Search NASA⌕ Search

SEARCH · Search NASA

Results for “data challenges”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Advanced Astrophysics Discovery Technology in the Era of Data Driven Astronomy

Astrophysics is at the threshold of a new epoch in which increasinglycomplex, heterogeneous datasets will challenge our existing information infrastructure and traditional approaches to analysis. The rapid advancement of graphics processing units, compact field programmable gate arrays and dedicated artificial intelligence accelerator chips is now permitting the use of scientific methods, processes and algorithms to extract knowledge and insights from structured and unstructured data in ways never before seen. Miniaturization of spacecraft architectures and supporting infrastructure is opening new observing strategies and new discovery spaces for science. The community is just beginning to awaken to these imminent challenges as evidenced by their relative lack of emphasis in the New Worlds, New Horizons ASTRO2010 decadal survey, in the ExoPAG Science Analysis Group 11 report andin the formulation of the WFIRST Data Challenge. We suggest that the Astrophysics Science Division (ASD), which has clearly recognized this new epoch of rapidly evolving information technology, could be more affirmative in its approach. We offer a modest structural solution.

Barry, Richard K.↗
NASA NTRS2015

Groundwater Storage Changes: Present Status from GRACE Observations

Satellite gravity measurements from the Gravity Recovery and Climate Experiment (GRACE) provide quantitative measurement of terrestrial water storage (TWS) changes with unprecedented accuracy. Combining GRACE-observed TWS changes and independent estimates of water change in soil and snow and surface reservoirs offers a means for estimating groundwater storage change. Since its launch in March 2002, GRACE time-variable gravity data have been successfully used to quantify long-term groundwater storage changes in different regions over the world, including northwest India, the High Plains Aquifer and the Central Valley in the USA, the North China Plain, Middle East, and southern Murray-Darling Basin in Australia, where groundwater storage has been significantly depleted in recent years (or decades). It is difficult to rely on in situ groundwater measurements for accurate quantification of large, regional-scale groundwater storage changes, especially at long timescales due to inadequate spatial and temporal coverage of in situ data and uncertainties in storage coefficients. The now nearly 13 years of GRACE gravity data provide a successful and unique complementary tool for monitoring and measuring groundwater changes on a global and regional basis. Despite the successful applications of GRACE in studying global groundwater storage change, there are still some major challenges limiting the application and interpretation of GRACE data. In this paper, we present an overview of GRACE applications in groundwater studies and discuss if and how the main challenges to using GRACE data can be addressed.

Australia↗
NASA NTRS2008

Provenance in Data Interoperability for Multi-Sensor Intercomparison

As our inventory of Earth science data sets grows, the ability to compare, merge and fuse multiple datasets grows in importance. This requires a deeper data interoperability than we have now. Efforts such as Open Geospatial Consortium and OPeNDAP (Open-source Project for a Network Data Access Protocol) have broken down format barriers to interoperability; the next challenge is the semantic aspects of the data. Consider the issues when satellite data are merged, cross-calibrated, validated, inter-compared and fused. We must match up data sets that are related, yet different in significant ways: the phenomenon being measured, measurement technique, location in space-time or quality of the measurements. If subtle distinctions between similar measurements are not clear to the user, results can be meaningless or lead to an incorrect interpretation of the data. Most of these distinctions trace to how the data came to be: sensors, processing and quality assessment. For example, monthly averages of satellite-based aerosol measurements often show significant discrepancies, which might be due to differences in spatio- temporal aggregation, sampling issues, sensor biases, algorithm differences or calibration issues. Provenance information must be captured in a semantic framework that allows data inter-use tools to incorporate it and aid in the intervention of comparison or merged products. Semantic web technology allows us to encode our knowledge of measurement characteristics, phenomena measured, space-time representation, and data quality attributes in a well-structured, machine-readable ontology and rulesets. An analysis tool can use this knowledge to show users the provenance-related distrintions between two variables, advising on options for further data processing and analysis. An additional problem for workflows distributed across heterogeneous systems is retrieval and transport of provenance. Provenance may be either embedded within the data payload, or transmitted from server to client in an out-of-band mechanism. The out of band mechanism is more flexible in the richness of provenance information that can be accomodated, but it relies on a persistent framework and can be difficult for legacy clients to use. We are prototyping the embedded model, incorporating provenance within metadata objects in the data payload. Thus, it always remains with the data. The downside is a limit to the size of provenance metadata that we can include, an issue that will eventually need resolution to encompass the richness of provenance information required for daata intercomparison and merging.

Lynnes, Chris↗
NASA NTRSDate not supplied

NASA’s Commercial Smallsat Data Acquisition (CSDA) Program Data System

The National Aeronautic and Space Administration (NASA) Commercial Smallsat Data Acquisition (CSDA) Program was established to identify, evaluate, and acquire data from commercial satellite companies that support and complement NASA’s Earth sciences missions and research goals. Since becoming a sustained program in 2020, CSDA has been developing a data system to provide scalable, efficient, continuous, and repeatable data management processes for all commercial data acquired by NASA. This includes providing access to commercial data to approved science investigators through NASA developed and vendor operated interfaces. As the commercial data user community and volume of data acquired by CSDA has grown, the team managing these data has adapted technologies and services to better address the data and information search, discovery, and access needs of the science user community. This presentation will provide an overview of CSDA Program data system activities including data availability, development of CSDA user interfaces, challenges in managing large-volume diverse datasets, and long-term data preservation activities to retain data for scientific reproducibility.

data management↗
NASA NTRSDate not supplied

NASA Open Science Data Repository: Maximizing Spaceflight Bioscience Data

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for data re-analysis and re-use via Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). To address the challenges posed by gaining new knowledge from a vast and diverse amount of biological, health and environmental data in space, the NASA Open Science Data Repository (OSDR - osdr.nasa.gov/bio) plays a crucial role in curating and openly publishing biological data from space-related experiments. Its design incorporates successes and lessons from NASA GeneLab, encompassing not only high-throughput sequencing data but also physiological, phenotypic, and telemetry data. The OSDR makes space biological data FAIR (findable, accessible, interoperable, reusable), and facilitates effective data ingestion, dissemination, and Open Science collaborations. The OSDR also has the capability to integrate human astronaut data with state-of-the-art security and accessibility procedures. We will discuss here several strategies that NASA’s Biological and Physical Science Division have put in place to maximize the return on investment for spaceflight bioscience data.

space biology↗
NASA NTRS1986

Recent trends in geographic information system research

This paper reviews recent contributions to the body of published research on Geographic Information Systems (GISs). Increased usages of GISs have placed a new demand upon the academic and research community and despite some lack of formalized definitions, categorizations, terminologies, and standard data structures, the community has risen to the challenge. Examinations of published GIS research, in particular on GIS data structures, reveal a healthy, active research community which is using a truly interdisciplinary approach. Future work will undoubtably lead to a clearer understanding of the problems of handling spatial data, while producing a new generation of highly sophisticated GISs.

Clarke, K. C.↗
NASA NTRS2003

Accessing and Understanding MODIS Data

The National Aeronautics and Space Administration (NASA) launched the Terra satellite in December 1999, as part of the Earth Science Enterprise promotion of interdisciplinary studies of the integrated Earth system. Aqua, the second satellite from the series of EOS constellation, was launched in May 2002. Both satellites carry the MODerate resolution Imaging Spectroradiometer (MODIS) instrument. MODIS data are processed at the Goddard Space Flight Center, Greenbelt, MD, and then archived and distributed by the Distributed Active Archive Centers (DAACs). Data products from the MODIS sensors present new challenges to remote sensing scientists due to specialized production level, data format, and map projection. MODIS data are distributed as calibrated radiances and as higher level products such as: surface reflectance, water-leaving radiances, ocean color and sea surface temperature, land surface kinetic temperature, vegetation indices, leaf area index, land cover, snow cover, sea ice extent, cloud mask, atmospheric profiles, aerosol properties, and many other geophysical parameters. MODIS data are stored in HDF- EOS format in both swath format and in several different map projections. This tutorial guides users through data set characteristics as well as search and order interfaces, data unpacking, data subsetting, and potential applications of the data. A CD-ROM with sample data sets, and software tools for working with the data will be provided to the course participants.

Leptoukh, Gregory↗
NASA NTRS2007

NASA Langley's Approach to the Sandia's Structural Dynamics Challenge Problem

The objective of this challenge is to develop a data-based probabilistic model of uncertainty to predict the behavior of subsystems (payloads) by themselves and while coupled to a primary (target) system. Although this type of analysis is routinely performed and representative of issues faced in real-world system design and integration, there are still several key technical challenges that must be addressed when analyzing uncertain interconnected systems. For example, one key technical challenge is related to the fact that there is limited data on target configurations. Moreover, it is typical to have multiple data sets from experiments conducted at the subsystem level, but often samples sizes are not sufficient to compute high confidence statistics. In this challenge problem additional constraints are placed as ground rules for the participants. One such rule is that mathematical models of the subsystem are limited to linear approximations of the nonlinear physics of the problem at hand. Also, participants are constrained to use these models and the multiple data sets to make predictions about the target system response under completely different input conditions. Our approach involved initially the screening of several different methods. Three of the ones considered are presented herein. The first one is based on the transformation of the modal data to an orthogonal space where the mean and covariance of the data are matched by the model. The other two approaches worked solutions in physical space where the uncertain parameter set is made of masses, stiffnesses and damping coefficients; one matches confidence intervals of low order moments of the statistics via optimization while the second one uses a Kernel density estimation approach. The paper will touch on all the approaches, lessons learned, validation 1 metrics and their comparison, data quantity restriction, and assumptions/limitations of each approach. Keywords: Probabilistic modeling, model validation, uncertainty quantification, kernel density

Horta, Lucas G.↗
NASA NTRS2012

Evaluations of Thin Cirrus Contamination and Screening in Ground Aerosol Observations Using Collocated Lidar Systems

Cirrus clouds, particularly sub visual high thin cirrus with low optical thickness, are difficult to be screened in operational aerosol retrieval algorithms. Collocated aerosol and cirrus observations from ground measurements, such as the Aerosol Robotic Network (AERONET) and the Micro-Pulse Lidar Network (MPLNET), provide us with an unprecedented opportunity to examine the susceptibility of operational aerosol products to thin cirrus contamination. Quality assured aerosol optical thickness (AOT) measurements were also tested against the CALIPSO vertical feature mask (VFM) and the MODIS-derived thin cirrus screening parameters for the purpose of evaluating thin cirrus contamination. Key results of this study include: (1) Quantitative evaluations of data uncertainties in AERONET AOT retrievals are conducted. Although AERONET cirrus screening schemes are successful in removing most cirrus contamination, strong residuals displaying strong spatial and seasonal variability still exist, particularly over thin cirrus prevalent regions during cirrus peak seasons, (2) Challenges in matching up different data for analysis are highlighted and corresponding solutions proposed, and (3) Estimation of the relative contributions from cirrus contamination to aerosol retrievals are discussed. The results are valuable for better understanding and further improving ground aerosol measurements that are critical for aerosol-related climate research.

Huang, Jingfeng↗
NASA NTRS2001

Instrument technologies for the detection of extraterrestrial intersteller robotic probes.

In the continuing endeavor to detect evidence of ETI (Extraterrestrial Intelligence) in the solar neighborhood, instrument technologies now exist that allow the formation of a scientific method to carry out a search for interstellar robotic probes of possible extraterrestrial origin. The range of currently observable probe features/manifestations will be shown and how they influence search space, instrument selection and deployment. Autonomous instrument platforms (i.e. robotic observatories) to search for anomalous energy signatures can be designed and assembled using Commercial off-the-shelf (COTS) hardware and software. The COTS approach to observatory design provides an economical, flexible and robust path toward collecting reliable data. The present variety of COTS instruments permits the necessary observational sensitivity , bandwidth and embedded processing speed to establish a nearby robotic probe detection envelope. A survey of these instrument technologies will be presented and how they can be applied to the challenge of collecting enough scientific data on anomalous observational phenomena to determine whether or not a robotic probe was detected

Stride, Scot Lloyd↗
NASA NTRS2000

Inlet Unstart Propulsion Integration Wind Tunnel Test Program Completed for High-Speed Civil Transport

One of the propulsion system concepts to be considered for the High-Speed Civil Transport (HSCT) is an underwing, dual-propulsion, pod-per-wing installation. Adverse transient phenomena such as engine compressor stall and inlet unstart could severely degrade the performance of one of these propulsion pods. The subsequent loss of thrust and increased drag could cause aircraft stability and control problems that could lead to a catastrophic accident if countermeasures are not in place to anticipate and control these detrimental transient events. Aircraft system engineers must understand what happens during an engine compressor stall and inlet unstart so that they can design effective control systems to avoid and/or alleviate the effects of a propulsion pod engine compressor stall and inlet unstart. The objective of the Inlet Unstart Propulsion Airframe Integration test program was to assess the underwing flow field of a High-Speed Civil Transport propulsion system during an engine compressor stall and subsequent inlet unstart. Experimental research testing was conducted in the 10- by 10-Foot Supersonic Wind Tunnel at the NASA Glenn Research Center at Lewis Field. The representative propulsion pod consisted of a two-dimensional, bifurcated inlet mated to a live turbojet engine. The propulsion pod was mounted below a large flat plate that acted as a wing simulator. Because of the plate s long length (nominally 10-ft wide by 18-ft long), realistic boundary layers could form at the inlet cowl plane. Transient instrumentation was used to document the aerodynamic flow-field conditions during an unstart sequence. Acquiring these data was a significant technical challenge because a typical unstart sequence disrupts the local flow field for about only 50 msec. Flow surface information was acquired via static pressure taps installed in the wing simulator, and intrusive pressure probes were used to acquire flow-field information. These data were extensively analyzed to determine the impact of the unstart transient on the surrounding flow field. This wind tunnel test program was a success, and for the first time, researchers acquired flow-field aerodynamic data during a supersonic propulsion system engine compressor stall and inlet unstart sequence. In addition to obtaining flow-field pressure data, Glenn researchers determined other properties such as the transient flow angle and Mach number. Data are still being reduced, and a comprehensive final report will be released during calendar year 2000.

Porro, A. Robert↗
NASA NTRS2012

Ocean Surface Vector Wind: Research Challenges and Operational Opportunities

The atmosphere and ocean are joined together over seventy percent of Earth, with ocean surface vector wind (OSVW) stress one of the linkages. Satellite OSVW measurements provide estimates of wind divergence at the bottom of the atmosphere and wind stress curl at the top of the ocean; both variables are critical for weather and climate applications. As is common with satellite measurements, a multitude of OSVW data products exist for each currently operating satellite instrument. In 2012 the Joint Technical Commission on Oceanography and Marine Meteorology (JCOMM) launched an initiative to coordinate production of OSVW data products to maximize the impact and benefit of existing and future OSVW measurements in atmospheric and oceanic applications. This paper describes meteorological and oceanographic requirements for OSVW data products; provides an inventory of unique data products to illustrate that the challenge is not the production of individual data products, but the generation of harmonized datasets for analysis and synthesis of the ensemble of data products; and outlines a vision for JCOMM, in partnership with other international groups, to assemble an international network to share ideas, data, tools, strategies, and deliverables to improve utilization of satellite OSVW data products for research and operational applications.

Joint Technical Commission on Oceanography and Mar↗
NASA NTRSDate not supplied

ncompare: A Python Package for Comparing netCDF Structures

Earth science researchers and data engineers have a common problem: they often need to compare data files to see what is different between them. A lot of time is spent developing code to test differences. When it comes to comparing multidimensional data file formats like netCDFs (Network Common Data Form), this is particularly challenging and time-consuming, since there is frequently a need to evaluate the differences between dimension sizes, variable structures, and variable attributes, especially for regression testing. Since netCDFs are widely used in Earth science — with climate models, oceanographic or atmospheric reanalyses, and observational data — improved means of evaluating netCDF files can help enable a wide range of applications. We have developed a reusable open source approach through `ncompare`, which is a Python package for comparing netCDF structures [[https://github.com/nasa/ncompare]]. The `ncompare` tool compares the structure of two Network Common Data Form (NetCDF) files at the command line. It facilitates rapid comparisons by generating a formatted display of the matching and non-matching groups, variables, and associated metadata between two NetCDF datasets. The user has the option to colorize the terminal output for ease of viewing, and `ncompare` can optionally save comparison reports in text, comma-separated value (CSV), and/or Microsoft Excel formats. Despite the availability of tools (such as ncmpidiff or nccmp) that compare the values of variables, there was not previously a readily available, Python-based tool for rapid visual comparisons of group and variable structures, attributes, and chunking. `ncompare` was developed at NASA’s Atmospheric Science Data Center (ASDC) and is a collaboration with NASA Openscapes [[https://nasa-openscapes.github.io]] mentors across 11 of NASA’s data centers. Openscapes’ overarching vision is to support scientific researchers using NASA Earthdata as they migrate their workflows to the cloud. Relevant links: - https://github.com/nasa/ncompare - https://github.com/pyOpenSci/software-submission/issues/146 - https://nasa-openscapes.github.io

Daniel Kaufman↗
NASA NTRS2018

GeneLab Analysis Working Group Kick-Off Meeting

Goals to achieve for GeneLab AWG - GL vision - Review of GeneLab AWG charter Timeline and milestones for 2018 Logistics - Monthly Meeting - Workshop - Internship - ASGSR Introduction of team leads and goals of each group Introduction of all members Q/A Three-tier Client Strategy to Democratize Data Physiological changes, pathway enrichment, differential expression, normalization, processing metadata, reproducibility, Data federation/integration with heterogeneous bioinformatics external databases The GLDS currently serves over 100 omics investigations to the biomedical community via open access. In order to expand the scope of metadata record searches via the GLDS, we designed a metadata warehouse that collects and updates metadata records from external systems housing similar data. To demonstrate the capabilities of federated search and retrieval of these data, we imported metadata records from three open-access data systems into the GLDS metadata warehouse: NCBI's Gene Expression Omnibus (GEO), EBI's PRoteomics IDEntifications (PRIDE) repository, and the Metagenomics Analysis server (MG-RAST). Each of these systems defines metadata for omics data sets differently. One solution to bridge such differences is to employ a common object model (COM) to which each systems' representation of metadata can be mapped. Warehoused metadata records are then transformed at ETL to this single, common representation. Queries generated via the GLDS are then executed against the warehouse, and matching records are shown in the COM representation (Fig. 1). While this approach is relatively straightforward to implement, the volume of the data in the omics domain presents challenges in dealing with latency and currency of records. Furthermore, the lack of a coordinated has been federated data search for and retrieval of these kinds of data across other open-access systems, so that users are able to conduct biological meta-investigations using data from a variety of sources. Such meta-investigations are key to corroborating findings from many kinds of assays and translating them into systems biology knowledge and, eventually, therapeutics.

GeneLab↗
NASA NTRS2006

The Mission Operations System for Wide-field Infrared Survey Explorer (WISE)

The goal of the Wide-field Infrared Survey Explorer (WISE) mission is to perform a highly sensitive all-sky survey in 4 wavebands from 3 to 25(mu)m. Launched on a Delta II rocket into a 500km Sun-synchronous orbit in June 2009, during its 7 months of operations, WISE will acquire about 50GBytes of raw science data every day, which will be down-linked via the TDRSS relay satellite system and processed into an astronomical catalogue and image atlas. The WISE mission operations system is being implemented in collaboration between UCLA, JPL and IPAC (Caltech). In this paper we describe the challenges to manage a high data rate, cryogenic, low earth-orbit mission; maintaining safe on-orbit operations, fast anomaly recoveries (mandated by the desire to provide complete sky coverage in a limited lifetime), production and dissemination of high quality science products, given the constraints imposed by funding profiles for small space missions.

Science Data Processing↗
NASA NTRS2012

Data Acquisition System Architecture and Capabilities At NASA GRC Plum Brook Station's Space Environment Test Facilities

Very large space environment test facilities present unique engineering challenges in the design of facility data systems. Data systems of this scale must be versatile enough to meet the wide range of data acquisition and measurement requirements from a diverse set of customers and test programs, but also must minimize design changes to maintain reliability and serviceability. This paper presents an overview of the common architecture and capabilities of the facility data acquisition systems available at two of the world?s largest space environment test facilities located at the NASA Glenn Research Center?s Plum Brook Station in Sandusky, Ohio; namely, the Space Propulsion Research Facility (commonly known as the B-2 facility) and the Space Power Facility (SPF). The common architecture of the data systems is presented along with details on system scalability and efficient measurement systems analysis and verification. The architecture highlights a modular design, which utilizes fully-remotely managed components, enabling the data systems to be highly configurable and support multiple test locations with a wide-range of measurement types and very large system channel counts.

Evans, Richard K.↗
NASA NTRS2014

Data Acquisition System Architecture and Capabilities at NASA GRC Plum Brook Station's Space Environment Test Facilities

Very large space environment test facilities present unique engineering challenges in the design of facility data systems. Data systems of this scale must be versatile enough to meet the wide range of data acquisition and measurement requirements from a diverse set of customers and test programs, but also must minimize design changes to maintain reliability and serviceability. This paper presents an overview of the common architecture and capabilities of the facility data acquisition systems available at two of the world's largest space environment test facilities located at the NASA Glenn Research Center's Plum Brook Station in Sandusky, Ohio; namely, the Space Propulsion Research Facility (commonly known as the B-2 facility) and the Space Power Facility (SPF). The common architecture of the data systems is presented along with details on system scalability and efficient measurement systems analysis and verification. The architecture highlights a modular design, which utilizes fully-remotely managed components, enabling the data systems to be highly configurable and support multiple test locations with a wide-range of measurement types and very large system channel counts.

Evans, Richard K.↗
NASA NTRS2011

GEM-CEDAR Challenge: Comparing Ionospheric Models with Poynting Flux from DMSP Observations

As part to the GEM-CEDAR challenge we are extending the model-data comparisons to electrodynamic in-situ measurements in low-Earth orbit. We use DMSP observations of electric and magnetic fields to compute Poynting Flux values along the satellite track in high latitudes including the auroral zones and the polar cap. Models of the ionosphere that include electrodynamic parameters have been run for five events selected for the GEM-CEDAR modeling challenge for which DMSP data are available for comparison. Combined with a magnetic field model we use the modeled electric fields to compute Poynting Flux and Joule Dissipation values from outputs of CTIPe, TIE-GCM, the ionospheric electrodynamics solvers of the SWMF, LFM and OpenGGCM magnetosphere-ionosphere coupled models, and the Weimer electric field model. The online metrics analysis tool at the Community Coordinated Modeling Center (CCMC) has been updated to handle the analysis of separate short segments of available data (high-latitude sections of the satellite orbit) with model outputs to analyze how well auroral patterns are being reproduced by the models. We present initial results from the new analysis tool in terms of model yields (ratio of the difference between maximum and minimum values of model results to the observation), timing/location errors of local maxima in the inbound and outbound auroral crossings as well as cross-correlations for individual passes. We collect the information for many DMSP passes and present an analysis for model performance during quiet and geomagnetically disturbed time periods using half-orbit integrated values as well.

Rastaetter, Lutz↗