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NASA NTRS2019

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, student recruitment, accomplishments, goals, and feedback from alumni and mentors since 2013. To date, 71 students from 53 different academic institutions, 13 staffers, and 29 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.

Rask, Jon↗
NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗
NASA NTRS2019

An Automated Behavioral Analysis of Drosophila Melanogaster

Behavioral characteristics of D.melanogaster are strongly influenced by intrinsic and extrinsic factors, allowing scientists to assess how changes in physiology or environment manifest into behavior. Conversely, assessing changes in behavior of specimens provides valuable information about how the physiology of that organism responds to external changes. In this project, we developed a computer program to automate behavioral analyses of larvae and adult D. melanogaster aboard the International Space Station using on-board video recordings. Utilizing freely available libraries for Python, we set parameters to compute the number of animals, amount of locomotion as distance or movement, and the change in the perimeter of the larvae's outer shape to quantify behaviors such as curling or peristaltic full body wall contractions. Results show that our program is an efficient tool for analysis of larvae and adult locomotive behavior, thus providing scientists with a low-cost, efficient, and reliable method of quantifying behavioral data.

Zavaleta, Jhony↗
NASA NTRS2019

Citizen Science Twitter Data Management for Earth Science Applications

Social media data can provide useful real-time and historical information relating to the natural world, but managing this data poses challenges. Scientists at GES DISC are exploring the potential of Twitter data to augment precipitation data from the Global Precipitation Measurement (GPM) mission. However, the format of Twitter data is unconventional in the context of NASA data centers, resulting in frustration for scientists who need to work with the data. This study investigated procedures and standards needed to properly manage Twitter data to make them compatible with these data centers. After comparing databases, the study found that the MongoDB database was best suited for the storage of raw Twitter data due to its flexibility, ability to be accessed by multiple users, and querying functionality. The study used the Python package Zarr to transform processed Twitter data into a gridded format similar to that of satellite data. Each Tweet was mapped onto a time-space grid; each grid location contained information about Tweet attributes and precipitation. The study developed a pipeline for downloading, storing, and gridding Twitter data and transformed Twitter data into an understandable format for users of NASA satellite data.

Li, Rachel↗
NASA NTRS2019

Bringing Analysis Closer to Data: Developing a Visualization Tool for L2 Earth Science Satellite Data

Earth Science satellite missions provide a unique opportunity for scientists to visualize complex and multifaceted observations projected geospatially across maps of the Earth. While visualization tools can help scientists comprehend, analyze, and share data, visualizing Level-2 Earth Sciences data poses its own specific set of challenges. Since the geospatial information in Level-2 data files is stored as independent variables, the plotting process involves matching dimensional information from latitude and longitude with a desired variable. Variables are stored in different ways across various Earth Science data file formats, which complicates the process of extracting data and plotting variables from a given file without requiring extensive user input and prerequisite familiarity with the file type variable structure. In coordination with NASA’s Goddard Earth Sciences Data Information Services Center (GES DISC), the team developed a Level-2 Earth Science data visualization tool that aims to address some of the complexities associated with plotting Level-2 data. This tool offers command-line and user interface support for file and variable selection to accommodate varying use cases and degrees of user familiarity with the structure of a given file. The visualization tool is written in Python 3 and utilizes a modular approach to facilitate continued expansion and reuse. In addressing some common complications involved in plotting Level-2 Earth Sciences data, the tool aims to help to link the process of analysis more directly with data acquisition and visualization, bringing analysis closer to data across levels of processing.

Li, Angela W.↗
NASA NTRS2018

Parallelization of a Six Degree of Freedom Entry Vehicle Trajectory Simulation Using OpenMP and OpenACC

The art and science of writing parallelized software, using methods such as Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC), is dominated by computer scientists. Engineers and non-computer scientists looking to apply these techniques to their project applications face a steep learning curve, especially when looking to adapt their original single threaded software to run multi-threaded on graphics processing units (GPUs). There are significant changes in mindset that must occur; such as how to manage memory, the organization of instructions, and the use of if statements (also known as branching). The purpose of this work is twofold: 1) to demonstrate the applicability of parallelized coding methodologies, OpenMP and OpenACC, to tasks outside of the typical large scale matrix mathematics; and 2) to discuss, from an engineer’s perspective, the lessons learned from parallelizing software using these computer science techniques. This work applies OpenMP, on both multi-core central processing units (CPUs) and Intel® Xeon Phi™ 7210, and OpenACC on GPUs. These parallelization techniques are used to tackle the simulation of thousands of entry vehicle trajectories through the integration of six degree of freedom (DoF) equations of motion (EoM). The forces and moments acting on the entry vehicle, and used by the EoM, are estimated using multiple models of varying levels of complexity. Several benchmark comparisons are made on the execution of six DoF trajectory simulation: single thread Intel® Xeon® E5-2670 CPU, multi-thread CPU using OpenMP, multi-thread Xeon Phi™ 7210 using OpenMP, and multi-thread NVIDIA® Tesla® K40 GPU using OpenACC. These benchmarks are run on the Pleiades Supercomputer Cluster at the National Aeronautics and Space Administration (NASA) Ames Research Center (ARC), and a Xeon Phi™ 7210 node at NASA Langley Research Center (LaRC).

Green, Justin S.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-31966-DND

Solar eclipses provide the scientific community with a natural experiment of the processes within the Earth system. Through the rapid reduction and recovery of surface insolation, the short-term interrelationships between surface fluxes, clouds, temperature, and other meteorological components. Past observations of the eclipse-induced temperature perturbation show a wide range of values dependent on multiple factors. One of the primary factors is prevailing cloud cover, which is challenging to quantify. The large public interest in the 21 August 2017 eclipse provided an opportunity for a study of the eclipse using citizen science. Inspired by past citizen science efforts, the GLOBE Observer (GO) program organized a citizen science campaign for the 2017 eclipse. The large number of observers combined with their ability to travel to meteorologically important areas provided nearly unbroken coverage of the path of totality, and provides a useful complementary view of the eclipse with the automated networks. Primary questions: 1) What is the quantitative relationship between prevailing cloud cover and eclipse-induced temperature perturbation according to citizen scientist observations? 2) How well do the results from the citizen scientist observations compare with those from conventional ground observing stations?

J. Brant Dodson↗
NASA NTRSDate not supplied

TPSAS-NF1676L-18153-DND

During this session, NASA scientists and educators will discuss the importance of sky observations to better understand our atmosphere. Through participation in NASA education projects (S?COOL, GLOBE, and MY NASA DATA) K-12 students get practice in being scientists by collecting data and conducting their own inquiry investigations. Workshop participants will engage in hands-on activities showcasing how aerosols can impact the color and clarity of the sky. Teachers will explore NASA satellite imagery and data to predict global patterns of aerosols and observe interactions within Earth atmosphere. The workshop will be structured following the 5E Learning Model. Workshop activities are classroom ready, aligned to the Next Generation Science Standards, and align well with Georgia science standards for Habits of Mind and Nature of Science, as well as the following content standards: S4E3, S4E4, S6E4, S8SR1, S8SR2, S8SR4, SES3, SES5 and SES6.

Sarah Crecelius↗
NASA NTRSDate not supplied

OpenNEX: An open collaboration platform for the earth science community

Satellite data from the past several decades provide the most consistent record of land-surface processes that form the basis for scientific assessments of the impacts of climate variations and changes on the environment and human social-economical activities. During this time, scientific research on the characterization and assessment of environmental changes had tended to focus on large-scale land-surface changes with significant social-economic impacts. Increasingly, attention is shifting toward changes that occur more locally and that most directly relate to the everyday life of the majority of the population. In addition, there has been needs to develop management and policy decision support systems that are based on local environmental information. Almost at the same time, the advancement in sensor technology has allowed us to collect an unprecedented volume of environmental data. These data must be curated and analyzed to extract useful information for research and decision support purposes. Established in 2013 and funded by NASA, the Open NASA Earth eXchange (OpenNEX; https://opennex.org/ , Jia et al., 2019 ) project partnered with Amazon Web Services (AWS) to make available a large amount of Earth observing data, modeling results, and analysis tools on the AWS. OpenNEX provides researchers, developers, educators, and ordinary users with easy access to an integrated Earth science computational and data platform, enabling citizen scientists and application developers to realize the full value of NASA data assets and software tools. To encourage the public's engagement in this project, NASA ran virtual workshops and prize competitions. The virtual workshops provided online lectures and tutorials about how prominent scientists used the data in their research and the tutorials gave examples how to use the tools to interrogate the data in the Amazon cloud. Finally, the prize competitions allowed much wider participation in the OpenNEX project and enable testing the non-traditional projects and out-of-box ideas. OpenNEX has continued to evolve and mature. Here, we highlight new features and functionalities available to the community.

Jian Zhang↗
NASA NTRSDate not supplied

GeneLab: The NASA Systems Biology Platform for Space Omics Repository, Analysis and Visualization

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics dataand collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretationof the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLabhave begunand will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Samrawit Getachew Gebre↗
NASA NTRSDate not supplied

GeneLab: The NASA Systems Biology Platform for Space Omics Repository, Analysis and Visualization

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data, and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Samrawit Gebre↗
NASA NTRSDate not supplied

WEBINAR, May 6: New Discoveries Using GeneLab

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetry data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Sylvain V. Costes↗
NASA NTRS2020

NASA ESDS Citizen Science Data Working Group

This document provides guidelines for legal, policy, and ethical issues; standards for citizen science data collection and management; information on ensuring usability of citizen science data and communication regarding its use; and best practices for long-term archival of citizen science data.Section1 contains a detailed discussion of policy, ethical, and legal considerations influencing citizen science data collection. Section 2 considers standards for documentation, including documentation of instrumentation, procedures, and the data itself. It concludes with a discussion of how citizen science data should be attributed. Section 3 provides guidance about how to ensure citizen science data are collected and stored in a useable way. It also considers how NASA and data producers should notify the scientific community, including citizen scientists and the public, about citizen science datasets and the scientific conclusions reached using them. Finally, Section 4 provides detailed information regarding what should be archived from projects using a citizen science approach, including data and code. It provides guidance about archive location, process, and timeframe, as well as information about data access and distribution services provided by NASA that may be relevant to data producers working with citizen scientists.

Citizen Science↗
NASA NTRSDate not supplied

Examining Artifacts from GLOBE Program Research Symposia & Using Network Analysis Techniques to Characterize Students’ Authentic STEM Investigations

For the past several years, the GLOBE Program's International Virtual Science Symposia (IVSS) and Student Research Symposia (SRS) have provided opportunities for U.S. and international students to present their Earth science research investigations to the GLOBE community through online or in-person events. This presentation will share the techniques and findings of an evaluation study that used student posters and written reports to characterize their research investigations through multiple lenses and frameworks. The study began with a list of characteristics drawn from a literature review, an analysis of sample projects, and several reviews by expert stakeholders and scientists, which comprehensively covered diverse relevant frameworks including citizen science, student STEM learning through authentic experiences, and The GLOBE Program model. Once applied to 207 student projects, this list of codes revealed the frequency and prevalence of various qualities and experiences represented by GLOBE student research investigations. An innovative application of social network analysis techniques to the coded dataset revealed frequently cooccurring characteristics. This networking approach identified and conceptually mapped several "clusters" of characteristics that typified student projects, empirically based on the submitted projects themselves. The basic quantitative investigation of frequencies indicates the extent to which various characteristics are present in - or absent from - GLOBE SRS and IVSS projects, while the network analysis provides a descriptive framework for typifying projects. Ultimately, the descriptive framework fostered a suite of assessment tools to help The GLOBE Program's staff, scientists, and research project judges understand the diversity of student research projects. GLOBE can use these tools to identify and respond to areas of need; for instance, the descriptive framework illustrates the potential for further education and training resources related to data analysis, interpretation of data, and credibility of scientific claims. This presentation will share the novel utilization of network analysis techniques to holistically assess and react to student research contributions.

Ann Martin↗
NASA NTRSDate not supplied

Presenting Model-Based Systems Engineering Information to Non-Modelers

NASA’s Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element adopted Systems Engineering (SE) principles and Model Based Systems Engineering (MBSE) tools to capture the system functions, system architecture, requirements, interfaces, and clinical capabilities for a future exploration medical system. There are many different stakeholders who may use the information in the model: systems engineers, requirement engineers, clinicians (doctors, nurses, and pharmacists), scientists, and program managers. Many of these individuals do not have access to MBSE modeling toolsor have never used these tools. Many of these individuals (clinicians, scientists, even program managers)may have no experience with SE in general let alone interpreting a systems model. The challenge faced by ExMC was how to present the content in the model to non-modelers in a way they could understand with limited to no training in MBSE or the Systems Modeling Language (SysML) without using the modeling tool. Therefore, from the model, ExMC created an HTML report that is accessible to anyone with a browser. When creating the HTML report, the ExMC SE team talked to stakeholders and received their feedback on what content they wanted and how to display this content. Factoring in feedback, the report arranges the content in a way that not only directs readers through the SE process taken to derive the requirements, but also helps them to understand the fundamental steps in an SE approach. The report includes links to source information (i.e., NASA documentation that describes levels of care) and other SE deliverables (e.g., Concept of Operations). These links were provided to aid in the understanding of how the team created this content through a methodical SE approach. This paper outlines the process used to develop the model, the data chosen to share with stakeholders, many of the model elements used in the report, the review process stakeholders followed, the comments received from the stakeholders, and the lessons ExMC learned through producing this HTML report.

Jeff Cohen↗
NASA NTRSDate not supplied

GeoNEX: A Geostationary Earth Observatory

The latest generation of geostationary satellites (Himawari 8/9, GOES-16/17, FY-4, GK-2A) carries sensors that closely mimic the spatial and spectral characteristics of widely used polar-orbiting, global monitoring sensors such as MODIS and VIIRS. When combined, data from various currently operating/planned geostationary platforms provide a geo-ring of hyper-temporal (5-10 minutes), multispectral observations at spatial resolutions as high as 500 m. These high frequency observations offer exciting new possibilities for monitoring our planet, including better retrievals of geophysical variables by overcoming cloud cover, enabling studies of diurnally varying phenomena in the atmosphere, land, and the oceans, and support operational decision-making in agriculture, hydrology and disaster management. The NASA Earth Exchange (NEX) team, in collaboration with scientists from JAXA, KARI, NOAA and other international institutions, created the GeoNEX (www.nasa.gov/geonex) pipeline to integrate data from all available geostationary platforms and produce and distribute spatially, temporally, and radiometrically consistent data for the earth science community. We envision various institutions adapting the Geo component (e.g., GeoNOAA, GeoKARI, GeoChiba, GeoJAXA, GeoCMA) and customizing the pipeline and downstream products to serve the local/regional research and applied science communities. To facilitate collaborative work among the partners, we have established the OpenNEX platform on the public cloud. OpenNEX provides researchers, developers, educators, and ordinary users with easy access to an integrated Earth science computational and data platform, enabling citizen scientists and application developers to realize the full value of GeoNEX data assets and software tools.

GeoNEX↗
NASA NTRS2020

Nonbinary Systems: Looking Towards the Future of Gender Equity in Planetary Science

Gender equity remains a major issue facing the field of planetary science, and there is broad interest in addressing gender disparities within space science and related disciplines. Many studies of these topics have been performed by professional planetary scientists who are relatively unfamiliar with research in fields such as gender studies and sociology. As a result, they adopt a normative view of gender as a binary choice of ‘male’ or ‘female,’ leaving planetary scientists whose genders do not fit within that model out of such research entirely. Reductive frameworks of gender and an overemphasis on quantification as an indicator of gendered phenomena are harmful to people of marginalized genders, especially those who live at the intersections of multiple axes of marginalization such as race, disability, and socioeconomic status. In order for the planetary science community to best serve its marginalized members as we move into the next decade, a new paradigm must be established. This paper aims to address the future of gender equity in planetary science by recommending better survey practices and institutional policies based on a more profound approach to gender.

Beck E Strauss↗
NASA NTRSDate not supplied

Presenting Model-Based Systems Engineering Information to Non-Modelers

NASA’s Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element adopted Systems Engineering (SE) principles and Model Based Systems Engineering (MBSE) tools to capture the system functions, system architecture, requirements, interfaces, and clinical capabilities for a future exploration medical system. There are many different stakeholders who may use the information in the model: systems engineers, clinicians (physicians, nurses, and pharmacists), scientists, and program managers. Many of these individuals do not have access to MBSE modeling tools or have never used these tools. Many of these individuals (clinicians, scientists, even program managers) may have no experience with SE in general let alone interpreting a systems model. The challenge faced by ExMC was how to present the content in the model to non-modelers in a way they could understand with limited to no training in MBSE or the Systems Modeling Language (SysML) without using the modeling tool. Therefore, from the model, ExMC created an HTML report that is accessible to anyone with a browser. When creating the HTML report, the ExMC SE team talked to stakeholders and received their feedback on what content they wanted and how to display this content. Factoring in feedback, the report arranges the content in a way that not only directs readers through the SE process taken to derive the requirements, but also helps them to understand the fundamental steps in an SE approach. The report includes links to source information (i.e., NASA documentation that describes levels of care) and other SE deliverables (e.g., Concept of Operations). These links were provided to aid in the understanding of how the team created this content through a methodical SE approach. This paper outlines the process used to develop the model, the data chosen to share with stakeholders, many of the model elements used in the report, the review process stakeholders followed, the comments received from the stakeholders, and the lessons ExMC learned through producing this HTML report.1Trade names and trademarks are used in this report for identification only. Their usage does not constitute an official endorsement, either expressed or implied, by the National Aeronautics and Space Administration.

Jeffrey R. Cohen↗