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DOE OSTI2024

Anchoring

This software provides methods and functions for training deep image classification models based on the principle of anchoring. It features a user-friendly PyTorch wrapper that facilitates the easy conversion of any model into an anchored model. The software supports various standard datasets and includes scripts for conducting evaluations. Developed with PyTorch, it is compatible with common neural network architectures used for image data. Additionally, it offers tools for computing evaluation metrics to assess model performance.

Narayanaswamy, Vivek Sivaraman↗

DOE OSTI2025

HALLUFIELD: DETECTING LLM HALLUCINATIONS VIA FIELD-THEORETIC MODELING

A research-focused Python package that implements our hallucination-detection method for large language models(LLMs). The code computes stability signals from LLM predictions across various hyperparameters sweeps and combines free-energy/entropy–style metrics to flag likely hallucinations, with tunable thresholds for batch scoring. The repo includes evaluation scripts, config files, and example notebooks to reproduce benchmark results and ablations; it depends on standard open-source libraries (PyTorch, Hugging Face) and runs on CPU/GPU. The repository examples only use public models/datasets only.

Bhattarai, Manish [Los Alamos National Labs]↗

DOE OSTI2023

Acute stress reduces population-level metabolic and proteomic variation

Abstract Background Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. Results We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. Conclusions RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2007

Process Improvement in a Radically Changing Organization

This presentation describes how the NASA Glenn Research Center planned and implemented a process improvement effort in response to a radically changing environment. As a result of a presidential decision to redefine the Agency's mission, many ongoing projects were canceled and future workload would be awarded based on relevance to the Exploration Initiative. NASA imposed a new Procedural Requirements standard on all future software development, and the Center needed to redesign its processes from CMM Level 2 objectives to meet the new standard and position itself for CMMI. The intended audience for this presentation is systems/software developers and managers in a large, research-oriented organization that may need to respond to imposed standards while also pursuing CMMI Maturity Level goals. A set of internally developed tools will be presented, including an overall Process Improvement Action Item database, a formal inspection/peer review tool, metrics collection spreadsheet, and other related technologies. The Center also found a need to charter Technical Working Groups (TWGs) to address particular Process Areas. In addition, a Marketing TWG was needed to communicate the process changes to the development community, including an innovative web site portal.

Varga, Denise M.↗

NASA NTRSDate not supplied

Nearfield Anisotropic Mesh Adaptation for the Third AIAA Sonic Boom Workshop

Sonic boom prediction is critical for the development of low-boom supersonic aircraft and encouraging a replacement of the overland commercial supersonic ban with a certification standard. The Third AIAA Sonic Boom Workshop nearfield test cases provide a unique opportunity to review anisotropic adaptive-mesh strategies for complex geometries with important propulsion interaction effects. Anisotropic metrics formulated to control estimated Mach interpolation and integrated nearfield pressure signature (goal-oriented) errors are compared with the results obtained on workshop-provided meshes. Detailed flow solutions and mesh renderings are combined with computational schlieren images to reveal the complex interactions present in a shock-plume interaction wind tunnel model and a prototype of the Low-Boom Flight Demonstrator. A favorable comparison of independent implementations of the entire simulation process provides evidence that each process is implemented correctly and sets a solid foundation for confidence in the nearfield prediction of each method.

sonic boom↗

NASA NTRSDate not supplied

Short Circuiting the Controller – Missteps in Maintenance and Inspection of Process and Wiring in STS-93

The primary objective of the Space Transportation System mission 93 (STS-93) was to deploy the Chandra X-Ray Observatory. Chandra, the world's most powerful X-Ray telescope, allowed scientists from around the world to study some of the most distant and dynamic objects in the universe. Stripped of nearly 7,000 pounds of its own gear to make room for the payload, the orbiter assigned to this mission was Space Shuttle Columbia. Prior to STS-93, Columbia had flown 25 flights and was NASA's oldest and heaviest orbiter. On July 23, 1999, after two prior launch scrubs, Eileen Collins and her crew of four launched from Kennedy Space Center. About five seconds after launch, Mission Control at Johnson Space Center detected a voltage drop on one of Columbia's electrical buses. As a result of this power fluctuation, a primary and back-up Main Engine controller dropped offline. Given design redundancy, the two remaining controllers supported all three engines. If there had been any other AC bus issues, one engine of the three on the Orbiter would have shut down. The redundant set of digital computer units in each engine controller saved Columbia and her crew from a very risky contingency abort. Post-flight inspection revealed soot on a screw head and a hole in an adjacent Kapton insulated wire. The single strand of polyimide wire was located nearly half-way down the payload bay. The Shuttle Independent Assessment Team (SIAT) reported that the wire had rubbed and chaffed against a burred screw head. The burr was later determined to be the result of overtightening of the screw by a technician during a maintenance refurbishment. Alone, the burr may not have been problematic, but later, during another ground processing event, possibly years after, someone inadvertently stepped on the wiring harness. With the pressure and motion of unintended contact, some of the Kapton insulation rubbed off against the burred screw head. The SIAT suspected the wire damage was pre-existing and was caused 4 or 5 years prior to the flight. Finally, the intense vibrations during the launch sequence allowed contact between the exposed conductor and exposed metal area on the burred screw head, resulting in the arcing and shorting of the wire. Due to the quick turnaround times of Space Shuttle Orbiters, wiring issues caused from multiple maintenance events were often overlooked. Additionally, failing to incorporate thorough and early inclusion of human systems integration (HSI) applications as a crucial part of the decision process can result in these types of misshaps. In order to reduce human error associated with integrated manufacturing, maintenance, refurbishment and flight preparations, wire inspection criteria should be refined and standardized, visual inspection processes should be quantified, and technicians should be certified by specially trained instructors. This case, among many others, unveils why human error management and development of safety metrics is a vital piece in the development of complex systems, and why it should be supported aggressively and implemented program wide.

Human Systems Integration↗

NASA NTRS2025

Time Course of Effects of Carbon Dioxide Exposure on Physical and Cognitive Performance in a Simulated Surface Extravehicular Activity Contingency Scenario

INTRODUCTION Carbon dioxide (CO 2 ) is a metabolic byproduct produced by humans that must be scrubbed from the atmosphere by environmental control and life support systems in space vehicles and spacesuits to prevent buildup. Current nominal CO 2 limits are set by NASA Standard 3001, however, limits during contingency situations are not specified. The purpose of this study was to characterize and quantify the physiological, cognitive, and self-assessed symptom and performance metrics as a function of inspired CO 2 (P I CO 2 ) during a simulated 1-hour contingency lunar EVA scenario in virtual reality (VR). METHODS Fifteen healthy subjects completed 7 simulated EVA scenarios walking on a passive treadmill for one hour breathing dry, ambient air mixed with added CO 2 in a blind, counterbalanced manner (P I CO 2 : 0, 5, 10, 15, 20, 25, 30 mmHg) simulating a 2.0 km contingency EVA walkback to a habitat on the lunar surface in the event of a CO 2 scrubber malfunction. Subjects were immersed in a Lunar VR environment and asked to maintain their walking speed between 2.1 km/h and an upper limit individually calibrated to a fixed metabolic workload of 1.3 L/min of O 2 consumption, and performed periodic cognitive tasks and continuous cardiopulmonary measurements. Self-assessments of symptoms and task performance were measured via verbal Likert-scale survey at the onset of the walkback and at 10-minute intervals thereafter. RESULTS The time course of cardiopulmonary, cognitive, and self-reported responses at each level of P I CO 2 will be presented. All 15 subjects were able to complete the 1-hr walk back task at all CO 2 levels. Physiological responses were rapid, typically within 1 to 5 minutes. No effects of time were detected for cognitive measures. Symptomatic ratings of headache, shortness of breath, and fatigue demonstrated a time-course development, reaching asymptote after 40 minutes of CO 2 exposure. DISCUSSION These findings will inform the posture of risk associated with CO 2 exposure on human health and performance during microgravity and planetary surface operations, This will help inform decisions related to space suit and environmental life support hardware requirements and EVA operational limits for contingency extravehicular activity (EVA) scenarios.

carbon dioxide↗

DOE OSTI2025

Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions

Understanding protein-protein interactions (PPIs) between viruses and host organisms is crucial for uncovering infection mechanisms and identifying potential therapeutic targets. The ability to generalize PPI predictive models across understudied viruses presents a significant challenge. In this work, we use arenavirus-human PPIs to illustrate the difficulties associated with model generalization, which are compounded by a lack of both positive and negative data. We employ a Transfer Learning approach to investigate arenavirus-human PPIs by utilizing models trained on better-studied virus-human and human-human PPIs. Additionally, we curate and assess four types of negative sampling datasets to evaluate their impact on model performance. Despite the overall high accuracies (93–99 %) and AUPRC scores (0.8–0.9) appearing promising, further analysis indicates that these performance metrics can be misleading due to data leakage, data bias, and overfitting, especially concerning under-represented viral proteins. We reveal these gaps and assess the impact of data imbalance using standard k-fold cross-validation and Independent Blind Testing with a Balanced Dataset, resulting in a drop in accuracy below 50 %. We propose a viral protein-specific evaluation framework that categorizes viral proteins into majority and minority classes based on their representation in the dataset, enabling comparison of model performance across these groups using balanced accuracies. This framework offers a more robust evaluation of model generalizability, addressing biases inherent in standard evaluation techniques and paving the way for more reliable PPI prediction models for understudied viruses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

A simulation framework for evaluating electronic order workflows in integrated health records

Electronic health record (EHR) systems are critical to modern healthcare delivery, yet the dynamic workflows that govern electronic order processing remain underexplored. Inefficiencies in these digital pathways can cause delays in care, repetitive workloads, and even patient harm. This study presents a discrete-event simulation framework used to reconstruct and evaluate EHR-based order workflows in a large integrated healthcare system. Using real-world data extracted from the Veterans Health Administration’s Corporate Data Warehouse, the authors mapped order events to standardized state transitions and modeled their progression across different facilities of varying complexity levels. After being calibrated with empirical distributions of transition times and validated against observed time-in-system metrics, the simulation demonstrates close alignment with historical performance. Scenario analyses reveal that resource capacity constraints significantly amplify the impact of electronic order surges, which are reflected in the disproportionate growth in backlogs and processing delays. Adjustments in transition probabilities further increased recirculation and extended workflow paths. Network-based analysis identified Reserved, InProgress, and Completed as structurally critical states that function as hubs within the process network but the transitions in-between also act as major bottlenecks. These results showcased the effectiveness of simulation-based approaches in monitoring EHR order processing performance and evaluating consequences of workflow changes on healthcare network resources planning. The proposed simulation framework provides a scalable data-driven tool to support operational decision-making and improve the efficiency of electronic order management in complex healthcare environments.

Engineering↗

DOE OSTI2023

Validation of semi-analytical, semi-empirical covariance matrices for two-point correlation function for early DESI data

ABSTRACT We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative of luminous red galaxies (LRGs) data collected during the initial 2 months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). We run the pipeline on multiple effective Zel’dovich (EZ) mock galaxy catalogs with the corresponding cuts applied and compare the results with the mock sample covariance to assess the accuracy and its fluctuations. We propose an extension of the previously developed formalism for catalogs processed with standard reconstruction algorithms. We consider methods for comparing covariance matrices in detail, highlighting their interpretation and statistical properties caused by sample variance, in particular, non-trivial expectation values of certain metrics even when the external covariance estimate is perfect. With improved mocks and validation techniques, we confirm a good agreement between our predictions and sample covariance. This allows one to generate covariance matrices for comparable data sets without the need to create numerous mock galaxy catalogs with matching clustering, only requiring 2PCF measurements from the data itself. The code used in this paper is publicly available at https://github.com/oliverphilcox/RascalC.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Data from: "Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics"

This data package was generated to support the manuscript “Towards CONUS-Wide Machine Learning-Augmented Conceptually Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics.” It provides input files, model outputs, plotting data, scripts, notebooks, and documentation used to develop, evaluate, and reproduce Mass-Conserving Perceptron (MCP)-based hydrologic modeling experiments across 513 selected Catchment Attributes and Meteorology for Large-sample Studies in the United States (CAMELS-US) basins. The files are organized by modeling component and analysis purpose, including rainfall–runoff experiments, snow module experiments, coupled hydrologic-snow experiments, Long Short-Term Memory (LSTM) benchmark results, model skill metrics, initialization and epoch records, cell-state normalization files, Akaike Information Criterion (AIC)-based model comparison files, and data used to generate manuscript figures. Tabular files can be opened using standard spreadsheet software or Python/R data-analysis tools. Python scripts, Jupyter notebooks, and selected MATLAB scripts are included for model execution, postprocessing, plotting, and statistical analysis. Quality assurance and quality control were conducted through the source-data selection and modeling workflow. Meteorological forcing, streamflow, and static catchment attributes were derived from the CAMELS-US dataset, and snow water equivalent data were derived from the University of Arizona (UA) Snow Water Equivalent dataset. Selected basins and time periods were screened during the associated research workflow to avoid missing observations or poor-quality cases. Static geospatial features were processed primarily using Quantum Geographic Information System (QGIS) and Geospatial Data Abstraction Library (GDAL) workflows. Additional details are provided in the associated manuscript and documentation.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗

NASA NTRS2007

Robust Design Optimization via Failure Domain Bounding

This paper extends and applies the strategies recently developed by the authors for handling constraints under uncertainty to robust design optimization. For the scope of this paper, robust optimization is a methodology aimed at problems for which some parameters are uncertain and are only known to belong to some uncertainty set. This set can be described by either a deterministic or a probabilistic model. In the methodology developed herein, optimization-based strategies are used to bound the constraint violation region using hyper-spheres and hyper-rectangles. By comparing the resulting bounding sets with any given uncertainty model, it can be determined whether the constraints are satisfied for all members of the uncertainty model (i.e., constraints are feasible) or not (i.e., constraints are infeasible). If constraints are infeasible and a probabilistic uncertainty model is available, upper bounds to the probability of constraint violation can be efficiently calculated. The tools developed enable approximating not only the set of designs that make the constraints feasible but also, when required, the set of designs for which the probability of constraint violation is below a prescribed admissible value. When constraint feasibility is possible, several design criteria can be used to shape the uncertainty model of performance metrics of interest. Worst-case, least-second-moment, and reliability-based design criteria are considered herein. Since the problem formulation is generic and the tools derived only require standard optimization algorithms for their implementation, these strategies are easily applicable to a broad range of engineering problems.

Crespo, Luis G.↗

NASA NTRS2013

Achieving Climate Change Absolute Accuracy in Orbit

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5-50 micron), the spectrum of solar radiation reflected by the Earth and its atmosphere (320-2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a "NIST [National Institute of Standards and Technology] in orbit." CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.

Wielicki, Bruce A.↗

NASA NTRS2017

The Relationships Between the Trends of Mean and Extreme Precipitation

This study provides a better understanding of the relationships between the trends of mean and extreme precipitation in two observed precipitation data sets: the Climate Prediction Center Unified daily precipitation data set and the Global Precipitation Climatology Program (GPCP) pentad data set. The study employs three kinds of definitions of extreme precipitation: (1) percentile, (2) standard deviation and (3) generalize extreme value (GEV) distribution analysis for extreme events based on local statistics. Relationship between trends in the mean and extreme precipitation is identified with a novel metric, i.e. area aggregated matching ratio (AAMR) computed on regional and global scales. Generally, more (less) extreme events are likely to occur in regions with a positive (negative) mean trend. The match between the mean and extreme trends deteriorates for increasingly heavy precipitation events. The AAMR is higher in regions with negative mean trends than in regions with positive mean trends, suggesting a higher likelihood of severe dry events, compared with heavy rain events in a warming climate. AAMR is found to be higher in tropics and oceans than in the extratropics and land regions, reflecting a higher degree of randomness and more important dynamical rather than thermodynamical contributions of extreme events in the latter regions.

global warming↗

NASA NTRS2019

Quantifying the Storm Time Thermospheric Neutral Density Variations Using Model and Observations

Accurate determination of thermospheric neutral density holds crucial importance for satellited rag calculations. The problem is twofold and involves the correct estimation of the quiet time climatology and storm time variations. In this work, neutral density estimations from two empirical and threephysics based models of the ionosphere thermosphere are compared with the neutral densities along the Challenging MicroSatellite Payload satellite track for six geomagnetic storms. Storm time variations are extracted from neutral density by (1) subtracting the mean difference between model and observation (bias),(2) setting climatological variations to zero, and (3) multiplying model data with the quiet time ratio between the model and observation. Several metrics are employed to evaluate the model performances. We find that the removal of bias or climatology reveals actual performance of the model in simulating the storm time variations. When bias is removed, depending on event and model, storm time errors in neutral density can decrease by an amount of 113% or can increase by an amount of 12% with respect to error in models with quiet time bias. It is shown that using only average and maximum values of neutral density to determine the model performances can be misleading since a model can estimate the averages fairly well but may not capture the maximum value or vice versa. Since each of the metrics used for determining model performances provides different aspects of the error, among these, we suggest employing mean absolute error, prediction efficiency, and normalized root mean square error together as a standard set ofmetrics for the neutral density.

Eyiguler, E. Ceren Kalafatoglu↗

NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

In engineering and aerospace applications, it is vital to operational success to have insight into the expected performance and health of physical systems. The field of prognostics and health management provides quantitative methods for monitoring, predicting, and managing system health. Prognostics algorithms can be employed to assess the current state of a system, propagate the system throughout time, and predict potential anomalies or failures that may occur. While they can provide accurate prediction results, effective prognostics algorithms can be challenging to use in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for future remaining useful life predictions. In this work, we implement new algorithmic approaches for prediction, quantitatively compare them via a battery degradation use-case, and provide recommendations of potential improvements to a prognostics framework. One approach to prediction is through sampling, whereby the current state of a physical system is sampled many times and each sample is propagated forward until failure is reached, resulting in a distribution of failure values. To improve the efficiency of this process, we implemented five new algorithmic approaches to prediction, including three distinct sampling methods (standard Monte Carlo, Quasi-Monte Carlo, and Latin Hypercube Sampling), a variable time step algorithm, and a variable sample size algorithm. To compare the algorithms, we employ a variety of metrics designed specifically to analyze both computational efficiency and model accuracy. Our metrics include accuracy to compare the average predicted value to ground truth, mean absolute deviation to illustrate dispersion, specific percentile error to describe accuracy within a user-defined risk tolerance, and code run-time. To quantitatively analyze our results, we employ a use-case of degradation of a Lithium-ion battery. We use an electrochemistry-based model to describe the current health state of the battery, and implement our prediction algorithms to propagate forward in time until end-of-discharge (EOD) is reached. Notably, through this work it was found that none of our sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. We find that while the sampling methods are unique, the distributions they generate are similar, ultimately producing final predictions that are nearly identical. In exploring the effect of the time step within the prediction algorithm, we found that prediction accuracy was highly dependent on the time step used, and that implementing a variable time step within a particular prediction may provide an increase in computational efficiency while also maintaining prediction accuracy. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment can result in improved computation speed and maintained prediction accuracy. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency.

prognostics↗

NASA NTRS2023

How Have Hydrological Extremes Changed Over the Past 20 Years?

Severe floods and droughts, including their back-to-back occurrences (weather whiplash), have been increasing in frequency and severity around the world. Improved understanding of systematic changes in hydrological extremes is essential for preparation and adaptation. In this study, we identified and quantified extreme wet and dry events globally by applying a clustering algorithm to terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (FO). The most intense events, ranked using an intensity metric, often reflect impacts of large-scale oceanic oscillations such as El Niño–Southern Oscillation and consequences of climate change. The severity of both wet and dry events, represented by standardized TWS anomalies, increased significantly in most cases, likely associated with intensification of wet and dry weather regimes in a warmer world, and consequently, exhibited strongest correlation with global temperature. In the Dry climate, the number of wet events decreased while the number of dry events increased significantly, suggesting a drying trend that may be attributed to climate variability and possible increases in irrigation and reliance on groundwater. In the Continental climate where temperature has risen faster than global average, dry events increased significantly. Characteristics of extreme events often showed strong correlations with global temperature, especially when averaged over all climates. These results suggest changes in hydrological extremes and underscore the importance of quantifying total water storage changes when studying hydrological extremes. Extending the GRACE/FO record, which spans 2002 to the present, is essential to continuously tracking changes in TWS and hydrological extremes.

Bailing Li↗

NASA NTRSDate not supplied

Visualizing Geospatial Data through ESRI Story Maps for Earth Science Education: Lessons Learned from My NASA Data

For 20 years My NASA Data (MND) has curated NASA Earth science data and provided the data to educators in engaging learner-centered resources. MND has recently featured story maps as an innovative way to engage students in NASA Earth data. A story map is a cloud-based lesson that engages the learner in interactive geospatial maps using NASA data, and other multimedia content, text, and tasks that can be seamlessly incorporated in classroom instruction. This immersive technology eliminates the need for the user to move among tricky interfaces to access and visualize Earth science data, and no special software is required to be downloaded. Each story map integrates data from different NASA satellite missions, retrieved from Distributed Active Archive Centers (DAACs). Story maps also employ data analysis tools, such as time series options and swipe tools that allow learners to view and analyze relationships between scientific variables. MND has produced 25 story map lesson plans on the topics of air quality, the urban heat island effect, Earth’s energy budget, phytoplankton distribution, hurricane formation, solar eclipses, ocean circulation patterns, sea ice extent, and volcanic eruptions. Nine of them are extended story maps and written in the 5E format, which is internationally recognized as best practice based on how children learn science. Each story map resource is developed by the MND team featuring a GIS programming specialist, a lead scientist, and educational specialist/s to ensure the context, content, and methods are scientifically and educationally sound. The MND story maps are written for middle and high school science teachers and students as they connect with the Earth Systems Science phenomena featured in the Next Generation Science Standards. Each story map includes supporting resources for smooth integration in the classroom. During Fiscal Year 2023, The My NASA Data website received over 100,000 story map engagements during. These metrics highlight the interest in story maps as an Earth Science educational resource.

Desiray Wilson↗