It has been a little while since I last wrote about the work we’re doing to develop a research data management (RDM) guide for researchers. Since then, we’ve thought a lot about the goals of this project and settled on a concrete plan for building out our materials. Because we will soon be proactively seeking feedback on the different elements of this project, I wanted to provide an update on what we’re doing and why.
Communication Barriers and Research Data Management
Several weeks ago I wrote about addressing Research Data Management (RDM) as a “wicked problem”, a problem that is difficult to solve because different stakeholders define and address it in different ways. My own experience as a researcher and library postdoc bears this out. Researchers and librarians often think and talk about data in very different ways! But as researchers face changing expectations from funding agencies, academic publishers, their own peers, and other RDM stakeholders about how they should manage and share their data, overcoming such communication barriers becomes increasingly important.
From visualizations like the ubiquitous research data lifecycle to instruments like the Data Curation Profiles, there are a wide variety of excellent tools that can be used to facilitate communication between different RDM stakeholders. Likewise, there are also discipline-specific best practice guidelines and tools like the Research Infrastructure Self Evaluation Framework (RISE) that allow researchers and organizations to assess and advance their RDM activities. What’s missing is a tool that combines these two elements that enables researchers the means to easily self-assess where they are in regards to RDM and allows data service providers to provide easily customizable guidance about how to advance their data-related practices.
Enter our RDM guide for researchers.
Our RDM Guide for Researchers
What I want to emphasize most about our RDM guide is that it is, first and foremost, designed to be a communication tool. The research and library communities both have a tremendous amount of knowledge and expertise related to data management. Our guide is not intended to supplant tools developed by either, but to assist in overcoming communication barriers in a way that removes confusion, grows confidence, and helps people in both communities find direction.
While the shape of RDM guide has not changed significantly since my last post, we have refined its basic structure and have begun filling in the details.
The latest iteration of our guide consists of two main elements:
- A RDM rubric which allows researchers to self-assess their data-related practices using language and terminology with which they are familiar.
- A series of one page guides that provide information about how to advance data-related practices as necessary, appropriate, or desired.
The rubric is similar to the “maturity model” described in my earlier blog posts. In this iteration, it consists of a grid containing three columns and a number of rows. The leftmost column contains descriptions of different phases of the research process. At present, the rubric contains four such phases: Planning, Collection, Analysis, and Sharing. These research data lifecycle-esque terms are in place to provide a framing familiar to data service providers in the library and elsewhere.
The next column includes phrases that describe specific research activities using language and terminology familiar to researchers. The language in this column is, in part, derived from the unofficial survey we conducted to understand how researchers describe the research process. By placing these activities beside those drawn from the research data lifecycle, we hope to ground our model in terms that both researchers and RDM service providers can relate to.
The rightmost column then contains a series of declarative statements which a researcher can use to identify their individual practices in terms of the degree to which they are defined, communicated, and forward thinking.
Each element of the rubric is designed to be customizable. We understand that RDM service providers at different institutions may wish to emphasize different services toggled to different parts data lifecycle and that researchers in different disciplines may have different ways of describing their data-related activities. For example, while we are working on refining the language of the declarative statements, I have left them out of the diagram above because they are likely the rubric that will remain most open for customization.
Each row within the rubric will be complemented by a one page guide that will provide researchers with concrete information about data-related best practices. If the purpose of the rubric is to allow researchers to orient themselves in RDM landscape, the purpose of these guides is to help them move forward.
Generating Outputs
Now that we’ve refined the basic structure of our model, it’s time to start creating some outputs. Throughout the remainder of the summer and into the autumn, members of the UC3 team will be meeting regularly to review the content of the first set of one page guides. This process will inform our continual refinement of the RDM rubric which will, in turn, shape the writing of a formal paper.
Moving forward, I hope to workshop this project with as many interested parties as I can, both to receive feedback on what we’ve done so far and to potentially crowdsource some of the content. Over the next few weeks I’ll be soliciting feedback on various aspects of the RDM rubric. If you’d like to provide feedback, please either click through the links below (more to be added in the coming weeks) or contact me directly.
Provide feedback on our guide!
More coming soon!
