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At least 109 records · Page 6

Fostering Open Science in Earth Data Science Research: Insights From Earthdata Forum By ASDC

In the dynamic landscape of Earth Science research, the promotion of open science principles is paramount for advancing knowledge and collaboration. The Earthdata Forum is an actively maintained and operational user forum for all participating National Aeronautics and Space Administration (NASA) Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Centers (DAACs), and the Global Change Master Directory (GCMD). The Forum serves as a cross-DAAC platform from which user communities can obtain authoritative information relating to NASA Earth Science. This abstract explores the role of the Earthdata Forum forum.earthdata.nasa.gov as a pivotal platform in fostering open science within the Earth Science community. The platform serves as a hub for researchers to actively engage in discussions, share datasets, and collaboratively tackle challenges in the field. Key aspects discussed include the platform's contribution to data accessibility, collaboration, and knowledge sharing. Forum.earthdata.nasa.gov provides a space where researchers transparently ask questions, discuss methodologies, share insights, and seek advice from a vibrant community. The resulting collaborative environment not only facilitates the exchange of ideas but also bolsters the collective knowledge base.

Earthdata FORUM↗

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗

Nuclear Science User Facilities High Performance Computing: Provide a Science Gateway for HPC Users

Idaho National Laboratory (INL), supported by the Department of Energy Office of Nuclear Energy (DOE-NE) through the Nuclear Science User Facilities (NSUF), provides direct access to the Barracuda Virtual Reactor and 18 Multiphysics Object-Oriented Simulation Environment (MOOSE) applications via a web-based science gateway developed using Open Ondemand on the INL high performance computing (HPC) systems. This gateway features the computational tools of the Nuclear Computational Resource Center (NCRC) and the computing resources of the INL high performance computing systems. These computational tools are a key foundation of collaboration and innovation in nuclear energy systems research. High performance computing resources and INL staff directly support the mission and objectives of DOE-NE. The Barracuda Virtual Reactor was the first science gateway deployed in Jan 2021 to support NSUF users. In July 2021, the science gateway was expanded to support access to NCRC codes for use across all supported INL HPC systems. The HPC science gateway currently supports 20 total applications. The gateway also includes access to training resources specific to NCRC tools.

99 GENERAL AND MISCELLANEOUS↗

In silico environmental chemical science: properties and processes from statistical and computational modelling

Quantitative structure–activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with “in silico” results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for “in silico environmental chemical science” are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.

54 ENVIRONMENTAL SCIENCES↗

Basic Energy Sciences Roundtable: Opportunities for Basic Research at the Frontiers of XFEL Ultrafast Science

Advances in science and technology over the past century have been driven by an improved understanding of matter on ultrashort length scales, reaching down to atomic dimensions. In contrast, methods aimed at understanding dynamics on the ultrafast time scales of atomic motion are comparatively new. Ultrafast characterization has already yielded crucial insights not attainable from slower measurements. The interplay between atomic-scale structure and the associated ultrafast dynamics governs the macroscopic functionality observed in matter. Understanding and controlling materials and chemical processes at these length and time scales are key to discovery and innovation to advance energy and related national priorities. The recent availability of x-ray free-electron lasers (XFELs) provides a probe that simultaneously reaches the required resolution in both space and time. X-ray wavelengths extend down to the atomic scale, while x-ray pulse durations now lie in the femtosecond (10 -15 seconds) range. This capability allows the evolution of materials and chemical processes to be followed on their natural time and length scales, providing fundamental scientific understanding of the complexity of the world around us. Because of the transformative potential of new ultrafast x-ray characterization tools provided by XFELs, it is imperative to lay out a roadmap for the exciting scientific opportunities that can be explored using these research tools. To identify the highest priority research opportunities, the U.S. Department of Energy Office of Basic Energy Sciences (BES) convened a roundtable of experts in chemistry, materials physics, and ultrafast and x-ray science. This group of experimentalists and theorists met on October 25–26, 2017 to explore research opportunities that will leverage current and imminent ultrafast XFEL capabilities and advance the broader BES science mission. This report summarizes major new scientific frontiers that can be addressed by emerging XFEL capabilities. The conclusions of the roundtable discussion are summarized in the following three Priority Research Opportunities (PROs).

36 MATERIALS SCIENCE↗

Science of the Van Allen Probes Science Operations Centers

The Van Allen Probes mission operations materialized through a distributed model in which operational responsibility was divided between the Mission Operations Center (MOC) and separate instrument specific SOCs. The sole MOC handled all aspects of telemetering and receiving tasks as well as certain scientifically relevant ancillary tasks. Each instrument science team developed individual instrument specific SOCs proficient in unique capabilities in support of science data acquisition, data processing, instrument performance, and tools for the instrument team scientists. In parallel activities, project scientists took on the task of providing a significant modeling tool base usable by the instrument science teams and the larger scientific community. With a mission as complex as Van Allen Probes, scientific inquiry occurred due to constant and significant collaboration between the SOCs and in concert with the project science team. Planned cross-instrument coordinated observations resulted in critical discoveries during the seven-year mission. Instrument cross-calibration activities elucidated a more seamless set of data products. Specific topics include post-launch changes and enhancements to the SOCs, discussion of coordination activities between the SOCs, SOC specific analysis software, modeling software provided by the Van Allen Probes project, and a section on lessons learned. One of the most significant lessons learned was the importance of the original decision to implement individual team SOCs providing timely and well-documented instrument data for the NASA Van Allen Probes Mission scientists and the larger magnetospheric and radiation belt scientific community.

47 OTHER INSTRUMENTATION↗

Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

We introduce atomate, an open-source Python framework for computational materials science simula-tion, analysis, and design with an emphasis on automation and extensibility. Built on top of open sourcePython packages already in use by the materials community such as pymatgen, FireWorks, and custodian,atomate provides well-tested workflow templates to compute various materials properties such as elec-tronic bandstructure, elastic properties, and piezoelectric, dielectric, and ferroelectric properties. Atomatealso enables the computational characterization of materials by providing workflows that calculate X-rayabsorption (XAS), Electron energy loss (EELS) and Raman spectra. One of the major features of atomate isthat it provides both fully functional workflows as well as reusable components that enable one to com-pose complex materials science workflows that use a diverse set of computational tools. Additionally,atomate creates output databases that organize the results from individual calculations and contains abuilder framework that creates summary reports for each computed material based on multiplesimulations.

Mathew, Kiran↗

Tracking materials science data lineage to manage millions of materials experiments and analyses

Abstract In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research.

Soedarmadji, Edwin↗

The National Microbiome Data Collaborative: Enabling Microbiome Science

To harness the potential of microbiome science across the broad range of relevant disciplines, new approaches to data infrastructure and transdisciplinary collaboration are necessary. The National Microbiome Data Collaborative (NMDC) is a new initiative to support microbiome data exploration and discovery through a collaborative integrative data science ecosystem.

Wood-Charlson, Elisha M.↗

The life science x-ray scattering beamline at NSLS-II

We report the current development status of the High Brightness X-ray Scattering for Life Sciences (or Life Science X-ray Scattering, LiX) beamline at the NSLS-II facility of Brookhaven National Laboratory. This instrument will operate in the x-ray energy range of 2.1-18 keV, provide variable beam sizes from 1 micron to ∼0.5 mm, and support user experiments in three scientific areas: (1) high-throughput solution scattering, in-line size exclusion chromatography and flow mixers-based time-resolved solution scattering of biological macro-molecules, (2) diffraction from single- and multi-layered lipid membranes, and (3) scattering-based scanning probe imaging of biological tissues. In order to satisfy the beam stability required for these experiments and to switch rapidly between different types of experiments, we have adopted a secondary source with refractive lenses for secondary focusing, a detector system consisting of three Pilatus detectors, and specialized experimental modules that can be quickly exchanged and each dedicated to a defined set of experiments. The construction of this beamline is on schedule for completion in September 2015. User experiments are expected to start in Spring 2016.

43 PARTICLE ACCELERATORS↗

Photometric redshift analysis in the Dark Energy Survey Science Verification data

In this study, we present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification period of observations in late 2012 and early 2013 that provided science-quality images for almost 200 sq. deg. at the nominal depth of the survey. We assess the photometric redshift (photo-z) performance using about 15 000 galaxies with spectroscopic redshifts available from other surveys. These galaxies are used, in different configurations, as a calibration sample, and photo-z's are obtained and studied using most of the existing photo-z codes. A weighting method in a multidimensional colour–magnitude space is applied to the spectroscopic sample in order to evaluate the photo-z performance with sets that mimic the full DES photometric sample, which is on average significantly deeper than the calibration sample due to the limited depth of spectroscopic surveys. In addition, empirical photo-z methods using, for instance, artificial neural networks or random forests, yield the best performance in the tests, achieving core photo-z resolutions σ68 ~ 0.08. Moreover, the results from most of the codes, including template-fitting methods, comfortably meet the DES requirements on photo-z performance, therefore, providing an excellent precedent for future DES data sets.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Combining Dark Energy Survey Science Verification data with near-infrared data from the ESO VISTA Hemisphere Survey

We present the combination of optical data from the Science Verification phase of the Dark Energy Survey (DES) with near-infrared (NIR) data from the European Southern Observatory VISTA Hemisphere Survey (VHS). The deep optical detections from DES are used to extract fluxes and associated errors from the shallower VHS data. Joint seven-band ( grizYJK ) photometric catalogues are produced in a single 3 sq-deg dedicated camera field centred at 02h26m-04d36m where the availability of ancillary multiwavelength photometry and spectroscopy allows us to test the data quality. Dual photometry increases the number of DES galaxies with measured VHS fluxes by a factor of ~4.5 relative to a simple catalogue level matching and results in a ~1.5 mag increase in the 80 per cent completeness limit of the NIR data. Almost 70 per cent of DES sources have useful NIR flux measurements in this initial catalogue. Photometric redshifts are estimated for a subset of galaxies with spectroscopic redshifts and initial results, although currently limited by small number statistics, indicate that the VHS data can help reduce the photometric redshift scatter at both z < 0.5 and z > 1. We present example DES+VHS colour selection criteria for high-redshift luminous red galaxies (LRGs) at z ~ 0.7 as well as luminous quasars. Using spectroscopic observations in this field we show that the additional VHS fluxes enable a cleaner selection of both populations with <10 per cent contamination from galactic stars in the case of spectroscopically confirmed quasars and <0.5 per cent contamination from galactic stars in the case of spectroscopically confirmed LRGs. The combined DES+VHS data set, which will eventually cover almost 5000 sq-deg, will therefore enable a range of new science and be ideally suited for target selection for future wide-field spectroscopic surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

The Fusion Nuclear Science Facility, the Critical Step in the Pathway to Fusion Energy

The proposed Fusion Nuclear Science Facility (FNSF) represents the first facility to enter the complex fusion nuclear regime, and its technical mission and attributes are being developed. The FNSF represents one part of the fusion energy development pathway to the first commercial power plant with other major components being the pre-FNSF research and development, research in parallel with the FNSF, pre-DEMO research and development, and the demonstration power plant (DEMO). Finally, the Fusion Energy Systems Studies group is developing the technical basis for the FNSF in order to provide a better understanding of the demands on the fusion plasma and fusion nuclear science programs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Basic Energy Sciences Roundtable: Opportunities for Quantum Computing in Chemical and Materials Sciences

Fundamental transformations in the basic logic of computing are few and far between. Since the invention of digital computers in the early 1940s, the logic underlying computation has remained the same, even as computing hardware evolved from vacuum tubes to silicon transistors. With the advent of quantum computation, a fundamental transformation is near. Quantum computation is based on a different type of logic: rather than being in one of the two states of a classical bit, a quantum bit or qubit can be in a superposition of two states simultaneously. Operations and measurements on these qubits obey the constraints of quantum mechanics. It is now understood that quantum computers have great power in principle to go beyond classical computers, but that not every application is well suited to implementation on quantum computers. For reasons explained in more detail in the Introduction, scientific problems in chemical and materials sciences are uniquely suited to take advantage of quantum computing in the relatively near future. Indeed, quantum computing offers the best hope to solve many of the most important and difficult problems in this field. For example, quantum materials, such as superconductors and complex magnetic materials, show novel kinds of ordered phases that are natural from the point of view of quantum mechanics but difficult to access via computation on classical computers. Quantum sensors based on solid materials are already widely used but could be greatly improved with insight from quantum computations, as could materials for information technologies. Quantum chemical dynamics is another example of a problem that is intrinsically well suited to studies on quantum computers. Applications of quantum chemical dynamics include catalysis, artificial photosynthesis, and other industrially important processes. Quantum computers exist in the laboratory and are beginning to exceed 50 qubits, which is roughly the size beyond which their behavior cannot be predicted or emulated on present-day classical supercomputers. While a quantum computer of 50 qubits is almost certainly not powerful enough to tackle the major scientific challenges in chemical and materials sciences, some of these major challenges start to become accessible with a few hundred qubits if error rates can be kept small. This roundtable was convened to ask how emerging quantum computers can be applied to major scientific problems in chemical and materials sciences, in light of Basic Energy Sciences’s leading role in these fields and the Department of Energy’s leading role in high-performance scientific computation more generally. The main outcome of the roundtable was a consensus that there are scientific problems of great importance on which emerging quantum computers have the potential for disruptive impact, and where comparable progress is unlikely to occur by other means.

36 MATERIALS SCIENCE↗

FY21 NNSA-IAEC Science Area V, Environmental ISR, Waste Management and Subsurface Science (Final Report)

The FY21 NNSA NA-22 Final Report represents the progress achieved over the past year (FY21) by the research team for Topic Area 3, Waste Management & Subsurface Science (WM3), and Topic Area 7, Radiation and Thermal Effects on Bituminous Rocks (WM7). WM3 and WM7 are part of Science Area V (Subsurface Science and Waste Management) in the NNSA-IAEC Science and Technology Working Group. The project was initiated four years ago (2017) with an MOU agreement between the U.S. National Nuclear Security Administration (NNSA) and the Israel Atomic Energy Commission (IAEC) to evaluate the feasibility of geological subsurface disposal of radioactive waste in Israel. The core WM3 and WM7 teams are comprised of scientists and engineers from Los Alamos National Laboratory, the Geologic Survey of Israel (GSI), and the Nuclear Research Center - Negev (NRCN), with a close collaboration to the teams from Sandia National Laboratory (SNL) and Lawrence Livermore Laboratory (LLNL)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Improving science literacy and education through space life sciences

The National Space Biomedical Research Institute (NSBRI) encourages open involvement by scientists and the public at large in the Institute's activities. Through its Education and Public Outreach Program, the Institute is supporting national efforts to improve Kindergarten through grade twelve (K-12) and undergraduate education and to communicate knowledge generated by space life science research to lay audiences. Three academic institution Baylor College of Medicine, Morehouse School of Medicine and Texas A&M University are designing, producing, field-testing, and disseminating a comprehensive array of programs and products to achieve this goal. The objectives of the NSBRI Education and Public Outreach program are to: promote systemic change in elementary and secondary science education; attract undergraduate students--especially those from underrepresented groups--to careers in space life sciences, engineering and technology-based fields; increase scientific literacy; and to develop public and private sector partnerships that enhance and expand NSBRI efforts to reach students and families. c 2001. Elsevier Science Ltd. All rights reserved.

NASA Discipline General Space Life Sciences↗

Science Off the Earth: an Integrated Approach to Science Operations in the Artemis Era and Beyond

Humanity is standing on the cusp of its next giant leap – an international, sustainable, and commercial return to the moon. Unlike the Apollo missions, the expectation that a permanent human presence will be established is well founded. Initially, crewed missions to the lunar surface are expected to last between 6 and 30 days and occur approximately once per year, however the robust suite of landers and orbiting assets proposed by both commercial and government entities provide an opportunity to conduct science research on the moon and in cis-lunar space, 24x7 for many years. In the 50 years since the conclusion of Apollo, NASA’s crewed space science missions have been evolving. This can be seen clearly from Skylab and the Space Shuttle’s Spacelab to the International Space Station (ISS). Presently aboard the ISS, international crews spend nearly half their time working on experiments while many payloads collect science non-stop with either very limited, or no crew interaction. Additionally, a fleet of orbiting experiments complement and validate those investigations performed on the ISS. As the number, variety, and complexity of these investigation increase, it becomes ever more necessary to integrate across the many entities involved to prevent duplication of effort, ensure complementary results, reduce cost, and create a sustainable environment to conduct groundbreaking scientific research in space. In this paper, we examine how the various elements of the Artemis program including NASA’s Lunar Gateway, Commercial Lunar Payload Services (CLPS) endeavor, the Human Landing Systems (HLS), surface habitats, rovers and many more will be operated to ensure interoperability, maximize science return, and enable success for government, commercial and private partners.

Science Operations↗

Science Off the Earth: An Integrated Approach to Science Operations in the Artemis Era and Beyond

Humanity is standing on the cusp of its next giant leap – an international, sustainable, and commercial return to the moon. Unlike the Apollo missions, the expectation that a permanent human presence will be established is well founded. Initially, crewed missions to the lunar surface are expected to last between 6 and 30 days and occur approximately once per year, however the robust suite of landers and orbiting assets proposed by both commercial and government entities provide an opportunity to conduct science research on the moon and in cis-lunar space, 24x7 for many years. In the 50 years since the conclusion of Apollo, NASA’s crewed space science missions have been evolving. This can be seen clearly from Skylab and the Space Shuttle’s Spacelab to the International Space Station (ISS). Presently aboard the ISS, international crews spend nearly half their time working on experiments while many payloads collect science non-stop with either very limited, or no crew interaction. Additionally, a fleet of orbiting experiments complement and validate those investigations performed on the ISS. As the number, variety, and complexity of these investigation increase, it becomes ever more necessary to integrate across the many entities involved to prevent duplication of effort, ensure complementary results, reduce cost, and create a sustainable environment to conduct groundbreaking scientific research in space. In this paper, we examine how the various elements of the Artemis program including NASA’s Lunar Gateway, Commercial Lunar Payload Services (CLPS) endeavor, the Human Landing Systems (HLS), surface habitats, rovers and many more will be operated to ensure interoperability, maximize science return, and enable success for government, commercial and private partners.

Science Operations↗