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NASA NTRS2002

ISAIA: Interoperable Systems for Archival Information Access

The ISAIA project was originally proposed in 1999 as a successor to the informal AstroBrowse project. AstroBrowse, which provided a data location service for astronomical archives and catalogs, was a first step toward data system integration and interoperability. The goals of ISAIA were ambitious: '...To develop an interdisciplinary data location and integration service for space science. Building upon existing data services and communications protocols, this service will allow users to transparently query hundreds or thousands of WWW-based resources (catalogs, data, computational resources, bibliographic references, etc.) from a single interface. The service will collect responses from various resources and integrate them in a seamless fashion for display and manipulation by the user.' Funding was approved only for a one-year pilot study, a decision that in retrospect was wise given the rapid changes in information technology in the past few years and the emergence of the Virtual Observatory initiatives in the US and worldwide. Indeed, the ISAIA pilot study was influential in shaping the science goals, system design, metadata standards, and technology choices for the virtual observatory. The ISAIA pilot project also helped to cement working relationships among the NASA data centers, US ground-based observatories, and international data centers. The ISAIA project was formed as a collaborative effort between thirteen institutions that provided data to astronomers, space physicists, and planetary scientists. Among the fruits we ultimately hoped would come from this project would be a central site on the Web that any space scientist could use to efficiently locate existing data relevant to a particular scientific question. Furthermore, we hoped that the needed technology would be general enough to allow smaller, more-focused community within space science could use the same technologies and standards to provide more specialized services. A major challenge to searching for data across a broad community is that information that describe some data products are either not relevant to other data or not applicable in the same way. Some previous metadata standard development efforts (e.g., in the earth science and library communities) have produced standards that are very large and difficult to support. To address this problem, we studied how a standard may be divided into separable pieces. Data providers that wish to participate in interoperable searches can support only those parts of the standard that are relevant to them. We prototyped a top-level metadata standard that was small and applicable to all space science data.

Hanisch, Robert J.↗

NASA NTRSDate not supplied

Development of an Improved Spatial Metadata Simplification Algorithm

The National Aeronautics and Space Administration's (NASA) Atmospheric Science Data Center (ASDC) at NASA Langley Research Center in Hampton, VA provides atmospheric science data products and services to the science community, including enhanced search and subsetting capabilities for numerous Earth Science datasets. The ASDC is the official Distributed Active Archive Center (DAAC) of record for the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument. TEMPO is situated on a geostationary satellite positioned at a longitude near the center of the conterminous United States and focused on North America, making hourly swaths of its field of regard from east to west. Spatial metadata is an essential component for the discovery and distribution of Earth Science data. The simplified polygonal boundaries representing the archived data files ensure that any granule can be identified quickly and accurately by a geospatial query. Historically the Douglas-Peucker algorithm has been used for polygon simplification; however, due to the nature of the algorithm, a buffer must be added to the polygon before simplification to ensure pivotal points are not removed by the algorithm. This adds in additional error to the polygon simplification. ASDC’s goal is to test other methods of polyline simplification, such as Visvalingan-Whyatt and Opheim simplification alongside of Douglas-Peucker and different buffering methods, to produce less error during polygon simplification of TEMPO data swaths, and special spatial query geometries such as EPA non-attainment regions, and geopolitical boundaries.

Spatial Metadata↗

NASA NTRSDate not supplied

Use of Spatial Metadata Simplification for TEMPO

Spatial Metadata↗

NASA NTRS2018

GeneLab: Omics Database for Spaceflight Experiments

Motivation - To curate and organize expensive spaceflight experiments conducted aboard space stations and maximize the scientific return of investment, while democratizing access to vast amounts of spaceflight related omics data generated from several model organisms. Results - The GeneLab Data System (GLDS) is an open access database containing fully coordinated and curated "omics" (genomics, transcriptomics, proteomics, metabolomics) data, detailed metadata and radiation dosimetry for a variety of model organisms. GLDS is supported by an integrated data system allowing federated search across several public bioinformatics repositories. Archived datasets can be queried using full-text search (e.g., keywords, Boolean and wildcards) and results can be sorted in multifactorial manner using assistive filters. GLDS also provides a collaborative platform built on GenomeSpace for sharing files and analyses with collaborators. It currently houses 172 datasets and supports standard guidelines for submission of datasets, MIAME (for microarray), ENCODE Consortium Guidelines (for RNA-seq) and MIAPE Guidelines (for proteomics).

omics↗

NASA NTRSDate not supplied

An Overview of NASA’s Catalog of Archived Suborbital Earth Science Investigations (CASEI): Supporting FAIR and Open Access to Airborne and Field Data

Since 2019, NASA’s Airborne Data Management Group (ADMG) within the Interagency Implementation and Advanced Concepts Team (IMPACT) has worked to promote and ensure the discoverability and accessibility of the agency’s non-satellite Earth science observations. A primary component of this effort is the development of NASA’s Catalog of Archived Suborbital Earth Science Investigations (CASEI) and the vetting of key contextual details required to sustain this unique inventory of airborne and field metadata. CASEI provides information on the science objectives motivating data collection, key events/time periods in the observational record aligned with the science objectives, complementary simultaneous observations, programmatic details, and much more. The diverse set of data formats and disciplines served by CASEI have required the implementation of a common data model to organize suborbital observation metadata and efficiently connect appropriate campaigns, platforms, and instruments. The CASEI inventory provides a single entry point for users to search and browse NASA’s airborne and field data archives, regardless of which repository is responsible for their stewardship. This presentation will provide a summary of the motivations for and the development of the CASEI system. Particular attention will be granted to how CASEI facilitates discovery and reuse of these lesser-known NASA data, supporting the Open Science vision and enhancing the return on investments made to collect these unique and varied observations. An up-to-date summary of CASEI inventory content and initial metrics will be provided. Current and future avenues ADMG is pursuing to enhance both CASEI and specific components of suborbital data stewardship at various stages of the data life cycle will also be discussed.

Stephanie M. Wingo↗

NASA NTRSDate not supplied

Assessing the Use of UAS-Related Terms in ASRS Using Seed Topic Modeling

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential system that disseminates reports received from personnel involved in aviation operations after de-identifying them. These reports are used by the community to improve overall aviation system safety. Aim: We propose and execute an experiment to assess the use of seed term topic modeling over the database narratives to identify Unmanned Aircraft System (UAS) reports. The use of seed term topic modeling enables users to identify groups of conceptually similar narratives associated to a topic of their interest. Method: We use a collection of narratives, expert-selected words, and report metadata that separates UAS from non-UAS reports to assess if seed topic modeling can be used to improve ASRS searches. Results: For simpler queries, seed topic search observes a higher recall and lower precision than the existing DBOL (DataBase OnLine) search in operation. However, the best results are obtained when seed topic search is used as a search suggestion system to be executed on the DBOL. Conclusion: Utilizing a combination of both the existing method and the proposed method, users can expand their search vocabulary about subjects of interest while improving the quality of results.

Text Mining↗

NASA NTRSDate not supplied

Assessing the Use of UAS-Related Terms in ASRS using Seeds for Topic Modeling

LDA↗

NASA NTRS2006

Progress and Plans in Support of the Polar Community

Feedback provided by the Antarctic community has proven instrumental in positively influencing the direction of the GCMD's development. For example, in response to requests for a stand alone metadata authoring tool, a new shareable software package called docBUILDER solo will be released to the public in March 2006. This tool permits researchers to document their data during experiments and observational periods in the field. The international polar community has also played a key role in encouraging support for the foreign language character set in the metadata display and tools (10% of the records in the AMD hold foreign characters). In the upcoming release, the full ISO character set, which also includes mathematical symbols, will be supported. Additional upgrades include the ability for users to search for data sets based on pre-selected temporal and spatial resolution ranges. Data providers are strongly encouraged to populate the resolution fields for their data sets, although these fields are not currently required. In prior versions, browser incompatibilities often resulted in unreliable performance for users attempting to initiate a spatial search using a map based on Java applet technology. The GCMD will offer an integrated Google map and date search, replacing the applet technology and enhancing the geospatial and temporal searches. It is estimated that 30% of the records in the AMD have direct access to data. A growing number of these records can be accessed through data service links. Related data services are therefore becoming valuable assets in facilitating the use and visualization of data. Users will gain the ability to refine services using the same options as those available for data set searches. Data providers are encouraged to describe available data-related services through the directory. Future plans include offering web services through a SOAP interface and extending semantic queries for the polar regions through the use of ontologies. The Open Archives Initiative's (OAI) Protocol for Metadata Harvesting (PMH) has been successfully tested with several organizations and appears to be a prime candidate for sharing metadata within the community. The GCMD anticipates contributing to the design of the data management system for the International Polar Year and to the ongoing efforts in the years to come. Further enhancements will be discussed at the meeting.

Olsen, Lola M.↗

NASA NTRS2011

SPASE: Current Uses, Tools, and Plans

The Space Physics Archive Search and Extract (SPASE) project is an international collaboration among Heliophysics (solar and space physics) groups concerned with data acquisition and archiving. Within this community there are a variety of old and new data centers, resident archives, "virtual observatories", etc. acquiring, holding, and distributing data. The main product of the SPASE group is an XML-based SPASE Data Model now in operational use to enable searches for and ultimate acquisition of data of interest to a researcher. The SPASE Data Model defines the content of resource descriptions (metadata). The intent is to describe all SCientifically usable Heliophysics data sets using the Data Model. Another product of the SPASE group, in collaboration with NASA's Virtual Observatories, is a set of tools and services which work with SPASE meta data. This includes Registry Services which can retrieve and render metadata using resource identifiers and facilitate the downloading of the data referenced by the meta data. The SPASE Data Model has also been used as a vocabulary in specialized data models. One example is the Heliophysics Event List Manager (HELM) model. The SPASE Data Model is also being expanded to provide the means for more detailed description of data sets with the aim of enabling more automated ingestion and use of the data through detailed format descriptions. The evolution is based on a number of lessons learned and feedback from our community. Some of the lessons learned are unique to Heliophysics, and some are common to the various data diSCiplines. We will discuss the present state of SPASE usage, the role the SPASE Data Model can play in speCialized data models and how we foresee the development direction in the future.

Thieman, J. R.↗

NASA NTRS2020

Recovery, Restoration and Archiving of Previously Lost Data and Metadata from the Apollo Lunar Surface Experiments Package (ALSEP)

The Apollo Lunar Surface Experiments Package (ALSEP) is the name used to collectively represent the geophysical instruments deployed on the lunar surface by the astronauts on Apollo 12, 14, 15, 16, and 17. These instruments were active from the times of their deployment (November 1969 – December 1972) to September 1977. During that time, fourteen types of experiments were conducted, and their data were transmitted to Earth. The experiment PIs processed them. At the conclusion of the experiments, some of these data were submitted to the NASA Space Science Data Coordinated Archive (NSSDCA) for archiving, while others were not. The raw instrument data received from the Moon prior to March 1976 were not archived, either. The unarchived data, resided on open-reel magnetic tapes, became lost in the decades since, along with much of the metadata (the information necessary/useful in properly processing/analyzing the data). This article retraces the history of the ALSEP data archiving efforts in the 1970s, the subsequent loss of the data tapes, and the search, recovery, and restoration of the lost data by contemporary researchers in the 21st century. In 2006, NSSDCA began reformatting some of the ALSEP data archived in the 1970s to conform with the current Planetary Data System (PDS). In 2010, 440 of the previously lost magnetic tapes containing the raw ALSEP data were recovered. From these tapes, the data were extracted, re-packaged for individual experiments, and, for those with sufficient metadata, processed into higher order data readily usable by researchers. All of these data products have been recently archived with either PDS or NSSDCA. These newly restored data fill a number of gaps in the previously existing archive of the ALSEP data. In addition, tens of thousands of pages of Apollo era documents have been optically scanned and compiled into an online searchable catalog. This article also describes the content, organization, and usage of the restored raw ALSEP data and metadata.

S Nagihara↗

NASA NTRS2018

NASA Earth eXchange (NEX) App Store

NASA Earth Exchange (NEX), and her public cloud version OpenNEX, have become platforms supporting scientific collaboration, knowledge sharing and research for the entire Earth science community. To date, a number of custom tools and capabilities have been integrated into the platforms. However, such integration has to undergo a case-by-case manual process thus lacks scalability. This timely project builds an App Store onto OpenNEX as a building block. Climate data analytics tools/programs can be easily uploaded, shared, organized, searched, and recommended like photos and videos on the YouTube. The foundation of our App Store is a provenance server, which not only records metadata but also execution history of climate data analytics apps including the input data and parameters, output data and products, who runs the app for which purpose, and how apps may be chained into workflows. Researchers can thus understand, reproduce, and repurpose existing apps and workflows. Machine learning approaches are applied to mine provenance to provide recommend-as-you-go services for Earth scientists, such as to recommend suitable apps and workflow snippets. A browser-based workflow tool is also provided for researchers to explore the provenance server and design value-added workflows. Scalability, sustainability, extensibility, usability, adaptability, security and privacy are considered in the App Store.

eXchange↗

NASA NTRS1992

A spatial data handling system for retrieval of images by unrestricted regions of user interest

The Intelligent Data Management (IDM) project at NASA/Goddard Space Flight Center has prototyped an Intelligent Information Fusion System (IIFS), which automatically ingests metadata from remote sensor observations into a large catalog which is directly queryable by end-users. The greatest challenge in the implementation of this catalog was supporting spatially-driven searches, where the user has a possible complex region of interest and wishes to recover those images that overlap all or simply a part of that region. A spatial data management system is described, which is capable of storing and retrieving records of image data regardless of their source. This system was designed and implemented as part of the IIFS catalog. A new data structure, called a hypercylinder, is central to the design. The hypercylinder is specifically tailored for data distributed over the surface of a sphere, such as satellite observations of the Earth or space. Operations on the hypercylinder are regulated by two expert systems. The first governs the ingest of new metadata records, and maintains the efficiency of the data structure as it grows. The second translates, plans, and executes users' spatial queries, performing incremental optimization as partial query results are returned.

Dorfman, Erik↗

NASA NTRSDate not supplied

ES2Vec: Earth Science Metadata Suggestions and Analogical Reasoning

As the volume of text-based Earth science research grows, it is increasingly possible to discover latent relationships in the literature. However, traditional methodologies are restricted by limited computational capabilities and intractable problem spaces. Advancements in natural language processing (NLP) have allowed us to use an extensive Earth science corpus to create a domain-specific word vector model, Es2Vec, which we have used to surface latent relationships between Earth science concepts and generate improved keyword tags. Earth science metadata keyword assignment is a challenging problem. Dataset curators select appropriate keywords from the Global Change Master Directory (GCMD) set of keywords. The keywords an are integral part of the search and discovery of these datasets. Hence, the selection of keywords is crucial to increasing the discoverability of datasets. Utilizing machine learning techniques, we provide users with automated keyword suggestions to complement manual selection. We trained a machine learning model that leverages the semantic embedding ability of Word2Vec models to process abstracts and suggest relevant keywords. A user interface tool we built to assist data curators in the assignment of such keywords is also described.

word vectors↗

NASA NTRS2003

Enabling knowledge discovery: taxonomy development for NASA

This white paper provides the background for why it is important to take the next steps with the NASA taxonomy including test and validation, XML schema development, integration with the FirstGov federal search engine, the OneNASA portal and its supporting web content management system.

taxonomy metadata classification schema NASA porta↗

NASA NTRS2008

The NASA NEESPI Data Portal: Products, Information, and Services

Studies have indicated that land cover and use changes in Northern Eurasia influence global climate system. However, the procedures are not fully understood and it is challenging to understand the interactions between the land changes in this region and the global climate. Having integrated data collections form multiple disciplines are important for studies of climate and environmental changes. Remote sensed and model data are particularly important die to sparse in situ measurements in many Eurasia regions especially in Siberia. The NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) NEESPI data portal has generated infrastructure to provide satellite remote sensing and numerical model data for atmospheric, land surface, and cryosphere. Data searching, subsetting, and downloading functions are available. ONe useful tool is the Web-based online data analysis and visualization system, Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure), which allows scientists to assess easily the state and dynamics of terrestrial ecosystems in Northern Eurasia and their interactions with global climate system. Recently, we have created a metadata database prototype to expand the NASA NEESPI data portal for providing a venue for NEESPI scientists fo find the desired data easily and leveraging data sharing within NEESPI projects. The database provides product level information. The desired data can be found through navigation and free text search and narrowed down by filtering with a number of constraints. In addition, we have developed a Web Map Service (WMS) prototype to allow access data and images from difference data resources.

Shen, Suhung↗

NASA NTRS2009

NASA's Earth Observing Data and Information System

NASA's Earth Observing System Data and Information System (EOSDIS) has been a central component of NASA Earth observation program for over 10 years. It is one of the largest civilian science information system in the US, performing ingest, archive and distribution of over 3 terabytes of data per day much of which is from NASA s flagship missions Terra, Aqua and Aura. The system supports a variety of science disciplines including polar processes, land cover change, radiation budget, and most especially global climate change. The EOSDIS data centers, collocated with centers of science discipline expertise, archive and distribute standard data products produced by science investigator-led processing systems. Key to the success of EOSDIS is the concept of core versus community requirements. EOSDIS supports a core set of services to meet specific NASA needs and relies on community-developed services to meet specific user needs. EOSDIS offers a metadata registry, ECHO (Earth Observing System Clearinghouse), through which the scientific community can easily discover and exchange NASA s Earth science data and services. Users can search, manage, and access the contents of ECHO s registries (data and services) through user-developed and community-tailored interfaces or clients. The ECHO framework has become the primary access point for cross-Data Center search-and-order of EOSDIS and other Earth Science data holdings archived at the EOSDIS data centers. ECHO s Warehouse Inventory Search Tool (WIST) is the primary web-based client for discovering and ordering cross-discipline data from the EOSDIS data centers. The architecture of the EOSDIS provides a platform for the publication, discovery, understanding and access to NASA s Earth Observation resources and allows for easy integration of new datasets. The EOSDIS also has developed several methods for incorporating socioeconomic data into its data collection. Over the years, we have developed several methods for determining needs of the user community including use of the American Customer Satisfaction Index and a broad metrics program.

Mitchell, Andrew E.↗

NASA NTRSDate not supplied

Enhancing Dataset Discovery With Knowledge Graph Link Prediction Techniques

● In the evolving landscape of open science, the ability to navigate and discover pertinent datasets is increasingly significant. This primarily hinges on the presence of detailed metadata, delineating the dataset’s content, and potential spheres of application. ● The GES DISC datasets are characterized by science keywords to enable dataset discovery in web search interfaces. ● A problem may arise where a dataset lacks a science keyword that it otherwise should have. ● Machine learning techniques such as link prediction can be used to detect these missing science keywords by estimating the probability of new links forming between dataset and keyword nodes.

machine learning↗

NASA NTRS2003

Organizing Diverse, Distributed Project Information

SemanticOrganizer is a software application designed to organize and integrate information generated within a distributed organization or as part of a project that involves multiple, geographically dispersed collaborators. SemanticOrganizer incorporates the capabilities of database storage, document sharing, hypermedia navigation, and semantic-interlinking into a system that can be customized to satisfy the specific information-management needs of different user communities. The program provides a centralized repository of information that is both secure and accessible to project collaborators via the World Wide Web. SemanticOrganizer's repository can be used to collect diverse information (including forms, documents, notes, data, spreadsheets, images, and sounds) from computers at collaborators work sites. The program organizes the information using a unique network-structured conceptual framework, wherein each node represents a data record that contains not only the original information but also metadata (in effect, standardized data that characterize the information). Links among nodes express semantic relationships among the data records. The program features a Web interface through which users enter, interlink, and/or search for information in the repository. By use of this repository, the collaborators have immediate access to the most recent project information, as well as to archived information. A key advantage to SemanticOrganizer is its ability to interlink information together in a natural fashion using customized terminology and concepts that are familiar to a user community.

Keller, Richard M.↗