Institutional Data & Library Research

Institutional data, collected by campus units to assist with decision making and organizational direction, can inform scope and provide context to library assessment and research projects.
Institutional data, collected by campus units to assist with decision making and organizational direction, can inform scope and provide context to library assessment and research projects.
In the UMich Research Data Services (RDS) group, we see and work with all sorts of data. One particularly thorny variety is netCDF. In Deep Blue Data, we have been getting regular deposits of data in this format, and we didn't know much about it. We had many questions how do we open it, what's its structure, how do researchers create these files and why can the size vary so widely from 100s of MBs to 100s of GBs or even TBs? Jake Carlson, Director of RDS, and I hashed out the idea of...
The U-M Library’s Shapiro Design Lab and the U-M Museum of Natural History are happy to announce a new Community and Citizen Science Project Incubator program for University of Michigan faculty, staff, and students! Community and citizen science projects can help scientists conduct extensive, quality research while engaging with members of their community. The program will explore questions about project design, ethics, learning goals, and data management. Participants will create project...
Asia Library will hold a Korean Data Services Workshop in November.
Last year, the Lieberthal-Rogel Center for Chinese Studies (LRCCS) and the Asia Library began co-sponsorship of a new series of guest lectures and workshops under the title Deep Dive into Digital and Data Methods in Chinese Studies.
This past week, the University of Michigan Library was pleased to host the second annual Midwest Data Librarian Symposium (MDLS). The goal of the symposium was to offer librarians who work with research data in the midwest a chance to network and discuss issues in their fields.
It is common scholarly practice to publish results of research, and it is becoming increasingly more important to share the underlying data. Data sharing allows for the replicability and verification of experimental findings and allow for reuse in new and unexpected ways. Sharing your data may also increase the impact of your research.
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