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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↗

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↗

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.↗

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.↗

Study of airborne science experiment management concepts for application to space shuttle, volume 2

Airborne research management and shuttle sortie planning at the Ames Research Center are reported. Topics discussed include: basic criteria and procedures for the formulation and approval of airborne missions; ASO management structure and procedures; experiment design, development, and testing aircraft characteristics and experiment interfaces; information handling for airborne science missions; mission documentation requirements; and airborne science methods and shuttle sortie planning.

Mulholland, D. R.↗

Study of airborne science experiment management concepts for application to space shuttle. Volume 1: Executive summary

The management concepts and operating procedures are documented as they apply to the planning of shuttle spacelab operations. Areas discussed include: airborne missions; formulation of missions; management procedures; experimenter involvement; experiment development and performance; data handling; safety procedures; and applications to shuttle spacelab planning. Characteristics of the airborne science experience are listed, and references and figures are included.

Mulholland, D. R.↗

ICESat (GLAS) Science Processing Software Document Series: Science Software Management Plan - Volume 1

This document provides the Software Management Plan for the GLAS Standard Data Software (SDS) supporting the GLAS instrument of the EOS ICESat Spacecraft. The SDS encompasses the ICESat Science Investigator-led Processing System (I-SIPS) Software and the Instrument Support Terminal (IST) Software. For the I-SIPS Software, the SDS will produce Level 0, Level 1, and Level 2 data products as well as the associated product quality assessments and descriptive information. For the IST Software, the SDS will accommodate the GLAS instrument support areas of engineering status, command, performance assessment, and instrument health status.

Hancock, David W., III↗

ICESat (GLAS) Science Processing Software Document Series: Science Data Management Plan - Volume 2

This document provides the Data Management Plan for the GLAS Standard Data Software (SDS) supporting the GLAS instrument of the EOS ICESat Spacecraft. The SDS encompasses the ICESat Science Investigator-led Processing System (I-SIPS) Software and the Instrument Support Facility (ISF) Software. This Plan addresses the identification, authority, and description of the interface nodes associated with the GLAS Standard Data Products and the GLAS Ancillary Data.

Jester, Peggy L.↗