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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MetaCat - metadata catalog for data management systems

Metadata management is one of three major areas and parts of functionality of scientific data management along with replica management and workflow management. Metadata is the information describing the data stored in a data item, a file or an object. It includes the data item provenance, recording conditions, format and other attributes. MetaCat is a metadata management database designed and developed for High Energy Physics experiments. As a component of a data management system, it’s main objectives are to provide efficient metadata storage and management and fast data items selection functionality. MetaCat is supposed to work on the scale of 100 million files (or objects) and beyond. The article will discuss the functionality of MetaCat and technological solutions used to implement the product.

Mandrichenko, Igor↗
DOE OSTI2023

Control systems and data management for high-power laser facilities

The next generation of high-power lasers enables repetition of experiments at orders of magnitude higher frequency than what was possible using the prior generation. Facilities requiring human intervention between laser repetitions need to adapt in order to keep pace with the new laser technology. A distributed networked control system can enable laboratory-wide automation and feedback control loops. These higher-repetition-rate experiments will create enormous quantities of data. A consistent approach to managing data can increase data accessibility, reduce repetitive data-software development and mitigate poorly organized metadata. An opportunity arises to share knowledge of improvements to control and data infrastructure currently being undertaken. We compare platforms and approaches to state-of-the-art control systems and data management at high-power laser facilities, and we illustrate these topics with case studies from our community

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Nuclear Data Management and Analysis System Plan

The United States Department of Energy Advanced Reactor Technologies Program was formed in Fiscal Year 2015 and encompasses the Next Generation Nuclear Plant Project and Very High Temperature Reactor (VHTR) Program as they were known previously. The VHTR Program was created to support design and licensing of the first VHTR nuclear plant. Data created for and used by the program must be qualified for use, stored in a readily accessible electronic form, categorized to assure the correct data are used, and controlled to prevent data corruption or inadvertent changes. The Nuclear Data Management and Analysis System was designed to support the data needs of the VHTR Program, at the time and now the Advanced Reactor Technologies Program. Since its inception, use of the Nuclear Data Management and Analysis System has expanded to support additional projects and programs with similar requirements for control, analysis, and availability of large data sets.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

High-performance data management for whole slide image analysis in digital pathology

When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we’ve developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario (“CPU scenario”), and (2) a GPU-based deep learning framework scenario (“GPU scenario”). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios.

Wang, Xiao↗
DOE OSTI2024

LSE-61: Rubin Observatory Data Management System (DMS) Requirements

This document contains the top-level requirements for the Data Management subsystem of the LSST, when combined with the Data Management-related requirements in the LSST System Requirements (LSR, LSE-29) and Observatory System Specifications (OSS, LSE-30).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Assessment of Data-Management Infrastructure Needs for Production Use of Advanced Machine Learning and Artificial Intelligence: Tri-Lab Level II Milestone (8554)

A robust data-management infrastructure is a key enabler for National Security Enterprise (NSE) capabilities in artificial intelligence and machine learning. This document describes efforts from a team of researchers at Sandia National Laboratories, Los Alamos National Laboratory, and Livermore National Laboratory to complete ASC Level II milestone #8854 “Assessment of Data-Management Infrastructure Needs for Production use of Advanced Machine learning and Artificial Intelligence.”

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

NDMAS Data Management Capabilities for Metallic Fuels

This poster summarizes the capabilities of the Nuclear Data Management and Analysis System and its applicability to the Metallic Fuels program in the Advanced Fuel Campaign.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

FY 2025 Multidimensional Data Correlation Platform: Unified Software Architecture for Advanced Materials and Manufacturing Technologies Data Management and Processing

The Advanced Materials and Manufacturing Technologies (AMMT) program continues to advance a data-driven approach to demonstrate the utility of additive manufacturing for fabricating components for nuclear applications. A key scientific goal is to leverage data to better understand manufacturing outcomes and thereby improve the performance, reliability, and lifespan of nuclear components. Ultimately, this effort supports the development of standards for certification and qualification of additively manufactured components, enabling broader industry adoption. In support of this objective, the AMMT program is building and deploying a data management platform to record, index, analyze, and make available the manufacturing data generated across the AMMT program. In FY 2023, the team conceptualized the architecture of the platform and, in FY 2024, deployed the first functional version at the Oak Ridge National Laboratory (ORNL) Manufacturing Demonstration Facility (MDF). In FY 2025, the platform was officially opened to all AMMT members. To enable this expansion, core modifications and enhancements were developed, including improvements to the user interface and workflows for data entry and retrieval. Most notably, robust security and access control mechanisms were implemented to protect data and manage information sharing. This effort featured a logging system, protected views, and controlled access mechanisms. This report documents these enhancements and the transition of the platform into program-wide use.

36 MATERIALS SCIENCE↗
DOE OSTI2020

BinaryCIF and CIFTools—Lightweight, efficient and extensible macromolecular data management

3D macromolecular structural data is growing ever more complex and plentiful in the wake of substantive advances in experimental and computational structure determination methods including macromolecular crystallography, cryo-electron microscopy, and integrative methods. Efficient means of working with 3D macromolecular structural data for archiving, analyses, and visualization are central to facilitating interoperability and reusability in compliance with the FAIR Principles. We address two challenges posed by growth in data size and complexity. First, data size is reduced by bespoke compression techniques. Second, complexity is managed through improved software tooling and fully leveraging available data dictionary schemas. To this end, we introduce BinaryCIF, a serialization of Crystallographic Information File (CIF) format files that maintains full compatibility to related data schemas, such as PDBx/mmCIF, while reducing file sizes by more than a factor of two versus gzip compressed CIF files. Moreover, for the largest structures, BinaryCIF provides even better compression—factor ten and four versus CIF files and gzipped CIF files, respectively. Herein, we describe CIFTools, a set of libraries in Java and TypeScript for generic and typed handling of CIF and BinaryCIF files. Together, BinaryCIF and CIFTools enable lightweight, efficient, and extensible handling of 3D macromolecular structural data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

PACT Data Management Plan (V.1.0)

The Perovskite PV Accelerator for Commercial Technology (PACT) is an independent validation center for the evaluation of perovskite PV technologies and their bankability. The center is led by Sandia National Laboratories and the National Renewable Energy Laboratory (NREL) and includes as part of its team Los Alamos National Laboratory (LANL), CFV Labs, Black and Veatch (B&V), and the Electric Power Research Institute (EPRI). The goals of the center are to: Develop and improve indoor and outdoor performance characterization methods, Develop and validate accelerated qualification testing for early failures (5-10 years), Research degradation and failure modes, Validate outdoor performance, and Provide bankability services to US perovskite PV (PSC) industry. The importance of data and data management to the success and outcomes of the PACT center is paramount. This report describes how data will be managed and protected by PACT and identifies important data management principles that will guide our approach.

14 SOLAR ENERGY↗
DOE OSTI2023

Advanced Long-Term Environmental Monitoring Systems (ALTEMIS) Artificial Intelligence Data Management Plan

Across the Department of Energy’s Environmental and Legacy Management sites, complex groundwater plumes exist that will require long-term monitoring to ensure remedial actions that have been put in place remain effective decades into the future. The current monitoring paradigm predominantly consists of groundwater well sampling, whereby samples are collected, concentrations analyzed, and plume anomalies are detected after they have occurred. The Advanced Long Term Environmental Monitoring Systems (ALTEMIS) program is a multi-lab, multi-institution team of researchers that is deploying spatially integrative technologies (i.e., real-time in situ sensor networks), coupled with artificial intelligence and machine learning, to establish a more proactive monitoring paradigm. Within this approach, plume anomalies can be predicted, and corrective actions can be established prior to the occurrence, offering a more cost-effective and robust approach to long-term monitoring. The team has deployed a variety of different in situ sensing technologies at the Savannah River Site’s F-Area Hazardous Waste Management Facility around the F-Area Seepage Basins, which are unlined basins that received 7 billion liters of acidic low-level radioactive waste from the 1950s until the late 1980s. The technologies and techniques that the team is deploying are intended to ensure that the remedial actions that have been taken by the site remain effective decades into the future. Foundational to this approach is a robust, integrated data management and analysis plan to ensure accurate and timely reporting from the variety of sensor systems that are in place. This report will outline the data management plan that has been implemented by the ALTEMIS team at the Savannah River Site and will serve as a blueprint as the technology is translated to new sites across the DOE Complex.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DMTN-065: Data Management for LSST Special Programs

Special Programs are additional survey areas and/or observing strategies that are driven by specific science goals which build on, or are beyond, the core science pillars of the Wide Fast Deep Main Survey. In order to meet the requirements and enable science related to Special Programs, this document provides recommendations for Rubin Data Management regarding processing and serving data products for Special Programs. Hardware and processing boundaries on the potential diversity of data from Special Programs are discussed along with scenarios in which user-generated processing and data products might be needed to meet Special Programs' science goals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research

Modern microbial and ecosystem sciences require diverse interdisciplinary teams that are often challenged in “speaking” to one another due to different languages and data product types. Here we introduce the IsoGenie Database, a de novo developed data management and exploration platform, as a solution to this challenge of accurately representing and integrating heterogenous environmental and microbial data across ecosystem scales. The IsoGenieDB is a public and private data infrastructure designed to store and query data generated by the IsoGenie Project, a ~10 year DOE-funded project focused on discovering ecosystem climate feedbacks in a thawing permafrost landscape. The IsoGenieDB provides (i) a platform for IsoGenie Project members to explore the project’s interdisciplinary datasets across scales through the inherent relationships among data entities, (ii) a framework to consolidate and harmonize the datasets needed by the team’s modelers, and (iii) a public venue that leverages the same spatially explicit, disciplinarily integrated data structure to share published datasets. The IsoGenieDB is also being expanded to cover the NASA-funded Archaea to Atmosphere (A2A) project, which scales the findings of IsoGenie to a broader suite of Arctic peatlands, via the umbrella A2A Database (A2A-DB). The IsoGenieDB’s expandability and flexible architecture allow it to serve as an example ecosystems database.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Supply Chain Risk Management: Data Structuring

Supply chain risk management (SCRM) is an area of research that addresses both logistics concepts to maximize efficiency, reliability, and revenue as well as risk features, such as potential weak points, break points, and vulnerabilities within the supply chain. SCRM is used to find risks introduced at each node in a supply chain and how these risks can impact a company’s products, individuals, customers, and reputation. SCRM is a relatively new field, so standardized processes including data structuring are not fully documented. This paper explains the importance of a standard data structuring methodology and how it can enhance current SCRM efforts. Data ingest, structuring, and analysis are predominantly managed by humans. Automating some of the less complex steps can positively impact SCRM by allowing human analysts to focus on more strategic analyses. Types of data to be collected and structured are collected via publicly available information related to hardware, software, and corporate entities. After the data has been collected, the information is formatted in a specific manner, conforming to a schema, to allow for more effective and efficient ingest for further analysis. This paper outlines data structures used by Pacific Northwest National Laboratory for SCRM research and analysis purposes. These structures have been used for hundreds of analyses and have been successful in developing a common baseline. Data structuring is one of the first steps in data standardization, which will further mature and enhance the SCRM research area.

supply chain risk management, data structuring, re↗
DOE OSTI2023

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. Here, in this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Advances in building data management for building performance standards using the SEED platform

Reducing energy consumption and greenhouse gas emissions in the built environment is a critical step in achieving emission goals to mitigate climate change impacts. Local, federal, and international jurisdictions are deploying several methods to reduce energy and emissions such as voluntary and mandatory benchmarking and building performance standards, requiring building owners to reach energy and emission targets. Jurisdictions leveraging benchmarking and building performance standards require knowledge of the buildings covered; which is a large task due to staffing constraints, limited information on building characteristics and tax parcel data, and the need for advanced data management techniques to align datasets. This paper describes an open-source platform's recent advances to create consistent taxonomies, identify erroneous data, enable auditability, and track building performance. The paper concludes with two use cases on how the platform has been used by jurisdictions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

DUNE Data Management: Network Visualization Monitoring Software

Fermilab’s fagship Deep Underground Neutrino Experiment (DUNE) seeks to better understand the nature of neutrinos within the context of Leptogenesis, neutrino oscillations, multi-messenger Astronomy, and other scientifc phenomena. The experiment will send a beam of neutrinos from the Fermilab site in Illinois to the Sanford Underground Neutrino Facility (SURF) in South Dakota, generating petabytes of scientifc data. Given the high volume of data expected when measurements begin at the end of the decade, DUNE computing and the data management group must carefully monitor data transfers across the 15 remote storage sites and, more generally, the 36 global DUNE computing sites. This report will describe both the frontend and backend data monitoring software designed to analyze and visualize these data transfers. Specifc emphasis will be placed on the software’s setup, usage, and methods for future implementations. The full software code can be found under the DUNE/data-mgmt-testing GitHub repository.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗