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DOE OSTI2021

sas-temper

Modeling remains a considerable challenge for practitioners of SAXS and SANS because the materials being studied are not highly ordered, the length scales being studied are large, and the information content of the data is low. Data analysis is both challenging and time consuming. Novices often rely on the assistance of an expert, such as the instrument scientist who supported them at the facility where the experiment was performed, to analyze their data. The high flux provided by modern facilities makes it possible to study dozens of samples or sample conditions during a single trip. Ultimately, the high throughput of modern instruments limits access to instrument scientists. A bottleneck in the research effort results that reduces facility productivity. Sas-temper seeks to address this problem, as well as the intrinsic difficulties of being confident in non-linear least squared fitting of data, by providing tools that automate much of the manual process of initial data fitting and refinement, as well as by providing tools for characterizing the nature of the parameter space that fits the measured data. The tools provided by sas-temper help small-angle scattering practitioners transition data into results.

Heller, William [Oak Ridge National Lab. (ORNL), O↗

DOE OSTI2025

Ascribe XR v0.1.0

Ascribe XR is an immersive visualization software designed for scientists and engineers working with 3D data sets. Its key features include interactive exploration, multi-user collaboration, and flexible data import capabilities, supporting various formats such as meshes, volumes, and terrain maps. The software utilizes Godot, OpenXR and PC-VR technology to provide an immersive experience. Ascribe XR is used for data analysis, visualization, and collaboration in various fields, enabling users to gain deeper insights into complex data sets. Its advantages over similar technologies include its flexibility, customizability, and ease of use. Ascribe XR's interactive and immersive environment facilitates collaboration and accelerates the discovery process. Compared to traditional 2D visualization tools, Ascribe XR offers a more engaging and intuitive experience, allowing users to explore complex data sets in a more natural and interactive way. Its ability to support multi-user collaboration and flexible data import capabilities make it a versatile tool for various applications. Overall, Ascribe XR provides a unique combination of features, usability, and performance, making it an attractive solution for scientists and engineers working with 3D data sets.

Pandolfi, Ronald [Lawrence Berkeley National Labor↗

DOE OSTI2022

Heavy-tailed distribution of the number of papers within scientific journals

Scholarly publications represent at least two benefits for the study of the scientific community as a social group. First, they attest to some form of relation between scientists (collaborations, mentoring, heritage, …), useful to determine and analyze social subgroups. Second, most of them are recorded in large databases, easily accessible and including a lot of pertinent information, easing the quantitative and qualitative study of the scientific community. Understanding the underlying dynamics driving the creation of knowledge in general, and of scientific publication in particular, can contribute to maintaining a high level of research, by identifying good and bad practices in science. In this article, we aim to advance this understanding by a statistical analysis of publication within peer-reviewed journals. Namely, we show that the distribution of the number of papers published by an author in a given journal is heavy-tailed, but has a lighter tail than a power law. Interestingly, we demonstrate (both analytically and numerically) that such distributions match the result of a modified preferential attachment process, where, on top of a Barabási-Albert process, we take the finite career span of scientists into account.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena

Abstract Climate variability and weather phenomena can cause extremes and pose significant risk to society and ecosystems, making continued advances in our physical understanding of such events of utmost importance for regional and global security. Advances in machine learning (ML) have been leveraged for applications in climate variability and weather, empowering scientists to approach questions using big data in new ways. Growing interest across the scientific community in these areas has motivated coordination between the physical and computer science disciplines to further advance the state of the science and tackle pressing challenges. During a recently held workshop that had participants across academia, private industry, and research laboratories, it became clear that a comprehensive review of recent and emerging ML applications for climate variability and weather phenomena that can cause extremes was needed. This article aims to fulfill this need by discussing recent advances, challenges, and research priorities in the following topics: sources of predictability for modes of climate variability, feature detection, extreme weather and climate prediction and precursors, observation–model integration, downscaling, and bias correction. This article provides a review for domain scientists seeking to incorporate ML into their research. It also provides a review for those with some ML experience seeking to broaden their knowledge of ML applications for climate variability and weather.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

The U.S. DOE ARM User Facility Establishes a New Site for Studies of Land–Aerosol–Cloud Interactions in the Southeastern United States

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility has established a new site in the Bankhead National Forest (BNF) in northern Alabama that will gather data on how clouds, the land surface, and aerosols interact at a hierarchy of scales important to understanding and simulating the Earth system. Starting its operations in October 2024, the BNF site provides a multi-year opportunity for scientists to unravel complex land-atmosphere interactions. A suite of ground-based sensors, elevated tower-based instrumentation, and aerial facilities will enable scientists to investigate those interactions from within the canopy to the clouds. The Southeastern United States was recommended by the DOE ARM and its collaborators in the broader community as an important region to address their common scientific questions given the region’s abundant surface-forced convective clouds and mesoscale convective systems that pose ongoing challenges in Earth system models. The region is also home to significant terrain complexity and land-use heterogeneity that will unleash new understanding of anthropogenic and biogenic aerosol processes, boundary-layer aerosol-cloud interactions, and the interactions between the terrestrial ecosystem and coupled aerosol-cloud-radiation processes.

Aerosols↗

DOE OSTI2024

Landmark Publications in Analytical Atomic Spectrometry: Fundamentals and Instrumentation Development

The almost-two-centuries history of spectrochemical analysis has generated a body of literature so vast that it has become nearly intractable for experts, much less for those wishing to enter the field. Authoritative, focused reviews help to address this problem but become so granular that the overall directions of the field are lost. This broader perspective can be provided partially by general overviews but then the thinking, experimental details, theoretical underpinnings, and instrumental innovations of the original work must be sacrificed. In the present compilation, this dilemma is overcome by assembling the most impactful publications in the area of analytical atomic spectrometry. Each entry was proposed by at least one current expert in the field and supported by a narrative that justifies its inclusion. The entries were then assembled into a coherent sequence and returned to contributors for a round-robin review. A total of 48 scientists participated in this endeavor, contributing a combined list of 1055 individual articles spanning 17 sub-disciplines of spectrochemical analysis into what the current community views as “key” publications. Of these cited articles, 60 received nominations from four or more scientists, establishing them as the most indispensable reading materials. The outcome of this collaborative effort is intended to serve as a valuable resource not only for current practitioners in atomic spectroscopy but also for present and future students who represent coming generations of analytical atomic spectroscopists.

Instruments & Instrumentation↗

DOE OSTI2023

HUMAN-ARTIFICIAL INTELLIGENCE TEAMING FOR THE U.S. NAVY: DEVELOPING A HOLISTIC RESEARCH ROADMAP

With the ever-increasing deluge of data and demand for warfighters to make decisions upon its analysis, U.S. defense strategy has prioritized the development of artificial intelligence (AI)/machine learning (ML) systems that can analyze multi-source data streams and suggest courses of action. However, there is a history of systems that have failed to be adopted by the warfighter due not only to unsolved technical challenges, but also a lack of usability or contributions to mission effectiveness, perceived or otherwise. To avoid this, research into human-AI teaming shows promise for developing AI systems that work with frontline operators. A recent National Academies of Sciences, Engineering, and Medicine report (NASEM, 2022) presented 57 research objectives in this area; however, the U.S. Navy requires a more-focused set of priorities, as it is impossible to tackle every priority. A workshop involving 23 human factors scientists, computer scientists, and active-duty sailors was organized at the Naval Information Warfare Center Pacific, resulting in a set of five research priorities spanning near-, mid-, and far-term time frames. This panel will summarize the results of this workshop, with a focus on the big questions both going into this workshop and coming out of it. The panel participants come from government, academia, and industry, providing perspective from the different kinds of organizations required to accomplish these research goals.

Wong, Jason↗

DOE OSTI2020

A terminology for in situ visualization and analysis systems

The term "in situ processing" has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over 50 experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. This paper discusses these axes, evaluates existing systems within the axes, and explores how currently used terms relate to the axes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

The founding charter of the Genomic Observatories Network

The co-authors of this paper hereby state their intention to work together to launch the Genomic Observatories Network (GOs Network) for which this document will serve as its Founding Charter. We define a Genomic Observatory as an ecosystem and/or site subject to long-term scientific research, including (but not limited to) the sustained study of genomic biodiversity from single-celled microbes to multicellular organisms. An international group of 64 scientists first published the call for a global network of Genomic Observatories in January 2012. The vision for such a network was expanded in a subsequent paper and developed over a series of meetings in Bremen (Germany), Shenzhen (China), Moorea (French Polynesia), Oxford (UK), Pacific Grove (California, USA), Washington (DC, USA), and London (UK). While this community-building process continues, here we express our mutual intent to establish the GOs Network formally, and to describe our shared vision for its future. The views expressed here are ours alone as individual scientists, and do not necessarily represent those of the institutions with which we are affiliated.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Elviz – exploration of metagenome assemblies with an interactive visualization tool

Metagenomics, the sequencing of DNA collected from an entire microbial community, enables the study of natural microbial consortia in their native habitats. Metagenomics studies produce huge volumes of data, including both the sequences themselves and metadata describing their abundance, assembly, predicted functional characteristics and environmental parameters. The ability to explore these data visually is critically important to meaningful biological interpretation. Current genomics applications cannot effectively integrate sequence data, assembly metadata, and annotation to support both genome and community-level inquiry. Elviz (Environmental Laboratory Visualization) is an interactive web-based tool for the visual exploration of assembled metagenomes and their complex metadata. Elviz allows scientists to navigate metagenome assemblies across multiple dimensions and scales, plotting parameters such as GC content, relative abundance, phylogenetic affiliation and assembled contig length. Furthermore Elviz enables interactive exploration using real-time plot navigation, search, filters, axis selection, and the ability to drill from a whole-community profile down to individual gene annotations. Thus scientists engage in a rapid feedback loop of visual pattern identification, hypothesis generation, and hypothesis testing. Compared to the current alternative of generating a succession of static figures, Elviz can greatly accelerate the speed of metagenome analysis. Elviz can be used to explore both user-submitted datasets and numerous metagenome studies publicly available at the Joint Genome Institute (JGI). Elviz is freely available at http://genome.jgi.doe.gov/viz and runs on most current web-browsers.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

ESS-DIVE guidelines for archiving terrestrial model data

This dataset contains supporting documents and images for ESS-DIVE terrestrial model data archiving guidelines.Terrestrial models are broadly defined as numerical models that couple both land dynamics and energy, water, carbon, or nutrient fluxes. We created these guidelines based on input from the U.S. Department of Energy’s Biological and Environmental Research land modeling community. The guidelines are intended to help modelers determine which components of their terrestrial model data associated with publication should be archived. Based on input from the land modeling community, the guidelines recommend archiving both model input and testing data, as well as code, script, and metadata. The guidelines also recommend archiving model data output, depending on the limitations set by data repositories. Lastly, we provide recommendations for bundling data files for publication as well as a discussion about tools that can facilitate model data archiving and reuse.This dataset is an archive of the associated GitHub repository for our model archiving guidelines (https://github.com/ess-dive-community/essdive-model-data-archiving-guidelines). The ‘README.pdf’ file gives a general introduction to the guidelines, and the ‘instructions.pdf’ file provides more detailed steps for following the guidelines. We also provide 2 figures in this data package: 1) a decision tree (model_data_guidelines_decision_tree.png) that can help users determine which components of their model data to archive. and 2) the ‘model_data_guidelines_flmd.png’ file depicts the different files that can be archived in addition to the model data itself. Lastly, we include 3 digitized tables from our associated manuscript and 3 CSV files with anonymized input from DOE scientists about the importance of different aspects of model data archiving from which we developed the guidelines.Dataset updates for v1.1.0: We updated this data package on 2021-11-22 in response to review comments on our related manuscript. In this update we removed one figure so that the model archiving guidelines are conveyed in text rather than an image. We updated the file-level metadata (FLMD) figure to be in accord with the most recent FLMD recommendations. We made minor edits to the README file to update the recommended citation and added two co-authors. We also added 6 new data files (3 are anonymized input from DOE scientists that helped to inform guidelines, and 3 are digitized tables from our manuscript.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems

We present the current status of a large-scale computing framework to address the need of the multidisciplinary effort to study chemical dynamics. Specifically, we are enabling scientists to process and store experimental data, run large-scale computationally expensive high-fidelity physical simulation, and analyze these results using the state-of-the-art data analytics tools, machine learning, and uncertainty quantification methods using heterogeneous computing resources, such as CPU and GPU cluster. The framework can integrate or abstract out multiple domains based on roles. In order to develop this framework, we have leveraged an existing framework coupled with in-house heterogeneous computing resources. We present the results of using this framework on a single metadata triggered workflow to accelerate an additive manufacturing use case.

heterogeneous computing, hpc, workflows, reproduci↗

DOE OSTI2021

CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation

Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model improvements. In this paper we demonstrate CrossCheck, an interactive visualization tool for rapid crossmodel comparison and reproducible error analysis. We describe the tool and discuss design and implementation details. We then present three use cases (named entity recognition, reading comprehension, and clickbait detection) that show the benefits of using the tool for model evaluation. CrossCheck allows data scientists to make informed decisions to choose between multiple models, identify when the models are correct and for which examples, investigate whether the models are making the same mistakes as humans, evaluate models’ generalizability and highlight models’ limitations, strengths and weaknesses. Furthermore, CrossCheck is implemented as a Jupyter widget, which allows rapid and convenient integration into data scientists’ model development workflows.

Arendt, Dustin L.↗

DOE OSTI2021

PyGDH: Python Grid Discretization Helper

Mathematical models expressed in the form of discretized equations play an important role inmany scientific disciplines. In our experience, few domain scientists have sufficient backgroundin numerical computing (or the time required to acquire such a background) to use manyflexible and powerful but complex open source packages, such as FEniCS (Alnæs et al., 2015)and OpenFOAM (The OpenFOAM Foundation Ltd, n.d.). Many user-friendly open sourcepackages, such as FiPy (J. E. Guyer & Warren, 2009), and many commercial packages, suchas COMSOL Multiphysics (COMSOL AB, n.d.) and Simcenter STAR-CCM+ (Siemens DigitalIndustries Software, n.d.), provide limited flexibility in the equations that users can express.Additionally, the use of commercial packages, which by nature do not perform calculationstransparently, can hinder reproducibility, which is vital to the scientific process. PyGDH(“pigged”) is a Python 2 / Python 3 (Python Software Foundation, 1991–2020) package thatis meant to be accessible to scientists who might not be specialists in scientific computing,while approaching the level of flexibility associated with writing dedicated programs tailoredto solving specific problems. The PyGDH User’s Guide provides detailed instructions forcreating numerical models, including a brief introduction to necessary command line andPython (Python Software Foundation, 1991–2020) skills, and discussions of discretization andvalidation. Note that PyGDH emphasizes flexibility and simplicity over performance, and wasnot designed for high-performance applications or models describing complex spatial domains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Unraveling Crop Residue Harvest Effects on Soil Organic Carbon

Crop residues protect soil resources, sustain soil organic carbon (SOC), cycle nutrients, support microbial communities, and provide bioenergy feedstock. Herein we (i) summarize the origin of the “Crop Residues for Advanced Biofuels: Effects on Soil Carbon” workshop; (ii) review the workshop structure; and (iii) present consensus points, unanswered questions, and outcomes of the workshop. Initiated by a farmer/ethanol investor’s letter to the American Society of Agronomy (ASA) President, an ASA Working Group (WG) was established to make a recommendation to the ASA Board on how to respond. The WG concluded a Tri-Society sponsored workshop involving farmers, ethanol production and marketing groups, agronomists, crop scientists, soil scientists, engineers, non-governmental organizations, life cycle analysis (LCA) experts, and regulatory personnel was needed to address the complex question that had been posed and to ensure all viewpoints were fully represented. The WG was expanded to an ASA Task Force who organized the workshop in Sacramento, CA. Eighty workshop attendees identified four consensus areas (crop residue management [CRM]; severity of soil erosion; tillage, CRM and erosion linkages; and the importance of simulation [LCA] models) and four unresolved themes (carbon sequestration, CRM effects on SOC stocks, changing climate effects, and the need to focus on ecosystem services rather than single endpoints). The workshop resulted in three direct outcomes: (i) development of a stakeholder funded SOC database, (ii) LCA and SOC model improvements, and (iii) development of this special section in Agronomy Journal. Dale and Ong (2014) concluded that large scale, renewable energy systems are no longer just a “good idea”; but essential, and during the next few decades society must develop systems capable of producing clean energy at the multi-terawatt scale. Crop residues such as corn (Zea mays L.) stover, and other cellulosic materials were identified as potential feedstocks to meet this demand and have been rigorously evaluated for the past decade (e.g., Perlack et al., 2005; U.S. Department of Energy, 2011 and 2016). However, it is also well established that crop residues have important ecosystem service and productivity functions (Larson, 1979; Johnson, 2018; Ibrahim et al., 2018).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2010

Global Prospects Rooted in Soil Science

Th e biosphere, our fragile and exquisite home, is changing abruptly and irrevocably, largely from human interference. Most or all of the coming stresses have links to the land, so fi nding hopeful outcomes depend on wide and deep understanding of soils. In this review, we pose eight urgent issues confronting humanity in coming decades: demands for food, water, nutrients, and energy; and challenges of climate change, biodiversity, “waste” reuse, and global equity. We then suggest some steps soil scientists might take to address these questions: a refocusing of research, a broadening of vision, a renewed enticement of emerging scientists, and more lucid telling of past successes and future prospects. Th e questions posed and responses posited are incomplete and not yet fully refi ned. But the conversations they elicit may help direct soil science toward greater relevance in preserving our fragile home on this changing planet.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

The future of the correlated electron problem

A central problem in modern condensed matter physics is the understanding of materials with strong electron correlations. Despite extensive work, the essential physics of many of these systems is not understood and there is very little ability to make predictions in this class of materials. In this manuscript we share our personal views on the major open problems in the field of correlated electron systems. We discuss some possible routes to make progress in this rich and fascinating field. This manuscript is the result of the vigorous discussions and deliberations that took place at Johns Hopkins University during a three-day workshop January 27, 28, and 29, 2020 that brought together six senior scientists and 46 more junior scientists. Our hope, is that the topics we have presented will provide inspiration for others working in this field and motivation for the idea that significant progress can be made on very hard problems if we focus our collective energies.

Alexandradinata, Aris [Univ. of Illinois at Urbana↗

DOE OSTI2011

2010 Neutron Review: ORNL Neutron Sciences Progress Report

During 2010, the Neutron Sciences Directorate focused on producing world-class science, while supporting the needs of the scientific community. As the instrument, sample environment, and data analysis tools at High Flux Isotope Reactor (HFIR ) and Spallation Neutron Source (SNS) have grown over the last year, so has promising neutron scattering research. This was an exciting year in science, technology, and operations. Some topics discussed are: (1) HFIR and SNS Experiments Take Gordon Battelle Awards for Scientific Discovery - Battelle Memorial Institute presented the inaugural Gordon Battelle Prizes for scientific discovery and technology impact in 2010. Battelle awards the prizes to recognize the most significant advancements at national laboratories that it manages or co-manages. (2) Discovery of Element 117 - As part of an international team of scientists from Russia and the United States, HFIR staff played a pivotal role in the discovery by generating the berkelium used to produce the new element. A total of six atoms of ''ununseptium'' were detected in a two-year campaign employing HFIR and the Radiochemical Engineering Development Center at Oak Ridge National Laboratory (ORNL) and the heavy-ion accelerator capabilities at the Joint Institute for Nuclear Research in Dubna, Russia. The discovery of the new element expands the understanding of the properties of nuclei at extreme numbers of protons and neutrons. The production of a new element and observation of 11 new heaviest isotopes demonstrate the increased stability of super-heavy elements with increasing neutron numbers and provide the strongest evidence to date for the existence of an island of enhanced stability for super-heavy elements. (3) Studies of Iron-Based High-Temperature Superconductors - ORNL applied its distinctive capabilities in neutron scattering, chemistry, physics, and computation to detailed studies of the magnetic excitations of iron-based superconductors (iron pnictides and chalcogenides), a class of materials discovered in 2008. This research is yielding new insights into the relationship between magnetism and superconductivity and has established several key features of this family of high-temperature superconducting (HTS ) materials: the maximum magnetic field at which they can function, the nature of the electrons involved in the superconductivity, the dependence of the properties upon chemical substitution, and the character of the magnetic fluctuations in the material. The results suggest that despite important differences between these materials and the HTS copper oxides, a universal mechanism may be responsible for the unconventional superconductivity. (4) Coal Sequestration Research: A New Home for Greenhouse Gases - One possibility for slowing down the increasing levels of carbon dioxide (CO{sub 2}) in the atmosphere is to capture the gas in natural underground features such as coal seams. Critical to the feasibility of this technology is determining how much CO{sub 2} can be stored, no method for which has been found - until now. (5) Accelerator Reliability Passes 92% - In December 2010, SNS set a new record for itself when the accelerator ran at 1 MW with 100% reliability. Target Performance Exceeds All Expectations - The mercury target used at SNS is the first of its kind. During the design and planning for SNS, many people were skeptical that the target would work. In 2010, it was confirmed that the target was working not only well but much better than anyone would have imagined. (6) Changing the World of Data Acquisition - Researchers at SNS are starting to benefit from event-based data analysis. Event data mode captures and stores an individual data set for every single neutron that strikes a detector - precisely when and where the neutron is detected. This technique provides numerous advantages over traditional methods. Event data mode allows researchers to process their data at the highest resolution possible with no loss of data. This method of data collection provides a much more efficient way for users to gather data and get the most from their beam time. (7) New Laboratories for Users - The HFIR and SNS user communities continue to grow dramatically. In 2010, HFIR hosted 862 users and SNS 796, outpacing projections for both facilities. To meet the needs of those users, a new complex of 13 laboratories is now open for users at SNS. (8) Innovative Detectors Provide Relief from Helium-3 Shortage - Helium-3 ({sup 3}He) has been the gas of choice for gaseous detectors since the early days of neutron science. About two years ago, detector scientists worldwide faced the reality that stockpiles of {sup 3}He are dwindling rapidly, while demand for it has risen by a factor of five. The Neutron Sciences Detectors Group has developed two new types of detectors that don't rely on {sup 3}He: the Anger camera and the wavelength shifting fiber neutron detector, both of which use lithium ({sup 6}Li).

High Flux isotope reactor↗