Thoughts on CARL’s Research Data Management Course

Last month, I attended CARL’s 4-day course on Research Data Management Services in Toronto. (Jargon alert: CARL is the Canadian Association of Research Libraries). This was an intensive week of collaborating on research data management (RDM) practices and creating a community of practice within Canadian academic librarianship. Our concern for sound RDM practices at Canadian universities brought together librarians with all kinds and levels of expertise so that we could share tools and develop action plans that will make a positive impact in this field.

1. Research Data Management, Data Lifecycles, and Research Data Lifecycles

What is research data management? I won’t go into textbook-detail suffice to say we’re talking about systematic practices that govern how research data are defined, organized, collected, used and conserved before, during, and after the research process. That sentence is a mouthful and it covers a lot of ground, so I suggest you look to Chuck Humphrey’s Research Data Management Infrastructure (RDMI) site for a more focused definition. Chuck is hailed in Canada for his data management expertise, and he led many sessions at the workshop. He explains that:

Research data management involves the practices and activities across the research lifecycle that involve the operational support of data through design, production, processing, documentation, analysis, preservation, discovery and reuse.  Collectively, these data-related activities span the stages of project-based research as well as the extended stages that tend to be institutionally based.  The activities are about the “what” and “how” of research data. (source)

Chuck’s website is a great introduction to the existing RDM gap in Canada, and we referred to it several times in the course. It neatly summarizes key information such as the shaky progress and history of RDM in Canada, where the Canadian RDM community stands in the world today, the differences between data management and data stewardship, and why the Canadian research community should focus its attention on building infrastructure to support RDM as opposed to building a national institution to guide it.

The Data Lifecycle (Source: UK Data Archive)
The Data Lifecycle (Source: UK Data Archive)

Beyond talking about what RDM is and isn’t, we spent a lot of time studying where RDM sits within the research lifecycle. Many people are familiar with the data lifecycle model since it introduces us to the many facets of data management, however, the CARL course proposed that we instead examine data management practices as an integral part of the larger research lifecycle. Rather than focusing only on data at the expense of the larger research project, the course facilitators asked us to apply RDM within the entire research process, using the following model from the University of Virginia:

Research Lifecycle (Source: UVa Library
Research Lifecycle (Source: UVa Library)

The salient point is that research data management isn’t limited to only the data life cycle; it affects the entire research process. (A simple example: data management strategies should be discussed well before data are created or collected.). Furthermore, if we want to develop sound RDM practices, we need to think like the researcher, understand the researcher’s needs, and include our work within their processes. If you’re not working with the researcher, then your RDM plan isn’t working.

2. Local RDM Drivers and Activities

If understanding what research data management is and where it affects the research process was one takeaway of the course, analyzing our local data environments was another:

  • RDM drivers, such as your library’s consortial collaborations, number of staff, existing IT relationships, administrative support, etc., are the parameters that shape and support your local RDM programme.
  • The activities in your RDM programme, meanwhile can be broadly categorized into the four areas: collection, access, use, and preservation (note: activities can fall into more than one category, and the order is not linear).

Discussing the things that affect our data landscapes and the activities we could perform helped us understand what is possible at our own libraries. I think a lot of us found this useful because all of our unique circumstances (e.g., library and university sizes, existing infrastructure and knowledge, etc.) can make RDM a bit nebulous at times. Although our focus is the same – RDM – our individual goals and aims might be different – are we building our technical capability, or are we designing soft systems that focus on relationships? Are we only collecting new locally created data, or will we also gather existing, completed projects?  The answers are going to depend on your local situation.

RDM activities within the research process.
RDM activities within the research process.

 The course facilitators were careful to help participants understand RDM as a necessarily scalable enterprise. Don’t create a monster RDM plan. Instead, contextualize your local RDM drivers and your library’s capabilities and desires so that you can mitigate the risks of creating an RDM plan that doesn’t fit your organization. The aim is to create a system and process that brings clear benefits to the researchers.

3. Planning… and Doing

The final takeway from the CARL RDM course, which you may have noticed I’ve been building up to, was straight-up, no-nonsense, get’er-done planning. The course facilitators built opportunities for real action into the course, which is probably one of the best parts of the week. Generally speaking, the academic enterprise undertakes a lot of talk and high-level planning before things happen.  This is often a good thing (read: I demand critical inquiry), but it can also stifle action (read: I despise institutional inertia). However, this CARL course found a way to bring together discussion and action. It gave us theory, but it demanded practice. Before the week was out, we had all talked about 3-year planning, considered how such a plan might look locally, and started to write one. Of course, these drafts aren’t ready for prime time, but my point is that before I came back to the office on Monday, I already had written the skeleton of a research data management plan that shows my library’s potential RDM activities and stakeholders, outlines activities and scopes, and offers timelines and deliverables. It didn’t make me an expert (and neither do I claim to be one), but it did offer some tools to help the library step out and make positive change.

So was the CARL RDM course money well spent? It sure was.  It’s not too often you come back from an event with a new community of practice, insight on a vital part of the research enterprise, and a plan to put everything in action. Hat’s off to the course facilitators for putting on such a great week – I think you’ve started something necessary, and good, for Canadian research.

(And some time next week, I’ll start gathering up some of the key readings from some of the bibliographies they presented us…  I’ll try not to turn the next post into a lit review, but it may come close to it.)