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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

What if chondritic porous interplanetary dust particles are not the real McCoy

To select a target comet for a Comet Nucleus Sample Return Mission (CNSRM) it is necessary to have an experimental data base to evaluate the extent of diversity and similarity of comets. For example, the physical properties (e.g., low density) of chondritic porous (CP) interplanetary dust particles (IDPs) are believed to resemble these properties of cometary dust although it is yet to be demonstrated that the porous structure of CP IDPs is inherent to presolar dust particles stored in comet nuclei. Porous structures of IDPs could conceivably form during sublimation at the surface of active comet nuclei. Porous structures are also obtained during annealing of amorphous Mg-SiO smokes which initially forms porous aggregates of olivine + platey tridymite and which, upon continued annealing, react to fluffy enstatite aggregates. It is therefore uncertain that CP IDPs are entirely composed of unmetamorphosed presolar dust. Conceivably, new minerals and textures may form in situ in nuclei of active comets as a function of their individual thermal history. Unmetamorphosed comet dust is probably structurally amorphous. Thermal annealing of this dust can produce ultra fine-grained minerals and this ultrafine grain size of CP IDPs should be considered in assessments of aqueous alterations that could affect presolar dust in comet nuclei between 200 and 400 K. Devitrification and hydration may occur in situ in ice-dust mixtures and the mantle of active comet nuclei. Devitrification, or uncontrolled crystallization, of amorphous precursor dust can produce a range of chemical compositions of ultrafine-grained minerals and (non-equilibrium) mineral assemblages and textures in dust contained in comet nuclei as a function of period and trajectory of orbit and number of perihelion passages (not considering internal heating). Thus, experimental data on relevant processes and reaction rates between 200 and 400 K are needed in order to evaluate comet selection, penetration depth for sampling device and curation of samples for CNSRM.

Rietmeijer, Frans J. M.↗
DOE OSTI2024

Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

42 ENGINEERING↗
DOE OSTI2026

RTN-124: Photometric Redshifts for the Vera C. Rubin Observatory Data Preview

We present the photometric redshifts (photo-z) inferred using algorithms implemented in the Redshift Assessment Infrastructure Layers (RAIL) for the NSF-DOE Vera C. Rubin Observatory Data Preview 2 (DP2). We produce a compilation of reference redshift catalog using spectroscopic, grism and many band photometric redshift dataset hosted on the LIneA Photo-z Server. We curate training and testing set for assessing the scientific and technical performance of Rubin photo-z. The algorithm applied to the object catalog are FlexZBoost, BPZ, kNN, GPz, DNF and TPz; with a combination of 6-band and 4-band photo-z depending on availability of u and y photometry. The redshift point estimates and uncertainty estimation in tabular format through the Large Survey DataBase (LSDB).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Standardized naming of microbiome samples in Genomes OnLine Database

The power of next-generation sequencing has resulted in an explosive growth in the number of projects aiming to understand the metagenomic diversity of complex microbial environments. The interdisciplinary nature of this microbiome research community, along with the absence of reporting standards for microbiome data and samples, poses a significant challenge for follow-up studies. Commonly used names of metagenomes and metatranscriptomes in public databases currently lack the essential information necessary to accurately describe and classify the underlying samples, which makes a comparative analysis difficult to conduct and often results in misclassified sequences in data repositories. The Genomes OnLine Database (GOLD) (https:// gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute has been at the forefront of addressing this challenge by developing a standardized nomenclature system for naming microbiome samples. GOLD, currently in its twenty-fifth anniversary, continues to enrich the research community with hundreds of thousands of metagenomes and metatranscriptomes with well-curated and easy-to-understand names. Through this manuscript, we describe the overall naming process that can be easily adopted by researchers worldwide. Additionally, we propose the use of this naming system as a best practice for the scientific community to facilitate better interoperability and reusability of microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants ( k p ). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2026

RCSB protein data Bank: Next‐generation advanced search for exploration of experimental structures and computed structure models

Abstract The Protein Data Bank (PDB), established in 1971, is the primary global, open‐access archive for experimentally determined 3D macromolecular structures (proteins, RNA, DNA). The research‐focused RCSB.org web‐portal provides access to these data alongside more than one million machine‐learning‐predicted structure models, greatly expanding the available structural landscape. Rapid growth of both experimental and computational structures has increased the need for powerful yet accessible search tools that serve a broad and diverse scientific community. Herein, we describe a redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface. This interface integrates annotation‐, sequence‐, and 3D structure‐based searches, embeds an interactive 3D viewer, and incorporates curated biological knowledge, such as catalytic site definitions from Mechanism and Catalytic Site Atlas and ligand‐guided structural motifs, for constructing geometry‐driven queries. A new Chemical Search tool allows definition of chemical queries via an integrated drawing tool or standard identifiers, seamlessly combining them with annotation filters. By allowing query definition directly within spatial and chemical contexts, these search interfaces reduce the need for detailed knowledge of residue numbering, chain identifiers, or external cheminformatics software. This capability enables efficient exploration of structures, chemical diversity, and structure–function relationships across all life domains. The redesigned interfaces can be accessed directly at rcsb.org/search/advanced for Advanced Search and rcsb.org/search/chemical for Chemical Search.

Rose, Yana [Research Collaboratory for Structural ↗
DOE OSTI2021

COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. Although it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. Furthermore, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

The Science Discovery Engine: Connecting Heterogeneous Scientific Data and Information

Transformative science often occurs at the boundaries of different disciplines. Making interdisciplinary science data, software and documentation discoverable and accessible is essential to enabling transformative science. However, connecting this diverse and heterogeneous information is often a challenge due to several factors including the dispersed and sometimes isolated nature of data and the semantic differences between topical areas. NASA’s Science Discovery Engine (SDE) has developed several approaches to tackling these challenges. The SDE is a unified, insightful search experience that enables discovery of NASA’s open science data across five topical areas: astrophysics, biological and physical sciences, Earth science, heliophysics and planetary science. In this presentation, we will discuss our efforts to develop a systematic scientific curation workflow to integrate diverse content into a single search environment. We will also share lessons learned from our work to create a metadata crosswalk across the five disciplines.

Kaylin Bugbee↗
DOE OSTI2024

Underwater Target Detection Software Demonstration on the RivGen Turbine

This repository contains data and processing scripts necessary to train the object detection models utilized in the underwater target detection software demonstration on the RivGen turbine project and to produce performance metrics (precision, recall, mAP50, mAP50-95). - Contents - Data consist of "images" and "labels". Each image has an associated label, both share the same time string in its file name (e.g., 2024_05_25_09_01_57.98.jpg and 2024_05_25_09_01_57.98.txt). Time strings have the format %yyyy_%mm_%dd_%HH_%MM_%SS.%3f. Images and labels were curated from 2021 and 2024 smolt outmigration periods at the project site in Igiugig, AK. Images are monochrome 8-bit images of objects (smolt, debris, and other) passing through the field of view of the deployed cameras during various operational stages of the RivGen turbine. Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format. - Requirements - Python3.8+ is required to install and run the train and validation script. The README.md provides instruction for installing the requirements from the requirements.py file. - Instructions - The "example_train.py" file ingests the provided data, trains a model, and produces model performance metrics at completion. NOTE: model performance metrics will vary from run to run as a consequence of the random selection of training and validation data.

16 TIDAL AND WAVE POWER↗
NASA NTRS2016

Extending the Reach of IGSN Beyond Earth: Implementing IGSN Registration to Link Nasa's Apollo Lunar Samples and Their Data

The rock and soil samples returned from the Apollo missions from 1969-72 have supported 46 years of research leading to advances in our understanding of the formation and evolution of the inner Solar System. NASA has been engaged in several initiatives that aim to restore, digitize, and make available to the public existing published and unpublished research data for the Apollo samples. One of these initiatives is a collaboration with IEDA (Interdisciplinary Earth Data Alliance) to develop MoonDB, a lunar geochemical database modeled after PetDB (Petrological Database of the Ocean Floor). In support of this initiative, NASA has adopted the use of IGSN (International Geo Sample Number) to generate persistent, unique identifiers for lunar samples that scientists can use when publishing research data. To facilitate the IGSN registration of the original 2,200 samples and over 120,000 subdivided samples, NASA has developed an application that retrieves sample metadata from the Lunar Curation Database and uses the SESAR API to automate the generation of IGSNs and registration of samples into SESAR (System for Earth Sample Registration). This presentation will describe the work done by NASA to map existing sample metadata to the IGSN metadata and integrate the IGSN registration process into the sample curation workflow, the lessons learned from this effort, and how this work can be extended in the future to help deal with the registration of large numbers of samples.

Todd, Nancy S.↗
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 OSTI2024

Utilizing Ontology Structures To Curate the DOE-NETL Carbon Storage Open Database

The specialized ontology for the Carbon Storage Open Database will enable more rapid assignment of appropriate symbology standards for visualization improvements, optimize topical and spatial tagging within keywords, and improve flexibility for utilization in existing data repositories such as EDX. This effort also aims to establish a foundation for utilization of ontologies for organization of other data related to geologic carbon storage in the future.

Martin, Abigail↗
NASA NTRSDate not supplied

Analyzing Natural Language Context in Human-Machine Teaming using Supervised Machine Learning

Building a foundation for trustworthiness and trust verification in multi-asset teaming is the research challenge of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Design Reference Mission (DRM) for ATTRACTOR is a search and rescue mission objective governed by a multi-member team consisting of human and machine operators. A crucial component to the effort is the communication between humans and autonomous agents throughout both planning and execution stages of the mission. Intuitive communication methods and modalities are posited as critical enablers for certifying trust and trustworthiness. This paper reports on the data collection and analysis conducted in support of the Human Informed Natural-language GANs Evaluation (HINGE)project to attain explainable and trusted communication between human-machine assets. Two identically curated image description datasets were acquired for HINGE, both consisting of two unique input modalities (typed vs. verbal) and retrieved in two distinct contexts (general vs. specific). The gathered datasets were assessed and compared using Parts-of-Speech (POS)features, sentence similarity metrics, and linguistic analysis. Then, the datasets were modeled and tested separately and in combination with one another using machine learning algorithms. The comparison and testing results reveal a superior dataset, by which a preferred context and input is understood, for generating image representations of missing persons using a Generative Adversarial Network (GAN).

Bryan A Barrows↗
NASA NTRS2018

The Importance of Contamination Knowledge - Insights into Mars Sample Return

The Astromaterials Acquisition and Curation Office at NASA Johnson Space Center (JSC), in Houston, TX (henceforth Curation Office) manages the curation of all past, present, and future extraterrestrial samples returned by NASA missions and shared collections from international partners, preserving their integrity for future scientific study while providing the samples to the international community in a fair and unbiased way. The Curation Office also curates all reference and witness materials for each mission (e.g., flight and non-flight hardware coupons; lubricants; non-flight, flight-like, and flown witness plates). These reference and witness materials provide the scientific community with the fundamental ability to reconstruct the contamination/alteration history of the sample collection through the course of the mission, with the overall goal of strengthening the scientific conclusions drawn from the study of returned materials. The information gained from characterizing the physical, biological, inorganic, and organic chemical properties of reference and witness materials is defined as the Contamination Knowledge (CK) of the sample collection. Unlike the data collected for Contamination Control (CC) and Planetary Protection (PP), CK is exclusively concerned with preserving reference and witness materials for study by future scientists upon sample return. Although CC and PP data collected for sample integrity and forward contamination purposes can be complementary to CK, they are two separate data sets with distinct objectives. A robust collection of samples for CK is necessary to allow the extraterrestrial material in a returned sample to be distinguished from terrestrial contamination. Traditionally CK is utilized by sample scientists in order to accomplish the mission’s scientific objectives, however this information can also be utilized by the Office of Planetary Protection to help evaluate the presence of any back contamination. Mars 2020, the first phase of a potential multipart Mars Sample Return (MSR) campaign, is expected to contribute to NASA’s Mars Exploration Program Science Goals by filling in knowledge gaps concerning: 1) the existence of past or present life on Mars, 2) the past and present climate of Mars, 3) the geology of Mars, and 4) hazards associated with human exploration of Mars. Although there is debate concerning which samples will best answer these questions, the necessity for proper sample blanks is well-understood. The CC and PP requirements, driven by the restricted Class V mission designation, are the most stringent of any sample return mission in recent history. The extremely low levels of allowable terrestrial contamination on the spacecraft and rover can complicate these analyses given the detection limits of current analytical instrumentation, especially in the case of biological contamination. By collecting and curating unanalyzed samples specifically for CK, future sample scientists will not be relegated to: 1) relying on data collected using possibly obsolete tools and techniques for return sample blanks, or 2) using remnants of extracted and/or cultured samples from ATLO (Assembly, Test, and Launch Operations), which could be incompatible with the desired experimental endpoints or state-of-the-art techniques available at the time of sample return.The addition of biological experimental endpoints to a sample return campaign’s objectives broadens the requisite range in preservation environments (e.g. inert ultra-pure nitrogen gaseous environment at 18 degrees Centigrade versus less than or equal to minus 80 degrees Centigrade) and types of CK samples. As a result, the Curation Office will also curate the following CK samples at less than or equal to minus 80 degrees Centigrade for the Mars 2020 mission: 1) unanalyzed swabs and wipes in sterile containers, 2) all recirculation filters from the clean rooms used for sample and caching subsystem assembly and all filters from the laminar flow benches used to assemble sample intimate hardware, and 3) witness plates collecting airborne contamination within the assembly clean rooms. It has been Curation Office policy since the Apollo missions to preserve as many pristine samples as possible for future scientific research. Although CK is required to be collected for all stages of the MSR campaign, the CK for the Mars 2020 mission is the most critical for understanding contamination in the returned samples given the intimacy between the Martian samples and the Mars 2020 flight hardware. This presentation highlights the importance of CK for sample return missions as well as the traditional and novel types of CK samples required for a successful MSR campaign.

Harrington, A. D.↗
NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗