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NASA NTRS2010

Route Generation for a Synthetic Character (BOT) Using a Partial or Incomplete Knowledge Route Generation Algorithm in UT2004 Virtual Environment

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.

Hanold, Gregg T.↗
NASA NTRS2010

Analysis, Simulation, and Verification of Knowledge-Based, Rule-Based, and Expert Systems

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.

Hinchey, Mike↗
NASA NTRS2010

Linking Indigenous Knowledge and Observed Climate Change Studies

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.

Alexander, Chief Clarence↗
NASA NTRS2014

U.S. Spacesuit Knowledge Capture Accomplishments in Fiscal Years 2012 and 2013

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.

Chullen, Cinda↗
NASA NTRS2014

U.S. Spacesuit Knowledge Capture Accomplishments in Fiscal Years 2012 and 2013

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.

Chullen, Cinda↗
NASA NTRS2016

EVA Wiki - Transforming Knowledge Management for EVA Flight Controllers and Instructors

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.

Johnston, Stephanie↗
NASA NTRS2016

EVA Wiki - Transforming Knowledge Management for EVA Flight Controllers and Instructors

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.

Johnston, Stephanie S.↗
NASA NTRS2016

EVA Wiki - Transforming Knowledge Management for EVA Flight Controllers and Instructors

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.

Johnston, Stephanie S.↗
NASA NTRS2016

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

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).

data analytics↗
NASA NTRS2017

EcAMSat and BioSentinel: Autonomous Bio Nanosatellites Addressing Strategic Knowledge Gaps for 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 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.

Padgen, Michael R.↗
NASA NTRS2017

EcAMSat and BioSentinel: Autonomous Bio Nanosatellites Addressing Strategic Knowledge Gaps for 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.

Padgen, Mike↗
NASA NTRS2017

Next Generation Agricultural System Models and Knowledge Products: Synthesis and Strategy

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.

Next generation↗
NASA NTRS2018

Using a Knowledge Graph to Discover Earth Science Information

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.

Freitag, Brian↗
NASA NTRS2019

NASA Aeronautics: Knowledge Management and Digital Transformation in Partnership

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.

Smith, Tiffany L.↗
NASA NTRS2018

Application of a Knowledge-Based Optimization Method for Aerodynamic Design

The current research is investigating the application of an optimization technique to an existing knowledge-based design tool. The optimization method, referred to as CODISC, helps improve the results from a knowledge-based design by eliminating the required advanced design knowledge, or help fine-tune a well-performing vehicle. Three CODISC designs are presented using a generic transonic transport, the Common Research Model (CRM). One design optimizes the baseline CRM to demonstrate the ability to improve a well-performing vehicle. Another design is performed from the CRM with camber and twist removed, which highlights the ability to use CODISC in the conceptual design phase. The final design implements laminar flow on the CRM, showing how CODISC can optimize the extent of laminar flow to find the best aerodynamic performance. All three CODISC designs reduced the vehicle drag compared to the baseline CRM, and highlight the new optimization technique’s versatility in the aircraft design industry.

CODISC↗
NASA NTRSDate not supplied

Modeling Atmospheric Science Knowledge from Research Publications

NASA Earth Science Data Centers contain enormous amounts of remote sensing digital data. It is often a significant challenge for users to find data suitable for their research topic in these vast archives. One of the approaches is the usage-driven dataset discovery, where users seek publications on projects similar to their intended study. For this approach to be effective, users need a clear connection between the underlying data in the publications and the study objectives; this is not often apparent to non-expert users. Tools and methodologies that can help facilitate and organize these connections are therefore valuable for creating improved knowledge mappings, which can be further used by search engines to suggest data or publications best tailored to a user’s specific research goal. As an illustration of these challenges, in this work we focus on the atmospheric chemistry processes related to Earth environmental impacts such as ozone depletion, aerosols, smog formation, acid rain, and radiative forcing. We further limit our study to publications that use data from the Microwave Limb Sounder (MLS) instrument flown on the Aura Earth Observing System. To create knowledge representations of science carried out in these publications, we use existing ontologies such as the Global Change Master Directory (GCMD) and Semantic Web for Earth and Environmental Terminology (SWEET). These ontologies together encompass term dictionaries that include measured variables, names of molecules or radicals, mission and instrument names, locations, action words, among many others. Based on these terms acknowledge graph database was populated with the terms retrieved from scientific publications that study atmospheric chemistry. These databases can be used to further enhance the automation of knowledge discovery and facilitate machine learning and artificial intelligence algorithms or applications. These tools and methods can also be extended to apply to content from other related Earth science domains.

Irina Gerasimov↗
NASA NTRSDate not supplied

Verb Sense Disambiguation for Densifying Knowledge Graphs in Earth Science

We begin with an ambitious goal: to create a knowledge graph that spans the entire discipline of Earth science. In order to achieve this, we need to apply Natural Language Processing (NLP) techniques on Earth science journal articles to extract their semantic components for the graph. When sentences from Earth science journal articles are broken down into their semantic components and loaded onto a graph, the relationships among these semantic components are represented by the verbs in the sentences. However, since there are multiple verbs in English that can be used to denote the same meaning, the knowledge graph can become sparse and so can the results when we query the graph. In order to ensure quality results, it would be desirable to consolidate similar verbs into a single "class". So, this is the problem at hand: how do we make sure that multiple verbs that mean the same thing are represented as a single class of verb in the knowledge graph? Or in other words, how do we distinguish which meaning a particular verb takes given a particular sentence? In this poster, we demonstrate a potential technique to solve this problem.

Ashish Acharya↗
NASA NTRSDate not supplied

ISS External Microorganisms: A Payload to Close Planetary Protection Knowledge Gaps for Crewed Missions

Before NASA or COSPAR is able to set planetary protection requirements for crewed missions to locations like Mars there are a number of critical knowledge gaps that must be addressed (1). One of the most important knowledge gaps is an understanding of microbial leakage from crewed habitats and space suits. Current ECLSS (Environmental Control and Life Support System) and PLSS (Portable Life Support System) requirements do not include any provisions to control microbes that may escape along with vented or leaked gasses. The current generation of NASA space suits can leak at rates as high as 100 cm2 /min. during nominal operation (2). ISS (International Space Station) intentionally vents atmospheric gases like CO2 to maintain habitable conditions for the crew. Furthermore, every time an airlock is used for EVA (extravehicular activity)there is an accompanying release of internal atmosphere. Since it is not possible to sterilize a crewed mission, it is important that we understand what if any microbes are entrained in these vented and leaked products. It is also important to understand if these microbes can survive on exterior surfaces. Recent sampling of the Russian segments of ISS suggest that bacteria and fungi from inside ISS may be capable of surviving on external surfaces(3). NASA is developing an aseptic sampling tool for use during EVA and plans to collect samples from vents on ISS to build on these results. The results of this work will be used to develop planetary protection requirements for vented and leaked gasses from crewed volumes. NASA has developed and tested a tool kit for collecting microbiological samples during EVA(4). This tool kit contains eight commercially available, 23 mm. diameter, foam swabs that can be used to aseptically collect samples while at vacuum. The swabs are individually housed in aluminum canisters that are equipped with 0.2 μm Teflon filters. These filters allow the canisters to equilibrate to pressure changes while preventing microbiological contamination. The canisters will be cleaned and sterilized before flight. Results from ground-based testing indicate that this tool kit is capable of aseptically collecting microbes while at vacuum without becoming contaminated during pressure changes(5). Based on the results of this ground testing we have modified the tool kit to meet NASA safety requirements and improve the ergonomics. We added additional mounting points to the tool kit to give astronauts more options for securing it during use. We also changed the opening mechanism to improve the precision with which swabs can be extracted from the tool kit. We plan to use this kit on an upcoming EVA to collect samples from non-propulsive vents and areas near the U.S. airlock on ISS. These samples will be frozen at -80 ̊C and stored on station until they can be returned to Earth. We will analyze these returned samples using next generation DNA sequencing to determine the community composition and function of external ISS environments. The results of this study will close planetary protection knowledge gaps for crewed missions and will help NASA determine appropriate planetary protection requirements for life support systems. The tool kit will also be useful for collecting aseptic samples on upcoming crewed or robotic missions and could easily be modified to collect samples with organic contamination control requirements as well.

A B Regberg↗