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NASA's mandate is to take risks to got into space while applying its best knowledge. NASA's knowledge is the result of scientific insights from research, engineering wisdom from experience, project management skills, safety and team consciousness and institutional support and collaboration. This presentation highlights NASA's organizational knowledge, communication and growth efforts.
The Arctic is home to many indigenous peoples, including those who depend on reindeer herding for their livelihood, in one of the harshest environments in the world. For the largely nomadic peoples, reindeer not only form a substantial part of the Arctic food base and economy, but they are also culturally important, shaping their way of life, mythologies, festivals and ceremonies. Reindeer pastoralism or husbandry has been practiced by numerous peoples all across Eurasia for thousands of years and involves moving herds of reindeer, which are very docile animals, from pasture to pasture depending on the season. Thus, herders must adapt on a daily basis to find optimal conditions for their herds according to the constantly changing conditions. Climate change and variability plus rapid development are increasingly creating major changes in the physical environment, ecology, and cultures of these indigenous reindeer herder communities in the North, and climate changes are occurring significantly faster in the Arctic than the rest of the globe, with correspondingly dramatic impacts (Oskal, 2008). In response to these changes, Eurasian reindeer herders have created the EALAT project, a comprehensive new initiative to study these impacts and to develop local adaptation strategies based upon their traditional knowledge of the land and its uses - in targeted partnership with the science and remote sensing community - involving extensive collaborations and coproduction of knowledge to minimize the impacts of the various changes. This chapter provides background on climate and development challenges to reindeer husbandry across the Arctic and an overview of the EALAT initiative, with an emphasis on indigenous knowledge, remote sensing, Geographic Information Systems (GIS), and other scientific data to 'co-produce' datasets for use by herders for improved decision-making and herd management. It also provides a description of the EALAT monitoring data integration and sharing system and portal being developed for reindeer pastoralism. In addition, the chapter provides some preliminary results from the EALAT Project, including some early remote sensing research results.
Collaboration between Medical Informatics and Healthcare Systems (MIHCS) at NASA/Johnson Space Center (JSC) and the Texas Medical Center (TMC) Library was established to investigate technologies for facilitating knowledge discovery across multiple life sciences research disciplines in multiple repositories. After reviewing 14 potential Enterprise Search System (ESS) solutions, Collexis was determined to best meet the expressed needs. A three month pilot evaluation of Collexis produced positive reports from multiple scientists across 12 research disciplines. The joint venture and a pilot-phased approach achieved the desired results without the high cost of purchasing software, hardware or additional resources to conduct the task. Medical research is highly compartmentalized by discipline, e.g. cardiology, immunology, neurology. The medical research community at large, as well as at JSC, recognizes the need for cross-referencing relevant information to generate best evidence. Cross-discipline collaboration at JSC is specifically required to close knowledge gaps affecting space exploration. To facilitate knowledge discovery across these communities, MIHCS combined expertise with the TMC library and found Collexis to best fit the needs of our researchers including:
This software addresses the problem of how to efficiently execute a knowledge base in the presence of missing data. Computationally, this is an exponentially expensive operation that without heuristics generates a search space of 1 + 2n possible scenarios, where n is the number of rules in the knowledge base. Even for a knowledge base of the most modest size, say 16 rules, it would produce 65,537 possible scenarios. The purpose of this software is to reduce the complexity of this operation to a more manageable size. The problem that this system solves is to develop an automated approach that can reason in the presence of missing data. This is a meta-reasoning capability that repeatedly calls a diagnostic engine/model to provide prognoses and prognosis tracking. In the big picture, the scenario generator takes as its input the current state of a system, including probabilistic information from Data Forecasting. Using model-based reasoning techniques, it returns an ordered list of fault scenarios that could be generated from the current state, i.e., the plausible future failure modes of the system as it presently stands. The scenario generator models a Potential Fault Scenario (PFS) as a black box, the input of which is a set of states tagged with priorities and the output of which is one or more potential fault scenarios tagged by a confidence factor. The results from the system are used by a model-based diagnostician to predict the future health of the monitored system.
This paper presents a new Route Generation Algorithm that accurately and realistically represents human route planning and navigation for Military Operations in Urban Terrain (MOUT). The accuracy of this algorithm in representing human behavior is measured using the Unreal Tournament(Trademark) 2004 (UT2004) Game Engine to provide the simulation environment in which the differences between the routes taken by the human player and those of a Synthetic Agent (BOT) executing the A-star algorithm and the new Route Generation Algorithm can be compared. The new Route Generation Algorithm computes the BOT route based on partial or incomplete knowledge received from the UT2004 game engine during game play. To allow BOT navigation to occur continuously throughout the game play with incomplete knowledge of the terrain, a spatial network model of the UT2004 MOUT terrain is captured and stored in an Oracle 11 9 Spatial Data Object (SOO). The SOO allows a partial data query to be executed to generate continuous route updates based on the terrain knowledge, and stored dynamic BOT, Player and environmental parameters returned by the query. The partial data query permits the dynamic adjustment of the planned routes by the Route Generation Algorithm based on the current state of the environment during a simulation. The dynamic nature of this algorithm more accurately allows the BOT to mimic the routes taken by the human executing under the same conditions thereby improving the realism of the BOT in a MOUT simulation environment.
Mathematically sound techniques are used to view a knowledge-based system (KBS) as a set of processes executing in parallel and being enabled in response to specific rules being fired. The set of processes can be manipulated, examined, analyzed, and used in a simulation. The tool that embodies this technology may warn developers of errors in their rules, but may also highlight rules (or sets of rules) in the system that are underspecified (or overspecified) and need to be corrected for the KBS to operate as intended. The rules embodied in a KBS specify the allowed situations, events, and/or results of the system they describe. In that sense, they provide a very abstract specification of a system. The system is implemented through the combination of the system specification together with an appropriate inference engine, independent of the algorithm used in that inference engine. Viewing the rule base as a major component of the specification, and choosing an appropriate specification notation to represent it, reveals how additional power can be derived from an approach to the knowledge-base system that involves analysis, simulation, and verification. This innovative approach requires no special knowledge of the rules, and allows a general approach where standardized analysis, verification, simulation, and model checking techniques can be applied to the KBS.
We present indigenous knowledge narratives and explore their connections to documented temperature and other climate changes and observed climate change impact studies. We then propose a framework for enhancing integration of these indigenous narratives of observed climate change with global assessments. Our aim is to contribute to the thoughtful and respectful integration of indigenous knowledge with scientific data and analysis, so that this rich body of knowledge can inform science, and so that indigenous and traditional peoples can use the tools and methods of science for the benefit of their communities if they choose to do so. Enhancing ways of understanding such connections are critical as the Intergovernmental Panel on Climate Change Fifth Assessment process gets underway.
The NASA U.S. spacesuit knowledge capture (KC) program has been in operations since the beginning 2008. The program was designed to augment engineers and others with information about spacesuits in a historical way. A multitude of seminars have captured spacesuit history and knowledge over the last six years of the programs existence. Subject matter experts have provided lectures and were interviewed to help bring the spacesuit to life so that lessons learned will never be lost. As well, the program concentrated in reaching out to the public and industry by making the recorded events part of the public domain through the NASA technical library via You Tube media. The U.S. spacesuit KC topics have included lessons learned from some of the most prominent spacesuit experts and spacesuit users including current and former astronauts. The events have enriched the spacesuit legacy knowledge from Gemini, Apollo, Skylab, Space Shuttle and International Space Station Programs. As well, expert engineers and scientists have shared their challenges and successes to be remembered. The last few years have been some of the most successful years of the KC program program's life with numerous recordings and releases to the public. It is evidenced by the thousands that have view the recordings online. This paper reviews the events accomplished and archived over Fiscal Years 2012 and 2013 and highlights a few of the most memorable ones. This paper also communicates ways to access the events that are available internally to NASA as well as in the public domain.
The NASA U.S. Spacesuit Knowledge Capture (KC) program has existed since the beginning of 2008. The program was designed to augment engineers and other technical team members with historical spacesuit information to add to their understanding of the spacesuit, its evolution, its limitations, and its capabilities. Over 40 seminars have captured spacesuit history and knowledge over the last six years of the program's existence. Subject matter experts have provided lectures and some were interviewed to help bring the spacesuit to life so that lessons learned will never be lost. As well, the program concentrated in reaching out to the public and industry by making the recorded events part of the public domain through the NASA technical library through YouTube media. The U.S. Spacesuit KC topics have included lessons learned from some of the most prominent spacesuit experts and spacesuit users including current and former astronauts. The events have enriched the spacesuit legacy knowledge from Gemini, Apollo, Skylab, Space Shuttle and International Space Station Programs. As well, expert engineers and scientists have shared their challenges and successes to be remembered. Based on evidence by the thousands of people who have viewed the recordings online, the last few years have been some of the most successful years of the KC program's life with numerous digital recordings and public releases. This paper reviews the events accomplished and archived over Fiscal Years 2012 and 2013 and highlights a few of the most memorable ones. This paper also communicates ways to access the events that are available internally on the NASA domain as well as those released on the public domain.
The EVA (Extravehicular Activity) Wiki was recently implemented as the primary knowledge database to retain critical knowledge and skills in the EVA Operations group at NASA's Johnson Space Center by ensuring that information is recorded in a common, searchable repository. Prior to the EVA Wiki, information required for EVA flight controllers and instructors was scattered across different sources, including multiple file share directories, SharePoint, individual computers, and paper archives. Many documents were outdated, and data was often difficult to find and distribute. In 2011, a team recognized that these knowledge management problems could be solved by creating an EVA Wiki using MediaWiki, a free and open-source software developed by the Wikimedia Foundation. The EVA Wiki developed into an EVA-specific Wikipedia on an internal NASA server. While the technical implementation of the wiki had many challenges, the one of the biggest hurdles came from a cultural shift. Like many enterprise organizations, the EVA Operations group was accustomed to hierarchical data structures and individually-owned documents. Instead of sorting files into various folders, the wiki searches content. Rather than having a single document owner, the wiki harmonized the efforts of many contributors and established an automated revision control system. As the group adapted to the wiki, the usefulness of this single portal for information became apparent. It transformed into a useful data mining tool for EVA flight controllers and instructors, and also for hundreds of other NASA and contract employees. Program managers, engineers, astronauts, flight directors, and flight controllers in differing disciplines now have an easier-to-use, searchable system to find EVA data. This paper presents the benefits the EVA Wiki has brought to NASA's EVA community, as well as the cultural challenges it had to overcome.
The EVA Wiki was recently implemented as the primary knowledge database to retain critical knowledge and skills in the EVA Operations group at NASA's Johnson Space Center by ensuring that information is recorded in a common, easy to search repository. Prior to the EVA Wiki, information required for EVA flight controllers and instructors was scattered across different sources, including multiple file share directories, SharePoint, individual computers, and paper archives. Many documents were outdated, and data was often difficult to find and distribute. In 2011, a team recognized that these knowledge management problems could be solved by creating an EVA Wiki using MediaWiki, a free and open-source software developed by the Wikimedia Foundation. The EVA Wiki developed into an EVA-specific Wikipedia on an internal NASA server. While the technical implementation of the wiki had many challenges, one of the biggest hurdles came from a cultural shift. Like many enterprise organizations, the EVA Operations group was accustomed to hierarchical data structures and individually-owned documents. Instead of sorting files into various folders, the wiki searches content. Rather than having a single document owner, the wiki harmonized the efforts of many contributors and established an automated revision controlled system. As the group adapted to the wiki, the usefulness of this single portal for information became apparent. It transformed into a useful data mining tool for EVA flight controllers and instructors, as well as hundreds of others that support the EVA. Program managers, engineers, astronauts, flight directors, and flight controllers in differing disciplines now have an easier-to-use, searchable system to find EVA data. This paper presents the benefits the EVA Wiki has brought to NASA's EVA community, as well as the cultural challenges it had to overcome.
The EVA Wiki was recently implemented as the primary knowledge database to retain critical knowledge and skills in the EVA Operations group at NASA's Johnson Space Center by ensuring that information is recorded in a common, easy to search repository. Prior to the EVA Wiki, information required for EVA flight controllers and instructors was scattered across different sources, including multiple file share directories, SharePoint, individual computers, and paper archives. Many documents were outdated, and data was often difficult to find and distribute. In 2011, a team recognized that these knowledge management problems could be solved by creating an EVA Wiki using MediaWiki, a free and open-source software developed by the Wikimedia Foundation. The EVA Wiki developed into an EVA-specific Wikipedia on an internal NASA server. While the technical implementation of the wiki had many challenges, one of the biggest hurdles came from a cultural shift. Like many enterprise organizations, the EVA Operations group was accustomed to hierarchical data structures and individually-owned documents. Instead of sorting files into various folders, the wiki searches content. Rather than having a single document owner, the wiki harmonized the efforts of many contributors and established an automated revision controlled system. As the group adapted to the wiki, the usefulness of this single portal for information became apparent. It transformed into a useful data mining tool for EVA flight controllers and instructors, as well as hundreds of others that support EVA. Program managers, engineers, astronauts, flight directors, and flight controllers in differing disciplines now have an easier-to-use, searchable system to find EVA data. This paper presents the benefits the EVA Wiki has brought to NASA's EVA community, as well as the cultural challenges it had to overcome.
Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).
Manned missions beyond low Earth orbit (LEO) require that several strategic knowledge gaps about the effects of space travel on the human body be addressed. NASA Ames Research Center has been the leader in developing autonomous bio nanosatellites, including past successful missions for GeneSat, PharmaSat, and O/OREOS, that tackled some of these issues. These nanosatellites provide in situ measurements, which deliver insight into the dynamic changes in cell behavior in microgravity. In this talk, two upcoming bio nanosatellites developed at Ames, the E. coli Antimicrobial Satellite (EcAMSat) and BioSentinel, will be discussed. Both satellites contain microfluidic systems that precisely deliver nutrients to the microorganisms stored within wells of fluidic cards. Each well, in turn, has its own 3-color LED and detector system which is used to monitor changes in metabolic activity with alamarBlue, a redox indicator, and the optical density of the cells. EcAMSat investigates the effects of microgravity on bacterial resistance to antimicrobial drugs, vital knowledge for understanding how to maintain the health of astronauts in long-term and beyond LEO spaceflight. The behavior of wild type and mutant uropathic E. coli will be compared in microgravity and with ground data to help understand the molecular mechanisms behind antibiotic resistance and how these phenotypes might change in space. BioSentinel seeks to directly measure the effects of space radiation on budding yeast S. cerevisiae, particularly double strand breaks (DSB). While hitching a ride on the SLS EM-1 mission (Orion's first unmanned mission to the moon) in 2018, BioSentinel will be kicked off and enter into a heliocentric orbit, becoming the first study of the effects of radiation on living organisms outside LEO since the Apollo program. The yeast are stored in eighteen independent 16-well microfluidic cards, which will be individually activated over the 12 month mission duration. In addition to the wild type and radiation-sensitive mutant strains, a BioSentinel strain of yeast has been developed, which requires a DSB to reactivate growth, thereby allowing for a direct measurement of DSBs caused by radiation. These two missions demonstrate the utility of using autonomous nanosatellites to address strategic knowledge gaps in the push to once again extend manned spaceflight beyond LEO.
Manned missions beyond low Earth orbit (LEO) require that several strategic knowledge gaps about the effects of space travel on the human body be addressed. NASA Ames Research Center has been the leader in developing autonomous bio nanosatellites, including past successful missions for GeneSat, PharmaSat, and OOREOS, that tackled some of these issues. These nanosatellites provide in situ measurements, which deliver insight into the dynamic changes in cell behavior in microgravity. In this talk, two upcoming bio nanosatellites developed at Ames, the E. coli Antimicrobial Satellite (EcAMSat) and BioSentinel, will be discussed. Both satellites contain microfluidic systems that precisely deliver nutrients to the microorganisms stored within wells of fluidic cards. Each well, in turn, has its own 3-color LED and detector system which is used to monitor changes in metabolic activity with alamarBlue, a redox indicator, and the optical density of the cells. EcAMSat investigates the effects of microgravity on bacterial resistance to antimicrobial drugs, vital knowledge for understanding how to maintain the health of astronauts in long-term and beyond LEO spaceflight. The behavior of wild type and mutant uropathic E. coli will be compared in microgravity and with ground data to help understand the molecular mechanisms behind antibiotic resistance and how these phenotypes might change in space. BioSentinel seeks to directly measure the effects of space radiation on budding yeast S. cerevisiae, particularly double strand breaks (DSB). While hitching a ride on the SLS EM-1 mission (Orions first unmanned mission to the moon) in 2018, BioSentinel will be kicked off and enter into a heliocentric orbit, becoming the first study of the effects of radiation on living organisms outside LEO since the Apollo program. The yeast are stored in eighteen independent 16-well microfluidic cards, which will be individually activated over the 12 month mission duration. In addition to the wild type and radiation-sensitive mutant strains, a BioSentinel strain of yeast has been developed, which requires a DSB to reactivate growth, thereby allowing for a direct measurement of DSBs caused by radiation. These two missions demonstrate the utility of using autonomous nanosatellites to address strategic knowledge gaps in the push to once again extend manned spaceflight beyond LEO.
The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. In the Introduction to this Special Issue, we described a vision for accelerating the rate of agricultural innovation and meeting the growing global need for food and fiber. In this concluding article of the NextGen Special Issue we synthesize insights and formulate a strategy to advance data, models, and knowledge products that are consistent with this vision. This strategy is designed to facilitate a transition from the current, primarily supply-driven approach toward a more demand-driven approach that would address key Use Cases where better data, models and knowledge products are seen by end-users as essential to meet their needs.
Knowledge graphs link key entities within a specific domain to other entities via relationships. Researchers are able to mine these relationships from numerous sources to infer new knowledge. Text extraction from peer-reviewed papers and scientific reports are untapped resources that can be leveraged by knowledge graphs to accelerate scientific discovery.
Knowledge management and digital transformation share common ground. Both maintain an interest in driving mission success, maximizing information access, and leveraging digital technologies to enhance efficiency and effectiveness. As NASA's Aeronautics Research Mission Directorate explores emerging digital capabilities, we are applying KM principles to strengthen our approach. Bringing digital transformation to knowledge management practices also helps us to reinvent antiquated processes and take advantage of new opportunities. This session identifies areas of complementarity between knowledge management and digital transformation, highlights ways KM practitioners can contribute to digital transformation activities in any organization, and provides some examples from NASA Aeronautics' experience in both.