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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

A Post-2013 Dropoff in Total Ozone at a Third of Global Ozonesonde Stations: Electrochemical Concentration Cell Instrument Artifacts?

An international effort to improve ozonesonde data quality and to reevaluate historical recordshas made significant improvements in the accuracy of global network data. However, between 2014 and2016, ozonesonde total column ozone (TCO; O3) at 14 of 37 regularly reporting stations exhibited a suddendropoff relative to satellite measurements. The ozonesonde TCO drop is 3–7% compared to satellite andground‐based TCO, and 5–10% or more compared to satellite stratospheric O3profiles, compromising theuse of recent data for trends, although they remain reliable for other uses. Hardware changes in theozonesonde instrument are likely a major factor in the O3dropoff, but no single property of the ozonesondeexplains thefindings. The bias remains in recent data. Research to understand the dropoff is in progress; thisletter is intended as a caution to users of the data. Ourfindings underscore the importance of regularozonesonde data evaluation.

ozonesonde TCO drop↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗
NASA NTRS2015

Directed Research in Bone Discipline: Refining Previous Research Observations for Space Medicine

Dual-energy X-ray absorptiometry bone mass density, as a sole index, is an insufficient surrogate for fracture; Clinical Practice Guidelines using bone mass density (both World Health Organization and FRAX) are not specific for complicated subjects such as young, healthy persons following prolonged exposure to skeletal unloading (i.e. an attribute of spaceflight); Research data suggest that spaceflight induces changes to astronaut bones that could be profound, possibly irreversible and unlike age-related bone loss on Earth.; There is a need to objectively assess factors across human physiology that are also influenced by spaceflight (e.g., muscle) that contribute to fracture risk. Some of these objective assessments may require innovative technologies, analyses and modeling.; Astronauts are also exposed to novel situations that may overload their bones highlighting a need integrate biomechanics of physical activities into risk assessments.; As we accumulate data, which reflects the biomechanical competence of bone under specific mechanically-loaded scenarios (even activities of daily living), BONE expects Bone Fracture Module to be more sensitive and/or have less uncertainty in its assessments of fracture probability.; Fracture probability drives the requirement for countermeasures. Level of evidence will unlikely be obtained; hence, the Bone Research and Clinical Advisory Panel (like a Data Safety Monitoring Board) will provide the recommendations.

Sibonga, Jean D.↗
DOE OSTI2025

2025 TEM Workshop

The TEM Data Management Workshop will take place on August 26 from 9 a.m. to 12 p.m. MT, and will be held virtually on TEAMS. The primary goal of this workshop is to engage NSUF users and stakeholders in discussions about the data needs for the utilization of AI and ML in the analysis of TEM data. Key topics to be covered include data storage, data sharing, data tagging, metadata inclusion, standardized data formats, data augmentation, and annotated training datasets. Additionally, the workshop will provide valuable insights into resources such as the Nuclear Research Data System (NRDS) for data storage and sharing, as well as open-source codes for data analysis.

Bachhav, Mukesh↗
NASA NTRS1986

Program status

The Pilot Climate Data System (PCDS) was designed to support a variety of users that have been arbitrarily categorized into four groups: researchers, data producers, occasional users, and management. The expanding capabilities of the system are attracting the attention of both academic and other scientific institutions worldwide. Highlighted by progress in networking capabilities, hardware acquisitions, software developments, data set additions, and tutorial developments, exciting advances have taken place since the First PCDS Workshop. In the plans for the 1986 fiscal year, recommendations from an ad hoc users' group meeting in May 1985 and from the First PCDS workshop are apparent. This year's plans are listed, along with comments made at the users' group meeting. Although the PCDS is presently considered to be in a developmental phase, plans for making the transition to an operational phase are being implemented.

Smith, P.↗
NASA NTRS2014

Identification of Medical Training Methods for Exploration Missions

As the National Aeronautics and Space Administration (NASA) and its international partner agencies anticipate eventual exploration missions of longer duration, there is a need to plan for the medical capabilities necessary to maximize crew health and provide the best likelihood of mission success. Current spaceflights consist of 5- to 6-month excursions to the International Space Station (ISS) in low-Earth orbit (LEO), and a 12-month ISS mission is currently in planning stages. However, missions to a near-Earth asteroid (NEA), a return to the moon, or even a mission to Mars will demand unprecedented medical capabilities, particularly relating to the training of the crew medical officers (CMOs). In its attempts to address the questions about medical preparation for spaceflight beyond LEO, the Exploration Medical Capability (ExMC) element within NASA's Human Research Program (HRP) defines a series of gaps. These gaps are shortcomings in knowledge, training, or technology that require resolution before an exploration mission can be undertaken. The ExMC element maintains current information about measures to close these gaps while developing plans for further investigation and research. Data pertaining to the gaps and their present status are available to the general public on the NASA Human Research Wiki and the NASA Human Research Roadmap Web sites (34, 36). One such gap, Gap 3.01, identifies a lack of knowledge about the optimal training methods for in-flight medical conditions identified on the Exploration Medical Condition List (EMCL), taking into account the crew medical officer s (CMOs) clinical background (33). This broad statement encompasses several related issues with the current methods of training CMOs and the medical ground support staff, specifically flight surgeons and biomedical engineers (BMEs) located in mission control, in addition to questions pertaining to the ways in which training will need to be adapted for the medical contingencies unique to exploration missions. To determine the optimal methods of medical training for an exploration medical crew and their ground support team, the historical context of medical operations, the current CMO training methods, and potential alternative training methods were identified.

Long duration space flight↗
NASA NTRS2015

Can We Trust Computational Modeling for Medical Applications?

Operations in extreme environments such as spaceflight pose human health risks that are currently not well understood and potentially unanticipated. In addition, there are limited clinical and research data to inform development and implementation of therapeutics for these unique health risks. In this light, NASA's Human Research Program (HRP) is leveraging biomedical computational models and simulations (M&S) to help inform, predict, assess and mitigate spaceflight health and performance risks, and enhance countermeasure development. To ensure that these M&S can be applied with confidence to the space environment, it is imperative to incorporate a rigorous verification, validation and credibility assessment (VV&C) processes to ensure that the computational tools are sufficiently reliable to answer questions within their intended use domain. In this presentation, we will discuss how NASA's Integrated Medical Model (IMM) and Digital Astronaut Project (DAP) have successfully adapted NASA's Standard for Models and Simulations, NASA-STD-7009 (7009) to achieve this goal. These VV&C methods are also being leveraged by organization such as the Food and Drug Administration (FDA), National Institute of Health (NIH) and the American Society of Mechanical Engineers (ASME) to establish new M&S VV&C standards and guidelines for healthcare applications. Similarly, we hope to provide some insight to the greater aerospace medicine community on how to develop and implement M&S with sufficient confidence to augment medical research and operations.

Mulugeta, Lealem↗
NASA NTRS2015

Data Recipes: Toward Creating How-To Knowledge Base for Earth Science Data

Both the diversity and volume of Earth science data from satellites and numerical models are growing dramatically, due to an increasing population of measured physical parameters, and also an increasing variety of spatial and temporal resolutions for many data products. To further complicate matters, Earth science data delivered to data archive centers are commonly found in different formats and structures. NASA data centers, managed by the Earth Observing System Data and Information System (EOSDIS), have developed a rich and diverse set of data services and tools with features intended to simplify finding, downloading, and working with these data. Although most data services and tools have user guides, many users still experience difficulties with accessing or reading data due to varying levels of familiarity with data services, tools, and or formats. The data recipe project at Goddard Earth Science Data and Information Services Center (GES DISC) was initiated in late 2012 for enhancing user support. A data recipe is a How-To online explanatory document, with step-by-step instructions and examples of accessing and working with real data (http:disc.sci.gsfc.nasa.govrecipes). The current suite of recipes has been found to be very helpful, especially to first-time-users of particular data services, tools, or data products. Online traffic to the data recipe pages is significant, even though the data recipe topics are still limited. An Earth Science Data System Working Group (ESDSWG) for data recipes was established in the spring of 2014, aimed to initiate an EOSDIS-wide campaign for leveraging the distributed knowledge within EOSDIS and its user communities regarding their respective services and tools. The ESDSWG data recipe group is working on an inventory and analysis of existing data recipes and tutorials, and will provide guidelines and recommendation for writing and grouping data recipes, and for cross linking recipes to data products. This presentation gives an overview of the data recipe activites at GES DISC and ESDSWG. We are seeking requirements and input from a broader data user community to establish a strong knowledge base for Earth science data research and application implementations.

data recipe↗
DOE OSTI2026

Adaptable Standards for Discovery, Access, and Usability of Oak Ridge National Laboratory’s Data Portals and Catalogs

Oak Ridge National Laboratory (ORNL) is leveraging its established capabilities and subject matter expertise in data curation, governance, management, national security, and risk assessment and mitigation to support the US Department of Energy (DOE) Grid Modernization Initiative. Using standards modeled by the National Institute of Standards and Technology (NIST), the Data Curation Network (DCN), the Oak Ridge Leadership Computing Facility (OLCF), and other leading organizations in the fields of energy research, high-performance computing, and national and homeland security, ORNL seeks to provide a federated approach to research data discovery, use, and interoperability.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1989

NASA's climate data system primer, version 1.2

This is a beginner's manual for NASA's Climate Data System (NCDS), an interactive scientific information management system that allows one to locate, access, manipulate, and display climate-research data. Additional information on the use of the system is available from the system itself.

Closs, James W.↗
NASA NTRS2011

Towards a Standard for Provenance and Context for Preservation of Data for Earth System Science

Long-term data sets with data from many missions are needed to study trends and validate model results that are typical in Earth System Science research. Data and derived products originate from multiple missions (spaceborne, airborne and/or in situ) and from multiple organizations. During the missions as well as well past their termination, it is essential to preserve the data and products to support future studies. Key aspects of preservation are: preserving bits and ensuring data are uncorrupted, preserving understandability with appropriate documentation, and preserving reproducibility of science with appropriate documentation and other artifacts. Computer technology provides adequate standards to ensure that, with proper engineering, bits are preserved as hardware evolves. However, to ensure understandability and reproducibility, it is essential to plan ahead to preserve all the relevant data and information. There are currently no standards to identify the content that needs to be preserved, leading to non-uniformity in content and users not being sure of whether preserved content is comprehensive. Each project, program or agency can specify the items to be preserved as a part of its data management requirements. However, broader community consensus that cuts across organizational or national boundaries would be needed to ensure comprehensiveness, uniformity and long-term utility of archived data. The Federation of Earth Science Information Partners (ESIP), a diverse network of scientists, data stewards and technology developers, has a forum for ESIP members to collaborate on data preservation issues. During early 2011, members discussed the importance of developing a Provenance and Context Content Standard (PCCS) and developed an initial list of content items. This list is based on the outcome of a NASA and NOAA meeting held in 1998 under the auspices of the USGCRP, documentation requirements from NOAA and our experience with some of the NASA Earth science missions. The items are categorized into the following 8 high level categories: Preflight/Pre-Operations, Products (Data), Product Documentation, Mission Calibration, Product Software, Algorithm Input, Validation, Software Tools.

Ramaprian, Hampapuram K.↗
DOE OSTI2026

Public Data Set: Impurity Dynamics and Radiative Losses During Local Helicity Injection Startup in the Pegasus-III Spherical Tokamak

This public dataset contains openly-documented, machine readable digital research data corresponding to figures published in C. Rodriguez Sanchez et al., “ Impurity Dynamics and Radiative Losses During Local Helicity Injection Startup in the Pegasus-III Spherical Tokamak,” accepted for publication in Physics of Plasmas .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

HRP Data Management Plan

The purpose of Human Research Program Data Management Plan (DMP) is to define the processes and activities required for the overall management of the research data collected and managed by HRP throughout their life cycle. New updates to the Data Management Plan in 2023 include 1. CAPABILITIES AND SERVICES Data Repositories. Principal Investigators (PIs) funded by HRP may be asked to submit data to one of several NASA data repositories. HRP archives data in the NASA Life Sciences Portal (NLSP) that it considers to be unique and high value. This includes data from human subjects in space flight (ISS and commercial flights) and ground analogs to spaceflight; spaceflight tech demos involving humans; human omics data including the microbiome; parabolic flight studies; and the NASA Space Radiation Laboratory (NSRL). The Open Science Data Repository (OSDR) includes The Ames Life Sciences Data Archive (ALSDA), used to archive non-human biological data (e.g., animal) generated by the Human Research program, and GeneLab, available to HRP PIs to archive non-human omics data. Catalog for search and retrieval. A catalog of non-human HRP life science experiments, with all associated descriptions (mission, payload, hardware, and personnel related information), and biospecimens is provided on the NLSP public web site for search and retrieval. 2. IRB ROLE IN RETURN OF INDIVIDUAL RESEARCH RESULTS The NASA IRB manages the process for incidental findings and for returning results to subjects for studies for which NASA IRB is the IRB of record. Omics data, especially genomics data, may generate information significant to the health of or risk to a research subject. These data potentially hold the keys to understand lifetime risks of chronic diseases, such as cancer, as well as risks associated with exposures common in space flight. 3. UPDATE OF TERMS – IDENTIFIABLE AND ATTRIBUTABLE DATA HRP now follows Federal and NASA policy by using “identifiable” instead of “attributable” for Personally Identifiable Information (PII). 4. POLICY ABOUT INTERNAL NON-RESEARCH USE OF DATA The HRP Chief Scientist grants access to data from HRP-funded research for non-research internal use that includes program management, customer facilitation, strategic planning, and risk research planning. Typical HRP personnel granted access to HRP research data for internal use include the Element Scientist, Subject Matter Experts (SME), and Data/bioinformatics Scientists. If data accessed for Internal Use is provided to an intramural or extramural scientist for hypothesis driven research, all Federal and NASA regulations (e.g., IRB review) regarding human subject research apply.

Data Management Plan↗
NASA NTRS1986

Crash response data system for the controlled impact demonstration (CID) of a full scale transport aircraft

NASA Langley's Crash Response Data System (CRDS) which is designed to acquire aircraft structural and anthropomorphic dummy responses during the full-scale transport CID test is described. Included in the discussion are the system design approach, details on key instrumentation subsystems and operations, overall instrumentation crash performance, and data recovery results. Two autonomous high-environment digital flight instrumentation systems, DAS 1 and DAS 2, were employed to obtain research data from various strain gage, accelerometer, and tensiometric sensors installed in the B-720 test aircraft. The CRDS successfully acquired 343 out of 352 measurements of dynamic crash data.

Calloway, Raymond S.↗
NASA NTRS1993

Methods and means used in programming intelligent searches of technical documents

In order to meet the data research requirements of the Safety, Reliability & Quality Assurance activities at Kennedy Space Center (KSC), a new computer search method for technical data documents was developed. By their very nature, technical documents are partially encrypted because of the author's use of acronyms, abbreviations, and shortcut notations. This problem of computerized searching is compounded at KSC by the volume of documentation that is produced during normal Space Shuttle operations. The Centralized Document Database (CDD) is designed to solve this problem. It provides a common interface to an unlimited number of files of various sizes, with the capability to perform any diversified types and levels of data searches. The heart of the CDD is the nature and capability of its search algorithms. The most complex form of search that the program uses is with the use of a domain-specific database of acronyms, abbreviations, synonyms, and word frequency tables. This database, along with basic sentence parsing, is used to convert a request for information into a relational network. This network is used as a filter on the original document file to determine the most likely locations for the data requested. This type of search will locate information that traditional techniques, (i.e., Boolean structured key-word searching), would not find.

Gross, David L.↗