Search NASA⌕ Search

DOE OSTI · 1185058

CORE (Common Operating Response Environment) Software Technology Suite

Abstract

Agencies that oversee complex, multi-stakeholder programs need efficient, secure ways to link people and knowledge within and across organizations. The Common Operating Response Environment (CORE), a software suite developed by PNNL researchers does just that. The CORE tool—which is customizable for a multitude of uses—facilitates situational awareness by integrating diverse data streams without the need to reformat them, summarizing that information, and providing users with the information they need to rapidly understand and appropriately respond to situations. It is mobile device-ready, has a straightforward interface for ease of use across organizations and skill sets, and is incredibly configurable to the needs of each specific user, whether they require data summaries for high-level decision makers or tactical maps, operational data, or weather information for responders in the field. Information can be input into CORE and queried in a variety of ways—using customized forms, reports, visuals, or other organizational templates—according to the needs of each user’s organization, teams, and business processes. CORE data forms, for instance, could be accessed and used in real-time to capture information about vessels being inspected for nuclear material.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gelston, Gariann, Rohlfing, Kerrie. 2015-05-26. CORE (Common Operating Response Environment) Software Technology Suite. https://www.osti.gov/biblio/1185058

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

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗