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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Survey Protocol to Assess Meaningfulness and Usefulness of Automated Topic Finding in the NASA Aviation Safety Reporting System

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants and other involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in a number of ways including entry into an online database, Safety Alert Bulletins and For Your Information Notices, and the CALLBACK newsletter. Key to these publications are the timely processing (de-identification, coding and summarization) of new reports, which is currently done by ASRS expert safety analysts. Thus, we believe topic modelling could decrease effort in ASRS, if topics are comprehensible. Aim: We propose a methodology to evaluate whether automated topic finding using topic modelling provides meaningful and useful topics. Method: We extend the total error survey methodology to evaluate user topic comprehension of machine learning outputs. To accomplish this we performed a literature review to identify existing methods and define a construct for topic comprehension, utilizing existing ASRS synopsis writing practices to more precisely define meaningfulness and usefulness. Results: A survey protocol was created that addresses the limitations of other survey protocols found in the literature review, which we found lacking in rationale and clear protocol definition. Conclusion: The surveying of user understanding in machine learning outputs presents challenges due to the explosion of parameters to control for and the lack of systematic approach presented in the literature. More reproducible work and survey protocols are needed in the literature and our work is one step towards that direction.

topic finding↗
NASA NTRSDate not supplied

Identifying Emerging Safety Threats Through Topic Modeling in the Aviation Safety Reporting System: A COVID-19 Study

The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.

ASRS↗
NASA NTRSDate not supplied

Requesting Antarctic Meteorite Samples for Research

The U.S. Antarctic meteorite program began in the 1970’s and has provided more than 24,000 samples. The program is based on a three agency agreement between NASA, the National Science Foundation, and the Smithsonian Institution. The collection, stored at the Johnson Space Center and the Smithsonian, is one of the largest collections of meteorites in the world and features samples from the moon, Mars, asteroids, and material from the early solar system. A brief overview of the collection shows it contains 92.2% ordinary chondrites (7205 H, 9126 L, 3890 LL, 146 enstatite, 30 R chondrites, 3.2% (973) carbonaceous chondrites, 3.7% (560) achondrites (1.7% HED), 118 irons, 27 pallasites, 41 mesosiderites, as well as many puzzling, ungrouped meteorites. JSC has sent splits of over 20,000 meteorite samples to more than 500 scientists around the world since 1977. After the meteorites are collected in Antarctica, they are shipped frozen to JSC in Houston, TX, arriving in April following the field season. The Astromaterials Curation Office at JSC is responsible for: - providing supplies and tools for the field team. - receiving the frozen meteorites. - staging: repackaging and changing the samples’ field identification numbers with official names. - submitting the names to the Nomenclature Committee of the Meteoritical Society for approval as new meteorites. - providing storage and handling of the meteorites in a class 10,000 clean room. - initial processing: weighing, measuring, describing, and photographing the sample and providing a chip for classification to the Smithsonian Institution staff. - the issuing of two newsletters per year, announcing hundreds of new meteorites. - the handling of requests from the scientific community and the allocation of those requests that are approved. - making petrographic thin and thick sections for the JSC library and scientific investigators. - maintaining the meteorite database with more than 76,000 sample splits.

C.E. Satterwhite↗
NASA NTRSDate not supplied

NASA’s Human Data Repositories: An In Depth Look at the New Data Request Process

As NASA transitions its focus to travel back to the moon and on to new destinations, the need to ensure the capture, analysis, and application of research and medical data is of greater urgency than at any other previous time. In this era of limited resources and challenging schedules, the Human Research Program (HRP), based at NASA’s Johnson Space Center (JSC), recognizes the need to extract the greatest possible amount of information from the data already captured. To this end, the HRP Chief Scientist Office (CSO), HRP Program Planning and Control (PP&C) Office, and the Space Medicine Operations Division have been working together to make reuse of both research data and medical monitoring data more accessible to the user community through the Life Science Data Archive (LSDA) and the Lifetime Surveillance of Astronaut Health (LSAH) Repositories. The task of both LSDA and LSAH repositories is to acquire, preserve, and distribute retrospective research (LSDA) and medical (LSAH) data and information both within the NASA community and to the science community at large, for knowledge discovery, retrospective analysis, and planning of future research studies. An additional goal is to encourage collaboration with non-NASA institutions also faced with enhancing human performance in extreme environments. In September 2022, the LSDA website and its contents transitioned to a new NASA Life Sciences Portal (https://nlsp.nasa.gov/explore/lsdahome). This site continues to feature publicly releasable information such as non-attributable datasets, experiment descriptions (from Project Mercury to ISS, as well as from multiple flight analog missions), descriptions of medical monitoring data, and LSAH newsletters (1992 - 2022). The website also provides an updated portal to request additional research and medical data not accessible from the public website. This presentation will provide an in-depth look at the new system as it relates to finding and requesting retrospective data. We will also detail processes from making a request to delivering data for different types of data requests (i.e., attributable, or non-attributable). This includes descriptions of various approval boards, what information and actions the requestor is responsible for, and key milestones in making data available for reuse.

D. M. Thomas↗
NASA NTRSDate not supplied

The Dominion Range (DOM) Lunar Regolith Breccia Pairing Group

With the Chang-E missions, ANGSA sample analysis, and Artemis mission progress as three examples of excitement about lunar science, we want to reaffirm and emphasize the importance of lunar meteorites to our understanding of the Moon. There are over 600 lunar meteorites documented with a total combined mass over 1000 kg, roughly 3x more mass than the Apollo samples. The 2018-19 season ANSMET team recovered lunar meteorites, reported in three different newsletters. Because these have been announced across three years, 2019-2022, we here provide an overview of their characteristics, reported findings, and comparison to other lunar meteorites. In particular, we emphasize their unique properties and how they may contribute to advancing lunar science. Eight pieces were recovered in the 2018-19 ANSMET season. All were found near the northern edge of the blue ice tongue or at the edge in the moraine. The largest mass is 45.87 g, ranging down to lowest mass of 5.46 g.

meteorite↗
NASA NTRSDate not supplied

A Community for Polar Early Career Researchers Engaging With the Interagency Arctic Research Policy Committee

The Interagency Arctic Research Policy Committee (IARPC) is tasked with implementation of the Arctic Research Plan (ARP) by coordinating representatives from federal and state agencies, academic, industry, and Indigenous partners with interests in Arctic research. IARPC is organized into several collaboration teams and discipline-specific communities of practice that interface on the IARPC Collaborations website. The Early Career Community of Practice (ECCoP) is one such community that was established to promote the research of early career researchers (ECR) and increase ECR engagement with the ARP, program managers, collaboration teams, and communities of practice. The ECCoP has grown to develop regular newsletters promoting recent ECR research publications ad sharing training, job, funding, and collaboration opportunities relevant to community members at all life stages that self-identify as ECRs. Within IARPC, the ECCoP continues to promote ECR participation in monthly community of practice meetings and webinars and to report on ECR efforts that contribute to deliverables of the ARP Biennial Implementation Plan. Recent activity of the ECCoP includes developing an annual survey of the ECR cohort on IARPC to follow change in demographics over time and to identify community needs to improve diversity and inclusion in the polar research space and to address gaps in training needs for ECRs.

Katy Smith↗
NASA NTRSDate not supplied

Kaona: Deep Searching and Curating Data from Aviation Safety Reporting Systems

Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given to augmenting existing processes for mining these safety reporting system databases. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety database mining activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using publicly available NASA’s ASRS narratives and another using publicly available C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.

ASRS↗
NASA NTRSDate not supplied

Kaona: Deep Searching and Curating Aviation Safety Reporting Systems

Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given in augmenting existing processes of safety reporting systems. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety reporting systems activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using all public NASA's ASRS narratives and another using all public C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.

asrs↗
NASA NTRSDate not supplied

Kaona: Deep Searching and Curating Safety Reporting Systems

Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given in augmenting existing processes of safety reporting systems. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety reporting systems activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using all public NASA's ASRS narratives and another using all public C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.

asrs↗
DOE OSTI2020

New Energy Planning Tools from NREL

This is a very brief description of the State and Local Planning for Energy (SLOPE) platform and State and Local Energy Profiles datasets to be included in the Tools issue of the Colorado chapter of the American Planning Association newsletter.

48 EE - Weatherization and Intergovernmental Progr↗
DOE OSTI2020

Small UAS radiological survey flights at the NNSS

News highlight intended for a new publication, the "LDRD Quarterly Highlights." The quarterly newsletter contains highlights from LDRD and SDRD programs and will be published on a LANL-hosted website. This story was previously featured on the NNSS.gov website

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Western Burrowing Owl Radio-tracking Study

Summary of western burrowing owl satellite-tracking study on the NNSS for the Fall issue of the Nevada Chapter of The Wildlife Society Newsletter.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

NNSS R&D 100 awards winner and finalist have roots in SDRD

Newsletter article to be included in the 1Q FY21 LDRD Quarterly Highlights online. Website: https://www.lanl.gov/projects/ldrd-tri-lab/quarterly-highlights.php. The text of this article was excerpted from inSite article "NNSS honored as R&D 100 winner and finalist," dated October 1, 2020.

99 GENERAL AND MISCELLANEOUS↗