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At least 37 records · Page 2

National Virtual Biotechnology Laboratory: Report on Rapid R&D Solutions to the COVID-19 Crisis

With funding from the CARES Act, the U.S Department of Energy (DOE) established the National Virtual Biotechnology Laboratory (NVBL) in March 2020 to address key challenges associated with the COVID-19 crisis. NVBL brought together the broad scientific and technical expertise and resources of DOE’s 17 national laboratories to help tackle medical supply short ages, discover potential drugs to fight the virus, develop and validate COVID-19 testing methods, model disease spread and impact across the nation, and understand virus transport in buildings and the environment. National laboratory resources leveraged for this effort include a suite of world-leading user facilities broadly available to the research community, such as light and neutron sources, nanoscale science research centers, sequencing and biocharacterization facilities, and high-performance computing facilities. Within months, NVBL teams produced innovations in materials and advanced manufacturing that mitigated shortages in test kits and personal protective equipment (PPE), creating nearly 1,000 new jobs. They used DOE’s high-performance computers and light and neutron sources to identify promising candidates for antibodies and antivirals that universities and drug companies are now evaluating. NVBL researchers also developed new diagnostic targets and sample collection approaches, and supported U.S. Food and Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and U.S. Department of Defense (DoD) efforts to establish national guidelines used in administering millions of tests. Researchers used artificial intelligence and high-performance computing to produce near-real-time data analysis to forecast disease transmission, stress on public health infrastructure, and economic impact, which supported decision-makers at the local, state, and national levels. NVBL teams also studied how to control indoor virus movement to minimize uptake and protect human health. NVBL’s accomplishments demonstrate not only the powerful resource represented by DOE’s national laboratories working together to meet national needs, but also the effectiveness of the integrated NVBL framework for rapidly responding to emergencies with research and development (R&D) solutions. As the fight against COVID continues, sustained efforts are needed to confront this pandemic as well as future threats. Examples include: 1) Establishing “supply chains on demand” to meet emergency production needs by leveraging the materials and manufacturing expertise of DOE national laboratories and developing advances in electronics, sensing, robotics, and automation capabilities; 2) Improving the speed and robustness of drug discovery by integrating experimental platforms with DOE’s computational and experimental user facilities, which provide unique resources to support the discovery of high-potential therapeutic agents; 3) Protecting public, environmental, and animal health by developing new testing protocols and instrumentation adaptable to diverse sample types (both physiological and environmental) to quickly detect a wide range of pathogens and monitor other biorisks; 4) Supporting near-real-time data needs of decision-makers at the local, regional, state, and national levels by advancing data curation, analysis, and modeling using artificial intelligence and new data science tools for managing and evaluating large diverse datasets; 5) Harnessing DOE’s expertise in environmental modeling to design rooms and air handling for offices, classrooms, restaurants, and other structures to minimize biorisk transmissions. Going forward, NVBL is poised to apply the unique capabilities and expertise of the national laboratory complex to future national and international emergencies, both natural and engineered. Through this framework, the Office of Science will continue to be an integral component of agency wide efforts to prepare for and respond to biorisks and other crises.

42 ENGINEERING↗

Challenges of open data in aquatic sciences: issues faced by data users and data providers

Free use and redistribution of data (i.e., Open Data) increases the reproducibility, transparency, and pace of aquatic sciences research. However, barriers to both data users and data providers may limit the adoption of Open Data practices. Here, we describe common Open Data challenges faced by data users and data providers within the aquatic sciences community (i.e., oceanography, limnology, hydrology, and others). These challenges were synthesized from literature, authors’ experiences, and a broad survey of 174 data users and data providers across academia, government agencies, industry, and other sectors. Through this work, we identified seven main challenges: 1) metadata shortcomings, 2) variable data quality and reusability, 3) open data inaccessibility, 4) lack of standardization, 5) authorship and acknowledgement issues 6) lack of funding, and 7) unequal barriers around the globe. Our key recommendation is to improve resources to advance Open Data practices. This includes dedicated funds for capacity building, hiring and maintaining of skilled personnel, and robust digital infrastructures for preparation, storage, and long-term maintenance of Open Data. Further, to incentivize data sharing we reinforce the need for standardized best practices to handle data acknowledgement and citations for both data users and data providers. We also highlight and discuss regional disparities in resources and research practices within a global perspective.

54 ENVIRONMENTAL SCIENCES↗

CreelCat, a Catalog of United States Inland Creel and Angler Survey Data

The United States Inland Creel and Angler Survey Catalog (CreelCat) contains a national compilation of angler and creel survey data collected by natural resource management agencies across the United States (including Washington, D.C. and Puerto Rico). These surveys are used to help inform the management of recreational fisheries, by collecting information about anglers including what they are catching and harvesting, the amount of effort they expend, their angling preferences, and demographic information. As of May 1, 2023, CreelCat houses over 14,729 surveys from 33 states, Puerto Rico, and Washington, D.C., comprising 235 data fields across 8 tables. These tables contain 235,015 records of fish catch and harvest metrics, 27,250 angler preference metrics, 14,729 records of survey characteristics, 13,576 records of effort metrics, and 409 records of angler demographics. Though individual creel surveys are often deployed to meet local science and management objectives, creel data aggregated across jurisdictions has the potential to address larger scale research and management needs.

59 BASIC BIOLOGICAL SCIENCES↗

Materials data science using CRADLE: A distributed, data-centric approach

Abstract There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. Graphical abstract

97 MATHEMATICS AND COMPUTING↗

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↗

Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future

Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

59 BASIC BIOLOGICAL SCIENCES↗

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM). Final Report

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM) is a proposed system that will enable data from an infinite number of diverse sources to be organized and accessed from anywhere using any handheld or other computer device. The approach offers a powerful roadmap for the creation and integration of a unified knowledge base of an entire ecosystem, including its many geophysical, geographical, social, political, agricultural, energy, transportation, and cyber aspects. The resulting aggregation of data has the potential to generate an informational universe of unprecedented size that has never before been possible due to the prohibitive costs, managerial complexity, and technical barriers associated with ever-changing exponential-growth data flows. We envision that DREAM will accelerate discovery by enabling climate researchers, among other types of researchers, to manage, analyze, and visualize data from earth-scale measurements and simulations. DREAM’s success will be built on proven components that leverage existing services and resources. A key building block for DREAM will be the ESGF, chaired by Dean N. Williams. Expanding on the existing ESGF, the project will ensure that the access, storage, movement, and analysis of the large quantities of data that are processed and produced by diverse science projects can be dynamically distributed with proper resource management. Much of the Office of Science data is currently generated by multiple stand-alone facilities. DREAM can collect data accumulated from these facilities and incorporate it into a fully integrated network accessible from anywhere in the world. The result is a completely new paradigm shift for data management, analysis, and visualization enabling researchers to: Manage their calculations, data, tools, and research results; Ensure that all data are sharable, reproducible and (re)usable—accompanied by appropriate metadata describing its provenance, syntax, and semantics at creation; Advance application performance by selectively adapting APIs and services in response to scientific requirements and architectural complexities; and Provide scalable interactive resource management—navigate data and metadata at multiple levels, provide architecture-aware data integration, analysis and visualization tools. We will engage closely with DOE, NASA, and NOAA science groups working at the leading edge of computing. These engagements—in domains such as biology, climate, and hydrology—will allow us to advance disciplinary science goals and inform our development of technologies that can accelerate discovery across DOE more broadly. We will advertise and promote our technologies via dedicated workshops, tutorials, and sessions at conferences, stand-alone events with broad inter-disciplinary invitation, and engagements with leadership facilities.

54 ENVIRONMENTAL SCIENCES↗

Urbanization and malaria have a contextual relationship in endemic areas: A temporal and spatial study in Ghana

In West Africa, malaria is one of the leading causes of disease-induced deaths. Existing studies indicate that as urbanization increases, there is corresponding decrease in malaria prevalence. However, in malaria-endemic areas, the prevalence in some rural areas is sometimes lower than in some peri-urban and urban areas. Therefore, the relationship between the degree of urbanization, the impact of living in urban areas, and the prevalence of malaria remains unclear. This study explores this association in Ghana, using epidemiological data at the district level (2015–2018) and data on health, hygiene, and education. We applied a multilevel model and time series decomposition to understand the epidemiological pattern of malaria in Ghana. Then we classified the districts of Ghana into rural, peri-urban, and urban areas using administratively defined urbanization, total built areas, and built intensity. We converted the prevalence time series into cross-sectional data for each district by extracting features from the data. To predict the determinant most impacting according to the degree of urbanization, we used a cluster-specific random forest. We find that prevalence is impacted by seasonality, but the trend of the seasonal signature is not noticeable in urban and peri-urban areas. While urban districts have a slightly lower prevalence, there are still pockets with higher rates within these regions. These areas of high prevalence are linked to proximity to water bodies and waterways, but the rise in these same variables is not associated with the increase of prevalence in peri-urban areas. The increase in nightlight reflectance in rural areas is associated with an increased prevalence. We conclude that urbanization is not the main factor driving the decline in malaria. However, the data indicate that understanding and managing malaria prevalence in urbanization will necessitate a focus on these contextual factors. Finally, we design an interactive tool, ’malDecision’ that allows data-supported decision-making.

60 APPLIED LIFE SCIENCES↗

Data Privacy for the Grid: Toward a Data Privacy Standard for Inverter-Based and Distributed Energy Resources

The traditional approach to planning the distribution grid has focused on reliability in the context of gradual and reasonably predictable load growth. Forecasts of load growth, combined with asset management practices, were used by system planners to identify upgrades to the system to maintain or improve reliability. The decisions, typically based within load flow analysis tools, included considerations about contingency scenarios and corporate forecasts (i.e., top-down predictions at a summary level of what would happen in a particular area that could impact load growth and behavior). As a result, today, this traditional approach no longer fits all purposes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DMTN-102: LSST Alerts: Key Numbers

A quantitative review of the key numbers associated with the Legacy Survey of Space and Time (LSST) Alert Stream.

79 ASTRONOMY AND ASTROPHYSICS↗

Computational Math Problems for a Clean Energy Future

Cutting edge computational mathematics are ubiquitous in renewable energy research. Problems in resilient and reliable electric grid operations, infrastructure planning, wind farm yaw control, and more demand sophisticated and scalable computational tools that enable the transition of renewable energy technologies from proof of concept to deployment into our energy system. The mission of the Computational Science Center at NREL is to lead the lab's efforts to solve energy challenges using high-performance computing (HPC), computational science, applied mathematics, scientific data management, visualization, and informatics. In this poster, we provide a short overview of three areas of computational mathematics research at NREL: wind power scenario generation for stochastic grid operations and infrastructure planning, improved rational function approximations for electromagnetic transients codes, and wind farm yaw control using a combination of the Alternating Direction Method of Multipliers (ADMM) and reinforcement learning (RL). Increasing penetrations of renewable energy into power grids motivate the investigation of new approaches to characterizing uncertainty for five-minute economic dispatch problems. Similarly, as the penetration of distributed energy resources on power grids increases, it becomes important to revisit our methods of modelling transient phenomena, i.e. electromagnetic transients programs. Finally, the combination of ADMM and RL for wind farm yaw control presented here can potentially increase the efficiency of the deployed distributed controllers by orders of magnitude.

ADMM↗

Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data

The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.

97 MATHEMATICS AND COMPUTING↗

Report for the ASCR Workshop on the Management and Storage of Scientific Data

The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.

97 MATHEMATICS AND COMPUTING↗

2020 Multiscale Microbial Dynamics Modeling Course

The 2020 Multiscale Microbial Dynamics course is adapted from the virtual 2020 Mutliscale Microbial Dynamics Summer School that was hosted by Environmental Molecular Sciences Laboratory (EMSL), a U.S. Department of Energy (DOE) science user facility located on the Pacific Northwest National Laboratory (PNNL) campus, in collaboration with the Joint Genome Institute (JGI) and the DOE Systems Biology Knowledgebase (KBase). The course course covers how to incorporate microbial metagenomic and environmental metabolite data from watershed ecosystems into metabolic and community modeling using computational frameworks, such as KBase and PFLOTRAN. The curriculum includes lectures and software and data analysis tutorials. All materials are freely accessible to the community as part of the 2020 Microbial Dynamics Summer School Organization in KBase.

54 ENVIRONMENTAL SCIENCES↗

RAPIDS: Reconciling Availability, Accuracy, and Performance in Managing Geo-Distributed Scientific Data

In modern science, big data plays an increasingly important role. Many scientific applications, such as running simulations on supercomputers or conducting experiments on advanced instruments, produce huge amount of data at unprecedented speed. Analyzing and understanding such big data is the key for scientists to make scientific breakthroughs. However, data might become unavailable for scientists to access when outages or maintenance of the storage system occur, which severely hinders scientific discovery. To improve the data availability, data duplication and erasure coding (EC) are often used. But as the scientific data gets larger, using these two methods can cause considerable storage and network overhead.In this paper, we propose RAPIDS, a hybrid approach that combines the multigrid-based error-bounded lossy compression with erasure coding, to significantly reduce the storage and network overhead required for maintaining high data availability. Our experiments show that RAPIDS reduces the storage overhead by up to 7.5x and network overhead by up to 3x to achieve the same level of availability compared to the regular EC method. We improve RAPIDS by building two models to optimize the fault tolerance configurations and data gathering strategy. We demonstrate that RAPIDS significantly improves performance when running on many CPU cores in parallel or on GPUs.

Wan, Lipeng↗

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗

Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Abstract This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman, et al., 2021b, https://doi.org/10.1029/2021EO153180 ) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing; (Section 3) Machine learning for multiscale modeling; (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Our role in ICON science therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of findable, accessible, interoperable, and reusable (FAIR) research products and practices. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi‐scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery.

58 GEOSCIENCES↗