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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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Topics include: a technology focus on sensors, electronic components and systems, software, materials, materials, mechanics, manufacturing, physical sciences, information sciences, book and reports, motion control and a special section of Photonics Tech Briefs.
Topics include: a technology focus on engineering materials, electronic components and systems, software, mechanics, machinery/automation, manufacturing, bio-medical, physical sciences, information sciences book and reports, and a special section of Photonics Tech Briefs.
Topics include: special coverage on computer hardware and peripherals, electronic components and circuits, electronic systems, software, materials, mechanics, machinery/automation, manufacturing, physical sciences, information sciences, book and reports, and a special section of Photonics Tech Briefs. and a second special section of Motion Control Tech Briefs
Topics include: special coverage on data acquisition, also, electronic components and circuits, electronic systems, software, materials, mechanics, machinery/automation, physical sciences, information sciences, This issue contains a special sections of Electronics Tech Briefs and Motion Control Tech Briefs.
Topics include: special coverage sections on sensors/imaging and mechanical technology, and sections on electronic components and circuits, electronic systems, software, materials, machinery/automation, manufacturing/fabrication, physical sciences, information sciences, book and reports, and a special section of Photonics Tech Briefs.
Topics include: special coverage sections on test and measurement and sections on electronic components and circuits, electronic systems, software, materials, mechanics, machinery/automation, physical sciences, information sciences, book and reports, and special sections of Electronics Tech Briefs amd Rapid Product Development Tech Briefs.
Topics include: special coverage section on composites and plastics, electronic components and systems, software, mechanics, physical sciences, information sciences, book and reports, and a special sections of Photonics Tech Briefs and Motion Control Tech Briefs.
Topics include: special coverage section on data acquisition and sensors and sections on electronic components and systems, software, materials, mechanics, machinery/automation, manufacturing/fabrication, bio-medical, physical sciences, information sciences, book and reports, and special section of Electronics Tech Briefs and Motion Control Tech briefs
Topics include: special coverage sections on CAD, CAE, and PDM, and, Composites and Plastics, and sections on electronic components and systems, software, test and measurement, mechanics, manufacturing/fabrication, physical sciences, information sciences, book and reports, and special sections of Electronics Tech Briefs and Motion Control Tech Briefs
Topics covered include: Sensors; Electronic Components and Systems; Software Engineering; Materials; Manufacturing/Fabrication; physical Sciences; Information Sciences.
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The American Nurses Association (ANA) defines nursing informatics as the integration of “nursing science, computer science, and information science to manage and communicate data, information, knowledge, and wisdom in nursing practice. [1]” In additional to nursing education and clinical experience, this field requires additional education in areas of data analysis, database management, and clinical information systems. Nursing informaticists help bridge the communication gap between clinical and technical stakeholders which help align common goals. NASA is currently developing a new medical system trade analysis and decision support tool called IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces). The development team is comprised of multiple specialties including but not limited to clinicians (pharmacists, physicians, and nurse informaticists), software and program developers, human factors experts, and engineers. The nurse informaticists play a significant role in the development, operation, and results interpretation of IMPACT. Their expert knowledge and skills in both clinical nursing, information science, and database management place them in a unique position to collaborate and integrate with the clinical, engineering, and software development members of the team to achieve a cohesive, functional, and user-friendly model.
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We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.
The Goddard Earth Sciences Data Information and Services Center (GES DISC) archives and distributes a number of observational and model carbon cycle science data sets. We also provide services that facilitate data discovery, intercomparison, and visualization of these heterogeneous datasets for both research and applications users, such as subsetting, format conversion, How-To documentation, and the Help Desk.
Data quality plays a critical role in research and applications. The Earth Science Information Partners (ESIP) Information Quality Cluster (IQC) defines four aspects of information quality: Science, Product, Stewardship, and Services. The ESIP IQC has become internationally recognized as an authoritative and responsive resource of information and guidance to data producers and distributors on how to implement data quality standards and best practices for their science data systems, datasets, and data/metadata dissemination services. In recent years, cloud computing environments have provided scale-up capabilities such as data archives and services, enabling interdisciplinary science and applications. More value-added products are expected from data service providers, including Analysis Ready Data (ARD). ARD refers to data that has been preprocessed into a form that allows immediate analysis by the end user, processed to a minimum set of requirements and provides interoperability over time and across multiple datasets. Once a dataset has been developed from its original form to produce ARD, what quality characteristics should the derived dataset or ARD possess? Also, is it safe to assume that the quality of the ARD is consistent with the quality of the source data, or are there special attributes to an ARD that would warrant a secondary, independent quality assessment? What provenance (also called “data lineage”) information needs to be included in ARD? It is important to answer these questions, especially given the ease of use of ARD, and the consequent temptation by users to trust ARD without understanding the limitations or possible variations in quality compared to the source data. In this presentation, we will discuss data quality challenges for ARD products and services and introduce IQC for participation.
This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.
Results of the deliberations of the six multispectral imaging science working groups (Botany, Geography, Geology, Hydrology, Imaging Science and Information Science) are summarized. Consideration was given to documenting the current state of knowledge in terrestrial remote sensing without the constraints of preconceived concepts such as possible band widths, number of bands, and radiometric or spatial resolutions of present or future systems. The findings of each working group included a discussion of desired capabilities and critical developmental issues.