The goal of the RMap Project is to create a prototype service that can capture and preserve maps of relationships amongst the increasingly distributed components (article, data, software, workflow objects, multimedia, etc.) that comprise the new model for scholarly publication. The demonstration will provide a tour of some of the features of the initial web service prototype. This will include examples of Distributed Scholarly Complex Objects (DiSCOs) and associated provenance data in RMap, as well as some of the options that users might have for interacting with the framework.

Among the key services that institutional data management infrastructures must provide are provenance and lineage tracking and the ability to associate data with contextual information needed for understanding and use. These functionalities are critical for addressing a number of key issues faced by data collectors and users, including trust in data, results traceability, data transparency, and data citation support. In this paper, we describe the support for these services within the Data Conservancy Service (DCS) software. The DCS provenance, context, and lineage services cross the four layers in the DCS data curation stack model: storage, archiving, preservation, and curation.

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.

Verifiability and reproducibility are core tenets of the scholarly communication process. For many scientific publications, however, it is often the case that supporting datasets are not preserved, even when the article text is. And when they are, it is usually as a collection of files without relationships amongst one another or to the articles with which they are associated. There are some existing approaches that attempt to link datasets with articles after the fact (e.g.,NED ), but they are relatively few and involve substantial human intervention. The Digital Research and Curation Center in the Johns Hopkins University Sheridan Libraries, in conjunction with its partners has developed a proof-of-concept system that demonstrates an approach to capturing datasets during the process of submitting the associated article. As part of this process, linkages are established between the datasets and the article.

In the June 2003 issue of D-Lib Magazine, Kenney et al. (2003) discuss a comparative study between Cornell's email reference staff and Google's Answers service. This interesting study provided insights on the potential impact of "computing and simple algorithms combined with human intelligence" for library reference services. As mentioned in the Kenney et al. article, Bill Arms (2000) had discussed the possibilities of automated digital libraries in an even earlier D-Lib article. Arms discusses not only automating reference services, but also another library function that seems to inspire lively debates about automation—metadata creation. While intended to illuminate, these debates sometimes generate more heat than light.

Astronomy is similar to other scientific disciplines in that scholarly publication relies on the presentation and interpretation of data. But although astronomy now has archives for its primary research telescopes and associated surveys, the highly processed data that is presented in the peer-reviewed journals and is the basis for final analysis and interpretation is generally not archived and has no permanent repository. We have initiated a project whose goal is to implement an end-to-end prototype system which, through a partnership of a professional society, that society’s scholarly publications/publishers, research libraries, and an information technology substrate provided by the Virtual Observatory, will capture high-level digital data as part of the publication process and establish a distributed network of curated, permanent data repositories. The data in this network will be accessible through the research journals, astronomy data centers, and Virtual Observatory data discovery portals.