Results for “Group Processes” — Page 83

NASA NTRS2005

NASA's Software Safety Standard

NASA (National Aeronautics and Space Administration) relies more and more on software to control, monitor, and verify its safety critical systems, facilities and operations. Since the 1960's there has hardly been a spacecraft (manned or unmanned) launched that did not have a computer on board that provided vital command and control services. Despite this growing dependence on software control and monitoring, there has been no consistent application of software safety practices and methodology to NASA's projects with safety critical software. Led by the NASA Headquarters Office of Safety and Mission Assurance, the NASA Software Safety Standard (STD-18l9.13B) has recently undergone a significant update in an attempt to provide that consistency. This paper will discuss the key features of the new NASA Software Safety Standard. It will start with a brief history of the use and development of software in safety critical applications at NASA. It will then give a brief overview of the NASA Software Working Group and the approach it took to revise the software engineering process across the Agency.

Ramsay, Christopher M.↗

NASA NTRS2008

We Don't Need Any Stinking AFSRB!

Flight Test supports four needs: a) Management approval to proceed; b) Establish a record of accountability; c) Inject expert insight into the risk management process; and d) Mitigate the human factors vulnerabilities associated with small group teaming and project ownership.

Henwood, Barton↗

NASA NTRS2010

Fault Management Practice: A Roadmap for Improvement

Autonomous fault management (FM) is critical for deep space and planetary missions where the limited communication opportunities may prevent timely intervention by ground control. Evidence of pervasive architecture, design, and verification/validation problems with NASA FM engineering has been revealed both during technical reviews of spaceflight missions and in flight. These problems include FM design changes required late in the life-cycle, insufficient project insight into the extent of FM testing required, unexpected test results that require resolution, spacecraft operational limitations because certain functions were not tested, and in-flight anomalies and mission failures attributable to fault management. A recent NASA initiative has characterized the FM state-of-practice throughout the spacecraft development community and identified common NASA, DoD, and commercial concerns that can be addressed in the near term through the development of a FM Practitioner's Handbook and the formation of a FM Working Group. Initial efforts will focus on standardizing FM terminology, establishing engineering processes and tools, and training.

Fesq, Lorraine M.↗

NASA NTRS2015

Lunar Hydrospheric Explorer (HYDROX)

The Lunar Hydrospheric Explorer (HYDROX) is a 6U CubeSat designed to further confirm the existence of lunar exospheric water, and to determine source processes and surface sites, through ion mass spectrometer measurements of water group (O+, OH+, H2O+) and related ions at energy charge up to 2 keV/e. and mass/charge 1-40amu/e. HYDROX would follow up on the now-concluded exospheric compositional measurements by the Neutral Mass Spectrometer on the NASA LADEE mission and on other remote sensing surface and exospheric measurements (LADEE,LRO, etc.).

exospheric↗

NASA NTRS2016

Restoration of Apollo Data by the Lunar Data Project/PDS Lunar Data Node: An Update

The Apollo 11, 12, and 14 through 17 missions orbited and landed on the Moon, carrying scientific instruments that returned data from all phases of the missions, included long-lived Apollo Lunar Surface Experiments Packages (ALSEPs) deployed by the astronauts on the lunar surface. Much of these data were never archived, and some of the archived data were on media and in formats that are outmoded, or were deposited with little or no useful documentation to aid outside users. This is particularly true of the ALSEP data returned autonomously for many years after the Apollo missions ended. The purpose of the Lunar Data Project and the Planetary Data System (PDS) Lunar Data Node is to take data collections already archived at the NASA Space Science Data Coordinated Archive (NSSDCA) and prepare them for archiving through PDS, and to locate lunar data that were never archived, bring them into NSSDCA, and then archive them through PDS. Preparing these data for archiving involves reading the data from the original media, be it magnetic tape, microfilm, microfiche, or hard-copy document, converting the outmoded, often binary, formats when necessary, putting them into a standard digital form accepted by PDS, collecting the necessary ancillary data and documentation (metadata) to ensure that the data are usable and well-described, summarizing the metadata in documentation to be included in the data set, adding other information such as references, mission and instrument descriptions, contact information, and related documentation, and packaging the results in a PDS-compliant data set. The data set is then validated and reviewed by a group of external scientists as part of the PDS final archive process. We present a status report on some of the data sets that we are processing.

Lunar↗

NASA NTRS2014

Titan Science Return Quantification

Each proposal for a NASA mission concept includes a Science Traceability Matrix (STM), intended to show that what is being proposed would contribute to satisfying one or more of the agency's top-level science goals. But the information traditionally provided cannot be used directly to quantitatively compare anticipated science return. We added numerical elements to NASA's STM and developed a software tool to process the data. We then applied this methodology to evaluate a group of competing concepts for a proposed mission to Saturn's moon, Titan.

Saturn↗

NASA NTRS2019

GeneLab: Overview of Challenges and Opportunities

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 200 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently 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 100 scientists from NASA and academia. These pipelines are now being used by a group of bioinformatics interns to provide standard basic analysis of the data for incorporation into GLDS.

Galazka, Jonathan M.↗

NASA NTRS2019

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 200 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently 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 100 scientists from NASA and academia. These pipelines are now being used by a group of bioinformatics interns to provide standard basic analysis of the data for incorporation into GLDS.

Galazka, Jonathan M.↗

NASA NTRSDate not supplied

GeneLab: The NASA Systems Biology Platform for Space Omics Repository, Analysis and Visualization

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 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently 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 120 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. 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.

Samrawit Gebre↗

NASA NTRSDate not supplied

WEBINAR, May 6: New Discoveries Using GeneLab

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 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetry data from experiments flown on the Space Shuttle. GeneLab has also recently 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 120 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. 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.

Sylvain V. Costes↗

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

Discovery: Strategic Foresight – Helping Aviation Find Problems Worth Solving

Strategic foresight is used in the early stages of the CAS (Convergent Aeronautics Solutions) process. We discover trends, needs, and capabilities from interviews with diverse groups of people, data analytics tools, and from publications. These are analyzed in the context of future scenarios to uncover problem areas that have the highest possible impact on the broadest number of people while ensuring that we are prepared for the future.

Foresight Strategy Complex wicked↗

NASA NTRSDate not supplied

Discovery: Strategic Foresight – Helping Aviation Find Problems Worth Solving

Strategic foresight is used in the early stages of the CAS (Convergent Aeronautics Solutions) process. We discover trends, needs, and capabilities from interviews with diverse groups of people, data analytics tools, and from publications. These are analyzed in the context of future scenarios to uncover problem areas that have the highest possible impact on the broadest number of people while ensuring that we are prepared for the future.

Foresight↗

DOE OSTI2014

Technologies for Production of Heat and Electricity

Biomass is a desirable source of energy because it is renewable, sustainable, widely available throughout the world, and amenable to conversion. Biomass is composed of cellulose, hemicellulose, and lignin components. Cellulose is generally the dominant fraction, representing about 40 to 50% of the material by weight, with hemicellulose representing 20 to 50% of the material, and lignin making up the remaining portion [4,5,6]. Although the outward appearance of the various forms of cellulosic biomass, such as wood, grass, municipal solid waste (MSW), or agricultural residues, is different, all of these materials have a similar cellulosic composition. Elementally, however, biomass varies considerably, thereby presenting technical challenges at virtually every phase of its conversion to useful energy forms and products. Despite the variances among cellulosic sources, there are a variety of technologies for converting biomass into energy. These technologies are generally divided into two groups: biochemical (biological-based) and thermochemical (heat-based) conversion processes. This chapter reviews the specific technologies that can be used to convert biomass to energy. Each technology review includes the description of the process, and the positive and negative aspects.

09 BIOMASS FUELS↗

DOE OSTI2016

Amino-siloxane composition and methods of using the same

An amino-siloxane composition is presented. The amino-siloxane composition includes structure (I): ##STR00001## wherein R.sup.1 is independently at each occurrence a C.sub.1-C.sub.5 aliphatic radical; R.sup.2 is a C.sub.3-C.sub.4 aliphatic radical; R.sup.3 is a C.sub.1-C.sub.5 aliphatic radical or R.sup.4, wherein R.sup.4 comprises structure (II): ##STR00002## and X is an electron donating group. Methods of reducing an amount of carbon dioxide in a process stream using the amino-siloxane composition are also presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Amino-siloxane composition and methods of using the same

An amino-siloxane composition is presented. The amino-siloxane composition includes structure (I): ##STR00001## wherein R 1 is independently at each occurrence a C 1 -C 5 aliphatic radical; R 2 is a C 3 -C. 4 aliphatic radical; R 3 is a C 1 -C 5 aliphatic radical or R 4 , wherein R 4 comprises structure (II): ##STR00002## and X is an electron donating group. Methods of reducing an amount of carbon dioxide in a process stream using the amino-siloxane composition are also presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

BETO 2021 Peer Review - Analytical Development and Standardization for Biomass-Derived Thermochemical Liquids

This project began in FY14 to address the lack of standard chemical characterization analytical methods for bio-oils. Bio-oils are very complex and present numerous analytical challenges; yet reliable chemical information (quantification of both individual compounds and chemical functional groups) is needed to inform upgrading research and refinery co-processing. In this project, analysis needs are first determined from engaging the bioenergy community. Next, standard methods are developed to meet these needs, and then subsequently validated via inter-laboratory studies. Methods that are successfully validated (< 10% variability) are then shared as Laboratory Analytical Procedures (LAPs), which are free and publicly available. We have been tracking LAP use and have seen sustained usage as evidenced by an average of 500 pages views and 100 downloads per quarter, demonstrating the value of these methods to the bioenergy community. LAP methods that are particularly useful and reliable will be chosen for the next-level of standardization through ASTM. We have recently achieved approval by ASTM for our carbonyl titration method. This method (ASTM E3146) is the first example of an ASTM standard solely focused on the chemical characterization of bio-oils. Work in this project is meeting the analysis needs of the bioenergy community and will ultimately help enable the commoditization of bio-oils.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2015

A new code for spectrometric analysis for environmental radiological surveillance on monitors focused on gamma radioactivity on aerosols

pGamma is a code developed by the NERG group of the Technical University of Catalonia - Barcelona Tech for the analysis of gamma spectra generated by the Equipment for the Continuous Measurement and Identification of Gamma Radioactivity on Aerosols with Paper Filter developed for our group and Raditel Servies company. Nowadays the code is in the process of adaptation for the monitors of the Environmental Radiological Surveillance Network of the Local Government of Catalonia (Generalitat of Catalonia), Spain. The code is a Spectrum Analysis System, it identifies the gamma emitters on the spectrum, determines its Concentration of Activity, generates alarms depending on the Activity of the emitters and generates a report. The Spectrum Analysis System includes a library with emitters of interest, NORM and artificial. The code is being used on the three stations with the aerosol monitor of the Network (Asco and Vandellos, near both Nuclear Power Plants and Barcelona). (authors)

54 ENVIRONMENTAL SCIENCES↗