Results for “Data science, data management” — Page 26

DOE OSTI2022

McLennan Community College Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the 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. In March of 2022, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from LEARN and McLennan Community College (MCC) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

South Plains College Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the 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. Between October 2021 and January 2022, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from LEARN and South Plains College (SPC) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Sinclair Community College and OARnet Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the 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. Between October 2021 and March 2022 staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from Sinclair Community College (SCC) and OARnet with the purpose of performing a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

San Diego State University Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the 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. Between December 2021 and April 2022 staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from San Diego State University (SDSU) the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

Arizona State University Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the 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. Between October 2021 and February 2022 staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from Arizona State University (ASU) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

St. Mary’s University Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the 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. Between September and December 2022, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from LEARN and St. Mary’s University for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

National Institute of Standards and Technology Requirements (Analysis Report)

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the 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. In October of 2022, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from the National Institute of Standards and Technology (NIST) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community. Material for this event included the written documentation from each of the profiled research areas, documentation about the current state of technology support, and a write-up of the discussion that took place via e-mail and video conferencing. The case studies highlighted the ongoing challenges and opportunities that NIST has in supporting a cross-section of established and emerging research use cases. Each case study mentioned unique challenges which were summarized into common needs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification

We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Age-specific case data reveal varying dengue transmission intensity in US states and territories

Dengue viruses (DENV) are endemic in the US territories of Puerto Rico, American Samoa, and the US Virgin Islands, with focal outbreaks also reported in the states of Florida and Hawaii. However, little is known about the intensity of dengue virus transmission over time and how dengue viruses have shaped the level of immunity in these populations, despite the importance of understanding how and why levels of immunity against dengue may change over time. These changes need to be considered when responding to future outbreaks and enacting dengue management strategies, such as guiding vaccine deployment. We used catalytic models fitted to case surveillance data stratified by age from the ArboNET national arboviral surveillance system to reconstruct the history of recent dengue virus transmission in Puerto Rico, American Samoa, US Virgin Islands, Florida, Hawaii, and Guam. We estimated average annual transmission intensity (i.e., force of infection) of DENV between 2010 and 2019 and the level of seroprevalence by age group in each population. We compared models and found that assuming all reported cases are secondary infections generally fit the surveillance data better than assuming all cases are primary infections. Using the secondary case model, we found that force of infection was highly heterogeneous between jurisdictions and over time within jurisdictions, ranging from 0.00008 (95% CrI: 0.00002–0.0004) in Florida to 0.08 (95% CrI: 0.044–0.14) in American Samoa during the 2010–2019 period. For early 2020, we estimated that seropositivity in 10 year-olds ranged from 0.09% (0.02%–0.54%) in Florida to 56.3% (43.7%–69.3%) in American Samoa. In the absence of serological data, age-specific case notification data collected through routine surveillance combined with mathematical modeling are powerful tools to monitor arbovirus circulation, estimate the level of population immunity, and design dengue management strategies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

The CanBikeCO Mini Pilot: Preliminary Results and Lessons Learned

In fall 2020, the Colorado Energy Office, as part of the State of Colorado’s “Can Do Colorado” initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, starting in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The nonmotorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO 2 . Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

A dendritic strontium river isoscape for fisheries applications in the Sacramento River basin, California, USA

Objective Understanding the origins and movements of fish is fundamental to effective conservation and fisheries management. Strontium isotope ratios ( 87 Sr/ 86 Sr) in otoliths provide a powerful tracer of natal origin and migratory pathways. However, existing 87 Sr/ 86 Sr isoscapes for the Sacramento River basin, an ecosystem that supports ecologically and economically important salmon populations, rely on discrete classification approaches that overlook unsampled habitats and do not incorporate spatial uncertainty. Our objective was to develop a continuous, network-explicit 87 Sr/ 86 Sr isoscape with quantified uncertainty to fill in data gaps and enable probabilistic assignments of fish origin and movement. Methods We used river water 87 Sr/ 86 Sr data from 106 sites (1997–2021) to develop spatial stream network models that use dendritic connectivity and watershed characteristics (lithology, bedrock age, and land cover) to predict river water 87 Sr/ 86 Sr throughout the basin. Models were fitted using maximum and restricted likelihood and were evaluated via Akaike’s information criterion and leave-one-out cross validation. We produced both historical (pre-dam) and present-day (below-dam) isoscapes, delineated uncertainty-informed isotopic ranges using k -means clustering, and applied a proof-of-concept Bayesian assignment to estimate natal origins and early rearing habitats for two endangered winter-run Chinook Salmon Oncorhynchus tshawytscha. Results Cross validation indicated strong performance of the 87 Sr/ 86 Sr model (leave-one-out cross validation: R 2 = 0.91; root mean square error = 0.0005). Uncertainty-informed clustering identified 19 isotopic “suites” (reaches with indistinguishable 87 Sr/ 86 Sr values) in present-day anadromous habitats and 25 suites in the historical network. Example natal and early rearing assignments included predictions that challenged expectations for juvenile salmon migration based on predicted river 87 Sr/ 86 Sr compositions. Conclusions This study developed a continuous, network-explicit 87 Sr/ 86 Sr isoscape that integrates existing river data to predict 87 Sr/ 86 Sr in unsampled reaches and the likely achievable range and resolution of otolith-based origin and life history inference. The resulting river isoscape provides a valuable tool to predict salmon movements and identify habitats supporting their survival and growth that otherwise might remain undetected. Coupling these predictions with complementary approaches that ground-truth juvenile presence (e.g., targeted fish surveys) represents an important step toward science-informed restoration and management of critical habitats throughout the Sacramento River basin.

Environmental sciences↗

DOE OSTI2020

November 2019 Initial Deployment of LiDAR in the H-Canyon Exhaust Tunnel

The H-Canyon Exhaust Tunnel (CAEX) structure is periodically inspected under the Structural Integrity Program using camera equipped crawlers or poles to remotely perform visual inspections. To explore the use of enhanced inspection methods a “Proof of Concept” using the Light Detection and Ranging (LiDAR) technology was performed over a 2-day period in November 2019. The purpose of the “Proof of Concept” deployment was to confirm whether a commercially available LiDAR unit could successfully operate and remotely transmit data from the tunnel CAEX environment and whether the data would provide quantitative information to establish baseline measurements. The LiDAR performance requirement was a measurement accuracy of ± 0.25-inches over a 30-foot distance. This report documents the work activities leading to the deployment, the deployment and processing of data, lessons learned from the deployment and the post data processing methods that will be applied to future deployments.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Success Using A Granular-Activated Carbon Filtration Swab to Rehabilitate Westbay{sup R} Multi-level Groundwater Monitoring Wells at the INL - 20544

In November 2015, tetrachloroethylene (PCE) was discovered in groundwater samples collected from a Westbay{sup R} multi-level groundwater monitoring well at the Idaho National Laboratory (INL).a Subsequent investigation determined that PCE was not present in the groundwater. The detected PCE was instead due to inadvertent contamination of the internal tubing fluid in the Westbay well-monitoring system, which is isolated from the surrounding groundwater. Further investigation showed that three of the 11 Westbay wells at the INL contained PCE-contaminated tubing fluid at concentrations above the U.S. Environmental Protection Agency's maximum contaminant level for drinking water (5 μg/L). Loose sediment at the bottom of some Westbay wells was also found to contain PCE; the three wells were then identified for rehabilitation as part of the INL well maintenance program. The unique construction of the Westbay wells - i.e., the small diameter (5.72 cm [2.25 in.]) and the depths of the tubing fluid ranging from 144.8 to 419 m (475 to 1,376 ft) below ground surface - posed unique challenges to the rehabilitation. Because the tubing fluid provides structural integrity, the fluid cannot be removed and then replaced without damaging the integrity of the Westbay well. Additionally, the small diameter of the systems and the associated depths limit the availability of equipment that can be used for the rehabilitation. After an evaluation of potential methods, it was determined that the use of an in-well, custom-fabricated, granular-activated-carbon filtration swab deployed via a wire line would provide the safest and most cost-effective method for rehabilitation. Westbay well rehabilitation started at well MIDDLE-2051 in late June 2018 and paused in December 2018 for the season. The PCE concentration in the tubing fluid at MIDDLE-2051 was initially reduced to a maximum concentration of 3.81 μg/L from a maximum of 642 μg/L, while the concentration of PCE in the sediment had been reduced from 5,360 to 2,270 μg/kg. Sampling was performed at MIDDLE-2051 again in August 2019, and the tubing fluid increased to a maximum concentration of 45.4 μg/L, while the concentration in the sediment decreased further to 1,890 μg/kg due to desorption of PCE from the sediment to the tubing fluid. Rehabilitation resumed at well MIDDLE-2050A in late August 2019, and the PCE concentration in the tubing fluid had been reduced to a maximum concentration of 19.0 μg/L. Although the method has been shown to be time-consuming, it has proven to be effective and safe for personnel performing the work and to the integrity of the well. Future sample data results at all wells will help determine an estimated completion of the rehabilitation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

2020 LANL Contributions to the BATS Test in WIPP

The DOE Office of Nuclear Energy (DOE-NE) repository research and development program seeks to provide a sound technical basis for multiple viable disposal options, increase confidence in the robustness of generic disposal concepts, and develop science and engineering tools needed to support disposal concept implementation. Sandia, Los Alamos, and Lawrence Berkeley National labs are conducting research into salt, which includes the Brine Availability Test in Salt (BATS) field test at the Waste Isolation Pilot Plant (WIPP), a DOE Office of Environmental Management facility. BATS is leveraging the existing infrastructure associated with the WIPP to advance science informing generic disposal concepts. BATS is an ongoing heated borehole experiment being conducted in the WIPP underground. The goal of this experiment is to reduce the uncertainty associated with spent nuclear fuel disposition in geologic salt formations. The BATS experiments is designed to accomplish this by increasing our understanding of brine migration in salt, assessing damaged zones from mining and drilling, simulating a post-closure environment, confirming salt properties, and providing data for model validation. Phased testing for generic salt repository R&D was proposed in 2015. Subsequently, borehole thermal testing was developed as a first step in this process. This document summarizes the work conducted on the BATS experiment during fiscal year 2020 with an emphasis on the contributions by Los Alamos National Lab.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Reusability First: Toward FAIR Workflows

The FAIR principles of open science (Findable, Accessible, Interoperable, and Reusable) have had transformative effects on modern large-scale computational science. In particular, they have encouraged more open access to and use of data, an important consideration as collaboration among teams of researchers accelerates and the use of workflows by those teams to solve problems increases. How best to apply the FAIR principles to workflows themselves, and software more generally, is not yet well understood. We argue that the software engineering concept of technical debt management provides a useful guide for application of those principles to workflows, and in particular that it implies reusability should be considered as ‘first among equals’. Moreover, our approach recognizes a continuum of reusability where we can make explicit and selectable the tradeoffs required in workflows for both their users and developers.To this end, we propose a new abstraction approach for reusable workflows, with demonstrations for both synthetic workloads and real-world computational biology workflows. Through application of novel systems and tools that are based on this abstraction, these experimental workflows are refactored to rightsize the granularity of workflow components to efficiently fill the gap between end-user simplicity and general customizability. Our work makes it easier to selectively reason about and automate the connections between trade-offs across user and developer concerns when exposing degrees of freedom for reuse. Additionally, by exposing fine-grained reusability abstractions we enable performance optimizations, as we demonstrate on both institutional-scale and leadership-class HPC resources.

Wolf, Matthew↗

DOE OSTI2020

Methods and Experiences for Developing Abstractions for Data-intensive, Scientific Applications

Developing software for scientific applications that require the integration of diverse types of computing, instruments, and data present challenges that are distinct from commercial software. These applications require scale, and the need to integrate various programming and computational models with evolving and heterogeneous infrastructure. Pervasive and effective abstractions for distributed infrastructures are thus critical; however, the process of developing abstractions for scientific applications and infrastructures is not well understood. While theory-based approaches for system development are suited for well-defined, closed environments, they have severe limitations for designing abstractions for scientific systems and applications. The design science research (DSR) method provides the basis for designing practical systems that can handle real-world complexities at all levels. In contrast to theory-centric approaches, DSR emphasizes both practical relevance and knowledge creation by building and rigorously evaluating all artifacts. In this work, we show how DSR provides a well-defined framework for developing abstractions and middleware systems for distributed systems. Specifically, we address the critical problem of distributed resource management on heterogeneous infrastructure over a dynamic range of scales, a challenge that currently limits many scientific applications. We use the pilot-abstraction, a widely used resource management abstraction for high-performance, high throughput, big data, and streaming applications, as a case study for evaluating the DSR activities. For this purpose, we analyze the research process and artifacts produced during the design and evaluation of the pilot-abstraction. We find DSR provides a concise framework for iteratively designing and evaluating systems. Finally, we capture our experiences and formulate different lessons learned.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

ARM FY2026 Radar Plan

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility maintains a suite of advanced atmospheric radar systems that serve as critical tools in ARM’s mission to provide continuous, high-quality observations for advancing the understanding and modeling of atmospheric processes. These radar systems enable detailed characterization of clouds, precipitation, and dynamic structures in the atmosphere, supporting a broad range of scientific applications. The number of deployed systems exceeds what current staffing levels can fully support for continuous 24/7/365 operation. As such, it is essential to have a clearly defined and community-informed plan that prioritizes radar operations and communicates ARM’s strategy for sustaining and evolving these observational assets. This FY2026 Radar Plan outlines ARM’s approach to managing its radar portfolio—balancing scientific impact, operational feasibility, and long-term sustainability. It reflects ARM’s continued commitment to delivering calibrated, well-documented radar data products that enable process-level studies and support the development and evaluation of weather and climate models. Through this plan, ARM aims to ensure transparency in decision-making, alignment with user needs, and support for innovative science across the facility’s fixed and mobile observatories. Given uncertainties around the Fiscal Year (FY) 2026 budget, this plan was developed to assume business as usual and will be updated as budgets and plans may change. It should be noted that, given the limited timeframe involved, this plan will be more succinct than previous plans.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Reliability metrics and their management implications for open pond algae cultivation

The prevalence of contaminating organisms in outdoor algae cultivation, with the often-associated dramatic crop failures, necessitates the need for metrics describing production system reliability. Standard metrics for algae cultivation reliability are critically needed to be able to map improvements in operational parameters, but do not currently exist. In this work, we present a set of standard metrics including mean time to failure (MTTF) and mean time between failures (MTBF) as the basis for the calculation of pond failure rate (FR) and reliability coefficient (RC). Metrics associated with the numbers of contaminating organisms such as abundance ratio (AR), prevalence of infection (PI), and mean intensity of infection (MII) are also relevant. These metrics, based on measured experimental values of outdoor pond performance during the Algae Testbed Public-Private Partnership (ATP3) Unified Field Studies (UFS), provide a basis for quantifying pond failure and provide insight into potential pond management and contaminant mitigation strategies. From these reliability metrics applied to this dataset, we are able to link operational parameters, such as harvest frequency and inoculum source, to pond reliability for different algae strains and seasons, and from an assessment of AR, provide contamination thresholds beyond which a culture may be unrecoverable. Ultimately, the implementation of widely-practiced and simple-to-calculate algae pond reliability metrics calculated from rapid and easy to collect data or observations will reduce risk and uncertainty in large-scale algae deployment and aid in the development of integrated pest management (IPM) strategies.

09 BIOMASS FUELS↗