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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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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data

Zero-shot and prompt-based models have excelled at visual reasoning tasks by leveraging large-scale natural image corpora, but they often fail on sparse and domain-specific scientific image data. We introduce Zenesis, a no-code interactive computer vision platform designed to reduce data readiness bottlenecks in scientific imaging workflows. Zenesis integrates lightweight multimodal adaptation for zero-shot inference on raw scientific data, human-in-the-loop refinement, and heuristic-based temporal enhancement. We validate our approach on Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets of catalyst-loaded membranes. Zenesis outperforms baselines, achieving an average accuracy of 0.947, Intersection over Union (IoU) of 0.858, and Dice score of 0.923 on amorphous catalyst samples; and 0.987 accuracy, 0.857 IoU, and 0.923 Dice on crystalline samples. These results represent a significant performance gain over conventional methods such as Otsu thresholding and standalone models like the Segment Anything Model (SAM). Zenesis enables effective image segmentation in domains where annotated datasets are limited, offering a scalable solution for scientific discovery.

Mukherjee, Shubhabrata↗
DOE OSTI2021

cuZ-Checker: A GPU-Based Ultra-Fast Assessment System for Lossy Compressions

Lossy compression is becoming an indispensable technique for the success of today's extreme-scale high-performance computing projects that produce vast volumes of data during scientific simulations or instrument data acquisitions. Comprehensively understanding the compression quality and performance of different lossy compressors is critical to selecting the best-fit compressors and using them properly and efficiently in practice. A few lossy compression assessment tools (e.g., Z-checker) have been developed, but none of them support the execution in a GPU environment. This is a significant gap because many recent extreme-scale applications and lossy compressors (e.g., cuSZ) can run entirely within GPUs. In this work, we develop an efficient lossy compression measuring system (called cuZ-Checker) on the GPU platform, which aims to perform the lossy compression quality and performance assessment completely within the GPU environment. Our contribution is threefold. (1) We develop a novel GPU-based lossy compression measuring framework using a computation pattern-based design approach. This approach classifies the computing-intensive metrics into three categories based on their patterns which creates large opportunities for kernel fusion and data reuse. (2) For each pattern in cuZ-Checker, we develop a CUDA kernel and provide fine-grained optimizations to boost its performance. (3) We thoroughly evaluate our cuZ-checker on a V100 GPU using four real-world scientific application datasets. Experiments show that cuZ-Checker can significantly accelerate the overall lossy compression assessment performance by 23X similar to 31X compared with the OpenMP-based multithreading CPU performance. To the best of our knowledge, this is the first lossy compression measuring system designed for GPU devices.

GPU↗
DOE OSTI2021

Data Reduction for Science: Brochure from the Advanced Scientific Computing Research Workshop

Data reduction for science holds promise for addressing the challenges of moving, storing, and processing massive data sets produced by the scientific community. Pursuing the PRDs outlined here will enable advances in data streaming, fast feedback and/or autonomous control of experiments, and faster time to scientific insight. These advances will result in a significant improvement in the ability to transport, store, process and interpret experimental, observational, and computational data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Communication System Architecture for Planetary Exploration

Future human missions to Mars will require effective communications supporting exploration activities and scientific field data collection. Constraints on cost, size, weight and power consumption for all communications equipment make optimization of these systems very important. These information and communication systems connect people and systems together into coherent teams performing the difficult and hazardous tasks inherent in planetary exploration. The communication network supporting vehicle telemetry data, mission operations, and scientific collaboration must have excellent reliability, and flexibility.

Braham, Stephen P.↗
NASA NTRS2002

Simultaneous Spectral and Timing Observations of Accreting Neuron Stars

The goal of this proposal is to perform simultaneous x-ray spectral and millisecond timing observations of accreting neutron stars to further our understanding of their accretion dynamics and in the hope of using these systems as probes of the physics of strong gravitational fields. NAG5-9104 is the successor grant to NAG5-8408. Observations using the Rossi X-Ray Timing Explorer (RXTE) and BeppoSAX were performed of 4U1702-429, 4U1735-44, and Cyg X-2. Unfortunately, only a small fraction of the approved observing time was obtained for the first two targets and the data are of limited scientific value. Data analysis has been completed on the observations of Cyg X-2. We discovered a correlation between the frequency of the horizontal branch oscillations (HBO) and a soft, thermal component of the x-ray spectrum likely associated with emission from the accretion disk. This correlation may place constraints on models of the oscillations. A paper based on these results appeared in the Astrophysical Journal.

Kaaret, P.↗
NASA NTRS2010

Distinguishing Provenance Equivalence of Earth Science Data

Reproducibility of scientific research relies on accurate and precise citation of data and the provenance of that data. Earth science data are often the result of applying complex data transformation and analysis workflows to vast quantities of data. Provenance information of data processing is used for a variety of purposes, including understanding the process and auditing as well as reproducibility. Certain provenance information is essential for producing scientifically equivalent data. Capturing and representing that provenance information and assigning identifiers suitable for precisely distinguishing data granules and datasets is needed for accurate comparisons. This paper discusses scientific equivalence and essential provenance for scientific reproducibility. We use the example of an operational earth science data processing system to illustrate the application of the technique of cascading digital signatures or hash chains to precisely identify sets of granules and as provenance equivalence identifiers to distinguish data made in an an equivalent manner.

Tilmes, Curt↗
NASA NTRS1992

MODIS algorithm development and data visualization using ACTS

The study of the Earth as a system will require the merger of scientific and data resources on a much larger scale than has been done in the past. New methods of scientific research, particularly in the development of geographically dispersed, interdisciplinary teams, are necessary if we are to understand the complexity of the Earth system. Even the planned satellite missions themselves, such as the Earth Observing System, will require much more interaction between researchers and engineers if they are to produce scientifically useful data products. A key component in these activities is the development of flexible, high bandwidth data networks that can be used to move large amounts of data as well as allow researchers to communicate in new ways, such as through video. The capabilities of the Advanced Communications Technology Satellite (ACTS) will allow the development of such networks. The Pathfinder global AVHRR data set and the upcoming SeaWiFS Earthprobe mission would serve as a testbed in which to develop the tools to share data and information among geographically distributed researchers. Our goal is to develop a 'Distributed Research Environment' that can be used as a model for scientific collaboration in the EOS era. The challenge is to unite the advances in telecommunications with the parallel advances in computing and networking.

Abbott, Mark R.↗
DOE OSTI2024

Hybrid Approaches for Data Reduction of Spatiotemporal Scientific Applications

Scientists conduct large-scale simulations to compute derived quantities from primary data. Thus, it is crucial that data compression techniques maintain bounded errors on these derived quantities or quantities of interest (QOI). For many spatiotemporal applications, these QOIs are binary in nature and represent presence or absence of a physical phenomenon. In this work, we propose to use a hybrid approah for differential compression for such applications. We use a neural network (NN) approach to determine regions-of-interest (ROIs) where the binary QOIs are going to be prevalent. This is then used with traditional approaches that compress at a lower level (and higher accuracy) for these ROIs as compared to other regions.

Li, Xiao↗
NASA NTRS1974

Apollo data analysis experiment S-211. Low brightness image data analysis

The scientific analyses of photographic data in astronomy obtained by the Apollo Orbital Science Photographic Team during Apollo flights 14 through 17 are described. Some of the tasks are the collection and correlation of Quick-Look listings, working prints for further study, the generation of post-mission reports, and project reports. Postflight calibrations of the flight photographic systems are carried out, and lens vignetting effects are corrected. The data handling is described and includes photodigitization of photographic scenes, calibrated step-wedges, and vignetting patterns, which are followed by computer program processing of the film density readings.

Mercer, R. D.↗
NASA NTRSDate not supplied

INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY

Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods. REFERENCES [1] Open science in space. Nature Medicine, 2021. 27(9): p. 1485-1485. [2] Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. [3] Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5. [4] Whetzel, P.L., et al., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res, 2011. 39(Web Server issue): p. W541-5.

informatics↗
DOE OSTI2021

Science Capsule - Capturing the Data Life Cycle

The data generated from scientific workflows often become unusable due to the lack or incompleteness of information required for processing and analyzing the data. Reproducibility of scientific data and workflows facilitates efficient processing and analyses. A key to enabling reproducibility is to capture the end-to-end workflow life cycle, and any contextual metadata and provenance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1997

Study of the Source Regions of Coronal Mass Ejections Using Yohkoh SXT Data

The scientific objective of the program was to better understand how CMEs (Coronal Mass Ejections) are initiated at the sun by examining structures on the disk which are related to the origins of CMEs. CMEs represent important disruptions of large-scale structures of closed magnetic fields in the corona, and result in significant disturbances of the interplanetary medium and near-Earth space. The program pertained to NASA's objectives of understanding the physics of solar activity and the structured and evolution of the corona, and the results are being applied to understanding CMEs currently being observed by SOHO near the sun and by WIND and Ulysses in the heliosphere. Three general areas of research were pursued in the program. One was to use Yohkoh soft X-ray telescope (SXT) images of eruptive events visible against the solar disk to examine the coronal structures and the boundaries of the large-scale magnetic fields considered to be involved in coronal mass ejections (CMEs). The second area involved a survey and study of SXT X-ray arcade events which exhibit dimming, or the possible depletion of coronal material above and possibly before onset of the bright long-duration event (LDE). Finally, we studied the SXT data during periods when white light CMEs were observed the HAO Mauna Loa K-coronameter and, conversely, we examined the white light data during periods when expanding X-ray loops were observed at the limb.

Webb, David F.↗
DOE OSTI2025

Extracting Material Property Measurements from Scientific Literature with Limited Annotations

Extracting material property data from scientific text is pivotal for advancing data-driven research in chemistry and materials science; however, the extensive annotation effort required to produce training data for named entity recognition (NER) models for this task often makes it a barrier to extracting specialized data sets. Here, in this work, we present a comparative study of the conventional, supervised NER methodology to alternative few-shot learning architectures and large language model (LLM)-based approaches that mitigate the need to label large training data sets. We find that the best-performing LLM (GPT-4o) not only excels in directly extracting relevant material properties based on limited examples but also enhances supervised learning through data augmentation. We supplement our findings with error and data quality assessments to provide a nuanced understanding of factors that impact property measurement extraction.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Overview of the Shuttle Imaging Radar-B preliminary scientific results

Data collected with the Shuttle Imaging Radar-B (SIR-B) on the October 5, 1985 Shuttle mission are discussed. The design and capabilities of the sensor which operates in a fixed illumination geometry and has incidence angles between 15 and 60 deg with 1 deg increments are described. Problems encountered with the SIR-B during the mission are examined. the The radar stereo imaging capability of the sensor was verified and three-dimensional images of the earth surface were obtained. The oceanography experiments provided significant data on ocean wave and internal wave patterns, oil spills, and ice zones. The geological images revealed that the sensor can evaluate penetration effect in dry soil from buried receivers and the existence of subsurface dry channels in the Egyptian desert was validated. The use of multiincidence angle imaging to classify terrain units and derive vegetation maps and the development of terrain maps are confirmed.

Elachi, C.↗