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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
DOE OSTI2013

Transforming Epidemiology for 21st Century Medicine and Public Health

n 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving toward more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical, and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating big data science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy, and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology, in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.

Khoury, Muin J↗
DOE OSTI2025

Compilation of Experimental Yield Data for Spontaneous Fission of 252 Cf

We present a comprehensive compilation and curation of experimental fission yield (FY) data for the spontaneous fission of 252 Cf, extracted from the EXFOR database. The compilation follows a structured methodology developed for prior compilations of neutron-induced fission yields, and incorporates both independent (IFY) and cumulative (CFY) yields. A total of 62 datasets were reviewed, with entries spanning from 1955 to 2021. A significant portion of the literature reports pre-neutron emission yields, which were excluded from the present compilation due to limitations in format compatibility. Each accepted dataset was processed into a standardized JSON format, including metadata, uncertainties, and bibliographic references. Where available, decay radiation information was used to update the FY data using the latest ENSDF evaluations; 237 data points were corrected accordingly. These corrections are fully traceable and preserve original values. The result is a curated dataset suitable for use in nuclear data evaluations. This work is part of an ongoing effort to modernize the handling of FY data and provide evaluators with high-quality, machine-readable experimental inputs

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

DENOVA: Deduplication Extended NOVA File System

This paper shows mathematically and experimentally that inline deduplication is not suitable for file systems on ultra-low latency Intel Optane DC PM devices in terms of performance, and proposes DeNova, an offline deduplication specially designed for log-structured NVM file systems such as NOVA. DeNova offers high-performance and low-latency I/O processing and executes deduplication in the background without interfering with foreground I/Os. DeNova employs DRAM-free persistent deduplication metadata, favoring CPU cache line, and ensures failure consistency on any system failure. We implement DeNova in the NOVA file system. Evaluation with DeNova confirms a negligible performance drop of baseline NOVA of less than 1%, while gaining high storage space savings. Extensive experiments show DeNova is failure consistent in all failure scenario cases.

Khan, Awais↗
DOE OSTI2017

Citations and Readership are Poor Indicators of Research Excellence

In this paper we show that citation counts and Mendeley readership are poor indicators of research excellence. Our experimental design builds on the assumption that a good evaluation metric should be able to distinguish publications that have changed a research field from those that have not. The experiment has been conducted on a new dataset for bibliometric research which we call TrueImpactDataset. TrueImpactDataset is a collection of research publications of two types -- research papers which are considered seminal work in their area and papers which provide a survey (a literature review) of a research area. The dataset also contains related metadata, which include DOIs, titles, authors and abstracts. We describe how the dataset was built and provide overview statistics of the dataset. We propose to use the dataset for validating research evaluation metrics. By using this data, we show that widely used research metrics only poorly distinguish excellent research.

Herrmannova, Dasha↗
DOE OSTI2014

Experiences with making diffraction image data available: what metadata do we need to archive?

A local raw ‘diffraction data images’ archive was made available and some data sets were retrieved and reprocessed, which led to analysis of the anomalous difference densities of two partially occupied Cl atoms in cisplatin as well as a re-evaluation of the resolution cutoff in these diffraction data. General questions on storing raw data are discussed. It is also demonstrated that often one needs unambiguous prior knowledge to read the (binary) detector format and the setup of goniometer geometries. Recently, the IUCr (International Union of Crystallography) initiated the formation of a Diffraction Data Deposition Working Group with the aim of developing standards for the representation of raw diffraction data associated with the publication of structural papers. Archiving of raw data serves several goals: to improve the record of science, to verify the reproducibility and to allow detailed checks of scientific data, safeguarding against fraud and to allow reanalysis with future improved techniques. A means of studying this issue is to submit exemplar publications with associated raw data and metadata. In a recent study of the binding of cisplatin and carboplatin to histidine in lysozyme crystals under several conditions, the possible effects of the equipment and X-ray diffraction data-processing software on the occupancies and B factors of the bound Pt compounds were compared. Initially, 35.3 GB of data were transferred from Manchester to Utrecht to be processed with EVAL. A detailed description and discussion of the availability of metadata was published in a paper that was linked to a local raw data archive at Utrecht University and also mirrored at the TARDIS raw diffraction data archive in Australia. By making these raw diffraction data sets available with the article, it is possible for the diffraction community to make their own evaluation. This led to one of the authors of XDS (K. Diederichs) to re-integrate the data from crystals that supposedly solely contained bound carboplatin, resulting in the analysis of partially occupied chlorine anomalous electron densities near the Pt-binding sites and the use of several criteria to more carefully assess the diffraction resolution limit. General arguments for archiving raw data, the possibilities of doing so and the requirement of resources are discussed. The problems associated with a partially unknown experimental setup, which preferably should be available as metadata, is discussed. Current thoughts on data compression are summarized, which could be a solution especially for pixel-device data sets with fine slicing that may otherwise present an unmanageable amount of data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

The Imperial Valley Dark Fiber Project: Toward Seismic Studies Using DAS and Telecom Infrastructure for Geothermal Applications

We report that the Imperial Valley is a seismically active basin occupying the southern end of the Salton trough, an area of rapid extension, high heat flow, and abundant geothermal resources. This report describes an ongoing large-scale distributed acoustic sensing (DAS) recording study acquiring high-density seismic data on an array between Calipatria and Imperial, California. This 27 km array, operating on dark fiber since 9 November 2020, has recorded a wealth of local seismic events as well as ambient noise. The goal of the broader Imperial Valley Dark Fiber project is to evaluate passive DAS as a tool for geothermal exploration and monitoring. This report is intended to provide installation information, noise characteristics, and metadata for future studies utilizing the data set. Because of the relatively small number of basin-scale DAS studies that have been conducted to date, we also provide a range of lessons learned during the deployment to assist future researchers exploring this acquisition strategy.

15 GEOTHERMAL ENERGY↗
NASA NTRS2018

FAIRness and Usability for Open-access Omics Data Systems

Omics data sharing is crucial to the biological research community, and the last decade or two has seen a huge rise in collaborative analysis systems, databases, and knowledge bases for omics and other systems biology data. We assessed the "FAIRness" of NASA's GeneLab Data Systems (GLDS) along with four similar kinds of systems in the research omics data domain, using 14 FAIRness metrics. The range of overall FAIRness scores was 6-12 (out of 14), average 10.1, and standard deviation 2.4. The range of Pass ratings for the metrics was 29-79%, Partial Pass 0-21%, and Fail 7-50%. The systems we evaluated performed the best in the areas of data findability and accessibility, and worst in the area of data interoperability. Reusability of metadata, in particular, was frequently not well supported. We relate our experiences implementing semantic integration of omics data from some of the assessed systems for federated querying and retrieval functions, given their shortcomings in data interoperability. Finally, we propose two new principles that Big Data system developers, in particular, should consider for maximizing data accessibility.

Berrios, Daniel C.↗
NASA NTRS2018

FAIRness and Usability for Open-access Omics Data Systems

Omics data sharing is crucial to the biological research community, and the last decade or two has seen a huge rise in collaborative analysis systems, databases, and knowledge bases for omics and other systems biology data. We assessed the “FAIRness” of NASA’s GeneLab Data Systems (GLDS) along with four similar kinds of systems in the research omics data domain, using 14 FAIRness metrics. The range of overall FAIRness scores was 6-12 (out of 14), average 10.1, and standard deviation 2.4. The range of Pass ratings for the metrics was 29-79%, Partial Pass 0-21%, and Fail 7-50%. The systems we evaluated performed the best in the areas of data findability and accessibility, and worst in the area of data interoperability. Reusability of metadata, in particular, was frequently not well supported. We relate our experiences implementing semantic integration of omics data from some of the assessed systems for federated querying and retrieval functions, given their shortcomings in data interoperability. Finally, we propose two new principles that Big Data system developers, in particular, should consider for maximizing data accessibility.

Berrios, Daniel C.↗
DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Persistent Memory Object Storage and Indexing for Scientific Computing

This paper presents Mosiqs, a persistent memory object storage framework with metadata indexing and querying for scientific computing. We design Mosiqs based on the key idea that memory objects on shared PM pool can live beyond the application lifetime and can become the sharing currency for applications and scientists. Mosiqs provides an aggregate memory pool atop an array of persistent memory devices to store and access memory objects. Mosiqs uses a lightweight persistent memory key-value store to manage the metadata of memory objects such as persistent pointer mappings, which enables memory object sharing for effective scientific collaborations. Mosiqs is implemented atop PMDK. We evaluate the proposed approach on many-core server with an array of real PM devices. The preliminary evaluation confirms a 100% improvement for write and 30% in read performance against a PM-aware file system approach.

Khan, Awais↗
DOE OSTI2026

Livewire: A Model Platform for Data Quality Assessment and AI Readiness Across DOE Missions

High-quality, well-governed data is essential for accelerating discovery and achieving operational excellence across DOE and national laboratory missions. The Livewire Data Platform is a DOE-supported platform that offers automated assessments of data quality, standardization, provenance, and Artificial Intelligence (AI) readiness. It allows researchers and data practitioners to systematically and easily evaluate datasets against established governance criteria and prepare them for advanced analytics. Livewire addresses critical challenges in DOE's data ecosystem with integrated capabilities for metadata validation, provenance tracking, and schema alignment. This platform's automated workflows assist users in identifying data quality gaps, enhancing interoperability between datasets collected from various stakeholders, and ensuring compliance with DOE data standards, all while reducing manual curation efforts. Additionally, we will discuss its AI readiness framework, which is being developed to prepare datasets for training models, developing advanced analytic tools, and machine learning applications. Using some of the more than one hundred tabular datasets on Livewire, processed with this open-source methodology, we will demonstrate how Livewire can serve as a model for scalable, standards-driven data management. This approach provides a pathway to leverage existing and future datasets within the DOE, boosting innovation and efficiency across national laboratories.

33 - ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2016

Evolving a NASA Digital Object Identifiers System with Community Engagement

To demonstrate how the ESDIS (Earth Science Data and Information System) DOI (Digital Object Identifier) system and its processes have evolved over these years based on the recommendations provided by the user community (whether the community members create and manage DOI information or use DOIs in the data citations). The user community is comprised of people with common interests and needs for data identifiers who are actively involved in the creation and usage process. Engagement describes the interactive context wherein the community provides information, evaluates the proposed processes, and provides guidance in the area of identifiers.

Identifiers↗
NASA NTRSDate not supplied

TOLNet’s FAIR Journey: Yesterday, Today, and Tomorrow

The Tropospheric Ozone Lidar Network (TOLNet) has generated over a decade of ozone vertical profile data products over North America and contributed to several air quality focused field studies. The science value of the TOLNet data has been demonstrated in numerous peer-reviewed publications on air quality and ozone relevant research. As the broad scientific community has moved towards adopting FAIR Principles to make data more findable, accessible, interoperable, and (re)usable, the TOLNet team has been consistently making data more FAIR. This effort has many challenges, partially reflecting on the FAIR principles being domain agnostic while the implementation needs to be domain specific. The FAIR principles declare the dependence on the community standards, domain-relevant metadata, and rich metadata. This presentation uses the TOLNet data and data system as an example to explore the best practices to implement FAIR principle. Particularly, we will examine the metadata and the “richness” to support findability and usability as well as machine-to-machine actionability via API. Last year, as part of our FAIR journey, we launched the TOLNet website (https://tolnet.larc.nasa.gov/) and the API (https://tolnet.larc.nasa.gov/api/). Part of this process included extracting and cataloging metadata across the entire TOLNet mission timeframe. This enabled users to search through the mission by various metadata criteria, improving the findability and accessibility. And computers could connect directly to the TOLNet API to extract both metadata and data, providing a level of interoperability never present before for TOLNet data. On top of that, all new TOLNet data is now automatically validated using the API to ensure it complies with GEOMS standards, aiding in reusability. It takes both technology and scientists working together to make progress. The next step is to evaluate the current TOLNet offerings against NASA’s Practical Guide for Open, Free & FAIR NASA Earth Science Data Products (https://doi.org/10.5067/DOC/ESCO/ESDSWG-0002V1).

TOLNet↗
DOE OSTI2025

PPI DataHub Project Data Package: S. elongatus PCC 7942 Limited Proteolysis and Thermal Proteome Profiling Structural Proteomics (JM-PB-DP3)

The purpose of this experiment was to investigate structural alterations in proteins involved in central carbon metabolism and photosynthetic electron transfer pathways in Synechococcus elongatus PCC 7942. Sample data was obtained from S. elongatus cell lysates using three complementary mass spectrometry (MS) techniques using limited proteolysis (LiP-MS), thermal proteome profiling (TPP-MS), and redox enrichment (Redox-MS) in evaluating alterations solvent accessibility and structural stability caused by light perturbation at the molecular level. Experimentally processed sample data for LiP and TPP proteomic datasets were derived from the same cell culture stock, prepared simultaneously in parallel, and acquired by mass spectrometry. Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files, computed outputs, and supporting metadata materials. Experimental samples processed for LiP-MS label-free quantification (LFQ) or TPP-MS tandem mass tag (TMT) 10-plex were acquired using a Q-Exactive HF-X mass spectrometer and processed/compiled using either MSGF+ (v2024.03.26) or ​​​​PlexedPiper for proteome evaluation. Additional software supporting downstream proteomic analysis include FragPipe (v.4.0), MSFragger (v.22.1), and an adapted Microbial Isolate LiP Analysis Workflow (located at Zenodo). Processed proteomic data downloads include a sample naming key, normalized quantification results files, and processed protein annotated abundance files.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

With the increasing digitalization of processes throughout the lifecycle of buildings, data exchanged between stakeholders and between building systems has grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent, hindering the development of applications that can be reused across buildings and limiting the scalability of innovative solutions. Semantics refers to the description of the meaning of the data in a way that can be consistently understood by applications. Recently, several competing initiatives have been developing metadata schemas and ontologies to express this semantic information for different applications in the building domain. This paper systematically reviews these schemas and conducts an analysis of five of them to evaluate their applicability to three high-value use cases for building operations: energy audits, automated fault detection and diagnostics and optimal control. The survey finds 40 schemas published in the last 10 years but but their actual use in industry is difficult to estimate. Among the five selected ontologies, several gaps are highlighted in relation to the three use cases. Recommendations for the future include better harmonization of these initiatives, more centralized repositories and search engines for these schemas as well as better industry engagement to facilitate their adoption.

Smart Building, Sematic, Metadata, Ontology, Data ↗
NASA NTRS2004

User Evaluation of the NASA Technical Report Server Recommendation Service

We present the user evaluation of two recommendation server methodologies implemented for the NASA Technical Report Server (NTRS). One methodology for generating recommendations uses log analysis to identify co-retrieval events on full-text documents. For comparison, we used the Vector Space Model (VSM) as the second methodology. We calculated cosine similarities and used the top 10 most similar documents (based on metadata) as 'recommendations'. We then ran an experiment with NASA Langley Research Center (LaRC) staff members to gather their feedback on which method produced the most 'quality' recommendations. We found that in most cases VSM outperformed log analysis of co-retrievals. However, analyzing the data revealed the evaluations may have been structurally biased in favor of the VSM generated recommendations. We explore some possible methods for combining log analysis and VSM generated recommendations and suggest areas of future work.

Nelson, Michael L.↗
NASA NTRS2004

User Evaluation of the NASA Technical Report Server Recommendation Service

We present the user evaluation of two recommendation server methodologies implemented for the NASA Technical Report Server (NTRS). One methodology for generating recommendations uses log analysis to identify co-retrieval events on full-text documents. For comparison, we used the Vector Space Model (VSM) as the second methodology. We calculated cosine similarities and used the top 10 most similar documents (based on metadata) as recommendations . We then ran an experiment with NASA Langley Research Center (LaRC) staff members to gather their feedback on which method produced the most quality recommendations. We found that in most cases VSM outperformed log analysis of co-retrievals. However, analyzing the data revealed the evaluations may have been structurally biased in favor of the VSM generated recommendations. We explore some possible methods for combining log analysis and VSM generated recommendations and suggest areas of future work.

Nelson, Michael L.↗