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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Data And Informatics Working Group On Virtual Data Integration Workshop Report

This report is the outcome of a workshop that was commissioned by the Department of Energy’s Climate and Environmental Sciences Division (CESD) to examine current and future data infrastructure requirements that would be foundational to achieving CESD’s scientific mission goals. Over the past several years, data volumes in CESD disciplines have risen sharply to unprecedented levels (tens of petabytes). So too has the complexity and diversity of the research data (simulation, observation, and reanalysis) needing to be captured, stored, verified, analyzed, and integrated. With the trends of increased data volume (in the hundreds of petabytes), more complex analysis processes, and growing crossdisciplinary collaborations, it is timely to investigate whether the CESD community has the right computational and data support to realize the full scientific potential from its data collections. In recognition of the challenges, a partnership is forming across CESD and with national and international agencies to investigate the viability of creating an integrated, collaborative data infrastructure: a virtual laboratory. The overarching goal of this report is to identify the community’s key data technology requirements and high-priority development needs for sustaining and growing their scientific discovery potential. The report also aims to map these requirements to existing solutions and to identify gaps in current services, tools, and infrastructure that will need to be addressed in the short, medium, and long term so as not to impede scientific progress

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Collaborative Platforms Aid Emergency Decision Making

Terra. Aqua. Cloudsat. Landsat. NASA runs and partners in many missions dedicated to monitoring the Earth, and the tools used in these missions continuously return data on everything from shifts in temperature to cloud formation to pollution levels over highways. The data are of great scientific value, but they also provide information that can play a critical role in decision making during times of crisis. Real-time developments in weather, wind, ocean currents, and numerous other conditions can have a significant impact on the way disasters, both natural and human-caused, unfold. "NASA has long recognized the need to make its data from real-time sources compatible and accessible for the purposes of decision making," says Michael Goodman, who was Disasters Program manager at NASA Headquarters from 2009-2012. "There are practical applications of NASA Earth science data, and we d like to accelerate the use of those applications." One of the main obstacles standing in the way of eminently practical data is the fact that the data from different missions are collected, formatted, and stored in different ways. Combining data sets in a way that makes them useful for decision makers has proven to be a difficult task. And while the need for a collaborative platform is widely recognized, very few have successfully made it work. Dave Jones, founder and CEO of StormCenter Communications Inc., which consults with decision makers to prepare for emergencies, says that "when I talk to public authorities, they say, If I had a nickel for every time someone told me they had a common operating platform, I d be rich. But one thing we ve seen over the years is that no one has been able to give end users the ability to ingest NASA data sets and merge them with their own."

Source record↗
DOE OSTI2022

First Plant Cell Atlas symposium report

The Plant Cell Atlas (PCA) community hosted a virtual symposium on December 9 and 10, 2021 on single cell and spatial omics technologies. The conference gathered almost 500 academic, industry, and government leaders to identify the needs and directions of the PCA community and to explore how establishing a data synthesis center would address these needs and accelerate progress. This report details the presentations and discussions focused on the possibility of a data synthesis center for a PCA and the expected impacts of such a center on advancing science and technology globally. Community discussions focused on topics such as data analysis tools and annotation standards; computational expertise and cyber-infrastructure; modes of community organization and engagement; methods for ensuring a broad reach in the PCA community; recruitment, training, and nurturing of new talent; and the overall impact of the PCA initiative. These targeted discussions facilitated dialogue among the participants to gauge whether PCA might be a vehicle for formulating a data synthesis center. The conversations also explored how online tools can be leveraged to help broaden the reach of the PCA (i.e., online contests, virtual networking, and social media stakeholder engagement) and decrease costs of conducting research (e.g., virtual REU opportunities). Major recommendations for the future of the PCA included establishing standards, creating dashboards for easy and intuitive access to data, and engaging with a broad community of stakeholders. The discussions also identified the following as being essential to the PCA's success: identifying homologous cell-type markers and their biocuration, publishing datasets and computational pipelines, utilizing online tools for communication (such as Slack), and user-friendly data visualization and data sharing. In conclusion, the development of a data synthesis center will help the PCA community achieve these goals by providing a centralized repository for existing and new data, a platform for sharing tools, and new analytical approaches through collaborative, multidisciplinary efforts. A data synthesis center will help the PCA reach milestones, such as community-supported data evaluation metrics, accelerating plant research necessary for human and environmental health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Large-Scale Visualization of 3D Unstructured Groundwater Model Using Cave Automated Virtual Environment

The immersive three-dimensional (3D) virtual reality (VR) visualization of groundwater models allows us to deepen our understanding of aquifer systems and provide better solutions to present groundwater-related problems, such as groundwater recharge, water quality, and sustainability. Visualization assists in accurately developing groundwater models and revealing important subsurface features, including faulting, folding, and unconformity. However, assessing model accuracy poses challenges due to the complexity of geology and groundwater systems. This research demonstrates a workflow to visualize and analyze raw 3D unstructured groundwater model data using an immersive Cave Automated Virtual Environment (CAVE). To visualize the unstructured groundwater model data, the raw dataset is converted into interactive CAVE-compatible formats utilizing a set of tools: ParaView, Blender, and Unity. This enables researchers to immerse themselves in the data, identifying influential patterns and relationships. e resulting insights can inform the development of sophisticated machine-learning models for groundwater level prediction. The CAVE’s immersive capabilities allow intuitive exploration from various perspectives, providing a more holistic understanding of the factors affecting groundwater levels. These insights are crucial to improve predictive models. The CAVE results also facilitate collaborative analysis and have potential applications in training and education. is research demonstrates the value of immersive VR tools such as the CAVE for unraveling intricacies within high-dimensional scientific data to drive real-world forecasting and modeling applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

KBase: The DOE Systems Biology Knowledgebase: An Overview

The Department of Energy Systems Biology Knowledgebase (KBase) is a knowledge creation and discovery environment designed for both biologists and bioinformaticians. KBase integrates a large variety of public data and analysis tools into an easy-to-use graphical user interface and leverages DOE computational infrastructure to perform sophisticated systems biology analyses. KBase is a freely available system that enables scientists to upload their own data, analyze it alongside collaborator and public data, build validated systems biology models, and share workflows and conclusions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Working Group on Virtual Data Integration

This report is the outcome of a workshop commissioned by the U.S. Department of Energy’s (DOE) Climate and Environmental Sciences Division (CESD) to examine current and future data infrastructure requirements foundational for achieving CESD scientific mission goals in advancing a robust, predictive understanding of Earth’s climate and environmental systems. Over the past several years, data volumes in CESD disciplines have risen sharply to unprecedented levels (tens of petabytes). Moreover, the complexity and diversity of this research data— including simulations, observations, and reanalysis— have grown significantly, posing new challenges for data capture, storage, verification, analysis, and integration. With the trends of increased data volume (in the hundreds of petabytes), more complex analysis processes, and growing cross-disciplinary collaborations, it is timely to investigate whether the CESD community has the computational and data support needed to fully realize the scientific potential of its data collections. In recognition of the challenges, a partnership is forming across CESD and among national and international agencies to examine the viability of creating an integrated, collaborative data infrastructure: a Virtual Laboratory. The overarching goal of this report is to identify the community’s key data technology requirements and high-priority development needs for sustaining and growing its scientific discovery potential. The report also aims to map these requirements to existing solutions and to identify gaps in current services, tools, and infrastructure that will need to be addressed in the short, medium, and long term to advance scientific progress.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

The Doppler Wind and Temperature Sounder (DWTS) Flight Evaluation and Experiments (TES-16,17)

The Doppler Wind and Temperature Sounder instrument (DWTS) developed by Global Atmospheric Technologies and Sciences (GATS) is a simple yet powerful tool with the potential to become a new window through which the study of upper atmosphere dynamics can occur. Based around a defense-grade infrared camera peering through a static gas cell used as a scanning spectral filter, a DWTS instrument can infer wind velocities and kinetic temperatures throughout the stratosphere and lower thermosphere. The DWTS achieves this scanning by measuring the induced Doppler shift and Doppler broadening of emissions as they pass through the DWTS field of view (Gordley, Marshall, 2011). The DWTS holds promise in improving accuracy in weather determination among other terrestrial benefits, and the core technology can be easily adapted to study the dynamics of other planetary atmospheres. In partnership with GATS, NOAA, and other collaborators, NASA Ames and the Nano-Orbital Workshop (NOW) group have been working to evaluate the DWTS instrument on orbit and optimize it as a flexible payload for nanosatellites. The first mission selected for DWTS technical evaluation is preparing for flight in early 2024, which will be followed by a more capable science mission in 2025, with both missions being part of the TES-n/NOW heritage flight series. The first rapid technology demonstration flight, TES-16/DWTS-A, will demonstrate a single DWTS instrument in an approximately 2U payload volume. With an estimated power consumption of 50 watts, the instrument will maintain the imaging sensor plane at 80K during instrument performance evaluation periods using an integrated Stirling cryocooler. Data from DWTS will be captured and processed via a NOW-designed custom data interface unit before being transmitted via S-band radio back to select ground stations, with instrument command and control maintained via L-band global-coverage radio. The subsequent TES-17/DWTS-B mission will be a dedicated science mission tasked with validating the instrument’s full altitude coverage capabilities, currently estimated from 20 to 200 km during both day and night. This new atmospheric observational capability will come from a single small satellite equipped with three DWTS imagers, each hosting a different gas cell chemistry, to form a complete instrument. The intention of this flight series, and one of NASA’s interests in this instrument, is not only to advance Earth atmospheric dynamics, but to advance a Martian atmospheric study instrument as well (Colaprete, Gordley, et al) which, if successful, would greatly further understanding of Martian atmospheric dynamics. This document describes the flight series in detail, including challenges facing the TES-16 flight tests and the projected challenges and application of Mars study. Additional detail regarding the possible applications of a Cognitive Communication technique in current flight development by NOW collaborators at the NASA Glenn Research Center is also discussed, including the implications of using an automated User Initiated Service (UIS) protocol to maximize the data collected per orbit.

DWTS Nano-satellite TES-n↗
NASA NTRS1999

A Virtual Collaborative Environment for Mars Surveyor Landing Site Studies

Over the past year and a half, the Center for Mars Exploration (CMEX) at NASA Ames Research Center (ARC) has been working with the Mars Surveyor Project Office at JPL to promote interactions among the planetary community and to coordinate landing site activities for the Mars Surveyor Project Office. To date, CMEX has been responsible for organizing the first two Mars Surveyor Landing Site workshops, web-archiving resulting information from these workshops, aiding in science evaluations of candidate landing sites, and serving as a liaison between the community and the Project. Most recently, CMEX has also been working with information technologists at Ames to develop a state-of-the-art collaborative web site environment to foster interaction of interested members of the planetary community with the Mars Surveyor Program and the Project Office. The web site will continue to evolve over the next several years as new tools and features are added to support the ongoing Mars Surveyor missions.

Gulick, V.C.↗
NASA NTRS1999

A Virtual Collaborative Environment for Mars Surveyor Landing Site Studies

Over the past year and a half, the Center for Mars Exploration (CMEX) at NASA Ames Research Center (ARC) has been working with the Mars Surveyor Project Office at JPL to promote interactions among the planetary community and to coordinate landing site activities for the Mars Surveyor Project Office. To date, CMEX has been responsible for organizing the first two Mars Surveyor Landing Site workshops, web-archiving resulting information from these workshops, aiding in science evaluations of candidate landing sites, and serving as a liaison between the community and the Project. Most recently, CMEX has also been working with information technologists at Ames to develop a state-of-the-art collaborative web site environment to foster interaction of interested members of the planetary community with the Mars Surveyor Program and the Project Office. The web site will continue to evolve over the next several years as new tools and features are added to support the ongoing Mars Surveyor missions.

Gulick, V. C.↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, altered photoperiods and many other abiotic stressors. Open Science is the practice of making research available to all, while respecting diverse cultures, and fostering collaborations with equity. 2023 is the ‘Year of Open Science’, and NASA has a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) within NASA’s Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. GeneLab started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository, providing detailed metadata on investigation, sample, and assay levels. The addition of ALSDA to OSDR expands plant data analysis capabilities across both phenotypic and ‘omics data. Today, OSDR hosts 62+ plant datasets and has enabled 58 peer-reviewed publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate and set new standards for space-relevant data and metadata. The AWGs welcome any ASPB members interested in contributing plant expertise for space biology, and to serve as subject matter experts as we establish the framework for modern plant data archiving. Investigators are invited to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗
DOE OSTI2024

Water Atomized H13 Tool Steel Powders for Binder Jet Additive Manufacturing

ORNL (Contractor) and Ametek, Inc. (Participant) collaborated to determine the feasibility of adopting water atomized H13 tool steel powder for binder jet additive manufacturing (BJAM) for tooling applications. The capability to utilize water atomized powders can substantially reduce powder expenses for BJAM, enabling process competitiveness with casting whilst enabling new applications of high-temperature, net-shaped components.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, altered photoperiods and many other abiotic stressors. Open Science is the practice of making research available to all, while respecting diverse cultures, to foster collaborations with equity. NASA has declared 2023 as the ‘Year of Open Science’ and created a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) within the Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. GeneLab started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository, providing detailed metadata on investigation, sample, and assay levels. The addition of ALSDA to OSDR expands plant data analysis capabilities across both phenotypic and ‘omics data. Today, OSDR hosts 62+ plant datasets and has enabled 58 peer-reviewed publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate with community members and set new standards for space-relevant data and metadata. The AWGs welcome any ASGSR members interested in contributing plant expertise for space biology, and to serve as subject matter experts as we establish the framework for modern plant data archiving. Investigators are encouraged to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗
DOE OSTI2017

Brief history of agricultural systems modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. Furthermore, the lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2016

Brief History of Agricultural Systems Modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the next generation models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

agricultural systems↗
DOE OSTI2017

Predicting a Drug’s Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data

Membrane permeability is a key property to consider during the drug design process, and particularly vital when dealing with small molecules that have intracellular targets as their efficacy highly depends on their ability to cross the membrane. In this work, we describe the use of umbrella sampling Molecular Dynamics (MD) computational modeling to comprehensively assess the passive permeability profile of a range of compounds through a lipid bilayer. The model was initially calibrated through in vitro validation studies employing a Parallel Artificial Membrane Permeability Assay (PAMPA). The model was subsequently evaluated for its quantitative prediction of permeability profiles for a series of custom synthesized and closely related compounds. The results exhibited substantially improved agreement with the PAMPA data, relative to alternative existing methods. Our work introduces a computational model that underwent progressive molding and fine-tuning as a result of its synergistic collaboration with numerous in vitro PAMPA permeability assays. The presented computational model introduces itself as a useful, predictive tool for permeability prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

SU-F-T-163: Improve Proton Therapy Efficiency: Report of a Workshop

Purpose: The technology of proton therapy, especially the pencil beam scanning technique, is evolving very quickly. However, the efficiency of proton therapy seems to lag behind conventional photon therapy. The purpose of the abstract is to report on the findings of a workshop on improvement of QA, planning and treatment efficiency in proton therapy. Methods: A panel of physicists, clinicians, and vendor representatives from over 18 institutions in the United States and internationally were convened in Knoxville, Tennessee in November, 2015. The panel discussed several topics on how to improve proton therapy efficiency, including 1) lean principle and failure mode and effects analysis, 2) commissioning and machine QA, 3) treatment planning, optimization and evaluation, 4) patient positioning and IGRT, 5) vendor liaison and machine availability, and 6) staffing, education and training. Results: The relative time needed for machine QA, treatment planning & check in proton therapy was found to range from 1 to 2.5 times of that in photon therapy. Current status in proton QA, planning and treatment was assessed. Key areas for efficiency improvement, such as elimination of unnecessary QA items or steps and development of efficient software or hardware tools, were identified. A white paper to summarize our findings is being written. Conclusion: It is critical to improve efficiency by developing reliable proton beam lines, efficient software tools on treatment planning, optimization and evaluation, and dedicated proton QA device. Conscious efforts and collaborations from both industry leaders and proton therapy centers are needed to achieve this goal and further advance the technology of proton therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Performance and Regulation of Photosystem II Energy Conversion in Oxygenic Phototrophs: From PSII Crystals to Living Cells

New information not previously accessible to answer remaining questions about the mechanism of PSII catalyzed water oxidation, its intrinsic inefficiencies and the regulation of PSII in living cells were obtained. This effort was done in collaboration with an expert in PSII crystallography (led by Prof. Petra Fromme, ASU). We applied a set of tools for fluorometric, oximetric, potentiometric and absorbance measurements. Transformational knowledge from PSII crystals and a powerful new fluorescence technique were developed. Three aims were addressed and 18 peer-reviewed publications were generated.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Space-based Science Operations Grid Prototype

Grid technology is the up and coming technology that is enabling widely disparate services to be offered to users that is very economical, easy to use and not available on a wide basis. Under the Grid concept disparate organizations generally defined as "virtual organizations" can share services i.e. sharing discipline specific computer applications, required to accomplish the specific scientific and engineering organizational goals and objectives. Grids are emerging as the new technology of the future. Grid technology has been enabled by the evolution of increasingly high speed networking. Without the evolution of high speed networking Grid technology would not have emerged. NASA/Marshall Space Flight Center's (MSFC) Flight Projects Directorate, Ground Systems Department is developing a Space-based Science Operations Grid prototype to provide to scientists and engineers the tools necessary to operate space-based science payloads/experiments and for scientists to conduct public and educational outreach. In addition Grid technology can provide new services not currently available to users. These services include mission voice and video, application sharing, telemetry management and display, payload and experiment commanding, data mining, high order data processing, discipline specific application sharing and data storage, all from a single grid portal. The Prototype will provide most of these services in a first step demonstration of integrated Grid and space-based science operations technologies. It will initially be based on the International Space Station science operational services located at the Payload Operations Integration Center at MSFC, but can be applied to many NASA projects including free flying satellites and future projects. The Prototype will use the Internet2 Abilene Research and Education Network that is currently a 10 Gb backbone network to reach the University of Alabama at Huntsville and several other, as yet unidentified, Space Station based science experimenters. There is an international aspect to the Grid involving the America's Pathway (AMPath) network, the Chilean REUNA Research and Education Network and the University of Chile in Santiago that will further demonstrate how extensive these services can be used. From the user's perspective, the Prototype will provide a single interface and logon to these varied services without the complexity of knowing the where's and how's of each service. There is a separate and deliberate emphasis on security. Security will be addressed by specifically outlining the different approaches and tools used. Grid technology, unlike the Internet, is being designed with security in mind. In addition we will show the locations, configurations and network paths associated with each service and virtual organization. We will discuss the separate virtual organizations that we define for the varied user communities. These will include certain, as yet undetermined, space-based science functions and/or processes and will include specific virtual organizations required for public and educational outreach and science and engineering collaboration. We will also discuss the Grid Prototype performance and the potential for further Grid applications both space-based and ground based projects and processes. In this paper and presentation we will detail each service and how they are integrated using Grid

Bradford, Robert N.↗