Results for “SCIENCE” — Page 32

NASA NTRS2012

Agile Science Operations: A New Approach for Primitive Exploration Bodies

Primitive body exploration missions such as potential Comet Surface Sample Return or Trojan Tour and Rendezvous would challenge traditional operations practices. Earth-based observations would provide only basic understanding before arrival and many science goals would be defined during the initial rendezvous. It could be necessary to revise trajectories and observation plans to quickly characterize the target for safe, effective observations. Detection of outgassing activity and monitoring of comet surface activity are even more time constrained, with events occurring faster than round-trip light time. "Agile science operations" address these challenges with contingency plans that recognize the intrinsic uncertainty in the operating environment and science objectives. Planning for multiple alternatives can significantly improve the time required to repair and validate spacecraft command sequences. When appropriate, time-critical decisions can be automated and shifted to the spacecraft for immediate access to instrument data. Mirrored planning systems on both sides of the light-time gap permit transfer of authority back and forth as needed. We survey relevant science objectives, identifying time bottlenecks and the techniques that could be used to speed missions' reaction to new science data. Finally, we discuss the results of a trade study simulating agile observations during flyby and comet rendezvous scenarios. These experiments quantify instrument coverage of key surface features as a function of planning turnaround time. Careful application of agile operations techniques can play a significant role in realizing the Decadal Survey plan for primitive body exploration

Agile science↗

NASA NTRS2012

ExoMars/TGO Science Orbit Design

This paper describes the development of the science orbit for the 2016 ESA/NASA collaborative ExoMars/Trace Gas Orbiter (TGO) mission. The initial requirements for the ExoMars/TGO mission simply described the science orbit as circular with a 400 km altitude and a 74 deg inclination. Over the past year, the JPL mission design team worked with the TGO science teams to refine the science orbit requirements and recommend an orbit that would be operationally feasible, easy to maintain, and most important allow the science teams to best meet their objectives.

science orbit↗

NASA NTRS2013

Policy for Robust Space-based Earth Science, Technology and Applications

Satellite remote sensing technology has contributed to the transformation of multiple earth science domains, putting space observations at the forefront of innovation in earth science. With new satellite missions being launched every year, new types of earth science data are being incorporated into science models and decision-making systems in a broad array of organizations. Policy guidance can influence the degree to which user needs influence mission design and when, and ensure that satellite missions serve both the scientific and user communities without becoming unfocused and overly expensive. By considering the needs of the user community early on in the mission-design process, agencies can ensure that satellites meet the needs of multiple constituencies. This paper describes the mission development process in NASA and ESA and compares and contrasts the successes and challenges faced by these agencies as they try to balance science and applications within their missions.

Space-based↗

NASA NTRS2015

The Role and Evolution of NASA's Earth Science Data Systems

One of the three strategic goals of NASA is to Advance understanding of Earth and develop technologies to improve the quality of life on our home planet (NASA strategic plan 2014). NASA's Earth Science Data System (ESDS) Program directly supports this goal. NASA has been launching satellites for civilian Earth observations for over 40 years, and collecting data from various types of instruments. Especially since 1990, with the start of the Earth Observing System (EOS) Program, which was a part of the Mission to Planet Earth, the observations have been significantly more extensive in their volumes, variety and velocity. Frequent, global observations are made in support of Earth system science. An open data policy has been in effect since 1990, with no period of exclusive access and non-discriminatory access to data, free of charge. NASA currently holds nearly 10 petabytes of Earth science data including satellite, air-borne, and ground-based measurements and derived geophysical parameter products in digital form. Millions of users around the world are using NASA data for Earth science research and applications. In 2014, over a billion data files were downloaded by users from NASAs EOS Data and Information System (EOSDIS), a system with 12 Distributed Active Archive Centers (DAACs) across the U. S. As a core component of the ESDS Program, EOSDIS has been operating since 1994, and has been evolving continuously with advances in information technology. The ESDS Program influences as well as benefits from advances in Earth Science Informatics. The presentation will provide an overview of the role and evolution of NASAs ESDS Program.

Remote Sensing↗

NASA NTRS2014

An Analysis of Earth Science Data Analytics Use Cases

The increase in the number and volume, and sources, of globally available Earth science data measurements and datasets have afforded Earth scientists and applications researchers unprecedented opportunities to study our Earth in ever more sophisticated ways. In fact, the NASA Earth Observing System Data Information System (EOSDIS) archives have doubled from 2007 to 2014, to 9.1 PB (Ramapriyan, 2009; and https:earthdata.nasa.govaboutsystem-- performance). In addition, other US agency, international programs, field experiments, ground stations, and citizen scientists provide a plethora of additional sources for studying Earth. Co--analyzing huge amounts of heterogeneous data to glean out unobvious information is a daunting task. Earth science data analytics (ESDA) is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. It can include Data Preparation, Data Reduction, and Data Analysis. Through work associated with the Earth Science Information Partners (ESIP) Federation, a collection of Earth science data analytics use cases have been collected and analyzed for the purpose of extracting the types of Earth science data analytics employed, and requirements for data analytics tools and techniques yet to be implemented, based on use case needs. ESIP generated use case template, ESDA use cases, use case types, and preliminary use case analysis (this is a work in progress) will be presented.

data analytics↗

NASA NTRS2016

Earth Science Data Analytics: Bridging Tools and Techniques with the Co-Analysis of Large, Heterogeneous Datasets

The continuum of ever-evolving data management systems affords great opportunities to the enhancement of knowledge and facilitation of science research. To take advantage of these opportunities, it is essential to understand and develop methods that enable data relationships to be examined and the information to be manipulated. This presentation describes the efforts of the Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster to understand, define, and facilitate the implementation of ESDA to advance science research. As a result of the void of Earth science data analytics publication material, the cluster has defined ESDA along with 10 goals to set the framework for a common understanding of tools and techniques that are available and still needed to support ESDA.

science data analysis↗

NASA NTRS2017

Training Early Career Scientists in Flight Instrument Design Through Experiential Learning: NASA Goddard's Planetary Science Winter School.

The NASA Goddard Planetary Science Winter School (PSWS) is a Goddard Space Flight Center-sponsored training program, managed by Goddard's Solar System Exploration Division (SSED), for Goddard-based postdoctoral fellows and early career planetary scientists. Currently in its third year, the PSWS is an experiential training program for scientists interested in participating on future planetary science instrument teams. Inspired by the NASA Planetary Science Summer School, Goddard's PSWS is unique in that participants learn the flight instrument lifecycle by designing a planetary flight instrument under actual consideration by Goddard for proposal and development. They work alongside the instrument Principal Investigator (PI) and engineers in Goddard's Instrument Design Laboratory (IDL; idc.nasa.gov), to develop a science traceability matrix and design the instrument, culminating in a conceptual design and presentation to the PI, the IDL team and Goddard management. By shadowing and working alongside IDL discipline engineers, participants experience firsthand the science and cost constraints, trade-offs, and teamwork that are required for optimal instrument design. Each PSWS is collaboratively designed with representatives from SSED, IDL, and the instrument PI, to ensure value added for all stakeholders. The pilot PSWS was held in early 2015, with a second implementation in early 2016. Feedback from past participants was used to design the 2017 PSWS, which is underway as of the writing of this abstract.

Flight↗

NASA NTRS2016

Optimal Reorganization of NASA Earth Science Data for Enhanced Accessibility and Usability for the Hydrology Community

A long-standing "Digital Divide" in data representation exists between the preferred way of data access by the hydrology community and the common way of data archival by earth science data centers. Typically, in hydrology, earth surface features are expressed as discrete spatial objects (e.g., watersheds), and time-varying data are contained in associated time series. Data in earth science archives, although stored as discrete values (of satellite swath pixels or geographical grids), represent continuous spatial fields, one file per time step. This Divide has been an obstacle, specifically, between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. and NASA earth science data systems. In essence, the way data are archived is conceptually orthogonal to the desired method of access. Our recent work has shown an optimal method of bridging the Divide, by enabling operational access to long-time series (e.g., 36 years of hourly data) of selected NASA datasets. These time series, which we have termed "data rods," are pre-generated or generated on-the-fly. This optimal solution was arrived at after extensive investigations of various approaches, including one based on "data curtains." The on-the-fly generation of data rods uses "data cubes," NASA Giovanni, and parallel processing. The optimal reorganization of NASA earth science data has significantly enhanced the access to and use of the data for the hydrology user community.

data rods↗

NASA NTRS2017

The NASA Space Life Sciences Training Program: Accomplishments Since 2013

The NASA Space Life Sciences Training Program (SLSTP) provides undergraduate students entering their junior or senior years with professional experience in space life science disciplines. This challenging ten-week summer program is held at NASA Ames Research Center. The primary goal of the program is to train the next generation of scientists and engineers, enabling NASA to meet future research and development challenges in the space life sciences. Students work closely with NASA scientists and engineers on cutting-edge research and technology development. In addition to conducting hands-on research and presenting their findings, SLSTP students attend technical lectures given by experts on a wide range of topics, tour NASA research facilities, participate in leadership and team building exercises, and complete a group project. For this presentation, we will highlight program processes, accomplishments, goals, and feedback from alumni and mentors since 2013. To date, 49 students from 41 different academic institutions, 9 staffers, and 21 mentors have participated in the program. The SLSTP is funded by Space Biology, which is part of the Space Life and Physical Sciences Research and Application division of NASA's Human Exploration and Operations Mission Directorate. The SLSTP is managed by the Space Biology Project within the Science Directorate at Ames Research Center.

education↗

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗

NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗

NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗

DOE OSTI2024

The NASA ISRO SAR (NISAR) Mission - Validation of Science Measurement Requirements

The NASA ISRO Synthetic Aperture Radar (NISAR) is scheduled for launch early in 2024 from the Satish Dhawan Space Centre (SDSC), at Sriharikota, near Chennai, India. This mission is the result of a collaboration between NASA and Indian Space Research Organization (ISRO), where NASA has contributed elements of the mission such as an L-band SAR, and ISRO has contributed other elements, such as an S-band SAR. After successful launch, the NISAR mission will collect left-looking L-band SAR data over most of the Earth’s land areas twice during every 12-day exact repeat orbit. (once while in an ascending orbit direction and once while in a descending orbit direction). NASA and ISRO have individual and joint requirements on the mission that include the performance of the imaging radars onboard the spacecraft. For example, NASA must demonstrate that this L-band SAR will achieve a set of identified science measurement accuracy requirements that span Ecosystem science, Solid Earth science, and Cryosphere science disciplines. Likewise, ISRO has several applications objectives on both the L-band and S-band data from NISAR that the ISRO science team and project will be developing and testing. Pre-launch and post-launch activities have been planned to validate that these requirements are met. Here, we will discuss how the NASA plans are being executed and will present any initial results at the conference.

Chapman, Bruce↗

DOE OSTI2024

A prospective on machine learning challenges, progress, and potential in polymer science

Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science and engineering every year. Polymer science is no different, though implementation of data-driven algorithms in this subfield has unique challenges barring widespread application of these techniques to the study of polymer systems. In this Prospective, we discuss several critical challenges to implementation of ML in polymer science, including polymer structure and representation, high-throughput techniques and limitations, and limited data availability. Promising studies targeting resolution of these issues are explored, and contemporary research demonstrating the potential of ML in polymer science despite existing obstacles are discussed. Finally, we present an outlook for ML in polymer science moving forward. Graphical Abstract

Struble, Daniel C. (ORCID:0009000093410612)↗

DOE OSTI2024

Convergence QL: NSF/DOE Quantum Science Summer School: QS3 Final Report

Convergence QL: NSF/DOE Quantum Science Summer School (QS3) was a 2-week summer program held in 2017, 2018, 2019, and 2022. The program was aimed at graduate students and postdoctoral researchers working on a wide range of topics related to quantum science and quantum engineering. Expert leaders in this field served as lectures for the school. The school program included lectures, active learning, project activities, poster presentations, and industry panel discussions. The school covered four topics: Fundamentals and Applications of Quantum Computing (2017), Fundamentals and Applications of Quantum Materials (2018), Fundamentals and Applications of Quantum Devices (2019), and Modern Synthesis of Quantum Materials (2022). In terms of intellectual merit, the school brought together students from different areas of research related to quantum science to educate them on a broad range of related topics, providing a unique crucible for the development of educational content and learning opportunities in a cross-disciplinary fashion in the context of quantum science and quantum engineering. In terms of broader impacts, this may serve to create a workforce which would accelerate research in quantum science, quantum engineering, and the development of new quantum technologies beneficial to society. Additionally, this may add to the training of a cadre of scientists and engineers to serve as future leaders in this research field.

42 ENGINEERING↗

NASA NTRS1973

Science sequence design

The activities of the following members of the Navigation Team are recorded: the Science Sequence Design Group, responsible for preparing the final science sequence designs; the Advanced Sequence Planning Group, responsible for sequence planning; and the Science Recommendation Team (SRT) representatives, responsible for conducting the necessary sequence design interfaces with the teams during the mission. The interface task included science support in both advance planning and daily operations. Science sequences designed during the mission are also discussed.

Koskela, P. E.↗

NASA NTRS1974

Life sciences payloads for Shuttle

The Life Sciences Program for utilization of the Shuttle in the 1980's is presented. Requirements for life sciences research experiments in space flight are discussed along with study results of designs to meet these requirements. The span of life sciences interests in biomedicine, biology, man system integration, bioinstrumentation and life support/protective systems is described with a listing of the research areas encompassed in these descriptions. This is followed by a description of the approach used to derive from the life sciences disciplines, the research functions and instrumentation required for an orbital research program. Space Shuttle design options for life sciences experiments are identified and described. Details are presented for Spacelab laboratories for dedicated missions, mini-labs with carry on characteristics and carry on experiments for shared payload missions and free flying satellites to be deployed and retrieved by the Shuttle.

Dunning, R. W.↗

NASA NTRS1975

Science planning and mission strategy for the 1979 Jupiter/Uranus opportunity

An intensive science and mission planning effort has been underway for several years to develop the next logical candidate mission to the outer planets following the Mariner Jupiter/Saturn 1977 Mission. The highest priority candidate that emerges from this effort is the launch of a MJS-derived Mariner spacecraft to Uranus during the 1979 Jupiter/Uranus opportunity. Since 1972 when the Outer Planets Science Advisory Group recommended MJU79 to follow MJS77, four other science groups have addressed this issue and arrived at the same recommendation. Currently, the planning effort is most intensive and the paper will report the past and present determinations of the various science and mission planning activities, emphasizing science value, technical factors and fiscal constraints.

Moore, J. W.↗