The integration of heterogeneous data in varying formats and from diverse communities requires an improved understanding of the concept of a dataset, and of key related concepts, such as format, encoding, and version. Ultimately, a normative formal framework of such concepts will be needed to support the effective curation, integration, and use of shared multi-disciplinary scientific data. To prepare for the development of this framework we reviewed the definitions of dataset found in technical documentation and the scientific literature. Four basic features can be identified as common to most definitions: grouping, content, relatedness, and purpose. In this summary of our results we describe each of these features, indicating the directions a more formal analysis might take.

Site-Based Data Curation (SBDC) is an approach to managing research data that prioritizes sharing and reuse of data collected at scientifically significant sites. The SBDC framework is based on geobiology research at natural hot spring sites in Yellowstone National Park as an exemplar case of high value field data in contemporary, cross-disciplinary earth systems science. Through stakeholder analysis and investigation of data artifacts, we determined that meaningful and valid reuse of digital hot spring data requires systematic documentation of sampling processes and particular contextual information about the site of data collection. We propose a Minimum Information Framework for recording the necessary metadata on sampling locations, with anchor measurements and description of the hot spring vent distinct from the outflow system, and multi-scale field photography to capture vital information about hot spring structures. The SBDC framework can serve as a global model for the collection and description of hot spring systems field data that can be readily adapted for application to the curation of data from other kinds scientifically significant sites.

A comprehensive record of research data provenance is essential for the successful curation, management, and reuse of data over time. However, creating such detailed metadata can be onerous, and there are few structured methods for doing so. In this case study of data curation in support of geobiology research conducted at Yellowstone National Park, we describe a method of "Research Process Modeling" for documenting noncomputational data provenance in a structured yet flexible way. The method combines systems analysis techniques to model research activities, the World Wide Web Consortium Provenance (PROV) ontology to illustrate relationships between data products, and simple inventory methods to account for research processes and data products. It also supports collaborative data curation between information professionals and researchers, and is therefore a significant step toward producing more useable and interpretable research data. We demonstrate how this method describes data provenance more robustly than "flat" metadata alone and fills a critical gap in the documentation of provenance for field-based and noncomputational workflows. We discuss potential applications of this approach to other research domains.

Collections of artifacts, images, texts, and other cultural objects are not arbitrary aggregations, but are designed to support specific research and scholarly activities. Collection-level metadata directly supports this objective, providing critical contextual information. However, exploiting this information, especially in a semantic web environment of linked data, requires a precise formalization of the rules that characterize collection/item metadata relationships. Toward this end we are developing a logicbased framework of relationship rule categories for collection/item metadata. This framework will support metadata specification developers, metadata catalogers, and system designers. In earlier work we described three example rule categories for propagation of information from collections to items. Further reflection, and examination of metadata in an RDF testbed, has revealed eighteen categories, which form an interrelated system with three levels of specificity and formal constraints differentiating categories. This paper summarizes the results of a three year effort, part of the IMLS Digital Collections and Content project.

Heterogeneous digital data that has been produced by different communities with varying practices and assumptions, and that is organized according to different representation schemes, encodings, and file formats, presents substantial obstacles to efficient integration, analysis, and preservation. This is a particular impediment to data reuse and interdisciplinary science. An underlying problem is that we have no shared formal conceptual model of information representation that is both accurate and sufficiently detailed to accommodate the management and analysis of real world digital data in varying formats. Developing such a model involves confronting extremely challenging foundational problems in information science. We present two complementary conceptual models for data representation, the Basic Representation Model and the Systematic Assertion Model. We show how these models work together to provide an analytical account of digitally encoded scientific data. These models will provide a better foundation for understanding and supporting a wide range of data curation activities, including format migration, data integration, data reuse, digital preservation strategies, and assessment of identity and scientific equivalence.