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Research Data Management Assistance at Virginia Tech: Posts - Data Management and Curation Services

Presenting in AGU Fall Meeting Session on Building Data Management Capacity

by Jonathan Petters on 2018-12-21T08:57:18-05:00 | Comments

Jonathan Petters attended the 2018 American Geophysical Union Fall Meeting in Washington DC, presenting “Building Data Management Capacity for Field Projects In a Research Group”. In this presentation he described some of the follow-up impacts of working with Prof. Carola Haas’ research group in creating a data management training curriculum for their use. See this previous post for more information on this effort.

 

Now a year removed from working intensely with the research group, Jon reached out to ask them: “What improvements/changes have occurred with respect to data management for your field research projects?”

 

Their responses suggest a rousing success story. Both the type and frequency of errors seen in their databases have been drastically reduced, and channels of communication between the research group and data collectors in the field have been used to great effect.

 

Prof. Haas added: “We are starting a new field project in Virginia and are planning on using the [curriculum] materials for that too and would definitely recommend it to others.”

 

Jennifer Smith, a postdoc involved in this project and now a professor at University of Texas at San Antonio, added that “[t[he impact of these efforts extends beyond the Haas lab too – I’ve certainly become very aware of data management and best practices and am even dedicating a whole hour in my methods class next semester about data management!”

 

The slides for Jon’s presentation can be accessed here. Jon intends to write a journal article around this data management support effort. The tentative conclusion, supported by other case studies in the literature, will be that targeted training in data and project management for a research project can lead to improvements in the quality of data to be used both as a foundation for research and as data to be more widely shared in the future.


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