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.