CIRSS researchers presented two posters at the third annual annual ASIS&T Research Data Access and Preservation (RDAP) Summit (http://rdap12.posterous.com), held 22-23 March 2012 in New Orleans, LA.  Topics explored at this year's summit included data management plans and policies; training of data management practitioners; discovery of research data; data curation service models; sustainability of data management; and data curation.

The two posters report on CIRSS activities on the Data Conservancy project (http://dataconservancy.org), funded by NSF and led by partners at Johns Hopkins University.

What Dataset Descriptions Actually Describe: Using the Systematic Assertion Model to Connect Theory and Practice
Karen Wickett, Andrea Thomer, Simone Sacchi, Karen S. Baker, David Dubin
Available at: http://hdl.handle.net/2142/30470

Scientific data is encoded and described with the aim of supporting retrieval, meaningful interpretation and reuse. Encoding standards for datasets like FGDC, DwC, EML typically include tagged metadata elements along with the encoded data, suggesting that, per the Dublin Core 1:1 principle, those elements apply to one and only one entity (a specimen, observation, dataset, etc.).  However, in practice vocabularies are often used to describe different dimensions of scientific data collection and communication processes. Discriminating these aspects offers a more precise account of how symbols and the propositions they express acquire the status of “data” and “data content,” respectively.

In this poster we present an analysis of species occurrence records based on the Systematic Assertion Model (SAM) [DWS]. SAM is a framework for describing the encoding and representation of scientific data, bridging the gap between data preservation models and discipline-specific scientific ontologies. The model is intended to be general enough for any scientific domain, and not bound to any particular methodology or field of study. Since species occurrence records are a kind of data that is frequent re-used, migrated across systems and shared they are a good target for analysis.

Integrating Conceptual and Empirical Studies of Data to Guide Curatorial Processes
Carole L. Palmer, Tiffany C. Chao, Nicholas M. Weber, Simone Sacchi, Karen M. Wickett, Allen H. Renear, Karen Baker, Andrea Thomer, & David Dubin

Two research teams within the Data Conservancy (http://dataconservancy.org/) project are investigating different aspects of scientific data curation. Data Concepts is developing a conceptual model to foster shared understanding of identity conditions and representation levels for data sets. Data Practices is conducting qualitative studies of data production and use in the earth and life sciences, analyzing curation needs, cultures of sharing, and re-use potential across disciplines. This poster will illustrate the integration of results from three phases of research to develop a more comprehensive and practical analysis of fundamental aspects of data curation.