Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “science data management”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Seismology in the Cloud: A New Streaming Workflow

Data-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. Here, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.

58 GEOSCIENCES↗
DOE OSTI2021

DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters

Ensemble-based Data Assimilation (EDA), based on the Monte Carlo approach, has been effectively applied to estimate model parameters through inverse modeling in subsurface flow and transport problems. However, implementation of EDA approach involves a complicated workflow that include setting up and executing ensemble forward model simulations, processing observations and model simulation results for parameter updates, and repeat for sequential or iterative EDA. To facilitate the management of such workflow and lower the barriers for adopting EDA-based parameter estimation in subsurface science, we develop a generic software frame-work linking the Data Assimilation Research Testbed (DART) with a massively parallel subsurface FLOw and TRANsport code PFLOTRAN. The new DART-PFLOTRAN leverages both the core data assimilation engines in DART and the computational power afforded by PFLOTRAN. In addition to the standard smoother and filtering options, DART-PFLOTRAN enables an iterative EDA workflow based on the Ensemble Smoother for Multiple Data Assimilation method (ES-MDA) to improve estimation accuracy for nonlinear forward problems. Here, we verify the implementation of ES-MDA in DART-PFLOTRAN using two synthetic cases designed to estimate static permeability and dynamic exchange fluxes across the riverbed, respectively, from continuous temperature measurements made across a depth profile. One-dimensional hydro-thermal simulations are performed in both cases to relate temperature responses with the parameters of interest. In the case of estimating dynamic parameters, we demonstrate the flexibility of DART-PFLOTRAN in automating sequential ES-MDA workflow, which will significantly reduce the time researchers spend on managing complex workflows in similar applications. Both studies yield accurate estimations of the parameters compared to their synthetic truth, while ES-MDA leads to more accurate estimation when a high level of nonlinearity exist between observed responses and unknown parameters. With a code base in Python and Fortran, DART-PFLOTRAN paves the way for applications in large-scale subsurface inverse modeling by automating the complex workflow of sequential ES-MDA that can be executed on various computing platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Roadmap on data-centric materials science

Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

36 MATERIALS SCIENCE↗
DOE OSTI2026

FAIRmaterials: Ontology Tools with Data FAIRification in Development

The bilingual FAIRmaterials package simplifies the creation and visualization of materials and data science ontologies. FAIRmaterials, available in the Python and R languages, addresses the complexities associated with traditional ontology editors based on manual user input such as Protege with an intuitive workflow and easy-to-use templates, making it accessible to users both experienced and inexperienced with ontologies. The FAIRmaterials package is its ability to programatically convert simple and structured CSV inputs into rich, well-defined ontologies. This capability is designed to support the findability, accessibility, interoperability, and reusability (FAIR) of research data and serve as a tool in the process of data FAIRification. Its additional features, such as automated ontology merging, static visualizations, and comprehensive documentation for outputs extend its utility, making it a valuable tool for any researcher engaged in knowledge management.

Bradley, Alexander Harding [Case Western Reserve U↗
DOE OSTI2020

Open Source Scalable Data Services and Data Fusion for Biological and Environmental Sciences (SBIR Phase I Final Scientific/ Technical Report)

The overarching goal of the project is to develop an integrated open-source scientific data management system (Apache V2 license), ResonantEco, that meets the need of biological and environmental researchers and developers for data management, curation, and data processing for analyses with a wide range of scale and complexity. ResonantEco will provide web enabled data services with features such as unified data interfaces and federated views of data and metadata for heterogeneous data sources with an interactive web client for data exploration. Our use of the term fusion is taken from geospatial (GIS) domain where data fusion is often synonymous with data integration. In particular, data integration in ResonantEco involves combining data residing in different sources and providing users with a unified view of them.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Untargeted, tandem mass spectrometry (LC/MS-MS) metaproteomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization. This package contains soil metaproteomics data in the context of site specific metagenomes from soil depth profiles in three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. These metaproteomes were collected in 2018 after 4.5 years of warming from five depth intervals (0-10 cm, 10-30 cm, 30-45 cm, 45-60 cm, 60-80 cm). For protein identification, the collected spectra were searched following a target-decoy search strategy against a database of metagenome predicted proteins (covering 96 samples from 2014 to 2021) representing the complete sequence diversity at the site. Data was searched with mass spectrometry database search tool (MS-GF+) using Pacific Northwest National Laboratory (PNNL)'s Data Management System (DMS) Processing pipeline. The metagenomes are published as part of another data package. Raw metaproteomic data and the data products from MS-GF+ are deposited in the Mass Spectrometry Interactive Virtual Environment (MassIVE) database under accession no. MSV000097826. Here we present a dataset that includes spectral counts for the detected proteins across samples (EMSL50964_BrodieAllMAGs_Globals_SC.txt), the sequences of the detected proteins, and sample metadata file that contains site information for the soil metaproteome samples.

Belowground Biogeochemistry Science Focus Area↗
DOE OSTI2023

High Energy Physics Network Requirements Review: Two-Year Update

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet interconnects DOE national laboratories, user facilities, and major experiments so that scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large datasets, and access distributed data repositories. ESnet is specifically built to provide a range of network services tailored to meet the unique requirements of the DOE’s data-intensive science. In July 2023, the Energy Sciences Network (ESnet) and the High Energy Physics program (HEP) of the DOE SC organized an interim ESnet requirements review of HEP-supported activities, to follow up on the work started during the 2020 HEP Network Requirements Review. Preparation for these events included checking back with the key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare updates to their previously submitted case study documents, so that ESnet could update the understanding of any changes to the current, near-term, and long-term status, expectations, and processes that will support the science activities of the program.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

South Dakota Region Scientific Deep Dive

EPOC uses the Deep Dive process to discuss and analyze current and planned science use cases and anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Questions crafted to explore this space include the following: 1) How, and where, will new data be analyzed and used? 2) How will the process of doing science change over the next 5–10 years? and 3) How will changes to the underlying hardware and software technologies influence scientific discovery? Deep Dives help ensure that key stakeholders have a common understanding of the issues and the actions that a campus or regional network may need to undertake to offer solutions. The EPOC team leads the effort and relies on collaboration with the hosting site or network, and other affiliated entities that participate in the process. EPOC organizes, convenes, executes, and shares the outcomes of the review with all stakeholders

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2022

Oak Ridge National Laboratory FY 2023 Site Sustainability Plan With FY 2022 Performance Data

At the close of each fiscal year, the US Department of Energy (DOE) Sustainability Performance Division (SPD) issues guidance documents and technical resource aids/tools necessary for DOE sites and national laboratories to complete sustainability reporting requirements. SPD is part of the DOE Office of Asset Management. As required by DOE Order 436.1, Departmental Sustainability, “each site will develop and commit to an annual Site Sustainability Plan (SSP) that identifies its respective contribution toward meeting the DOE’s sustainability goals.” SPD collects and compiles information reported by each site to develop an agency-wide Sustainability Report and Implementation Plan, which is used to report DOE sustainability progress to the federal government as required by all major federal agencies. DOE launched a formal Sustainability Office and annual SSP process in 2011. Each year, Oak Ridge National Laboratory (ORNL), in concert with the Office of Science (SC), provides the resources essential to fulfill its commitment to deliver a complete and accurate SSP report and quality performance data for entry into the DOE Sustainability Dashboard as managed by SPD. The performance data entered by each DOE site are then combined to disclose the progress of each DOE Program Office and are further combined to show comprehensive progress for the agency. The Office of Asset Management provides assistance to program offices in sustaining their missions, freeing up resources by reducing waste, avoiding excess expenditure on utilities, maximizing productivity, and improving the efficiency of facilities and processes. By focusing on mission needs, programs and associated DOE sites can help the agency meet its sustainability goals, as outlined in federal statutory and regulatory requirements. In FY 2022, the SSP guidance was updated to capture requirements from Executive Order (EO) 14008, Tackling the Climate Crisis at Home and Abroad, the Energy Act of 2020 (EAct 20), actions outlined in DOE’s Climate Adaptation & Resilience Plan and Sustainability Plan, and EO 14057, Catalyzing Clean Energy Industries and Jobs Through Federal Sustainability. Updates in SSP guidance help to minimize and streamline reporting while simultaneously addressing updated federal requirements. Per DOE, each SSP report should provide an overview of the site’s planned actions, as well as an overview of efforts and accomplishments during the reporting period. SPD collects and compiles information reported by each site to develop DOE’s Annual Sustainability Report, Climate Adaptation & Resilience Plan, and Annual Energy Management Report to Congress. The agency goal has been to lower the reporting burden for sites and increase and improve the consistency of information available to decision makers, allowing them to better identify projects and potential for increased efficiency, as well as to reduce waste, lower emissions, and enhance operational resilience. Sites may elect to produce a more polished publication for their leadership and stakeholders, but this step is no longer required. The ORNL SSP narrative report (this document) and the reporting of DOE SPD Sustainability Dashboard performance data is a collaborative effort of approximately 30 subject matter experts (SMEs) from ORNL facility management and research divisions. Annually, these associates come together to provide a report that can be used by DOE to demonstrate continued agency progress in energy efficiency and sustainable federal operations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Coordinative Entities: Forms of Organizing in Data Intensive Science

Scientific collaboration is a long-standing subject of CSCW scholarship that typically focuses on the development and use of computing systems to facilitate research. The research presented in this article investigates the sociality of science by identifying and describing particular, common forms of organizing that researchers in four different scientific realms employ to conduct work in both local contexts and as part of distributed, global projects. This paper introduces five prototypical forms of organizing we categorize as coordinative entities: the Principal Group, Intermittent Exchange, Sustained Aggregation, Federation, and Facility Organization. Coordinative entities as a categorization help specify, articulate, compare, and trace overlapping and evolving arrangements scientists use to facilitate data intensive research. We use this typology to unpack complexities of data intensive scientific collaboration in four cases, showing how scientists invoke different coordinative entities across three types of research activities: data collection, processing, and analysis. Finally, our contribution scrutinizes the sociality of scientific work to illustrate how these actors engage in relational work within and among diverse, dispersed forms of organizing across project, funding, and disciplinary boundaries.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

A panel analysis of groundwater use in California

We report groundwater is a relevant source of drinking and agricultural water in many regions of the world, but many aquifers have been unsustainably over-drafted and polluted. This has significant environmental, health, and economic implications. We rely on panel analysis, with small-sample corrections for cluster-robust variance estimation and hypothesis testing, to investigate the dynamics of groundwater extraction. We focus on California, yet our approach could be helpful to analyze the dynamics of groundwater extraction in other groundwater-reliant regions of the world. In California, over-reliance on groundwater has led to significant overdraft, affecting long-term water supply reliability and groundwater pumping costs. It further caused subsidence and infrastructure damage, harmed groundwater-dependent ecosystems, and threatened the sustainability of groundwater resources in the state. We use panel data of the 56 California Water Plan planning areas over the 1998–2015 period. We concentrate on agricultural and urban water use and the major water projects in the state, to provide a better understanding of the relationships between groundwater extraction and water use and supply. Results suggest that reducing agricultural water in Central California and urban water in Southern California could reduce groundwater extraction in these regions by approximately the same amount of the reduced water. Other opportunities to reduce the stress on the groundwater resources in the state are available for other regions, yet with lower benefits. Results also suggest that a decrease in deliveries from the Central Valley Project to the southern part of the Central Valley would increase groundwater extraction by approximately the same proportion. Changes in deliveries from the major water projects in the state, as well as from other sources of surface water, would also have some, yet lower impacts on groundwater extraction in the Central and Southern California.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

SMART Deliverable 6.1.2a: Application of the ORION tool to the IBDP Carbon Storage Site

Forecasting and managing potential induced seismic activity is one of the challenges facing commercialscale geologic carbon sequestration (GCS), as well as other geologic energy extraction and byproduct disposal technologies. Historically, the process to develop robust, science-based forecasts of induced seismicity has required an integrated effort from experts in seismology, geomechanics, and reservoir engineering to manage data, develop and evaluate models of subsurface processes, and to calibrate and interpret the results from a range of models to understand site behavior relative to prescribed standards and in the context of uncertainty in geologic characterization data, forecasting models, and operational scenario uncertainty. The Operational Forecasting of Induced Seismicity (ORION) toolkit is an open-source, observation-based forecasting toolkit that is being co-developed by two U.S. DOE-funded initiatives: the National Risk Assessment Partnership (NRAP) and the Scienceinformed Machine Learning for Accelerating Real Time Decisions in Subsurface Applications (SMART) Initiative. ORION is designed to provide functionality to support decision making about seismic hazard analysis and risk management for GCS stakeholders ranging from the public to site operators to expert seismologists. The tool, which is written as open-source code in the Python programming language, is composed of a desktop graphical user interface (GUI) and an underlying forecasting engine. The forecasting engine uses available reservoir properties, well and fluid injection scenario details, and observed seismic catalog data as inputs to produce a set of temporal and spatio-temporal seismic forecasts.

58 GEOSCIENCES↗
DOE OSTI2025

Community Requirements Meta-Analysis: Characterizing Needs and Opportunities for HPDF

This High Performance Data Facility (HPDF) Project is creating a new scientific user facility to provide advanced infrastructure for data-intensive science, supporting the DOE’s Office of Science (SC) community. HPDF’s mission is to enable and accelerate scientific discovery by delivering state-of-the-art data management infrastructure, capabilities, and tools. This meta-analysis examines the needs of the breadth of the SC community, captured in publicly available community reports or mission documents. The meta-analysis identifies and provides initial characterization of fifteen core requirements for the HPDF Project team to consider during the conceptual design phase. The fifteen requirements illustrate how scientific work among SC communities requires modern, seamless user experiences across the ASCR Ecosystem to advance the use of large volumes of heterogeneous data. The scientific community requires support for the missing middle of compute between local and HPC to interactively and collaboratively use growing datasets. Data producers and end users will benefit from enhanced data catalogs and portals that improve data access through advanced search of well curated data. The fifteen requirements are examined here organized across five themes for discussion. Examples in each theme illustrate the array of scientific needs that convey the important role that the fully realized and operational High Performance Data Facility will be able to play as an integral part of the evolving ASCR Ecosystem. Our amalgamated data tables from ESnet reports demonstrate ranges to the volumes of data HPDF must be concerned with, but limitations are inherent to this meta-analysis (see Key Challenges & Limitations). Feedback and validation of these requirements along with additional details and emergent community requirements will be gathered through user research and design activities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Survey on the Expanding Scope and Interdisciplinary Opportunities for Processing-in-Memory Techniques

Processing-in-Memory (PIM) is emerging as a practical path to overcome the limitations of traditional von Neumann architectures. At its core, PIM systems implement computing primitives such as logic operations and multiply-accumulate acceleration through compute-in-memory, near-memory processing, or hybrid designs. The role of memory cells varies widely across technologies, acting as inputs, outputs, or analog accumulators through bit-lines and sense amplifiers. This diversity creates trade-offs in precision, bandwidth, latency, and programmability, making it difficult to build a unified understanding on the progress of the field. In this survey, we organize recent advances of PIM into three areas. First, we discuss the progress on the architectural optimizations of PIM and its integration with both DRAM and emerging non-volatile memories. Second, we examine how PIM is being used to accelerate key computing domains, including generative AI workloads and high-performance kernels, along with new approaches. Third, we highlight the growing adoption of PIM in computational sciences, where it is being applied to solve interdisciplinary problems such as genome analysis, mRNA quantification, mass spectrometry, quantum circuit simulation, wave modeling, and secure computation. Finally, we synthesize the major challenges that continue to slow PIM adoption, including manufacturing constraints, power delivery, thermal reliability, data consistency, runtime and memory-management coordination, and the difficulty of building portable software abstractions without sacrificing commercial viability. This work provides an updated, structured perspective on PIM’s potential across computing and computational sciences and the barriers that must be solved for it to reach its full impact.

Asifuzzaman, Kazi [Oak Ridge National Laboratory (↗