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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 OSTI2021

Making a Water Data System Responsive to Information Needs of Decision Makers

Evidence-based environmental management requires data that are sufficient, accessible, useful and used. A mismatch between data, data systems, and data needs for decision making can result in inefficient and inequitable capital investments, resource allocations, environmental protection, hazard mitigation, and quality of life. In this paper, we examine the relationship between data and decision making in environmental management, with a focus on water management. We focus on the concept of decision-driven data systems —data systems that incorporate an assessment of decision-makers' data needs into their design. The aim of the research was to examine the process of translating data into effective decision making by engaging stakeholders in the development of a water data system. Using California's legislative mandate for state agencies to integrate existing water and other environmental data as a case study, we developed and applied a participatory approach to inform data-system design and identify unmet data needs. Using workshops and focused stakeholder meetings, we developed 20 diverse use cases to assess data sources, availability, characteristics, gaps, and other attributes of data used for representative decisions. Federal and state agencies made up about 90% of the data sources, and could readily adapt to a federated data system, our recommended model for the state. The remaining 10% of more-specialized data, central to important decisions across multiple use cases, would require additional investment or incentives to achieve data consistency, interoperability, and compatibility with a federated system. Based on this assessment, we propose a typology of different types of data limitations and gaps described by stakeholders. We also propose technical, governance, and stakeholder engagement evaluation criteria to guide planning and building environmental data systems. Data-system governance involving both producers and users of data was seen as essential to achieving workable standards, stable funding, convenient data availability, resilience to institutional change, and long-term buy-in by stakeholders. Our work provides a replicable lesson for using decision-maker and stakeholder engagement to shape the design of an environmental data system, and inform a technical design that addresses both user and producer needs.

Cantor, Alida↗
DOE OSTI2022

Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)

Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1972

A 2000-Hour Durability Test of a 5-Centimeter Diameter Mercury Bombardment Ion Thruster

A 2000-hour durability test of a modified Hughes SIT-5 (Structurally Integrated Thruster, 5 cm) was conducted at the Lewis Research Center. The thruster operated with a translating screen thrust vector grid locked in position for 10 deg beam deflection. The test was essentially continuous except for seven stoppages of beam current. The neutralizer keeper voltage and thruster floating potential increased slightly with time. Performance profiles and maps of thruster characteristics were obtained at 453 and 2023 hours into the test. Overall efficiency was nearly constant at 31 - 32 percent, and operating characteristics were similar at both points in the test. A post-shutdown inspection showed negligible erosion damage to the accelerator and cathode baffle. Some erosion was found in the aperture of the neutralizer cathode.

Nakanishi, S.↗
NASA NTRS1990

Computer-assisted knowledge acquisition for hypermedia systems

The usage of procedural and declarative knowledge to set up the structure or 'web' of a hypermedia environment is described. An automated knowledge acquisition tool was developed that helps a knowledge engineer elicit and represent an expert's knowledge involved in performing procedural tasks. The tool represents both procedural and prerequisite, declarative knowledge that supports each activity performed by the expert. This knowledge is output and subsequently read by a hypertext scripting language to generate the link between blank, but labeled cards. Each step of the expert's activity and each piece of supporting declarative knowledge is set up as an empty node. An instructional developer can then enter detailed instructional material concerning each step and declarative knowledge into these empty nodes. Other research is also described that facilitates the translation of knowledge from one form into a form more readily useable by computerized systems.

Steuck, Kurt↗
NASA NTRS1996

The Role of Linear Acceleration in Visual-Vestibular Interactions and Implications in Aircraft Operations

While considerable attention has been given to visual-vestibular interaction (VVI) during angular motion of the head as might occur during an aircraft spin, much less attention has been given to VVI during linear motion of the head. Such interaction might occur, for example, while viewing a stationary or moving display during vertical take-off and landing operations Research into linear VVI, particularly during prolonged periods of linear acceleration, has been hampered by the unavailability of a programmable translator capable of large excursions We collaborated with Otis Elevator Co. and used their research tower and elevator, whose motion could be digitally programmed, to vertically translate human subjects over a distance of 92.3 meters with a peak linear acceleration of 2 meters/sec(exp 2) During pulsatile or sinusoidal translation, the subjects viewed moving stripes (optokinetic stimulus) or a fixed point source (light emitting diode, led, display), respectively and it was generally found that. The direction of linear acceleration relative to the cardinal head axes and the direction of the slow component of optokinetic nystagmus (OKN) determined the extent of VVI during concomitant stripe motion and linear acceleration. Acceleration along the z head axis (A(sub z)) produced the largest VVI, particularly when the slow component of OKN was in the same direction as eye movements produced by the linear acceleration and Eye movements produced by linear acceleration are suppressed by viewing a fixed target at frequencies below 10 Hz But, above this frequency the suppression produced by VVI is removed. Finally, as demonstrated in non-human primates, vergence of the eyes appears to modulate the vertical eye movement response to linear acceleration in humans.

Correia, Manning J.↗
DOE OSTI2025

TransPlatformer

We propose TransPlatformer for translating toxicogenomics from one platform to another. Transcriptomic profiling has evolved through multiple generations of technology, from microarrays (e.g., Affymetrix, CodeLink) to more recent high-throughput sequencing and targeted panels such as S1500+. Microarrays, which dominated gene expression studies in the early 2000s, provided affordable and high-throughput transcript quantification but suffered from cross-hybridization issues and limited dynamic range . RNA-Seq, introduced in the late 2000s, revolutionized transcriptomics by enabling unbiased and comprehensive gene expression analysis, albeit at higher costs and computational demands . Despite advances, many studies rely on historical microarray data, necessitating the translation of legacy data into modern platforms to ensure continuity and comparability. This translation is complicated by factors such as platform-specific probe design, differences in transcript coverage, and batch effects . Existing methods for cross-platform mapping include statistical normalization, machine learning models, and biological anchoring approaches. The ability to translate transcriptomic data between platforms has broad implications, including enhanced meta-analyses, improved toxicological modeling, and better integration of historical datasets with contemporary research. TransPlatformer seeks to contribute to this effort by evaluating translation methodologies and proposing novel strategies to improve cross-platform gene expression harmonization. In this repository there are code examples for TransPlatformer implementation

Cong, Guojing↗
NASA NTRS2003

Flywheels Upgraded for Systems Research

With the advent of high-strength composite materials and microelectronics, flywheels are becoming attractive as a means of storing electrical energy. In addition to the high energy density that flywheels provide, other advantages over conventional electrochemical batteries include long life, high reliability, high efficiency, greater operational flexibility, and higher depths of discharge. High pulse energy is another capability that flywheels can provide. These attributes are favorable for satellites as well as terrestrial energy storage applications. In addition to energy storage for satellites, the several flywheels operating concurrently can provide attitude control, thus combine two functions into one system. This translates into significant weight savings. The NASA Glenn Research Center is involved in the development of this technology for space and terrestrial applications. Glenn is well suited for this research because of its world-class expertise in power electronics design, rotor dynamics, composite material research, magnetic bearings, and motor design and control. Several Glenn organizations are working together on this program. The Structural Mechanics and Dynamics Branch is providing magnetic bearing, controls, and mechanical engineering skills. It is working with the Electrical Systems Development Branch, which has expertise in motors and generators, controls, and avionics systems. Facility support is being provided by the Space Electronic Test Engineering Branch, and the program is being managed by the Space Flight Project Branch. NASA is funding an Aerospace Flywheel Technology Development Program to design, fabricate, and test the Attitude Control/Energy Storage Experiment (ACESE). Two flywheels will be integrated onto a single power bus and run simultaneously to demonstrate a combined energy storage and 1-degree-of-freedom momentum control system. An algorithm that independently regulates direct-current bus voltage and net torque output will be experimentally demonstrated.

Jansen, Ralph H.↗
DOE OSTI2021

Critical Infrastructure Decision-Making under Long-Term Climate Hazard Uncertainty: The Need for an Integrated, Multidisciplinary Approach

U.S. critical infrastructure assets are often designed to operate for decades, and yet long-term planning practices have historically ignored climate change. With the current pace of changing operational conditions and severe weather hazards, research is needed to improve our ability to translate complex, uncertain risk assessment data into actionable inputs to improve decision-making for infrastructure planning. Decisions made today need to explicitly account for climate change – the chronic stressors, the evolution of severe weather events, and the wide-ranging uncertainties. If done well, decision making with climate in mind will result in increased resilience and decreased impacts to our lives, economies, and national security. We present a three-tier approach to create the research products needed in this space: bringing together climate projection data, severe weather event modeling, asset-level impacts, and contextspecific decision constraints and requirements. At each step, it is crucial to capture uncertainties and to communicate those uncertainties to decision-makers. While many components of the necessary research are mature (i.e., climate projection data), there has been little effort to develop proven tools for long-term planning in this space. The combination of chronic and acute stressors, spatial and temporal uncertainties, and interdependencies among infrastructure sectors coalesce into a complex decision space. By applying known methods from decision science and data analysis, we can work to demonstrate the value of an interdisciplinary approach to climate-hazard decision making for longterm infrastructure planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Pivotal trial characteristics and types of endpoints used to support Food and Drug Administration rare disease drug approvals between 2013 and 2022

Background/aims Rare disease drug development faces unique challenges, such as genotypic and phenotypic heterogeneity within small patient populations and a lack of established outcome measures for conditions without previously successful drug development programs. These challenges complicate the process of selecting the appropriate trial endpoints and conducting clinical trials in rare diseases. In this descriptive study, we examined novel drug approvals for non-oncologic rare diseases by the U.S. Food and Drug Administration’s Center for Drug Evaluation and Research over the past decade and characterized key regulatory and trial design elements with a focus on the primary efficacy endpoint utilized as the basis of approval. Methods Using the Food and Drug Administration’s Data Analysis Search Host database, we identified novel new drug applications and biologics license applications with orphan drug designation that were approved between 2013 and 2022 for non-oncologic indications. From Food and Drug Administration review documents and other external databases, we examined characteristics of pivotal trials for the included drugs, such as therapeutic area, trial design, and type of primary efficacy endpoints. Differences in trial design elements associated with primary efficacy endpoint type were assessed such as randomization and blinding. Then, we summarized the primary efficacy endpoint types utilized in pivotal trials by therapeutic area, approval pathway, and whether the disease etiology is well defined. Results One hundred and seven drugs that met our inclusion criteria were approved between 2013 and 2022. Assessment of the 107 drug development programs identified 150 pivotal trials that were subsequently analyzed. The pivotal trials were mostly randomized (80%) and blinded (69.3%). Biomarkers (41.1%) and clinical outcomes (42.1%) were commonly utilized as primary efficacy endpoints. Analysis of the use of clinical trial design elements across trials that utilized biomarkers, clinical outcomes, or composite endpoints did not reveal statistically significant differences. The choice of primary efficacy endpoint varied by the drug’s therapeutic area, approval pathway, and whether the indicated disease etiology was well defined. For example, biomarkers were commonly selected as primary efficacy endpoints in hematology drug approvals (70.6%), whereas clinical outcomes were commonly selected in neurology drug approvals (69.6%). Further, if the disease etiology was well defined, biomarkers were more commonly used as primary efficacy endpoints in pivotal trials (44.7%) than if the disease etiology was not well defined (27.3%). Discussion In the past 10 years, numerous novel drugs have been approved to treat non-oncologic rare diseases in various therapeutic areas. To demonstrate their efficacy for regulatory approval, biomarkers and clinical outcomes were commonly utilized as primary efficacy endpoints. Biomarkers were not only frequently used as surrogate efficacy endpoints in accelerated approvals, but also in traditionally approved rare disease drugs. The choice of primary efficacy endpoints varied by therapeutic area, approval pathway, and understanding of disease etiology.

Hong, Kyungwan [Rare Diseases Team, Office of New ↗
DOE OSTI2020

Frontiers in Thermal Transport and Energy Conversion: Proceedings of a Workshop

Thermal transport and energy conversion has remained an active field for at least 200 years, with numerous opportunities for discoveries and new applications. Recently, experiments have advanced researchers’ understanding of basic physics, and how new discoveries might translate into applications in energy, materials, quantum technologies, and other areas. The National Academies convened a workshop on April 11, 2019 to identify and assess the frontier of current research in the field of thermal transport and energy conversion. Discussions involved topics related to thermal transport and quasi-particle hydrodynamics, thermal transport beyond the quasiparticle paradigm, the thermal hall effect from neutral spin excitations in frustrated quantum magnets, quantization of the thermal hall conductivity at small hall angles, and thermal spin transport, including spin-Seebeck and magnon drag effects. These topics were strategically selected with the goal of uncovering key challenges, opportunities, and issues in order to guide future efforts and investments to advance the field. This publication offers a condensed summary of the discussions and presentations from the workshop, which was unclassified and open to the public.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1993

The (mis)use of subjective process measures in software engineering

A variety of measures are used in software engineering research to develop an understanding of the software process and product. These measures fall into three broad categories: quantitative, characteristics, and subjective. Quantitative measures are those to which a numerical value can be assigned, for example effort or lines of code (LOC). Characteristics describe the software process or product; they might include programming language or the type of application. While such factors do not provide a quantitative measurement of a process or product, they do help characterize them. Subjective measures (as defined in this study) are those that are based on the opinion or opinions of individuals; they are somewhat unique and difficult to quantify. Capturing of subjective measure data typically involves development of some type of scale. For example, 'team experience' is one of the subjective measures that were collected and studied by the Software Engineering Laboratory (SEL). Certainly, team experience could have an impact on the software process or product; actually measuring a team's experience, however, is not a strictly mathematical exercise. Simply adding up each team member's years of experience appears inadequate. In fact, most researchers would agree that 'years' do not directly translate into 'experience.' Team experience must be defined subjectively and then a scale must be developed e.g., high experience versus low experience; or high, medium, low experience; or a different or more granular scale. Using this type of scale, a particular team's overall experience can be compared with that of other teams in the development environment. Defining, collecting, and scaling subjective measures is difficult. First, precise definitions of the measures must be established. Next, choices must be made about whose opinions will be solicited to constitute the data. Finally, care must be given to defining the right scale and level of granularity for measurement.

Valett, Jon D.↗
NASA NTRS1999

Crystal Growth Using MEPHISTO

The shuttle flight experiment "In Situ Monitoring of Crystal Growth Using MEPHISTO" was accomplished during STS-87 as part of the fourth flight of the United States Microgravity Payload (USMP-4), which was flown from November 19 to December 5, 1997. The data returned from that flight are just now beginning to yield quantitative results. This project is an international collaboration: the furnace system known as MEPHISTO was built in France by CNES (French National Space Agency) and CEA (French Atomic Energy Commission); the principal investigator, Prof. Reza Abbaschian, is from the University of Florida at Gainesville; and numerical and analytical modeling support includes collaborators from the University of New South Wales, Australia, the University of Wisconsin at Milwaukee, the National Institute of Standards and Technology, and the NASA Lewis Research Center. MEPHISTO is a French acronym that translates into English as Materials for the Study of Interesting Phenomena of Solidification on Earth and in Orbit. Since this was the fourth flight of the MEPHISTO furnace, the experiment is referred to as MEPHISTO-4. MEPHISTO-4 was a directional solidification experiment that studied the liquid-to-solid transformation of bismuth alloyed with tin. Directional solidification is a freezing technique common to the processing of the electronic materials used in integrated circuits and detectors, such as silicon and germanium. When liquids are frozen on Earth, they must be cooled. The cooling causes stirring because of density variations in the liquid. This stirring, known as natural convection, influences the quality of the resulting solid. During freezing, regions of high and low concentrations of tin are created. This introduces another important phenomenon: diffusion, or the movement by molecular action of matter from regions of high concentration to regions of lower concentration. In MEPHISTO-4, it is tin that diffuses from the high-concentration region in front of the solid-liquid interface to more distant low-concentration regions.

deGroh, Henry C., III↗
DOE OSTI2022

Conversion of Resonance Parameters Between Wigner-Eisenbud R-matrix and Transition Matrix Pole Representation

The goal of this research is to develop a numerical code for translation between the current standard, Wigner-Eisenbud R-matrix parameters and a novel pole-representation of cross sections. The method follows Ducru et al. and constructs the conversion between these parametric representations of the scattering and reaction data. This pole representation, which expresses cross sections as a sum of simple poles (and their attendant residues), is examined in the context of the advantages provided by this compact, computationally efficient representation. These advantages include the ability to doppler broaden cross sections on-the-fly as well as a lower memory footprint when generating cross sections, an attractive feature to those interested in parallel GPU based Monte Carlo. Some conversion methods between R-matrix parameters and S-matrix poles have relied on curve fitting techniques. It is unknown weather these numerical methods necessarily preserve certain underlying physics properties of the standard R-matrix cross section model (such as unitarity). The direct conversion studied here aim to preserve such properties. This knowledge is of interest for future work into the generation of resonance parameter sensitivities.

CLUTCH↗
DOE OSTI2020

Production of Radiohalogens: Bromine and Astatine for Imaging and Therapy

Objective: This collaborative proposal between the University of Alabama at Birmingham (UAB), University of Wisconsin, Madison (UWisc) and the University of Pennsylvania (Penn) aims to research the efficient production of high purity, clinical grade radiohalogens for preclinical and translational applications by optimizing the cyclotron targetry and separation chemistry of 76,77Br and 211At for use in imaging and therapy. Proposed Research and Methods: During the course of this project, faculty and trainees at our three institutions will use the complementary infrastructure available to examine the 76Se(p,n)76Br, 77Se(p,n)77Br, 78Se(p,2n)77Br, natAs(α,2n)77Br ,80Kr(p,α)77Br and 209Bi(α, 2n)211At reactions as routes to the production of these valuable radiohalogens for imaging and therapy. Working together we aim to optimize both solid and gas targetry, including reclamation of expensive enriched material. We will develop high yielding recovery processes based on dry distillation and ion exchange techniques. We will also create a comprehensive set of quality control procedures to ensure we are producing our radioisotopes in the highest radiochemical and chemical purity possible. By bringing three institutions together to perform research and development we provide the unique opportunity to build an everlasting cooperative culture in the field of nuclear medicine research by enabling academic centers to delve into the fields of radiohalogen chemistry. In addition, each institution involved holds different skills and expertise and a collaborative grant will facilitate the cross-pollination of ideas, techniques, and build strong bonds that unite under the DOE mission. Additionally, our work does not stop at the completion of this proposal, rather this proposal allows us to gain the momentum needed to get our research and developmental efforts off the ground thereby creating a network of three institutions dedicated to the advancement of isotope production for medical research.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS2000

Formal Analysis of the Remote Agent Before and After Flight

This paper describes two separate efforts that used the SPIN model checker to verify deep space autonomy flight software. The first effort occurred at the beginning of a spiral development process and found five concurrency errors early in the design cycle that the developers acknowledge would not have been found through testing. This effort required a substantial manual modeling effort involving both abstraction and translation from the prototype LISP code to the PROMELA language used by SPIN. This experience and others led to research to address the gap between formal method tools and the development cycle used by software developers. The Java PathFinder tool which directly translates from Java to PROMELA was developed as part of this research, as well as automatic abstraction tools. In 1999 the flight software flew on a space mission, and a deadlock occurred in a sibling subsystem to the one which was the focus of the first verification effort. A second quick-response "cleanroom" verification effort found the concurrency error in a short amount of time. The error was isomorphic to one of the concurrency errors found during the first verification effort. The paper demonstrates that formal methods tools can find concurrency errors that indeed lead to loss of spacecraft functions, even for the complex software required for autonomy. Second, it describes progress in automatic translation and abstraction that eventually will enable formal methods tools to be inserted directly into the aerospace software development cycle.

Havelund, Klaus↗
NASA NTRSDate not supplied

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

On January 15, 2022, the Hunga Tonga-Hunga Ha’apai (hereafter, Hunga Tonga) submarine volcano had an explosive eruption that thrusted ash, gases, and water vapor through the troposphere into the stratosphere and mesosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using data retrieved from low earth orbiting satellite instruments (e.g., OMPS, OMI, and CALIPSO), this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), with prompt engineering can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline using NASA Earthdata and Openscapes, establishes a framework for systematically and rapidly studying extreme events, including volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of remote sensing data, this work demonstrates how AI and open science can accelerate research and generate actionable results. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions (e.g., the Atmosphere Observing System (AOS)), and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters and extreme events in a changing world.

David M. Giles↗
DOE OSTI2021

A holistic framework integrating plant-microbe-mineral regulation of soil bioavailable nitrogen

Abstract Soil organic nitrogen (N) is a critical resource for plants and microbes, but the processes that govern its cycle are not well-described. To promote a holistic understanding of soil N dynamics, we need an integrated model that links soil organic matter (SOM) cycling to bioavailable N in both unmanaged and managed landscapes, including agroecosystems. We present a framework that unifies recent conceptual advances in our understanding of three critical steps in bioavailable N cycling: organic N (ON) depolymerization and solubilization; bioavailable N sorption and desorption on mineral surfaces; and microbial ON turnover including assimilation, mineralization, and the recycling of microbial products. Consideration of the balance between these processes provides insight into the sources, sinks, and flux rates of bioavailable N. By accounting for interactions among the biological, physical, and chemical controls over ON and its availability to plants and microbes, our conceptual model unifies complex mechanisms of ON transformation in a concrete conceptual framework that is amenable to experimental testing and translates into ideas for new management practices. This framework will allow researchers and practitioners to use common measurements of particulate organic matter (POM) and mineral-associated organic matter (MAOM) to design strategic organic N-cycle interventions that optimize ecosystem productivity and minimize environmental N loss.

Environmental Sciences & Ecology↗
NASA NTRSDate not supplied

Evaluating Machine Learning Approaches to Plume Tracking

On July 15, 2022, the Hunga Tonga-Hunga Ha’apai (HTHH) submarine volcano erupted, propelling trace gasses and ash through the troposphere and up into the stratosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using imagery from NASA’s Earth Observing System, including MODIS aerosol products and OMI sulfur dioxide products, this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline, establishes a framework for systematically and rapidly studying natural disasters, including additional volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of NASA’s Earth Observation and remote sensing data, this work shows how AI and open science can accelerate research and generate actionable results, even for unprecedented events. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions, and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters in a changing world.

machine learning↗