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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

We Are All Potential Role Models: How We Can Support Scientist Engagement with Youth and the General Public

We all have the opportunity to be positive STEM role models. Yet scientists rarely identify themselves as a possible role model. In the training, “Empowering Effective STEM Role Models,” scientists practice research based strategies for engaging youth, particularly girls. This type of training is complementary to science communication, but is focused on equipping scientists with the skills needed to successfully encourage youth to pursue STEM careers. This training has been provided to over 150 STEM professionals at NASA and 14 federal agencies participated in a Train-the-Trainer version, organized by the White House Council for Women and Girls in STEM Working Group. Participants in this training reported that learning and practicing how to connect with and inspire children in STEM made them more confident stepping into further engagement opportunities. Confident, inspiring scientists are a highlight of a successful engagement event, but other factors make a positive experience possible.It’s important that all partners are thoughtful and intentional in preparing for public engagement events. The logistics are crucial, especially now while many engagements have moved to virtual platforms. Additional technology considerations make preparation essential. Two examples of virtual engagement highlight the support provided that led to successful engagements. The GLOBE Mission Earth project engages scientists in virtual classroom visits. To ensure both teacher and scientist expectations are met, a coordinator facilitates up front preparation by providing slide templates for the scientist to customize and providing video and other resources to the teacher and students to prompt student questions. Another example leverages the power of social media, particularly NASA Facebook. The NASA Earth Science Education Collaborative organizes citizen science challenges and engages the public on social. For one event, scientists were asked to share their connection to the investigation, but without access to NASA’s video production studio, the plans shifted. Scientists prepared audio files instead of videos and collaborated with communications leads to help craft their message. This weekly series has been flexible, adjusting to scientist's comfort levels, and has resulted in fresh and creative engagement.

Jessica Elaine Taylor↗
NASA NTRSDate not supplied

Enhancing NASA Earth Science Data Discovery from Scientific Publications

Earth observations from space borne instruments have evolved explosively in the past decades. Following closely are reanalysis systems assimilating model and observational data, yielding even longer records and larger number of variables. Thanks to advances in internet technology, it is now easier than ever to visualize and analyze these data using web interfaces. On the other hand, it also becomes an increasingly daunting task to build upon the existing knowledge published in various peer reviewed sources, and navigate toward the most relevant data, analysis, and visualization. We present an analysis of a subset of publications that utilized a popular visualization web interface at the NASA Goddard Earth Science Data and Information Services Center. Known as "Giovanni", it allows researchers from wide backgrounds to work with hundreds of variables from space observations and assimilation systems. Since coming online more than a decade ago, Giovanni has been credited in more than 100 papers per year, and the total count now is estimated to be nearly 1,500. Many of these papers contain valuable information about when, where and how Giovanni has been used, and hence forge an opportunity to learn and share the knowledge of which variables were used for what research projects. The purpose of our work is to retrieve the information from the papers and organize it as a knowledge repository which links together datasets, variables, places, dates and phenomena all of which reflect the essence of the published research. Since the publications are unstructured texts, we use natural language processing along with machine learning methods in the retrieval process. One of the challenges is deciphering the dataset names, because in many cases researchers refer to variables, rather than the datasets containing them. To constrain the number of terms, we deploy Earth Science ontologies as dictionaries for the term extraction. We demonstrate that storing these terms and underlying ontologies, along with datasets, variables and papers in the knowledge graph database, enables various linkages between all these entities facilitating the data discovery. Thus, we are setting a qualitatively new stage in improvements of web data interfaces, where machine learning techniques are used to establish and optimize usage-based discovery of data.

Irina V Gerasimov↗
NASA NTRS2022

Data-driven Expectations for Electromagnetic Counterpart Searches Based on LIGO/Virgo Public Alerts

Searches for electromagnetic counterparts of gravitational-wave signals have redoubled since the first detection in2017 of a binary neutron star merger with a gamma-ray burst, optical/infrared kilonova, and panchromatic after glow. Yet, one LIGO/Virgo observing run later, there has not yet been a second, secure identification of an electromagnetic counterpart. This is not surprising given that the localization uncertainties of events in LIGO and Virgo’s third observing run, O3, were much larger than predicted. We explain this by showing that improvements in data analysis that now allow LIGO/Virgo to detect weaker and hence more poorly localized events have increased the overall number of detections, of which well-localized, gold-plated events make up a smaller proportion overall. We present simulations of the next two LIGO/Virgo/KAGRA observing runs, O4 and O5, that are grounded in the statistics ofO3 public alerts. To illustrate the significant impact that the updated predictions can have, we study the follow-up strategy for the Zwicky Transient Facility. Realistic and timely forecasting of gravitational-wave localization accuracy is paramount given the large commitments of telescope time and the need to prioritize which events are followed up. We include a data release of our simulated localizations as a public proposal planning resource for astronomers

Polina Petrov↗
NASA NTRSDate not supplied

Towards Automated Analytics of Research Publications

For readers of scientific publications it remains a big challenge to unambiguously relate the published research with the data used. To a substantial degree it is attributed to authors, journals, editors, and reviewers not prioritizing correct data citation, which impacts traceability, repeatability, and giving credits to published authors and their funding sources. Furthermore, uniform classification of the content of the published research is hampered by journals using journal specific topics and letting authors to assign free text keywords to their papers. We demonstrate automated analytics methods for extracting and relating datasets used and the research application areas by processing 1,300 research papers that referenced the NASA Giovanni service (but probably not the datasets in particular) as supporting their publication process. This presentation was given during the 2022 ESIP January meeting held virtually in January 2022.

Irina Gerasimov↗
NASA NTRSDate not supplied

Utilizing Google Scholar as a Bibliographic Resource for Publications Search

This study focuses on the automated search for publication citations for the Earth Observing System Data and Information System (EOSDIS) datasets. The research investigates the feasibility of using automated search methods to gather published works from various bibliometric databases. A comparison is presented, highlighting the differences in citation counts obtained from Google Scholar compared to established bibliographic databases. The study also introduces a methodology and an open-source tool for getting publication citations from Google Scholar, utilizing dataset DOIs and keyword searches. The findings contribute to understanding the reliability and effectiveness of Google Scholar as a source for dataset citation retrieval and provide researchers with a valuable resource for obtaining comprehensive citation data.

Infometrics↗
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

The NASA Ames Mars Global Climate Model: Benchmarking Publicly Released Source Code and Model Output

We have recently publicly released source code from the new NASA Ames Mars Global Climate Model (MGCM), which is based on NOAA/GFDL cubed-sphere finite volume (FV3-based) dynamical core (https://github.com/nasa/AmesGCM). We also we have a manuscript in preparation that aims to document the status of the new MGCM, and present selected simulations generated from it with interpretations and comparisons to both observations and the Ames Legacy MGCM. Output from our reference simulation will be made publicly available as well. One of our ongoing goals is to understand the underlying causes for differences between results produced with the new FV3-based dynamical core compared to the Legacy C-grid dynamical core. While the thermal and dynamical fields predicted with the new and Legacy GCMs are broadly similar for much of the year, there are key differences at low resolution when no external drag is applied to the new MGCM. This is particularly clear during a seasonal window of ~100 degrees of Ls surrounding southern summer solstice, when the predicted northern hemisphere polar warming is significantly over-predicted in the new MGCM. When we apply Rayleigh drag throughout much of the tropics and sub-tropics to the new MGCM, the simulated zonal mean structure of the atmosphere is much more consistent with both MCS observations and Legacy MGCM simulations. We note that the Kling et al. (2023; this meeting) study demonstrates that the behavior that we see with Rayleigh drag here can be recovered with high resolution simulations (in the horizontal and in the vertical) or with parameterized orographic and non-orographic gravity waves at lower resolution. While this work is still in progress, our preliminary conclusion is that the new dynamical core is less dissipative than the Legacy dynamical core. At low to moderate resolution, users of the new MGCM will need to be careful to use some sort of external drag, either in the form of gravity wave drag parameterizations or the simpler Rayleigh drag.

Melinda April Kahre↗
NASA NTRS2023

Object Detection and Pose Estimation Public Competition Sample Data

This data was created with open-source tooling for the purpose of running a public competition. This dataset is a sample of what can be generated for the commissioned competition runner to be able to set up their testing infrastructure. It contains images of publicly available spacecraft models in a Blender scene composed of a light source, a background image, and the spacecraft model with bounding box labels.

James Berck↗
NASA NTRSDate not supplied

Monitoring Trends in Air Quality During a Drought: Case Study to Improve Public Health Response to Drought Threats

Drought has been an area of distinct concern in the Pacific Northwest since the mid-2010s. Recent studies have documented a correlation between air quality and drought in the United States, which is often observed in increased aerosols including airborne particulate matter (PM). This study partnered with the Oregon Health Authority and Washington State Department of Health to evaluate trends in air quality in the Pacific Northwest during the evolution of drought conditions using aerosol optical depth (AOD) observations collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellites. These satellite data were analyzed in conjunction with ground-based PM2.5 and PM10 data sourced from the Environmental Protection Agency (EPA) Air Quality System’s network of ground-based monitors and the Standardized Precipitation Evapotranspiration Index drought index. Based on recommendations by local health departments, this study examined air quality trends between 2015 and 2022 in 12 counties within Oregon and Washington that reflected diversity in population density, drought exposure, rural and urban status, and data availability from EPA AQS monitors. The findings from this study provided supplemental information to the ongoing efforts by community health stakeholders to study the public health impacts of drought and support public health responses to future drought events.

Taylor West↗
NASA NTRS2009

Mars Public Engagement Overview

This viewgraph presentation reviews the Mars public engagement goal to understand and protect our home planet, explore the Universe and search for life, and to inspire the next generation of explorers. Teacher workshops, robotics education, Mars student imaging and analysis programs, MARS Student Imaging Project (MSIP), Russian student participation, MARS museum visualization alliance, and commercialization concepts are all addressed in this project.

public outreach↗
NASA NTRS2016

Evaluation of Different MODIS AOD Retrieval Algorithms for PM (sub 2.5) Estimation in the Western, Midwestern and Southeastern United States with Implications for Public Health

This study was part of the research activities of the Center for Applied Atmospheric Research and Education (CAARE) funded by the NASA MUREP (Minority University Research and Education Project) Institutional Research Opportunity (MIRO) Program. Satellite measurements of Aerosol Optical Depth (AOD) have been shown to be correlated with ground measurements of fine particulate matter less than 2.5 microns PM (sub 2.5), which in turn has been linked to respiratory and heart diseases. The strength of the correlation between AOD and PM (sub 2.5) varies for different AOD retrieval algorithms and geographic regions. We evaluated several Moderate Resolution Imaging Spectrometer (MODIS) AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target versus Deep Blue), Collections (5.1 versus 6) and spatial resolutions (10-kilometers versus 3-kilometers) for cities in the Western, Midwestern and Southeastern U.S. We developed and validated PM (sub 2.5) prediction models using remotely-sensed AOD data, which were improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind speed, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the predictive power of all the PM (sub 2.5) models, especially in the Western U.S. Temperature, relative humidity and wind speed were the most significant meteorological variables throughout the year in the Western U.S. Wind speed was the most significant meteorological variable for the cold season while temperature was the most significant variable for the warm season in the Midwestern and Southeastern U.S. Our study re-establishes the connection between PM (sub 2.5) and public health concerns including respiratory and cardiovascular diseases (asthma, high blood pressure, coronary heart disease, heart attack, and stroke). Using PM (sub 2.5) data and health data from the Centers for Disease Control and Prevention (CDC)'s Behavioral Risk Factor Surveillance System (BRFSS), our statistical analysis showed that heart attack and stroke occurrences had the strongest correlations with PM (sub 2.5).

Environmental Public Health↗
NASA NTRSDate not supplied

Cumulative Distribution Overlap Technique for Artemis Mission Public Entry Risk Assessment

The Artemis missions use a skip-entry profile to accomplish a recovery near the western US coastline, however, the service module debris must still be disposed of safely. For certain contingency return scenarios, the expected dispersed entry profile is biased from the well-analyzed corridor. A novel method is presented for assessing the Cumulative Distribution Function (CDF) of these biased results to ensure they do not contribute additional probabilistic risk to the public. More specifically, the weight of the flight path angle dispersion "tail" of the CDF needs to remain below the well-assessed region. The details of how this method was actually used to screen and plan entry profiles for specific launch windows during the Artemis I mission are shown. This method has potential applicability in other problems where a biased distribution needs to be assessed against a well-defined "core" region.

Reentry↗
NASA NTRSDate not supplied

Cumulative Distribution Overlap Technique for Artemis Mission Public Entry Risk Assessment

The Artemis missions use a skip-entry profile to accomplish a recovery near the western US coastline, however, the service module debris must still be disposed of safely. For certain contingency return scenarios, the expected dispersed entry profile is biased from the well-analyzed corridor. A novel method is presented for assessing the Cumulative Distribution Function (CDF) of these biased results to ensure they do not contribute additional probabilistic risk to the public. More specifically, the weight of the flight path angle dispersion "tail" of the CDF needs to remain below the well-assessed region. The details of how this method was actually used to screen and plan entry profiles for specific launch windows during the Artemis I mission are shown. This method has potential applicability in other problems where a biased distribution needs to be assessed against a well-defined "core" region.

Reentry↗