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Goddard Atmospheric Composition Data Center: Aura Data and Services in One Place

The Goddard Atmospheric Composition Data and Information Services Center (AC-DISC) is a portal to the Atmospheric Composition specific, user driven, multi-sensor, on-line, easy access archive and distribution system employing data analysis and visualization, data mining, and other user requested techniques for the better science data usage. It provides convenient access to Atmospheric Composition data and information from various remote-sensing missions, from TOMS, UARS, MODIS, and AIRS, to the most recent data from Aura OMI, MLS, HIRDLS (once these datasets are released to the public), as well as Atmospheric Composition datasets residing at other remote archive site.

Leptoukh, G.

Intelligent Systems Technologies to Assist in Utilization of Earth Observation Data

With the launch of several Earth observing satellites over the last decade, we are now in a data rich environment. From NASA's Earth Observing System (EOS) satellites alone, we are accumulating more than 3 TB per day of raw data and derived geophysical parameters. The data products are being distributed to a large user community comprising scientific researchers, educators and operational government agencies. Notable progress has been made in the last decade in facilitating access to data. However, to realize the full potential of the growing archives of valuable scientific data, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications. Sponsored by NASA s Intelligent Systems Project within the Computing, Information and Communication Technology (CICT) Program, a conceptual architecture study has been conducted to examine ideas to improve data utilization through the addition of intelligence into the archives in the context of an overall knowledge building system. Potential Intelligent Archive concepts include: 1) Mining archived data holdings using Intelligent Data Understanding algorithms to improve metadata to facilitate data access and usability; 2) Building intelligence about transformations on data, information, knowledge, and accompanying services involved in a scientific enterprise; 3) Recognizing the value of results, indexing and formatting them for easy access, and delivering them to concerned individuals; 4) Interacting as a cooperative node in a web of distributed systems to perform knowledge building (i.e., the transformations from data to information to knowledge) instead of just data pipelining; and 5) Being aware of other nodes in the knowledge building system, participating in open systems interfaces and protocols for virtualization, and collaborative interoperability. This paper presents some of these concepts and identifies issues to be addressed by research in future intelligent systems technology.

Ramapriyan, Hampapuram K.

GeneLab: Current and Future Omics Data Integration Between Space Biology and HRP

For the past five years, the Biological and Physical Sciences Division has pioneered Open Science in Space Biology by funding the NASA GeneLab project. Along with the Ames Life Sciences Data Archive, GeneLab has quickly become the world leader in archiving and scientifically curating spaceflight and spaceflight relevant multi-omics data. Specifically, the GeneLab Data System has become a full enterprise solution providing advanced mining capabilities, several application programming interfaces for data federation and machine learning approaches, and delivering to the world an analytical and visualization platform which has enabled collaboration within the scientific community. Over the past three years, large meta-analysis and modeling studies have been published by the GeneLab Analysis Working Groups (AWGs), which are comprised of ~200 volunteer scientists. One natural extension of GeneLab data reuse has recently turned towards linking animal data with human data, which is the next necessary step to further validate animal models for inferring biological risks to humans conducting LEO, lunar or Martian missions. As such, data from the Human Research Program are an essential component of GeneLab and ALSDA. At the moment, simulated space radiation experiments conducted at Brookhaven National Laboratory make the most of HRP GeneLab data, and the scientific community has been eager to also link their animal spaceflight results to actual Astronaut data and human analog data. We will discuss further the current status of knowledge and future approaches to accelerate our basics understanding of the impact of space stressors on humans using latest omics technology.

omics

Intelligent Systems Technologies and Utilization of Earth Observation Data

The addition of raw data and derived geophysical parameters from several Earth observing satellites over the last decade to the data held by NASA data centers has created a data rich environment for the Earth science research and applications communities. The data products are being distributed to a large and diverse community of users. Due to advances in computational hardware, networks and communications, information management and software technologies, significant progress has been made in the last decade in archiving and providing data to users. However, to realize the full potential of the growing data archives, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications. Sponsored by NASA s Intelligent Systems Project within the Computing, Information and Communication Technology (CICT) Program, a conceptual architecture study has been conducted to examine ideas to improve data utilization through the addition of intelligence into the archives in the context of an overall knowledge building system (KBS). Potential Intelligent Archive concepts include: 1) Mining archived data holdings to improve metadata to facilitate data access and usability; 2) Building intelligence about transformations on data, information, knowledge, and accompanying services; 3) Recognizing the value of results, indexing and formatting them for easy access; 4) Interacting as a cooperative node in a web of distributed systems to perform knowledge building; and 5) Being aware of other nodes in the KBS, participating in open systems interfaces and protocols for virtualization, and achieving collaborative interoperability.

Ramapriyan, H. K.

Technology Alignment and Portfolio Prioritization (TAPP): Advanced Methods in Strategic Analysis, Technology Forecasting and Long Term Planning for Human Exploration and Operations, Advanced Exploration Systems and Advanced Concepts

The Advanced Concepts Office (ACO) at NASA, Marshall Space Flight Center is expanding its current technology assessment methodologies. ACO is developing a framework called TAPP that uses a variety of methods, such as association mining and rule learning from data mining, structure development using a Technological Innovation System (TIS), and social network modeling to measure structural relationships. The role of ACO is to 1) produce a broad spectrum of ideas and alternatives for a variety of NASA's missions, 2) determine mission architecture feasibility and appropriateness to NASA's strategic plans, and 3) define a project in enough detail to establish an initial baseline capable of meeting mission objectives ACO's role supports the decision­-making process associated with the maturation of concepts for traveling through, living in, and understanding space. ACO performs concept studies and technology assessments to determine the degree of alignment between mission objectives and new technologies. The first step in technology assessment is to identify the current technology maturity in terms of a technology readiness level (TRL). The second step is to determine the difficulty associated with advancing a technology from one state to the next state. NASA has used TRLs since 1970 and ACO formalized them in 1995. The DoD, ESA, Oil & Gas, and DoE have adopted TRLs as a means to assess technology maturity. However, "with the emergence of more complex systems and system of systems, it has been increasingly recognized that TRL assessments have limitations, especially when considering [the] integration of complex systems." When performing the second step in a technology assessment, NASA requires that an Advancement Degree of Difficulty (AD2) method be utilized. NASA has used and developed or used a variety of methods to perform this step: Expert Opinion or Delphi Approach, Value Engineering or Value Stream, Analytical Hierarchy Process (AHP), Technique for the Order of Prioritization by Similarity to Ideal Solution (TOPSIS), and other multi­‐criteria decision-making methods. These methods can be labor-intensive, often contain cognitive or parochial bias, and do not consider the competing prioritization between mission architectures. Strategic Decision-Making (SDM) processes cannot be properly understood unless the context of the technology is understood. This makes assessing technological change particularly challenging due to the relationships "between incumbent technology and the incumbent (innovation) system in relation to the emerging technology and the emerging innovation system." The central idea in technology dynamics is to consider all activities that contribute to the development, diffusion, and use of innovations as system functions. Bergek defines system functions within a TIS to address what is actually happening and has a direct influence on the ultimate performance of the system and technology development. ACO uses similar metrics and is expanding these metrics to account for the structure and context of the technology. At NASA technology and strategy is strongly interrelated. NASA's Strategic Space Technology Investment Plan (SSTIP) prioritizes those technologies essential to the pursuit of NASA's missions and national interests. The SSTIP is strongly coupled with NASA's Technology Roadmaps to provide investment guidance during the next four years, within a twenty-year horizon. This paper discusses the methods ACO is currently developing to better perform technology assessments while taking into consideration Strategic Alignment, Technology Forecasting, and Long Term Planning.

Funaro, Gregory V.

Development of sensitized pick coal interface detector system

One approach for detection of the coal interface is measurement of pick cutting loads and shock through the use of pick strain gage load cells and accelerometers. The cutting drum of a long wall mining machine contains a number of cutting picks. In order to measure pick loads and shocks, one pick was instrumented and telemetry used to transmit the signals from the drum to an instrument-type tape recorder. A data system using FM telemetry was designed to transfer cutting bit load and shock information from the drum of a longwall shearer coal mining machine to a chassis mounted data recorder. The design of components in the test data system were finalized, the required instruments were assembled, the instrument system was evaluated in an above-ground simulation test, and an underground test series to obtain tape recorded sensor data was conducted.

Burchill, R. F.

ERTS-1 data applied to strip mining

Two coal basins within the western region of the Potomac River Basin contain the largest strip-mining operations in western Maryland and West Virginia. The disturbed strip-mine areas were delineated along with the surrounding geological and vegetation features by using ERTS-1 data in both analog and digital form. The two digital systems employed were (1) the ERTS analysis system, a point-by-point digital analysis of spectral signatures based on known spectral values and (2) the LARS automatic data processing system. These two systems aided in efforts to determine the extent and state of strip mining in this region. Aircraft data, ground-verification information, and geological field studies also aided in the application of ERTS-1 imagery to perform an integrated analysis that assessed the adverse effects of strip mining. The results indicated that ERTS can both monitor and map the extent of strip mining to determine immediately the acreage affected and to indicate where future reclamation and revegetation may be necessary.

Anderson, A. T.

Data Mining for Anomaly Detection

The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

Biswas, Gautam

Data Mining at NASA: From Theory to Applications

This slide presentation demonstrates the data mining/machine learning capabilities of NASA Ames and Intelligent Data Understanding (IDU) group. This will encompass the work done recently in the group by various group members. The IDU group develops novel algorithms to detect, classify, and predict events in large data streams for scientific and engineering systems. This presentation for Knowledge Discovery and Data Mining 2009 is to demonstrate the data mining/machine learning capabilities of NASA Ames and IDU group. This will encompass the work done re cently in the group by various group members.

Srivastava, Ashok N.

Data Mining for ISHM of Liquid Rocket Propulsion Status Update

This document consists of presentation slides that review the current status of data mining to support the work with the Integrated Systems Health Management (ISHM) for the systems associated with Liquid Rocket Propulsion. The aim of this project is to have test stand data from Rocketdyne to design algorithms that will aid in the early detection of impending failures during operation. These methods will be extended and improved for future platforms (i.e., CEV/CLV).

Srivastava, Ashok

Underground coal mining section data

A set of tables which display the allocation of time for ten personnel and eight pieces of underground coal mining equipment to ten function categories is provided. Data from 125 full shift time studies contained in the KETRON database was utilized as the primary source data. The KETRON activity and delay codes were mapped onto JPL equipment, personnel and function categories. Computer processing was then performed to aggregate the shift level data and generate the matrices. Additional, documented time study data were analyzed and used to supplement the KETRON databased. The source data including the number of shifts are described. Specific parameters of the mines from which there data were extracted are presented. The result of the data processing including the required JPL matrices is presented. A brief comparison with a time study analysis of continuous mining systems is presented. The procedures used for processing the source data are described.

Gabrill, C. P.

Determining Component Probability using Problem Report Data for Ground Systems used in Manned Space Flight

During the shuttle era NASA utilized a failure reporting system called the Problem Reporting and Corrective Action (PRACA) it purpose was to identify and track system non-conformance. The PRACA system over the years evolved from a relatively nominal way to identify system problems to a very complex tracking and report generating data base. The PRACA system became the primary method to categorize any and all anomalies from corrosion to catastrophic failure. The systems documented in the PRACA system range from flight hardware to ground or facility support equipment. While the PRACA system is complex, it does possess all the failure modes, times of occurrence, length of system delay, parts repaired or replaced, and corrective action performed. The difficulty is mining the data then to utilize that data in order to estimate component, Line Replaceable Unit (LRU), and system reliability analysis metrics. In this paper, we identify a methodology to categorize qualitative data from the ground system PRACA data base for common ground or facility support equipment. Then utilizing a heuristic developed for review of the PRACA data determine what reports identify a credible failure. These data are the used to determine inter-arrival times to perform an estimation of a metric for repairable component-or LRU reliability. This analysis is used to determine failure modes of the equipment, determine the probability of the component failure mode, and support various quantitative differing techniques for performing repairable system analysis. The result is that an effective and concise estimate of components used in manned space flight operations. The advantage is the components or LRU's are evaluated in the same environment and condition that occurs during the launch process.

Monaghan, Mark W.

Grist : grid-based data mining for astronomy

The Grist project is developing a grid-technology based system as a research environment for astronomy with massive and complex datasets. This knowledge extraction system will consist of a library of distributed grid services controlled by a workflow system, compliant with standards emerging from the grid computing, web services, and virtual observatory communities. This new technology is being used to find high redshift quasars, study peculiar variable objects, search for transients in real time, and fit SDSS QSO spectra to measure black hole masses. Grist services are also a component of the 'hyperatlas' project to serve high-resolution multi-wavelength imagery over the Internet. In support of these science and outreach objectives, the Grist framework will provide the enabling fabric to tie together distributed grid services in the areas of data access, federation, mining, subsetting, source extraction, image mosaicking, statistics, and visualization.

grid computing

Large language models for batteries

Large Language Models (LLMs) are advanced artificial intelligence systems capable of solving diverse tasks using language, reasoning, and external tools. Despite their growing deployment in academia and industry, their potential remains underexplored in battery research. This review presents a comprehensive overview of existing and emerging applications of LLMs in batterie field, addressing two critical questions: What can LLMs offer to support battery-related tasks, and how to develop more effective models for this purpose. We begin by outlining the principles of LLMs and criteria for selecting appropriate models and tools for battery research and development. We then explore their roles in text-mining, data interpretation, and the development of intelligent battery systems. In parallel, we discuss technical challenges, such as data standardizing and sharing, model evaluation, and tool integration. Lastly, we propose future research directions with short-, medium-, and long-term goals and highlight more broad perspectives for connecting experts and cross-disciplinary collaborations.

SoC

Large Scale Data Mining to Improve Usability of Data: An Intelligent Archive Testbed

Research in certain scientific disciplines - including Earth science, particle physics, and astrophysics - continually faces the challenge that the volume of data needed to perform valid scientific research can at times overwhelm even a sizable research community. The desire to improve utilization of this data gave rise to the Intelligent Archives project, which seeks to make data archives active participants in a knowledge building system capable of discovering events or patterns that represent new information or knowledge. Data mining can automatically discover patterns and events, but it is generally viewed as unsuited for large-scale use in disciplines like Earth science that routinely involve very high data volumes. Dozens of research projects have shown promising uses of data mining in Earth science, but all of these are based on experiments with data subsets of a few gigabytes or less, rather than the terabytes or petabytes typically encountered in operational systems. To bridge this gap, the Intelligent Archives project is establishing a testbed with the goal of demonstrating the use of data mining techniques in an operationally-relevant environment. This paper discusses the goals of the testbed and the design choices surrounding critical issues that arose during testbed implementation.

Ramapriyan, Hampapuram

Foil Bearing Supported Compressor-Expander

R&D Dynamics Corporation (RDD) is developing a fuel cell system compressor-expander designed for heavy-duty vehicle applications. The system emphasizes high reliability, versatility, long service life, and cost-effective mass production. Its design integrates a high-speed centrifugal compressor/expander, oil-free foil air/gas bearings, a direct current permanent magnet (DCPM) motor, and an inverter utilizing silicon carbide (SiC) switches. Key advantages include high efficiency, compact size and reduced weight, enhanced reliability, and elimination of oil contamination risks. The technology targets multiple sectors such as long-haul semi-trucks, mining and construction equipment, power generation, data centers, marine systems, and other heavy machinery. Evaluation units are ready for shipment, and manufacturing capabilities are currently being developed. Contract Number: DE-EE0009617 (2021).

30 DIRECT ENERGY CONVERSION

GL4U: Training the next generation of bioinformaticians, one omics datatype at a time

Spaceflight modifies gene expression in every organism examined to date, including humans. Understanding how these gene expression changes affect physiology is crucial for the development of countermeasures to enable long-duration manned missions. NASA’s GeneLab project provides researchers open access to multi-omics data, including genetic and gene expression data, from spaceflight experiments that can be mined to understand the effects of spaceflight on biological systems. To ensure new knowledge generation through data re-use, it is important to maximize the number of scientists who utilize GeneLab data. Training students on the GeneLab platform is the best way to create long-term adopters of this NASA database and its tools. Turning students into future instructors and advocates will also accelerate the dissemination of these data and tools to the broader scientific community. Therefore, in collaboration with the GeneLab Educational Working Group (EWG), GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. During the bootcamp, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U direct training pilot program was conducted in June 2021 in collaboration with USRA and San Jose State University (SJSU). During the pilot, SJSU students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrates the capacity of GL4U for training young scientists and encouraging data re-use.

Jonathan Matthew Galazka

GL4U: Bioinformatics training for students and educators using space omics data

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. During the educator pilot, scheduled for June 2022, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative.

Amanda Marie Saravia-butler