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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Exploration Medical System Demonstration (EMSD) Project

The Exploration Medical System Demonstration (EMSD) is a project under the Exploration Medical Capability (ExMC) element managed by the Human Research Program (HRP). The vision for the EMSD is to utilize ISS as a test bed to show that several medical technologies needed for an exploration mission and medical informatics tools for managing evidence and decision making can be integrated into a single system and used by the on-orbit crew in an efficient and meaningful manner. Objectives: a) Reduce and even possibly eliminate the time required for on-orbit crew and ground personnel (which include Surgeon, Biomedical Engineer (BME) Flight Controller, and Medical Operations Data Specialist) to access and move medical data from one application to another. b) Demonstrate that the on-orbit crew has the ability to access medical data/information using an intuitive and crew-friendly software solution to assist/aid in the treatment of a medical condition. c) Develop a common data management framework and architecture that can be ubiquitously used to automate repetitive data collection, management, and communications tasks for all crew health and life sciences activities.

Chin, Duane↗
NASA NTRSDate not supplied

Dashboards to Explore Effects of COVID-19 Using Earth Observations

People are reeling from the impacts of the COVID-19 pandemic in every part of the world. As a result,changes in human activity have made visible impacts on ourplanet. To understand these impacts, NASA, ESA (European Space Agency), and JAXA (Japan Aerospace Exploration Agency) joined forces to develop a trilateral dashboard—a situational awareness tool backed by Earth observation derived indicators. An unprecedented collaboration followed for the next two months between the three agencies in which data, science, and technology experts addressed several challenges including indicator development, infrastructure, data management, content development, and communication. “COVID-19 Earth Observation Dashboard” was successfully released offering user-friendly tracking of changes in indicators that include air and water quality, climate, economic activity, and agriculture. This presentation will highlight the outcomes, coordination, technical approaches, collaboration, processes, and lessons learned from the dashboard development.

Manil Maskey↗
NASA NTRS2018

Use of Nack Oriented Reliable Multicast (NORM) Protocol for Transport of Spacecraft Telemetry in Ground Networks

Frequently it is necessary to distribute spacecraft telemetry to multiple destinations on Internet Protocol (IP) networks. There are various methods of delivering this data, but when the data is high rate and there are many destinations IP multicast provides the best solution from a network perspective. However, IP multicast only supports the User Datagram Protocol (UDP) which is connectionless and unreliable. This nature of UDP and by extension the low priority treatment of UDP packets by some network hardware creates a difficult environment for distribution of high rate telemetry to a number of recipients over IP networks. NACK (Negative ACKnowledgment) Oriented Reliable Multicast (NORM) is defined by IETF (Internet Engineering Task Force) RFC (Request for Comments) 5740 in 2009 by B. Adamson et al. NORM provides a mechanism of retransmission and or forward error correction for UDP packets, potentially overcoming the limits of UDP for the transmission of spacecraft telemetry. Marshall Space Flight Center’s (MSFC) Huntsville Operations Support Center (HOSC) manages the delivery of the International Space Station (ISS) payload science and health data. The ISS IP Ground Routed (IIGoR) network provides the delivery of the data from the ground stations to the control centers. HOSC personnel have been experimenting with the use of NORM on the link between the ground stations and the front end processor equipment in Huntsville, AL.

Baldwin, Patrick A.↗
NASA NTRS2016

Managing Sustainable Data Infrastructures: The Gestalt of EOSDIS

EOSDIS epitomizes a System of Systems, whose many varied and distributed parts are integrated into a single, highly functional organized science data system. A distributed architecture was adopted to ensure discipline-specific support for the science data, while also leveraging standards and establishing policies and tools to enable interdisciplinary research, and analysis across multiple scientific instruments. The EOSDIS is composed of system elements such as geographically distributed archive centers used to manage the stewardship of data. The infrastructure consists of underlying capabilities connections that enable the primary system elements to function together. For example, one key infrastructure component is the common metadata repository, which enables discovery of all data within the EOSDIS system. EOSDIS employs processes and standards to ensure partners can work together effectively, and provide coherent services to users.

remote sensing↗
NASA NTRSDate not supplied

Open-Source Science-led Development of the Atmosphere Observing System (AOS) Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David Giles↗
NASA NTRSDate not supplied

Open-Source Science-led Development of the AOS Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David M. Giles↗
NASA NTRS2015

NASA's Earth Science Data Systems

NASA's Earth Science Data Systems (ESDS) Program has evolved over the last two decades, and currently has several core and community components. Core components provide the basic operational capabilities to process, archive, manage and distribute data from NASA missions. Community components provide a path for peer-reviewed research in Earth Science Informatics to feed into the evolution of the core components. The Earth Observing System Data and Information System (EOSDIS) is a core component consisting of twelve Distributed Active Archive Centers (DAACs) and eight Science Investigator-led Processing Systems spread across the U.S. The presentation covers how the ESDS Program continues to evolve and benefits from as well as contributes to advances in Earth Science Informatics.

Data Systems↗
NASA NTRS2014

Human Exploration Science Office (KX) Overview

The Human Exploration Science Office supports human spaceflight, conducts research, and develops technology in the areas of space orbital debris, hypervelocity impact technology, image science and analysis, remote sensing, imagery integration, and human and robotic exploration science. NASA's Orbital Debris Program Office (ODPO) resides in the Human Exploration Science Office. ODPO provides leadership in orbital debris research and the development of national and international space policy on orbital debris. The office is recognized internationally for its measurement and modeling of the debris environment. It takes the lead in developing technical consensus across U.S. agencies and other space agencies on debris mitigation measures to protect users of the orbital environment. The Hypervelocity Impact Technology (HVIT) project evaluates the risks to spacecraft posed by micrometeoroid and orbital debris (MMOD). HVIT facilities at JSC and White Sands Test Facility (WSTF) use light gas guns, diagnostic tools, and high-speed imagery to quantify the response of spacecraft materials to MMOD impacts. Impact tests, with debris environment data provided by ODPO, are used by HVIT to predict risks to NASA and commercial spacecraft. HVIT directly serves NASA crew safety with MMOD risk assessments for each crewed mission and research into advanced shielding design for future missions. The Image Science and Analysis Group (ISAG) supports the International Space Station (ISS) and commercial spaceflight through the design of imagery acquisition schemes (ground- and vehicle-based) and imagery analyses for vehicle performance assessments and mission anomaly resolution. ISAG assists the Multi-Purpose Crew Vehicle (MPCV) Program in the development of camera systems for the Orion spacecraft that will serve as data sources for flight test objectives that lead to crewed missions. The multi-center Imagery Integration Team is led by the Human Exploration Science Office and provides expertise in the application of engineering imagery to spaceflight. The team links NASA programs and private industry with imagery capabilities developed and honed through decades of human spaceflight, including imagery integration, imaging assets, imagery data management, and photogrammetric analysis. The team is currently supporting several NASA programs, including commercial demonstration missions. The Earth Science and Remote Sensing Team is responsible for integrating the scientific use of Earth-observation assets onboard the ISS, which consist of externally mounted sensors and crew photography capabilities. This team facilitates collaboration on remote sensing and participates in research with academic organizations and other Government agencies, not only in conjunction with ISS science, but also for planetary exploration and regional environmental/geological studies. Human exploration science focuses on science strategies for future human exploration missions to the Moon, Mars, asteroids, and beyond. This function provides communication and coordination between the science community and mission planners. ARES scientists support the operation of robotic missions (i.e., Mars Exploration Rovers and the Mars Science Laboratory), contribute to the interpretation of returned mission data, and translate robotic mission technologies and techniques to human spaceflight.

Calhoun, Tracy A.↗
NASA NTRS2009

Life Sciences Data Archive (LSDA) in the Post-Shuttle Era

Now, more than ever before, NASA is realizing the value and importance of their intellectual assets. Principles of knowledge management, the systematic use and reuse of information/experience/expertise to achieve a specific goal, are being applied throughout the agency. LSDA is also applying these solutions, which rely on a combination of content and collaboration technologies, to enable research teams to create, capture, share, and harness knowledge to do the things they do well, even better. In the early days of spaceflight, space life sciences data were been collected and stored in numerous databases, formats, media-types and geographical locations. These data were largely unknown/unavailable to the research community. The Biomedical Informatics and Health Care Systems Branch of the Space Life Sciences Directorate at JSC and the Data Archive Project at ARC, with funding from the Human Research Program through the Exploration Medical Capability Element, are fulfilling these requirements through the systematic population of the Life Sciences Data Archive. This project constitutes a formal system for the acquisition, archival and distribution of data for HRP-related experiments and investigations. The general goal of the archive is to acquire, preserve, and distribute these data and be responsive to inquiries from the science communities.

Fitts, Mary A.↗
DOE OSTI2021

Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy

Atom probe tomography, and related methods, probe the three-dimensional architecture of a material. The software tools that microscopists use, and how these tools are connected into workflows, makes a substantial contribution to the accuracy and precision of such a material characterization experiment. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced that collaborating is useful. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Expanding Biological Repository Data Available for Sharing and Knowledge Discovery

Biology has developed next-generation data science and alternative analytical approaches with methodologies which require principal investigator (PI) experimental assay data be re-used. This new approach involves mining multiple datasets at once from various hierarchical organizations of biological complexity, while concurrently evaluating how experimental factors affect endpoints of standard assays. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make findable, accessible, interoperable, and reusable (FAIR) all non-human space-relevant biological data. These data include mission metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery, and subject-experienced telemetry (radiation, temperature, humidity, acoustics, vibrations). ALSDA has transformed to bring current biological repository data and all future collected data into this new scientific data mining reality. It has integrated into the ‘NASA Open Science’ group of projects to facilitate a suite of new tools and workflows to improve data accessibility and reusability by implementing data management plans, automating data submission agreements, and adopting the single-point-of-entry data submission portal, originally developed by NASA GeneLab. These systems required ALSDA to develop science assay configurations for the submission portal, capturing essential assay parameters according to established norms in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. ALSDA datasets are curated to maintain rich metadata, accuracy of datasets, data transparency, provenance, and additionally ensure data are machine-readable (e.g., R and Python languages). ALSDA integration with GeneLab and its analysis portals enable higher-order physiological-level datasets be mined in conjunction with -omics datasets. As ALSDA physiological-level datasets are published (micro-computed tomography, histology, intraocular pressure, hormonal assays, immunostaining, ultrasonography), the merging of hierarchical organizations of biological complexity from spaceflight will enable new knowledge discovery approaches.

Ryan T Scott↗
NASA NTRS2023

Perspectives on User Engagement of Satellite Earth Observation for Water Quality Management

The management and governance of our surface waters is core to life and prosperity on our planet. However, monitoring data are not available to many potential users and the disparate nature of water bodies makes consistent monitoring across so many systems difficult. While satellite Earth observation (EO) offers solutions, there are numerous challenges that limit the use of satellite EO for water monitoring. To understand the perceptions of using satellite EO for water quality monitoring, a survey was conducted within academia and the water quality management sector. Study objectives were to assess community understanding of satellite EO water quality data, identify barriers in the adoption of satellite EO data, and analyse trust in satellite EO data. Most (40 %) participants were beginners with little understanding of satellite EO. Participants indicated problems with satellite EO data accessibility (31 %) and interpretability (26 %). Results showed a high level of trust with satellite EO data and higher trust with in-situ EO data. This study highlighted the gap between water science, applied social science, and policy. A transdisciplinary approach to managing water resources is needed to bridge water disciplines and take a key role in areas such as social issues, knowledge brokering, and translation.

water quality↗
NASA NTRS1983

Research and technology, fiscal year 1983

The responibilities and programs of the Goddard Space Flight Center are ranged from basic research in the space and Earth sciences through the management of numerous flight projects to operational responsibility for the tracking of and data acquisition from NASA's Earth orbiting satellites, Progress in the areas of spacecraft technology, sensor development and data system development, as well as in the basic and applied to research in the space and Earth sciences that they support is highlighted.

Source record↗
NASA NTRS1984

Pilot land data system

During the fall of 1983, the Information Systems Office of NASA's Office of Space Science and Applications assembled a Working Group to develop initial plans for a Pilot Land Data System (PLDS). Workshops coordinated planning and concept development activities between land-related and computer science disciplines, and examined land research requirements, information science technology requirements, PLDS architecture, and methodologies for system evaluation. The PLDS will be a limited-scale distributed information system to explore scientific, technical and management approaches to satisfy land science research needs. PLDS will pave the way for a Land Data System to improve data access, processing, transfer and analysis, fostering an environment in which land science information synthesis can occur on a scale not previously possible owing to limits to data assembly and access and efficiency of processing.

Cressy, P. J.↗
NASA NTRS1997

STS-82 Flight Day 06 Highlights

The sixth day of the STS-82 mission begins with the crew, Commander Kenneth D. Bowersox, Pilot Scott J. Horowitz, Payload Commander Mark C. Lee, and Mission Specialists Gregory J. Harbaugh, Steven L. Smith, Joseph R. Tanner, and Steven A. Hawley conducting the third spacewalk of the mission. Lee and Smith are seen removing and replacing a Data Interface Unit which provides command and data interfaces between Hubble's data management system and other subsystems. They also replace an old reel-to-reel style Engineering and Science Tape Recorder with a new digital Solid State Recorder (SSR) that will allow simultaneous recording and playback of data. The final task for Lee and Smith is the change out of one of four Reaction Wheel Assembly units that use spin momentum to move the telescope toward a target and maintain it in a stable position.

Source record↗
DOE OSTI2025

LandScan Global 30 Arcsecond Annual Global Gridded Population Datasets from 2000 to 2022

Abstract Oak Ridge National Laboratory (ORNL) annually develops the LandScan Global (LSG) dataset, a 30 arcsecond global gridded population dataset representing global ambient human population distribution. This multivariable dasymetric model disaggregates census counts within administrative boundaries using ancillary data. Each country’s distribution reflects cultural and socioeconomic patterns; manual validations yield a unique global dataset for assessing populations at risk. For over two decades, LSG has been a standard for estimating populations at risk, aiding U.S. federal government, academia and humanitarian organizations. During disasters such as the 2004 Indian Ocean tsunami and the 2010 Haiti earthquake and geopolitical crises such as the Syrian civil war and the 2022 Russian invasion of Ukraine, LSG supported scientific and operational communities in emergency response and recovery. In 2022, LSG datasets from 2000 onward were made publicly available through ORNL’s LandScan Portal. This data descriptor details our methodology and the application of geospatial science and machine learning to geographic and demographic data, highlighting uses in urban resiliency, emergency management, disaster response, and human health and security.

Science & Technology - Other Topics↗
NASA NTRSDate not supplied

Novel Observing Strategies (NOS) and Earth System Digital Twins (ESDT) for Disaster Resilience

"Earth systems have now been observed continuously for more than 50 years, not only from space but also from airplanes, balloons and in-situ sensors. With the addition of commercial remote sensing providers, many more Internet-of-Things sensors and new NASA and international observatories being planned, these incredible amounts of data will soon be augmented by even larger amounts of diverse data and therefore will become more and more difficult to access, integrate, understand and utilize. At the same time, because of climate change and its impacts, in order to predict, manage, and mitigate the effects of extreme science events and disasters, the information produced by all of this data needs to be optimized, organized and analyzed in such a way that it can be utilized by traditional as well as many new non-traditional users. This calls for the development of observing systems that are agile, coordinated and responsive to events of interest, and for information systems to be dynamic, interactive and fast. With these objectives in mind, the Advanced Information Systems Technology (AIST) Program has been developing technologies that will allow for the development of Novel Observing Strategies (NOS) in a distributed, coordinated fashion (i.e., similar to an “Internet-of-Earth-Things”), as well as for the development of novel information systems or Earth System Digital Twins (ESDT) that will build a digital replica of the past and current states of the Earth systems, will derive forecasts of future states under nominal assumptions and will also offer the capability to investigate many hypothetical evolution scenarios under varying impact assumptions. Both NOS and ESDT will be facilitated by the unprecedented advances of Artificial Intelligence technologies, especially Machine Learning (ML), for extracting relevant information from these large amounts of data, for fusing and assimilating diverse data together and for running complex models faster. Together NOS and ESDT will help build the capabilities needed to anticipate, predict and mitigate the effects of future disasters."

Earth Science Remote Sensing; Information Systems↗