Results for “knowledge reuse” — Page 7

NASA NTRS1993

Using AI/expert system technology to automate planning and replanning for the HST servicing missions

This paper describes a knowledge-based system that has been developed to automate planning and scheduling for the Hubble Space Telescope (HST) Servicing Missions. This new system is the Servicing Mission Planning and Replanning Tool (SM/PART). SM/PART has been delivered to the HST Flight Operations Team (FOT) at Goddard Space Flight Center (GSFC) where it is being used to build integrated time lines and command plans to control the activities of the HST, Shuttle, Crew and ground systems for the next HST Servicing Mission. SM/PART reuses and extends AI/expert system technology from Interactive Experimenter Planning System (IEPS) systems to build or rebuild time lines and command plans more rapidly than was possible for previous missions where they were built manually. This capability provides an important safety factor for the HST, Shuttle and Crew in case unexpected events occur during the mission.

Bogovich, L.↗

NASA NTRSDate not supplied

Expanding Repository Data Available For Sharing and Knowledge Discovery

Some of the hardest space biology and space health challenges require data-intensive, bioinformatic, meta-analytical, and computer-assisted research approaches. These challenges include examining interdisciplinary space life science research across experiments and across interacting spaceflight hazards (radiation, altered gravity, confinement, hostile-closed environments, distance-duration from Earth). The approaches to confront these challenges involve mining multiple datasets simultaneously from various hierarchical organizations of biological complexity, all while concurrently evaluating how experimental design factors affect endpoints of standard assays. To enable this field, it is essential that principal investigators (PIs) submit data in a structure so it can be maximally re-used. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make publicly available all non-human space-relevant biological data. ALSDA must also ensure data are open-access, and maximally findable, accessible, interoperable, and reusable (FAIR). The scope of ALSDA data collected and submitted by PIs include subject and study design metadata, assay metadata parameters, raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). ALSDA recently integrated into a collaborative group of Open Science projects to facilitate a suite of new tools and workflows that will improve data submission, accessibility, and reusability by implementing digital data submission agreements, and adopting the data management system originally developed by NASA GeneLab. ALSDA intends to bring current biological repository data and all future collected data into this new scientific data reuse reality. This new suite of tools will enable ALSDA to deploy a science curation system using scientific assay configurations for the data submission portal. It will capture essential assay parameters according to established standards in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. Data submissions can be brought into cutting-edge informatic analysis portals to enable mining of physiological, behavioral, biochemical, and imaging datasets in conjunction with ‘omics-level datasets. As ALSDA datasets are submitted, curated, and published (e.g., micro-computed tomography, histology, pulse oximetry, serum metabolites, magnetic resonance imaging, intraocular pressure, novel object recognition, etc.), the merging together of spaceflight data along this multi-hierarchical complexity of biology will enable informatics and data-intensive approaches resulting in knowledge discoveries across missions, space hazards, and biological disciplines.

Biology↗

NASA NTRSDate not supplied

Expanding Repository Data Available For Sharing And Knowledge Discovery

Some of the hardest space biology and space health challenges require data-intensive, bioinformatic, meta-analytical, and computer-assisted research approaches. These challenges include examining interdisciplinary space life science research across experiments and across interacting spaceflight hazards (radiation, altered gravity, confinement, hostile-closed environments, distance-duration from Earth). The approaches to confront these challenges involve mining multiple datasets simultaneously from various hierarchical organizations of biological complexity, all while concurrently evaluating how experimental design factors affect endpoints of standard assays. To enable this field, it is essential that principal investigators (PIs) submit data in a structure so it can be maximally re-used. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make publicly available all non-human space-relevant biological data. ALSDA must also ensure data are open-access, and maximally findable, accessible, interoperable, and reusable (FAIR). The scope of ALSDA data collected and submitted by PIs include subject and study design metadata, assay metadata parameters, raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). ALSDA recently integrated into a collaborative group of Open Science projects to facilitate a suite of new tools and workflows that will improve data submission, accessibility, and reusability by implementing digital data submission agreements, and adopting the data management system originally developed by NASA GeneLab. ALSDA intends to bring current biological repository data and all future collected data into this new scientific data reuse reality. This new suite of tools will enable ALSDA to deploy a science curation system using scientific assay configurations for the data submission portal. It will capture essential assay parameters according to established standards in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. Data submissions can be brought into cutting-edge informatic analysis portals to enable mining of physiological, behavioral, biochemical, and imaging datasets in conjunction with ‘omics-level datasets. As ALSDA datasets are submitted, curated, and published (e.g., micro-computed tomography, histology, pulse oximetry, serum metabolites, magnetic resonance imaging, intraocular pressure, novel object recognition, etc.), the merging together of spaceflight data along this multi-hierarchical complexity of biology will enable informatics and data-intensive approaches resulting in knowledge discoveries across missions, space hazards, and biological disciplines.

life science↗

NASA NTRS2017

Towards a Preservation Content Standard for Earth Observation Data

Information from Earth observing missions (remote sensing with airborne and spaceborne instruments, and in situ measurements such as those from field campaigns) is proliferating in the world. Many agencies across the globe are generating important datasets by collecting measurements from instruments on board aircraft and spacecraft, globally and constantly. The data resulting from such measurements are a valuable resource that needs to be preserved for the benefit of future generations. These observations are the primary record of the Earths environment and therefore are the key to understanding how conditions in the future will compare to conditions today. Earth science observational data, derived products and models are used to answer key questions of global significance. In the near-term, as long as the missions data are being used actively for scientific research, it continues to be important to provide easy access to the data and services commensurate with current information technology. For the longer term, when the focus of the research community shifts toward new missions and observations, it is essential to preserve the previous mission data and associated information. This will enable a new user in the future to understand how the data were used for deriving information, knowledge and policy recommendations and to repeat the experiment to ascertain the validity and possible limitations of conclusions reached in the past and to provide confidence in long term trends that depended on data from multiple missions. Organizations that collect, process, and utilize Earth observation data today have a responsibility to ensure that the data and associated content continue to be preserved by them or are gathered and handed off to other organizations for preservation for the benefit of future generations. In order to ensure preservation of complete content necessary for understanding and reusing the data and derived digital products from todays missions, it is necessary to develop a specification of such preservation content. While there are existing standards that address archival and preservation in general, there are no existing international standards or specifications today to address what content should be preserved. The purpose of this paper is to outline briefly the existing standards that apply to preservation, describe a recent effort in getting an international standard in place for specifying preservation content for Earth observation data and derived digital data products and the remaining work needed to arrive at a standard.

ISO Standards↗

NASA NTRSDate not supplied

GeneLab: Current and Future Omics Data Integration Between Space Biology and HRP

For the past five years, the Biological and Physical Sciences Division has pioneered Open Science in Space Biology by funding the NASA GeneLab project. Along with the Ames Life Sciences Data Archive, GeneLab has quickly become the world leader in archiving and scientifically curating spaceflight and spaceflight relevant multi-omics data. Specifically, the GeneLab Data System has become a full enterprise solution providing advanced mining capabilities, several application programming interfaces for data federation and machine learning approaches, and delivering to the world an analytical and visualization platform which has enabled collaboration within the scientific community. Over the past three years, large meta-analysis and modeling studies have been published by the GeneLab Analysis Working Groups (AWGs), which are comprised of ~200 volunteer scientists. One natural extension of GeneLab data reuse has recently turned towards linking animal data with human data, which is the next necessary step to further validate animal models for inferring biological risks to humans conducting LEO, lunar or Martian missions. As such, data from the Human Research Program are an essential component of GeneLab and ALSDA. At the moment, simulated space radiation experiments conducted at Brookhaven National Laboratory make the most of HRP GeneLab data, and the scientific community has been eager to also link their animal spaceflight results to actual Astronaut data and human analog data. We will discuss further the current status of knowledge and future approaches to accelerate our basics understanding of the impact of space stressors on humans using latest omics technology.

omics↗

DOE OSTI2025

Historical and Future Learning for the New Era of Multi-Terawatt Photovoltaics

Solar photovoltaics (PV) is entering a new era of multi-terawatt deployment, with 2 TW already in service and more than 75 TW predicted in many scenarios by 2050. This next era has been enabled by over five decades of cumulative advances in PV module cost reduction, performance and reliability. The current scale of deployment also introduces new needs, opportunities and challenges. In this Perspective we frame a path forwards based on learning, broadly defined as a combination of expansion of knowledge and advances through research and development, experience and collaboration. We discuss historical topics where learning has driven PV deployment until now, and emerging areas that are required to sustain high levels of future deployment. We expect progress to continue in terms of module price, performance and reliability, driven by advances in PV cell and module design, the emergence of tandem devices and increased focus on extending module lifetimes. Large-scale deployment also means large-scale sustainability and responsibility. We therefore posit that additional metrics, such as the impact on global CO2 emissions, resource consumption and design for reuse and recycling, will become increasingly important to the PV industry and provide opportunities for further learning.

14 SOLAR ENERGY↗

DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗

NASA NTRS1999

Interaction of Space Suits with Windblown Soil: Preliminary Mars Wind Tunnel Results

Experiments in the Mars Wind Tunnel at NASA Ames Research Center show that under Mars conditions, spacesuit materials are highly susceptible to dust contamination when exposed to windblown soil. This effect was suspected from knowledge of the interaction of electrostatically adhesive dust with solid surfaces in general. However, it is important to evaluate the respective roles of materials, meteorological and radiation effects, and the character of the soil. The tunnel permits evaluation of dust contamination and sand abrasion of space suits by simulating both pressure and wind conditions on Mars. The long-term function of space suits on Mars will be primarily threatened by dust contamination. Lunar EVA activities caused heavy contamination of space suits, but the problem was never seriously manifest because of the brief utilization of the suits, and the suits were never reused. Electrostatically adhering dust grains have various detrimental effects: (1) penetration and subsequent wear of suit fabrics, (2) viewing obscuration through visors and scratching/pitting of visor surfaces, (3) penetration, wear, and subsequent seizing-up of mechanical suit joints, (4) changes in albedo and therefore of radiation properties of external heat-exchanger systems, (5) changes in electrical conductivity of suit surfaces which may affect tribocharging of suits and create spurious discharge effects detrimental to suit electronics/radio systems. Additional information is contained in the original.

Marshall, J.↗

NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship. 1. Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. 2. National Academies of Sciences, E. and Medicine, Open Science by Design: Realizing a Vision for 21st Century Research. 2018, Washington, DC: The National Academies Press. 232. 3. Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5.

Life Sciences data↗

NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship.

Life Sciences data↗

NASA NTRS1994

SCL: An off-the-shelf system for spacecraft control

In this age of shrinking military, civil, and commercial space budgets, an off-the-shelf solution is needed to provide a multimission approach to spacecraft control. A standard operational interface which can be applied to multiple spacecraft allows a common approach to ground and space operations. A trend for many space programs has been to reduce operational staff by applying autonomy to the spacecraft and to the ground stations. The Spacecraft Command Language (SCL) system developed by Interface and Control Systems, Inc. (ICS) provides an off-the-shelf solution for spacecraft operations. The SCL system is designed to provide a hyper-scripting interface which remains standard from program to program. The spacecraft and ground station hardware specifics are isolated to provide the maximum amount of portability from system to system. Uplink and downlink interfaces are also isolated to allow the system to perform independent of the communications protocols chosen. The SCL system can be used for both the ground stations and the spacecraft, or as a value added package for existing ground station environments. The SCL system provides an expanded stored commanding capability as well as a rule-based expert system on-board. The expert system allows reactive control on-board the spacecraft for functions such as electrical power systems (EPS), thermal control, etc. which have traditionally been performed on the ground. The SCL rule and scripting capability share a common syntax allowing control of scripts from rules and rules from scripts. Rather than telemeter over sampled data to the ground, the SCL system maintains a database on-board which is available for interrogation by the scripts and rules. The SCL knowledge base is constructed on the ground and uploaded to the spacecraft. The SCL system follows an open-systems approach allowing other tasks to communicate with SCL on the ground and in space. The SCL system was used on the Clementine program (launched January 25, 1994) and is required to have bidirectional communications with the guidance, navigation, and control (GNC) algorithms which were written as another task. Sequencing of the spacecraft maneuvers are handled by SCL, but the low-level thruster pulse commands are handled by the GNC software. Attitude information is reported back as telemetry, allowing the SCL expert system to inference on the changing data. The Clementine SCL flight software was largely reused from another Naval Center for Space Technology (NCST) satellite program. This paper details the SCL architecture and how an off-the-shelf solution makes sense for multimission spacecraft programs. The Clementine mission will be used as a case study in the application of the SCL to a 'fast track' program. The benefits of such a system in a 'better, cheaper, faster' climate will be discussed.

Buckley, Brian↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

NASA NTRS1993

Machine aided indexing from natural language text

The NASA Lexical Dictionary (NLD) Machine Aided Indexing (MAI) system was designed to (1) reuse the indexing of the Defense Technical Information Center (DTIC); (2) reuse the indexing of the Department of Energy (DOE); and (3) reduce the time required for original indexing. This was done by automatically generating appropriate NASA thesaurus terms from either the other agency's index terms, or, for original indexing, from document titles and abstracts. The NASA STI Program staff devised two different ways to generate thesaurus terms from text. The first group of programs identified noun phrases by a parsing method that allowed for conjunctions and certain prepositions, on the assumption that indexable concepts are found in such phrases. Results were not always satisfactory, and it was noted that indexable concepts often occurred outside of noun phrases. The first method also proved to be too slow for the ultimate goal of interactive (online) MAI. The second group of programs used the knowledge base (KB), word proximity, and frequency of word and phrase occurrence to identify indexable concepts. Both methods are described and illustrated. Online MAI has been achieved, as well as several spinoff benefits, which are also described.

Silvester, June P.↗

NASA NTRS2013

Work Practice Simulation of Complex Human-Automation Systems in Safety Critical Situations: The Brahms Generalized berlingen Model

The transition from the current air traffic system to the next generation air traffic system will require the introduction of new automated systems, including transferring some functions from air traffic controllers to on-board automation. This report describes a new design verification and validation (V&V) methodology for assessing aviation safety. The approach involves a detailed computer simulation of work practices that includes people interacting with flight-critical systems. The research is part of an effort to develop new modeling and verification methodologies that can assess the safety of flight-critical systems, system configurations, and operational concepts. The 2002 Ueberlingen mid-air collision was chosen for analysis and modeling because one of the main causes of the accident was one crew's response to a conflict between the instructions of the air traffic controller and the instructions of TCAS, an automated Traffic Alert and Collision Avoidance System on-board warning system. It thus furnishes an example of the problem of authority versus autonomy. It provides a starting point for exploring authority/autonomy conflict in the larger system of organization, tools, and practices in which the participants' moment-by-moment actions take place. We have developed a general air traffic system model (not a specific simulation of Überlingen events), called the Brahms Generalized Ueberlingen Model (Brahms-GUeM). Brahms is a multi-agent simulation system that models people, tools, facilities/vehicles, and geography to simulate the current air transportation system as a collection of distributed, interactive subsystems (e.g., airports, air-traffic control towers and personnel, aircraft, automated flight systems and air-traffic tools, instruments, crew). Brahms-GUeM can be configured in different ways, called scenarios, such that anomalous events that contributed to the Überlingen accident can be modeled as functioning according to requirements or in an anomalous condition, as occurred during the accident. Brahms-GUeM thus implicitly defines a class of scenarios, which include as an instance what occurred at Überlingen. Brahms-GUeM is a modeling framework enabling "what if" analysis of alternative work system configurations and thus facilitating design of alternative operations concepts. It enables subsequent adaption (reusing simulation components) for modeling and simulating NextGen scenarios. This project demonstrates that BRAHMS provides the capacity to model the complexity of air transportation systems, going beyond idealized and simple flights to include for example the interaction of pilots and ATCOs. The research shows clearly that verification and validation must include the entire work system, on the one hand to check that mechanisms exist to handle failures of communication and alerting subsystems and/or failures of people to notice, comprehend, or communicate problematic (unsafe) situations; but also to understand how people must use their own judgment in relating fallible systems like TCAS to other sources of information and thus to evaluate how the unreliability of automation affects system safety. The simulation shows in particular that distributed agents (people and automated systems) acting without knowledge of each others' actions can create a complex, dynamic system whose interactive behavior is unexpected and is changing too quickly to comprehend and control.

complex systems↗

NASA NTRS2014

Collaborative WorkBench for Researchers - Work Smarter, Not Harder

It is important to define some commonly used terminology related to collaboration to facilitate clarity in later discussions. We define provisioning as infrastructure capabilities such as computation, storage, data, and tools provided by some agency or similarly trusted institution. Sharing is defined as the process of exchanging data, programs, and knowledge among individuals (often strangers) and groups. Collaboration is a specialized case of sharing. In collaboration, sharing with others (usually known colleagues) is done in pursuit of a common scientific goal or objective. Collaboration entails more dynamic and frequent interactions and can occur at different speeds. Synchronous collaboration occurs in real time such as editing a shared document on the fly, chatting, video conference, etc., and typically requires a peer-to-peer connection. Asynchronous collaboration is episodic in nature based on a push-pull model. Examples of asynchronous collaboration include email exchanges, blogging, repositories, etc. The purpose of a workbench is to provide a customizable framework for different applications. Since the workbench will be common to all the customized tools, it promotes building modular functionality that can be used and reused by multiple tools. The objective of our Collaborative Workbench (CWB) is thus to create such an open and extensible framework for the Earth Science community via a set of plug-ins. Our CWB is based on the Eclipse [2] Integrated Development Environment (IDE), which is designed as a small kernel containing a plug-in loader for hundreds of plug-ins. The kernel itself is an implementation of a known specification to provide an environment for the plug-ins to execute. This design enables modularity, where discrete chunks of functionality can be reused to build new applications. The minimal set of plug-ins necessary to create a client application is called the Eclipse Rich Client Platform (RCP) [3]; The Eclipse RCP also supports thousands of community-contributed plug-ins, making it a popular development platform for many diverse applications including the Science Activity Planner developed at JPL for the Mars rovers [4] and the scientific experiment tool Gumtree [5]. By leveraging the Eclipse RCP to provide an open, extensible framework, a CWB supports customizations via plug-ins to build rich user applications specific for Earth Science. More importantly, CWB plug-ins can be used by existing science tools built off Eclipse such as IDL or PyDev to provide seamless collaboration functionalities.

Ramachandran, Rahul↗

DOE OSTI2026

Data Cards for Standardized Metadata Across DOE-Aligned Data Initiatives: Toward Transparent, Interoperable, and Governed Dataset Documentation

As data-intensive research, advanced computing, and artificial intelligence become increasingly central to scientific and operational workflows, the need for consistent, transparent, and machine-actionable documentation has grown correspondingly. Multiple DOE-aligned communities—including Office of Science, Genesis Mission, American Science Cloud (AmSC), National Nuclear Security Administration (NNSA) stewardship and governance, and related cross-laboratory collaborations—have independently developed metadata practices to support discovery, access, reuse, repository deposit, and compliance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS2017

Linearity Analysis and Efficiency Testing of The Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) Science Cameras for Flight

To unveil the complexity of the solar atmosphere, measurement of the magnetic field in the upper chromosphere and transition region is fundamentally important, as this is where the forces transition from plasma to magnetic field dominated. Measurements of the field are also needed to elucidate the energy transport from the lower atmospheric regions to the corona beyond. Such an advance in heliospheric knowledge became possible with the first flight of the international solar sounding rocket mission, CLASP. For the first time, linear polarization was measured in Hydrogen Lyman-Alpha at 121.60 nm in September 2015. For linear polarization measurements in this emission line, high sensitivity is required due to the relatively weak polarization signal compared to the intensity. To achieve this high sensitivity, a low-noise sensor is required with good knowledge of its characterization, including linearity. This work presents further refinement of the linearity characterization of the cameras flown in 2015. We compared the current from a photodiode in the light path to the digital response of the detectors. Pre-flight CCD linearity measurements were taken for all three flight cameras and calculations of the linear fits and residuals were performed. However, the previous calculations included a smearing pattern and a digital saturation region on the detectors which were not properly taken into account. The calculations have been adjusted and were repeated for manually chosen sub-regions on the detectors that were found not to be affected. We present a brief overview of the instrument, the calibration data and procedures, and a comparison of the old and new linearity results. The CLASP cameras will be reused for the successor mission, CLASP2, which will measure the Magnesium II h & k emission lines between 279.45 nm and 280.35 nm. The new approach will help to better prepare for and to improve the camera characterization for CLASP2.

linearit↗

NASA NTRS2018

Linearity Analysis and Efficiency Testing of The Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) Science Cameras for Flight

To unveil the complexity of the solar atmosphere, measurement of the magnetic field in the upper chromosphere and transition region is fundamentally important, as this is where the forces transition from plasma to magnetic field dominated. Measurements of the field are also needed to elucidate the energy transport from the lower atmospheric regions to the corona beyond. Such an advance in heliospheric knowledge became possible with the first flight of the international solar sounding rocket mission, CLASP. For the first time, linear polarization was measured in Hydrogen Lyman-Alpha at 121.60 nm in September 2015. For linear polarization measurements in this emission line, high sensitivity is required due to the relatively weak polarization signal compared to the intensity. To achieve this high sensitivity, a low-noise sensor is required with good knowledge of its characterization, including linearity. This work presents further refinement of the linearity characterization of the cameras flown in 2015. We compared the current from a photodiode in the light path to the digital response of the detectors. Pre-flight CCD linearity measurements were taken for all three flight cameras and calculations of the linear fits and residuals were performed. However, the previous calculations included a smearing pattern and a digital saturation region on the detectors which were not properly taken into account. The calculations have been adjusted and were repeated for manually chosen sub-regions on the detectors that were found not to be affected. We present a brief overview of the instrument, the calibration data and procedures, and a comparison of the old and new linearity results. The CLASP cameras will be reused for the successor mission, CLASP2, which will measure the Magnesium II h & k emission lines between 279.45 nm and 280.35 nm. The new approach will help to better prepare for and to improve the camera characterization for CLASP2.

detector↗