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Scientific content curation provides users across diverse disciplines and levels of experience with a valuable means of accessing relevant and reliable information amid the growing data landscape.

NASA's Visualization, Exploration, and Data Analysis (VEDA) project is an open-source science cyberinfrastructure for data processing, visualization, exploration, and geographic information systems (GIS) capabilities (https://www.earthdata.nasa.gov/esds/veda). VEDA was an ambitious platform and one that was only made possible in the past year by building upon existing NASA projects. The extensive technology community at NASA continues to come together to design, build and use VEDA’s interoperable APIs and datasets.This presentation will demo the current capabilities of VEDA and discuss how these capabilities were designed and architected with the central goals of science delivery, reproducible science, and interoperability to support re-use of data and APIs across NASA’s Earth Science ecosystem of tools. The presentation will close with VEDA’s future plans. In 2023, VEDA will support NASA’s Transform to Open Science (TOPS) program and open-source science initiatives through data, APIs and analytics platforms. In 2023 and beyond, VEDA will advance the state of the art in cloud-based Earth science as well as strengthening the ties of technology within NASA.The projects behind VEDA’s current features are:The Multi-Mission Algorithm and Analysis Platform (https://maap-project.org/, presented at EGU 2019): Recognizing the numerous advantages of open, reproducible science, NASA and ESA are working together to create the Joint ESA-NASA MAAP. The MAAP brings together relevant data and algorithms in a common virtual environment in order to support the global aboveground terrestrial carbon dynamics research community. The COVID-19 Earth Observation Dashboard (https://www.earthdata.nasa.gov/covid19/): Following the interest in this dashboard, NASA invested in the design and development of a new dashboard infrastructure. This infrastructure is highly configurable to support easily adding new datasets and discoveries. UI and config layers are built upon the VEDA STAC catalog and Cloud-Optimized GeoTIFFs. The Earthdata Information Systems (EIS) pilots (https://eis.smce.nasa.gov/): Scientists at NASA worked together on open science tools to develop new research projects using Earth Observation data across the domains of fire, freshwater, greenhouse gasses, and sea level rise. ArcGIS Enterprise in the Cloud (gis.earthdata.nasa.gov) provides GIS capabilities. The projects listed above have all made VEDA a reality in a year. The scientists from EIS are using the new dashboard infrastructure to tell their stories and the analytics backend from MAAP to scale their science.In 2023, VEDA plans many initiatives in the work to extend its reach within and beyond NASA. There are many advanced technologies at NASA and we see an opportunity for VEDA to support closing the information gaps across groups. For example, VEDA will support driving standards for using, publishing and visualizing NASA’s Earthdata Zarr archives and also deliver interoperable APIs for its data stores to support dynamic data visualization and storytelling.VEDA will also extend its reach beyond NASA by providing a JupyterHub for any user to explore the data behind NASA Earth Science, specifically the discoveries presented in the Earthdata Dashboard.

NASA’s Science Plan includes a strategy to advance discovery by leveraging cross-disciplinary opportunities between scientific disciplines. In addition, NASA is committed to building an inclusive, open science community over the next decade and is championing the new Open-Source Science Initiative (OSSI) to foster that community. The OSSI supports many activities to promote open science including the development of an empowering cyberinfrastructure to accelerate the time to actionable science. One component of the OSSI cyberinfrastructure is the Science Discovery Engine (SDE). The goal of the SDE is to enable the discovery of data, software and documentation across the five SMD divisions including Astrophysics, Biological and Physical Sciences, Earth Science, Heliophysics and Planetary Science. The SDE increases accessibility to NASA’s open science data and information and promotes interdisciplinary scientific discovery. In this presentation, we describe our work to develop the Science Discovery Engine in Sinequa, a Cognitive Search capability. We also share lessons learned about data governance, curation and information access.

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.

NASA’s Science Mission Directorate (SMD) is working to build an open-source science infrastructure to enable open, collaborative and interdisciplinary science. One key component in the open-source science infrastructure is the SMD data catalog project. The SMD data catalog project is building an integrated SMD enterprise search capability to enable discovery of open data across SMD’s five divisions, including Astrophysics, Biological and Physical Sciences, Earth Science, Heliophysics and Planetary Science. In order to efficiently integrate heterogeneous data types across the SMD enterprise, the SMD data catalog project considered three approaches for implementation including semantic mapping, graph data stores and Cognitive Search capabilities. In this presentation, we will describe the SMD data catalog project business case, the three identified technical approaches and our assessment of those approaches. We will also provide a summary of the overall technical assessment process so that it may inform other agencies and organizations who may be implementing similar initiatives.

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.

Access to valuable scientific research data is becoming increasingly more open, attracting a growing user community of scientists, decision makers and innovators. While these data are more openly available, accessibility continues to remain an issue due to the large volumes of complex, heterogeneous data that are available for analysis. This emerging accessibility issue is driving the development of specialized software stacks to instantiate new analysis platforms that enable users to quickly and efficiently work with large volumes of data. These platforms, typically found on the cloud or in a high performance computing environment, are optimized for large-scale data analysis. These platforms can be transient in nature, with a defined life span and a focus on improved capabilities as opposed to serving as an archive of record. While these transient, optimized platforms are not held to the same stewardship standards as a traditional archive, data must still be managed in a standardized and uniform manner throughout the platform. Valuable scientific research is conducted in these platforms, making these platforms subject to open science principles such as reproducibility and accessibility. In this presentation, we examine the differences between various data stewardship models and describe where transient optimized platforms fit within those models. We then describe in more detail a data and information governance framework for Earth Observation transient optimized analysis platforms. We will end our presentation by sharing our experiences of developing such a framework for the Multi-Mission Algorithm and Analysis Platform (MAAP).

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.

Algorithm Theoretical Basis Documents (ATBDs) are documents which accompany Earth observation data products generated from algorithms. While ATBDs are essential to scientific reproducibility, these key documents are not standardized and are often difficult to find. In this paper, we present the prototype Algorithm Publication Tool (APT), a cloud-based ATBD authoring and editing tool for NASA's Earth science data systems. A standardized ATBD information model is also described as well as lessons learned from developing the prototype tool.

Collaborative open science practices are changing the way research is conducted. These changes affect how scientists work together on data, code and information. Data systems enhance open science by offering forward thinking technological solutions, such as providing data and computation on the cloud, to enable collaboration, sharing and analysis. In this paper, we present our vision for a conceptual data system on the cloud that enables open science. We also present our work on the Multi-Mission Algorithm and Analysis Platform (MAAP) which has served as a pathfinder data system for this conceptual approach.

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.

The scientific method within the Earth sciences is rapidly evolving. Ever increasing volumes require new methods for processing and understanding data while an almost 60 year Earth observation record makes more data-intensive retrospective analyses possible. These new methods of data analysis are made possible by technological innovations and interdisciplinary scientific collaborations. While scientists are beginning to adopt new technologies and collaborations to more effectively conduct data-intensive research, both the data information infrastructure and the supporting data stewardship model have been slow to change. Standard data products are generated at a processing system which are then ingested into local archives. These local archive centers then provide metadata to a centralized repository for search and discovery. Each step in the data process occurs independently and on different siloed components. Similarly, the data stewardship process has a well-established but narrow view of data publication that may be too constrained for an ever-changing data environment. To overcome these obstacles, a new approach is needed for both the data information infrastructure and stewardship models. The data ecosystem approach offers a solution to these challenges by placing an emphasis on the relationships between data, technologies and people. In this presentation, we present the Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform’s (MAAP) data system as a forward-looking ecosystem solution. We will present the components needed to support the MAAP data ecosystem along with the key capabilities the MAAP data ecosystem supports. These capabilities include the ability for users to share data and software within the MAAP, the creation of analysis optimized data services, and the creation of an aggregated catalog for data discovery. We will also explore our data stewardship efforts within this new type of data system which includes developing a data management plan and a level of service plan.

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.

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...

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.

Alternative, or non-traditional, data sources can be used to generate datasets which can in turn be analyzed for temporal, spatial and climatological patterns. Events and case studies inferred from the analysis of these patterns can be used by the remote sensing community to more effectively search for Earth observation data. In this paper, we present a new alternative Earth science dataset created from the National Weather Service’s Area Forecast Discussion (AFD) documents. We then present an exploratory methodology for identifying interesting climatological patterns within the AFD data and a corresponding motivating example as to how these data and patterns can be used to search for relevant events or case studies.

New adaptive approaches to Earth observation data stewardship need to be adopted in order to allow for higher data volumes, heterogeneous data and constantly evolving technologies. The data ecosystem approach to stewardship offers a viable solution to this need by placing an emphasis on the relationships between data, technologies and people. In this paper, we present the Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform’s (MAAP) creation of a data ecosystem to support global aboveground terrestrial carbon dynamics research. We present the components needed to support the MAAP data ecosystem along with two data stewardship workflows used in the MAAP and the development of extended metadata for MAAP.

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.

In the current era of knowledge explosion, it is becoming increasingly critical to help researchers quickly grasp the core ideas and methods used in the sea of published articles. As a first step toward the aim, this paper proposes a novel approach that simulates the cognitive process of how human beings read Earth science articles, and automatically identifies semantic entities from the articles. Among others, one major objective is to identify the datasets studied in articles. Oftentimes, however, researchers do not explicitly cite the datasets used. Thus, we propose a profile-matching method strengthened by a neural network-based method to identify implicitly cited dataset entities based on the context. Our experiments have demonstrated the effectiveness of our approaches.

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.

A tremendous amount of Earth and Climate related data and information are available from the U.S. Federal government. Among the proposed actions of the President's Climate Action Plan are several efforts to foster the use of existing data to encourage development of additional data products and tools that can be used to improve community resilience and prepare for the impacts of climate change. Building on previous efforts to organize the presentation of this material from Federal web pages and data centers, the Climate Data Initiative is working together with other related efforts to link Earth observation systems through to data resulting from them and on to related web pages, case studies, decision making tools, and other relevant content. Often such information is not located in a single web site, data center, or even a single agency, but distributed across the Federal Government. Linking such information across the breadth of interagency holdings can increase understanding of the complexity of those holdings and their inter-relationships and allow a more cohesive presentation of all of the material.