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NASA NTRS2017

NASA's EOSDIS, Trust and Certification

NASA's Earth Observing System Data and Information System (EOSDIS) has been in operation since August 1994, managing most of NASA's Earth science data from satellites, airborne sensors, filed campaigns and other activities. Having been designated by the Federal Government as a project responsible for production, archiving and distribution of these data through its Distributed Active Archive Centers (DAACs), the Earth Science Data and Information System Project (ESDIS) is responsible for EOSDIS, and is legally bound by the Office of Management and Budgets circular A-130, the Federal Records Act. It must follow the regulations of the National Institute of Standards and Technologies (NIST) and National Archive and Records Administration (NARA). It must also follow the NASA Procedural Requirement 7120.5 (NASA Space Flight Program and Project Management). All these ensure that the data centers managed by ESDIS are trustworthy from the point of view of efficient and effective operations as well as preservation of valuable data from NASA's missions. Additional factors contributing to this trust are an extensive set of internal and external reviews throughout the history of EOSDIS starting in the early 1990s. Many of these reviews have involved external groups of scientific and technological experts. Also, independent annual surveys of user satisfaction that measure and publish the American Customer Satisfaction Index (ACSI), where EOSDIS has scored consistently high marks since 2004, provide an additional measure of trustworthiness. In addition, through an effort initiated in 2012 at the request of NASA HQ, the ESDIS Project and 10 of 12 DAACs have been certified by the International Council for Science (ICSU) World Data System (WDS) and are members of the ICSUWDS. This presentation addresses questions such as pros and cons of the certification process, key outcomes and next steps regarding certification. Recently, the ICSUWDS and Data Seal of Approval (DSA) organizations merged their Core Trustworthy Data Repositories Requirements and require that members be recertified every three years. Given the rigor with which NASA manages the ESDIS Project and the DAACs, the recertification through WDSDSA, while involving some additional work, is a relatively simple process.

Certification↗

NASA NTRS2015

The Use of UML for Software Requirements Expression and Management

It is common practice to write English-language "shall" statements to embody detailed software requirements in aerospace software applications. This paper explores the use of the UML language as a replacement for the English language for this purpose. Among the advantages offered by the Unified Modeling Language (UML) is a high degree of clarity and precision in the expression of domain concepts as well as architecture and design. Can this quality of UML be exploited for the definition of software requirements? While expressing logical behavior, interface characteristics, timeliness constraints, and other constraints on software using UML is commonly done and relatively straight-forward, achieving the additional aspects of the expression and management of software requirements that stakeholders expect, especially traceability, is far less so. These other characteristics, concerned with auditing and quality control, include the ability to trace a requirement to a parent requirement (which may well be an English "shall" statement), to trace a requirement to verification activities or scenarios which verify that requirement, and to trace a requirement to elements of the software design which implement that requirement. UML Use Cases, designed for capturing requirements, have not always been satisfactory. Some applications of them simply use the Use Case model element as a repository for English requirement statements. Other applications of Use Cases, in which Use Cases are incorporated into behavioral diagrams that successfully communicate the behaviors and constraints required of the software, do indeed take advantage of UML's clarity, but not in ways that support the traceability features mentioned above. Our approach uses the Stereotype construct of UML to precisely identify elements of UML constructs, especially behaviors such as State Machines and Activities, as requirements, and also to achieve the necessary mapping capabilities. We describe this approach in the context of a space-based software application currently under development at the Jet Propulsion Laboratory.

model-based engineering↗

NASA NTRS2018

Changes in Exercise Data Management

The suite of exercise hardware aboard the International Space Station (ISS) generates an immense amount of data. The data collected from the treadmill, cycle ergometer, and resistance strength training hardware are basic exercise parameters (time, heart rate, speed, load, etc.). The raw data are post processed in the laboratory and more detailed parameters are calculated from each exercise data file. Updates have recently been made to how this valuable data are stored, adding an additional level of data security, increasing data accessibility, and resulting in overall increased efficiency of medical report delivery. Questions regarding exercise performance or how exercise may influence other variables of crew health frequently arise within the crew health care community. Inquiries over the health of the exercise hardware often need quick analysis and response to ensure the exercise system is operable on a continuous basis. Consolidating all of the exercise system data in a single repository enables a quick response to both the medical and engineering communities. A SQL server database is currently in use, and provides a secure location for all of the exercise data starting at ISS Expedition 1 - current day. The database has been structured to update derived metrics automatically, making analysis and reporting available within minutes of dropping the inflight data it into the database. Commercial tools were evaluated to help aggregate and visualize data from the SQL database. The Tableau software provides manageable interface, which has improved the laboratory's output time of crew reports by 67%. Expansion of the SQL database to be inclusive of additional medical requirement metrics, addition of 'app-like' tools for mobile visualization, and collaborative use (e.g. operational support teams, research groups, and International Partners) of the data system is currently being explored.

Buxton, R. E.↗

NASA NTRS2018

Recent Developments in the Code RITRACKS (Relativistic Ion Tracks)

The code RITRACKS (Relativistic Ion Tracks) was developed to simulate detailed stochastic radiation track structures of ions of different types and energies. Many new capabilities were added to the code during the recent years. Several options were added to specify the times at which the tracks appear in the irradiated volume, allowing the simulation of dose-rate effects. The code has been used to simulate energy deposition in several targets: spherical, ellipsoidal and cylindrical. More recently, density changes as well as a spherical shell were implemented for spherical targets, in order to simulate energy deposition in walled tissue equivalent proportional counters. RITRACKS is used as a part of the new program BDSTracks (Biological Damage by Stochastic Tracks) to simulate several types of chromosome aberrations in various irradiation conditions. The simulation of damage to various DNA structures (linear and chromatin fiber) by direct and indirect effects has been improved and is ongoing. Many improvements were also made to the graphic user interface (GUI), including the addition of several labels allowing changes of units. A new GUI has been added to display the electron ejection vectors. The parallel calculation capabilities, notably the pre- and post-simulation processing on Windows and Linux machines have been reviewed to make them more portable between different systems. The calculation part is currently maintained in an Atlassian Stash® repository for code tracking and possibly future collaboration.

Plante, Ianik↗

NASA NTRS2017

Machine Learning Approaches to Increasing Value of Spaceflight Omics Databases

The number of spaceflight bioscience mission opportunities is too small to allow all relevant biological and environmental parameters to be experimentally identified. Simulated spaceflight experiments in ground-based facilities (GBFs), such as clinostats, are each suitable only for particular investigations -- a rotating-wall vessel may be 'simulated microgravity' for cell differentiation (hours), but not DNA repair (seconds) -- and introduce confounding stimuli, such as motor vibration and fluid shear effects. This uncertainty over which biological mechanisms respond to a given form of simulated space radiation or gravity, as well as its side effects, limits our ability to baseline spaceflight data and validate mission science. Machine learning techniques autonomously identify relevant and interdependent factors in a data set given the set of desired metrics to be evaluated: to automatically identify related studies, compare data from related studies, or determine linkages between types of data in the same study. System-of-systems (SoS) machine learning models have the ability to deal with both sparse and heterogeneous data, such as that provided by the small and diverse number of space biosciences flight missions; however, they require appropriate user-defined metrics for any given data set. Although machine learning in bioinformatics is rapidly expanding, the need to combine spaceflight/GBF mission parameters with omics data is unique. This work characterizes the basic requirements for implementing the SoS approach through the System Map (SM) technique, a composite of a dynamic Bayesian network and Gaussian mixture model, in real-world repositories such as the GeneLab Data System and Life Sciences Data Archive. The three primary steps are metadata management for experimental description using open-source ontologies, defining similarity and consistency metrics, and generating testing and validation data sets. Such approaches to spaceflight and GBF omics data may soon enable unique insight into which measured phenomena correlate to biological mechanisms that are truly affected by spaceflight conditions; which are most likely to be confounded by other variables; and which are insufficiently characterized, significantly increasing existing and future science return from ISS and spaceflight missions.

Gentry, Diana↗

NASA NTRS2017

Mouse Behavior on ISS: The Emergence of Distinctive, Organized Group Circling Behavior Unique to Spaceflight

As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. Only a handful of papers have previously reported behavior of mice and rats in the weightless environment of space (Andreev-Andrievskiy, et al., 2013; Cancedda et al., 2012; Ronca et al., 2008). The Rodent Research Hardware and Operations Validation Mission (Rodent Research-1; RR1) utilized the Rodent Habitat (RH) developed at NASA Ames Research Center to fly mice on the ISS. Ten adult (16-week-old) female C57BL6J mice were launched on September 21st, 2014 in an unmanned Dragon Capsule, and spent 37 days in flight. Here we report group behavioral phenotypes of the RR1 Flight (FLT) and environment-matched Ground Control (GC) mice in the RH during this long duration flight. Video was recorded for 34 days on the ISS, permitting daily assessments of overall health and well being of the mice, and providing a valuable repository for detailed behavioral analysis. As compared to GC mice, RR1 FLT mice exhibited the same range of behaviors, including eating, drinking, exploration, self- and allogrooming,and social interactions at similar or greater levels of occurrence. Overallactivity was greater in FLT as compared to GC mice, with spontaneous ambulatory behavior, including organized circling or race-tracking behavior that emerged within thefirst few days of flight following a common developmental sequence, comprising theprimary dark cycle activity of FLT mice. Circling participation by individual micepersisted throughout the mission. Analysis of group behavior over mission days revealed recruitment of mice into the group phenotype, coupled with decreasing numbers of collisions between circling mice. This analysis provides insights into the behavior of mice in microgravity, and clear evidence for the emergence of a distinctive,organized group behavior unique to the weightless space environment.

mice↗

NASA NTRS2017

NASA's Big Earth Data Initiative Accomplishments

The goal of NASA's effort for BEDI is to improve the usability, discoverability, and accessibility of Earth Observation data in support of societal benefit areas. Accomplishments: In support of BEDI goals, datasets have been entered into Common Metadata Repository(CMR), made available via the Open-source Project for a Network Data Access Protocol (OPeNDAP), have a Digital Object Identifier (DOI) registered for the dataset, and to support fast visualization many layers have been added in to the Global Imagery Browse Services (GIBS).

CMR↗

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 NTRS2018

Interactive Multi-Instrument Database of Solar Flares

The fundamental motivation of the project is that the scientific output of solar research can be greatly enhanced by better exploitation of the existing solar/heliosphere space-data products jointly with ground-based observations. Our primary focus is on developing a specific innovative methodology based on recent advances in "big data" intelligent databases applied to the growing amount of high-spatial and multi-wavelength resolution, high-cadence data from NASA's missions and supporting ground-based observatories. Our flare database is not simply a manually searchable time-based catalog of events or list of web links pointing to data. It is a preprocessed metadata repository enabling fast search and automatic identification of all recorded flares sharing a specifiable set of characteristics, features, and parameters. The result is a new and unique database of solar flares and data search and classification tools for the Heliophysics community, enabling multi-instrument/multi-wavelength investigations of flare physics and supporting further development of flare-prediction methodologies.

Heliophysics↗

NASA NTRS2017

New Developments in NASA's Rodent Research Hardware for Conducting Long Duration Biomedical and Basic Research in Space

Animal models, particularly rodents, are the foundation of pre-clinical research to understand human diseases and evaluate new therapeutics, and play a key role in advancing biomedical discoveries both on Earth and in space. The National Research Councils Decadal survey emphasized the importance of expanding NASA's life sciences research to perform long duration, rodent experiments on the International Space Station (ISS) to study effects of the space environment on the musculoskeletal and neurological systems of mice as model organisms of human health and disease, particularly in areas of muscle atrophy, bone loss, and fracture healing. To accomplish this objective, flight hardware, operations, and science capabilities were developed at NASA Ames Research Center (ARC) to enhance science return for both commercial (CASIS) and government-sponsored rodent research. The Rodent Research Project at NASA ARC has pioneered a new research capability on the International Space Station and has progressed toward translating research to the ISS utilizing commercial rockets, collaborating with academia and science industry, while training crewmembers to assist in performing research on orbit. The Rodent Research Habitat provides a living environment for animals on ISS according to standard animal welfare requirements, and daily health checks can be performed using the habitats camera system. Results from these studies contribute to the science community via both the primary investigation and banked samples that are shared in publicly available data repository such as GeneLab. Following each flight, through the Biospecimen Sharing Program (BSP), numerous tissues and thousands of samples will be harvested, and distributed from the Space Life and Physical Sciences (SLPS) to Principal Investigators (PIs) through the Ames Life Science Data Archive (ALSDA). Every completed mission sets a foundation to build and design greater complexity into future research and answer questions about common human diseases. Together, the hardware improvements (enrichment, telemetry sensors, cameras), new capabilities (live animal return), and experience that the Rodent Research team has gained working with principal investigator teams and ISS crew to conduct complex experiments on orbit are expanding capabilities for long duration rodent research on the ISS to achieve both basic science and biomedical research objectives.

Shirazi, Yasaman↗

NASA NTRS2018

WGISS-45 International Directory Network (IDN) Report

The objective of this presentation is to provide IDN (International Directory Network) updates on features and activities to the Committee on Earth Observation Satellites (CEOS) Working Group on Information Systems and Services (WGISS) and provider community. The following topics will be will be discussed during the presentation: Transition of Providers DIF-9 (Directory Interchange Format-9) to DIF-10 Metadata Records in the Common Metadata Repository (CMR); GCMD (Global Change Master Directory) Keyword Update; DIF-10 and UMM-C (Unified Metadata Model-Collections) Schema Changes; Metadata Validation of Provider Metadata; docBUILDER for Submitting IDN Metadata to the CMR (i.e. Registration); and Mapping WGClimate Essential Climate Variable (ECV) Inventory to IDN Records.

WGISS↗

NASA NTRS2018

Collaborative Metadata Curation in Support of NASA Earth Science Data Stewardship

Growing collection of NASA Earth science data is archived and distributed by EOSDIS’s 12 Distributed Active Archive Centers (DAACs). Each collection and granule is described by a metadata record housed in the Common Metadata Repository (CMR). Multiple metadata standards are in use, and core elements of each are mapped to and from a common model – the Unified Metadata Model (UMM). Work done by the Analysis and Review of CMR (ARC) Team.

data stewardship↗

NASA NTRS2017

Knowledge Base for Distributed Spacecraft Mission Design Using the Trade-Space Analysis Tool for Constellations (TAT-C)

Opportunities for multi-point measurements, greater revisit frequency, failure robustness, and improved cost effectiveness motivate consideration of Distributed Spacecraft Missions (DSMs) for future Earth science missions. However, careful analysis is required to assess the distributed sensing capabilities of a constellation compared to more mature monolithic spacecraft while also considering other important dimensions such as cost and risk. The large combinatorial DSM design space limits existing mission analysis tools and exploration methods which emphasize monolithic design variables. The Trade-space Analysis Tool for Constellations (TAT-C) under development at Goddard seeks to enumerate and evaluate alternative mission architectures to minimize cost and maximize scientific return for pre-defined goals during pre-phase A analysis.Similar to other model-centric engineering efforts, efficient data management is a significant challenge for DSM mission analysis. In TAT-C, a Knowledge Base (KB) is envisioned as a cumulative central repository of information and meta-information about DSMs. Initial KB concepts store related data for reuse within or across mission analyses; however, over time, the KB is envisioned to be an important layer to coordinate actions of both human analysts and automated design agents to search a large design space for desirable mission alternatives. Preliminary KB research builds on a modern web technology stack to provide the following functionality: 1) storage of trade-space search requests which set requirements and constraints for DSM concepts, 2) storage of analysis results which quantify performance metrics for evaluated DSM concepts, 3) a RESTful application programming interface (API) for scripted access to data from TAT-C modules, 4) web-based graphical user interface (GUI) for manual access to underlying data, and 5) access control and management restrictions relevant to data protection and security. These efforts have culminated in a prototype KB used by the research team during TAT-C development to assess opportunities for future work.

Pattern Recognition↗

NASA NTRS2018

Mission Configuration Tree for the James Webb Space Telescope (JWST)

The GSFC Mission Configuration web-based application is a means of documenting the structure of the many elements used in building each mission that NASA launches, as well as archiving the documents associated with each of these elements and keeping track of suppliers. This includes all available drawings, engineering orders, parts lists, block diagrams, and photos of each system, subsystem, and component. While the information for several of NASA's missions is being input into Mission Configuration, the ten weeks of this project focused on collecting information for the instrument systems of the James Webb Space Telescope. Documents were collected from a database called the Next Generation Integrated Network (NGIN) so that they can eventually be uploaded into the application. From these documents, including drawings and parts lists, a configuration tree was created in Microsoft Visio which showed the relationships between each system, subsystem, and component. When both these documents and the configuration tree are input into Mission Configuration, they will be place into a drill down tree structure, from which drawings and documents can be accessed directly. Mission Configuration also includes search functions that make all parts searchable by keywords such as vendor, part number, taxonomy, and alternate nomenclature. This application is an important resource for providing access to documents related to the components used on each mission. It has created a central repository for information which can be used to benefit Safety & Mission Assurance, but may also be useful across codes. The process of collecting the information and documents to put into Mission Configuration is also allowing for a concise breakdown of information from diverse formats, creating standardization which makes information more readily accessible, while retaining project-specific information that makes it more easily searchable.

Cain, Mary C.↗

NASA NTRS2018

2018 NASA Green Propulsion Technology Development Roadmap

The NASA Green Propulsion Working Group (GPWG) was tasked by the NASA Chemical Propulsion Subcapabilities Management (CPSM) with the development of this NASA Green Propulsion Technologies Development Roadmap, herein referred to as the Green Propulsion Roadmap, or simply the Roadmap, to provide guidance to NASA through the CPSM on green propulsion technology development. Other agencies or commercial partners may refer to this roadmap as well. It is envisioned that the synthesis of various Center-based activities and knowledge repositories will result in a cumulative knowledge gain, and will provide capabilities beyond the sum contribution of individual Centers. Ultimately, a well-defined roadmap of technology investment path, the enhanced coordination and alignment of activities among NASA Centers and other Federal Agencies, and a well-supported green propulsion community will facilitate the path towards the broader infusion of green propulsion technologies for science and human exploration missions, as well as a deeper understanding of the fundamental behaviors and characteristics of these systems that is on par with other historically used monopropellant propulsion systems, such as hydrazine.

Cavender, D. P.↗

NASA NTRS2018

GeneLab: "Omics" Data Systems for Spaceflight and Simulated Spaceflight Environment

NASA's GeneLab Data System is a repository that hosts multi-omics datasets generated by biological experiments flown onboard the International Space Station. Strategies regarding how GeneLab envisions the involvement of the scientific community and the public at large will be discussed, and current and future capabilities of the system will be described. Information describing how scientists can participate in analyzing the current datasets on plants, microbes, invertebrates or mammals will be provided, and initial findings from the current datasets will be discussed during this presentation. Anyone interested in genomics, transcriptomics, epigenomics and proteomics, and systems biology, or who is curious to understand how space modifies living organisms should attend.

Omics↗

NASA NTRS2018

GeneLab: "Omics" Data System for Space Biology Research

Determining the biological impact of spaceflight through novel approaches is essential to reduce the health risks to astronauts for long-term space missions. The current established health risks due to spaceflight are only reflecting known symptomatic and physiologic responses and do not reflect early onset of other potential diseases. There are many unknown variables which still need to be identified to fully understand the health impacts due to the environmental factors in space. One method to uncover potential novel biological mechanisms responsible for health risks in astronauts is by utilizing NASA's GeneLab Data Systems (genelab.nasa.gov). GeneLab is public repository that hosts multiple omics datasets generated from space biology experiments that include experiments flown in space, simulated cosmic radiation experiments, and simulated microgravity experiments. This presentation will provide an overview of GeneLab and examples of analysis that are being done with GeneLab datasets. These example will include novel data and work being generated with various scientists around the world involved with GeneLab's Analysis Working Groups (AWG) that are assisting with the development of pipelines and advancing GeneLab to the next phase, a publication from GeneLab discovering novel Carbon Dioxide impact due to rodent habitats, another publication from GeneLab discovering a potential master regulator responsible for health risk associated due to spaceflight, and potential cardiovascular risk from space radiation.

microgravity↗

NASA NTRS2018

GeneLab: Omics Database for Spaceflight Experiments

Motivation - To curate and organize expensive spaceflight experiments conducted aboard space stations and maximize the scientific return of investment, while democratizing access to vast amounts of spaceflight related omics data generated from several model organisms. Results - The GeneLab Data System (GLDS) is an open access database containing fully coordinated and curated "omics" (genomics, transcriptomics, proteomics, metabolomics) data, detailed metadata and radiation dosimetry for a variety of model organisms. GLDS is supported by an integrated data system allowing federated search across several public bioinformatics repositories. Archived datasets can be queried using full-text search (e.g., keywords, Boolean and wildcards) and results can be sorted in multifactorial manner using assistive filters. GLDS also provides a collaborative platform built on GenomeSpace for sharing files and analyses with collaborators. It currently houses 172 datasets and supports standard guidelines for submission of datasets, MIAME (for microarray), ENCODE Consortium Guidelines (for RNA-seq) and MIAPE Guidelines (for proteomics).

omics↗