Ready to find out how it works for your institution?
RDM white papers and case studies
Whitepaper: Research Data Management - Tracking Institutional Research Data
With today’s high level of interest in research integrity, transparency and openness, reinforced by policies sharing this direction, research data is rapidly rising up the agenda and now it’s time for action. Find out how to elevate your research excellence with Elsevier’s Research Intelligence toolset.
Read the whitepaper nowWird in neuem Tab/Fenster geöffnet
Use cases for research trackingWird in neuem Tab/Fenster geöffnet
University of Canberra Case study: Building an integrated research data management framework around Pure
Discover what the University of Canberra did in response to The Australian Code for the Responsible Conduct of Research which came into effect in 2019 and dictated principles of how data is stored, accessed and shared, and laid the onus of compliance on institutions and individual researchers.
Download the University of Canberra case studyWird in neuem Tab/Fenster geöffnet
University of Groningen Case study: How Groningen is drawing on the insights gleaned from Data Monitor to shape strategic thinking
The university was tasked with overhauling their research data storage in order to meet new research data guidelines and practices for funders. Scientific Information Specialist Christina Elsenga needed to find a way to track down research data and input into Pure, which is how their work with Data Monitor began.
Read the University of Groningen Case studyWird in neuem Tab/Fenster geöffnet
Fact sheet for librarians
10 things I wish I had known before my institution introduced RDM
Download the fact sheet for librariansWird in neuem Tab/Fenster geöffnet
Factsheet for researchers
6 great reasons you should engage with research data management
Download the fact sheet for researchersWird in neuem Tab/Fenster geöffnet
Metadata Matters: Identifying Research Communities and Estimating Author Influence in Networks
This work illustrates how clustering of author communities can be improved by using metadata about the authors (such as subject area or institutional affiliation) to supplement network structure based on co-authorship or citation relationships. Using such metadata can improve clustering significantly, both in terms of human interpretability and by allowing detection of smaller groups – but weighting metadata too highly in relation to network structure can be detrimental.
