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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

An Integrated Economics Model for ISRU in Support of a Mars Colony - Initial Results Report

This database, the Mars Colony Architecture Model (MCAM), is then linked to a variety of “downsteam” analytic models. In particular, we integrated an Extraction Process (i.e., “Mining”) Model, an Infrastructure and Integrated Logistics Support (ILS) Model, and an Economics Integration Model. The Extraction Process Model focuses on the technologies associated with in situ resource extraction, processing, storage and handling, and delivery. For each mined resource, which may involve multiple cooperating In Situ Resource Utilization (ISRU) systems in a given architecture, the Extraction Process Model computes the production rate as a function of the systems’ technical parameters and the local Mars environment. As with our earlier work, this model focuses on the extraction and processing of Mars water/ice.

Shishko, Robert↗
NASA NTRS2006

NASA Stennis Space Center Integrated System Health Management Test Bed and Development Capabilities

Integrated System Health Management (ISHM) is a capability that focuses on determining the condition (health) of every element in a complex System (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK)-not just data-to control systems for safe and effective operation. This capability is currently done by large teams of people, primarily from ground, but needs to be embedded on-board systems to a higher degree to enable NASA's new Exploration Mission (long term travel and stay in space), while increasing safety and decreasing life cycle costs of spacecraft (vehicles; platforms; bases or outposts; and ground test, launch, and processing operations). The topics related to this capability include: 1) ISHM Related News Articles; 2) ISHM Vision For Exploration; 3) Layers Representing How ISHM is Currently Performed; 4) ISHM Testbeds & Prototypes at NASA SSC; 5) ISHM Functional Capability Level (FCL); 6) ISHM Functional Capability Level (FCL) and Technology Readiness Level (TRL); 7) Core Elements: Capabilities Needed; 8) Core Elements; 9) Open Systems Architecture for Condition-Based Maintenance (OSA-CBM); 10) Core Elements: Architecture, taxonomy, and ontology (ATO) for DIaK management; 11) Core Elements: ATO for DIaK Management; 12) ISHM Architecture Physical Implementation; 13) Core Elements: Standards; 14) Systematic Implementation; 15) Sketch of Work Phasing; 16) Interrelationship Between Traditional Avionics Systems, Time Critical ISHM and Advanced ISHM; 17) Testbeds and On-Board ISHM; 18) Testbed Requirements: RETS AND ISS; 19) Sustainable Development and Validation Process; 20) Development of on-board ISHM; 21) Taxonomy/Ontology of Object Oriented Implementation; 22) ISHM Capability on the E1 Test Stand Hydraulic System; 23) Define Relationships to Embed Intelligence; 24) Intelligent Elements Physical and Virtual; 25) ISHM Testbeds and Prototypes at SSC Current Implementations; 26) Trailer-Mounted RETS; 27) Modeling and Simulation; 28) Summary ISHM Testbed Environments; 29) Data Mining - ARC; 30) Transitioning ISHM to Support NASA Missions; 31) Feature Detection Routines; 32) Sample Features Detected in SSC Test Stand Data; and 33) Health Assessment Database (DIaK Repository).

Figueroa, Fernando↗
NASA NTRS2012

QuakeSim 2.0

QuakeSim 2.0 improves understanding of earthquake processes by providing modeling tools and integrating model applications and various heterogeneous data sources within a Web services environment. QuakeSim is a multisource, synergistic, data-intensive environment for modeling the behavior of earthquake faults individually, and as part of complex interacting systems. Remotely sensed geodetic data products may be explored, compared with faults and landscape features, mined by pattern analysis applications, and integrated with models and pattern analysis applications in a rich Web-based and visualization environment. Integration of heterogeneous data products with pattern informatics tools enables efficient development of models. Federated database components and visualization tools allow rapid exploration of large datasets, while pattern informatics enables identification of subtle, but important, features in large data sets. QuakeSim is valuable for earthquake investigations and modeling in its current state, and also serves as a prototype and nucleus for broader systems under development. The framework provides access to physics-based simulation tools that model the earthquake cycle and related crustal deformation. Spaceborne GPS and Inter ferometric Synthetic Aperture (InSAR) data provide information on near-term crustal deformation, while paleoseismic geologic data provide longerterm information on earthquake fault processes. These data sources are integrated into QuakeSim's QuakeTables database system, and are accessible by users or various model applications. UAVSAR repeat pass interferometry data products are added to the QuakeTables database, and are available through a browseable map interface or Representational State Transfer (REST) interfaces. Model applications can retrieve data from Quake Tables, or from third-party GPS velocity data services; alternatively, users can manually input parameters into the models. Pattern analysis of GPS and seismicity data has proved useful for mid-term forecasting of earthquakes, and for detecting subtle changes in crustal deformation. The GPS time series analysis has also proved useful as a data-quality tool, enabling the discovery of station anomalies and data processing and distribution errors. Improved visualization tools enable more efficient data exploration and understanding. Tools provide flexibility to science users for exploring data in new ways through download links, but also facilitate standard, intuitive, and routine uses for science users and end users such as emergency responders.

Donnellan, Andrea↗
NASA NTRS1994

A data and information system for processing, archival, and distribution of data for global change research

Work on this project was focused on information management techniques for Marshall Space Flight Center's EOSDIS Version 0 Distributed Active Archive Center (DAAC). The centerpiece of this effort has been participation in EOSDIS catalog interoperability research, the result of which is a distributed Information Management System (IMS) allowing the user to query the inventories of all the DAAC's from a single user interface. UAH has provided the MSFC DAAC database server for the distributed IMS, and has contributed to definition and development of the browse image display capabilities in the system's user interface. Another important area of research has been in generating value-based metadata through data mining. In addition, information management applications for local inventory and archive management, and for tracking data orders were provided.

Graves, Sara J.↗
NASA NTRS2017

Mass and Reliability Source (MaRS) Database

The Mass and Reliability Source (MaRS) Database consolidates components mass and reliability data for all Oribital Replacement Units (ORU) on the International Space Station (ISS) into a single database. It was created to help engineers develop a parametric model that relates hardware mass and reliability. MaRS supplies relevant failure data at the lowest possible component level while providing support for risk, reliability, and logistics analysis. Random-failure data is usually linked to the ORU assembly. MaRS uses this data to identify and display the lowest possible component failure level. As seen in Figure 1, the failure point is identified to the lowest level: Component 2.1. This is useful for efficient planning of spare supplies, supporting long duration crewed missions, allowing quicker trade studies, and streamlining diagnostic processes. MaRS is composed of information from various databases: MADS (operating hours), VMDB (indentured part lists), and ISS PART (failure data). This information is organized in Microsoft Excel and accessed through a program made in Microsoft Access (Figure 2). The focus of the Fall 2017 internship tour was to identify the components that were the root cause of failure from the given random-failure data, develop a taxonomy for the database, and attach material headings to the component list. Secondary objectives included verifying the integrity of the data in MaRS, eliminating any part discrepancies, and generating documentation for future reference. Due to the nature of the random-failure data, data mining had to be done manually without the assistance of an automated program to ensure positive identification.

Valdenegro, Wladimir↗
NASA NTRS1998

Mining for Data

AbTech Corporation used an F-18 HARV (High Alpha Research Vehicle) simulation developed by NASA to create an interactive computer-based prototype of the MQ (Model Quest) SV (System Validator) tool. Dryden Flight Research Center provided support to develop, test, and rapidly reprogram the validation function. AbTech's ModelQuest Enterprises highly automated and outperforms other modeling techniques to quickly discover meaningful relationships, patterns, and trends in databases. Applications include technical and business professionals in finance, marketing, business, banking, retail, healthcare, and aerospace.

Source record↗
NASA NTRSDate not supplied

GL4U: Training the next generation of bioinformaticians, one omics datatype at a time

Spaceflight modifies gene expression in every organism examined to date, including humans. Understanding how these gene expression changes affect physiology is crucial for the development of countermeasures to enable long-duration manned missions. NASA’s GeneLab project provides researchers open access to multi-omics data, including genetic and gene expression data, from spaceflight experiments that can be mined to understand the effects of spaceflight on biological systems. To ensure new knowledge generation through data re-use, it is important to maximize the number of scientists who utilize GeneLab data. Training students on the GeneLab platform is the best way to create long-term adopters of this NASA database and its tools. Turning students into future instructors and advocates will also accelerate the dissemination of these data and tools to the broader scientific community. Therefore, in collaboration with the GeneLab Educational Working Group (EWG), GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. During the bootcamp, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U direct training pilot program was conducted in June 2021 in collaboration with USRA and San Jose State University (SJSU). During the pilot, SJSU students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrates the capacity of GL4U for training young scientists and encouraging data re-use.

Jonathan Matthew Galazka↗
NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Data, Analysis Tools, and Informatic Collaborations

Increased biomedical risks and challenges associated with deep space missions require knowledge discovery, health countermeasures, and biomedical support capabilities. Maximally open-access and reusable data is needed by developers, scientists, and engineers to develop these systems. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database (ie., findable, accessible, interoperable, and reusable), and meets various scientific, technical, and operational needs. It offers users and submitters the ability to upload, download, search, share, analyze, cite, and visualize data across ‘omics, physiological, phenotypic, payload, hardware, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR is an expanded database, based upon the successes of NASA GeneLab. OSDR has >460 studies with datasets covering model organisms to 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 with raw files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) developed from industry norms. OSDR is collecting and curating biomedical human data from a new sub-orbital research flight and is open to more space life science/biomedical submissions from the international and commercial sectors. OSDR also recently began a collaboration with the European Space Agency (ESA) to collect and curate >200 terabytes of human and model organism data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics and ~50 physiological-phenotypic-imaging assay data types. Tools available for OSDR users include: 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, and 3) a Multi-study visualization tool which enables users to look across and combine ‘omics datasets. There are ~600 volunteer OSDR Analysis Working Group (AWG) members providing feedback on scientific data/metadata standards and collaborating to mine-reuse OSDR in research. OSDR/GeneLab has enabled ~60 publications reusing data as of October 2023.

space biology↗
NASA NTRSDate not supplied

Mining the Gravity Mutants of Arabidopsis

Gravity mutants are a valuable resource for understanding gravity perception, signaling, and response in plants. A review of 190 publications resulted in a list of 97 loci with mutations that caused a gravitropic phenotype. While all these mutants show some form of gravity phenotype, several are also generally defective in growth. After removing these nonspecific mutants, 76 loci were deemed to have true gravity mutants. The gene list was then used to create a sortable database containing key factors of gravity signaling as well as data on methodologies and the development of mutant lines. Indexing this database has allowed us to pull out trends that were not visible in individual publications. For example, mutants that are unable to rearrange starch statoliths make up a larger proportion of the described inflorescence stems mutants than any other organ. Comparing these statolith mutants across organs shows that root and inflorescence stems consistently display different phenotypes for the same class of defect. The more severe gravity defects are most commonly described in root tissue while other tissues are more likely to show only a delayed or reduced response to gravity. The experience of members of the GeneLab plant Analytics Working Group (AWG) in data visualization and gene mapping have sparked new ideas for utilizing this database. Knowledge of shared traits, mutant development, and growth can provide a new resource for the production of seed lines specialized for their response to gravity.

gravitropism↗
NASA NTRSDate not supplied

Role of Antecedent Soil Moisture and Vegetation Stress in Lightning-Initiated Wildfires

Lightning-caused wildfires are a small percentage of all wildfire events within the Conterminous U.S. (CONUS), but they account for over 56% of the acreage burned. The atmospheric conditions favoring wildfire and rapid growth are well understood: large dewpoint depressions, unstable planetary boundary layer, strong winds, etc. However, antecedent land surface conditions affecting dead and live fuel moisture is more difficult to quantify. This study examines over 20 years of antecedent land surface, vegetation stress, and wildfire characteristic data associated with nearly 77,000 lightning-initiated wildfires from the U.S. Forest Service Wildfire Database. We will invoke two in-house databases generated by the NASA Short-term Prediction Research and Transition (SPoRT) Center: an observations-driven, climatological run of the Noah land surface model within the NASA Land Information System (i.e., SPoRT-LIS) to depict soil moisture deficits / anomalies, and a satellite-constrained Evaporative Stress Index (ESI) product to denote areas of stressed vegetation. We will mine these datasets associated with lightning-caused (and null) events to determine important relationships, distributions, and delineators that correspond to elevated threat areas for lightning-initiated wildfires.

Wildfire↗
NASA NTRS2012

NASA Tech Briefs, March 2012

The topics include: 1) Spectral Profiler Probe for In Situ Snow Grain Size and Composition Stratigraphy; 2) Portable Fourier Transform Spectroscopy for Analysis of Surface Contamination and Quality Control; 3) In Situ Geochemical Analysis and Age Dating of Rocks Using Laser Ablation-Miniature Mass Spectrometer; 4) Physics Mining of Multi-Source Data Sets; 5) Photogrammetry Tool for Forensic Analysis; 6) Connect Global Positioning System RF Module; 7) Simple Cell Balance Circuit; 8) Miniature EVA Software Defined Radio; 9) Remotely Accessible Testbed for Software Defined Radio Development; 10) System-of-Systems Technology-Portfolio-Analysis Tool; 11) VESGEN Software for Mapping and Quantification of Vascular Regulators; 12) Constructing a Database From Multiple 2D Images for Camera Pose Estimation and Robot Localization; 13) Adaption of G-TAG Software for Validating Touch and Go Asteroid Sample Return Design Methodology; 14) 3D Visualization for Phoenix Mars Lander Science Operations; 15) RxGen General Optical Model Prescription Generator; 16) Carbon Nanotube Bonding Strength Enhancement Using Metal Wicking Process; 17) Multi-Layer Far-Infrared Component Technology; 18) Germanium Lift-Off Masks for Thin Metal Film Patterning; 19) Sealing Materials for Use in Vacuum at High Temperatures; 20) Radiation Shielding System Using a Composite of Carbon Nanotubes Loaded With Electropolymers; 21) Nano Sponges for Drug Delivery and Medicinal Applications; 22) Molecular Technique to Understand Deep Microbial Diversity; 23) Methods and Compositions Based on Culturing Microorganisms in Low Sedimental Fluid Shear Conditions; 24) Secure Peer-to-Peer Networks for Scientific Information Sharing; 25) Multiplexer/Demultiplexer Loading Tool (MDMLT); 26) High-Rate Data-Capture for an Airborne Lidar System; 27) Wavefront Sensing Analysis of Grazing Incidence Optical Systems; 28) Foam-on-Tile Damage Model; 29) Instrument Package Manipulation Through the Generation and Use of an Attenuated-Fluent Gas Fold; 30) Multicolor Detectors for Ultrasensitive Long-Wave Imaging Cameras; 31) Lunar Reconnaissance Orbiter (LRO) Command and Data Handling Flight Electronics Subsystem; and 32) Electro-Optic Segment-Segment Sensors for Radio and Optical Telescopes.

Source record↗
NASA NTRS2011

NASA Instrument Cost/Schedule Model

NASA's Office of Independent Program and Cost Evaluation (IPCE) has established a number of initiatives to improve its cost and schedule estimating capabilities. 12One of these initiatives has resulted in the JPL developed NASA Instrument Cost Model. NICM is a cost and schedule estimator that contains: A system level cost estimation tool; a subsystem level cost estimation tool; a database of cost and technical parameters of over 140 previously flown remote sensing and in-situ instruments; a schedule estimator; a set of rules to estimate cost and schedule by life cycle phases (B/C/D); and a novel tool for developing joint probability distributions for cost and schedule risk (Joint Confidence Level (JCL)). This paper describes the development and use of NICM, including the data normalization processes, data mining methods (cluster analysis, principal components analysis, regression analysis and bootstrap cross validation), the estimating equations themselves and a demonstration of the NICM tool suite.

JCL↗
NASA NTRS2018

The Ocean Carbon States Database: A Proof-of-Concept Application of Cluster Analysis in the Ocean Carbon Cycle

In this paper, we present a database of the basic regimes of the carbon cycle in the ocean, the 'ocean carbon states', as obtained using a data mining/pattern recognition technique in observation-based as well as model data. The goal of this study is to establish a new data analysis methodology, test it and assess its utility in providing more insights into the regional and temporal variability of the marine carbon cycle. This is important as advanced data mining techniques are becoming widely used in climate and Earth sciences and in particular in studies of the global carbon cycle, where the interaction of physical and biogeochemical drivers confounds our ability to accurately describe, understand, and predict CO2 concentrations and their changes in the major planetary carbon reservoirs. In this proof-of-concept study, we focus on using well-understood data that are based on observations, as well as model results from the NASA Goddard Institute for Space Studies (GISS) climate model. Our analysis shows that ocean carbon states are associated with the subtropical-subpolar gyre during the colder months of the year and the tropics during the warmer season in the North Atlantic basin. Conversely, in the Southern Ocean, the ocean carbon states can be associated with the subtropical and Antarctic convergence zones in the warmer season and the coastal Antarctic divergence zone in the colder season. With respect to model evaluation, we find that the GISS model reproduces the cold and warm season regimes more skillfully in the North Atlantic than in the Southern Ocean and matches the observed seasonality better than the spatial distribution of the regimes. Finally, the ocean carbon states provide useful information in the model error attribution. Model air-sea CO2 flux biases in the North Atlantic stem from wind speed and salinity biases in the subpolar region and nutrient and wind speed biases in the subtropics and tropics. Nutrient biases are shown to be most important in the Southern Ocean flux bias.

carbon cycle↗
NASA NTRS2017

Framework for Processing Citizens Science Data for Applications to NASA Earth Science Missions

Citizen science (or crowdsourcing) has drawn much high-level recent and ongoing interest and support. It is poised to be applied, beyond the by-now fairly familiar use of, e.g., Twitter for natural hazards monitoring, to science research, such as augmenting the validation of NASA earth science mission data. This interest and support is seen in the 2014 National Plan for Civil Earth Observations, the 2015 White House forum on citizen science and crowdsourcing, the ongoing Senate Bill 2013 (Crowdsourcing and Citizen Science Act of 2015), the recent (August 2016) Open Geospatial Consortium (OGC) call for public participation in its newly-established Citizen Science Domain Working Group, and NASA's initiation of a new Citizen Science for Earth Systems Program (along with its first citizen science-focused solicitation for proposals). Over the past several years, we have been exploring the feasibility of extracting from the Twitter data stream useful information for application to NASA precipitation research, with both "passive" and "active" participation by the twitterers. The Twitter database, which recently passed its tenth anniversary, is potentially a rich source of real-time and historical global information for science applications. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. Mining the Twitter stream could augment these validation programs and, potentially, help tune existing algorithms. Our ongoing work, though exploratory, has resulted in key components for processing and managing tweets, including the capabilities to filter the Twitter stream in real time, to extract location information, to filter for exact phrases, and to plot tweet distributions. The key step is to process the "precipitation" tweets to be compatible with satellite-retrieved precipitation data. These key components for processing and managing "precipitation" tweets (and additional ones to be developed) are not limited to precipitation, nor are they limited to the Twitter social medium. Indeed, to maximize the value of our work for NASA earth science programs, these components should be generalized and be part of an overall framework for processing citizen science data for science research. In this paper, we outline such a framework.

earth science satellite data↗
NASA NTRS2017

Mars Target Encyclopedia: Information Extraction for Planetary Science

We created a new reference database called the Mars Target Encyclopedia (MTE) that contains compositional information about science targets (usually rocks or soils) on Mars. Users can search for all targets that contain an element (e.g., “calcium”) or mineral (e.g., “hematite”) and see a map view of their spatial locations (see Fig. 1). Clicking on a search result or searching for a specific target of interest (e.g., “Dillinger”) brings up a page that compiles previous findings about its composition (see Fig. 2). The information that populates the MTE was mined from the planetary science literature using information extraction technology. All information in the MTE is linked to the source publication from which it was extracted, so users can easily browse the full context in the original document (Fig. 2).

Singh, K.↗
NASA NTRS2020

Systems Development, Data Mining, and Knowledge Discovery

I worked as a NASA OSTEM virtually during the Fall 2020 term. Working in the IT division under my mentor Dr. Ali Shaykhian, our overall goal for the duration of this internship is to get a better understanding of the Visual Basic Language and how it can be used to make forms and collaboration with other workers to be more dynamics. I also worked with 3 other interns throughout this internship, using Microsoft Office to help each other to get a better understanding of how we approached our projects individually. Every week, my mentor Dr. Ali assigned me a task and a goal to finish by the end of each week. My overall project was figuring out a way to make email submissions and emails in general more dynamic for the NASA database. For example, instead of only using the same generic email for hundreds of different workers, a code can be used to send an individual email with more personalization such as each recipient’s name and personal info. I have also been assigned to create an email graphical user interface by the end of this internship. For these tasks to be made possible, I had to learn more about the capabilities of Microsoft Office, such as Macros in Microsoft Excel and Visual Basic in both Microsoft Excel and Word. Although it was challenging at first, I developed new programming skilled in a new language and actually made it possible to create this code along the way. Alongside doing the individual assignments, Dr. Ali also assigned up to replicate other intern’s work to get a better understanding of their approach and learn how to do it ourselves.

Janelisse Morales Gonzalez↗
NASA NTRSDate not supplied

Assessing the Use of UAS-Related Terms in ASRS Using Seed Topic Modeling

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential system that disseminates reports received from personnel involved in aviation operations after de-identifying them. These reports are used by the community to improve overall aviation system safety. Aim: We propose and execute an experiment to assess the use of seed term topic modeling over the database narratives to identify Unmanned Aircraft System (UAS) reports. The use of seed term topic modeling enables users to identify groups of conceptually similar narratives associated to a topic of their interest. Method: We use a collection of narratives, expert-selected words, and report metadata that separates UAS from non-UAS reports to assess if seed topic modeling can be used to improve ASRS searches. Results: For simpler queries, seed topic search observes a higher recall and lower precision than the existing DBOL (DataBase OnLine) search in operation. However, the best results are obtained when seed topic search is used as a search suggestion system to be executed on the DBOL. Conclusion: Utilizing a combination of both the existing method and the proposed method, users can expand their search vocabulary about subjects of interest while improving the quality of results.

Text Mining↗
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↗