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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

NASA's astrophysics archives at the National Space Science Data Center

NASA maintains an archive facility for Astronomical Science data collected from NASA's missions at the National Space Science Data Center (NSSDC) at Goddard Space Flight Center. This archive was created to insure the science data collected by NASA would be preserved and useable in the future by the science community. Through 25 years of operation there are many lessons learned, from data collection procedures, archive preservation methods, and distribution to the community. This document presents some of these more important lessons, for example: KISS (Keep It Simple, Stupid) in system development. Also addressed are some of the myths of archiving, such as 'scientists always know everything about everything', or 'it cannot possibly be that hard, after all simple data tech's do it'. There are indeed good reasons that a proper archive capability is needed by the astronomical community, the important question is how to use the existing expertise as well as the new innovative ideas to do the best job archiving this valuable science data.

Vansteenberg, M. E.↗
DOE OSTI2021

Data Science in Chemical Engineering: Applications to Molecular Science

Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science—the application tools for molecular discovery and property optimization at the atomic scale. Here, we summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1996

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems.

Kiely, Aaron B.↗
NASA NTRS2017

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

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

earth science satellite data↗
NASA NTRS1993

The European HST Science Data Archive

The paper describes the European HST Science Data Archive. Particular attention is given to the flow from the HST spacecraft to the Science Data Archive at the Space Telescope European Coordinating Facility (ST-ECF); the archiving system at the ST-ECF, including the hardware and software system structure; the operations at the ST-ECF and differences with the Data Management Facility; and the current developments. A diagram of the logical structure and data flow of the system managing the European HST Science Data Archive is included.

Pasian, F.↗
NASA NTRS2020

NASA ESDS Citizen Science Data Working Group

This document provides guidelines for legal, policy, and ethical issues; standards for citizen science data collection and management; information on ensuring usability of citizen science data and communication regarding its use; and best practices for long-term archival of citizen science data.Section1 contains a detailed discussion of policy, ethical, and legal considerations influencing citizen science data collection. Section 2 considers standards for documentation, including documentation of instrumentation, procedures, and the data itself. It concludes with a discussion of how citizen science data should be attributed. Section 3 provides guidance about how to ensure citizen science data are collected and stored in a useable way. It also considers how NASA and data producers should notify the scientific community, including citizen scientists and the public, about citizen science datasets and the scientific conclusions reached using them. Finally, Section 4 provides detailed information regarding what should be archived from projects using a citizen science approach, including data and code. It provides guidance about archive location, process, and timeframe, as well as information about data access and distribution services provided by NASA that may be relevant to data producers working with citizen scientists.

Citizen Science↗
NASA NTRS2000

Landsat 7 Science Data Processing: A System's Overview

The Landsat Science Data Processing System, developed by NASA for the Landsat 7 Project provides science data handling infrastructure used at the EROS Data Center Landsat 7 Data Handling Facility of the USGS Department of Interior. This paper presents an overview the designs, architectures, and details of the various systems used in the processing of the Landsat 7 Science Data.

Schweiss, Robert↗
DOE OSTI2025

Enabling pan-repository reanalysis for big data science of public metabolomics data

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.

El Abiead, Yasin↗
DOE OSTI2026

Ontologies for Intelligent Data Science

As anyone even vaguely aware of current technology can tell you, machine learning (ML) and artificial intelligence (AI) have made exceptional breakthroughs in recent years. Generative artificial intelligence (GAI) emerged circa 2022 dominated by Large Language Models (LLMs) and generative tools for images emerged at about the same time.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. Our workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. These methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. In addition, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Energy Materials Chemistry Integrating Theory, Experiment and Data Science (Final Report)

The Energy Materials Chemistry Integrating Theory, Experiment and Data Science (EM-CITED) project is a multidisciplinary research effort focused on accelerating discovery of scientific knowledge via incorporation of data science and artificial intelligence in materials chemistry research. The project aims to advance materials chemistry-aware data science to unify theory and experiment knowledge streams. The work resulted in foundational AI frameworks for materials chemistry – Deep Reasoning Networks (DRNets), Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP), and Material-to-Spectrum (Mat2Spec) prediction – as well as a host of strategies for accelerated scientific discoveries through principled incorporation of data science in computational and experimental research.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Telemetry and Science Data Software System

The Telemetry and Science Data Software System (TSDSS) was designed to validate the operational health of a spacecraft, ease test verification, assist in debugging system anomalies, and provide trending data and advanced science analysis. In doing so, the system parses, processes, and organizes raw data from the Aquarius instrument both on the ground and while in space. In addition, it provides a user-friendly telemetry viewer, and an instant pushbutton test report generator. Existing ground data systems can parse and provide simple data processing, but have limitations in advanced science analysis and instant report generation. The TSDSS functions as an offline data analysis system during I&T (integration and test) and mission operations phases. After raw data are downloaded from an instrument, TSDSS ingests the data files, parses, converts telemetry to engineering units, and applies advanced algorithms to produce science level 0, 1, and 2 data products. Meanwhile, it automatically schedules upload of the raw data to a remote server and archives all intermediate and final values in a MySQL database in time order. All data saved in the system can be straightforwardly retrieved, exported, and migrated. Using TSDSS s interactive data visualization tool, a user can conveniently choose any combination and mathematical computation of interesting telemetry points from a large range of time periods (life cycle of mission ground data and mission operations testing), and display a graphical and statistical view of the data. With this graphical user interface (GUI), the data queried graphs can be exported and saved in multiple formats. This GUI is especially useful in trending data analysis, debugging anomalies, and advanced data analysis. At the request of the user, mission-specific instrument performance assessment reports can be generated with a simple click of a button on the GUI. From instrument level to observatory level, the TSDSS has been operating supporting functional and performance tests and refining system calibration algorithms and coefficients, in sync with the Aquarius/SAC-D spacecraft. At the time of this reporting, it was prepared and set up to perform anomaly investigation for mission operations preceding the Aquarius/SAC-D spacecraft launch on June 10, 2011.

Bates, Lakesha↗
NASA NTRS2020

Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.

snow cover extent↗
NASA NTRS2018

NASA Earth Science Data Rescue Efforts

Historically, at the end of a NASA mission, earth and space science data were stored at NASA's National Space Science Data Center (NSSDC). The original data archive consisted of both magnetic tapes and film media. As data storage technology improved, data from later missions were stored on disks and platters and higher capacity magnetic media for online accessibility. To conserve physical space at NASA archive sites and to meet disaster recovery guidelines, historical data originally stored on magnetic tapes and film were moved to the Federal Archives and Record Center (FRC) as a temporary holding area until its long-term value was determined by NASA. All records at the FRC are controlled by the NASA Records Retention Schedule (NRRS) which determines the disposal date for each record. On that date, responsible NASA parties are notified that all scheduled records should be reviewed and assessed to determine if they continue to hold significant historical, scientific or administrative value. For Earth Science data records being held at FRC, the Earth Science Data and Information System (ESDIS) Project office is the party responsible for making the value assessment that determines which records warrant preservation and which are ready for proper disposal according to NASA guidelines. Once the data's long-term value is determined, ESDIS takes definitive steps to preserve this data for future discovery and access. Deteriorating media containing historic data of value are recalled from FRC and brought back to ESDIS. Through a tedious, laborious process, digital data are recovered and restored to modern formats with improved metadata and documentation to aid discovery. The restored digital products are then incorporated into our modern online archive, and made immediately accessible to the public. In this paper, we will discuss how we identify data-at-risk, ways to minimize data loss, how we plan for recovery, how we delegate recovery activities to our archive facilities, and how we make recovered data more accessible.

Data Systems; Social and Information sciences↗
DOE OSTI2017

Transdisciplinary Foundations of Geospatial Data Science

Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement.

Xie, Yiqun↗