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NASA NTRS2012

An Integrated and Collaborative Approach for NASA Earth Science Data

Earth science research requires coordination and collaboration across multiple disparate science domains. Data systems that support this research are often as disparate as the disciplines that they support. These distinctions can create barriers limiting access to measurements, which could otherwise enable cross-discipline Earth science. NASA's Earth Observing System Data and Information System (EOSDIS) is continuing to bridge the gap between discipline-centric data systems with a coherent and transparent system of systems that offers up to date and engaging science related content, creates an active and immersive science user experience, and encourages the use of EOSDIS earth data and services. The new Earthdata Coherent Web (ECW) project encourages cohesiveness by combining existing websites, data and services into a unified website with a common look and feel, common tools and common processes. It includes cross-linking and cross-referencing across the Earthdata site and NASA's Distributed Active Archive Centers (DAAC), and by leveraging existing EOSDIS Cyber-infrastructure and Web Service technologies to foster re-use and to reduce barriers to discovering Earth science data (http://earthdata.nasa.gov).

Murphy, K.↗

NASA NTRS2008

Semantic Web Data Discovery of Earth Science Data at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

Mirador is a web interface for searching Earth Science data archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Mirador provides keyword-based search and guided navigation for providing efficient search and access to Earth Science data. Mirador employs the power of Google's universal search technology for fast metadata keyword searches, augmented by additional capabilities such as event searches (e.g., hurricanes), searches based on location gazetteer, and data services like format converters and data sub-setters. The objective of guided data navigation is to present users with multiple guided navigation in Mirador is an ontology based on the Global Change Master directory (GCMD) Directory Interchange Format (DIF). Current implementation includes the project ontology covering various instruments and model data. Additional capabilities in the pipeline include Earth Science parameter and applications ontologies.

Hegde, Mahabaleshwara↗

NASA NTRS2010

Multi-Instrument Tools and Services to Access NASA Earth Science Data from the GSFC Earth Sciences Data and Information Services Center

The presentation purpose is to describe multi-instrument tools and services that facilitate access and usability of NASA Earth science data at Goddard Space Flight Center (GSFC). NASA's Earth observing system includes 14 satellites. Topics include EOSDIS facilities and system architecture, and overview of GSFC Earth Science Data and Information Services Center (GES DISC) mission, Mirador data search, Giovanni, multi-instrument data exploration, Google Earth[TM], data merging, and applications.

Kempler, Steve↗

NASA NTRSDate not supplied

Transforming NASA Earth Science Data Systems: A Journey from Big Earth Data Initiative (BEDI) to Open-Source Science Initiative (OSSI)

NASA's Earth Science Data and Information Systems (ESDIS) have undergone a significant evolution, particularly with the introduction of the Big Earth Data Initiative (BEDI) and the Open-Source Science Initiative (OSSI). In this talk, I will provide an overview of NASA's Earth Science Data and Information systems, highlighting key components such as EOSDIS, ESDIS, and ESDS. Moving forward, I will delve into the BEDI initiative, discussing its objectives, key players, and lessons learned. The second part of the talk will cover the OSSI initiative, exploring its objectives, strategy, and innovative solutions. Throughout the presentation, I will provide insights into the requests, strategies, and solutions behind both BEDI and OSSI. By the end, you will gain a comprehensive understanding of how NASA's Earth Science Data Systems have evolved over the years and witness the organization's commitment to advancing an open-source and collaborative approach to data science. Join me for an enlightening exploration into the future of Earth science data and the pivotal role played by NASA in shaping this transformative landscape.

Jennifer Wei↗

NASA NTRS2010

Distinguishing Provenance Equivalence of Earth Science Data

Reproducibility of scientific research relies on accurate and precise citation of data and the provenance of that data. Earth science data are often the result of applying complex data transformation and analysis workflows to vast quantities of data. Provenance information of data processing is used for a variety of purposes, including understanding the process and auditing as well as reproducibility. Certain provenance information is essential for producing scientifically equivalent data. Capturing and representing that provenance information and assigning identifiers suitable for precisely distinguishing data granules and datasets is needed for accurate comparisons. This paper discusses scientific equivalence and essential provenance for scientific reproducibility. We use the example of an operational earth science data processing system to illustrate the application of the technique of cascading digital signatures or hash chains to precisely identify sets of granules and as provenance equivalence identifiers to distinguish data made in an an equivalent manner.

Tilmes, Curt↗

NASA NTRS2016

NASA Earth Observing System Data and Information System (EOSDIS): A U.S. Network of Data Centers Serving Earth Science Data: A Network Member of ICSU WDS

NASA's Earth Observing System Data and Information System (EOSDIS) has been in operation since August 1994, and serving a diverse user community around the world with Earth science data from satellites, aircraft, field campaigns and research investigations. The ESDIS Project, responsible for EOSDIS is a Network Member of the International Council for Sciences (ICSU) World Data System (WDS). Nine of the 12 Distributed Active Archive Centers (DAACs), which are part of EOSDIS, are Regular Members of the ICSUWDS. This poster presents the EOSDIS mission objectives, key characteristics of the DAACs that make them world class Earth science data centers, successes, challenges and best practices of EOSDIS focusing on the years 2014-2016, and illustrates some highlights of accomplishments of EOSDIS. The highlights include: high customer satisfaction, growing archive and distribution volumes, exponential growth in number of products distributed to users around the world, unified metadata model and common metadata repository, flexibility provided to uses by supporting data transformations to suit their applications, near-real-time capabilities to support various operational and research applications, and full resolution image browse capabilities to help users select data of interest. The poster also illustrates how the ESDIS Project is actively involved in several US and international data system organizations.

Earth Science↗

NASA NTRS2024

Strategizing Earth Science Data Development

Developing Earth science data products that meet the needs of diverse users is a challenging task for both data producers and service providers, as user requirements can vary significantly and evolve over time. In this comment, we discuss several strategies to improve Earth science data products that everyone can use.

Earth science↗

NASA NTRS2015

Deriving Earth Science Data Analytics Requirements

Data Analytics applications have made successful strides in the business world where co-analyzing extremely large sets of independent variables have proven profitable. Today, most data analytics tools and techniques, sometimes applicable to Earth science, have targeted the business industry. In fact, the literature is nearly absent of discussion about Earth science data analytics. Earth science data analytics (ESDA) is the process of examining large amounts of data from a variety of sources to uncover hidden patterns, unknown correlations, and other useful information. ESDA is most often applied to data preparation, data reduction, and data analysis. Co-analysis of increasing number and volume of Earth science data has become more prevalent ushered by the plethora of Earth science data sources generated by US programs, international programs, field experiments, ground stations, and citizen scientists.Through work associated with the Earth Science Information Partners (ESIP) Federation, ESDA types have been defined in terms of data analytics end goals. Goals of which are very different than those in business, requiring different tools and techniques. A sampling of use cases have been collected and analyzed in terms of data analytics end goal types, volume, specialized processing, and other attributes. The goal of collecting these use cases is to be able to better understand and specify requirements for data analytics tools and techniques yet to be implemented. This presentation will describe the attributes and preliminary findings of ESDA use cases, as well as provide early analysis of data analytics toolstechniques requirements that would support specific ESDA type goals. Representative existing data analytics toolstechniques relevant to ESDA will also be addressed.

data analytics↗

NASA NTRS2017

A Relevancy Algorithm for Curating Earth Science Data Around Phenomenon

Earth science data are being collected for various science needs and applications, processed using different algorithms at multiple resolutions and coverages, and then archived at different archiving centers for distribution and stewardship causing difficulty in data discovery. Curation, which typically occurs in museums, art galleries, and libraries, is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest. Curating data sets around topics or areas of interest addresses some of the data discovery needs in the field of Earth science, especially for unanticipated users of data. This paper describes a methodology to automate search and selection of data around specific phenomena. Different components of the methodology including the assumptions, the process, and the relevancy ranking algorithm are described. The paper makes two unique contributions to improving data search and discovery capabilities. First, the paper describes a novel methodology developed for automatically curating data around a topic using Earthscience metadata records. Second, the methodology has been implemented as a standalone web service that is utilized to augment search and usability of data in a variety of tools.

earth science phenomena↗

NASA NTRS2017

Use of Schema on Read in Earth Science Data Archives

Traditionally, NASA Earth Science data archives have file-based storage using proprietary data file formats, such as HDF and HDF-EOS, which are optimized to support fast and efficient storage of spaceborne and model data as they are generated. The use of file-based storage essentially imposes an indexing strategy based on data dimensions. In most cases, NASA Earth Science data uses time as the primary index, leading to poor performance in accessing data in spatial dimensions. For example, producing a time series for a single spatial grid cell involves accessing a large number of data files. With exponential growth in data volume due to the ever-increasing spatial and temporal resolution of the data, using file-based archives poses significant performance and cost barriers to data discovery and access. Storing and disseminating data in proprietary data formats imposes an additional access barrier for users outside the mainstream research community. At the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), we have evaluated applying the schema-on-read principle to data access and distribution. We used Apache Parquet to store geospatial data, and have exposed data through Amazon Web Services (AWS) Athena, AWS Simple Storage Service (S3), and Apache Spark. Using the schema-on-read approach allows customization of indexing spatially or temporally to suit the data access pattern. The storage of data in open formats such as Apache Parquet has widespread support in popular programming languages. A wide range of solutions for handling big data lowers the access barrier for all users. This presentation will discuss formats used for data storage, frameworks with This presentation will discuss formats used for data storage, frameworks with support for schema-on-read used for data access, and common use cases covering data usage patterns seen in a geospatial data archive.

cloud applications↗

NASA NTRS2020

Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.

snow cover extent↗

NASA NTRS2015

NASA's Earth Science Data Systems

NASA's Earth Science Data Systems (ESDS) Program has evolved over the last two decades, and currently has several core and community components. Core components provide the basic operational capabilities to process, archive, manage and distribute data from NASA missions. Community components provide a path for peer-reviewed research in Earth Science Informatics to feed into the evolution of the core components. The Earth Observing System Data and Information System (EOSDIS) is a core component consisting of twelve Distributed Active Archive Centers (DAACs) and eight Science Investigator-led Processing Systems spread across the U.S. The presentation covers how the ESDS Program continues to evolve and benefits from as well as contributes to advances in Earth Science Informatics.

Data Systems↗

NASA NTRS2004

Digital Archive Issues from the Perspective of an Earth Science Data Producer

Contents include the following: Introduction. A Producer Perspective on Earth Science Data. Data Producers as Members of a Scientific Community. Some Unique Characteristics of Scientific Data. Spatial and Temporal Sampling for Earth (or Space) Science Data. The Influence of the Data Production System Architecture. The Spatial and Temporal Structures Underlying Earth Science Data. Earth Science Data File (or Relation) Schemas. Data Producer Configuration Management Complexities. The Topology of Earth Science Data Inventories. Some Thoughts on the User Perspective. Science Data User Communities. Spatial and Temporal Structure Needs of Different Users. User Spatial Objects. Data Search Services. Inventory Search. Parameter (Keyword) Search. Metadata Searches. Documentation Search. Secondary Index Search. Print Technology and Hypertext. Inter-Data Collection Configuration Management Issues. An Archive View. Producer Data Ingest and Production. User Data Searching and Distribution. Subsetting and Supersetting. Semantic Requirements for Data Interchange. Tentative Conclusions. An Object Oriented View of Archive Information Evolution. Scientific Data Archival Issues. A Perspective on the Future of Digital Archives for Scientific Data. References Index for this paper.

Barkstrom, Bruce R.↗

NASA NTRS2016

Optimal Reorganization of NASA Earth Science Data for Enhanced Accessibility and Usability for the Hydrology Community

A long-standing "Digital Divide" in data representation exists between the preferred way of data access by the hydrology community and the common way of data archival by earth science data centers. Typically, in hydrology, earth surface features are expressed as discrete spatial objects (e.g., watersheds), and time-varying data are contained in associated time series. Data in earth science archives, although stored as discrete values (of satellite swath pixels or geographical grids), represent continuous spatial fields, one file per time step. This Divide has been an obstacle, specifically, between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. and NASA earth science data systems. In essence, the way data are archived is conceptually orthogonal to the desired method of access. Our recent work has shown an optimal method of bridging the Divide, by enabling operational access to long-time series (e.g., 36 years of hourly data) of selected NASA datasets. These time series, which we have termed "data rods," are pre-generated or generated on-the-fly. This optimal solution was arrived at after extensive investigations of various approaches, including one based on "data curtains." The on-the-fly generation of data rods uses "data cubes," NASA Giovanni, and parallel processing. The optimal reorganization of NASA earth science data has significantly enhanced the access to and use of the data for the hydrology user community.

data rods↗

NASA NTRS2015

Data Recipes: Toward Creating How-To Knowledge Base for Earth Science Data

Both the diversity and volume of Earth science data from satellites and numerical models are growing dramatically, due to an increasing population of measured physical parameters, and also an increasing variety of spatial and temporal resolutions for many data products. To further complicate matters, Earth science data delivered to data archive centers are commonly found in different formats and structures. NASA data centers, managed by the Earth Observing System Data and Information System (EOSDIS), have developed a rich and diverse set of data services and tools with features intended to simplify finding, downloading, and working with these data. Although most data services and tools have user guides, many users still experience difficulties with accessing or reading data due to varying levels of familiarity with data services, tools, and or formats. The data recipe project at Goddard Earth Science Data and Information Services Center (GES DISC) was initiated in late 2012 for enhancing user support. A data recipe is a How-To online explanatory document, with step-by-step instructions and examples of accessing and working with real data (http:disc.sci.gsfc.nasa.govrecipes). The current suite of recipes has been found to be very helpful, especially to first-time-users of particular data services, tools, or data products. Online traffic to the data recipe pages is significant, even though the data recipe topics are still limited. An Earth Science Data System Working Group (ESDSWG) for data recipes was established in the spring of 2014, aimed to initiate an EOSDIS-wide campaign for leveraging the distributed knowledge within EOSDIS and its user communities regarding their respective services and tools. The ESDSWG data recipe group is working on an inventory and analysis of existing data recipes and tutorials, and will provide guidelines and recommendation for writing and grouping data recipes, and for cross linking recipes to data products. This presentation gives an overview of the data recipe activites at GES DISC and ESDSWG. We are seeking requirements and input from a broader data user community to establish a strong knowledge base for Earth science data research and application implementations.

data recipe↗

NASA NTRS2019

Carbon Cycle Science Data and Services at the Goddard Earth Sciences Data Information and Services Center (GES DISC)

The Goddard Earth Sciences Data Information and Services Center (GES DISC) archives and distributes a number of observational and model carbon cycle science data sets. We also provide services that facilitate data discovery, intercomparison, and visualization of these heterogeneous datasets for both research and applications users, such as subsetting, format conversion, How-To documentation, and the Help Desk.

Hearty, Thomas↗

NASA NTRS2019

Using Big Data Technologies with Earth Science Data in HDF5: HDF5 Scalable Solutions

HDF5 (Hierarchical Data Format 5) is open-source, high-performance software that consists of an abstract data model, library, and fileformat used for storing and managing extremely large and/or complex data collections. NASA Earth Observing System (EOS) Data and Information Systems use HDF5 as an archival format to store remote sensing data from EOS satellites. HDF5 is also used to store other types of Geoscience and Strophysical data, e.g., seismic data and data from Low-Frequency Array (LOFAR) radio telescopes. Data stored in HDF5 has reached tens of petabytes and is growing at an accelerated rate.With the growing amout of HDF5 Earth Science data to analyze and process, scientists need to adopt big data technologies including new storage paradigms such as cloud and object storage. To run models and perform data analysis they also need to utilizied efficient and diverse ways to access data, from high-performance computing's (HPC) Message Passing Interface (MPI) I/O and deep memory hierarchies (DMH) to non-HPC frameworks such as Apache Hadoop, Spark, and Drill. The HDF Group continually works to enable usage of big data technologies in HDF software.

Knox, Larry↗

NASA NTRS2016

Virtual Collections: An Earth Science Data Curation Service

The role of Earth science data centers has traditionally been to maintain central archives that serve openly available Earth observation data. However, in order to ensure data are as useful as possible to a diverse user community, Earth science data centers must move beyond simply serving as an archive to offering innovative data services to user communities. A virtual collection, the end product of a curation activity that searches, selects, and synthesizes diffuse data and information resources around a specific topic or event, is a data curation service that improves the discoverability, accessibility, and usability of Earth science data and also supports the needs of unanticipated users. Virtual collections minimize the amount of the time and effort needed to begin research by maximizing certainty of reward and by providing a trustworthy source of data for unanticipated users. This presentation will define a virtual collection in the context of an Earth science data center and will highlight a virtual collection case study created at the Global Hydrology Resource Center data center.

Data↗