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This presentation is for the paper INL/CON-22-65800
Final report
Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.
Science investigators need to quickly and effectively assess past observations of specific locations on a planetary surface. This innovation involves a location-based search technology that was adapted and applied to planetary science data to support a spatial query capability for mission operations software. High-performance location-based searching requires the use of spatial data structures for database organization. Spatial data structures are designed to organize datasets based on their coordinates in a way that is optimized for location-based retrieval. The particular spatial data structure that was adapted for planetary data search is the R+ tree.
This article describes new features in the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni), a user-friendly online tool that enables visualization, analysis, and assessment of NASA Earth science data sets without downloading data and software. Since the satellite era began, data collected from Earth-observing satellites have been widely used in research and applications; however, using satellite-based data sets can still be a challenge to many. To facilitate data access and evaluation, as well as scientific exploration and discovery, the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) has developed Giovanni for a wide range of users around the world. This article describes the latest capabilities of Giovanni with examples, and discusses future plans for this innovative system.
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Earth Science satellite missions provide a unique opportunity for scientists to visualize complex and multifaceted observations projected geospatially across maps of the Earth. While visualization tools can help scientists comprehend, analyze, and share data, visualizing Level-2 Earth Sciences data poses its own specific set of challenges. Since the geospatial information in Level-2 data files is stored as independent variables, the plotting process involves matching dimensional information from latitude and longitude with a desired variable. Variables are stored in different ways across various Earth Science data file formats, which complicates the process of extracting data and plotting variables from a given file without requiring extensive user input and prerequisite familiarity with the file type variable structure. In coordination with NASA’s Goddard Earth Sciences Data Information Services Center (GES DISC), the team developed a Level-2 Earth Science data visualization tool that aims to address some of the complexities associated with plotting Level-2 data. This tool offers command-line and user interface support for file and variable selection to accommodate varying use cases and degrees of user familiarity with the structure of a given file. The visualization tool is written in Python 3 and utilizes a modular approach to facilitate continued expansion and reuse. In addressing some common complications involved in plotting Level-2 Earth Sciences data, the tool aims to help to link the process of analysis more directly with data acquisition and visualization, bringing analysis closer to data across levels of processing.
Background: The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods: Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results: Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion: Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.
Quantum machine learning—and specifically Variational Quantum Algorithms (VQAs)—offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.
Social media data can provide useful real-time and historical information relating to the natural world, but managing this data poses challenges. Scientists at GES DISC are exploring the potential of Twitter data to augment precipitation data from the Global Precipitation Measurement (GPM) mission. However, the format of Twitter data is unconventional in the context of NASA data centers, resulting in frustration for scientists who need to work with the data. This study investigated procedures and standards needed to properly manage Twitter data to make them compatible with these data centers. After comparing databases, the study found that the MongoDB database was best suited for the storage of raw Twitter data due to its flexibility, ability to be accessed by multiple users, and querying functionality. The study used the Python package Zarr to transform processed Twitter data into a gridded format similar to that of satellite data. Each Tweet was mapped onto a time-space grid; each grid location contained information about Tweet attributes and precipitation. The study developed a pipeline for downloading, storing, and gridding Twitter data and transformed Twitter data into an understandable format for users of NASA satellite data.
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of the major Distributed Active Archive Centers (DAACs) archiving and distributing remote sensing data from the NASA's Earth Observing System. In addition to providing just data, the GES DISC/DAAC has developed various value-adding processing services. A particularly useful service is data processing a t the DISC (i.e., close to the input data) with the users' algorithms. This can take a number of different forms: as a configuration-managed algorithm within the main processing stream; as a stand-alone program next to the on-line data storage; as build-it-yourself code within the Near-Archive Data Mining (NADM) system; or as an on-the-fly analysis with simple algorithms embedded into the web-based tools (to avoid downloading unnecessary all the data). The existing data management infrastructure at the GES DISC supports a wide spectrum of options: from data subsetting data spatially and/or by parameter to sophisticated on-line analysis tools, producing economies of scale and rapid time-to-deploy. Shifting processing and data management burden from users to the GES DISC, allows scientists to concentrate on science, while the GES DISC handles the data management and data processing at a lower cost. Several examples of successful partnerships with scientists in the area of data processing and mining are presented.
The topics are presented in viewgraph form and include the following: technical objectives; technology challenges; state-of-the-art assessment; mass storage comparison; SODR drive and system concepts; program description; vertical Bloch line (VBL) device concept; relationship to external programs; and backup charts for memory and storage.
The technical objectives are to develop high-performance, space-qualifiable, onboard computing, storage, and networking technologies. The topics are presented in viewgraph form and include the following: justification; technology challenges; program description; and state-of-the-art assessment.
We demonstrate how students and teachers can register to use the NASA Earthdata Forums. The NASA Earthdata forums provide a venue where registered users can pose questions regarding NASA Earth science data in a moderated forum, and have their questions answered by data experts and scientific subject matter experts connected with NASA Earth science missions and projects. Since the forums are also available for research scientists to pose questions and discuss pertinent topics, the NASA Earthdata Forums provide a unique opportunity for students and teachers to gain insight from expert scientists and enhance their knowledge of the many different ways that NASA Earth observations can be used in research and applications.
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Data for the report (ORNL/TM-2025/3807) that describes the calculations and the Monte Carlo Ray Tracing simulations performed using the McStas package to determine the coatings and geometry for the NB-6 guide. It provides the information to inform the mechanical design, validation tests and verification that it meets the science requirements.
Prenyltransfer is an early-stage carbon-hydrogen bond (C-H) functionalization prevalent in the biosynthesis of a diverse array of biologically active bacterial, fungal, plant, and metazoan diketopiperazine (DKP) alkaloids. Toward the development of a unified strategy for biocatalytic construction of prenylated DKP indole alkaloids, we sought to identify and characterize a substrate-permissive C2 reverse prenyltransferase (PT). As the first tailoring event within the biosynthesis of cytotoxic notoamide metabolites, PT NotF catalyzes C2 reverse prenyltransfer of brevianamide F. Solving a crystal structure of NotF (in complex with native substrate and prenyl donor mimic dimethylallyl S-thiolodiphosphate (DMSPP)) revealed a large, solvent-exposed active site, intimating NotF may possess a significantly broad substrate scope. To assess the substrate selectivity of NotF, we synthesized a panel of 30 sterically and electronically differentiated tryptophanyl DKPs, the majority of which were selectively prenylated by NotF in synthetically useful conversions (2 to > 99%). Quantitative representation of this substrate library and development of a descriptive statistical model provided insight into the molecular origins of NotF's substrate promiscuity. This approach enabled the identification of key substrate descriptors (electrophilicity, size, and flexibility) that govern the rate of NotF-catalyzed prenyltransfer, and the development of an "induced fit docking (IFD)-guided" engineering strategy for improved turnover of our largest substrates. We further demonstrated the utility of NotF in tandem with oxidative cyclization using flavin monooxygenase, BvnB. This one-pot, in vitro biocatalytic cascade enabled the first chemoenzymatic synthesis of the marine fungal natural product, (-)-eurotiumin A, in three steps and 60% overall yield.
Science and application results appearing in peer-reviewed journal papers in 2021 are highlighted in this paper. With over six years of science data acquisition, science data products of the NASA Soil Moisture Active Passive (SMAP)satellite project are now being applied in diverse subdisciplines in Earth System science. In 2021, there were close to two-hundred papers appearing in peer-reviewed disciplinary journals. In this paper we highlight a few of the research and applications findings that were reported in the calendar year.