Search NASA⌕ Search
Home/Archive/NASA NTRS

NASA NTRS · 20210004822

DataHub: Science Data Management in Support of Interactive Exploratory Analysis

Rubin, M.·Handley, T. Jr.
View original source ↗Export metadata ↓

Abstract

The DataHub addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, videlicet knowledge-based expert systems, science visualization, and science data management. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system.

Explore related subjects

sciencedatamanagementinteractiveexploratoryanalysisDataHub

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rubin, M., Handley, T. Jr.. 1993-10-19. DataHub: Science Data Management in Support of Interactive Exploratory Analysis. https://ntrs.nasa.gov/citations/20210004822

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

NASA NTRS2024

2024 Software for NASA Science Mission Directorate Workshop Report

The 2024 Software for the NASA Science Mission Directorate Workshop was the first workshop of its kind in over 10 years. The numerous attendees and high level of interaction in this hybrid workshop portrayed the untapped interest and energy in the NASA SMD community about software. Over 100 takeaways were collected from the hosted discussions in four key areas - Communication, Communities, Funding and Clarity - as summarized in this report. One takeaway was representative of all four categories: “Software is not hardware; it is organic and needs a different model. You often don't know which components will be Open-Source reusable until later in the development cycle.” For the communication category, this takeaway motivates the reduction of silos by shifting towards a model that better supports community collaboration on common challenges and a more streamlined and improved software release process. For the communities category, the same statement points to forming communities of practice with varying scopes and a new approach to recognition and incentives for open-source software contributions. Concerning funding, this statement motivates a more sustained and flexible funding model that better supports the software foundation needed for NASA’s long-term success, including the collaboration and infrastructure a good foundation requires. The new approach to clarity motivated by this statement calls for significant changes to the software release process and related policies to streamline compliance, align those policies with the open-source science culture NASA is promoting and with each other, and simplify use of the cloud. It is time to recognize software as a foundational component of NASA with an organic nature not properly supported by current approaches. Different models are needed in all four areas to shift the NASA SMD software community and governance structures into a more efficient, open, and collaborative ecosystem - one that enables ground-breaking science and daring exploration into the coming decades

science↗
NASA NTRS2018

ExMASS: A viable model for authentic student-scientist research partnerships

The Exploration of the Moon and Asteroids by Secondary Students (ExMASS) program provides students the opportunity to conduct authentic, student-led, open-inquiry research with assistance from their teacher and a scientist. One goal of the program is to enhance participants’ attitudes toward science. Most students enter the ExMASS program with positive attitudes toward science. However, the attitude survey developed for ExMASS measured small but statistically significant increases in attitudes. This paper discusses the ExMASS program, an attitude towards science survey designed for the program’s specific audience, and the results of statistical analyses of the survey. The validity of the attitudes survey was tested with an exploratory factor analysis (EFA). The results of the EFA revealed two salient factors measured by the survey: 1) Personal Importance of Science and 2) Importance of Science to Society. A reliability analysis revealed a Cronbach alpha value of 0.89. Paired t-tests revealed statistically significant differences in students’ attitudes before and after the program. For Factor 1, participants’ before scores were statistically significantly lower than their post scores, t(88) = 3.16, p<0.05. For Factor 2, participants’ before scores were statistically significantly lower than their post scores, t(88) = 4.47, p<0.05. ExMASS has been shown to be a practical model for programs designed to increase high school student attitudes toward science. More information on the ExMASS program is available at https://www.lpi.usra.edu/exploration/education/hsResearch/.

science↗
NASA NTRS2018

Flood Analysis of the Thomas Flood in California Using NASA Data in a GIS

In December 2017, the Thomas Fire burned areas in Ventura and Santa Barbara Counties in California, consuming 281,893 acres (440 square miles), and becoming the largest wildfire in California history. On January 8, 2018, a winter storm made landfall in Santa Barbara County, releasing more than 5 inches of rain onto the same scarred area, causing San Ysidro and Montecito Creeks to overflow and leading to devastating flash flooding and debris flows by January 9, 2018. Several areas that were not typically prone to flooding were at risk due to the higher than usual flows in the nearby streams and creeks. The unprecedented flooding killed at least 20 people and left several others missing. NASA's Earth Observing System Data Information System (EOSDIS) manages Earth Observation satellites and the Distributed Active Archive Centers (DAACs), where the data is stored and processed. This poster presents the use of satellite data from NASA's inventory that have the potential for use in identification and analysis of forest fire risk and subsequent after-effects within a geographic information system (GIS).

science↗