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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sources NASA NTRS DOE OSTI

DOE OSTI2017

Empirical Analysis of the Subjective Impressions and Objective Measures of Domain Scientists’ Visual Analytic Judgments

Scientists often use specific data analysis and presentation methods familiar within their domain. But does high familiarity drive better analytical judgment? This question is especially relevant when familiar methods themselves can have shortcomings: many visualizations used conventionally for scientific data analysis and presentation do not follow established best practices. This necessitates new methods that might be unfamiliar yet prove to be more effective. But there is little empirical understanding of the relationships between scientists’ subjective impressions about familiar and unfamiliar visualizations and objective measures of their visual analytic judgments. To address this gap and to study these factors, we focus on visualizations used for comparison of climate model performance. We report on a comprehensive survey-based user study with 47 climate scientists and present an analysis of : i) relationships among scientists’ familiarity, their perceived lev- els of comfort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.

Dasgupta, Aritra↗

DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Time allocations across collaborations of academic scientists and their impact on efforts to commercialize novel technologies: is more always better?

We investigate how time spent in different collaborative research arrangements by academic scientists affect their propensity of involvement in the commercialization of novel, university‐originated technologies. Three common collaborative strategies used by academic scientists: (1) internal (within the research group or within the home university) research collaboration; (2) cosmopolitan research collaboration (with scientists in other US or foreign universities); or (3) university–industry research collaboration are assessed. Drawing on the concepts of cognitive and spatial distance, the empirical findings suggest that only one internal research collaboration strategy has a significant impact on the propensity of academic scientists to engage in the commercialization of novel technologies with a private firm; however, this relationship is inverse U shaped. More importantly, academic scientists adopting a university–industry collaboration strategy and spending more research time in such an arrangement have a significantly stronger propensity for being involved in technology commercialization with a private firm; however, this relationship is inverse U shaped as well. We discuss the managerial and policy implications of the findings.

Libaers, Dirk↗

DOE OSTI2021

Fostering Radiation Oncology Physician Scientist Trainees Within a Diverse Workforce: The Radiation Oncology Research Scholar Track

There is a need to foster future generations of radiation oncology physician scientists, but the number of radiation oncologists with sufficient education, training, and funding to make transformative discoveries is relatively small. A large number of MD/PhD graduates have entered he field of radiation oncology over the past 2 decades, but this has not led to a significant cohort of externally funded physician scientists. Because radiation oncologists leading independent research labs have the potential to make transformative discoveries that advance our field and positively affect patients with cancer, we created the Duke Radiation Oncology Research Scholar (RORS) Program. In crafting this program, we sought to eliminate barriers preventing radiation oncology trainees from becoming independent physician scientists. The RORS program integrates the existing American Board of Radiology Holman Pathway with a 2-year post-graduate medical education instructor position with 80% research effort at the same institution. We use a separate match for RORS and traditional residency pathways, which we hope will increase the diversity of our residency program. Since the inception of the RORS program, we have matched 2 trainees into our program. We encourage other radiation oncology residency programs at peer institutions to consider this training pathway as a means to foster the development of independent physician scientists and a diverse workforce in radiation oncology.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2020

ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists

Recently, advances in machine learning and artificial intelligence have been playing more and more critical roles in a wide range of areas. For the last several years, industries have shown that how learning from data, identifying patterns and making decisions with minimal human intervention can be extremely useful to their business (e.g., image classification, recommending a product to a customer, finding friends in a social network, predicting customers actions, etc.). These success stories have been motivating scientists who study physics, chemistry, materials, medicine, and many other subjects, to explore a new pathway of utilizing machine learning techniques like regression and classification for their scientific activities. However, most existing machine learning tools, systems, and methodologies have been developed for programming experts but not for scientists (or any users) who have no or little knowledge of programming. ASCENDS is a toolkit that is developed to assist scientists (or any persons) who want to use their data for machine learning tasks, more specifically, correlation analysis, regression, and classification. ASCENDS does not require programming skills. Instead, it provides a set of simple but powerful CLI (Command Line Interface) and GUI (Graphic User Interface) tools for non-data scientists to be able to intuitively perform advanced data analysis and machine learning techniques. ASCENDS has been implemented by wrapping around opensource software including Keras, TensorFlow, and scikit-learn.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Los Alamos Scientists Support Planetary Defense Efforts Worldwide

Every two years the International Academy of Astronautics brings scientists, engineers, spacecraft developers, disaster specialists, and government officials together to share research and ideas, and to consider how to find, and ultimately deflect or destroy, an asteroid that might be threatening Earth. Attending these Planetary Defense Conferences are scientists from Los Alamos National Laboratory who contribute considerable expertise in the areas of computer modeling and asteroid mitigation technologies. Los Alamos scientists work with NASA's Planetary Defense Coordination Office to assist in the location and tracking near-Earth objects, characterization of those objects to determine their orbit trajectory, size, shape, mass, composition, rotational dynamics and other parameters, and planning and implementation of measures to deflect or disrupt an object on an impact course, along with measures that can be taken on Earth to protect lives and property include evacuation of the impact area and movement of critical infrastructure. At the Planetary Defense Conferences scientists share research findings, collaborate on planetary defense strategies, and work on hypothetical scenarios that consider the many timelines and options that might occur during an asteroid threat. To be sure, that threat is rare, but these emergency exercises provide officials with valuable information that may one day protect us all during the real thing.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

Nuclear Science User Facilities: Instrument Scientist Program

The traditional single institution User Facility model prevalent in the DOE complex funds Instrument Scientists (IS) full-time to collaborate with and guide users, disseminate results through publication, build a broad and competent user base, continuously improve instrumentation, enhance data collection methods, and improve data analysis methods. This model focuses the Instrument Scientist on ensuring that visiting researchers obtain the best and most comprehensive data. Instrument Scientists are also expected to establish personal research programs based on the use of the instrument(s) under their charge, which allows for further advances in instrumentation and science. This model ensures that the User Facility offers unique, world-leading instruments and data analysis to the user community, creates high demand among top researchers for the use of these capabilities, and as a result, positively impacts science and technology in the United States.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Mind the gaps! Climate scientists should heed lessons in collaborative storytelling from William Shakespeare

Abstract To co‐produce locally relevant climate knowledge, climate scientists are engaging in new collaborations—with stakeholders and with scholars in the humanities, arts, and social sciences. In our work as a Shakespeare scholar‐turned‐public‐humanist and a climate scientist, we have created a methodology that allows researchers and communities to co‐produce knowledge by co‐producing narratives. We combine principles from emerging climate “storylines” research with collaborative storytelling inspired by William Shakespeare's plays and theatrical practices. Shakespeare's plays spark collaborations and interpretations, in part, because of how Shakespeare leaves gaps in the narrative. These gaps allow others to enter as collaborators, creating a “cognitive ecology” that fosters knowledge and action among all engaged. Integrating these methods into climate storyline‐making offers a radical paradigm: it upends the scientist's role as the focal storyteller and expert, and fosters, instead, partnership, equity, and a co‐exploration of multiple uncertainties. It is time for researchers to cede control to a cognitive ecology of collaborative action. This article is categorized under: Trans‐Disciplinary Perspectives > Humanities and the Creative Arts

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Why scientists should learn to program in Python

Scientists have been central to the historical development of the computer industry, but the importance of software only continues to grow for all areas of scientific research and in particular for powder diffraction. Knowing how to program a computer is a basic and useful skill for scientists. The article introduces the three types of programming languages and why scripting languages are now preferred for scientists. Of them, the authors assert Python is the most useful and easiest to learn. Python is introduced. Also presented is an overview to a few of the many add-on packages available to extend the capabilities of Python, for example, for numerical computations, scientific graphics and graphical user interface programming.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Reducing the Analytical Bottleneck for Domain Scientists: Lessons from a Climate Data Visualization Case Study

The gap between large-scale data production rate and the rate of generation of data-driven scientific insights has led to an analytical bottleneck in scientific domains like climate, biology, etc. This is primarily due to the lack of innovative analytical tools that can help scientists efficiently analyze and explore alternative hypotheses about the data, and communicate their findings effectively to a broad audience. In this paper, by reflecting on a set of successful collaborative research efforts between with a group of climate scientists and visualization researchers, we introspect how interactive visualization can help reduce the analytical bottleneck for domain scientists.

climate↗

DOE OSTI2018

Early-Career Systems Microbiology Scientists

Early-career scientists, especially those just stepping into their first independent roles, represent the tenacity of our creed to continue exploring the microbial world through structured questioning, testing, and observation. While sometimes confused with the epithet “young scientist,” which may represent people with either considerable or limited experience, those in their early career are just starting to craft their professional reputation. This is a most exciting time; all of us can remember that first time we sat down at a desk or bench and realized that for all intents and purposes we were now responsible for our own fate. Let loose from the comparative shackles of graduate school and postdoctoral martyrdom, this period in our growth as professional scientists often presents so many opportunities that it can be bewildering.

Gilbert, Jack A.↗

DOE OSTI2021

Nuclear Targeting Terms for Engineers and Scientists Revised Edition

This is a revised version of LA-UR-17-20752, Nuclear Targeting Terms for Engineers and Scientists, March 1, 2017. The discussion of compounding for damage and compounding for reliability was reworded for clarity. The Department of Defense has a methodology for targeting nuclear weapons, and a jargon that is used to communicate between the analysts, planners, aircrews, and missile crews. The typical engineer or scientist in the Department of Energy may not have been exposed to the nuclear weapons targeting terms and methods. This report provides an introduction to the terms and methodologies used for nuclear targeting. Its purpose is to prepare engineers and scientists to participate in wargames, exercises, and discussions with the Department of Defense. Terms such as Circular Error Probable, probability of hit and damage, damage expectancy, and the physical vulnerability system are discussed. Methods for compounding damage from multiple weapons applied to one target are presented.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2014

Specificity and Engagement: Increasing ELSI s Relevance to Nano Scientists

Scholars studying the ethical, legal, and social issues (ELSI) associated with emerging technologies maintain the importance of considering these issues throughout the research and development cycle, even during the earliest stages of basic research. Embedding these considerations within the scientific process requires communication between ELSI scholars and the community of physical scientists who are conducting that basic research. We posit that this communication can be effective on a broad scale only if it links societal issues directly to characteristics of the emerging technology that are relevant to the physical and natural scientists involved in research and development. In this article, we examine nano-ELSI literature from 2003 to 2010 to discern the degree to which it makes these types of explicit connections. We find that, while the literature identifies a wide range of issues of societal concern, it generally does so in a non-specific manner. It neither links societal issues to particular forms or characteristics of widely divergent nanotechnologies nor to any of the many potential uses to which those nanotechnologies may be put. We believe that these kinds of specificity are essential to those engaged in nano-scale research. We also compare the literature-based findings to observations from interviews we conducted with nanoscientists and conclude that ELSI scholars should add technical- and application-related forms of specificity to their work and their writings to enhance effectiveness and impact in communicating with one important target audience members of the nanoscale science community.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2018

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image-like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience , where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation-based analysis, inversion of network-extracted features, reduced-order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem-specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

An agentic artificially intelligent X-ray scientist

Executing experimental tasks in both normal research laboratories and large-scale scientific facilities often requires extensive human supervision and remains a key challenge on the path to fully autonomous, artificial intelligence (AI)-driven science. Here we demonstrate a large language model-driven agent that autonomously performs X-ray sample alignment on a synchrotron beamline by planning actions, executing instrumental commands, interpreting observations and iterating towards experimental goals. Based on existing large language models with structured tool-use via the model context protocol, our AI X-ray scientist was guided and tested using an in-house-built virtual experimental setup that mirrors a six-circle diffractometer at an operational synchrotron beamline. The agentic workflow developed in the virtual environment was directly deployed on a real beamline, where it correctly identified reference reflections and determined the orientation matrix, an essential first step in any type of single-crystal scattering experiment. Our AI X-ray scientist responded effectively to unexpected experimental conditions, demonstrating adaptive problem-solving and readiness for addressing practical experimental situations. Our study provides a step towards autonomous operation across diverse experimental environments at large-scale scattering facilities.

Chen, Zhantao (ORCID:0000000319543868)↗

DOE OSTI2023

Shower thoughts: why scientists should spend more time in the rain

Abstract Stormwater is a vital resource and dynamic driver of terrestrial ecosystem processes. However, processes controlling interactions during and shortly after storms are often poorly seen and poorly sensed when direct observations are substituted with technological ones. We discuss how human observations complement technological ones and the benefits of scientists spending more time in the storm. Human observation can reveal ephemeral storm-related phenomena such as biogeochemical hot moments, organismal responses, and sedimentary processes that can then be explored in greater resolution using sensors and virtual experiments. Storm-related phenomena trigger lasting, oversized impacts on hydrologic and biogeochemical processes, organismal traits or functions, and ecosystem services at all scales. We provide examples of phenomena in forests, across disciplines and scales, that have been overlooked in past research to inspire mindful, holistic observation of ecosystems during storms. We conclude that technological observations alone are insufficient to trace the process complexity and unpredictability of fleeting biogeochemical or ecological events without the shower thoughts produced by scientists’ human sensory and cognitive systems during storms.

ecosystem functioning↗

DOE OSTI2024

The role of emerging scientists in restoration ecology: insights from a SER2023 conference workshop

Emerging professionals are crucial in advancing ecological restoration by connecting newcomers with established leaders and blending new ideas with traditional practices. This paper highlights the vital role emerging scientists play in restoration ecology and the Society for Ecological Restoration (SER). A survey conducted during the SER2023 conference workshop, organized by the Students and Emerging Professionals committee, showed that emerging professionals contribute significantly to every stage of scientific work, from planning to publication. However, participants emphasized the scarcity of funding opportunities for non‐senior scientists, which limits academic growth. The workshop underscored the importance of nurturing emerging professionals to develop future leaders for SER and ensure continuity. Fostering non‐hierarchical and inclusive networking is key to engaging and empowering these professionals, ultimately contributing to the long‐term success of ecological restoration efforts.

Weidlich, Emanuela W. A.↗

DOE OSTI2019

The Second ARM Training and Science Application Event: Training the Next Generation of Atmospheric Scientists

The Atmospheric Radiation Measurement (ARM) user facility (Turner and Ellingson 2016) collects valuable observations of atmospheric variables affecting the Earth’s radiation budget. These include properties of aerosol, cloud, precipitation, dynamic, and thermodynamic fields. The ARM user facility also supports routine high-resolution modeling efforts at its Southern Great Plains (SGP) site, and over the years it has had significant impacts on cloud-resolving and Earth system model development (Krueger et al. 2016; Randall et al. 2016). In an effort to promote use of the ARM data by the next generation of atmospheric scientists, the second ARM Summer Training and Science Applications Event on observations and modeling of clouds and precipitation took place from 14 to 21 July 2018. The event was sponsored by the ARM User Facility and was catered toward graduate students and early career scientists interested in observations and modeling of aerosol, cloud, and precipitation processes. Theoretical and practical instructions on the application of ground- and aircraft-based observations from a wide array of active and passive sensors, and finescale-resolution model simulations were provided during the training. This informed the participants about innovative methods for using the ARM data and numerical models to address complex scientific questions. The event was locally organized by the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and was held at the National Weather Center in Norman, Oklahoma. The training was attended by 12 instructors and 24 participants.

54 ENVIRONMENTAL SCIENCES↗