Mercury is a federated metadata harvesting, search and retrieval tool based on both open source packages and custom software developed at Oak Ridge National Laboratory (ORNL). It was originally developed for the National Aeronautics and Space Administration (NASA), and the consortium now includes funding from NASA, U.S. Geological Survey (USGS), and U.S. Department of Energy (DOE). Mercury is itself a reusable software application which uses a service-oriented architecture (SOA) approach to capturing and managing metadata in support of twelve Earth science projects. Mercury also supports the reuse of metadata by enabling searches across a range of metadata specification and standards including XML, Z39.50, FGDC, Dublin-Core, Darwin-Core, EML, and ISO-19115. It collects metadata and key data from contributing project servers distributed around the world and builds a centralized index. The Mercury search interfaces allows the users to perform simple, fielded, spatial, temporal and other hierarchical searches across these metadata sources. This centralized repository of metadata with distributed data sources provides extremely fast search results (Table 1) to the user, while allowing data providers to advertise the availability of their data and yet maintain complete control and ownership of that data.

To address the need for published data, considerable effort has gone into formalizing the process of data publication. From funding agencies to publishers, data publication has rapidly become a requirement. Digital Object Identifiers (DOI) and data citations have enhanced the integration and availability of data. The challenge facing data publishers now is to deal with the increased number of publishable data products and most importantly the difficulties of publishing diverse data products into an online archive. The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), a NASA-funded data center, faces these challenges as it deals with data products created by individual investigators. This paper summarizes the challenges of curating data and provides a summary of a workflow solution that ORNL DAAC researcher and technical staffs have created to deal with publication of the diverse data products. The workflow solution presented here is generic and can be applied to data from any scientific domain and data located at any data center.