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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Critical parameters in the faculty application process: A data-driven analysis

We report the process of applying for science and engineering faculty positions requires a significant time investment for both the applicant and hiring committees. Applicants face the daunting task of sifting through a seemingly endless amount of information related to crafting their application packages in preparation for a review process that is inherently highly subjective. Furthermore, while scientific supervisors are often the most valuable resource during this stage of career development, many years may have passed since they went through this process themselves, and they may not have experience with recent developments in the application process. Because expectations and job responsibilities of new faculty members are rapidly evolving, certain guidelines are often not clearly specified, and it is therefore becoming difficult for applicants to most effectively prepare their application documents. Without access to adequate information and assistance, early-career researchers in particular can be placed at a significant disadvantage in the hiring process. Several recent articles can help prospective applicants in materials science and engineering land a faculty position,1,2,3 but there is nevertheless a need for clarification on expectations for different aspects of the faculty application materials.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Quasar classification using color and variability

We conduct a pilot investigation to determine the optimal combination of color and variability information to identify quasars in current and future multi-epoch optical surveys. We use a Bayesian quasar selection algorithm to identify 35,820 type 1 quasar candidates in a 239 deg{sup 2} field of the Sloan Digital Sky Survey (SDSS) Stripe 82, using a combination of optical photometry and variability. Color analysis is performed on 5-band single- and multi-epoch SDSS optical photometry to a depth of r∼22.4. From these data, variability parameters are calculated by fitting the structure function of each object in each band with a power-law model using 10 to >100 observations over timescales from ∼1 day to ∼8 years. Selection was based on a training sample of 13,221 spectroscopically confirmed type-1 quasars, largely from the SDSS. Using variability alone, colors alone, and combining variability and colors we achieve 91%, 93%, and 97% quasar completeness and 98%, 98%, and 97% efficiency, respectively, with particular improvement in the selection of quasars at 2.7<z<3.5 where quasars and stars have similar optical colors. The 22,867 quasar candidates that are not spectroscopically confirmed reach a depth of i∼22.0; 21,876 (95.7%) are dimmer than coadded i-band magnitude of 19.9, the cutoff for spectroscopic follow-up for SDSS on Stripe 82. Brighter than 19.9, we find 5.7% more quasar candidates without confirming spectra in sky regions otherwise considered complete. The resulting quasar sample has sufficient purity (and statistically correctable incompleteness) to produce a luminosity function comparable to those determined by spectroscopic investigations. We discuss improvements that can be made to the process in preparation for performing similar photometric selection and science on data from post-SDSS sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Integrated parameter and process learning for hydrologic and biogeochemical modules in Earth System Models

Focus area: Primary focal area #2; secondary focal area #3: Learning about parameters and processes of land surface hydrologic and biogeochemical models in Earth System models by integrating machine learning, physics, and big data. Science challenges: How do we maximally leverage big-data observations to improve hydrobiogeochemical process description and parameterization so that such modules more realistically capture hydrologic and vegetation responses and feedbacks under the future climate? For example, how can we leverage physics, limited observations of vegetation and streamflow to better estimate evapotranspiration, and, relatedly, net primary productivity, especially for drought areas? Vegetation plays a critical role in regional and global water cycles; however, existing vegetation models have failed to predict vegetation response to droughts (McDowell & Xu, 2017) , arctic greening (Keenan & Riley, 2018) , and critical transitions between forest and savanna (Hirota et al., 2011) . These studies suggest that when we build process-based models (PBM) parameterized from regional and global plant traits, we tend to poorly describe plant adaptation and local-scale competition processes. The models and their associated parameters assigned for different regions in the world are not capturing essential heterogeneity in vegetation responses at finer spatial scales. Many parameters of the land surface models control hydrology and vegetation dynamics at the same time. The heterogeneity in vegetation response is a function of (i) plant type, (ii) plant size, (iii) competition and succession, (iv) environmental controls, and (v) local variations due to the unique ecological community that are very difficult to describe (e.g., the size of gaps resulting from fire that facilitated the coexistence of pioneering species). In the demographic models, only factors (i) and (iv) were captured, and plant types were generally described only by leaf phenology and climate zones. With current demographic models, we generally consider more traits to define plant types (i) and calibrate these traits to consider factors (ii), (iii) and (iv); however, it is substantially challenging to scale to regional and global simulations due to trait variations across space (Ali et al., 2016). Moreover, it has been noted that hillslope processes, including ridge-to-valley flow and sunny vs. shady slopes are primary organizers of water, energy, and vegetation (Clark et al., 2015; Fan et al., 2019) . Although gradual improvements in the hydrologic model component in earth system models may reduce this error (at a remarkably slow pace), the long-term, gradual impact of hydrology on plant traits are not well captured. Recent work showed that the hydrologic controls exerted by groundwater and lateral flow are primary regulators of rooting depth (Fan et al., 2017) . Such hydrologic controls have seldom been reflected in vegetation model parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Automatic Analyte-Ion Recognition and Background Removal for Ambient Mass-Spectrometric Data Based on Cross-Correlation

Ambient mass spectrometry is a powerful approach for rapid, high-throughput, and direct sample analysis. Due to the open-air desorption and ionization processes, random fluctuations of ambient conditions can lead to large variances in mass-spectral signals over time. The mass-spectral data also can be further complicated due to multiple analytes present in the sample, background-ion signals stemming from the desorption/ionization source itself, and other laboratory-specific conditions (e.g., ambient laboratory air, nearby hardware). Thus, background removal and analyte-ion recognition can be quite difficult, particularly in non-targeted analyses. Here, we demonstrate the use of a cross-correlation-based approach to exploit chemical information encoded in the time domain to group/categorize mass-spectral peaks from a single analysis dataset. Ions that originate from ambient (or other) background species were readily flagged and removed from spectra; the result was a decrease in mass-spectral complexity by over 70% due to the removal of these background ions. Meanwhile, analyte ions were differentiated and categorized based on their time-domain profiles. With sufficient mass resolving-power and mass-spectral acquisition rate, isolated mass spectra containing ions from the same species in a sample could be extracted, leading to a reduction in mass-spectral complexity by more than 98% in some cases. The cross-correlation approach was tested with different ionization sources as well as reproducible and irreproducible sample introduction. Software built in-house enabled fully automated data processing, which can be performed within a few seconds. Ultimately, this approach provides an additional dimension of analyte separation in ambient mass-spectrometric analyses with information that is already recorded throughout the analysis. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design

Human land-use activities have resulted in large changes to the Earth's surface, with resulting implications for climate. In the future, land-use activities are likely to expand and intensify further to meet growing demands for food, fiber, and energy. The Land Use Model Intercomparison Project (LUMIP) aims to further advance understanding of the impacts of land-use and land-cover change (LULCC) on climate, specifically addressing the following questions. (1) What are the effects of LULCC on climate and biogeochemical cycling (past-future)? (2) What are the impacts of land management on surface fluxes of carbon, water, and energy, and are there regional land-management st rategies with the promise to help mitigate climate change? In addressing these questions, LUMIP will also address a range of more detailed science questions to get at process-level attribution, uncertainty, data requirements, and other related issues in more depth and sophistication than possible in a multi-model context to date. There will be particular focus on the separation and quantification of the effects on climate from LULCC relative to all forcings, separation of biogeochemical from biogeophysical effects of land use, the unique impacts of land-cover change vs. land-management change, modulation of land-use impact on climate by land-atmosphere coupling strength, and the extent to which impacts of enhanced CO 2 concentrations on plant photosynthesis are modulated by past and future land use.LUMIP involves three major sets of science activities: (1) development of an updated and expanded historical and future land-use data set, (2) an experimental protocol for specific LUMIP experiments for CMIP6, and (3) definition of metrics and diagnostic protocols that quantify model performance, and related sensitivities, with respect to LULCC. In this paper, we describe LUMIP activity (2), i.e., the LUMIP simulations that will formally be part of CMIP6. These experiments are explicitly designed to be complementary to simulations requested in the CMIP6 DECK and historical simulations and other CMIP6 MIPs including ScenarioMIP, C4MIP, LS3MIP, and DAMIP. LUMIP includes a two-phase experimental design. Phase one features idealized coupled and land-only model simulations designed to advance process-level understanding of LULCC impacts on climate, as well as to quantify model sensitivity to potential land-cover and land-use change. Phase two experiments focus on quantification of the historic impact of land use and the potential for future land management decisions to aid in mitigation of climate change. This paper documents these simulations in detail, explains their rationale, outlines plans for analysis, and describes a new subgrid land-use tile data request for selected variables (reporting model output data separately for primary and secondary land, crops, pasture, and urban land-use types). It is essential that modeling groups participating in LUMIP adhere to the experimental design as closely as possible and clearly report how the model experiments were executed.

58 GEOSCIENCES↗
DOE OSTI2024

Global Corn Heat Stress: Mean and SD of Degree Days Above 29°C based on NEX-GDDP-CMIP6 Climate Projections

Description This global dataset provides the estimated mean and standard deviation (SD) of corn heat stress (degree days above 29°C) for a set of climate models in NEX-GDDP-CMIP6 at 0.25-degree resolution. The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6). The current dataset includes: Long-Term Average Degree Days Above 29°C- Historical Long-Term Average Degree Days Above 29°C- SSP245 Long-Term Standard Deviation of Degree Days Above 29°C- Historical Long-Term Standard Deviation of Degree Days Above 29°C- SSP245 The mean and SD are calculated over 1985-2014 for the historical period and over 2035-2064 for future projections. A full description of methods, including growing season, daily temperature distribution, and statistical coefficients, can be found in Haqiqi (2024). The source climate data are obtained from https://ds.nccs.nasa.gov/thredds2/catalog/catalog.html and are described in Thrasher et al (2022). The codes used to create this dataset are available at https://github.com/ihaqiqi/dd29c_nex_cmip6. Acknowledgments This work was supported by the US Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics under Cooperative Agreement DE-SC0022141. The data processing, computation, and storage were completed on Purdue Anvil supercomputer and cyberinfrastructure supported by the National Science Foundation HDR award # 2118329: "NSF Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE)". References Haqiqi. I. (2024). Trade can buffer climate-induced risks and volatilities in crop supply. Environmental Research: Food Systems. https://doi.org/10.1088/2976-601X/ad7d12 Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T., & Nemani, R. (2022). NASA global daily downscaled projections, CMIP6. Scientific Data, 9(1), 262. https://doi.org/10.1038/s41597-022-01393-4

Climate Change↗
DOE OSTI2015

Monitoring system for the quality assessment in additive manufacturing

Additive Manufacturing (AM) refers to a process by which a set of digital data -representing a certain complex 3dim design - is used to grow the respective 3dim real structure equal to the corresponding design. For the powder-based EOS manufacturing process a variety of plastic and metal materials can be used. Thereby, AM is in many aspects a very powerful tool as it can help to overcome particular limitations in conventional manufacturing. AM enables more freedom of design, complex, hollow and/or lightweight structures as well as product individualisation and functional integration. As such it is a promising approach with respect to the future design and manufacturing of complex 3dim structures. On the other hand, it certainly calls for new methods and standards in view of quality assessment. In particular, when utilizing AM for the design of complex parts used in aviation and aerospace technologies, appropriate monitoring systems are mandatory. In this respect, recently, sustainable progress has been accomplished by joining the common efforts and concerns of a manufacturer Additive Manufacturing systems and respective materials (EOS), along with those of an operator of such systems (MTU Aero Engines) and experienced application engineers (Carl Metrology), using decent know how in the field of optical and infrared methods regarding non-destructive-examination (NDE). The newly developed technology is best described by a high-resolution layer by layer inspection technique, which allows for a 3D tomography-analysis of the complex part at any time during the manufacturing process. Thereby, inspection costs are kept rather low by using smart image-processing methods as well as CMOS sensors instead of infrared detectors. Moreover, results from conventional physical metallurgy may easily be correlated with the predictive results of the monitoring system which not only allows for improvements of the AM monitoring system, but finally leads to an optimisation of the quality and insurance of material security of the complex structure being manufactured. Both, our poster and our oral presentation will explain the data flow between the above mentioned parties involved. A suitable monitoring system for Additive Manufacturing will be introduced, along with a presentation of the respective high resolution data acquisition, as well as the image processing and the data analysis allowing for a precise control of the 3dim growth-process.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Transparent Dielectric Metasurfaces for Spatial Mode Multiplexing

Expanding the use of physical degrees of freedom to employ spatial multiplexing of data in optical communication is considered to be the most disruptive and effective solution for meeting the capacity demand of the growing information traffic. Development of space division–multiplexing methods stimulated research on spatial encoding, detection, and processing of data, attracting interest from various fields of science. Here a passive all–dielectric metasurface with near–unity transmission is demonstrated that engineers spatial mode profiles, potentially of an arbitrary complexity. The broadband response of the metasurface covers all S, C, and L bands of fiber communications. Unlike conventional phase plates, the metasurface allows for both phase and polarization conversion, providing full flexibility for the mode engineering. The dielectric metasurface is employed for mode multiplexing in a free–space optical communication system with an extinction ratio in excess of 20 dB over the whole C–band with negligible penalty even for 100 Gb s –1 data transmission. Furthermore, these results merge two seemingly different fields, optical communication and metamaterials , and they suggest a novel approach for an ultimate miniaturization of mode multiplexers and advanced LiFi technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Future Trends in Nuclear Physics Computing

In nuclear physics (NP) today the study of quarks, gluons and their strong interactions extends across a broad research program at a varied range of collaborative scales, from a few collaborators up to large experiments at scales comparable to those typical of high energy physics (HEP). Overall, the software and computing efforts vary accordingly, from pragmatic do-it-yourself approaches among a few, to substantial organized software and computing activities within large experiments. With new experiments starting up and on the horizon [1], and rapidly increasing data volumes [2, 3] and processing demands even at small experiments, the NP community has in recent years been thinking about the next generation of data processing and analysis workflows that will maximize the science output. One context for this discussion has been a series of workshops, “Future Trends in Nuclear Physics Computing” [4]. The most recent in this series took place in Fall 2020, organized by the authors together with colleagues. The workshop focused on identifying the unique aspects of software and computing in NP, and discussing how the NP community could strengthen common efforts and chart a path forward for the next decade, sure to be an exciting one with rich ongoing scientific programs at Brookhaven National Laboratory (BNL), Jefferson Lab (JLab), and other NP facilities, and culminating in datataking at the Electron-Ion Collider (EIC) [5,6,7] in the early 2030s. Without claiming to present a collective view from the workshop and discussions since—fortunately this is not expected of us in this opinion editorial—we offer here our reflections on the topic, informed by the workshop and the summary we authored with our colleagues [8], as well as discussions and developments in the eventful time since.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints

A hybrid method of combining linear programming (LP) and physical constraints is developed to estimate specific differential phase (K DP ) and to improve rain estimation. Moreover, the hybrid K DP estimator and the existing estimators of LP, least squares fitting, and a self-consistent relation of polarimetric radar variables are evaluated and compared using simulated data. Our simulation results indicate the new estimator's superiority, particularly in regions where backscattering phase (δ hv ) dominates. Further, a quantitative comparison between auto-weather-station rain-gauge observations and K DP -based radar rain estimates for a Meiyu event also demonstrate the superiority of the hybrid K DP estimator over existing methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

SU-D-210-03: Limited-View Multi-Source Quantitative Photoacoustic Tomography

Purpose: This work is to investigate a novel limited-view multi-source acquisition scheme for the direct and simultaneous reconstruction of optical coefficients in quantitative photoacoustic tomography (QPAT), which has potentially improved signal-to-noise ratio and reduced data acquisition time. Methods: Conventional QPAT is often considered in two steps: first to reconstruct the initial acoustic pressure from the full-view ultrasonic data after each optical illumination, and then to quantitatively reconstruct optical coefficients (e.g., absorption and scattering coefficients) from the initial acoustic pressure, using multi-source or multi-wavelength scheme.Based on a novel limited-view multi-source scheme here, We have to consider the direct reconstruction of optical coefficients from the ultrasonic data, since the initial acoustic pressure can no longer be reconstructed as an intermediate variable due to the incomplete acoustic data in the proposed limited-view scheme. In this work, based on a coupled photo-acoustic forward model combining diffusion approximation and wave equation, we develop a limited-memory Quasi-Newton method (LBFGS) for image reconstruction that utilizes the adjoint forward problem for fast computation of gradients. Furthermore, the tensor framelet sparsity is utilized to improve the image reconstruction which is solved by Alternative Direction Method of Multipliers (ADMM). Results: The simulation was performed on a modified Shepp-Logan phantom to validate the feasibility of the proposed limited-view scheme and its corresponding image reconstruction algorithms. Conclusion: A limited-view multi-source QPAT scheme is proposed, i.e., the partial-view acoustic data acquisition accompanying each optical illumination, and then the simultaneous rotations of both optical sources and ultrasonic detectors for next optical illumination. Moreover, LBFGS and ADMM algorithms are developed for the direct reconstruction of optical coefficients from the acoustic data. Jing Feng and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Generalized Canonical Polyadic Tensor Decomposition

Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback--Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. Furthermore, we demonstrate the flexibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Alteration of the α 1 β 2 /α 2 β 1 subunit interface contributes to the increased hemoglobin-oxygen affinity of high-altitude deer mice

Deer mice (Peromyscus maniculatus) that are native to high altitudes in the Rocky Mountains have evolved hemoglobins with an increased oxygen-binding affinity relative to those of lowland conspecifics. To elucidate the molecular mechanisms responsible for the evolved increase in hemoglobin-oxygen affinity, the crystal structure of the highland hemoglobin variant was solved and compared with the previously reported structure for the lowland variant. Highland hemoglobin yielded at least two crystal types, in which the longest axes were 507 and 230 Å. Using the smaller unit cell crystal, the structure was solved at 2.2 Å resolution. The asymmetric unit contained two tetrameric hemoglobin molecules. The analyses revealed that αPro50 in the highland hemoglobin variant promoted a stable interaction between αHis45 and heme that was not seen in the αHis50 lowland variant. The αPro50 mutation also altered the nature of atomic contacts at the α 1 β 2 /α 2 β 1 intersubunit interfaces. These results demonstrate how affinity-altering changes in intersubunit interactions can be produced by mutations at structurally remote sites.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

2020 Postclosure Groundwater Monitoring and Inspection Report, Central Nevada Test Area, Subsurface Corrective Action Unit 443

This report presents the groundwater monitoring data collected by the U.S. Department of Energy (DOE) Office of Legacy Management (LM) from the Central Nevada Test Area (CNTA), Nevada, Site, Subsurface Corrective Action Unit (CAU) 443 in Nye County, Nevada (Figure 1). The CNTA is the site of an underground nuclear test in 1968 that resulted in residual contamination near the detonation depth of 3200 feet (ft); the contamination requires long-term monitoring. Responsibility for the environmental restoration and long-term monitoring was transferred from DOE’s National Nuclear Security Administration, Nevada Field Office, to LM on October 1, 2006. The environmental restoration and site closure process were completed in 2015 in accordance with the amended 1996 Nevada Federal Facility Agreement and Consent Order (FFACO) (State of Nevada et al. 1996, as amended) and all applicable Nevada Division of Environmental Protection (NDEP) policies and regulations. The Closure Report, Central Nevada Test Area, Subsurface Corrective Action, Unit 443 (DOE 2018), also called the Closure Report, originally was completed in January 2016 and revised in October 2018; it describes LM’s plan for long-term postclosure monitoring. This includes monitoring of the radioisotopes of interest and water elevations, inspecting the site and maintaining the institutional controls (ICs), evaluating and reporting data, and documenting the site’s records and data management processes (DOE 2018).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Magnetic Field Observations at Purcell, Oklahoma Field Campaign Report

The campaign “Magnetic Field Observations at Purcell, Oklahoma” installed a ground-based magnetometer at Purcell’s U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility boundary installation at the Kessler Atmospheric and Ecological Field Station, University of Oklahoma, to measure local magnetic field variations. It is a part of the nine stations of the Mid-continent MAgnetoseismic Chain (McMAC) placed as close to the 330° magnetic longitude as possible. This is the meridian in the world where land covers the greatest continuous range in magnetic latitude. Figure 1 shows the map of the magnetometer stations along the 330th magnetic meridian, including the Purcell (PCEL) station. The main scientific objective of the campaign is to detect the field line resonance (FLR) frequencies of the magnetic field line connected to the Purcell station. This magnetic field line extends from Purcell to the outer space at distances as far as 2 Earth radii (RE). To accurately identify FLR frequencies, however, simultaneous measurements at slightly different latitudes along the same meridian are necessary to allow the use of the cross-phase technique. This consideration explains the arrangement to operate magnetometers at the Americus (AMER) and Richardson (RICH) stations nearby. The measured resonant frequency can infer the plasma mass density along the field line through the method of normal-mode magnetoseismology. The magnetometer at the Purcell station can detect many other types of magnetic field fluctuations associated with the changes in the electric currents in the ionosphere and the magnetosphere, which by large are affected by the solar activity. In other words, the magnetic field data collected by this campaign are also useful for understanding space weather phenomena. The magnetometer was installed at Purcell’s ARM boundary facility in March 27, 2006. The construction of the triaxial fluxgate magnetometer used by the campaign, as well as the data processing and analysis, was sponsored by the National Science Foundation. Except during occasional downtimes, the magnetometer collected measurements from June 6, 2006 to July 26, 2016.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future

Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

59 BASIC BIOLOGICAL SCIENCES↗