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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
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 NTRS2010

Combining Epidemiologic Information Across Space Agencies

Space flight is a very unique occupational exposure with potential hazards that are not fully understood. A limited number of individuals have experienced the exposures incurred during space flight, and epidemiologic research would benefit from shared information across space agencies. However, data sharing can be problematic due to agency protection policies for personally identifiable information as well as medical records. Compliance with these protocols in the astronaut population is particularly difficult given the small, high-profile population under study. Creativity in combining data is necessary in order to overcome these difficulties and improve statistical power in research. This study presents methods in meta-analysis that may be used to combine non-attributable data across space agencies so that meaningful conclusions may be drawn about study interests. Methods for combining epidemiologic data across space agencies are presented, and the processes are demonstrated using life-time mortality data in U.S. astronauts and Russian cosmonauts. This proof of concept was found to be an acceptable way of sharing data across agencies, and will be used in the future as more relevant research interests are identified.

Minard, Charles G.↗
NASA NTRS2010

GLobal Integrated Design Environment (GLIDE): A Concurrent Engineering Application

The GLobal Integrated Design Environment (GLIDE) is a client-server software application purpose-built to mitigate issues associated with real time data sharing in concurrent engineering environments and to facilitate discipline-to-discipline interaction between multiple engineers and researchers. GLIDE is implemented in multiple programming languages utilizing standardized web protocols to enable secure parameter data sharing between engineers and researchers across the Internet in closed and/or widely distributed working environments. A well defined, HyperText Transfer Protocol (HTTP) based Application Programming Interface (API) to the GLIDE client/server environment enables users to interact with GLIDE, and each other, within common and familiar tools. One such common tool, Microsoft Excel (Microsoft Corporation), paired with its add-in API for GLIDE, is discussed in this paper. The top-level examples given demonstrate how this interface improves the efficiency of the design process of a concurrent engineering study while reducing potential errors associated with manually sharing information between study participants.

McGuire, Melissa L.↗
DOE OSTI2024

RBDMS, FracFocus, State Support, and Produced Water Initiatives

Award DE-FE-0027702 from the Department of Energy to the Ground Water Protection Council (GWPC) focused on state and federal priorities in the areas of state Risk Based Data Management System (RBDMS) development, connectivity between state systems and FracFocus.org, and data sharing initiatives across agencies. The primary objective was to enhance the RBDMS by adding new components relevant to current environmental topics such as hydraulic fracturing, increasing field inspection capabilities, creating linkages between FracFocus and state programs, upgrading eForm capabilities, and analyzing potential for data sharing. The recipient worked with state agencies developing RBDMS module(s) that meet these needs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

An automated integrated web-based smart tool for open stope design

The Stability Graph is a widely used tool for the design of open stopes in underground mining. Many users of the Stability Graph still apply this design method manually. Although the manual approach has benefits, using multiple graphs and stability number computation charts for each stope surface is time-consuming, even for the experienced mining engineer. Current practice in the use of the method also limits data sharing. This paper presents a StopeSoft web-based tool for open stope stability prediction that is developed on the basis of the Stability Graph method and is available at openstope.com. StopeSoft incorporates flexibility in terms of Stability Graph options and incorporates additional critical factors often overlooked. As a web-based tool, StopeSoft encourages and makes data sharing possible globally, focused on expanding the database and improving the current limitations of the Stability Graph to provide practical, reliable solutions for mining engineers, consultants, and academics. The StopeSoft automated process facilitates the process of open stope stability prediction, saving time and minimizing potential human errors. Statistical treatment of the data accounts for the variability of input parameters to emphasize the probabilistic nature of the Stability Graph method. The probabilistic interpretation of the stability states of stope surfaces eliminates the false feeling of absolute stope performance based on its location on the Stability Graph , as implied by the deterministic approach.

58 GEOSCIENCES↗
NASA NTRS1993

Study of the Alsys implementation of the Catalogue of Interface Features and Options for the Ada language for 80386 Unix

A set of programs was written to test the functionality and performance of the Alsys Ada implementation of the Catalogue of Interface Features and Options (CIFO), a set of optional Ada packages for real-time applications. No problems were found with the task id, preemption control, or shared-data packages. Minor problems were found with the dispatching control, dynamic priority, events, non-waiting entry call, semaphore, and scheduling packages. The Alsys implementation is derived mostly from Release 2 of the CIFO standard, but includes some of the features of Release 3 and some modifications unique to Alsys. Performance measurements show that the semaphore and shared-data features are an order-of-magnitude faster than the same mechanisms using an Ada rendezvous. The non-waiting entry call is slightly faster than a standard rendezvous. The existence of errors in the implementation, the incompleteness of the documentation from the published standard impair the usefulness of this implementation. Despite those short-comings, the Alsys CIFO implementation might be of value in the development of real-time applications.

Gibson, James S.↗
NASA NTRS2014

Effects of Long Duration Spaceflight on Venous and Arterial Compliance

The visual impairment and intracranial pressure syndrome (VIIP) is a newly described space flight-associated medical condition made up of a constellation of symptoms affecting at least 34% of American astronauts who have flown International Space Station (ISS) missions. VIIP is defined primarily by visual acuity deficits and anatomical changes to eye structures, and is thought to be related to elevated intracranial pressure secondary to space flightinduced cephalad fluid shifts. Loss of visual acuity could be a significant threat to crew health and performance and may be suggestive of other adaptations with implications for years post-flight. Our primary objective is to determine whether vascular compliance is altered by space flight and whether such adaptations are related to the incidence of VIIP. In particular, we will measure ocular parameters and vascular compliance in vessels of the head and neck in astronauts who have no space flight experience, in astronauts before, during, and after space flight, and in bed rest subjects with conditions similar to space flight. Additionally, we will analyze astronaut data from the Lifetime Surveillance of Astronaut Health (LSAH) archive to determine which factors might be predictive of the development of VIIP. The project will be conducted in four separate but related parts. To understand the baseline condition of astronauts without any prior space flight experience, we will study 10 astronauts who have never flown in space by performing a comprehensive evaluation of the vasculature of the head, neck and eyes. Hemodynamic data (stroke volume and blood pressure), ocular (tonometry and ocular ultrasound), venous and arterial parameters will be acquired across a range of tilt angles (20, 10, 0, -10, -20 degrees). Vessels to be studied include the temporal, jugular, and vertebral veins and the cerebral, carotid and vertebral arteries. Ophthalmic data from the annual physical will be obtained through data sharing. To examine the relation between vascular compliance in the head and neck and the development of VIIP after a long duration space flight, we will study 10 astronauts before, during, and after long-duration ISS missions. Pre- and post-flight testing will be identical to that described above. During flight, images of the same vessels of interest will be obtained for later analysis. Ophthalmic data including VIIP scores will be obtained through data sharing from medically-required tests. To investigate the effects of age and elevated sodium intake, two potential contributors to VIIP, we will study 24 men (in two age groups: 25-35 and 45-55) during a 14 day 6deg head-down bed rest, a well-accepted analog of space flight. Standard NASA bed rest conditions will be maintained except for dietary sodium. Sodium intake will be similar to that of ISS astronauts, which is higher than consumed in previous bed rest studies. Pre- and post-bed rest testing procedures will be identical to the testing protocol described above for astronauts. Ophthalmic testing (optical coherence tomography, fundoscopy, and tonometry) will be conducted on the same day that vascular compliance measures are obtained. To identify parameters that may relate to an increase in an astronaut's susceptibility to developing VIIP, we will use data mining techniques to evaluate astronaut data obtained from the LSAH. Medical history, family history, space flight history and its related exposures, and history of high performance jet aircraft exposure will be examined for their potential relationship to ocular data. We hypothesize that the cephalad fluid shift induced by space flight will result in structural and functional adaptations in head and neck vessels leading to decreased vascular compliance and related to the development of VIIP symptoms. Further, although VIIP has not been observed in previous bed rest studies, we hypothesize that an elevated sodium intake will increase the incidence of VIIP symptoms in this space flight analog. Finally, we hypothesize that data mining analyses will reveal relationships between health history, previous exposures (including space flight and high performance aircraft), and the development of VIIP in the astronaut population.

Ribeiro, L. C.↗
NASA NTRS2011

Development of a Web-Based Visualization Platform for Climate Research Using Google Earth

Recently, it has become easier to access climate data from satellites, ground measurements, and models from various data centers, However, searching. accessing, and prc(essing heterogeneous data from different sources are very tim -consuming tasks. There is lack of a comprehensive visual platform to acquire distributed and heterogeneous scientific data and to render processed images from a single accessing point for climate studies. This paper. documents the design and implementation of a Web-based visual, interoperable, and scalable platform that is able to access climatological fields from models, satellites, and ground stations from a number of data sources using Google Earth (GE) as a common graphical interface. The development is based on the TCP/IP protocol and various data sharing open sources, such as OPeNDAP, GDS, Web Processing Service (WPS), and Web Mapping Service (WMS). The visualization capability of integrating various measurements into cE extends dramatically the awareness and visibility of scientific results. Using embedded geographic information in the GE, the designed system improves our understanding of the relationships of different elements in a four dimensional domain. The system enables easy and convenient synergistic research on a virtual platform for professionals and the general public, gr$tly advancing global data sharing and scientific research collaboration.

Sun, Xiaojuan↗
DOE OSTI2026

NGEE Arctic Authorship Guidelines

Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

Iversen, Colleen [ORNL] (ORCID:0000000182933450)↗
DOE OSTI2024

SEAS Communication Engine: An Extensible, Flexible Wrapper for Co-Simulation Agents

When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.

co-simulation↗
NASA NTRS1994

The network data delivery service: A real-time data connectivity system

A network data-sharing system, the Network Data Delivery Service (NDDS), is discussed. The NDDS system relies on a bipartite model consisting of the following: information producers (sources) and information consumers (sinks). The example provided by a dual-arm robotic system is examined.

Pardo-Castellote, Gerardo↗
NASA NTRSDate not supplied

Standard Measures During Spaceflight

The key goal of the Spaceflight Standard Measures project is to ensure that a set of measures, representing the Human Research Program’s key risks and acquired with minimal impact on time and resources, is consistently captured from crewmembers through the end of the International Space Station (ISS) Program. Data collected under the Spaceflight Standard Measures project include assessments of sleep/wake cycles, cognition, immune status and function, general blood and urine chemistry (urine is collected only before flight and after landing), microbiome composition (gastrointestinal tract, saliva, and body surface), cardiovascular structure and function (carotid intima-media thickness, orthostatic responses), sensorimotor function, and team processes. Data is collected once or twice before the flight (180 and 90 days before launch), twice during the 6-month missions (fight day 30 and 30 days before return to Earth) with the exception of actigraphy, which is recorded continuously during the mission, and during two-week periods before and after the mission. In this presentation, we will review the data collected to date on twelve ISS crew members. These data are placed in the NASA Life Sciences Data Archive and are available for occupational surveillance (using non-identifiable data) Institutional Review Board-approved data sharing requests, and retrospective data requests. This data repository enables high-level monitoring of the effectiveness of countermeasures and meaningful interpretation of health and performance outcomes for various mission durations. The knowledge gained from this project informs and supports future hypothesis-driven research that will enable the success of planetary missions.

G R Clement↗
NASA NTRSDate not supplied

Standard Measures During Spaceflight

The goal of the Spaceflight Standard Measures project is to ensure that a set of measures, representing the Human Research Program’s key risks and acquired with minimal impact on time and resources, is consistently captured from crewmembers through the end of the International Space Station (ISS) Program. Data collected under the Spaceflight Standard Measures project include assessments of sleep/wake cycles, cognition, immune status and function, general blood and urine chemistry (urine is collected only before flight and after landing), microbiome composition (gastrointestinal tract, saliva, and body surface), cardiovascular structure and function (carotid intima-media thickness, orthostatic responses), sensorimotor function, sleep quality, and team processes. Data is collected once or twice before the flight (180 and 90 days before launch), twice during the 6-month missions (flight day 30 and 30 days before return to Earth) with the exception of actigraphy, which is recorded during two-week periods before, during, and after the mission. In this presentation, we will review the data collected to date on 31 ISS crewmembers. These data are placed in the NASA Life Sciences Portal (NLSP) and are available for occupational surveillance (using non-identifiable data), Institutional Review Board-approved data sharing requests, and retrospective data requests. This data repository enables high-level monitoring of the effectiveness of countermeasures and meaningful interpretation of health and performance outcomes for various mission durations. The knowledge gained from this project informs and supports future hypothesis-driven research that will enable the success of planetary missions.

G R Clement↗
NASA NTRS2016

Distributed Visualization Project

Distributed Visualization allows anyone, anywhere to see any simulation at any time. Development focuses on algorithms, software, data formats, data systems and processes to enable sharing simulation-based information across temporal and spatial boundaries without requiring stakeholders to possess highly-specialized and very expensive display systems. It also introduces abstraction between the native and shared data, which allows teams to share results without giving away proprietary or sensitive data. The initial implementation of this capability is the Distributed Observer Network (DON) version 3.1. DON 3.1 is available for public release in the NASA Software Store (https://software.nasa.gov/software/KSC-13775) and works with version 3.0 of the Model Process Control specification (an XML Simulation Data Representation and Communication Language) to display complex graphical information and associated Meta-Data.

Tech Port↗
DOE OSTI2024

Challenges of open data in aquatic sciences: issues faced by data users and data providers

Free use and redistribution of data (i.e., Open Data) increases the reproducibility, transparency, and pace of aquatic sciences research. However, barriers to both data users and data providers may limit the adoption of Open Data practices. Here, we describe common Open Data challenges faced by data users and data providers within the aquatic sciences community (i.e., oceanography, limnology, hydrology, and others). These challenges were synthesized from literature, authors’ experiences, and a broad survey of 174 data users and data providers across academia, government agencies, industry, and other sectors. Through this work, we identified seven main challenges: 1) metadata shortcomings, 2) variable data quality and reusability, 3) open data inaccessibility, 4) lack of standardization, 5) authorship and acknowledgement issues 6) lack of funding, and 7) unequal barriers around the globe. Our key recommendation is to improve resources to advance Open Data practices. This includes dedicated funds for capacity building, hiring and maintaining of skilled personnel, and robust digital infrastructures for preparation, storage, and long-term maintenance of Open Data. Further, to incentivize data sharing we reinforce the need for standardized best practices to handle data acknowledgement and citations for both data users and data providers. We also highlight and discuss regional disparities in resources and research practices within a global perspective.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Collaborative Data Publication Utilizing the Open Data Repository's Data Publisher

For small communities in multidisciplinary fields such as astrobiology, publishing and sharing data can be challenging. While large, homogenous fields often have repositories and existing data standards, small groups of independent researchers have few options for publishing data that can be utilized within their community. In conjunction with teams at NASA Ames and the University of Arizona, a number of pilot studies are being conducted to assess the needs of these research groups and to guide the software development so that it allows them to publish and share their data collaboratively.

Human-readable interfaces↗