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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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The Wind-Wildlife Impacts Literature Database (WILD), developed and maintained by the National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory (NREL), is comprised of over 1,000 citations pertaining to the effects of land-based wind, offshore wind, marine and hydrokinetic, power lines, and communication and television towers on wildlife.
US President Barack Obama issued Executive Order 13642-Making Open and Machine Readable the New Default for Government - on May, 9 2013, mandating, wherever legally permissible and possible, that US Government information be made open to the public.[1] This edict accelerated the construction of and framework for data repositories, and data citation principles and practices, such as data.gov. As a corollary, researchers across the country's national laboratories found themselves creating data management plans, applying data set metadata standards, and ensuring the long-term access of data for federally funded scientific research.
Patterns of scientific collaboration and their effect on scientific production have been the subject of many studies. In this paper, we analyze the nature of ties between co-authors and study collaboration patterns in science from the perspective of semantic similarity of authors who wrote a paper together and the strength of ties between these authors (i.e. how frequently have they previously collaborated together). These two views of scientific collaboration are used to analyze publications in the TrueImpactDataset (Herrmannova et al., 2017) (Herrmannova et al., 2017), a new dataset containing two types of publications – publications regarded as seminal and publications regarded as literature reviews by field experts. We show there are distinct differences between seminal publications and literature reviews in terms of author similarity and the strength of ties between their authors. In particular, we find that seminal publications tend to be written by authors who have previously worked on dissimilar problems (i.e. authors from different fields or even disciplines), and by authors who are not frequent collaborators. On the other hand, literature reviews in our dataset tend to be the result of an established collaboration within a discipline. This demonstrates that our method provides meaningful information about potential future impacts of a publication which does not require citation information.
This document provides example language for referencing the use of Uniform Methods Project (UMP) protocols followed by examples of language that is currently in use, which helps the reader understand when and how the protocols are used and ensures consistent citation of relevant UMP protocols.
A novel laser stripping scheme based on sequential ex-citation has been proposed for foil-less charge-exchange injection of hydrogen ion (H-) beams. Our analysis indi-cates that the new scheme can save the laser power by an order of magnitude for certain particle beam energies. An application to 1.3-GeV hydrogen ion beams is described.
Different learning channels (i.e., learning from upstream actors, local network, global codified knowledge, etc.) coexist in firms’ technological innovation processes. Although firms are exposed to globalization and convenient communication and transportation tools, researchers still argue about the role of geography in technology innovation. Geographic proximity can facilitate knowledge spillover and network-building. Localized learning and the significance of geography indicate the importance of local knowledge in innovative activities. Local knowledge tends to be more closely aligned with local firms’ technological demand and easier to understand and absorb, because they are created and applied in the same context. However, following Schumpeter, innovation researchers believe that diverse knowledge, especially knowledge from external industries and geographically distant places, is a valuable source for innovation, especially disruptive innovation. Therefore, firms must balance different learning channels, especially between localized learning and global learning. However, it is still unclear about the extent to which localized learning is important for technology innovations and which factors influence the degree of localized learning. Meanwhile, most studies in the area of localized learning usually take no account of the international dimension and the interdependent of knowledge at different geographic scales. Different learning channels (i.e., learning from upstream actors, local network, global codified knowledge, etc.) coexist in firms’ technological innovation processes. Although firms are exposed to globalization and convenient communication and transportation tools, researchers still argue about the role of geography in technology innovation. Geographic proximity can facilitate knowledge spillover and network-building. Localized learning and the significance of geography indicate the importance of local knowledge in innovative activities. Local knowledge tends to be more closely aligned with local firms’ technological demand and easier to understand and absorb, because they are created and applied in the same context. However, following Schumpeter, innovation researchers believe that diverse knowledge, especially knowledge from external industries and geographically distant places, is a valuable source for innovation, especially disruptive innovation. Therefore, firms must balance different learning channels, especially between localized learning and global learning. However, it is still unclear about the extent to which localized learning is important for technology innovations and which factors influence the degree of localized learning. Meanwhile, most studies in the area of localized learning usually take no account of the international dimension and the interdependent of knowledge at different geographic scales. To address this research gap, this paper empirically investigated firms’ learning and innovation processes in relation to the geography. The aim is to answer the following three questions: 1) how does geographic distance influence knowledge spillover; 2) how technological characteristics influence knowledge acquisition choices as regards local vs. non-local knowledge? and 3) how do local and non-local knowledge affect the quality of innovation? The subject of our empirical inquiry is photovoltaic balance-of-systems (PV BOS) components. This choice is primarily motivated by two factors: (1) large-scale deployment of distributed solar PV technologies is widely considered to be an important piece in addressing the environmental impacts of the electricity sector, making PV an important technology to study; furthermore, there is a general consensus that the biggest barrier for a wider deployment of PV is the high price of non-module components, and (2) technological aspects of non-module components of PV systems make local conditions (climate, regulation, learning networks, etc.) salient, thus affording a rich empirical setting to explore the geography-innovation-economy interactions at the local level. We find that the localization effect of technological learning in the solar PV BOS industry is at state level, the number of citations-based linkages decreases with distance, and we also find that international knowledge significantly contributes to the quality of patents, which suggests that state border might be a barrier of current knowledge spillover, and only focusing on local innovation networks is not sufficient for promoting PV BOS innovation.
The talk is given at the IPAC19 on the occasion of acceptance of the Nishikawa Tetsuji Prize for a recent, significant, original contribution to the accelerator field, with no age limit with citation “for original work on electron lenses in synchrotron colliders, outstanding contribution to the construction and operation of high-energy, high-luminosity hadron colliders and for tireless leadership in the accelerator community.”
This article provides background for the High-Energy Collider Parameter Tables that follow and some additional information; see in-depth review and a comprehensive list of references in [1]; citations below are limited to widely used textbooks and open access seminal papers and reviews.
Managing technical references for projects that have long histories is hampered by the large collection of documents, each of which might contain discrete pieces of information relevant to the site conceptual model. A database application has been designed to improve the efficiency of retrieving technical information for a project. Although many databases are currently used for accessing analytical and geo-referenced data, applications designed specifically to manage technical reference material for projects are scarce. Retrieving site data from the array of available references becomes an increasingly inefficient use of labor. The electronic-Knowledge Information Tool (e-KIT) is designed as a project-level resource to access and communicate technical information. The e-KIT is a living tool that grows as new information becomes available, and its value to the project increases as the volume of site information increases. Having all references assembled in one location with complete reference citations and links to elements of the site conceptual model offers a way to enhance communication with outside groups. The published and unpublished references are incorporated into the e-KIT, while the compendium of references serves as a complete bibliography for the project. (authors)
Transmutation of minor actinides such as Np, Am, and Cm in spent nuclear fuel is of international interest because of its potential for reducing the long-term health and safety hazards caused by the radioactivity of the spent fuel. For this reason, the DOE Fuel Cycle Research and Development (FCRD) Advanced Fuels Campaign (AFC) has been performing research on nuclear fuel technologies that could be used in conjunction with a future actinide transmutation mission. One important approach to transmutation involves incorporating the minor actinides into U-Pu-Zr alloys, which can be used as fuel in fast reactors. It is, therefore, important to understand the properties of U-Pu-Zr alloys. The Idaho National Laboratory has recently published a handbook intended to summarize key properties of these alloys for researchers. This handbook focuses on phases and key thermophysical properties of elements and alloys in the U-Pu-Zr system. It is envisioned as the first of a series, which will serve as a summary of current knowledge about important properties of transmutation fuels. Future volumes will include information about properties and materials not covered in the first edition, as well as updates to information in this edition of the handbook. The handbook is intended to serve two audiences. One audience is researchers, who may find it useful to know what has been measured, where it has been published, and what kinds of information are needed. A second audience is modelers, who need a concise summary of information about specific properties and the accuracy with which they are known. The two audiences have different needs, and the handbook is a compromise intended to be useful to both. Therefore, it includes summaries of research results and needs, but generally does not present the original data. It also includes numerous graphs and, where available, equations that provide convenient approximations to materials properties for modelers. It is important to understand that most of the equations provided in this handbook are derived by fitting polynomials to data and have no physical basis; while they can be used with caution for design studies, fuel modelers will probably want to use the original experimental data to develop or validate their own equations and/or models. Although the handbook includes some citations to research involving development of models, its primary focus is experimental data. Many of the measurements of the U-Pu-Zr system were made before 1980. Where possible, the handbook relies on previous reviews of experimental data rather than presenting new interpretations of the old information. It is expected that researchers will refer to the original literature for details that are not presented in the handbook. Metals and alloys in the U-Pu-Zr system are uniquely difficult to study for a number of reasons. Experimental designs may need to be modified to allow researchers to work with these hazardous materials safely. Kinetics of phase transformations are commonly sluggish, and it is highly unlikely that all of the samples in previous research had equilibrium phases and microstructures. Thermal cycling effects (changes in sample characteristics that are not fully reversed during cooling) are commonly reported and may be significant. Careful control of impurities (particularly oxygen) during sample fabrication and data collection is both crucial and difficult, and concentrations below 100 ppm in samples may significantly affect thermophysical properties. For all of these reasons, differences between measured values for similar alloys are commonly far larger than the estimated errors for individual measurements. Further research on well-characterized materials with careful control of impurities is needed for all of the alloys discussed here. The 2015 FCRD Handbook summarizes currently available information about U, Pu, Zr, and their binary and ternary alloys. It contains information about phase diagrams and related information (including phases and phase transformations); heat capacity, entropy, and enthalpy; thermal expansion; and thermal conductivity and diffusivity. In addition to presenting information about materials properties, it attempts to provide information about how well the property is known and how much variation exists between measurements. Although the handbook includes some references to publications about modeling, its primary focus is experimental data. (authors)
The aim of this document is to lay out a first draft of the specifications for the MEDUSAL database (Machine-readable Experimental Data User App & Library) that is being described by OECD/NEA/WPEC SG-50. The EXFOR database (Otuka et al., 2014) has a format that is based on code-value pairs, and a significant portion of the information in the EXFOR entry is contained in free text sections. Several high-level requirements for the MEDUSAL database, as laid out in the Use Cases and Requirements Working Paper (citation), relate to the definition of the specifications
As the use of artificial intelligence spreads across most professions, projections for the expected energy consumption of these virtual assistants/workers dictate a large increase in energy production. Recent published estimates refer to up to a necessary tripling of nuclear energy production~\cite{iaea}. % TODO add citations This is supported by the new administration, calling on February 5th to "unleash Commercial Nuclear Power in the United States" and to launch the "long-awaited American nuclear renaissance"~\cite{doe-order}. The Department of Energy (DOE) is tasked with "enabl[ing] the rapid deployment and export of next generation nuclear technology". In terms of research of development, we must seek to "unleash American Energy Innovation", by "focus[ing] our time and resources on technologies that will advance basic science, grow America’s scientific leadership, reduce costs for American families [...]". To that end, the Department will "prioritize affordable, reliable, and secure energy technologies, including fossil fuels, advanced nuclear, geothermal, and hydropower." Following this call to action, we elect to examine the role of the open-source Multiphysics Object Oriented Simulation Environment (MOOSE)~\cite{moose3} in meeting the stated goals.
Operational safety in mission-critical environments requires AI systems that are accurate, interpretable, and resistant to hallucination. We present an agentic Retrieval-Augmented Generation (RAG) framework, REFSafe, for grounded hazard analysis and automated safety report generation. The system integrates Large Language Models (LLMs) with structured operational data, historical incident repositories, policy documents, and external authoritative sources. Through iterative agentic reasoning, the framework retrieves, verifies, and synthesizes evidence prior to generation, enforcing citation-backed outputs with explicit source attribution (documents, links, and prior events) to ensure traceability and trust. To mitigate hallucinations and unsupported claims, all risk assessments and forecasts are constrained to retrieved evidence, with confidence signals derived from retrieval relevance and source consistency. A transparent pipeline enables subject matter experts (SMEs) to validate predictions, and provide structured feedback, forming a continuous performance calibration loop. Preliminary deployment demonstrates improved reliability in hazard detection and safety/vulnerability report generation. This work advances trustworthy, evidence-grounded AI for predictive safety intelligence in mission-critical operations.
Differential cross sections for forward-angle photoproduction of p0, ¿, and ¿ 1 pseudoscalar mesons were measured using data from the RadPhi experiment conducted in Hall B at Jef ferson Lab. RadPhi utilized a tagged bremsstrahlung photon beam incident on a stationary 9Be target, with a detector system configured to trigger on a recoil proton in coincidence with multiple neutral showers in the calorimeter. Events were reconstructed and subjected to kinematic constraints, with background suppressed via sideband subtraction guided by Monte Carlo modeling of background contributions. Cross sections were extracted over the photon energy range 4.4– 5.4 GeV and binned in invariant momentum transfer t, providing measurements from one of the first high-statistics experiments of forward ¿ and ¿1 pro duction from a nuclear target at these energies. Acceptance corrections were applied using a detailed GEANT-based simulation of the detector geometry and response. The resulting cross sections are consistent with 2020 CLAS results, when scaled by the number of protons in beryllium, and show broad agreement with other data and theoretical models. In parallel, a high-resolution photon tagger detector, the Tagger Microscope (TAGM), was designed, constructed, and commissioned for the GlueX experiment in Hall D at Jefferson Lab. The TAGM was developed to provide high-rate tagging capability in the coherent bremsstrahlung peak by detecting post-bremsstrahlung electrons across a one GeV range along the focal plane of the tagging spectrometer. The detector consists of a 5ˆ102 array of 2ˆ2 mm2 square BCF-20 plastic scintillating fibers thermally fused to BCF-98 light guide fibers optically coupled to silicon photomultipliers. These fibers are mounted in a precision machined framework enabling fine positional adjustments to maintain precise alignment with post-bremsstrahlung electron trajectories, while ensuring mechanical rigidity, thermal stability, optical isolation, minimal inactive area, and radiation shielding for electronics. The construction effort involved extensive testing of fiber quality, light transmission, thermal fusing, radiation hardness, and defect analysis using SEM and EDX techniques. Following its installation and commissioning, the TAGM became a critical component of the GlueX beamline, enabling high-rate tagging essential for studies of hybrid mesons and gluonic ex citations.
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Scientific datasets are increasingly cited in peer-reviewed journal publications, facilitating easy access to research utilizing those datasets. Datasets undergo a life cycle where older versions of datasets are replaced by newer versions often due to improvements in data resolution, algorithms, and other factors. Unlike peer reviewed documents registered with a single Digital Unique Identifier (DOI), datasets can be updated over time and the newer version of the datasets are registered with a new DOI which is not necessarily linked to the previous version of the dataset. It is challenging when publications citing a dataset need to be traced over the entire life cycle of that dataset. We provide an innovative approach to link the dataset versions and publications using a knowledge graph (KG). KG can help to trace the dataset cited in publications over the entire dataset life cycle and shed light into dataset usage in various applied research areas. We fine-tuned the pretrained NASA IMPACTINDUS Large Language Model (LLM) on a set of labeled publications abstracts. Our results showed that 87% of the publications were classified into one of twenty applied research areas, while the remaining 13% were classified into non-applied research areas. By linking datasets to applied research areas through the KG and employing Global Change Master Directory(GCMD), a well-established controlled vocabulary of scientific keywords describing Earth science datasets, we contribute to a transparent and advanced search and discovery mechanism for datasets across the Earth data ecosystem. The integrated KG and LLM approach is now incorporated and operational in dataset publication management at one of NASA’s Earth science data archival centers.
In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.
This article attempts to summarize current best practices that support the movement towards enabling researchers to cite and receive credit for their data. The authors are a small representation of the people and organizations trying to make this happen, and we acknowledge that it is not possible to capture all efforts behind this endeavor in 10 Simple Rules. We encourage interested readers to dive deeper by providing related resources along the way.