Building an RDM Maturity Model: Part 4
By John Borghi
Researchers are faced with rapidly evolving expectations about how they should manage and share their data, code, and other research products. These expectations come from a variety of sources, including funding agencies and academic publishers. As part of our effort to help researchers meet these expectations, the UC3 team spent much of last year investigating current practices. We studied how neuroimaging researchers handle their data, examined how researchers use, share, and value software, and conducted interviews and focus groups with researchers across the UC system. All of this has reaffirmed our that perception that researchers and other data stakeholders often think and talk about data in very different ways.
Such differences are central to another project, which we’ve referred to alternately as an RDM maturity model and an RDM guide for researchers. Since its inception, the goal of this project has been to give researchers tools to self assess their data-related practices and access the skills and experience of data service providers within their institutional libraries. Drawing upon tools with convergent aims, including maturity-based frameworks and visualizations like the research data lifecycle, we’ve worked to ensure that our tools are user friendly, free of jargon, and adaptable enough to meet the needs of a range of stakeholders, including different research, service provider, and institutional communities. To this end, we’ve renamed this project yet again to “Support your Data”.
What’s in a name?
Because our tools are intended to be accessible to a people with a broad range of perceptions, practices, and priorities, coming up with a name that encompasses complex concepts like “openness” and “reproducibility” proved to be quite difficult. We also wanted to capture the spirit of terms like “capability maturity” and “research data management (RDM)” without referencing them directly. After spending a lot of time trying to come up with something clever, we decided that the name of our tools should describe their function. Since the goal is to support researchers as they manage and share data (in ways potentially influenced by expectations related to openness and reproducibility), why not just use that?
In addition to thinking through the name, we’ve also refined the content of our tools. The central element, a rubric that allows researchers to quickly benchmark their data-related practices, is shown below. As before, it highlights how the management of research data is an active and iterative process that occurs throughout the different phases of a project. Activities in different phases represented in different rows. Proceeding left to right, a series of declarative statements describe specific activities within each phase in order of how well they are designed to foster access to and use of data in the future.
The four levels “ad hoc”, “one-time”, “active and informative” and “optimized for re-use”, are intended to be descriptive rather than prescriptive.
- Ad hoc — Refers to circumstances in which practices are neither standardized or documented. Every time a researcher has to manage their data they have to design new practices and procedures from scratch.
- One time — Refers to circumstances in which data management occurs only when it is necessary, such as in direct response to a mandate from a funder or publisher. Practices or procedures implemented at one phase of a project are not designed with later phases in mind.
- Active and informative — Refers to circumstances in which data management is a regular part of the research process. Practices and procedures are standardized, well documented, and well integrated with those implemented at other phases.
- Optimized for re-use — Refers to circumstances in which data management activities are designed to facilitate the re-use of data in the future
Each row of the rubric is tied to a one page guide that provides specific information about how to advance practices as desired or required. Development of the content of the guides has proceeded sequentially. During the autumn and winter of 2017, members of the UC3 team met to discuss issues relevant to each phase, reduce the use of jargon, and identify how content could be localized to meet the needs of different research and institutional communities. We are currently working on revising the content based suggestions made during these meetings.
Now that we have scoped out the content, we’ve begun to focus on the design aspect of our tools. Working with CDL’s UX team, we’ve begun to think through the presentation of both the rubric and the guides in physical media and online.
As always, we welcome any and all feedback about content and application of our tools.