Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

Data Management Case Studies

Community Research Projects - Setting responsibilities

A research group at a Prestige University employed community science practices to collect data about the environment. The project came together quickly, while concerns that people wouldn't be able to collect the data for much longer. The emergency project attracted thousands of volunteers who collected over 4 terabytes of data. 

After the data collection, it became clear that most of the environments where data was collected were not going to be impacted as expected. Regardless, the group wants to keep the data as important historical evidence of those environments and the of project.

The data was collected using a software developed by a collaborator of the project. Some datasets were moved from that software onto the commercial servers the research group procured,  but some remain in the software. The collaborator has since broken ties with the research group and is unwilling to help them get the remaining datasets transferred out of the software. The software was designed with an obscure programming language that no one on the team is familiar with.

To make matters worse, the team member who was managing the commercial data servers has left the team for a new position. The team has no one else who knows how to maintain the servers to make sure the data are secure. They are looking around the university for support but the project doesn't fit within the scope of most services because it was collected by volunteers around the world, and not by Prestige researchers.

How could the research team have avoided these problems? If they had created a data management plan with their collaborators at the beginning, they could have requested the data collection tool be created with tools and languages familiar to the team, and a collaborative agreement could have been agreed upon to ensure that support for the tool would continue until the completion of the project. Similarly, the group could have made arrangements for the maintenance of the commercial data servers taking turnover into account. Turnover is common on long term research projects. Finally, the group could have worked with other units at Prestige University to establish a long term plan for the curation of the collected data. 

When projects pop up quickly, it can be hard to plan ahead, but taking a moment to think about how responsibilities and ownership of different project components can save many headaches in the future. When you have a framework for how you manage your data on more traditional projects, this process becomes easier as you will have a set of questions and scenarios to plan for.