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The Nuclear Material and Composition (NuMaCo) server is a web-based platform for storing, managing, and serving material composition data. It includes the SCALE Standard Composition Library and the PNNL Compendium, along with their associated references and uncertainty data. NuMaCo also provides tools to generate, compare, and validate material input definitions for both SCALE and MCNP.
This data package contains global-scale Earth System Model simulations run with DOE's E3SM Model. The package includes one set of simulations run in the default configuration and a second set of simulations run with the dynamic root module enabled. These two datasets can be used to assess the impact that dynamic roots have on Land Surface fluxes such as carbon and water. The outputs are provided here on a 0.5 x 0.5 global grid at monthly resolution. The simulations were run with atmospheric forcing from the Global Soil Wetness Project. We provide specifically here Gross Primary Production, Transpiration, relative root fraction per soil layer as well as Air Temperature, Precipitation and all the parameterizations used for the simulations. Our purpose for generating and analyzing these simulations was to assess the role that root dynamics and foraging for water has in the recovery timescale of ecosystems following climate perturbation. All data provided are in the "netcdf" format using standard CF-1 (Climate and Forecast) Convention. The data are broken up into 20 year chunks to facilitate easier read-in. The data are all machine readable using various software platforms including Matlab, NCO, Panoply and GrADS.
This data package contains point-scale Earth System Model simulations run with DOE's E3SM Model. The package includes four sets of simulations, one set of simulations run in the default configuration, a second set of simulations run with the dynamic root module enabled, the third set with dynamic leaf to fine root allocation, and the fourth with the combination of dynamic roots and dynamic allocation. These simulations were run in point mode a Ameriflux sites, US-MMS, US-Moz, US-Syv, and US-NR1. These datasets can be used to assess the impact that dynamic roots have on Land Surface fluxes such as carbon and water. The simulations were run with atmospheric forcing from the Global Soil Wetness Project. We provide specifically here Gross Primary Production, Transpiration, relative root fraction per soil layer as well as Air Temperature, Precipitation and all the parameterizations used for the simulations. Our purpose for generating and analyzing these simulations was to assess the role that root dynamics and foraging for water has in the recovery timescale of ecosystems following climate perturbation. All data provided are in the "netcdf" format using standard CF-1 (Climate and Forecast) Convention. The data are all machine readable using various software platforms including Matlab, NCO, Panoply and R.
Historically, extreme waves have been the most destructive force in the Gulf of Mexico, destroying and damaging thousands of offshore structures. The American Petroleum Institute has revised its design criteria for offshore platforms several times over the decades, as new data continues to become available, showing that data for our present climate is insufficient. This problem is exacerbated in a changing climate. By using synthetic physics-based tropical cyclones to force a third-generation wave model, we can generate adequate statistics for present and future climates. These simulations have been completed using Joule Supercomputer.
This talk covers our work on errant beam prognostics at the Spallation Neutron Source (SNS), focusing on the end-to-end process from data collection to the development and deployment of predictive models in specific. A short overview of AIML work done for accelerators and current trends will be presented. We will walk through key steps involved in creating robust Machine Learning (ML) models, including model training, validation, and deployment in an operational setting. In addition to presenting our technical approach, we will share valuable lessons learned, emphasizing the importance of infrastructure to support the continuous adaptation of models to evolving data and system behaviors. This talk will provide insights into the challenges and solutions involved in applying ML to real-world operational environments, with a particular focus on managing data drift and changes in accelerator setup while ensuring model resilience over time.
This report describes the results of more advanced use cases including Ancillary Services, Black-sky-day operation, and Mode Switching from Phase 3 of the “Beyond DERMS” project aiming to build, deploy, and demonstrate a holistic platform that supports the integrated operation and planning of future power distribution networks with bi-directional power flows, many diverse distributed energy resources (DERs), and inverter-based resources. These test results demonstrated how a Beyond DERMS platform can fuse together AMI, SCADA, and DER data to provide a utility with deep insights into distribution network operations and planning, extending the value of DERMS and BTM DER resources.
This report presents a specification for the Portals 4 network programming interface. Portals 4 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4 is well suited to massively parallel processing and embedded systems. Portals 4 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandia's Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4 is targeted to the next generation of machines employing advanced network interface architectures that support enhanced offload capabilities.
The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOF-MS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Therefore, our accomplishment in this CRADA improved the efficiency and accuracy of strain testing by developing and implementing fast analytical methods, robust processing tools, and software for predicting the best methods for UHPLC analysis.
In the coming decade, it is anticipated that deep-sea mining activities will commence throughout the deep ocean, at depths around 4000-6000m where vast deposits of baseball-sized rocks called polymetallic nodules lay on the seabed. These nodules contain resources such as cobalt, copper and nickel which are needed to produce batteries for electric vehicles. Collector machines driving on the seabed and gathering these nodules will stir up clouds of sediment, and there is concern about how the resulting concentration of sediment in the near-bottom ocean water will affect deep and mid-ocean biology. There is currently no technology capable of comprehensively monitoring the concentrations and properties of stirred up sediment in the deep. This project addressed this critical gap by developing a deep-sea particle measurement system (Real-Time Size and Settling Velocity, RTSSV) that uses a multi-camera high-resolution video imaging system, with accompanying image processing, to measure the concentration, size distribution and settling speed of sediment. Importantly, these measurements are made in-situ, so that delicate sediment aggregates will not be disturbed, as is the case when collecting samples at depth to be measured in the lab. The instrument can be mounted on a variety of established deep-sea platforms such as moorings, AUVs and ROVs, providing data in near real time, where the key measurements need to be made—from the source of the sediment disturbance and throughout the resulting plume—vital for determining if deep-sea mining can be done in an environmentally responsible manner. The developed system (RTSSV) will possess the only such capability worldwide, giving a US-based group a substantial competitive advantage as this newly evolving global industry develops. As added value, the technology will also have widespread application to the offshore oil and gas industry, dredging industry, and to sediment science research.
This report is intended to help your state identify the most effective investments to improve grid resilience and reliability, based on analysis of outage data, weather events, and the current state of the electric grid. The information presented is derived from the best available data. The discussions and recommendations included provide context and valuable insights into potential high returns on investment (ROI). Specifically, the report offers an overview of electric grid resilience in Oregon, covering outage information, reasons for outages, calculated "resilience scores" across the state, and recommendations for both immediate and long-term investments in grid infrastructure. The document provides a concise overview of the analytical capabilities of the TASTI-GRID team. By utilizing additional data and resources, it synthesizes insights from the platform to present a distinct perspective on investing in grid resilience throughout Oregon. This document is not intended to influence or direct state-level decisions related to investments in grid resilience and reliability. TASTI-GRID should not be used for real-time monitoring, supporting the ESF #12 emergency response functions, or predicting the restoration times for outages.
The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.
The State and Local Planning for Energy (SLOPE) Platform is a tool to enable more data-driven state and local energy planning by integrating dozens of distinct sources of energy efficiency, renewable energy, and (coming in 2020) sustainable transportation data and analyses.
The Validation Hub will be a clearinghouse for the transfer of novel algorithms and software from the PV research community to industry. Potential algorithms tested in the Hub could include the estimation of various PV loss factors and the detection of various operational issues. The primary function of the Hub will be for developers to submit executable code which will run on hosted data sets. Developers will receive private reports on the accuracy and performance (e.g., run-time) of the submitted algorithms, and public high level summaries will be hosted. These summaries will indicate the organization who submitted the algorithm (e.g., links to GitHub pages, documentation websites, etc.), high-level accuracy metrics, and standardized performance metrics. These results will be stored in a publicly available database, accessible through the Hub, with the ability for users to sort and filter the results. In short, the Hub will be presented to public users as a collection of interactive leaderboards, organized around specific analysis tasks pertinent to the PV data science community. These tasks include things such as the estimation of various PV loss factors and the detection of various operational issues. We will present progress on the development of this hub, including preliminary results of comparative validation of PV data science algorithms and progress towards building the platform itself.
Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able to match the needs of each application with an appropriate type of memory. As the primary generators of memory usage, applications create a great deal of information that can be useful for guiding memory management, but the community still lacks tools to collect, organize, and leverage this information effectively. To address this gap, this work introduces a novel software framework that collects and analyzes object-level information to guide memory tiering. The framework includes tools to monitor the capacity and usage of individual data objects, routines that aggregate and convert this information into tier recommendations for the host platform, and mechanisms to enforce these recommendations according to user-selected policies. Moreover, the developed tools and techniques are fully automatic, work on standard Linux systems, and do not require modification or recompilation of existing software. Using this framework, this study evaluates and compares the impact of a variety of design choices for memory tiering, including different policies for prioritizing objects for the fast memory tier as well as the frequency and timing of migration events. In conclusion, the results, collected on a modern Intel platform with conventional DDR4 SDRAM as well as Intel Optane NVRAM, show that guiding data tiering with object-level information can enable significant performance and efficiency benefits compared with standard hardware- and software-directed data-tiering strategies for a diverse set of memory-intensive workloads.
Understanding the behavior of software in execution is a key step in identifying and fixing performance issues. This is especially important in high performance computing contexts where even minor performance tweaks can translate into large savings in terms of computational resource use. To aid performance analysis, developers may collect an execution trace —a chronological log of program activity during execution. As traces represent the full history, developers can discover a wide array of possibly previously unknown performance issues, making them an important artifact for exploratory performance analysis. However, interactive trace visualization is difficult due to issues of data size and complexity of meaning. Traces represent nanosecond-level events across many parallel processes, meaning the collected data is often large and difficult to explore. The rise of asynchronous task parallel programming paradigms complicates the relation between events and their probable cause. Here, to address these challenges, we conduct a continuing design study in collaboration with high performance computing researchers. We develop diverse and hierarchical ways to navigate and represent execution trace data in support of their trace analysis tasks. Through an iterative design process, we developed Traveler , an integrated visualization platform for task parallel traces. Traveler provides multiple linked interfaces to help navigate trace data from multiple contexts. We evaluate the utility of Traveler through feedback from users and a case study, finding that integrating multiple modes of navigation in our design supported performance analysis tasks and led to the discovery of previously unknown behavior in a distributed array library.
Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform, the Osprey, operated by the Terrestrial Ecosystem Science and Technology (TEST) group (https://www.bnl.gov/testgroup). This package includes data from three flights flown over the NGEE-Arctic Council, Kougarok and Teller sites in July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface 'skin' temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range 350 - 1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model (CHM). In addition, a VNIR surface reflectance file is provided for the trigger locations collected during each flight campaign. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Unprocessed and processed data products are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *.kml, *hdr, *.enp), tabular (*.dat, *.csv, *.waypoint, ENVI format (no extension)), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
A better understanding of the genetic and metabolic mechanisms that confer stress resistance and tolerance in plants is key to engineering new crops through advanced breeding technologies. This requires a systems biology approach that builds on a genome-wide understanding of the regulation of gene expression, plant metabolism, physiology and growth. In this study, we examine the response to drought stress in Sorghum, as we leverage the tools for transcriptomics and plant metabolic modeling we have implemented at the U.S. Department of Energy Systems Biology Knowledgebase (KBase). KBase enables researchers worldwide to collaborate and advance research by allowing them to upload private or public data into the KBase Narrative Interface, empowering them to analyze this data using a rich, extensible array of computational and data-analytics tools, and allows them to securely share scientific workflows and conclusions. We demonstrate how to use the current RNA-seq tools in KBase, applicable to both plants and microbes, to assemble and quantify long transcripts and identify differentially expressed genes effectively. More specifically, we demonstrate the utility of the platform by identifying key genes that are differentially expressed during drought-stress in Sorghum bicolor, which is an important sustainable production crop plant. We then show how to use KBase tools to predict the membership of genes in metabolic pathways and examine expression data in the context of metabolic subsystems. We demonstrate the power of the platform by making the data analysis and interpretation available to the biologists in the reproducible, re-usable, point-and-click format of a KBase Narrative thus promoting FAIR (Findable, Accessible, Interoperable and Reusable) guiding principles for scientific data management and stewardship.
The optoelectronic properties of semiconducting polymers and device performance rely on a delicate interplay of design and processing conditions. However, screening and optimizing the relationships between these parameters for reliably fabricating organic electronics can be an arduous task requiring significant time and resources. To overcome this challenge, Polybot is developed—a robotic platform within a self‐driving lab that can efficiently produce organic field‐effect transistors (OFETs) from various semiconducting polymers via high‐throughput blade coating deposition. Polybot not only handles the fabrication process but also can conduct characterization tests on the devices and autonomously analyze the data gathered, thus facilitating the rapid acquisition of data on a large scale. This work leverages the capabilities of this platform to investigate the fabrication of OFETs using hydrogen bonding‐containing semiconducting polymers. Through high‐throughput fabrication and characterization, various data trends are analyzed, and large extents of anisotropic charge mobility are observed in devices. The materials are thoroughly characterized to understand the role of processing conditions in solid state and electronic properties of these organic semiconductors. The findings demonstrate the effectiveness of automated fabrication and characterization platforms in uncovering novel structure–property relationships, facilitating refinement of rational chemical design, and processing conditions, ultimately leading to new semiconducting materials.