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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 sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Towards Commercialization of Low-Cost, Crack-Tolerant, Screen-Printable Metallization by Full-Size Module Testing and Field Characterization

This project is motivated by the need to develop a materials engineering solution to reduce solar module degradation caused by cell cracks. The cell-crack-induced power loss is a long-term degradation mechanism and one of the main causes of solar panel field failures. Cell cracks can occur during module fabrication, transportation, installation, and long-term operation due to thermomechanical stressors. Our team’s internal estimation – based on the national weather pattern, frequency of severe weather, and 39- GW asset survey by Heliolytics – reveals that the cell-crack-induced module degradation translates to lost revenues >$17B over an average 5-year period for solar farm owners and to reduced reliability (<25 years panel lifetime) for solar energy consumers. In response to this challenge, the prime recipient offers a metal matrix composite (MMC) silver paste that is tailor-engineered for screen-printing gridlines and busbars, which serve as the electrical contacts on solar cells. We formulate our MMC paste by adding low-cost (∼0.02¢/WDC for research grade), surface-engineered carbon nanotubes to commercially available silver paste. The MMC paste offers a drop-in, cost-effective solution to cell cracks for solar cell and module manufacturers. The main goal of this project was to conduct field-relevant, module-level analysis and qualification of MMC metallization.

14 SOLAR ENERGY↗
DOE OSTI2021

iBLAST: Incremental BLAST of new sequences via automated e-value correction

Search results from local alignment search tools use statistical scores that are sensitive to the size of the database to report the quality of the result. For example, NCBI BLAST reports the best matches using similarity scores and expect values (i.e., e-values) calculated against the database size. Given the astronomical growth in genomics data throughout a genomic research investigation, sequence databases grow as new sequences are continuously being added to these databases. As a consequence, the results (e.g., best hits) and associated statistics (e.g., e-values) for a specific set of queries may change over the course of a genomic investigation. Thus, to update the results of a previously conducted BLAST search to find the best matches on an updated database, scientists must currently rerun the BLAST search against the entire updated database, which translates into irrecoverable and, in turn, wasted execution time, money, and computational resources. To address this issue, we devise a novel and efficient method to redeem past BLAST searches by introducing iBLAST. iBLAST leverages previous BLAST search results to conduct the same query search but only on the incremental (i.e., newly added) part of the database, recomputes the associated critical statistics such as e-values, and combines these results to produce updated search results. Our experimental results and fidelity analyses show that iBLAST delivers search results that are identical to NCBI BLAST at a substantially reduced computational cost, i.e., iBLAST performs (1 + δ )/ δ times faster than NCBI BLAST, where δ represents the fraction of database growth. We then present three different use cases to demonstrate that iBLAST can enable efficient biological discovery at a much faster speed with a substantially reduced computational cost.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

R&D to Ensure a Scientific Basis for Qualification Tests and Standards (Final Report: FY19-FY22)

This is the final technical report for the FY19-FY21 research program "R&D to Ensure a Scientific Basis for Qualification Tests and Standards." This research was performed because project return on investment in a photovoltaic (PV) system depends increasingly on maintaining high energy yields, and the system lifetime is a major factor in levelized cost of electricity (LCOE). Thus, the rate of PV deployment and the success of these assets depends upon reliable long-term power generation. This program advances PV module reliability of commercial products through identification of reliability needs, characterization that provides scientific understanding of targeted degradation mechanisms, and translation of those data into practical and predictive test protocols and standards. This report describes program scope, results, milestones, and accomplishments over the three-year period of performance.

14 SOLAR ENERGY↗
DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗
DOE OSTI2025

Modulating Oxide‐Based Quantum Materials by Ion Implantation

Ion implantation has emerged as a powerful tool for manipulating and investigating oxide quantum materials, where strong electron correlations give rise to exotic phases and functionalities. This Review examines how controlled ion bombardment enables targeted modulation of structural, electronic, and magnetic properties in complex oxides. Recent advances in ion beam techniques is analyzed that provide unprecedented control over defect profiles, from point defects to extended damage cascades. In application, ion irradiation has demonstrated the ability to tune quantum phenomena including metal‐insulator transitions, magnetism, and superconductivity through selective doping, defect creation, and lattice strain. The ability to write quantum states with nanoscale precision using focused ion beams is opening new paths for quantum device fabrication. Challenges in understanding radiation damage mechanisms in strongly correlated systems is discussed, and identify opportunities for designing quantum functionality through defect‐engineering approaches. The technique's demonstrated capability to write quantum states with nanoscale precision, combined with its compatibility with existing semiconductor processing infrastructure, suggests a clear pathway for translating fundamental discoveries in quantum materials into practical technological applications.

defect engineering↗
DOE OSTI2021

The Making of a Metric: Co-Producing Decision-Relevant Climate Science

Developing decision-relevant science for adaptation requires the identification of climatic parameters that are both actionable for practitioners as well as tractable for modelers. In many sectors, these decision-relevant climatic metrics and the approaches that enable their identification remain largely unknown. “Co-production” of science with scientists and decision-makers is one potential way to identify these metrics, but there is little research describing specific and successful co-production approaches. This paper examines the negotiations and outcomes from Project Hyperion, wherein scientists and water managers jointly developed decision-relevant climatic metrics for adaptive water management. We identify successful co-production strategies by analyzing the project’s numerous back-and-forth engagements and tracing the evolution of the science during these engagements. We found that effective mediation between scientists and managers needed dedicated “boundary spanners” with significant modeling expertise. Translating practitioners’ information needs into tractable climatic metrics required direct and indirect methods of eliciting knowledge. We identified four indirect methods that were particularly salient for extracting tacitly held knowledge and enabling shared learning: developing a hierarchical framework linking management issues with metrics, starting discussions from the planning challenges, collaboratively exploring the planning relevance of new scientific capabilities, and using analogies of other “good” metrics. The decision-relevant metrics we developed provide insights into advancing adaptation-relevant climate science in the water sector. The co-production strategies we identified can be used to design and implement productive scientist–decision-maker interactions. Overall, the approaches and metrics we developed can help climate science to expand in new and more use-inspired directions.

Planning↗
DOE OSTI2025

Closeout Report of BNL LDRD 23-050 A Second EIC Detector: Physics Case and Conceptual Design

This document is the closeout report for LDRD 23-050, a type-A LDRD project awarded in FY2022 under the title “ A Second EIC Detector: Physics Case and Conceptual Design ”. The project was motivated by the strong interest within the EIC community in a second general-purpose detector and interaction region, and by the recognition that such a detector is essential to fully exploit the scientific potential of the EIC over its multi-decade lifetime. The key goals of the LDRD were to (i) strengthen the case for a second EIC detector, building on the arguments already articulated in the community Yellow Report, (ii) provide a realistic detector concept that is complementary to the current project detector, ePIC, in terms of physics reach, precision, and control of systematics, and (iii) broaden the overall physics program of the EIC facility. Since a possible second detector is expected to be realized with a delay of several years relative to the first detector, the project explicitly aimed at identifying technologies that are not yet sufficiently mature for ePIC but could be deployed on the later timescale of a second detector, thereby providing genuine complementarity and room for innovation. LDRD 23-050 provided support for two postdoctoral researchers and partial support for several BNL staff members, and the effort ramped up in mid-2023. Work was carried out in close collaboration with colleagues from the EIC project, the Cold QCD and STAR groups at BNL, and with substantial input from members of the EIC User Group and the ePIC Collaboration as reflected in the author list. Over the period from October 2022 through September 2025, this team developed and refined a physics program tailored to a second detector, translated the resulting physics requirements into detector-level performance targets, and explored multiple conceptual detector layouts capable of meeting these targets within the constraints of the second interaction region. The expected outcome, as stated in the original proposal, was a document detailing the physics potential and requirements of a second EIC detector, accompanied by a comprehensive conceptual detector design and an outline of the remaining R&D needed to bring the relevant technologies to maturity. The present report summarizes the progress that has been achieved toward these goals. It consolidates the physics studies, detector concepts, and technology assessments developed under this LDRD and places them in the broader context of worldwide detector R&D. While the evolving priorities of the EIC project and the substantial community effort required to design and construct ePIC naturally limited the scope of EIC-wide activities toward a second detector during this period, the work documented here is intended to provide a foundation and a point of reference for future efforts. Whatever form a second EIC detector may ultimately take, we hope that this report will serve as a useful guide for colleagues who continue to advance this program in the near- and mid-term future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Integrating catalytic fractionation and microbial funneling to produce 2-pyrone-4,6-dicarboxylic acid and ethanol

Replacing biorefinery designs that use stepwise fractionation, depolymerization, and conversion processes with designed tandem process steps can greatly reduce a biorefinery's operating costs and environmental impact. Reductive Catalytic Fractionation (RCF) is a highly efficient lignin-first approach that combines biomass fractionation and lignin depolymerization to generate a hydrogenolysis oil and pulp. The oil is composed of a complex mixture of phenolic monomers, dimers, and oligomers. Intergrating this chemical deconstruction process with microbial funneling of phenolics can simplify the product mixture and make high-value products. We applied RCF to poplar biomass in a biomass-to-bioproduct processing chain in which the phenolics were funneled to 2-pyrone-4,6-dicarboxylic acid (PDC) by an engineered strain of Novosphingobium aromaticivorans DSM12444. The pulp was enzymatically digested and the glucose and xylose was funneled to ethanol by an engineered strain of Saccharomyces cerevisiae GLBRCY945. By combining biomass fractionation and lignin depolymerization we removed a costly processing step that directly translated into a 29% reduction in the minimum selling price of PDC. This work combines experimentation with process modeling of an integrated biorefinery design to show how utilizing tandem process steps can significantly reduce operating expenses and environmental impact of upgrading lignocellulosic biomass to a portfolio of high value products.

Sener, Canan [Great Lakes Bioenergy Research Cente↗
DOE OSTI2019

Application of Electronics Packaging Fundamentals to Photovoltaic Interconnects and Packaging

The energy capacity of photovoltaic (PV) installations worldwide has increased significantly in the last decade, increasing the demand for rigorous investigation of the physical phenomena causing degradation and failures in PV modules. As PV reliability science develops, established methods and approaches from longer-standing industries can inform and expedite PV reliability advances. This work demonstrates how thermomechanical solder bond fatigue models derived for electronics packaging applications can be applied to both standard and emerging PV interconnect designs. This expertise cannot be directly translated to PV however, as target PV module lifetimes are significantly longer than most electronics and the modules must withstand the natural climate wherever they are deployed. Verification of analytical reliability models is then an additional challenge, due to model size, test time, and climate variability. Furthermore, unconventional materials, such as low-temperature solders, are now being integrated into PV designs, for which appropriate material models must be selected and material parameters derived. In this paper, the current state of PV interconnect research is explored, with an emphasis on the experimental and simulation approaches and models being used for this work. Recent results for standard module architectures as well as emerging interconnect schemes are discussed.

14 SOLAR ENERGY↗
DOE OSTI2025

Warm-phase microphysical evolution in large-eddy simulations of tropical cumulus congestus: evaluating drop size distribution evolution using polarimetry retrievals, in situ measurements, and a thermal-based framework

Owing to uncertainties in convective microphysics processes, improving parameterizations in Earth system models (ESMs) can benefit from observationally constrained cases suitable for scaling between cloud-resolving models and ESMs. We propose a benchmark large-eddy simulation (LES) cumulus congestus case study from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP 2 Ex) for evaluating and improving ESMs in single-column model (SCM) mode. We seek observational constraints using novel polarimetric retrievals and in situ cloud microphysics measurements. Simulations using bulk and bin microphysics initialized with observed aerosol profiles are compared to cloud-top retrievals of cloud droplet effective radius (R eff ), effective variance (ν eff ), and number concentration (N d ) from the airborne Research Scanning Polarimeter (RSP). Both schemes reproduce characteristics of cloud-top N d and R eff that increase and decrease with altitude, respectively. Cloud-top N d is low-biased relative to RSP retrievals in both schemes, potentially due to limitations in both simulations and retrieval assumptions. Cloud-top R eff is low-biased in the bulk scheme but reasonably reproduced by the bin scheme. Profiles of N d and R eff are sensitive to the collision–coalescence process and the vertical variation in aerosol size distribution. Comparison of simulated and in situ droplet size distributions (DSDs) shows that, to first order, integrated moments are always sensitive to sizes < ~ 30 µm and can also be sensitive to larger sizes if the DSDs are sufficiently broad, with implications for the assumed maximum observed size retrieved by the RSP. The bin scheme captures the observed extended tail of the DSD, while the bulk scheme is unable to due to parametric constraints. Differences in expected relationships between in situ measurements of cloud cores and cloud-top retrievals by RSP demonstrate difficulty in constraining well the case presented herein. Finally, a thermal-tracking framework demonstrates that the dilution of N d throughout a thermal's lifetime is heavily determined by collision–coalescence and the height-varying aerosol distribution and that, in the absence of these, the impact of entrainment on diluting N d is largely offset by secondary activation. Implications for evaluating warm-phase convective microphysics schemes in ESMs and translating results for use on global, space-based polarimetry platforms are discussed.

Stanford, McKenna Wallace [Columbia Univ., New Yor↗
DOE OSTI2024

Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders

Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities. This necessitates the development of the machine learning methods for discovery of physical and chemical phenomena from the data, such as manifestations of symmetry breaking phenomena in electron and scanning tunneling microscopy images, or variability of the nanoparticles. Variational autoencoders (VAEs) are emerging as a powerful paradigm for the unsupervised data analysis, allowing to disentangle the factors of variability and discover optimal parsimonious representation. Here, we summarize recent developments in VAEs, covering the basic principles and intuition behind the VAEs. The invariant VAEs are introduced as an approach to accommodate scale and translation invariances present in imaging data and separate known factors of variations from the ones to be discovered. We further describe the opportunities enabled by the control over VAE architecture, including conditional, semi-supervised, and joint VAEs. Several case studies of VAE applications for toy models and experimental datasets in Scanning Transmission Electron Microscopy are discussed, emphasizing the deep connection between VAE and basic physical principles. Python codes and datasets discussed in this article are available at https://github.com/saimani5/VAE-tutorials and can be used by researchers as an application guide when applying these to their own datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Addressing Uncertainty in MultiSector Dynamics Research

MultiSector Dynamics (MSD) research explores the dynamics and co-evolutionary pathways of human and Earth systems and the emerging interdependent sectors of energy, water, agriculture, and transportation among others. The interactions between these sectors are central to MSD science, as they capture how processes and feedbacks across Earth, environmental, infrastructure, and societal systems shape transitions, socioeconomic risks, and the provision of services. By definition, MSD research requires deep integration across diverse scientific disciplines, ranging from the natural to the social sciences and engineering. All these disciplines apply a variety of numerical simulation models to study and understand their underlying systems of focus. The utility of these models hinges on the fidelity with which they represent the real systems and their ability to produce novel insights about systems and their interactions. The coupled human-natural systems typically represented are shaped by a multitude of interdependent human and natural processes which, when modeled, translate to highly complex, non-linear, interacting behaviors. This ever-increasing complexity massively expands the uncertainty space of a model and can be found in model inputs, processes and parameters. This is further amplified when several models are combined to answer multisectoral questions, as additional uncertainty regarding coupling relationships and interactions is introduced.

IM3, Uncertainty and Sensitivity Analysis, multise↗
DOE OSTI2026

The birth of a field through a marriage of scales: urban meteorological modeling meets regional climate modeling

Urban meteorological modeling and regional climate modeling developed largely independently, with each discipline addressing different aspects of atmospheric processes across space and time. In this review, I show that urban climate modeling did not arise as a simple scaling extension of urban meteorological modeling or an add-on to regional climate modeling, but instead emerged through the selective inheritance of complementary strengths from its parents after both disciplines reached sufficient methodological maturity. By tracing the parallel evolution of these disciplines, I demonstrate that urban climate modeling inherited the physically explicit treatment of the built environment developed within urban meteorological modeling, and the hierarchical scale translation and climatological framing that matured within regional climate modeling, allowing urban effects to influence climate-relevant outcomes. Building on this synthesis, I propose an objective, and methodologically-grounded definition of urban climate modeling that distinguishes it from urban meteorological modeling. This distinction is increasingly important as urban climate data informs decisions with long-lived societal consequences, and as ambiguity in terminology risks conflating fundamentally different modeling frameworks with distinct physical meaning and decision relevance. In addition to a clarifying definition, this review outlines a research framework for advancing urban climate modeling through scale-aware coupling strategies that preserve physically consistent urban-atmosphere interactions.

regional climate↗
DOE OSTI2025

Real-Time Ammonia and Humidity Monitoring with Ultra-Fast Conductometric Sensors Based on Porphyrin and Phthalocyanine Complexes

Organic semiconductors like porphyrins and phthalocyanines are attracting a wide range of researchers due to their versatile electrical properties and sensing performances in conductometric sensors. In this study, we investigate two types of π-extended porphyrins, which share the same macrocyclic structure but differ in their central metal. These porphyrins are employed as sublayers in bilayer heterojunction devices, with the lutetium bisphthalocyanine complex, LuPc 2 , serving as the common top layer. Remarkably, the central metal in the porphyrin macrocycle significantly influences the solubility of the materials and, consequently, the surface topography of the resulting bilayer heterojunction devices. This structural variation translates into distinct electrical and sensing performances. The device incorporating nickel as metal centre (AM2) demonstrates superior sensitivity towards NH3, with a relative response (RR) of ca. -7% at 90 ppm, an ultra-fast response time of about 9 s, and an impressive limit of detection (LOD) of 250 ppb, whereas, the device that has zinc as metal centre in sublayer (AM3) exhibits RR value of ca. -0.9% at 90 ppm with t 90 of ca. 120 s and LOD of 2 ppm. Both devices are evaluated under randomly varying NH 3 concentration and RH value. The results shows that the AM2-based sensor allows following NH 3 in real-time, while the AM3-based sensor delivers an average concentration over time. On the other hand, the AM2-based sensor exhibits slow kinetics under RH exposure, while the AM3-based sensor precisely mirrors the pattern of random RH changes generated by the software, demonstrating its exceptional responsiveness and accuracy in tracking humidity fluctuations. In conclusion, these findings underscore the critical role of the metal centre in tuning the electrical and sensing properties of the heterojunction devices.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

pyEGAF: An open-source Python library for the Evaluated Gamma-ray Activation File

The Evaluated Gamma-ray Activation File (EGAF) is one of the most comprehensive resources for thermal neutron-capture data. This database contains data from prompt gamma activation analysis measurements carried out in a consistent manner using the same experimental configuration at the Budapest Research Reactor for 245 isotopes. Although these valuable datasets have been freely available for many years, one of the drawbacks is the outdated and cryptic Evaluated Nuclear Structure Data File (ENSDF) format that is currently adopted for dissemination, making it difficult for users unfamiliar with the format to access and utilize the data contained therein. Furthermore, the ENSDF format does not readily lend itself to modern computational technologies and a parser is required to interpret the complicated mixed-record format. To help overcome these challenges, we have developed a translator to convert the ENSDF-formatted datasets into an open standard JavaScript Object Notation (JSON) format enabling accessibility to applications using different programming languages running in different environments. To compliment this effort, we have also developed an open-source software package implemented in Python, pyEGAF, that is designed to interact with the JSON data structures for general purpose access, manipulation, and analysis of the neutron-capture $\gamma$-ray data in EGAF. The new format, together with the pyEGAF library, greatly enhances access to the wider applications community where EGAF data may be useful or is required.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Atomic dynamics in fluids: Normal mode analysis revisited

Developing microscopic understanding of the thermal properties of liquids is challenging due to their strong dynamic disorder, which prevents characterization of the atomic degrees of freedom. There have been significant research interests in the past few decades to extend the normal mode analysis for solids to instantaneous structures of liquids. However, the nature of normal modes that arise from these unstable structures is still elusive. Here, in this paper, we explore the instantaneous eigenmodes of dynamical matrices of various Lennard-Jones argon liquid and gas systems at high temperatures and show that the normal modes can be interpreted as an interpolation of T →∞ (gas) and T=0 (solid) mode descriptions. We find that normal modes become increasingly collisional and translational, recovering atomistic gaslike behavior rather than vibrational with increase in temperature, suggesting that normal modes in liquids may be described by both solidlike and gaslike modes.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

The Pan-Arctic Vegetation Cover (PAVC) database v1.1

The Pan-Arctic Vegetation Cover (PAVC) database contains synthesized field-data observations of vegetation cover from 978 Arctic Alaska plots with observations from 2010 to 2021. The cover datasets contain plot data at both the plant functional type (PFT) and species-level resolution, with standardized PFT definitions and species names. We synthesized publicly available point-intercept and visual estimate plots from the Arctic Vegetation Archive of Alaska, the Alaska Vegetation Plots Database, the North Slope Science Catalog, and the National Ecological Observatory Network; as well as previously unpublished data from the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic).Users will find four synthesized datasets, 4 associated data descriptor (dd) files, and 1 metadata file in the PAVC database:synthesized_species_fcover.csv contains fractional cover (fcover) for unique accepted species names, where names include vegetation identified at the family, genus, species, subspecies, and variety levels, as well as general functional types across all 5 data sources. The synthesized_species_fcover_dd.csv accompanies this dataset with header information.synthesized_pft_fcover.csv contains fcover for the following PFTs: non-vascular plants with lichen and bryophyte subcategories, trees with deciduous and evergreen subcategories, shrubs with deciduous and evergreen subcategories, graminoids (grasses), and forbs (herbaceous flowering plants) measured as total cover. Litter and “other” cover are also included as total cover. Additional “types” include water and bare ground, which were measured as top cover. The synthesized_pft_fcover_dd.csv accompanies this dataset with header information.species_pft_checklist.csv is a lookup table containing the translation from a dataset species name to an accepted species name and to a PFT. This table can be used to clarify our species to PFT adjudications, and to aid users in assigning their own PFTs. Any issues found in this checklist should be reported in the Issues tab of our github.survey_unit_information.csv contains auxiliary information about the plots synthesized in this database. It contains useful information for filtering plots of interest based on temporal, geospatial, and contextual information about the plot surveys.flmd.csv contains metadata information about each file in the database.This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗