Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1820765

Research Software Engineering Efforts for DataFlow: FY2021 Developments

McDonnell, Marshall·Malviya, Addi Thakur·Shutt, Gregory·Somnath, Suhas·Stansberry, Dale·Kuchar, Olga
View original source ↗Read source document ↗Export metadata ↓

Abstract

DataFlow is a web application that helps scientific data to flow from one source location to another destination location. DataFlow helps scientists easily capture scientific metadata associated with an experiment and transmit both metadata and experimental data to a designated, centralized data storage resource. This report describes the software engineering efforts and architecture of the project for the fiscal year 2021 developments. We hope it effectively communicates findings from our work, challenges we have overcome, and how we will continue our development of DataFlow in the future.

Explore related subjects

42 ENGINEERING97 MATHEMATICS AND COMPUTING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

McDonnell, Marshall, Malviya, Addi Thakur, Shutt, Gregory, Somnath, Suhas, Stansberry, Dale, Kuchar, Olga. 2021-09-01. Research Software Engineering Efforts for DataFlow: FY2021 Developments. https://doi.org/10.2172/1820765

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

KEEP EXPLORING

Related reports

DOE OSTI2026

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↗