Results for “Data Visualization” — Page 33

NASA NTRS2003

Accelerating Large Data Analysis By Exploiting Regularities

We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical to Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid-body transformation of another. Our algorithms can also discover rigid-body motion of meshes in time-series data. Next, we describe a data model where we can utilize the results from the discovery process in order to accelerate large data visualizations. Where possible, we replace general curvilinear zones with rectilinear or cylindrical zones. In rigid-body motion cases we replace a time-series of meshes with a transformed mesh object where a reference mesh is dynamically transformed based on a given time value in order to satisfy geometry requests, on demand. The data model enables us to make these substitutions and dynamic transformations transparently with respect to the visualization algorithms. We present results with large data sets where we combine our mesh replacement and transformation techniques with out-of-core paging in order to achieve significant speed-ups in analysis.

Moran, Patrick J.↗

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗

NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, 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.

open access↗

NASA NTRS2008

Experimental Investigation of the DLR-F6 Transport Configuration in the National Transonic Facility

An experimental aerodynamic investigation of the DLR (German Aerospace Center) F6 generic transport configuration has been conducted in the NASA NTF (National Transonic Facility) for CFD validation within the framework of the AIAA Drag Prediction Workshop. Force and moment, surface pressure, model deformation, and surface flow visualization data have been obtained at Reynolds numbers of both 3 million and 5 million. Flow-through nacelles and a side-of-body fairing were also investigated on this wing-body configuration. Reynolds number effects on trailing edge separation have been assessed, and the effectiveness of the side-of-body fairing in eliminating a known region of separated flow has been determined. Data obtained at a Reynolds number of 3 million are presented together for comparison with data from a previous wind tunnel investigation in the ONERA S2MA facility. New surface flow visualization capabilities have also been successfully explored and demonstrated in the NTF for the high pressure and moderately low temperature conditions required in this investigation. Images detailing wing surface flow characteristics are presented.

Gatlin, Gregory M.↗

NASA NTRSDate not supplied

Material Property Estimation in Thin Battery Components Using Guided Wave Measurement, Experimental Dispersion Curve Extraction and Finite Element Modeling

At NASA, we are investigating nondestructive evaluation (NDE) and structural health monitoring (SHM) techniques to detect precursors of thermal runaway failure in lithium metal based lithium ion batteries (LIB). The approach is centered on computational simulation models to guide inspection and aid in interpretation of results. Since lithium metal LIBs have combinations of solid and fluid-filled porous materials, obtaining accurate material properties is both challenging and critical for successful simulation of battery inspection. To this end, we investigated a multi-verification approach for material characterization of thin battery components. First, a laser Doppler vibrometer (LDV) was used to measure guided wave fields in thin (microns-thick) battery components subject to broadband excitation. The time-space wavefield data was converted to frequency-wave number data to extract guided wave dispersion curves. A data visualization and post processing graphics user interface (GUI) was developed at NASA to aid the data exploration and analysis. Due to the thinness of the samples, low frequency-thickness-product plate wave approximations allowed for the calculation of closed-form solutions for material elastic property estimation. These approximations were then verified by calculating the Lamb wave solutions using the previously obtained material properties. Finally, the estimated elastic material properties were implemented in a COMSOL simulation model, and dispersion curves were extracted from simulation results. The dispersion curves and derived material properties were then compared to the analysis results from the experimental data. These comparisons informed on the accuracy of the measured material properties and helped demonstrate the accuracy of the finite element analysis (FEA) computational models. This assessment will prove vital when we start simulating more complex multi-layer components and poroelastic media. This paper gives a brief background of the problem space, outlines the workflow for data analysis and verification, shows results from the workflow, and gives an overview of future plans for simulation of lithium metal LIB inspection.

Peter Juarez↗

NASA NTRS2017

Weather from 250 Miles Up: Visualizing Precipitation Satellite Data (and Other Weather Applications) Using CesiumJS

Geospatial weather visualization remains predominately a two-dimensional endeavor. Even popular advanced tools like the Nullschool Earth display 2-dimensional fields on a 3-dimensional globe. Yet much of the observational data and model output contains detailed three-dimensional fields. In 2014, NASA and JAXA (Japanese Space Agency) launched the Global Precipitation Measurement (GPM) satellite. Its two instruments, the Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) observe much of the Earth's atmosphere between 65 degrees North Latitude and 65 degrees South Latitude. As part of the analysis and visualization tools developed by the Precipitation Processing System (PPS) Group at NASA Goddard, a series of CesiumJS [Using Cesium Markup Language (CZML), JavaScript (JS) and JavaScript Object Notation (JSON)] -based globe viewers have been developed to improve data acquisition decision making and to enhance scientific investigation of the satellite data. Other demos have also been built to illustrate the capabilities of CesiumJS in presenting atmospheric data, including model forecasts of hurricanes, observed surface radar data, and gridded analyses of global precipitation. This talk will present these websites and the various workflows used to convert binary satellite and model data into a form easily integrated with CesiumJS.

web applications↗

DOE OSTI2024

Remote operation of the DIII-D National Fusion Facility

Abstract Full remote scientific operation of the DIII-D National Fusion Facility is now possible through significant advances in the computer science hardware and software infrastructure made over the last decade. Capabilities around information visualization, data movement, and communication have all been enhanced. The level of capability deployed to remotely operate DIII-D required an infrastructure advancement over what had previously been achieved in the fusion community. The large quantity of real-time data that is automatically displayed on DIII-D’s control room screens can now be visualized by remote participants via web-based applications. New audio/video solutions using the VoIP and instant messaging application Discord have been implemented to mimic the dynamic and ad-hoc scientific conversations that are critical in successfully operating an experimental campaign. Discord’s ability for a user to rapidly move between audio channels, text with images, and share screens is a significant enhancement over traditional videoconferencing tools. In addition, multiple combinations of broadcast audio are made available via a web-based application to allow remote participants to simultaneously listen to general announcements/sounds while conducting their own specific conversations. Secure methodologies have been put into place to allow remote control of hardware including DIII-D’s plasma control system application. Secure methods also included the ability of the on-site team to closely coordinate their work with remote team members which has been enhanced through extensions to the wireless network and the use of tablet computers for audio/video/screen sharing. However, no amount of software can fully replace the need for ‘hands on hardware.’ This infrastructure was severely stress tested during the COVID-19 pandemic where occupancy of the DIII-D control room was restricted. Operational efficiency during the pandemic, measured in discharges per hour, remained high (3.8 ± 0.8) compared to values obtained pre-pandemic (3.7 ± 0.8).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1984

Propfan experimental data analysis

A data reduction method, which is consistent with the performance prediction methods used for analysis of new aircraft designs, is defined and compared to the method currently used by NASA using data obtained from an Ames Res. Center 11 foot transonic wind tunnel test. Pressure and flow visualization data from the Ames test for both the powered straight underwing nacelle, and an unpowered contoured overwing nacelle installation is used to determine the flow phenomena present for a wind mounted turboprop installation. The test data is compared to analytic methods, showing the analytic methods to be suitable for design and analysis of new configurations. The data analysis indicated that designs with zero interference drag levels are achieveable with proper wind and nacelle tailoring. A new overwing contoured nacelle design and a modification to the wing leading edge extension for the current wind tunnel model design are evaluated. Hardware constraints of the current model parts prevent obtaining any significant performance improvement due to a modified nacelle contouring. A new aspect ratio wing design for an up outboard rotation turboprop installation is defined, and an advanced contoured nacelle is provided.

Vernon, David F.↗

NASA NTRS2003

Multi-Disciplinary Analysis for Future Launch Systems Using NASA's Advanced Engineering Environment (AEE)

A new engineering environment constructed for the purposes of analyzing and designing Reusable Launch Vehicles (RLVs) is presented. The new environment has been developed to allow NASA to perform independent analysis and design of emerging RLV architectures and technologies. The new Advanced Engineering Environment (AEE) is both collaborative and distributed. It facilitates integration of the analyses by both vehicle performance disciplines and life-cycle disciplines. Current performance disciplines supported include: weights and sizing, aerodynamics, trajectories, propulsion, structural loads, and CAD-based geometries. Current life-cycle disciplines supported include: DDT&E cost, production costs, operations costs, flight rates, safety and reliability, and system economics. Involving six NASA centers (ARC, LaRC, MSFC, KSC, GRC and JSC), AEE has been tailored to serve as a web-accessed agency-wide source for all of NASA's future launch vehicle systems engineering functions. Thus, it is configured to facilitate (a) data management, (b) automated tool/process integration and execution, and (c) data visualization and presentation. The core components of the integrated framework are a customized PTC Windchill product data management server, a set of RLV analysis and design tools integrated using Phoenix Integration's Model Center, and an XML-based data capture and transfer protocol. The AEE system has seen production use during the Initial Architecture and Technology Review for the NASA 2nd Generation RLV program, and it continues to undergo development and enhancements in support of its current main customer, the NASA Next Generation Launch Technology (NGLT) program.

Monell, D.↗

DOE OSTI2025

Designing a User Interface for Real-Time Magnetometer Data Acquisition

The Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) is a next-generation quantum sensor designed to search for ultralight dark matter and explore new frontiers in quantum mechanics. Due to the experiment s sensitivity to magnetic interference, a magnetometer trolley system was developed to scan magnetic fields along a vacuum tube. Interacting with the system required command-line inputs, creating usability challenges. To improve accessibility and streamline data acquisition, I developed a graphical user interface (GUI) using Python and the customtkinter library. The GUI supports real-time data display, state/mode switching, command execution, and CSV file management. I collaborated with another intern to integrate data visualization features into the GUI, allowing users to generate 3D plots of post-acquisition magnetic field data. In the future, I aim to fix the real-time plotting feature as it results in an unresponsive GUI.

Mendez, Milagros [DuPage Coll.]↗

NASA NTRS2019

Lunar Science for Landed Missions Sites Visualized with NASA's Moon Trek Portal

The Lunar Science for Landed Missions Workshop, held in January 2018 at NASA Ames, asked the important question, “Where should we explore next on the Moon?” Representatives from NASA, other international space agencies, commercial exploration companies, and the international lunar science and exploration communities gathered to present and debate the scientific and exploration relevance of a range of potential future lunar landing sites. From this workshop, a list of representative high-value sites was reported. In this presentation, we will present the sites and summarize their individual merits as described in the workshop report. We will augment this with detailed visualizations of each of the sites through the capabilities of NASA’s Moon Trek (https://trek.nasa.gov/moon) data visualization and analysis portal with the goal of highlighting each site’s outstanding geomorphological features along with of the scientific and exploration contexts of each site.

Day, Brian↗

DOE OSTI2025

Designing a User Interface for Real-Time Magnetometer Data Acquisition

The Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) is a next-generation quantum sensor designed to search for ultralight dark matter and explore new frontiers in quantum mechanics. Due to the experiment’s sensitivity to magnetic interference, a magnetometer trolley system was developed to scan magnetic fields along a vacuum tube. Interacting with the system required command-line inputs, creating usability challenges. To improve accessibility and streamline data acquisition, I developed a graphical user interface (GUI) using Python and the customtkinter library. The GUI supports real-time data display, state/mode switching, command execution, and CSV file management. I collaborated with another intern to integrate data visualization features into the GUI, allowing users to generate 3D plots of post-acquisition magnetic field data. In the future, I aim to fix the real-time plotting feature as it results in an unresponsive GUI.

Mendez, Milagros [DuPage Coll.]↗

NASA NTRS1992

A fuzzy measure approach to motion frame analysis for scene detection

This paper addresses a solution to the problem of scene estimation of motion video data in the fuzzy set theoretic framework. Using fuzzy image feature extractors, a new algorithm is developed to compute the change of information in each of two successive frames to classify scenes. This classification process of raw input visual data can be used to establish structure for correlation. The algorithm attempts to fulfill the need for nonlinear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments.

Leigh, Albert B.↗

NASA NTRS1992

Automated digital motion-video abstraction - A fuzzy measure approach

This paper addresses a solution to the problem of scene estimation of motion video data in the fuzzy set theoretic framework. Using fuzzy image feature extractors, a new algorithm is developed to compute the change of information in each of two successive frames to classify scenes. This classification process of raw input visual data can be used to establish structure for correlation. The algorithm attempts to fulfill the need for non-linear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments.

Leigh, Albert B.↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

NASA NTRS2011

NASA's Hyperwall Revealing the Big Picture

NASA:s hyperwall is a sophisticated visualization tool used to display large datasets. The hyperwall, or video wall, is capable of displaying multiple high-definition data visualizations and/or images simultaneously across an arrangement of screens. Functioning as a key component at many NASA exhibits, the hyperwall is used to help explain phenomena, ideas, or examples of world change. The traveling version of the hyperwall is typically comprised of nine 42-50" flat-screen monitors arranged in a 3x3 array (as depicted below). However, it is not limited to monitor size or number; screen sizes can be as large as 52" and the arrangement of screens can include more than nine monitors. Generally, NASA satellite and model data are used to highlight particular themes in atmospheric, land, and ocean science. Many of the existing hyperwall stories reveal change across space and time, while others display large-scale still-images accompanied by descriptive, story-telling captions. Hyperwall content on a variety of Earth Science topics already exists and is made available to the public at: eospso.gsfc.nasa.gov/hyperwall. Keynote and PowerPoint presentations as well as Summary of Story files are available for download on each existing topic. New hyperwall content and accompanying files will continue being developed to promote scientific literacy across a diverse group of audience members. NASA invites the use of content accessible through this website but requests the user to acknowledge any and all data sources referenced in the content being used.

Sellers, Piers↗

NASA NTRS1990

Expert systems and imaging - NASA's start-up work in intelligent image management

The problem of image management for NASA's Space Shuttle program and the future role of expert systems are considered. The difficulties involved in the storage and retrieval of the estimated 100,000 color photographs used for verification of essential installation, test, and repair procedures for Space Shuttle prelaunch are discussed. It is noted that the collection, classification, storage, and retrieval of high-quality pictoral images is possible through the use of pixels and an expert systems-assisted relational data base management system. The development of a query by pictorial example process is discussed. The NASA Intelligent Interactive Visual Data Base Management Systems project is detailed. It aims to conduct a systems analysis of a large-scale traditional image processing operation, collect data on critical human performance factors in the areas of image creation, processing, classification, storage, and retreival, and validate the attributes identified through the creation and testing of prototype models.

Ragusa, James M.↗

NASA NTRS2007

Using Selection Pressure as an Asset to Develop Reusable, Adaptable Software Systems

The Goddard Earth Sciences Data and Information Services Center (GES DISC) at NASA has over the years developed and honed several reusable architectural components for supporting large-scale data centers with a large customer base. These include a processing system (S4PM) and an archive system (S4PA) based upon a workflow engine called the Simple Scalable Script based Science Processor (S4P) and an online data visualization and analysis system (Giovanni). These subsystems are currently reused internally in a variety of combinations to implement customized data management on behalf of instrument science teams and other science investigators. Some of these subsystems (S4P and S4PM) have also been reused by other data centers for operational science processing. Our experience has been that development and utilization of robust interoperable and reusable software systems can actually flourish in environments defined by heterogeneous commodity hardware systems the emphasis on value-added customer service and the continual goal for achieving higher cost efficiencies. The repeated internal reuse that is fostered by such an environment encourages and even forces changes to the software that make it more reusable and adaptable. Allowing and even encouraging such selective pressures to software development has been a key factor In the success of S4P and S4PM which are now available to the open source community under the NASA Open source Agreement

Berrick, Stephen↗