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NASA NTRSDate not supplied

Scientific Content Curation in an Open Science Era

Today’s open science environment, in combination with the Big Data era, means more scientific data, software, tools, documentation, publications and other resources are available than ever. The promise of the open science era is that scientists will spend less time reinventing the wheel and more time doing actionable research. Yet navigating this vast and complex information landscape can feel overwhelming to scientists trying to get their bearings. In this presentation, we define and discuss the importance of scientific content curation for enhancing discovery and use of scientific data and information. We also share two examples of scientific content curation in action: the Catalog of Archived Suborbital Earth Science Investigations (CASEI) and the Science Discovery Engine (SDE).

Kaylin Bugbee↗
NASA NTRS2011

NLSI Focus Group on Missing ALSEP Data Recovery: Progress and Plans

On the six Apollo landed missions, the Astronauts deployed the Apollo Lunar Surface Experiments Package (ALSEP) science stations which measured active and passive seismic events, magnetic fields, charged particles, solar wind, heat flow, the diffuse atmosphere, meteorites and their ejecta, lunar dust, etc. Today's scientists are able to extract new information and make new discoveries from the old ALSEP data utilizing recent advances in computer capabilities and new analysis techniques. However, current-day investigators are encountering problems trying to use the ALSEP data. In 2007 archivists from NASA Goddard Space Flight Center (GSFC) National Space Science Data Center (NSSDC) estimated only about 50 percent of the processed ALSEP lunar surface data-of-interest to current lunar science investigators were in the NSSDC archives. The current-day lunar science investigators found most of the ALSEP data, then in the NSSDC archives. were extremely difficult to use. The data were in forms often not well described in the published reports and rerecording anomalies existed in the data which could only be resolved by tape experts. To resolve this problem, the DPS Lunar Data Node was established in 2008 at NSSDC and is in the process of successfully making the existing archived ALSEP data available to current-day investigators in easily useable forms. In July of 2010 the NASA Lunar Science Institute (NLSI) at Ames Research Center established the Recovery of Missing ALSEP Data Focus Group in recognition of the importance of the current activities to find the raw and processed ALSEP data missing from the NSSDC archives.

Lewis, L. R.↗
NASA NTRS2012

NLSI Focus Group on Recovery of Missing ALSEP Data: Status Update for 2012 NLSI Science Forum

On the six Apollo lunar landed missions, the Astronauts deployed the Apollo Lunar Surface Experiments Package (ALSEP) science stations which measured active and passive seismic events, magnetic fields, charged particles, solar wind, heat flow, the diffuse atmosphere, meteorites and their ejecta, lunar dust, etc. Today s investigators are able to extract new information and make new discoveries from the old ALSEP data utilizing recent advances in computer capabilities and new analysis techniques. However, current-day investigators are encountering problems in trying to use the ALSEP data. The data were in formats often not well described in the published reports and contained rerecording anomalies which required tape experts to resolve. To solve these problems the DPS Lunar Data Node was established at NASA Goddard Space Flight Center (GSFC) NASA Space Science Data Center (NSSDC) in 2008 and is currently in the process of making the existing archived ALSEP data available to current-day investigators in easily useable forms. However, current estimates by NSSDC archivists are that only about 60 percent of the PI processed ALSEP data and less than 30 percent of the raw experiment ALSEP data-of-interest to current lunar science investigators are currently in the NSSDC archives.

Lewis, Lyach R.↗
NASA NTRS2014

Use of Semantic Technology to Create Curated Data Albums

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discover tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out nonrelevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science.

Ramachandran, Rahul↗
NASA NTRSDate not supplied

Advancing Open Science through Public-Private Partnerships

Rapid technology developments are changing the way data-driven research is performed within the science community. With the emergence of cloud computing, this has quickly become a viable approach for enabling “science at scale”. Researchers are no longer hindered by obstacles of data management and data wrangling, allowing them to quickly discover, access and perform analysis on extremely large datasets. Infrastructures that move data out of institutional silos and into a computational platform, will ensure that data and tools are accessible to all users. NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT) seeks to address these rapid technology developments by establishing Space Act Agreements with selected partners from the public-private sector working in the area of cloud computing. These agreements aim to explore new opportunities with commercial cloud providers to accelerate open science and enable discovery, access and use of data sets on the cloud. In addition, they will also help establish training workshops for the science community to help researchers utilize the cloud for science. In this talk, we will present an overview of current and new partnerships we are developing to support open science and open data initiatives.

Elizabeth Fancher↗
NASA NTRS2010

Pion Production Data Needed for Space Radiation

A recent discovery concerning the importance of hadron production in space radiation is that pions can contribute up to twenty percent of the dose from galactic cosmic ray interactions (S. Aghara, S. Blattnig, J. Norbury, R. Singleterry, Nuclear Instruments and Methods, Vol. 267, 2009, p. 1115). Although the contribution for dose equivalent will be smaller, the dose contribution could be important for fluence based radiation models. Pion production cross sections will be an essential ingredient to such models, and it is of interest to investigate the adequacy of the pion production experimental data base for energies relevant to space radiation. The pion production threshold in nucleon - nucleon reactions is at 280 MeV and, in an interesting accident of nature, this lies near the peak of the galactic cosmic ray proton spectrum. Therefore, pion production data are needed from threshold up to energies around 50 GeV/nucleon, where the galactic cosmic ray fluence is of decreasing importance. Total and differential cross section data for pion production in this energy range will be reviewed. The availability and accuracy of theoretical models will also be discussed. It will be shown that there are a significant lack of data in this important energy range and that theoretical models still need improvement.

Norbury, John W.↗
NASA NTRS1999

Discovery of a New Cassini-Size Basin on Mars from MOLA Topographic Data

MOLA profile data collected during the Science Phasing Operations have revealed a previously unknown, 450 km wide, 2 km deep basin on Mars centered at 30N, 312W near the Phison Rupes. This structure, as large as and somewhat deeper than the very obvious Cassini impact basin located 1000 km to the SW, is not apparent in the existing good quality Viking imagery. Gridded MOLA data show the basin as a closed depression, but elevation contours show only weak correlation with what little structure exists in the area and with mapped geologic units. From analysis of slope breaks readily visible in two MOLA profiles we suggest this is at least a three-ring basin. Portions of rings from concentric fits to slope breaks align with some of the linear ridge-like structures of the Phison Rupes, and outline a region of lower crater density and smoother inter-crater plains. The discovery of such a pronounced topographic depression which lacks obvious visible structure suggests more such previously unknown features may exist on Mars, and that MOLA data may be useful in finding them.

Frey, H.↗
NASA NTRS1987

Analysis and synthesis of abstract data types through generalization from examples

The discovery of general patterns of behavior from a set of input/output examples can be a useful technique in the automated analysis and synthesis of software systems. These generalized descriptions of the behavior form a set of assertions which can be used for validation, program synthesis, program testing and run-time monitoring. Describing the behavior is characterized as a learning process in which general patterns can be easily characterized. The learning algorithm must choose a transform function and define a subset of the transform space which is related to equivalence classes of behavior in the original domain. An algorithm for analyzing the behavior of abstract data types is presented and several examples are given. The use of the analysis for purposes of program synthesis is also discussed.

Wild, Christian↗
NASA NTRS1987

Analysis and synthesis of abstract data types through generalization from examples

The discovery of general patterns of behavior from a set of input/output examples can be a useful technique in the automated analysis and synthesis of software systems. These generalized descriptions of the behavior form a set of assertions which can be used for validation, program synthesis, program testing, and run-time monitoring. Describing the behavior is characterized as a learning process in which the set of inputs is mapped into an appropriate transform space such that general patterns can be easily characterized. The learning algorithm must chose a transform function and define a subset of the transform space which is related to equivalence classes of behavior in the original domain. An algorithm for analyzing the behavior of abstract data types is presented and several examples are given. The use of the analysis for purposes of program synthesis is also discussed.

Wild, Christian↗
NASA NTRS2009

Evolution of Web Services in EOSDIS: Search and Order Metadata Registry (ECHO)

During 2005 through 2008, NASA defined and implemented a major evolutionary change in it Earth Observing system Data and Information System (EOSDIS) to modernize its capabilities. This implementation was based on a vision for 2015 developed during 2005. The EOSDIS 2015 Vision emphasizes increased end-to-end data system efficiency and operability; increased data usability; improved support for end users; and decreased operations costs. One key feature of the Evolution plan was achieving higher operational maturity (ingest, reconciliation, search and order, performance, error handling) for the NASA s Earth Observing System Clearinghouse (ECHO). The ECHO system is an operational metadata registry through which the scientific community can easily discover and exchange NASA's Earth science data and services. ECHO contains metadata for 2,726 data collections comprising over 87 million individual data granules and 34 million browse images, consisting of NASA s EOSDIS Data Centers and the United States Geological Survey's Landsat Project holdings. ECHO is a middleware component based on a Service Oriented Architecture (SOA). The system is comprised of a set of infrastructure services that enable the fundamental SOA functions: publish, discover, and access Earth science resources. It also provides additional services such as user management, data access control, and order management. The ECHO system has a data registry and a services registry. The data registry enables organizations to publish EOS and other Earth-science related data holdings to a common metadata model. These holdings are described through metadata in terms of datasets (types of data) and granules (specific data items of those types). ECHO also supports browse images, which provide a visual representation of the data. The published metadata can be mapped to and from existing standards (e.g., FGDC, ISO 19115). With ECHO, users can find the metadata stored in the data registry and then access the data either directly online or through a brokered order to the data archive organization. ECHO stores metadata from a variety of science disciplines and domains, including Climate Variability and Change, Carbon Cycle and Ecosystems, Earth Surface and Interior, Atmospheric Composition, Weather, and Water and Energy Cycle. ECHO also has a services registry for community-developed search services and data services. ECHO provides a platform for the publication, discovery, understanding and access to NASA s Earth Observation resources (data, service and clients). In their native state, these data, service and client resources are not necessarily targeted for use beyond their original mission. However, with the proper interoperability mechanisms, users of these resources can expand their value, by accessing, combining and applying them in unforeseen ways.

Mitchell, Andrew↗
NASA NTRSDate not supplied

Identifying Improvements to Airborne and Field Data Stewardship from Data User and Producer Needs (Findings from the March 2022 Airborne and Field Data Workshop)

Workshop Purpose: to gather valuable feedback from data users and data producers in order to improve the acquisition and use of NASA’s airborne and field data. Workshop Goals: - Identify new ways to improve the discovery, access, and reuse of NASA’s airborne and field data - Intentionally include the airborne community in future planning to ensure the community needs are met - Understand how NASA can better help researchers realize the full value of airborne and field data - Enable Open Source Science and determine what this means for airborne and field data communities

Geoffrey Stano↗
NASA NTRS1998

Polar Hydra Data Analysis

The science activities are: 1) Hydra is still operating successfully on orbit. 2) A large amount of analysis and discovery has occurred with the Hydra ground data processing this past year. 3) Full interdetector calibration has been implemented and documented. This intercalibration was necessitated by the incorrect installation of bias resistors in the pre-acceleration stage to the electron channeltrons. This had the effect of making the counting efficiency for electrons energy dependent as well as channeltron specific. The nature of the error had no impact on the ion detection efficiency since they have a different bias arrangement. This intercalibration is so effective, that the electron and ion moment densities are routinely produced with a level of agreement better than 20%. 4) The data processing routinely removes glint in the sensors and produces public energy time spectrograms on the web overnight. 6) Routine, but more intensive computer processing codes are operational that determine for electrons and ions, the density, the flow vector, the pressure tensor and the heat flux by numerical integration. These codes use the magnetic field to sustain the quality of their output. To gain access to this high quality magnetic field within our data stream we have monitored Russell's web page for zero levels and timing files (since his data acquisition is not telemetry synchronous) and have a local reconstruction of B for our use. We have also detected a routine anomaly in the magnetometer data stream that we have documented to Chris Russell and developed an editing algorithm to intercept these "hits" and remove them from the geophysical analysis.

Scudder, J. D.↗
NASA NTRS2018

NASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval System

NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.

Machine learning↗
NASA NTRS1973

Microwave maps of the polar ice of the earth

Synoptic views of the entire polar regions of earth were obtained free of the usual persistent cloud cover using a scanning microwave radiometer operating at a wavelength of 1.55 cm on board the Nimbus-5 satellite. Three different views at each pole are presented utilizing data obtained at approximately one-month intervals during the winter of 1972-1973. The major discoveries resulting from an analysis of these data are as follows: (1) Large discrepancies exist between the climatic norm ice cover depicted in various atlases and the actual extent of the canopies. (2) The distribution of multiyear ice in the north polar region is markedly different from that predicted by existing ice dynamics models. (3) Irregularities in the edge of the Antarctic sea ice pack occur that have neither been observed previously nor anticipated. (4) The brightness temperatures of the Greenland and Antarctica glaciers show interesting contours probably related to the ice and snow morphologic structure.

Gloersen, P.↗
NASA NTRS1974

An analysis of pilot error-related aircraft accidents

A multidisciplinary team approach to pilot error-related U.S. air carrier jet aircraft accident investigation records successfully reclaimed hidden human error information not shown in statistical studies. New analytic techniques were developed and applied to the data to discover and identify multiple elements of commonality and shared characteristics within this group of accidents. Three techniques of analysis were used: Critical element analysis, which demonstrated the importance of a subjective qualitative approach to raw accident data and surfaced information heretofore unavailable. Cluster analysis, which was an exploratory research tool that will lead to increased understanding and improved organization of facts, the discovery of new meaning in large data sets, and the generation of explanatory hypotheses. Pattern recognition, by which accidents can be categorized by pattern conformity after critical element identification by cluster analysis.

Kowalsky, N. B.↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism, behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRS1993

Visualization techniques to aid in the analysis of multi-spectral astrophysical data sets

The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions, and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists.

Brugel, Edward W.↗