DataStaR: An Institutional Approach to Research Data Curation

Towards Linked Research Data: an Institutional Approach

For Open Science to be widely adopted, a strong institutional support for scientists will be essential. Bielefeld University and the associated Center of Excellence Cognitive Interaction Technology (CITEC) have developed a platform that enables researchers to manage their publications and the underlying research data in an easy and efficient way. Following a Linked Data approach we integrate th...

Institutional Repositories and Research Data Curation in a Distributed Environment

Broadly speaking, the lack of a framework for organizing, preserving, and making research data available for the long term has resulted in valuable datasets becoming lost or discarded. The approach of the Distributed Data Curation Center of the Purdue University Libraries has been to integrate librarians and the principles of library and archival sciences with domain sciences, computer and info...

DataStaR: a data staging repository to support the sharing and publication of research data

The opportunities for new discoveries made possible by the widespread availability of large amounts of data are much in the news, as are the challenges associated with sharing research data and preserving it for the long term. Cornell University Library is engaged in a pilot project (funded by the National Science Foundation) to test the feasibility of a local “staging” repository to support da...

Practices of research data curation in institutional repositories: A qualitative view from repository staff

The importance of managing research data has been emphasized by the government, funding agencies, and scholarly communities. Increased access to research data increases the impact and efficiency of scientific activities and funding. Thus, many research institutions have established or plan to establish research data curation services as part of their Institutional Repositories (IRs). However, i...

Continuous quality control for research data to ensure reproducibility : an institutional approach