Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “citizen science methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

29 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Biology IS the Technology: the Microbial Ecology of Space Food Production and the Power of Aquaponics as a Learning Tool

To accomplish the objective of human missions to Mars and/or the long-term colonization of the moon, bioregenerative life support systems and food production systems will be absolutely necessary. Microbes are an essential and unavoidable component of these systems. In fact, these systems are driven by complex microbial communities about which we know very little, a glaring strategic knowledge gap in our ability to support extended human exploration in closed systems. Our laboratory has been working to use molecular ecological methods, including nanopore sequencing technology already deployed on the International Space Station, to understand the microbes in food production systems on Earth. Our ultimate goal is to inform the implementation of food production systems off-world. To date, we have sampled and sequenced the microbiomes of aquaponics systems, hydroponics systems, and fish ponds. Our results have revealed that the microbial communities in these systems are extremely diverse, and highly variable between systems. Along the way, we have discovered the power of aquaponics systems as teaching tools, and the capacity of students to perform high quality citizen science. By designing, constructing, and operating aquaponics systems, students better understand the role of microbes in the cycling of the elements in natural ecosystems, and in the human built environment. In partnership with schools and colleges, contributing new knowledge as citizen scientists, we are now exploring the relationships between the functioning of these systems and their microbial flora.

Bebout, Brad↗
NASA NTRSDate not supplied

High School Citizen Scientists Use AI/ML to Predict Intra-Ocular Pressure From Gene Expression Data for Spaceflown Mice

Artificial Intelligence (AI) and Machine Learning (ML) have increasingly become pivotal in biological and biomedical research, largely due to the culture of open data sharing and its associated benefits. The methodologies inherent in AI/ML are particularly adept at identifying and forecasting biological phenotypes from the vast amounts of data generated by next-generation sequencing technologies. These techniques offer substantial promise for advancing research in space biosciences and for the development of automated systems for monitoring space health. Nevertheless, there are crucial aspects to consider when training, validating, and testing machine learning models in both biological research and clinical contexts. It is essential that Open Science principles, including data sharing and the availability of open-source code, are complemented by high-quality, publicly accessible training resources. These resources should focus on best practices and include modules based on real-world scientific cases and data to ensure that future AI/ML practitioners gain practical experience with genuine problems. Addressing this knowledge gap, we have designed, developed, and delivered both interactive and self-paced training programs for citizen scientists worldwide, enabling them to utilize AI/ML for space biology research. This initiative was made possible through generous funding from a Transformation to Open Science Training grant. The interactive training sessions, conducted this summer, utilized AI/ML techniques to analyze data from the Open Science Data Repository, specifically targeting the effects of spaceflight on ocular structure and function. The dataset OSD-583, from the Rodent Research 9 mission, provides experimental data detailing the ocular responses of mice subjected to a 35-day spaceflight, compared with ground control counterparts. Using OSD-583 as observational data, our summer training participants applied AI/ML methods to predict intraocular pressure from RNA-seq data and identify the genes most predictive of the observed responses. Further analysis through pathway enrichment and gene set enrichment revealed that these genes are involved in molecular and cellular processes contributing to retinal degeneration.

James Casaletto↗
NASA NTRS2022

Disks in Nearby Young Stellar Associations Found Via Virtual Reality Journal Article

The Disk Detective citizen science project recently released a new catalog of disk candidates found by visual inspection of images from NASA's Wide-field Infrared Survey Explorer mission and other surveys. We applied this new catalog of well-vetted disk candidates to search for new members of nearby young stellar associations (YSAs) using a novel technique based on Gaia data and virtual reality (VR). We examined AB Doradus, Argus, β Pictoris, Carina, Columba, Octans-Near, Tucana–Horologium, and TW Hya by displaying them in VR together with other nearby stars, color coded to show infrared excesses found via Disk Detective. Using this method allows us to find new association members in mass regimes where isochrones are degenerate. We propose 10 new YSA members with infrared excesses: three of AB Doradus (HD 44775, HD 40540 and HD 44510), one of β Pictoris (HD 198472), two of Octans-Near (HD 157165 and BD+35 2953), and four disk-hosting members of a combined population of Carina, Columba, and Tucana–Horologium: CPD-57 937, HD 274311, HD 41992, and WISEA J092521.90-673224.8. This last object (J0925) appears to be an extreme debris disk with a fractional infrared luminosity of 3.7 × 10−2. We also propose two new members of AB Doradus that do not show infrared excesses: TYC 6518-1857-1 and CPD-25 1292. We find HD 15115 appears to be a member of Tucana–Horologium rather than β Pictoris. We advocate for membership in Columba–Carina of HD 30447, CPD-35 525, and HD 35841. Finally, we propose that three M dwarfs, previously considered members of Tucana–Horologium are better considered a separate association, tentatively called "Smethells 165."

Susan Higashio↗
DOE OSTI2021

Science & Technology Review: The Road to Exascale Computing

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world. The Department of Energy’s Exascale Computing Project (ECP) and Lawrence Livermore’s RADIUSS (Rapid Application Development via an Institutional Universal Software Stack) initiative benefit from strategically developed software tools. The front cover shows a simulation of advection under twisting rotation that uses high-order finite elements from Livermore’s Modular Finite Element Methods (MFEM) software library and GLVis visualization tool. On the back cover, the logo (also created with GLVis) for the MFEM project illustrates the curved mesh and sub-element resolution used in high-order simulations. MFEM and GLVis are key components of the ECP’s co-design Center for Efficient Exascale Discretizations (CEED) and RADIUSS. MFEM is also part of ECP’s Extreme-Scale Scientific Software Development Kit (xSDK).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

Evaluation of Student Outcomes in Materials Science and Technology

This paper specifies 14 benchmarks and exit standards for the introduction of Materials Science and Technology in a secondary school education. Included is the standard that students should be able to name an example of each category of technological materials including metals, glass/ceramics, polymers (plastics) and composites. Students should know that each type of solid material has specific properties that can be measured. Students will learn that all solid materials have either a long range crystalline structure or a short range amorphous structure (i.e., glassy). They should learn the choice of materials for a particular application depends on the properties of the material, and the properties of the material depends on its crystal structure and microstructure. The microstructure may be modified by the methods by which the material is processed; students should explain this by the example of sintering a ceramic body to reduce its porosity and increase its densification and strength. Students will receive exposure to the world of work, post secondary educational opportunities, and in general a learning that will lead to a technologically literate intelligent citizen.

Piippo, Steven↗
NASA NTRS2003

Citizen Explorer: An Earth Observer With New Small Satellite Technology - 1

Citizen Explorer-I (CX-I), designed and built by students at Colorado Space Grant Consortium in Boulder to provide global ozone monitoring, employs a unique mission architecture and several innovative technologies during its mission. The mission design allows K-12 schools around the world to be involved as ground stations available to receive science data and telemetry from CX-I. Another important technology allows the spacecraft to be less reliant on ground operators. Spacecraft Command Language (SCL) allows mission designers to set constraints on the satellite operations. The satellite then automatically adheres to the constraints when the satellite is out of contact with Mission Operations. In addition to SCL, a low level of artificial intelligence will be supplied to the spacecraft through the use of the Automated Scheduling and Planning ENvironment (ASPEN). ASPEN is used to maintain a spacecraft schedule in order to achieve the objectives a mission operator would normally have to complete. Within the communications system of CX-I, internet of CX-I, internet protocols are the main method for communicating with the satellite. As internet protocols have not been widely used in satellite communication, CX-I provides an opportunity to study the effectiveness of using internet protocols over radio links. The Attitude Determination and Control System (ADCS) on CX-I uses a gravity gradient boom as a means of orienting the satellite's science instruments toward nadir. The boom design is unique because it is constructed of tape measure material. These new technologies' effectiveness will be tested for use on future small satellite projects within the space satellite industry.

Allen, Zachary↗
DOE OSTI2022

PurpleAir Sensors as Effective Indicators of PM Exposure in Urban Areas

Particulate matter that is 2.5 microns or less in diameter (PM 2.5 ) is a biproduct of combustion reactions used for energy production. Populations that are exposed to consistently high levels of aerosolized PM 2.5 face serious health risks. This project compared low-cost PM 2.5 sensors with federally recognized methods to look for a cost-effective way to expand the air quality monitor network. Within metropolitan areas that face inconsistent spatial distribution of PM 2.5 , there may not be the necessary network density to indicate neighborhood-levels of PM 2.5 . This project aimed to examine the sensitivity of low-cost PM 2.5 sensor measurements on a neighborhood scale (< 4 km diameter) in an urban area to prevent citizens from being exposed to unsafe levels of PM 2.5 without their knowledge. Using publicly available sensor data from Livermore, CA and Bakersfield, CA, it was determined, based on the revealed patterns, that the analyzed low-cost sensors were able to display representative PM 2.5 levels for neighborhoodscale areas exposed to pollution from PM 2.5 sources.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Comparing multi-source urban flood indicators: satellite, simulation, and citizen-reported data

Urban flooding arises from complex mechanisms, making it challenging to capture accurately with a single detection method. This study evaluates three complementary approaches to detect flooding across three Chicago neighborhoods: (i) Sentinel-1 synthetic aperture radar (SAR), offering weather-independent, high-resolution (10 m) imagery of surface inundation; (ii) the storm water management model (SWMM), simulating combined sewer overflow and drainage performance; and (iii) citizen-generated 311 service requests, capturing observed flooding impacts. By analyzing six storms ranging from severe to mild, we examine how each source uniquely contributes to identifying urban flood events. SAR imagery effectively identifies standing water but can miss brief flooding due to satellite revisit constraints. SWMM provides detailed insights into system-wide drainage behavior yet may underestimate localized street-level flooding. Meanwhile, 311 calls reflect real-world flooding impacts but are vulnerable to underreporting. Statistical overlap analysis highlights chronic flood hotspots repeatedly identified across multiple detection methods, indicating persistent infrastructure and topographic vulnerabilities. Temporal analysis further reveals that while SWMM flooding aligns closely with rainfall peaks, 311 calls typically precede or persist beyond these peaks. Our findings emphasize the value of using satellite observations, hydrological modeling, and resident-reported data in a complementary manner to better interpret patterns in flood timing, severity, and spatial distribution—providing insights that can inform targeted infrastructure improvements and contribute to urban flood resilience planning.

311↗
NASA NTRS1992

Atmospheric detectives: Atlas 2 teacher's guide with activities. For use with middle-school students

Can you imagine doing a science project in space? This is the challenging and exciting situation that researchers experience in Spacelab, the laboratory carried inside the Shuttle. Here, hundreds of kilometers above Earth's surface, the crews of the ATLAS missions scan, probe, and measure concentrations of chemicals and water vapor in Earth's protective bubble. So far, one ATLAS crew has rocketed into the atmosphere, watching many sunrises and sunsets come and go while activating delicate instruments and conducting experiments that monitor the complicated interactions between the Sun, the atmosphere, and Earth. We, the crew of ATLAS 2, will continue this important work aboard the Space Shuttle. Together, we will gather data that will be compared with information from satellites, balloons, and instruments on the ground. As part of the National Aeronautics and Space Administration's (NASA's) contribution to Mission to Planet Earth, ATLAS 2 will help develop a thorough picture of the Sun's output, its interaction with the atmosphere, and the well-being of Earth's middle atmosphere. Because the health of the atmosphere is of vital importance to all Earth's inhabitants, everyone should be part of this investigation. You can be active participants in exciting and vital activities: recycling and practicing other conservation methods and gathering information to learn more about how you can keep our atmosphere healthy now, as students, and in the future as informed citizens, scientists, technicians, and mathematicians.

Source record↗
NASA NTRS2019

Simplifying NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns, are large, complex and evolving. Such characteristics pose great challenges for end users (e.g., Earth science and applied science users, students, citizen scientists), particularly for those who are unfamiliar with NASA's EOSDIS and thus unable to access and utilize datasets effectively. For example, a novice user may simply ask: what is the total rainfall for a flooding event in my county yesterday? For an experienced user (e.g., algorithm developer), a question can be: how did my rainfall product perform, compared to ground observations, during a flooding event? Nonetheless, with rapid information technology development such as natural language processing, it is possible to develop simplified Web interfaces and back-end processing components to handle such questions and deliver answers in terms of text, data, or graphic results directly to users.In this presentation, we describe the main challenges for end users with different levels of expertise in accessing and utilizing NASA Earth science data. Surveys reveal that most non-professional users normally do not want to download and handle raw data as well as conduct heavy-duty data processing tasks. Often they just want some simple graphics or data for various purposes. To them, simple and intuitive user interfaces are sufficient because complicated ones can be difficult and time-consuming to learn. Professionals also want such interfaces to answer many questions from datasets. One solution is to develop a natural language based search box like Google and the search results can be text, data, graphics and more. Now the challenge is, with natural language processing, can we design a system to process a scientific question typed in by a user? In this presentation, we describe our plan for such a prototype. The workflow is: 1) extract needed information (e.g., variables, spatial and temporal information, processing methods, etc.) from the input, 2) process the data in the backend, and 3) deliver the results (data or graphics) to the user.

Liu, Zhong↗
DOE OSTI2022

Feasibility of Adding Twitter Data to Aid Drought Depiction: Case Study in Colorado

The use of social media, such as Twitter, has changed the information landscape for citizens’ participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise, such as how the usage of Twitter by a large population may change during the development of a major drought alongside how the changing usage facilitates drought detection. For this reason, contemporary analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Three different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is found that the integration of data resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Eight of the ten models tested showed quantifiable improvements in the performance over the control run model, suggesting that the Twitter-based model was superior in predicting drought severity. Future work lies in expanding this method to depict drought in the western U.S.

54 ENVIRONMENTAL SCIENCES↗