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DOE OSTI2025

Electric Vehicle Charging Analytics and Reporting Tool (EV-ChART): Data Format and Preparation Guidance (V.5.0)

The Joint Office of Energy and Transportation maintains the Electric Vehicle Charging Analytics and Reporting Tool (EV-ChART), which provides a centralized hub for submitting electric vehicle (EV) charging infrastructure data directed by the Federal Highway Administration (23 CFR 680.112(a)-(c)). EV-ChART provides a streamlined data submission process and an integrated set of analytic tools, connects to other data sources, and empowers data sharing and access across stakeholders, including the public. Any data shared publicly will be aggregated and anonymized to stay in accordance with 23 CFR 680. This EV-ChART Data Format and Preparation Guidance provides a comprehensive overview of the data reporting requirements as authorized under 23 CFR 680.112(a)-(c)). The guidance is intended to be used alongside the EV-ChART Data Input Template, which defines the tabular data structure that these data submissions must follow. Per 23 CFR 680.112(a)-(c), the annual and quarterly data submissions are required of all National Electric Vehicle Infrastructure (NEVI) Formula Program projects, as well as projects for the construction of publicly accessible EV chargers that are funded with funds made available under Title 23, United States Code, including any EV charging infrastructure project funded with federal funds that is treated as a project on a federal-aid highway. One-time data submissions are required of both the NEVI Formula Program projects and grants awarded under 23 U.S.C. 151(f) for projects that are for EV charging stations located along and designed to serve the users of designated Alternative Fuel Corridors (AFCs). Other information and data required in 23 CFR 680, such as 23 CFR 680.112(d), 23 CFR 680.116(c), and 23 CFR 680.106(a), are not discussed in this guidance.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)

The scientific community is faced with a need for greatly improved data sharing, analysis, visualization and advanced collaboration based firmly on open science principles. Recent and upcoming launches of new satellite missions with more complex and voluminous data, as well as the ever more urgent need to better understand the global carbon budget and related ecological processes, provided the immediate rational for the ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP). This highly collaborative joint project of ESA and NASA established a framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by ESA and NASA EO data users. Presented to the public in October 2021, the current version of MAAP provides a common cloud-based platform with computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of global above-ground biomass. Data from the Global Ecosystem Dynamics Investigation (GEDI) mission on the International Space Station and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) have been instrumental in the first products of MAAP including the first comprehensive map of Boreal above-ground Biomass and a current Global Biomass Harmonization Activity, but the platform is also being specifically designed to support the forthcoming ESA Biomass mission and incorporate data from the upcoming NASA-ISRO SAR (NISAR) mission. While these missions and the corresponding research which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of data and information relating to global biomass estimation, they also present data storing, processing and sharing challenges. The NISAR mission alone will produce about 80TB/day. These large data volumes present a challenge that would otherwise place accessibility limits on the scientific community and impact scientific progress. Other challenges being addressed by MAAP include: 1) Enabling researchers to easily discover, process, visualize and analyze large volumes of data from both agencies; 2) Providing a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation; 3) Providing a version-controlled science algorithm development environment that supports tools, co-located data and processing resources; and 4) Addressing intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms. MAAP products can be explored on the MAAP Dashboard at https://earthdata.nasa.gov/maap-biomass or the joint platform entrance at scimaap.net. MAAP also can be accessed through individual NASA (https://maap-project.org) and ESA (https://esa-maap.org/) landing pages.

cloud computing↗
NASA NTRS2019

Use of UAS Reports (UREPs) during TCL3 Field Testing

During the NASA Unmanned Aircraft System (UAS) Traffic Management (UTM) Project’s Technical Capability Level 3 (TCL3) demonstration, a service for stakeholders to share weather and aircraft observations was tested. The overall goal was to increase awareness of airspace and weather activity to increase a pilot’s ability to fly safely. To achieve this goal, a mechanism to share data was created, called “UAS Reports” or UREPs, which were generated by client systems and sent to a central data service. The data service provided subscriptions and allowed for data requests to share the reports that had been sent in by stakeholders. To execute this functionality, four FAA (Federal Aviation Administration)-designated UAS test sites performed UREP testing as part of TCL3. NASA provided the centralized service and test site partners flew missions and simulated activity at the test sites to generate data to send to the service. The loop was closed by having other clients (usually other small UAS operators) request those data from the service or subscribe to feeds from the service. Overall, the tests demonstrated the utility of such a service. In this report, the testing setup, data collection, and analysis of results are presented. The concept of UREPs has since been incorporated as a service within NASA’s Conflict Mitigation Model for UTM. The concept will continue to be tested in NASA’s TCL4 activities.

UTM↗
DOE OSTI2025

Electrical Load Forecasting Over Multihop Smart Metering Networks With Federated Learning

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting, but require data sharing, which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. Here, this article presents a novel personalized FL (PFL) method for high-quality load forecasting in metering networks. A meta-learning-based strategy is developed to address data heterogeneity at local SMs in the collaborative training of local load forecasting models. Moreover, to minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs. A theoretical convergence analysis is also conducted to provide insights into FL design for federated load forecasting. Extensive simulations from real-world datasets show that our method outperforms existing approaches regarding better load forecasting and reduced operational latency costs.

Rahman, Ratun [Univ. of Alabama, Huntsville, AL (U↗
NASA NTRSDate not supplied

Maximizing Spaceflight Biological Data with Omics Analytics: The NASA GeneLab Database

NASA’s GeneLab includes an open-access repository of some 250+ omics datasets generated by biological experiments relevant to spaceflight including simulated cosmic radiation and microgravity. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics background, GeneLab has become a knowledgebase platform converting raw genetic and proteomic signatures found in flight samples into biological and physiological meanings. A large community of more than 100 scientists has rallied behind GeneLab and organized into four Analysis Working Groups (AWGs: Animal, Plant, Microbe, and Multi-Omics). Together, the AWGs have gained scientific recognition worldwide by establishing a consortium in charge of adopting new complex standards for data analysis workflows and omics sample processing in a rapidly evolving field. We will demonstrate the usage of the repository with smart search capability, an online controlled-access toolshed "Galaxy" to process user data with vetted standard workflows, a workspace for data sharing and a data submission portal with ontology control for better metadata curation. The GeneLab visualization portal will also be demonstrated, showing how anyone without formal training in bioinformatics can now browse the space biology omics data to discover new biology and potential solutions to improve life in space.

Sylvain Vincent Costes↗
NASA NTRSDate not supplied

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

NASA’s GeneLab includes an open-access repository of some 250+ omics datasets generated by biological experiments relevant to spaceflight including simulated cosmic radiation and microgravity. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics background, GeneLab has become a knowledgebase platform converting raw genetic and proteomic signatures found in flight samples into biological and physiological meanings. A large community of more than 100 scientists has rallied behind GeneLab and organized into four Analysis Working Groups (AWGs: Animal, Plant, Microbe, and Multi-Omics). Together, the AWGs have gained scientific recognition worldwide by establishing a consortium in charge of adopting new complex standards for data analysis workflows and omics sample processing in a rapidly evolving field. We will demonstrate the usage of the repository with smart search capability, an online controlled-access toolshed "Galaxy" to process user data with vetted standard workflows, a workspace for data sharing and a data submission portal with ontology control for better metadata curation. The GeneLab visualization portal will also be demonstrated, showing how anyone without formal training in bioinformatics can now browse the space biology omics data to discover new biology and potential solutions to improve life in space.

GeneLab↗
NASA NTRSDate not supplied

NASA Omics Archive Project

The space environment consists of a complex set of hazards including altered gravity, radiation, psychological/physiological stress, isolation, and confinement leading to complex biological responses. Advances in biotechnology capabilities offer considerable potential to provide novel insight into those responses as well as innovative diagnostic, treatment, and countermeasure solutions for astronauts as NASA begins to travel beyond low Earth orbit. Omics data (genomics, transcriptomics, proteomics, etc.) is one example that can provide NASA with critical knowledge of how a crewmember’s genetics, environment, and lifestyle can be used to develop individualized approaches for disease prevention, advance diagnostics, and improve treatment strategies. NASA ventured into the field of omics on human subjects with the successful completion of the NASA Twins Study which was the first step in mapping the multi-omic profile of astronauts to understand and mitigate the health consequences of spaceflight. The Human Research Program aims to build upon the success of the Twins Study with the NASA Omics Archive flight study, establishing a longitudinal biospecimen archive and efficiently generating a comprehensive high-quality multi-omic dataset from astronauts for the purpose of studying molecular, metabolic, and microbial changes associated with longduration spaceflight missions. The goal is to facilitate scientific and medical research community efforts to characterize and mitigate spaceflight health and performance risks. In this presentation, we will review details regarding the biospecimen and data archive to be generated by the NASA Omics Archive flight study. Data generated as part of this project will be archived in the NASA Life Sciences Portal (NLSP) and be made available for future hypothesis-driven research efforts or occupational surveillance through Institutional Review Board-approved data sharing and retrospective data requests submitted to the Life Sciences Data Archive (LSDA) team. We will also present results of a ground study performed to evaluate in-house procedures, new sample collection hardware, and vendor capabilities. The data repository generated and the samples to be archived by this study will enable future research efforts to assess an astronauts’ unique molecular and genetic profile with respect to individual spaceflight responses. Results of which will be instrumental in enabling precision health capabilities to better assess and mitigate spaceflight risks, detect disease states earlier, and actively monitor countermeasure treatments, ultimately improving clinical outcomes during future exploration class missions.

C. A. Theriot↗
NASA NTRS1994

Performance of BLAS 3, FFTs and NAS Parallel Benchmarks on Cray T3D

Recently, a Cray T3D Emulator has been made available on the Cray Y-MP and C90 computers. The Pittsburgh Supercomputer Center has acquired a CRAY T3D system and many other centers like Jet Propulsion Laboratory (JPL) will have it by the end of 1994. The Cray T3D system is the firstphase system in Cray Research, Inc.'s (CRI) three-phase massively parallel processing (MPP) program. This system features a heterogeneous architecture that closely couples DEC's ALPHA microprocessors and CRI's parallel-vector technology, i.e. the Cray Y-MP and Cray C90. The Cray T3D Emulator will give prospective users a valuable experience in developing high performance applications on the MPP system. This emulator runs programs written in CRI's MPP Fortran programming model (data sharing and work sharing) or Parallel Virtual Machine (PVM) programming model. It will help the users to study data layout, data locality, and data reference patterns thereby providing feedback which will enable one to write more efficient parallel codes. An overview of the Cray T3D hardware, software, and three of its available programming models is presented.The Cray Fortran Programming Model comprising (a) Data Sharing, (b) Worksharing and (c) Message Passing, will be discussed with examples. We have also implemented distributed BLAS 3 (matrix-matrix multiplication) in data parallel model (using only CSHIFT); worksharing model using block distribution and collapsed distribution; and message passing model using PVM. We have also implemented 2D and 3D FFTs for radix-2 using PVM. The performance of NAS Parallel 'Benchmarks (NPB) on CRAY T3D will be compared with other highly parallel systems such as CM-5, Paragon, C90 etc.

Saini, Subhash↗
DOE OSTI2025

LinkML: an open data modeling framework

Background Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, nonstandardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult. Findings LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics. Conclusions LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at their origin. LinkML documentation and code are available at https://linkml.io/.

AI-ready data↗
NASA NTRS2016

Access to Archived Astronaut Data for Human Research Program Researchers: Update on Progress and Process Improvements

Since the 2010 NASA directive to make the Life Sciences Data Archive (LSDA) and Lifetime Surveillance of Astronaut Health (LSAH) data archives more accessible by the research and operational communities, demand for astronaut medical data has increased greatly. LSAH and LSDA personnel are working with Human Research Program on many fronts to improve data access and decrease lead time for release of data. Some examples include the following: Feasibility reviews for NASA Research Announcement (NRA) data mining proposals; Improved communication, support for researchers, and process improvements for retrospective Institutional Review Board (IRB) protocols; Supplemental data sharing for flight investigators versus purely retrospective studies; Work with the Multilateral Human Research Panel for Exploration (MHRPE) to develop acceptable data sharing and crew consent processes and to organize inter-agency data coordinators to facilitate requests for international crewmember data. Current metrics on data requests crew consenting will be presented, along with limitations on contacting crew to obtain consent. Categories of medical monitoring data available for request will be presented as well as flow diagrams detailing data request processing and approval steps.

Lee, L. R.↗
NASA NTRS2016

Storage of Physical Sample Metadata in the Astrobiology Habitable Environments Database (AHED)

The National Aeronautics and Space Administration has begun an effort to store, curate, and publish information about physical samples collected and analyzed in conjunction with NASA-funded astrobiology research. Astrobiology is a multidisciplinary area of scientific research being conducted by collaborating teams of biologists, chemists, geologists, atmospheric scientists, oceanographers, astrophysicists, astronomers, and other specialists. Astrobiology studies the origin, evolution, and distribution of life in the Universe. NASA uses the results of astrobiology research to focus its future missions on targets of opportunity for the discovery of life off Earth. Astrobiology researchers conduct both field-based and laboratory-based research, during which physical samples are collected, processed, and catalogued. The cataloguing practices employed by different teams of astrobiologists vary widely, and there are no specific standards available to guide the collection and recording of astrobiology sample data. The disparity in data collection approaches and the lack of a centralized sample repository makes it difficult for astrobiology teams to share data and benefit from resultant synergies.To facilitate data sharing within the astrobiology community, NASA is developing a prototype database the Astrobiology Habitable Environments Database (AHED) and an associated set of data collection templates. The database will store information about samples, along with associated measurements and analyses, including information about biological cultures enriched or isolated from samples, and the results of analyses performed on the samples (e.g., via spectrography, microscopy, etc.). In addition, the system will store contextual information about field sites where samples were collected, the instruments or equipment used for analysis, and people and institutions involved in their collection. AHED is being implemented on top of Open Data Repository's Data Publisher [1], an open source software platform for the publication of scientific datasets. The data collection templates under development represent an initial attempt to propose a set of metadata for capture and storage within AHED. The design of these templates is being conducted by a consolidated group of astrobiologists from active research teams at NASA Ames Research Center, assisted by data science and software engineering specialists. These initial templates must be vetted with the broader astrobiology community through a defined process to ensure that they meet community needs. Each template captures a different type of data collection record. For each template, we are developing a list of fields to be captured, including a set of required entry fields, a set of recommended but optional fields, and a set of discretionary fields. A datatype selected from a variety of text and numeric types is specified for each field. Included is a 'choice' type that restricts user input to an enumerated list of values. Many of the fields and field values capture information of particular interest to the astrobiology community, and are intended to facilitate search and retrieval of relevant data across multiple datasets.

Keller, Rich↗
NASA NTRSDate not supplied

NASA R&M Efficiency through Findable, Accessible, Interoperable, and Reusable (FAIR) Digital Assets

At the intersection of mission, technology, and place is NASA’s need to modernize for a digital-forward future. Digitalization, the process of moving toward digital business, is occurring everywhere and remains an ongoing process across the federal government.”[1] Whereas, Digital Transformation is “employing digitization/digital technologies (e.g., Artificial Intelligence (AI), mobile, cloud, data) to change a process, product, or capability so dramatically (e.g., real-time, intelligent, personalized, anywhere, anytime) that it is unrecognizable compared to its traditional form.” [2] In order to facilitate a digital transformation it is essential for NASA to understand and identify where data exists today and which data are value-needed in the future, understand where there are unfulfilled data needs that limit the advancement of NASA work, and ensure NASA efficiency through Findable, Accessible, Interoperable, and Reusable (FAIR) digital assets in the future. Therefore, NASA’s Reliability & Maintainability (R&M) Enterprise Data Sharing team is working to leverage both Digitization and Digital Transformation to achieve their vision of developing an R&M data discovery framework that enables our community, our partners, and our stakeholders with the ability to efficiently, robustly, and seamlessly access information that enables real-time knowledge and model-based, analytics driven, decision-making impacting R&M. As a result the R&M Enterprise Data Sharing team has conducted a survey of its Reliability, Maintainability, and Availability (RMA) community members to identify data existence (created or used) and where there are corresponding barriers to data acquisition and/or R&M or other issues as shown within this presentation.

Digital Transformation↗
NASA NTRSDate not supplied

Trilateral Task Force – Reliability Analysis Supporting Mission Extension/Post Mission Disposal

At the intersection of mission, technology, and place is NASA’s need to modernize for a digital-forward future. Digitalization, the process of moving toward digital business, is occurring everywhere and remains an ongoing process across the federal government.”[1] Whereas, Digital Transformation is “employing digitization/digital technologies (e.g., Artificial Intelligence (AI), mobile, cloud, data) to change a process, product, or capability so dramatically (e.g., real-time, intelligent, personalized, anywhere, anytime) that it is unrecognizable compared to its traditional form.” [2] In order to facilitate a digital transformation it is essential for NASA to understand and identify where data exists today and which data are value-needed in the future, understand where there are unfulfilled data needs that limit the advancement of NASA work, and ensure NASA efficiency through Findable, Accessible, Interoperable, and Reusable (FAIR) digital assets in the future. Therefore, NASA’s Reliability & Maintainability (R&M) Enterprise Data Sharing team is working to leverage both Digitization and Digital Transformation to achieve their vision of developing an R&M data discovery framework that enables our community, our partners, and our stakeholders with the ability to efficiently, robustly, and seamlessly access information that enables real-time knowledge and model-based, analytics driven, decision-making impacting R&M. As a result the R&M Enterprise Data Sharing team has conducted a survey of its Reliability, Maintainability, and Availability (RMA) community members to identify data existence (created or used) and where there are corresponding barriers to data acquisition and/or R&M or other issues as shown within this presentation.

Digital Transformation, Reliability Engineering↗
NASA NTRS2006

Integrating International Engineering Organizations For Successful ISS Operations

The International Space Station (ISS) is a multinational orbiting space laboratory that is built in cooperation with 16 nations. The design and sustaining engineering expertise is spread worldwide. As the number of Partners with orbiting elements on the ISS grows, the challenge NASA is facing as the ISS integrator is to ensure that engineering expertise and data are accessible in a timely fashion to ensure ongoing operations and mission success. Integrating international engineering teams requires definition and agreement on common processes and responsibilities, joint training and the emergence of a unique engineering team culture. ISS engineers face daunting logistical and political challenges regarding data sharing requirements. To assure systematic information sharing and anomaly resolution of integrated anomalies, the ISS Partners are developing multi-lateral engineering interface procedures. Data sharing and individual responsibility are key aspects of this plan. This paper describes several examples of successful multilateral anomaly resolution. These successes were used to form the framework of the Partner to Partner engineering interface procedures, and this paper describes those currently documented multilateral engineering processes. Furthermore, it addresses the challenges experienced to date, and the forward work expected in establishing a successful working relationship with Partners as their hardware is launched.

Blome, Elizabeth↗
NASA NTRS2017

Collaborative Data Publication Utilizing the Open Data Repository's (ODR) Data Publisher

Introduction: For small communities in diverse fields such as astrobiology, publishing and sharing data can be a difficult challenge. While large, homogenous fields often have repositories and existing data standards, small groups of independent researchers have few options for publishing standards and data that can be utilized within their community. In conjunction with teams at NASA Ames and the University of Arizona, the Open Data Repository's (ODR) Data Publisher has been conducting ongoing pilots to assess the needs of diverse research groups and to develop software to allow them to publish and share their data collaboratively. Objectives: The ODR's Data Publisher aims to provide an easy-to-use and implement software tool that will allow researchers to create and publish database templates and related data. The end product will facilitate both human-readable interfaces (web-based with embedded images, files, and charts) and machine-readable interfaces utilizing semantic standards. Characteristics: The Data Publisher software runs on the standard LAMP (Linux, Apache, MySQL, PHP) stack to provide the widest server base available. The software is based on Symfony (www.symfony.com) which provides a robust framework for creating extensible, object-oriented software in PHP. The software interface consists of a template designer where individual or master database templates can be created. A master database template can be shared by many researchers to provide a common metadata standard that will set a compatibility standard for all derivative databases. Individual researchers can then extend their instance of the template with custom fields, file storage, or visualizations that may be unique to their studies. This allows groups to create compatible databases for data discovery and sharing purposes while still providing the flexibility needed to meet the needs of scientists in rapidly evolving areas of research. Research: As part of this effort, a number of ongoing pilot and test projects are currently in progress. The Astrobiology Habitable Environments Database Working Group is developing a shared database standard using the ODR's Data Publisher and has a number of example databases where astrobiology data are shared. Soon these databases will be integrated via the template-based standard. Work with this group helps determine what data researchers in these diverse fields need to share and archive. Additionally, this pilot helps determine what standards are viable for sharing these types of data from internally developed standards to existing open standards such as the Dublin Core (http://dublincore.org) and Darwin Core (http://rs.twdg.org) metadata standards. Further studies are ongoing with the University of Arizona Department of Geosciences where a number of mineralogy databases are being constructed within the ODR Data Publisher system. Conclusions: Through the ongoing pilots and discussions with individual researchers and small research teams, a definition of the tools desired by these groups is coming into focus. As the software development moves forward, the goal is to meet the publication and collaboration needs of these scientists in an unobtrusive and functional way.

easy to use and implement software tool↗
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.↗