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High quality descriptive metadata is essential to enabling the effective discovery of Earth observation data to a growing number of diverse users. In this paper, we define a framework to assess the quality of NASA’s Earth observation metadata with the overarching goal of improving the discoverability, accessibility and usability of the data it describes. The framework, developed by the Analysis and Review of the Common Metadata Repository (ARC) team, focuses on the metadata quality dimensions of correctness, completeness, and consistency. The methodology used by the ARC team to implement the framework is described, as well as best practices, lessons learned and recommendations for implementing similar metadata quality assessment processes. Initial results from the project indicate that this methodology, in combination with community and stakeholder collaboration, is effective in improving metadata quality.

The open science movement continues to gain momentum, attention, and discussion. However, there are a number of different interpretations, viewpoints, and perspectives as to what the term “open science” means. In this study, we define open science as a collaborative culture enabled by technology that empowers the open sharing of data, information, and knowledge within the scientific community and the wider public to accelerate scientific research and understanding. As science has become increasingly data driven, data programs now play a critical role in enabling and accelerating open science. In this study, we describe specific actions that data programs can take to make the open science paradigm shift a reality. These actions range from implementing open data and software policies to reimaging data systems that move data out of organizational silos and into cyberinfrastructures that enable efficient research processes and accelerate knowledge dissemination. There are still a number of obstacles to be overcome by data programs which range from mitigating the risk of open data misuse to overcoming the inertia of legacy systems. Data programs need to support open science through the thoughtful development of open policies, systematic investment in innovative and collaborative infrastructures, and the promotion of cultural change. On the other hand, individual researchers play an equally important role by serving as advocates for open science principles and by adopting a number of best practices outlined in this study. By working together, a new and more open age of scientific research can be achieved to benefit science and society.

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 Earth science metadata records. Second, the methodology has been implemented as a stand-alone web service that is utilized to augment search and usability of data in a variety of tools.

Open science is a concept that represents a fundamental change in scientific culture. This change is characterized by openness, where research objects and results are shared as soon as possible, and connectivity to a wider audience. Understanding about what Open Science actually means differs from various stakeholders.Thoughts on Open Science fall into four distinct viewpoints. The first viewpoint strives to make science accessible to a larger community by focusing on allowing non-scientists to participate in the research process through citizen science project and by more effectively communicating research results to the broader public. The second viewpoint considers providing equitable knowledge access to everyone by not only considering access to journal publications but also to other objects in the research process such as data and code. The third viewpoint focuses on making both the research process and the communication of results more efficient. There are two aspects to this component which can be described as social and technical components. The social component is driven by the need to tackle complex problems that require collaboration and a team approach to science while the technical component focuses on creating tools, services and especially scientific platforms to make the scientific process more efficient. Lastly, the fourth viewpoint strives to develop new metrics to measure scientific contributions that go beyond the current metrics derived solely from scientific publications and to consider contributions from other research objects such as data, code or knowledge sharing through blogs and other social media communication mechanisms. Technological change is a factor in all four of these viewpoints on Open Science. New capabilities in compute, storage, methodologies, publication and sharing enable technologists to better serve as the primary drivers for Open Science by providing more efficient technological solutions. Sharing knowledge, information and other research objects such as data and code has become easier with new modalities of sharing available to researchers. In addition, technology is enabling the democratization of science at two levels. First, researchers are no longer constrained by lack of infrastructure resources needed to tackle difficult problems. Second, the Citizen Science projects now involve the public at different steps of the scientific process from collecting the data to analysis.This presentations investigates the four described viewpoints on Open Science from the perspective of any large organization involved in scientific data stewardship and management. The presentation will list possible technological strategies that organizations may adopt to further align with all aspects of the Open Science movement.

Algorithm Theoretical Basis Documents (ATBDs) accompany Earth observation data generated from algorithms. ATBDs describe the physical theory, mathematical procedures and assumptions made for the algorithms that convert radiances received by remote sensing instruments into geophysical quantities. While ATBDs are critical to scientific reproducibility and data reuse, there have been technical, social and informational issues surrounding the creation and maintenance of these key documents. A standard ATBD structure has been lacking, resulting in inconsistent documents of varying levels of detail. Due to the lack of a minimum set of requirements, there has been very little formal guidance on the ATBD publication process. Additionally, ATBDs have typically been provided as static documents that are not machine readable, making search and discovery of the documents and the content within the documents difficult for users. To address the challenges surrounding ATBDs, NASA has prototyped the Algorithm Publication Tool (APT), a centralized cloud-based publication tool that standardizes the ATBD content model and streamlines the ATBD authoring process. This presentation will describe our approach in developing a common information model for ATBDs and our efforts to provide ATBDs as dynamic documents that are available for both human and machine utilization. We will also include our vision for APT within the broader NASA Earth science data system and how this tool may assist in standardizes and easing the ATBD creation and maintenance process.

A digital, or virtual, collection is a value added service developed by libraries that curates information and resources around a topic, theme or organization. Adoption of the virtual collection concept as an Earth science data service improves the discoverability, accessibility and usability of data both within individual data centers but also across data centers and disciplines. In this paper, we introduce a methodology for systematically and rigorously curating Earth science data and information into a cohesive virtual collection. This methodology builds on the geocuration model of searching, selecting and synthesizing Earth science data, metadata and other information into a single and useful collection. We present our experiences curating a virtual collection for one of NASA's twelve Distributed Active Archive Centers (DAACs), the Global Hydrology Resource Center (GHRC), and describe lessons learned as a result of this curation effort. We also provide recommendations and best practices for data centers and data providers who wish to curate virtual collections for the Earth sciences.

Curation is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest and typically occurs in museums, art galleries, and libraries. The task of organizing data around specific topics or themes is a vibrant and growing effort in the biological sciences but to date this effort has not been actively pursued in the Earth sciences. In this paper, we introduce the concept of geocuration and define it as the act of searching, selecting, and synthesizing Earth science data/metadata and information from across disciplines and repositories into a single, cohesive, and useful collection. We present the Climate Data Initiative (CDI) project as a prototypical example. The CDI project is a systematic effort to manually curate and share openly available climate data from various federal agencies. CDI is a broad multi-agency effort of the U.S. government and seeks to leverage the extensive existing federal climate-relevant data to stimulate innovation and private-sector entrepreneurship to support national climate-change preparedness. We describe the geocuration process used in the CDI project, lessons learned, and suggestions to improve similar geocuration efforts in the future.

This paper presents two research applications exploiting unused metadata resources in novel ways to aid data discovery and exploration capabilities. The results based on the experiments are encouraging and each application has the potential to serve as a useful standalone component or service in a data system.