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NASA NTRS2003

Oh, Develop

In a mature view of the subject, career development is not simply four years of college or a week at training, culminating in a diploma or a certificate to hang on an office wall. That's why we wanted to take a broad look at career development in this issue of ASK. Take for example, Dr. Gerald Mulenburg s contribution, Fly on the Wall. When Mulenburg and other members of a knowledge-sharing group at Ames were invited to observe an upcoming project review, Mulenburg thought it would be interesting to learn how another project does its reviews. Note that Mulenburg is no fresh out who's never attended a NASA project review. Not only has he been through a fair share of them as the reviewed, he has also been on the other side of the table as a reviewer. This experienced project manager recognizes that at any stage of a career there is room to grow and develop one's repertoire. Too often people associate career development with textbooks and role classroom training, far removed from project life. But classroom training need not be like this, as you'll find in our Special Feature, The Enterprise Project by Wendy Dolci, which sprung out of an APPL Advanced Project Management class in July 2003 at Ames Research Center. In addition to Dolci, some of her classmates contribute to the story. Mike Sander of the Jet Propulsion Laboratory, project manager for the Mars Science Laboratory mission, who provided the assignment on which the story is based, also has a cameo in the story. We think Dolci's story is an inspiring example of what classroom training can be if it's approached imaginatively and made to serve a practical purpose. Another story from Ames, by Frank Larsen, takes a different twist on career development. At the annual Experimental Aircraft Association Fly-in in Oshkosh, Wisconsin, Larsen represented Ames at a NASA booth. While there, Larsen met a colleague from Glenn Research Center. Months later on a project with a quick turnaround, he remembered his colleague from Glenn who had equipment that might help Larsen save time and money on his project. Although they had never worked together and they had to unravel a lot of red tape before they could collaborate, they managed a way to get the job done. There are other stories in this issue that deal directly with this theme of career development. Then there are others in which it is less explicit. But if you take the view that career development happens all the time, and is as necessary to your survival as breathing, then you can read almost any story in ASK with your career development in mind. In that case, all the best, and so then-start developing.

Post, Todd↗
NASA NTRSDate not supplied

The Light Microscopy Module A Facility Overview-History and Science

We will share a brief history and provide a science overview of the many accomplishments of the Light Microscopy Module (LMM), a microscope that has been operating in the microgravity environment of the International Space Station (ISS) since 2010. It will be removed in October 2021. It was initially outfitted for the Constrained Vapor Bubble – Wickless Heat Pipe experiment. It was later reconfigured and upgraded many times to support a broad portfolio of Physical science and Biological experiments including: Complex Fluids, Colloids, Macro-Molecular Biophysics, Protein Crystals, and Plant Biology. The LMM has enabled many significant studies and discoveries and has seen its fair share of pleasant surprises. These include recording order arising out of disorder, e.g., systems of colloids that were glasses on Earth crystallizing to form large defect-free crystals, nematic ordering of elliptical colloids, and colloidal-polymer systems crystallizing. The LMM science teams have also seen how to improve product stabilizers once the effects of sedimentation on Earth were removed; they were able to see which tagged genes express in plants when gravity is removed, telling them which genes are essential for growing plants in space; they’ve tested protein crystal growth models; checked models for creating bijel electrodes that turn batteries into fast-charging supercapacitors; tested many forms of colloidal self-assembly: including those using depletion attraction, magnetic fields, and critical Casimir forces; they’ve observed explosive bubble nucleation in a wickless heat pipe, and much more. This work has been supported by the NASA Biological and Physical Sciences (BPS) Division and the ISS Program Office, Johnson Space Center Code OZ and Code OB, the Center for the Advancement of Science in Space (CASIS) / ISS National Lab, EPSCoR, and ISS international partners from ESA – the Netherlands and Italy, CSA, JAXA and S. Korea, and by Space Act Agreements with Procter and Gamble (P&G).

Colloids↗
NASA NTRS1995

Job Management Requirements for NAS Parallel Systems and Clusters

A job management system is a critical component of a production supercomputing environment, permitting oversubscribed resources to be shared fairly and efficiently. Job management systems that were originally designed for traditional vector supercomputers are not appropriate for the distributed-memory parallel supercomputers that are becoming increasingly important in the high performance computing industry. Newer job management systems offer new functionality but do not solve fundamental problems. We address some of the main issues in resource allocation and job scheduling we have encountered on two parallel computers - a 160-node IBM SP2 and a cluster of 20 high performance workstations located at the Numerical Aerodynamic Simulation facility. We describe the requirements for resource allocation and job management that are necessary to provide a production supercomputing environment on these machines, prioritizing according to difficulty and importance, and advocating a return to fundamental issues.

Saphir, William↗
NASA NTRS1995

Job Scheduling Under the Portable Batch System

The typical batch queuing system schedules jobs for execution by a set of queue controls. The controls determine from which queues jobs may be selected. Within the queue, jobs are ordered first-in, first-run. This limits the set of scheduling policies available to a site. The Portable Batch System removes this limitation by providing an external scheduling module. This separate program has full knowledge of the available queued jobs, running jobs, and system resource usage. Sites are able to implement any policy expressible in one of several procedural language. Policies may range from "bet fit" to "fair share" to purely political. Scheduling decisions can be made over the full set of jobs regardless of queue or order. The scheduling policy can be changed to fit a wide variety of computing environments and scheduling goals. This is demonstrated by the use of PBS on an IBM SP-2 system at NASA Ames.

Henderson, Robert L.↗
NASA NTRS2008

Learning from Space Entrepreneurs

The early days of rocketry and space exploration in the United States were marked by incredibly rapid progress: a seemingly endless parade of firsts. Not coincidentally, this period also saw more than its fair share of failure, especially in the infamous "kaputnik" days prior to the successful launch of Explorer. Without a standard canon of known quantities to turn to, the early pioneers of rocketry and space flight were forced to dream up new ideas that ranged from the elegant to the bizarre and to accept the fact that the price of radical progress is occasional failure. Nowadays, rapid prototyping and testing have slowed, as we rely more and more on the extensive knowledge pined by our predecessors and on the embarrassment of riches modern engineers get from computational modeling and computer assisted design. In many cases, this leads to much improved or phenomenally more efficient designs. It also, however, fosters a culture so terrified of failure that we over-engineer and overanalyze everything, often tweaking designs for decades before a new system takes flight. (This is not a problem unique to rockets; the same phenomenon seems to have occurred in high-performance jets.) This is one reason why it was possible for President Kennedy to dream of the completion of the Mercury and Gemini missions and a successful landing on the moon in under a decade, while returning to the moon may take nearly twice as long. Lacking access to the tremendous computational resources of the national space program-and, just as importantly, removed from the harsh judgment of public shareholders or congressional appropriations committees-the hungry entrepreneurs who compete for our prizes tend not to display such fear of failure. Instead, most of them follow a rapid "build, test, fly" program. They are willing to throw a handful of concepts against the wall and see what sticks. They often go from drawing on the back of a napkin to firing engines or even flying vehicles in a matter of weeks or months, learning valuable lessons along the way. Indeed, our teams have repeatedly learned many of the most valuable lessons after only a few moments of working with real hardware-lessons that could never have been learned from a CAD drawing, like finding the failure modes of different welding practices or tracking down the interference between an onboard camera and a GPS unit. As Paul Breed, the leader of a Northrop Grumman Lunar Lander Challenge team (playfully called Unreasonable Rocket), is fond of saying, "In computer simulations the plumbing never leaks. In real life, it always does."

Pomerantz, William↗
NASA NTRS2022

On the Current State of Sheaf Theoretic Networking

Advancing the Delay Tolerant Networking (DTN) effort has proven to be a unique and difficult challenge. In the journey to implementing space networking, NASA has considered several strategies. From direct translations of Internet Protocol (IP) to satellite networks to pre-planned routing structures in Contact Graph Routing (CGR), each potential solution for DTN has come with its fair share of setbacks and challenges. Frequently, these challenges can be traced back to specific assumptions made in the development of each protocol that do not carry over from ground networks to space networks. To address these fundamental assumptions, a more general and foundational networking theory is required. In this paper, we survey a novel mathematical foundation for networking using the theory of cellular sheaves. Sheaves form a mathematical tool for modeling local phenomenon that leads to global effects. Sheaves have been introduced a number of times, and our goal here is to summarize the applications and point towards important future work that must be done to build a stronger, cohesive theory for networking.

Sheaves↗
NASA NTRSDate not supplied

Data Sharing in Radiobiology; Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally „Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗
NASA NTRS2023

Data Sharing in Radiation Biology: Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally "Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗
NASA NTRSDate not supplied

INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY

Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods. REFERENCES [1] Open science in space. Nature Medicine, 2021. 27(9): p. 1485-1485. [2] Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. [3] Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5. [4] Whetzel, P.L., et al., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res, 2011. 39(Web Server issue): p. W541-5.

informatics↗
NASA NTRSDate not supplied

INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY

Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods.

knowledge↗
NASA NTRS1985

Parallel computation with the force

A methodology, called the force, supports the construction of programs to be executed in parallel by a force of processes. The number of processes in the force is unspecified, but potentially very large. The force idea is embodied in a set of macros which produce multiproceossor FORTRAN code and has been studied on two shared memory multiprocessors of fairly different character. The method has simplified the writing of highly parallel programs within a limited class of parallel algorithms and is being extended to cover a broader class. The individual parallel constructs which comprise the force methodology are discussed. Of central concern are their semantics, implementation on different architectures and performance implications.

Jordan, H. F.↗
NASA NTRS2017

Cultivating a Grassroots Aerospace Innovation Culture at NASA Ames Research Center

This paper details the adaptation of specific 'knowledge production' methods to implement a first of its kind, grassroots event that provokes a cultural change in how the NASA Ames civil servant community engages in the creation and selection of innovative ideas. Historically, selection of innovative proposals at NASA Ames Research Center is done at the highest levels of management, isolating the views and perspectives of the larger civil servant community. Additionally, NASA innovation programs are typically open to technical organizations and do not engage non-technical organizations to bring forward innovative processes/business practices. Finally, collaboration on innovative ideas and associated solutions tend to be isolated to organizational silos. In this environment, not all Ames employees feel empowered to innovate and opportunities for employee collaboration are limited. In order to address these issues, the 'innovation contest' method was adapted to create the NASA Ames Innovation Fair, a unique, grassroots innovation opportunity for the civil servant community. The Innovation Fair consisted of a physical event with a virtual component. The physical event provided innovators the opportunity to collaborate and pitch their innovations to the NASA Ames community. The civil servant community then voted for the projects that they viewed as innovative and would contribute to NASA's core mission, making this event a truly grassroots effort. The Innovation Fair website provided a location for additional knowledge sharing, discussion, and voting. On March 3rd, 2016, the 'First Annual NASA Ames Innovation Fair' was held with 49 innovators and more than 300 participants collaborating and/or voting for the best innovations. Based on the voting results, seven projects were awarded seed funding for projects ranging from innovative cost models to innovations in aerospace technology. Surveys of both innovators and Fair participants show the Innovation Fair was successful in fostering cross-organizational collaborations, soliciting participation of non-technical innovations, and increasing employee engagement in influencing the future of NASA Ames Research Center. The grassroots component of the Innovation Fair has been bench marked by the agency as a solid foundation for increasing employee engagement in the development of game changing aerospace technology and processes in support of NASA's mission.

Innovation↗
NASA NTRS2005

Layered Deposits and Pitted Terrain in the Circum Hellas Region

Much of the southern highlands has been mantled since the Noachian, including a general blanket of possibly airfall-derived sediment that softens the landscape, the Electris mantle including knobby chaos in several basins, and a variety of deposits that are the subject of this study that share the common characteristics of being generally confined to basins and crater floors and that manifest irregular interior depressions. Many of these features occur in a zone surrounding Hellas. These deposits share the general characteristics of having fairly smooth, nearly planar surfaces and abrupt scarps bordering interior and marginal depressions. Despite these common characteristics, a wide range of morphologies occurs. Several end-members are discussed below. Additional information is included in the original extended abstract.

Moore, J. M.↗
NASA NTRS2024

A Fair, Economically-Efficient, Incentive-Aligned, Scalable Airspace Auction Mechanism for UAV Traffic Management

Unmanned Aerial Vehicles (UAVs) are increasingly used in a wide range of applications such as cinematography, package delivery, and surveying. As a result, regulators have become interested in developing UAV Traffic Management (UTM) systems to coordinate UAV traffic. One possible framework for UTM is a combinatorial auction. Under this framework, airspace is modeled as a grid of space-time cells. UAV operators bid on sets of cells which collectively form flight paths for their UAVs. An ideal airspace auction should: be fair, be incentive-aligned, be scalable, allocate airspace economically-efficiently, enable price discovery, and reduce the work required to participate where possible. In this paper, we propose the first auction mechanism for airspace allocation that meets the criteria above. Our mechanism: (a) is provably economically-efficient, fair and incentive-aligned, (b) shares pricing information with bidders and (c) has features which reduce the burden of participating. We evaluate our mechanism on scenarios based on a Japan Aerospace Exploration Agency (JAXA) case study and find that it can scale to 26,000 bids.

Robert Allan Morris↗
NASA NTRS2018

FAIRness and Usability for Open-access Omics Data Systems

Omics data sharing is crucial to the biological research community, and the last decade or two has seen a huge rise in collaborative analysis systems, databases, and knowledge bases for omics and other systems biology data. We assessed the "FAIRness" of NASA's GeneLab Data Systems (GLDS) along with four similar kinds of systems in the research omics data domain, using 14 FAIRness metrics. The range of overall FAIRness scores was 6-12 (out of 14), average 10.1, and standard deviation 2.4. The range of Pass ratings for the metrics was 29-79%, Partial Pass 0-21%, and Fail 7-50%. The systems we evaluated performed the best in the areas of data findability and accessibility, and worst in the area of data interoperability. Reusability of metadata, in particular, was frequently not well supported. We relate our experiences implementing semantic integration of omics data from some of the assessed systems for federated querying and retrieval functions, given their shortcomings in data interoperability. Finally, we propose two new principles that Big Data system developers, in particular, should consider for maximizing data accessibility.

Berrios, Daniel C.↗
NASA NTRS2018

FAIRness and Usability for Open-access Omics Data Systems

Omics data sharing is crucial to the biological research community, and the last decade or two has seen a huge rise in collaborative analysis systems, databases, and knowledge bases for omics and other systems biology data. We assessed the “FAIRness” of NASA’s GeneLab Data Systems (GLDS) along with four similar kinds of systems in the research omics data domain, using 14 FAIRness metrics. The range of overall FAIRness scores was 6-12 (out of 14), average 10.1, and standard deviation 2.4. The range of Pass ratings for the metrics was 29-79%, Partial Pass 0-21%, and Fail 7-50%. The systems we evaluated performed the best in the areas of data findability and accessibility, and worst in the area of data interoperability. Reusability of metadata, in particular, was frequently not well supported. We relate our experiences implementing semantic integration of omics data from some of the assessed systems for federated querying and retrieval functions, given their shortcomings in data interoperability. Finally, we propose two new principles that Big Data system developers, in particular, should consider for maximizing data accessibility.

Berrios, Daniel C.↗
NASA NTRS2018

FAIRness and Usability for Open-Access Omics Data Systems

Omics data sharing is especially crucial to the biological research community, and the last decade or two has seen a huge rise in collaborative analysis systems, databases, and knowledge bases for omics and other systems biology data. We assessed the "FAIRness" of NASA's GeneLab Data Systems (GLDS) along with four similar kinds of systems in the research omics data domain, using 14 FAIRness metrics. 14 metrics. The range of Pass ratings was 29-79% of the 14 metrics, Partial Pass 0-21%, and Fail 7-50%. The range of overall FAIRness scores was 5-12 (out of 14). The systems we evaluated performed the best in the areas of data findability and accessibility, and worst in the area of data interoperability. We propose two new principles that Big Data systems, in particular, should consider for increasing data accessibility. We relate our experiences implementing semantic integration of omics data from several systems for the federated querying and retrieval functions of the GLDS, given the shortcomings in data interoperability of these systems.

Berrios, Daniel C.↗
NASA NTRS2014

Testing Orions Fairing Separation System

Traditional fairing systems are designed to fully encapsulate and protect their payload from the harsh ascent environment including acoustic vibrations, aerodynamic forces and heating. The Orion fairing separation system performs this function and more by also sharing approximately half of the vehicle structural load during ascent. This load-share condition through launch and during jettison allows for a substantial increase in mass to orbit. A series of component-level development tests were completed to evaluate and characterize each component within Orion's unique fairing separation system. Two full-scale separation tests were performed to verify system-level functionality and provide verification data. This paper summarizes the fairing spring, Pyramidal Separation Mechanism and forward seal system component-level development tests, system-level separation tests, and lessons learned.

Martinez, Henry↗