Search NASA⌕ Search

SEARCH · Search NASA

Results for “data curation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 3

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 4

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Hyaloscypha finlandica Metabolome Repository

This repository provides the curated data tables, manuscript figure and table exports, dependency records, and workflow scripts supporting an integrated comparative genomics and untargeted LC-MS/MS metabolomics analysis of Hyaloscypha finlandica strain PMI 746, a root-associated dark septate endophyte of poplar. The repository includes genome-mining summaries from antiSMASH, FunBGCeX, BGC-Prophet, and BiG-SCAPE; processed metabolomics inputs; metabolite annotation evidence; statistical outputs; and publication-facing figures and tables. Raw LC-MS/MS spectra, full genome/protein downloads, and large generated tool outputs are referenced through public archive/accession records and are not stored in Git.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2019

Lunar Glovebox Balance with Wireless Technology

The most important equipment required for processing lunar samples is a high-quality mass balance for maintaining accurate weight inventory, security, and scientific study. After careful review, a Curation Office memo by Michael Duke in 1978 chose the Mettler PL200 to be used for sample weight measurements inside the gloveboxes (Fig. 3). These commercial off-the-shelf (COTS) balances did not meet the strict accepted material requirements in the Lunar lab. As a result, each balance housing, weighing pan, and wiring was custom retrofitted to meet Lunar Operating Procedure (LOP) 54 requirements [for material construction restrictions]. The original design drawings for the custom housings, readout support stands, and wiring were done by the JSC engineering directorate. The 1977- 1978 schematics, drawings, and files are now housed in the curation Data Center. Per the design specifications, the housing was fabricated from aluminum grade 6061 T6, seamless welds, and anodized per MIL-A-8625 type I, class I. The balance feet were TFE Teflon and any required joints were sealed with Viton A gaskets. The readout display and support stands outside the glovebox were fabricated from 300 series stainless steel with #4 finish and mounted to the glovebox with welded bolts. Wire harnesses that linked the balance with the outside display and power were encapsulated with TFE Teflon and transported through custom Deutsch wire bulk head pass-through systems from inside to outside the glovebox. These Deutsch connectors were custom fabricated with 316L stainless steel bodies, Viton A O-rings, aluminum 6061 with electroless nickel plating, Teflon (replacing the silicone), and gold crimp connectors (no soldering). Many of the Deutsch connectors may have been used in the Apollo program high vacuum complex in building 37 and date to about 1968 to 1970.

Zeigler, Ryan A.↗
NASA NTRSDate not supplied

Extravehicular Activity Mission System Software (EMSS) - Enabling Human Planetary Exploration Data Within The Broader Planetary Data Ecosystem

The planetary science community is once again on the verge of generating, capturing and analyzing human planetary exploration data, this time via the Artemis program. Artemis missions will involve robotic missions in addition to human extravehicular activity (EVA) where crew will be generating scientific data [1]. Present-day robotic mission data expectations for data archiving involves ingesting data into the Planetary Data System (PDS), but how might PDS be leveraged/adapted/ready (or not) for human spaceflight mission data, particularly EVA data that includes non-scientific data that provides important context to the scientific data gathered on the lunar surface? This question has broader implications than what this abstract can answer, but we wanted to pose the question to 1) get conversations started and 2) highlight how operations software data handling could play a role in overall data curation.

M J Miller↗
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 NTRS2023

NLSP: NASA Life Sciences Portal

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. Some of these improvements will at the same time support the twin pillars of Open Science: transparency of methods and reproducibility of results. This video is a high level overview of the NLSP for existing and new users.

Life Sciences data↗
NASA NTRS2011

Technical Tension Between Achieving Particulate and Molecular Organic Environmental Cleanliness: Data from Astromaterial Curation Laboratories

NASA Johnson Space Center operates clean curation facilities for Apollo lunar, Antarctic meteorite, stratospheric cosmic dust, Stardust comet and Genesis solar wind samples. Each of these collections is curated separately due unique requirements. The purpose of this abstract is to highlight the technical tensions between providing particulate cleanliness and molecular cleanliness, illustrated using data from curation laboratories. Strict control of three components are required for curating samples cleanly: a clean environment; clean containers and tools that touch samples; and use of non-shedding materials of cleanable chemistry and smooth surface finish. This abstract focuses on environmental cleanliness and the technical tension between achieving particulate and molecular cleanliness. An environment in which a sample is manipulated or stored can be a room, an enclosed glovebox (or robotic isolation chamber) or an individual sample container.

Allton, J. H.↗
NASA NTRSDate not supplied

AI Curation Methods for NASA Scientific Data

The NASA Open Science Data Repository (OSDR) serves as a central hub for sharing and accessing NASA's vast collection of scientific data, supporting researchers across diverse fields. To enhance the efficiency, accuracy, and accessibility of this data, we are leveraging advanced artificial intelligence (AI) techniques as part of the AI for Curation project. By integrating large language models (LLMs) into our data curation workflow, we aim to streamline the entire process—from data submission to user interaction. This initiative focuses on improving key areas, including data ingestion, curation, and user engagement with curated datasets, impacting multiple domains and a wide user base. First, we are developing tools that can automatically parse data in various formats, using LLMs to convert unstructured data into structured, standardized formats. This reduces the manual effort required for curation, allowing curators to focus on more critical scientific analyses. Additionally, AI and machine learning (ML) models are being implemented to automate data validation and verification, ensuring the highest standards of data quality and reliability. Finally, we are creating a conversational AI agent to interact with the curated scientific studies in OSDR, helping users easily navigate the repository and access relevant data. By enhancing data discoverability and accessibility, these advancements will foster new research opportunities and promote the principles of open science.

Walter Alvarado↗
NASA NTRS1984

Configuration and Implementation

The main objectives of a Planetary Data System (PDS) are to curate planetary data - that is to store and maintain complete planetary data sets and the necessary supporting documentation to make them useful - and to facilitate scientific study of these data through improved organization and access. A hardware system, no matter how sophisticated and supportive, cannot replace the live colleague interaction needed for good science.System users with only a general knowledge of a data file or data set will need guidance in determining whether that data is most appropriate to support a theory. The PDS is meant to allow easy access to data, to provide useful presentations of that data, and to facilitate data analysis; however it is not designed to supercede the critical examination and interactions that data analysis requires. The Planetary Data System will be a powerful research tool which allows the entire planetary science community access to a complete data and will facilitate the archiving of pass, current, and future mission data sets.

Source record↗
DOE OSTI2026

Curation and Dissemination of Complex Multi-Modal Datasets for Radiation Detection, Localization, and Tracking

The PANDAWN sensor network in Chicago, IL, is a state-of-the-art testbed for networked, multi-modal sensing. It integrates AI/data science methods into its operation, from data acquisition to automated data labeling and curation workflows. The curation and dissemination of diverse multi-modal datasets will enable the development of new radiological/nuclear (R/N) detection, localization, and tracking algorithms and methods relevant across the nonproliferation mission space. This article first introduces the PANDAWN sensor network and the features that make it stand out from previous multi-modal data acquisition efforts. We then review the various data streams acquired on the PANDAWN nodes and present the implementation of an automated data curation pipeline that includes the labeling of radiation and contextual data streams. Here, we finally provide a short overview of different studies that leveraged the curated datasets.

Data curation↗
NASA NTRSDate not supplied

Managing Multi-Instrument Data Streams in Secure Environments

The capture and curation of all primary instrument data is a potentially valuable source of added insight into experiments or diagnostics in laboratory experiments. The data can, when properly curated, enable analysis beyond the current practice that uses just a subset of the as-measured data. Complete curated data can also be input for machine learning and other data exploration tools. Conveniently storing and accessing instrument data requires that the instruments are connected to databases and users through a networking infrastructure. This infrastructure needs to accommodate a wide array of instruments which can range from single laboratory mounted probes for environment monitoring to computers managing multiple instruments. These resources may also include mobile devices on which researchers record instrument and experiment state related notes. These varied data sources bring with them the challenges of different communications capabilities and protocols as well as the primary data typically being produced in proprietary formats. These challenges are further compounded when the instruments need to operate in secure environments such as required in national laboratories. We will discuss the SmartLab, an ongoing effort to set up a system for instrument and simulation data curation at NASA Langley Research Center. We will outline the challenges faced in managing the data sources required for ongoing research activities and the solutions that are being considered and implemented to address those challenges.

instrument data management↗
NASA NTRS2014

Data Albums: An Event Driven Search, Aggregation and Curation Tool for Earth Science

Approaches used in Earth science research such as case study analysis and climatology studies involve discovering and gathering diverse data sets and information to support the research goals. To gather relevant data and information for case studies and climatology analysis is both tedious and time consuming. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. In cases where researchers are interested in studying a significant event, they have to manually assemble a variety of datasets relevant to it by searching the different distributed data systems. This paper presents a specialized search, aggregation and curation tool for Earth science to address these challenges. The search rool automatically creates curated 'Data Albums', aggregated collections of information related to a specific event, containing links to relevant data files [granules] from different instruments, tools and services for visualization and analysis, and information about the event contained in news reports, images or videos to supplement research analysis. Curation in the tool is driven via an ontology based relevancy ranking algorithm to filter out non relevant information and data.

Ramachandran, Rahul↗