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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Expedition Earth and Beyond: Engaging Classrooms in Student-Led Research Using NASA Data, Access to Scientists, and Integrated Educational Strategies

Classroom teachers are challenged with engaging and preparing today s students for the future. Activities are driven by state required skills, education standards, and high-stakes testing. Providing educators with standards-aligned, inquiry-based activities that will help them engage their students in student-led research in the classroom will help them teach required standards, essential skills, and help inspire their students to become motivated learners. The Astromaterials Research and Exploration Science (ARES) Education Program, classroom educators, and ARES scientists at the NASA Johnson Space Center created the Expedition Earth and Beyond education program to help teachers promote student-led research in their classrooms (grades 5-14) by using NASA data, providing access to scientists, and using integrated educational strategies.

Graff, P. V.↗
NASA NTRS2014

Colorado Lightning Mapping Array Collaborations through the GOES-R Visiting Scientist Program

For the past two years, the GOES‐R Proving Ground has solicited proposals for its Visiting Scientist Program. NASA's Short‐term Prediction Research and Transition (SPoRT) Center has used this opportunity to support the GOES‐R Proving Ground by expanding SPoRT's total lightning collaborations. In 2012, this expanded the evaluation of SPoRT's pseudo‐geostationary lightning mapper product to the Aviation Weather Center and Storm Prediction Center. This year, SPoRT has collaborated with the Colorado Lightning Mapping Array (COLMA) and potential end users. In particular, SPoRT is collaborating with the Cooperative Institute for Research in the Atmosphere (CIRA) and Colorado State University (CSU) to obtain these data in real‐time. From there, SPoRT is supporting the transition of these data to the local forecast offices in Boulder, Colorado and Cheyenne, Wyoming as well as to Proving Ground projects (e.g., the Hazardous Weather Testbed's Spring Program and Aviation Weather Center's Summer Experiment). This presentation will focus on the results of this particular Visiting Scientist Program trip. In particular, the COLMA data are being provided to both forecast offices for initial familiarization. Additionally, several forecast issues have been highlighted as important uses for COLMA data in the operational environment. These include the utility of these data for fire weather situations, situational awareness for both severe weather and lightning safety, and formal evaluations to take place in the spring of 2014.

Stano, Geoffrey T.↗
NASA NTRS2017

HRP Chief Scientist's Office: Conducting Research to Enable Deep Space Exploration

The HRP Chief Scientist's Office sets the scientific agenda for the Human Research Program. As NASA plans for deep space exploration, HRP is conducting research to ensure the health of astronauts, and optimize human performance during extended duration missions. To accomplish this research, HRP solicits for proposals within the U.S., collaborates with agencies both domestically and abroad, and makes optimal use of ISS resources in support of human research. This session will expand on these topics and provide an opportunity for questions and discussion with the HRP Chief Scientist. Presentations in this session will include: NRA solicitations - process improvements and focus for future solicitations, Multilateral Human Research Panel for Exploration - future directions (MHRPE 2.0), Extramural liaisons - National Science Foundation (NSF) and Department of Defense (DOD), Standardized Measures for spaceflight, Ground-based Analogs - international collaborations, and International data sharing.

Charles, J. B.↗
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 NTRS2018

Bringing Climate Scientist's Tools into Classrooms to Improve Conceptual Understandings

Efforts to address anthropogenic global climate change (AGCC) require public understanding of Earth and climate science. To meet this need, educational reforms and prominent scientists have called for instructional approaches that teach students how climate scientists examine AGCC. Yet, only a few educational studies have reported clear empirical results on what instructional approaches and climate education technologies best accomplish this goal. This manuscript presents detailed analysis and statistically significant results on the educational impact pre to post of students learning to use a National Aeronautics and Space Administration (NASA) global climate model (GCM). This series of case studies demonstrates that differing instructional approaches and climate education technologies result in differing levels of understanding of AGCC and ability to engage with policies addressing it. Students who learned the scientific process of climate modeling scored significantly higher pre to post on exams (quantitatively) and gained more complete conceptual understandings of the issue (qualitatively). Yet, teaching students to conduct research with complex technology can be difficult. This study also found lecture-based learning better improved recall of facts about GCMs tested by multiple-choice questions. Our findings indicate what educational systems and related technologies might provide the public with the conceptual understandings necessary to engage in the political debate over AGCC.

Public understanding↗
NASA NTRS2017

Scientist-Guided Autonomy for Self-Reliant Rovers

Although Mars rover missions have been highly successful in accomplishing scientific objectives, mission productivity is limited by challenges stemming from the need for commanding ground-based targeted observations under communication constraints imposed by the large distance between Earth and Mars. With an aging fleet of sun-synchronous relay orbiters, the opportunities for regular communication with rovers may become even more limited. In addition to on-board planning, robust navigation, and health assessment, there are strategies to make future rovers more self-reliant by enabling them to perform autonomous scientific characterizations of new areas during periods without an opportunity for ground-based targeted observations. In particular, we have studied how a ”walkabout” strategy, in which an initial high-level characterization of a region is used to informed subsequent passes with specific targeted observations, was used successfully during the investigation of Pahrump Hills by the Mars Science Laboratory. Inspired by this approach, we have identified several capabilities that could allow a rover to autonomously perform some of these initial high-level characterization steps. In this paper, we describe technologies for identifying specific geologic units, regions, or features of interest, identifying areas of contact between two adjacent units, detecting and determining the orientation of layering within rock units, identifying novel and interesting features, and planning observations of regions with different sampling strategies using remote sensing instruments. The observations acquired with these approaches are driven by scientists’ guidance and can provide scientists with data to help inform their decisions about where to make more resource-intensive targeted observations.

Doran, Gary↗
NASA NTRS2021

PIXLISE-C: Exploring The Data Analysis Needs of NASA Scientists for Mineral Identification

NASA JPL scientists working on the micro x-ray fluorescence (microXRF) spectroscopy data collected from Mars surface perform data analysis to look for signs of past microbial life on Mars. Their data analysis workflow mainly involves identifying mineral com- pounds through the element abundance in spatially distributed data points. Working with the NASA JPL team, we identified pain points and needs to further develop their existing data visualization and analysis tool. Specifically, the team desired improvements for the process of creating and interpreting mineral composition groups. To address this problem, we developed an interactive tool that enables scientists to (1) cluster the data using either manual lasso-tool selection or through various machine learning clustering algorithms, and (2) compare the clusters and individual data points to make informed decisions about mineral compositions. Our preliminary tool supports a hybrid data analysis workflow where the user can manually refine the machine-generated clusters.

Davidoff, Scott↗
NASA NTRS2014

Climate Change: On Scientists and Advocacy

Last year, I asked a crowd of a few hundred geoscientists from around the world what positions related to climate science and policy they would be comfortable publicly advocating. I presented a list of recommendations that included increased research funding, greater resources for education, and specific emission reduction technologies. In almost every case, a majority of the audience felt comfortable arguing for them. The only clear exceptions were related to geo-engineering research and nuclear power. I had queried the researchers because the relationship between science and advocacy is marked by many assumptions and little clarity. This despite the fact that the basic question of how scientists can be responsible advocates on issues related to their expertise has been discussed for decades most notably in the case of climate change by the late Stephen Schneider.

climate change↗
DOE OSTI2021

Soil organic carbon is not just for soil scientists: measurement recommendations for diverse practitioners

Soil organic carbon (SOC) regulates terrestrial ecosystem functioning, provides diverse energy sources for soil microorganisms, governs soil structure, and regulates the availability of organically bound nutrients. Investigators in increasingly diverse disciplines recognize how quantifying SOC attributes can provide insight about ecological states and processes. Today, multiple research networks collect and provide SOC data, and robust, new technologies are available for managing, sharing, and analyzing large data sets. Here, we advocate that the scientific community capitalize on these developments to augment SOC data sets via standardized protocols. We describe why such efforts are important and the breadth of disciplines for which it will be helpful, and outline a tiered approach for standardized sampling of SOC and ancillary variables that ranges from simple to more complex. We target scientists ranging from those with little to no background in soil science to those with more soil-related expertise, and offer examples of the ways in which the resulting data can be organized, shared, and discoverable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Apollo Next Generation Sample Analysis (ANGSA): an Apollo Participating Scientist Program to Prepare the Lunar Sample Community for Artemis

As a first step in preparing for the return of samples from the Moon by the Artemis Program, NASA initiated the Apollo Next Generation Sample Analysis Program (ANGSA). ANGSA was designed to function as a low-cost sample return mission and involved the curation and analysis of samples previously returned by the Apollo 17 mission that remained unopened or stored under unique conditions for 50 years. These samples include the lower portion of a double drive tube previously sealed on the lunar surface, the upper portion of that drive tube that had remained unopened, and a variety of Apollo 17 samples that had remained stored at -27 °C for approximately 50 years. ANGSA constitutes the first preliminary examination phase of a lunar “sample return mission” in over 50 years. It also mimics that same phase of an Artemis surface exploration mission, its design included placing samples within the context of local and regional geology through new orbital observations collected since Apollo and additional new “boots-on-the-ground” observations, data synthesis, and interpretations provided by Apollo 17 astronaut Harrison Schmitt. ANGSA used new curation techniques to prepare, document, and allocate these new lunar samples, developed new tools to open and extract gases from their containers, and applied new analytical instrumentation previously unavailable during the Apollo Program to reveal new information about these samples. Most of the 90 scientists, engineers, and curators involved in this mission were not alive during the Apollo Program, and it had been 30 years since the last Apollo core sample was processed in the Apollo curation facility at NASA JSC. There are many firsts associated with ANGSA that have direct relevance to Artemis. ANGSA is the first to open a core sample previously sealed on the surface of the Moon, the first to extract and analyze lunar gases collected in situ, the first to examine a core that penetrated a lunar landslide deposit, and the first to process pristine Apollo samples in a glovebox at -20 °C. All the ANGSA activities have helped to prepare the Artemis generation for what is to come. The timing of this program, the composition of the team, and the preservation of unopened Apollo samples facilitated this generational handoff from Apollo to Artemis that sets up Artemis and the lunar sample science community for additional successes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Tutorial: Lessons Learned for Behavior Analysts from Data Scientists

Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. Here, we hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.

42 ENGINEERING↗
DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. Herein, we cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.

36 MATERIALS SCIENCE↗
DOE OSTI2021

First Simultaneous Observation of STEVE and SAR Arc Combining Data From Citizen Scientists, 630.0 nm All‐Sky Images, and Satellites

Abstract On September 28, 2017 citizen scientist observations at Alberta, Canada (51°N, 113° W) detected aurora and a thin east‐west purplish arc, known as strong thermal emission velocity enhancement (STEVE) that lasted less than 20 min. All‐sky imagers at subauroral latitudes measured stable auroral red (SAR) arcs during the entire night. The imager at Bridger, MT (45.3°N, 108.9°W) also measured a STEVE. The overlapping geometry allowed to determine that the height of STEVE was 225–275 km. STEVE is brighter in the 630.0 nm images in the West and almost merges with the SAR arc in the East. A DMSP satellite pass in the southern hemisphere was at the conjugate location of the Bridger imager during the STEVE observation. When mapped into the northern hemisphere intense subauroral ion drift and subauroral polarization streams were detected associated with the two optical signatures measured in 630.0 nm.

Martinis, C.↗
DOE OSTI2022

Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.

Computer Science↗