How to build and promote your RDM skills and why you should

Tufts University’s Rebecca Morin would be the first to acknowledge that many people think you need to be a statistician or coder to practice good research data management (RDM).

But according to Rebecca, who is Head of Research and Instruction at the Hirsh Health Sciences Library, “you can be data-literate and understand the principles of RDM without ever running analyses, building visualizations, or standing up a server.”

In an April Library Connect webinar, she highlighted four ways that librarians can hone and share their research data management skills and some compelling reasons why they should.

1. Draw on existing skills sets

Rebecca has found that researchers tend to focus on finding, creating, analyzing, synthesizing and interpreting data, but that’s only the beginning of the RDM story. And she believes that librarians are perfectly placed to help researchers complete the remaining data steps, including:

• Preservation

• Organization

• Application

• Appraisal

• Access

She explains: “In most industries, there are product experts and process experts. I see researchers as the former – they know their data, experiments and equipment. Librarians, however, have the process knowledge to suggest sensible file structures, describe data in plain language and make it reusable.”

She adds: “Right now, with so many researchers having to learn how to access materials remotely, it’s a great time for librarians to mention that they can not only solve those problems, but help with RDM; for example, teach their teams about it, help them write data management plans and comply with grant data management requirements.”

2. A little extra training never hurts

If there is an opportunity to learn more about areas of RDM outside your usual remit, take it, especially if the stakes are low. According to Rebecca, this might mean auditing the biostatistics for an epidemiology class or taking a programming course. “A little bit of comfort with the language and principles that researchers use goes a really long way towards building trust and close collaboration. Right now, many of us are home with time for professional development and there are plenty of courses out there.”

“A good option is the Research Data Management Librarian Academy (RDMLA) 打開新的分頁／視窗, a free online professional development program that delivers essential knowledge and skills needed to collaborate effectively with researchers on data management."

3. Grab opportunities to pass on what you’ve learned – but be realistic

With so many university staff working from home, many libraries, including Rebecca’s, are on the hunt for content that they can offer remotely. RDM skills tick all the boxes.

“Consider offering some stressed faculty with spare class time, a guest lecture about RDM, or you can build remote interactive workshops. The Responsible Conduct of Research training, which is required for many government grants, is another great opportunity to share RDM information. So too are data management plans: they are required for just about all Federal grants now. If you can say that taking your class is going to play a role in getting grant funding, people will be more invested.

“But remember what I call the 10-6-4 model - if there are 10 RDM principles you think people need to understand, focus on teaching them the six most important, but be really, really happy if they walk away understanding four.”

4. Leverage the FAIR data principles

FAIR stands for Findability, Accessibility, Interoperability and Reuse, all principles that researchers can usually relate to, according to Rebecca. “For example, if a researcher is really resistant about creating a data dictionary, you can emphasize that appropriate description is a key aspect of the reuse element of the FAIR principles. Having reusable data is very important, not just for future research, but often to meet grant requirements.”