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The size of the National Aeronautics and Space Administration (NASA) Science Mission Directorate (SMD) is growing exponentially, allowing researchers to make discoveries. However, making discoveries is challenging and time-consuming due to the size of the data catalogs, and as many concepts and data are indirectly connected. This paper proposes a pipeline to generate knowledge graphs (KGs) representing different NASA SMD domains. These KGs can be used as the basis for dataset search engines, saving researchers time and supporting them in finding new connections. We collected textual data and used several modern natural language processing (NLP) methods to create the nodes and the edges of the KGs. We explore the cross-domain connections, discuss our challenges, and provide future directions to inspire researchers working on similar challenges.

This presentation was given at the NASA Open Source Science Data Repositories Workshopwhich occurred September 27 - 30, 2022 at Langley Research Center in Hampton Virginia. The purpose of the workshop was to engage the NASA data repositories on the next steps in sharing NASA scientific information and open science. This presentation was given during the session on open source science knowledge management. The goal of the presentation was to inform the repositories community of the work done to date on the Science Discovery Engine and to ask for feedback.

Presentation on NASA's Open Source Science Initiative include the steps that are being taken to share Open Science, update NASA policy, ROSES elements to support open science, and Transform to Open Science.

The Science Mission Directorate has adopted SPD-41 that is a consolidation of existing policies applicable to SMD. These policies are based on our understanding of existing NASA and Federal guidance, and they are already part of solicitations for funding such as ROSES or SALMON Announcement of Opportunities. This applies to all SMD-funded activities related to producing scientific information, but the policy excludes restricted information such as ITAR, export control, CUI. •These policies are applicable to all current or future awards, contracts, or cooperative agreements for scientific activities. •SPD-41: The Science Information Policy •Science Information Policy Website In addition, SMD will be releasing a Request for Information on proposed additions to the information policy based on new Federal guidance, NASA policy, National Academy studies, or community best practices.

The objective of this meeting was toprovide a forum for our science data providers, stewards, and partners to find out more details about the Open Source Science Initiative (OSSI), ask questions about the number of initiatives that are currently underway related to Open Science, and participate in forum-style breakout sessions where attendees will have the opportunity to engage with SMD and the community on further developing these initiatives. Initiatives included projects involving machine learning and artificial intelligence, discoverability of SMD data and publications, and new policies to support open science. In addition, we will also introduced Transform to Open Science (TOPS), our initiative to help jump start the adoption of open science across our communities. Open Source Science Workshop Website:https://science.nasa.gov/researchers/science-data/open-source-science-workshop

In order to help scientists in the above ground biomass community and to support the science behind the upcoming BIOMASS, NISAR, and GEDI satellite missions, ESA and NASA are collaborating on the Multi-Mission Algorithm and Analysis Platform (MAAP). The MAAP is a jointly developed and implemented platform that will include not only data (satellite, airborne, in situ data, and products), but also computing capabilities and sets of tools and algorithms developed to support this specific field of research. To best ensure that users are able to collaborate across the platform and to access needed resources, the MAAP requires all data, algorithms, and software to conform to open access and open source policies. In addition to aiding researchers, the MAAP exercise is establishing a collaboration framework between ESA and NASA that focuses on sharing data, science algorithms, and computable resources in order to foster and accelerate scientific research.

Under the auspices of the Earth Science Information Partners (ESIP) and with collaboration among members of the ESIP Information Quality Cluster (IQC), the Barcelona Supercomputing Center (BSC) Evaluation and Quality Control (EQC) team, and the Australia/New Zealand Data Quality Interest Group (AU/NZ DQIG), a community effort has been undertaken by international Earth Science domain experts. The objective of this effort is to develop global community guidelines with practical recommendations to promote the representation, sharing and reuse of quality information at the dataset level, leveraging the experiences and expertise of a team of interdisciplinary domain experts and community best practices. The community guidelines are inspired by the guiding principles of findability, accessibility, interoperability, and reusability (FAIR) and aim to help stakeholders such as science data centers, repositories, data producers and publishers, data managers and stewards, etc., i) to capture, describe, and represent quality information of their datasets in a way that is in line with the FAIR guiding principles; ii) to allow for the maximum discovery, trust, sharing, reuse and value of their datasets; and iii) to enable global access to and integration of dataset quality information. The vision of developing these guidelines is to promote the creation and use of freely and openly shared dataset quality information that is consistently described, readily available in community standardized formats, and capable of being integrated across commonly-used Earth science systems and tools for search and access with explicitly expressed usage licenses.

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.

Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science. The virtual pre-ESIP workshop held on July 13, 2020 was sponsored by ESIP and co-organized by the ESIP IQC and the BSC EQC team, in collaboration with the ARDC AU/NZ DQIG. An additional community engagement event was carried out by the AU/NZ DQIG prior to the pre-ESIP workshop. ESIP is primarily supported by the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA) and the United States Geological Survey (USGS). The technological and infrastructural support during the preparation and conduct of the workshop was invaluable. In particular, we thank Megan Carter, ESIP Community Director, for supporting us throughout the workshop and providing helpful advice during the planning stage of the virtual workshop, and ESIP Community Fellow, Alexis Garretson, for supporting the ESIP SM20 report-out session. We thank all participants for attending the pre-ESIP workshop and the ESIP SM20 session and contributing to productive discussions during the live sessions and the two weeks of the ESIP SM20 period. Portions of this work have been extracted from Peng et al. (2020a), which reported on the workshop and the ESIP SM20 report-out session. The Australian participants acknowledge the support of the ARDC. The constructive suggestions from two anonymous reviewers of Data Science Journal have helped improve the quality of the paper. Peer Reviewed "Article signat per 33 autors/es: Ge Peng , Robert R. Downs, Carlo Lacagnina, Hampapuram Ramapriyan, Ivana Ivánová, David Moroni, Yaxing Wei, Gilles Larnicol, Lesley Wyborn, Mitch Goldberg, Jörg Schulz, Irina Bastrakova, Anette Ganske, Lucy Bastin, Siri Jodha S. Khalsa, Mingfang Wu, Chung-Lin Shie, Nancy Ritchey, Dave Jones, Ted Habermann, Christina Lief, Iolanda Maggio, Mirko Albani, Shelley Stall, Lihang Zhou, Marie Drévillon, Sarah Champion, C. Sophie Hou, Francisco Doblas-Reyes, Kerstin Lehnert, Erin Robinson, Kaylin Bugbee"

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.

The ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP) is a platform designed to meet the need of the international scientific community to collaborate on the generation and analysis of increasingly massive datasets from space-based, airborne, and field observations for aboveground terrestrial carbon dynamics. The MAAP is an open-source, cloud-based platform that distinguishes itself from other science platforms by being agnostic to any science disciplines and allows scientists to write, develop, and execute their own algorithms in shared workspaces that are tailored to their specific area of research. We present an overview of the capabilities of the Pilot implementation of MAAP. Users can explore and visualize ESA and NASA data that is ingested in the MAAP data catalog, develop and test algorithms to generate new datasets and act upon existing ones, launch matured algorithms as large-scale processing jobs, and analyze the results. This allows scientists to follow the entire scientific process...

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.