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The Site-Based Data Curation (SBDC) project is developing a framework of guidelines and processes for the curation of research data generated at scientifically significant sites. SBDC will advance the professional work of curation by articulating principles, workflows and inter-institutional relationships essential for site-based curation. Work to date has concentrated on stakeholder analysis, with results informing metadata guidelines for capture of essential site-based specifics and clarifying the value in aggregating data around site factors for both scientist and YNP stakeholder groups.

The nature of science and scholarship is being transformed by the ability to collect and integrate vast quantities of information. Some sciences such as ecology and environmental science are inherently integrative, requiring the combination of many types of information from many sources in order to answer more complex questions than has been previously possible. This new information and the information management tools designed to deal with this volume of data will help us make informed decisions that will impact human health and prosperity. To enable this cross-scale, interdisciplinary integration for the coming generations of scholars, data must be managed to facilitate interoperability, preservation, and sharing. This panel will explore best practices in data curation and models of education for new data curation professionals.

1 Abstract Digital research data can only be managed and preserved over time through a sustained institutional commitment. Research data curation is a multi-faceted issue, requiring technologies, organizational structures, and human knowledge and skills to come together in complementary ways. This article provides a high-level description of the Data Conservancy Instance, an implementation of infrastructure and organizational services for data collection, storage, preservation, archiving, curation, and sharing. While comparable to institutional repository systems and disciplinary data repositories in some aspects, the DC Instance is distinguished by featuring a data-centric architecture, discipline-agnostic data model, and a data feature extraction framework that facilitates data integration and cross-disciplinary queries. The Data Conservancy Instance is intended to support, and be supported by, a skilled data curation staff, and to facilitate technical, financial, and human sustainability of organizational data curation services. The Johns Hopkins University Data Management Services (JHU DMS) are described as an example of how the Data Conservancy Instance can be deployed.

As initiatives such as Google Book Search (http://books.google.com/) and the Open Content Alliance (http://www.opencontentalliance.org/) advance efforts to digitize millions of books, there is great potential to make available vast amounts of information. To truly unlock this knowledge, however, it will be necessary to process the resulting digital page images to recognize important content, including both the semantic and structural aspects. Given the vast diversity of fonts, symbols, tables, languages and a host of other elements, it will be necessary to create flexible, modular, scalable document recognition systems. Document recognition involves extracting features from the images and even transcriptions of other documents in order to group diverse content.

This article provides an overview of evaluation studies for libraries, a brief introduction to the CAPM Project, a description of the theoretical background for the CAPM methodology and, finally, a discussion of the implementation of the methodology for the CAPM Project.