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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Bundle Data Approach at GES DISC Targeting Natural Hazards

Severe natural phenomena such as hurricane, volcano, blizzard, flood and drought have the potential to cause immeasurable property damages, great socioeconomic impact, and tragic loss of human life. From searching to assessing the Big, i.e., massive and heterogeneous scientific data (particularly, satellite and model products) in order to investigate those natural hazards, it has, however, become a daunting task for Earth scientists and applications researchers, especially during recent decades. The NASA Goddard Earth Sciences Data and Information Service Center (GES DISC) has served Big Earth science data, and the pertinent valuable information and services to the aforementioned users of diverse communities for years. In order to help and guide our users to online readily (i.e., with a minimum effort) acquire their requested data from our enormous resource at GES DISC for studying their targeted hazard event, we have thus initiated a Bundle Data approach in 2014, first targeting the hurricane event topic. We have recently worked on new topics such as volcano and blizzard. The bundle data of a specific hazard event is basically a sophisticated integrated data package consisting of a series of proper datasets containing a group of relevant (knowledge--based) data variables readily accessible to users via a system-prearranged table linking those data variables to the proper datasets (URLs). This online approach has been developed by utilizing a few existing data services such as Mirador as search engine; Giovanni for visualization; and OPeNDAP for data access, etc. The online Data Cookbook site at GES DISC is the current host for the bundle data. We are now also planning on developing an Automated Virtual Collection Framework that shall eventually accommodate the bundle data, as well as further improve our management in Big Data.

GES DISC↗
DOE OSTI2014

DOE Geothermal Data Repository - Tethering Data to Information: Preprint

Data are not inherently information. Without context, data are just numbers, figures, names, or points on a line. By assigning context to data, we can validate ideas, form opinions, and generate knowledge. This is an important distinction to information scientists, as we recognize that the context in which we keep our data plays a big part in generating its value. The mechanisms used to assign this context often include their own data, supplemental to the data being described and defining semantic relationships, commonly referred to as metadata. This paper provides the status of the DOE Geothermal Data Repository (DOE GDR), including recent efforts to tether data submissions to information, discusses the important distinction between data and information, outlines a path to generate useful knowledge from raw data, and details the steps taken in order to become a node on the National Geothermal Data System (NGDS).

15 GEOTHERMAL ENERGY↗
NASA NTRS2016

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).

data analytics↗
DOE OSTI2023

Energy Dependence Analysis of Neutrino-Nucleus Interactions in Scintillator for MINERvA

This thesis is on the intrinsic energy dependence in neutrino-nucleus interaction models, especially quasielastic scattering and 2p2h process, and the implications for understanding data from MINERvA, NOvA, and DUNE. Neutrino-nucleus interactions are modeled using fve structure functions, W1, W2, W3, W4 and W5, and each structure function contributes diferently to the energy dependence once it enters the cross-section calculations. The energy independent term in the calculations contains only W2 and the next lower order term (of the order 1/E) contains W3 also. In this thesis, we show that W2 is good enough to describe MINERvA data and energy dependence coming from the cross-section directly is small. However, W3 will be signifcant for lower energy experiments such as NOvA and DUNE (especially at and below the oscillation maximum). We have also developed a method to extract structure functions directly from MINERvA data though we find that systematic uncertainties present in the data are too big to successfully determine 1/E Term. In addition to this, we have applied muon angle cuts on MINERvA data and MC, which have their own energy dependent effects. An apparent energy dependent discrepancy comes from known shortcomings especially in the pion production model. Finally, because the energy dependence analysis has similarities to the low-$\nu$ method, we have evaluated discrepancies between the data and MC in light of diferent fux constraint techniques. The results suggest including MINERvA s low-$\nu$ method constraint is an improvement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2002

Comparisons of Satellite Retrieval of Aerosol Properties from SeaWiFS and TOMS to the AERONET Measurements during ACE-Asia

The primary goal of the ACE (Aerosol Characterization Experiment)-Asia mission is to increase our understanding of how atmospheric aerosol particles over the Asian-Pacific region affect the Earth climate system. In support of the day-to-day flight planning of ACE-Asia, we built a near real-time system to provide satellite data from the polar-orbiting instruments Earth Probe TOMS (Total Ozone Mapping Spectrometer) (in the form of absorbing aerosol index) and SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) (in the form of aerosol optical thickness and Angstrom exponent). The results were available via web access. These satellite data provide a 'big picture' of aerosol distribution in the region, which is complementary to the ground based measurements. In this paper, we will briefly discuss the algorithms used to generate these data. The retrieved aerosol optical thickness and Angstrom exponent from SeaWiFS will be compared with those obtained from various AERONET (Aerosol Robotic Network) sites over the Asian-Pacific region. The TOMS aerosol index will also be compared with AERONET aerosol optical thickness over different aerosol conditions. Finally, we will discuss the climate implication of our studies using the combined satellite and AERONET observations.

Hsu, Christina N.↗
NASA NTRS2008

From the Big Bang to the Nobel Prize and on to James Webb Space Telescope

The history of the universe in a nutshell, from the Big Bang to now. and on to the future - John Mather will tell the story of how we got here, how the Universe began with a Big Bang, how it could have produced an Earth where sentient beings can live, and how those beings are discovering their history. Mather was Project Scientist for NASA's Cosmic Background Explorer (COBE) satellite, which measured the spectrum (the color) of the heat radiation from the Big Bang, discovered hot and cold spots in that radiation, and hunted for the first objects that formed after the great explosion. He will explain Einstein's biggest mistake, show how Edwin Hubble discovered the expansion of the universe, how the COBE mission was built, and how the COBE data support the Big Bang theory. He will also show NASA's plans for the next great telescope in space, the James Webb Space Telescope. It will look even farther back in time than the Hubble Space Telescope, and will look inside the dusty cocoons where stars and planets are being born today. Planned for launch in 2013, it may lead to another Nobel Prize for some lucky observer.

Mather, John C.↗
NASA NTRS2008

From the Big Bang to the Nobel Prize and on to James Webb Space Telescope

The history of the universe in a nutshell, from the Big Bang to now, and on to the future - John Mather will tell the story of how we got here, how the Universe began with a Big Bang, how it could have produced an Earth where sentient beings can live, and how those beings are discovering their history. Mather was Project Scientist for NASA's Cosmic Background Explorer (COBE) satellite, which measured the spectrum (the color) of the heat radiation from the Big Bang, discovered hot and cold spots in that radiation, and hunted for the first objects that formed after the great explosion. He will explain Einstein's biggest mistake, show how Edwin Hubble discovered the expansion of the universe, how the COBE mission was built, and how the COBE data support the Big Bang theory. He will also show NASA's plans for the next great telescope in space, the James Webb Space Telescope. It will look even farther back in time than the Hubble Space Telescope, and will look inside the dusty cocoons where stars and planets are being born today. Planned for launch in 2013, it may lead to another Nobel Prize for some lucky observer.

Mather, John C.↗
NASA NTRS2008

From the Big Bang to the Nobel Prize and on to the James Webb Space Telescope

The history of the universe in a nutshell, from the Big Bang to now. and on to the future - John Mather will tell the story of how we got here, how the Universe began with a Big Bang, how it could have produced an Earth where sentient beings can live, and how those beings are discovering their history. Mather was Project Scientist for NASA's Cosmic Background Explorer (COBE) satellite, which measured the spectrum (the color) of the heat radiation from the Big Bang, discovered hot and cold spots in that radiation, and hunted for the first objects that formed after the great explosion. He will explain Einstein's biggest mistake, show how Edwin Hubble discovered the expansion of the univerre, how the COBE mission was built, and how the COBE data support the Big Bang theory. He will also show NASA's plans for the next great telescope in space, the Jarnes Webb Space Telescope. It will look even farther back in time than the Hubble Space Telescope, and will look inside the dusty cocoons where rtars and planets are being born today. Planned for launch in 2013, it may lead to another Nobel Prize for some lucky observer.

Mather, John C.↗
NASA NTRS2009

From the Big Bang to the Nobel Prize and on to James Webb Space Telescope

The history of the universe in a nutshell, from the Big Bang to now, and on to the future - John Mather will tell the story of how we got here, how the Universe began with a Big Bang, how it could have produced an Earth where sentient beings can live, and how those beings are discovering their history. Mather was Project Scientist for NASA s Cosmic Background Explorer (COBE) satellite, which measured the spectrum (the color) of the heat radiation from the Big Bang, discovered hot and cold spots in that radiation, and hunted for the first objects that formed after the great explosion. He will explain Einstein s biggest mistake, show how Edwin Hubble discovered the expansion of the universe, how the COBE mission was built, and how the COBE data support the Big Bang theory. He will also show NASA s plans for the next great telescope in space, the James Webb Space Telescope. It will look even farther back in time than the Hubble Space Telescope, and will look inside the dusty cocoons where stars and planets are being born today. Planned for launch in 2013, it may lead to another Nobel Prize for some lucky observer.

Mather, John C.↗
DOE OSTI2021

Code Artifact for: Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data [SWR-21-96]

This repository contains data ingestion, feature extraction, and analysis code used in NREL Technical report "Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data." The code is written in Python. The ETL and feature extraction code must be run in a Spark context. The analysis code can be run without Spark, provided you have pre-computed features in a CSV file. Analysis code related to the NREL Technical Report NREL/TP-2C00-74212. Includes PySpark functions to perform trip segmentation and feature extraction over big time series data in Apache Spark. Includes "domain specific" features such as Aerodynamic Speed (ft/s), Characteristic Acceleration (ft/s2), Percent Below 55 (%), Percent Zero (%), Stops Per Mile, Average Speed (mph), Maximum Speed (mph), and Speed Standard Deviation (mph). Includes Pyspark UDF to compute "domain agnostic" features using the TSFresh library. This software record also includes the analysis notebooks and code to generate the results in the previously mentioned technical report.

Perr-Sauer, Jordan↗
DOE OSTI2023

Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic

This article presents the design, implementation, and use cases of the Chattanooga Digital Twin (CTwin) towards the vision for next-generation smart city applications for urban mobility management. CTwin is an end-to-end web-based platform that incorporates various aspects of the decision-making process for optimizing urban transportation systems in Chattanooga, Tennessee, to reduce traffic congestion, incidents, and vehicle fuel consumption. The platform serves as a cyberinfrastructure to collect and integrate multi-domain urban mobility data from various online repositories and Internet of Things (IoT) sensors, covering multiple urban aspects (e.g., traffic, natural hazards, weather, and safety) that are relevant to urban mobility management. The platform enables advanced capabilities for: (a) real-time situational awareness on traffic and infrastructure conditions on highways and urban roads, (b) cyber-physical control for optimizing traffic signal timing, and (c) interactive visual analytics on big urban mobility data and various metrics for traffic prediction and transportation performance evaluation. The platform is designed using a multi-level componentization paradigm and is implemented using modular and adaptive architecture, rendering it as a generalizable and extendable prototype for other urban management applications. We present several use cases to demonstrate CTwin's core capabilities for supporting decision-making in smart urban mobility management.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Motivation Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification. Results To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex.

Biochemistry & Molecular Biology↗
DOE OSTI2024

Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants

Biotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production. Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, light doses of stress result in the production of positive attributes in crops, like tolerance to stress and biosynthesis of metabolites, called hormesis. Advancement in artificial intelligence (AI) has enabled the development of high-throughput gadgets such as high-resolution imagery sensors and robotic aerial vehicles, i.e., satellites and unmanned aerial vehicles (UAV), to overcome biotic and abiotic stresses. These High throughput (HTP) gadgets produce accurate but big amounts of data. Significant datasets such as transportable array for remotely sensed agriculture and phenotyping reference platform (TERRA-REF) have been developed to forecast abiotic stresses and early detection of biotic stresses. For accurately measuring the model plant stress, tools like Deep Learning (DL) and Machine Learning (ML) have enabled early detection of desirable traits in a large population of breeding material and mitigate plant stresses. In this review, advanced applications of ML and DL in plant biotic and abiotic stress management have been summarized.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1981

Application of remote sensing to selected problems within the state of California

Specific case studies undertaken to demonstrate the usefulness of remote sensing technology to resource managers in California are highlighted. Applications discussed include the mapping and quantization of wildland fire fuels in Mendocino and Shasta Counties as well as in the Central Valley; the development of a digital spectral/terrain data set for Colusa County; the Forsythe Planning Experiment to maximize the usefulness of inputs from LANDSAT and geographic information systems to county planning in Mendocino County; the development of a digital data bank for Big Basin State Park in Santa Cruz County; the detection of salinity related cotton canopy reflectance differences in the Central Valley; and the surveying of avocado acreage and that of other fruits and nut crops in Southern California. Special studies include the interpretability of high altitude, large format photography of forested areas for coordinated resource planning using U-2 photographs of the NASA Bucks Lake Forestry test site in the Plumas National Forest in the Sierra Nevada Mountains.

Colwell, R. N.↗
NASA NTRS1988

Structure of magnetic fields on the quiet sun

To obtain quantitative temporal and spatial information on the network magnetic fields, auto- and cross-correlation techniques are applied to the Big Bear videomagnetogram data. The average size of the network magnetic elements derived from the auto-correlation curve is about 5700 km. The distance between the primary and secondary peak in the auto-correlation curve is about 17,000 km, which is half of the size of the supergranule as determined from the velocity map. The canceling features and the emergence of ephemeral regions are the major sources for the loss and replenishment of magnetic flux on the quiet sun.

Wang, Haimin↗
NASA NTRS1993

Solar activity and oscillation frequency splittings

Solar p-mode frequency splittings, parameterized by the coefficients through order N = 12 of a Legendre polynomial expansion of the mode frequencies as a function of m/L, were obtained from an analysis of helioseismology data taken at Big Bear Solar Observatory during the 4 years 1986 and 1988-1990 (approximately solar minimum to maximum). Inversion of the even-index splitting coefficients confirms that there is a significant contribution to the frequency splittings originating near the solar poles. The strength of the polar contribution is anti correlated with the overall level or solar activity in the active latitudes, suggesting a relation to polar faculae. From an analysis of the odd-index splitting coefficients we infer an uppor limit to changes in the solar equatorial near-surface rotatinal velocity of less than 1.9 m/s (3 sigma limit) between solar minimum and maximum.

Woodard, M. F.↗
NASA NTRS1993

On the dynamic activity in sheared corridors of large delta spots

A study of registered, highly time-compressed, white-light movies, made from digital data obtained at Big Bear, revealed dynamic and complex photospheric activity for the first time inside the large delta spot of the March 1989 region. Similar activity in two other regions of large delta spots with sheared penumbral structure was found. They are NOAA Nos. 5629 and 5747 in August and October of 1989, respectively. Both are extraordinarily flare-prolific regions as well. The unusual dynamic activity consists of two parts: the motion of the penumbra and the emergence of new spots in the midst of the penumbral motion. The manner and place of emergence are different from those in ordinary emerging flux regions, and often the spots are without observable opposite polarity flux.

Tang, F.↗
NASA NTRS1981

Development of a digital geographic data base for resource planning in a wildland environment

Multiple resource planning requires the ability to access information for several parameters in a coordinated way. Attempts to do this manually, through the use of multiple transparent overlays or similar techniques can become awkward if there are more than a few parameters under consideration. One solution to this problem is to use a computer system to collect and organize the information into a data base that will make access and analysis easier, even for large numbers of parameters. The increase in the types and forms of remote sensing data and the decrease in costs for computer systems in the last decade has made this approach more popular than in the past. This paper describes the development of one such data base for the Big Basin Redwoods State Park in the Santa Cruz Mountains in California. The data base contains information for satellite spectral data, soil type, vegetation type, and hypsographic data and was developed for use in a cooperative project being conducted by personnel from the Remote Sensing Research Program and the California Department of Parks and Recreation

Ritter, P. R.↗