Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 3366083

Metadata Standards for the NSE: Extended Field Standards

Offtermatt, Mark James [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000013230903)·Gregonis, Kathryn Anne [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000250458146)·Mathieu, Camille [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0000000170306329)·Brunner, Katharine [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0009000191712200)·McGovern, Sean [Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)] (ORCID:0009000495768262)·McGuiness, Laura [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0009000252322557)·Miller, Julie Ann [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000855651237)·Wilson, Sonia [Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)] (ORCID:0009000578469823)
View original source ↗Read source document ↗Export metadata ↓

Abstract

This standard presents a set of optional metadata fields for managed digital objects within the Nuclear Security Enterprise (NSE) and provides a deeper look at data representation in metadata by looking at the representation of 1) Records Management required metadata, and 2) common representations of technical/scientific data. Metadata standardization is a critical enabler for effectively sharing data, documents, and other digital objects between NSE sites, and for tracing the digital thread at the object level. Standardization is necessary for both schemas and vocabularies, meaning that both field standards and value standards must be specified. This document serves as a complementary field standard, recommending an optional set of fields that should be uniformly built for all managed digital objects within the NSE. This document specifically focuses on extending the shared discovery layer defined in the first white paper by introducing additional descriptive and data representation fields that improve cross-site search and interpretation.

Explore related subjects

96 KNOWLEDGE MANAGEMENT AND PRESERVATIONarchivescontrolled vocabulariesdisciplinary metadataDublin Core metadatametadata

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Offtermatt, Mark James [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000013230903), Gregonis, Kathryn Anne [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000250458146), Mathieu, Camille [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0000000170306329), Brunner, Katharine [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0009000191712200), McGovern, Sean [Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)] (ORCID:0009000495768262), McGuiness, Laura [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0009000252322557), Miller, Julie Ann [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000855651237), Wilson, Sonia [Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)] (ORCID:0009000578469823). 2026-05-29. Metadata Standards for the NSE: Extended Field Standards. https://doi.org/10.2172/3366083

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Ontologies for Intelligent Data Science

As anyone even vaguely aware of current technology can tell you, machine learning (ML) and artificial intelligence (AI) have made exceptional breakthroughs in recent years. Generative artificial intelligence (GAI) emerged circa 2022 dominated by Large Language Models (LLMs) and generative tools for images emerged at about the same time.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Data Cards for Standardized Metadata Across DOE-Aligned Data Initiatives: Toward Transparent, Interoperable, and Governed Dataset Documentation

As data-intensive research, advanced computing, and artificial intelligence become increasingly central to scientific and operational workflows, the need for consistent, transparent, and machine-actionable documentation has grown correspondingly. Multiple DOE-aligned communities—including Office of Science, Genesis Mission, American Science Cloud (AmSC), National Nuclear Security Administration (NNSA) stewardship and governance, and related cross-laboratory collaborations—have independently developed metadata practices to support discovery, access, reuse, repository deposit, and compliance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Verification and Validation of Performance with Dissemination of Best Practices in District Energy and CHP for Enhanced Resiliency, Energy Efficiency, and Cybersecurity

This report contains the results of the International District Energy Association’s work to analyze, validate, and verify performance data of existing district energy systems and identify industy best practices for the purpose of improving system reliability, resiliency, and efficiency, and to accellerate decarbonization. In addition to a technical evaluation of the surveyed systems and identification of a series of technical performance metrics, the report illustrates the accompanying operations and financial best practices employed by surveyed systems to fully serve their customer base. Additionally, the third chapter of the report describes the current landscape of cybersecurity threats and counteracting measures, and recommends a series of steps for effectively guarding highly networked district energy systems against cybersecurity attacks.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗