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At least 91 records · Page 5

Solar PV Transmission Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from transmission. A setback requirement is a minimum distance from transmission infrastructure that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a 30-meter setback, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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Solar PV Railroad Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from railroads. A setback requirement is a minimum distance from a railroad that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a 30-meter setback, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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Solar PV Oil and Gas Pipeline Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from oil and gas pipelines. A setback requirement is a minimum distance from a pipeline that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a 30-meter setback, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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Solar PV Water Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from bodies of water based on county ordinances as of April 2022. A setback requirement is a minimum distance from water that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 30 meters was applied in counties without specific water setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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Solar PV Structure Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from structures based on county ordinances as of April 2022. A setback requirement is a minimum distance from a structure that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 61 meters was applied in counties without specific structure setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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Solar PV Road Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from roads based on county ordinances as of April 2022. A setback requirement is a minimum distance from a road that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 30 meters was applied in counties without specific road setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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A Study of Scientometric Methods to Identify Emerging Technologies via Modeling of Milestones

This work examines a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, patents, news archives, and online mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain we investigated.

99 GENERAL AND MISCELLANEOUS↗

Comments on extracting the resonance strength parameter from yield data

The F(α,n) reaction is the focus of on-going research in part because it is an important source of neutrons in the nuclear fuel cycle which can be exploited to assay nuclear materials, especially uranium in the form of UF 6 . At the present time there remains some considerable uncertainty (of the order of ± 20%) in the thick target integrated over angle (α,n) yield from 19 F (100% natural abundance) and its compounds as discussed. An important thin target cross-section measurement is that of Wrean and Kavanagh who explore the region from below threshold (2.36 MeV) to approximately 3.1 MeV with fine energy resolution. Integration of their cross-section data over the slowing down history of a stopping α-particle allows the thick target yield to be calculated for incident energies up to 3.1 MeV. This trend can then be combined with data from other sources to obtain a thick target yield curve over the wider range of interest to the fuel cycle (roughly threshold to 10 MeV to include all relevant α-emitters). To estimate the thickness of the CaF 2 target they used, Wrean and Kavanagh separately measured the integrated yield of the 6.129 MeV γ-rays from the resonance at 340.5 keV (laboratory α-particle kinetic energy) in the 19 F(p,αγ) reaction. To interpret the data they adopted a resonance strength parameter of (22.3 ± 0.8) eV based on a determination by Becker et al. The value and its uncertainty directly affects the thickness estimate and the extracted (α,n) cross-section values. In their citation to Becker et al's work, Wrean and Kavanagh comment that they did not make use of an alternative value of (23.7±1.0) eV reported by Croft because they were unable to reproduce the value from the data given in that paper. The value they calculated for the resonance strength from the thick target yield given by Croft was 21.4 eV. The purpose of this communication is to revisit the paper by Croft published in this journal and specifically to explain the origin of the reported resonance strength. Fortunately the original notes spanning the period 12 January 1988 to 16 January 1990 were available to consult. Finally, in hindsight there is certainly a case of excessive brevity to rectify. In essence the step requiring explanation is how to compute the resonance strength, ω γ , from the reported thick target resonance yield Y.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Analysis of Search Results for the Clarification and Identification of Technology Emergence (AR-CITE)

This work examines a scientometric model that clarifies and identifies the technology of emergence from initial discovery (via original scientific and conference literature), through critical discoveries(via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. The purpose of this study therefore, is to address the relationships among multiple disparate sources of information as a way to explain systematically the emergence of new technologies from innovation on through to commercial application. In one example, we investigate the combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, patents, news archives, and on-line mapping networks) as they are assembled to become one collective network (a data set for analysis of relations). In another example, we investigate the combinations of five distinct sources (i.e., university R&D, industry R&D, product emergence, and two levels of annual market revenue [$1B and $10B]). These established networks and relationship become the basis from which to quickly analyze the temporal flow of activity (searchable events) for multiple example subject domains we investigated.

Abercrombie, Robert K↗

Soil microbiome resilience to short-term (30 days, 90 days) and long-term (1000 days) drought

This dataset contains data used for the paper "Drought duration does not impact soil microbiome resilience". The Related References will be updated with a full citation when available. Increasing global droughts exert large but poorly understood effects on the microbial communities and ecology of soil. Microbial communities generally show resilience and return to pre-drought conditions when short-term droughted soils are rewet; soils exposed to long-term drought, however, often show a lag upon rewetting, after which microbial communities may or may not return to their pre-stressed conditions. Though short-term droughts have been widely studied, long-term drought manipulation experiments remain rare, especially those that compare microbial response to short-term and long-term drought in tandem. We conducted a 1000-day drought simulation in controlled laboratory conditions with soil cores collected from a tidal freshwater ecosystem in Washington state, USA, and subsequently exposed them to rewetting for two weeks. We also included short-term (30-day and 90-day) drought and rewet treatments to directly compare microbial community and organic matter responses across drought durations. We found distinct microbial taxa belonging to Firmicutes and Actinobacteria enriched after the 1000-day drought, but not after the short-term droughts. While we hypothesized that the microbial community would recover from a short-term drought after rewetting to resemble pre-drought conditions, our results revealed community dissimilarities between rewet and pre-drought conditions across all drought durations. These findings suggest unique microbial life history strategies within certain microbial phyla that make them successful colonizers during an extended drought period, and the influence of environmental and physiological context on microbial responses to rewetting. The 16SrRNA gene amplicon dataset contains processed DNA sequences in the form of an ASV table with raw unrarefied read counts and representative sequences in .fasta format as described in the ESS-DIVE amplicon sequence reporting format (https://ess-dive.gitbook.io/amplicon-sequencing-reporting-format/instructions). The Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) dataset consists of processed files containing presence absence data of molecular formulae and molecular characterization of FTICR resolved peaks. The Nuclear Magnetic Resonance (NMR) dataset contains files relevant to NMR spectra and peaks. A sample key file and a sample metadata file is included for the FTICR/NMR and 16S dataset respectively.

1000-day drought↗

Standing on the Shoulders of Giants: Results From the Radiation Oncology Academic Development and Mentorship Assessment Project (ROADMAP)

Purpose: To analyze survey information regarding mentorship practices and cross-correlate the results with objective metrics of academic productivity among academic radiation oncologists at US Accreditation Council for Graduate Medical Education (ACGME)-accredited residency training programs. Methods and Materials: An institutional review board-approved survey for the Radiation Oncology Academic Development and Mentorship Assessment Project (ROADMAP) was sent to 1031 radiation oncologists employed at an ACGME-accredited residency training program and administered using an international secure web application designed exclusively to support data capture for research studies. Data collected included demographics, presence of mentorship, and the nature of specific mentoring activities. Productivity metrics, including number of publications, number of citations, h-index, and date of first publication, were collected for each survey respondent from a commercially available online database, and m-index was calculated. Results: A total of 158 academic radiation oncologists completed the survey, 96 of whom reported having an academic/scientific mentor. Faculty with a mentor had higher numbers of publications, citations, and h- and m-indices. Differences in gender and race/ethnicity were not associated with significant differences in mentorship rates, but those with a mentor were more likely to have a PhD degree and were more likely to have more time protected for research. Bivariate fit regression modeling showed a positive correlation between a mentor's h-index and their mentee's h-index (R{sup 2} = 0.16; P<.001). Linear regression also showed significant correlates of higher h-index, in addition to having a mentor (P=.001), included a longer career duration (P<.001) and fewer patients in treatment (P=.02). Conclusions: Mentorship is widely believed to be important to career development and academic productivity. These results emphasize the importance of identifying and striving to overcome potential barriers to effective mentorship.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Plant Metabolic Network 16: expansion of underrepresented plant groups and experimentally supported enzyme data

Abstract The Plant Metabolic Network (PMN) is a free online database of plant metabolism available at https://plantcyc.org. The latest release, PMN 16, provides metabolic databases representing >1200 metabolic pathways, 1.3 million enzymes, >8000 metabolites, >10 000 reactions and >15 000 citations for 155 plant and green algal genomes, as well as a pan-plant reference database called PlantCyc. This release contains 29 additional genomes compared with PMN 15, including species listed by the African Orphan Crop Consortium and nonflowering plant species. Furthermore, 52 new enzymes with experimentally supported function information have been included in this release. The single-species databases contain a combination of experimental information from the literature and computationally predicted information obtained through PMN’s database generation pipeline for a single species, while PlantCyc contains only experimental information but for any species within Viridiplantae. PMN is a comprehensive resource for querying, visualizing, analyzing and interpreting omics data with metabolic knowledge. It also serves as a useful and interactive tool for teaching plant metabolism.

Hawkins, Charles (ORCID:0000000312849047)↗

Final project report for DE-SC0010620: Scale-aware, improved hydrological and biogeochemical simulations of the Amazon under a changing climate

Shen was the PI for DOE project entitled Scale-Aware, Improved Hydrological and Biogeochemical Simulations of the Amazon Under a Changing Climate (contract DE-SC0010620; PI: Chaopeng Shen; co-PI, John Melack, William Riley, $976,000, 2013-2018). Under this project, the group has made substantial progress in (a) scientific advancement: (b) model and software engineering development; (c) aided parallel DoE project; (d) generating datasets; and (e) graduate and postdoc education that are useful global data fusion and model validation; and (f) participated and presented in several conferences. In summary, we have published 15 peer-reviewed papers (citations bolded below) and numerous conference presentations. One more paper is still under review. Our software progress has been shared with the wider community.

97 MATHEMATICS AND COMPUTING↗

U.S. Solar Siting Regulation and Zoning Ordinances

A machine readable collection of documented solar siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled based on a locality-by-locality review zoning ordinances after completing an initial review of scholarly legal articles. The citations for each ordinance are included in the spreadsheet.

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Soil pore network response to freeze-thaw cycles in permafrost aggregates

This dataset contains data used for the paper "Pore network response to freeze-thaw cycles in permafrost aggregates". The Related References field will be updated with a full citation when available.Climate change in Arctic landscapes may increase freeze-thaw frequency within the active layer as well as newly thawed permafrost. A highly disruptive process, freeze-thaw can deform soil pores and alter the architecture of the soil pore network with varied impacts to water transport and retention, redox conditions, and microbial activity. Our objective was to investigate how freeze-thaw cycles impacted the pore network of newly thawed permafrost aggregates to improve understanding of what type of transformations can be expected from warming Arctic landscapes. We measured the impact of freeze-thaw on pore morphology, pore throat diameter distribution, and pore connectivity with X-ray computed tomography (XCT) using six permafrost aggregates with sizes of 2.5 cm3 from a mineral soil horizon (Bw; 28-50 cm depths) in Toolik, Alaska. Freeze-thaw cycles were performed using a laboratory incubation consisting of five freeze-thaw cycles (-10˚C to 20˚C) over five weeks. Our findings indicated decreasing spatial connectivity of the pore network across all aggregates with higher frequencies of singly connected pores following freeze-thaw. Water-filled pores that were connected to the pore network decreased in volume while the overall connected pore volumetric fraction was not affected. Shifts in the pore throat diameter distribution were mostly observed in pore throats ranges of 100 microns or less with no corresponding changes to the pore shape factor of pore throats. Responses of the pore network to freeze-thaw varied with aggregate, suggesting that initial pore morphology may play a role in driving freeze-thaw response. Our research suggests that freeze-thaw alters the microenvironment of permafrost aggregates during the incipient stage of deformation following permafrost thaw, impacting soil properties and function in Arctic landscapes undergoing transition. This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv format, which can be accessed and processed using MS Excel or R. This archive can also be accessed on GitHub at https://github.com/Erin-Rooney/XCT-freezethaw (DOI: 10.5281/zenodo.5816355).

54 ENVIRONMENTAL SCIENCES↗

Data for "Soil carbon dynamics during drying vs. rewetting: Importance of antecedent moisture conditions"

This dataset contains data used for the paper "Soil carbon dynamics during drying vs. rewetting: importance of antecedent moisture conditions". The Related References field will be updated with a full citation when available.Soil moisture influences soil carbon dynamics, including microbial growth and respiration. The response of such ‘soil respiration’ to moisture changes is generally assumed to be linear and reversible, i.e. to depend only on the current moisture state. Current models thus do not account for antecedent soil moisture conditions when determining soil respiration or the available substrate pool. We conducted a laboratory incubation to determine how the antecedent conditions of drought and flood influenced soil organic matter (SOM) chemistry, bioavailability, and respiration. We sampled soils from an upland coastal forest, Beaver Creek, WA USA, and subjected them to drying and rewetting treatments. For the drying treatment, field moist soils were saturated and then dried to 75, 50, 35, and 5 % saturation. In the rewetting treatment, field moist soils were air-dried and then rewet to 35, 50, 75, and 100 % saturation. We measured respiration and water extractable organic carbon (WEOC) concentrations and used 1H-NMR and FT-ICR-MS to characterize the WEOC pool across the treatments. The drying vs. wetting treatment strongly influenced SOM bioavailability, as rewet soils (with antecedent drought) had greater WEOC concentrations and respiration fluxes compared to the drying soils (with antecedent flood). In addition, air-dry soils had the highest WEOC concentrations, and the NMR-resolved peaks showed a strong contribution of protein groups in these soils. Both NMR and FT-ICR-MS analyses indicated increased contribution of complex aromatic groups/molecules in the rewet soils, compared to the drying soils. We suggest that drying introduced organic matter into the WEOC pool via desorption of aromatic molecules and/or by microbial cell lysis, and this stimulated microbial mineralization rates. Our work indicates that even short-term shifts in antecedent moisture conditions can strongly influence soil C dynamics at the core scale. The predictive uncertainties in current soil models may be reduced by a more accurate representation of soil water and C persistence that includes a mechanistic and quantitative understanding of the impact of antecedent moisture conditions.This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv and .txt format, which can be accessed and processed using MS Excel or R. NMR data are provided as raw output data (accessed in Bruker TopSpin or MestreNova) as well as the MestreNova-processed files. This archive can also be accessed on GitHub at https://github.com/kaizadp/hysteresis_and_soil_carbon (DOI: 10.5281/zenodo.4432885).

1H-NMR↗

Data for "Soil texture and environmental conditions influence the biogeochemical responses of soils to drought and flooding"

This dataset contains data used for the paper "Soil texture and environmental conditions influence the biogeochemical responses of soils to drought and flooding". The Related References field will be updated with a full citation when available.Climate change is intensifying the global water cycle, with increased frequency of drought and flood. Water is an important driver of soil carbon dynamics, and it is crucial to understand how moisture disturbances will affect carbon availability and fluxes in soils. Here we investigate the role of water in substrate-microbe connectivity and soil carbon cycling under extreme moisture conditions. We collected soils from Alaska, Florida, and Washington USA, and incubated them under Drought and Flood conditions. Drought had a stronger effect on soil respiration, pore-water carbon, and microbial community composition than flooding. Soil response was not consistent across sites, and was influenced by site-level pedological and environmental factors. Soil texture and porosity can influence microbial access to substrates through the pore network, driving the chemical response. Further, the microbial communities are adapted to the historic stress conditions at their sites and therefore show site-specific responses to drought and flood.This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv and .txt format, which can be accessed and processed using MS Excel or R. This archive can also be accessed on GitHub at https://github.com/kaizadp/TES_3Soils_2021 (DOI: 10.5281/zenodo.4792655).

54 ENVIRONMENTAL SCIENCES↗

REFSafE: A RAG-Enabled Framework for Predictive Risk Analysis and Automated Safety Report Generation in Mission-Critical Environments

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.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗