Modeling for Integrated Science Management and Resilient Systems Development
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Shelhamer, Mark↗
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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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Integrating Science & Management: Florida Scrub-Jay Conservation along the Central Florida's Atlantic Coast
Florida scrub-jays are a species listed under the Endangered Species Act. The NASA Ecology program has been a partner for conservation, recovery, and translocation across the species range. The objectives of this talk are to update members of the Archie Carr Working Group recovery, conservation, and translocation activities and describe how the Archie Carr National Wildlife Refuge and nearby conservation lands relate to species recovery actions.
translocation↗
Market-Based Approaches to Managing Science Return from Planetary Missions
The return of science is the fundamental objective of any planetary mission. However, which constellation of science observations constitute the best return of science is hard to evaluate. Past approaches toward planning science observations have been based on co-location of payload scientists who debate the merits of which investigation had the stronger science. This advocacy approach is time-consuming and does not provide appropriate incentives for science teams to reveal their trade-offs. An alternative approach, currently under evaluation by the Cassini Mission to Saturn, is one based on providing better incentives to the science teams. Incentives can produce better tradeoffs because the individuals who can make the best decisions about which science observations to propose, what resources are required to implement the observations, and which observations are most important are the science team's Principle Investigators (PI) themselves.
mission↗
Science Management: NASA Human Research Program
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Cherie M Oubre↗
Citizen Science Twitter Data Management for Earth Science Applications
Social media data can provide useful real-time and historical information relating to the natural world, but managing this data poses challenges. Scientists at GES DISC are exploring the potential of Twitter data to augment precipitation data from the Global Precipitation Measurement (GPM) mission. However, the format of Twitter data is unconventional in the context of NASA data centers, resulting in frustration for scientists who need to work with the data. This study investigated procedures and standards needed to properly manage Twitter data to make them compatible with these data centers. After comparing databases, the study found that the MongoDB database was best suited for the storage of raw Twitter data due to its flexibility, ability to be accessed by multiple users, and querying functionality. The study used the Python package Zarr to transform processed Twitter data into a gridded format similar to that of satellite data. Each Tweet was mapped onto a time-space grid; each grid location contained information about Tweet attributes and precipitation. The study developed a pipeline for downloading, storing, and gridding Twitter data and transformed Twitter data into an understandable format for users of NASA satellite data.
Li, Rachel↗
DataHub: Science Data Management in Support of Interactive Exploratory Analysis
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.
science↗
Research program on the management of science and technology Annual report, 1964 - 1965
Research program on management of science and technology
PROGRAM MANAGEMENT↗
DataHub - Science data management in support of interactive exploratory analysis
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, viz. knowledge-based expert systems, science visualization, and science data management. This integration is based on a concept called the DataHub. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system. Both computational nodes and interactive nodes are able to effectively and efficiently use the data services (access, retrieval, update, etc.) in a distributed, interdisciplinary information system in a uniform and standard way. This allows the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis is on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to information. The DataHub includes all the required end-to-end components and interfaces to demonstrate the complete concept.
Handley, Thomas H., Jr.↗
DataHub: Science data management in support of interactive exploratory analysis
The DataHub addresses four areas of significant needs: 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, viz. knowledge-based expert systems, science visualization, and science data management. This integration is based on a concept called the DataHub. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system. Both computational nodes and interactives nodes are able to effectively and efficiently use the data services (access, retrieval, update, etc), in a distributed, interdisciplinary information system in a uniform and standard way. This allows the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to information. The DataHub includes all the required end-to-end components and interfaces to demonstrate the complete concept.
Handley, Thomas H., Jr.↗
1994 Science Information Management and Data Compression Workshop
This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on September 26-27, 1994, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival and retrieval of large quantities of data in future Earth and space science missions. It consisted of eleven presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center.
Tilton, James C.↗
The 1995 Science Information Management and Data Compression Workshop
This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival, and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The Workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center.
Tilton, James C.↗
FY21 NNSA-IAEC Science Area V, Environmental ISR, Waste Management and Subsurface Science (Final Report)
The FY21 NNSA NA-22 Final Report represents the progress achieved over the past year (FY21) by the research team for Topic Area 3, Waste Management & Subsurface Science (WM3), and Topic Area 7, Radiation and Thermal Effects on Bituminous Rocks (WM7). WM3 and WM7 are part of Science Area V (Subsurface Science and Waste Management) in the NNSA-IAEC Science and Technology Working Group. The project was initiated four years ago (2017) with an MOU agreement between the U.S. National Nuclear Security Administration (NNSA) and the Israel Atomic Energy Commission (IAEC) to evaluate the feasibility of geological subsurface disposal of radioactive waste in Israel. The core WM3 and WM7 teams are comprised of scientists and engineers from Los Alamos National Laboratory, the Geologic Survey of Israel (GSI), and the Nuclear Research Center - Negev (NRCN), with a close collaboration to the teams from Sandia National Laboratory (SNL) and Lawrence Livermore Laboratory (LLNL)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DataHub--Knowledge-Based Science Data Management for Exploratory Data Analysis
It is our belief that new modes of research and new tools will be required to handle the massive amount of diverse data that is to be stored, organized, accessed, distributed, visualized, and analyzed. The fundamental innovation required is the integration of three automation technologies, videlicet knowledge-based expert systems, science visualization and science data management. This integration is based on a concept caled the DataHub, which we describe here.
DataHub↗
A critical review of the life sciences project management at Ames Research Center for the Spacelab Mission development test 3
A management study was initiated by ARC (Ames Research Center) to specify Spacelab Mission Development Test 3 activities and problems. This report documents the problems encountered and provides conclusions and recommendations to project management for current and future ARC life sciences projects. An executive summary of the conclusions and recommendations is provided. The report also addresses broader issues relevant to the conduct of future scientific missions under the constraints imposed by the space environment.
Helmreich, R. L.↗
Science operations management
The operation teams engaged in the IR Astronomical Satellite (IRAS) project included scientists from the IRAS International Science Team. The detailed involvement of these scientists in the design, testing, validation, and operations phases of the IRAS mission contributed to the success of this project. The Project Management Group spent a substantial amount of time discussing science-related issues, because science team coleaders were members from the outset. A single scientific point-of-contact for the Management Group enhanced the depth and continuity of agreement reached in decision-making.
Squibb, G. F.↗
Cassini Radio Science Data Management & Team Website
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Cassini radio science data team website↗
Clarifying the trophic state concept to advance macroscale freshwater science and management
For over a century, ecologists have used the concept of trophic state (TS) to characterize an aquatic ecosystem's biological productivity. However, multiple TS classification schemes, each relying on a variety of measurable parameters as proxies for productivity, have emerged to meet use‐specific needs. Frequently, chlorophyll a, phosphorus, and Secchi depth are used to classify TS based on autotrophic production, whereas phosphorus, dissolved organic carbon, and true color are used to classify TS based on both autotrophic and heterotrophic production. Both classification approaches aim to characterize an ecosystem's function broadly, but with varying degrees of autotrophic and heterotrophic processes considered in those characterizations. Moreover, differing classification schemes can create inconsistent interpretations of ecosystem integrity. For example, the US Clean Water Act focuses exclusively on algal threats to water quality, framed in terms of eutrophication in response to nutrient loading. This usage lacks information about non‐algal threats to water quality, such as dystrophication in response to dissolved organic carbon loading. Consequently, the TS classification schemes used to identify eutrophication and dystrophication may refer to ecosystems similarly (e.g., oligotrophic and eutrophic), yet these categories are derived from different proxies. These inconsistencies in TS classification schemes may be compounded when interdisciplinary projects employ varied TS frameworks. Even with these shortcomings, TS can still be used to distill information on complex aquatic ecosystem function into a set of generalizable expectations. The usefulness of distilling complex information into a TS index is substantial such that usage inconsistencies should be explicitly addressed and resolved. To emphasize the consequences of diverging TS classification schemes, we present three case studies for which an improved understanding of the TS concept advances freshwater research, management efforts, and interdisciplinary collaboration. To increase clarity in TS, the aquatic sciences could benefit from including information about the proxy variables, ecosystem type, as well as the spatiotemporal domains used to classify TS. As the field of aquatic sciences expands and climatic irregularity increases, we highlight the importance of re‐evaluating fundamental concepts, such as TS, to ensure their compatibility with evolving science.
classification↗