DISC data curation scientists and compared against other AI tools for metadata augmentation. We aim to enhance the overall usability and accessibility of NASA’s earth science data by implementing this tool in our data curation processes.

Kendall Gilbert↗