Plans for Data Management or Mobilisation?

I had a recent conversation with a researcher who had just been informed that her letter of intent for a major grant competition had been accepted and that she was invited to submit a full application. As she started this process, she organised a committee to provide her advice on a knowledge mobilisation plan. Such a plan is a requirement of the funding agency to which she is applying. One person she asked to serve on her committee inquired if she had also thought about preparing a data management plan (DMP). Because Canada’s three major federal funding councils have yet to institute DMP policies, she was unfamiliar with such plans and was referred to me to learn more about them.

When we met to discuss her research, I discovered that the project consisted of an international team with data from several countries. The data will be a mix of qualitative and quantitative information, some of which will be collected by the project and some of which will be obtained from national agencies. We reviewed the types of consent required from her research participants that would enable sharing these data with other researchers and preserving it for long-term access. We talked about the technical options for making the data safely accessible to the researchers on her team from other countries. We spoke about developing a data charter for the project that addresses governance issues around the use of the data by everyone within the project. As we went through these and other data topics, she paused and said, “I now understand that I will need a data mobilisation plan as well as a knowledge mobilisation plan.” This observation struck me that the “M” in DMP should possibly be mobilisation instead of management.

Data Mobilisation Plans

For most funding councils, the administrative purpose of data management plans is to learn the steps that researchers will apply to share the data from their projects. One way to frame this data-sharing goal is to address expectations around data stewardship. In taking this direction, I believe that mobilisation is a more appropriate concept than management.

  • First, management is about controlling or administering activities and resources, while mobilisation is about organising or preparing something for use (see remarks in the previous blog entry about organising versus managing). From the perspective of data stewardship, the planning steps for sharing data have more to do with organising the custodial care of data across the lifecycle than with controlling the details of data management.

    Mark Parsons, Secretary General for the Research Data Alliance, illustrated this point in a comment that he made during the CASRAI Reconnect 2014 Conference. He noted that when his staff at the National Snow and Ice Data Centre at the University of Colorado helped researchers prepare DMPs for U.S. funding agencies, the researchers had difficulty describing in advance the details around how they would manage their data. Such decisions often come later in the project and depend on the technology available at that time. My response was that assuming DMPs to be statements about the nuts and bolts of managing data misses the policy intent of the plan to elicit how the data will be shared. Instead of small details, these plans should be about the strategies that researchers will follow throughout their project around managing their data.

    Preparing strategies in a DMP should draw upon the data stewards with whom solutions might be formulated. For example, if a DMP asks a researcher to identify the data repository with which she or he will deposit the project’s data, one answer might be to discuss with a liaison librarian the identity of an appropriate domain or campus data repository. Another strategy might be to contact a curator from a data repository about her or his involvement in the project from its beginning. A DMP consisting of strategies for finding solutions that can be implemented during a project directs the researcher’s focus toward mobilising data stewards and services to deal with data management requirements as they arise.

  • Second, knowledge mobilisation plans are a funding agency requirement already known to many Canadian researchers, although some agencies may identify them as knowledge transfer or translation plans. Researchers see the value of these plans, which chart the dissemination activities of research findings. The rewards of having such plans are well understood by researchers. These statements identify pathways to influence other researchers, policy makers, and practitioners that will increase the likelihood of a larger readership of the researcher’s findings and potentially more citations of the researcher’s work. These valued outcomes translate into increased prestige and greater promotion opportunities.

    Data mobilisation plans may benefit from the widely recognised value already attributed to knowledge mobilisation plans. We may soon see rewards structured around data sharing, especially if data citation takes root and the linkage between data and research articles becomes universally adopted through the use of persistent digital identifiers. The more incentives are associated with data sharing, the more data mobilisation plans will be linked to researcher rewards.
  • Third, one should not lose sight of the role that a DMP plays as an administrative tool to promote research practices supportive of an organisation’s data policy. This connection between data policy and a DMP is fundamental to its function. Whether the data policy is directed at data stewardship, data sharing, reproducible research, or a combination of these, the DMP should elicit responses that are expressive of the policy’s values. The level of abstraction called for in this context is more directed at organising than managing things. As a policy instrument, the goal of DMPs keeps our attention more centred on mobilising than managing resources.

DMP Rights & Responsibilities

A consultation on Open Data was conducted in the United Kingdom in February 2012 providing valuable insight into governing principles for open data.  In particular, a series of rights and responsibilities regarding researchers, public and private funders, and  the public was identified in the study’s final report.  Emerging from this dialogue was a prominent policy role for data management plans (DMPs) to record agreements among stakeholders and to state clearly their rights and responsibilities associated with the data.  Viewing data management plans this way is closely associated to the position taken in the previous entry to this blog, The Value of Data Management Plans.  In this context, DMPs serve as a document of relationships and agreements.

Page 35 of this report contains a table summarizing rights and responsibilities among stakeholders.  Four stand out about DMPs:

  1. Researchers have a responsibility to “develop data management plans;”
  2. Funders have a right to expect researchers to prepare and implement data management plans;
  3. Funders have a responsibility to “enforce and publish data management policies and practices,” including DMPs; and
  4. The public has a right to know about research data in the public interest, which can be partially achieved through publishing DMPs.

The discussion in this report addresses several ways in which DMPs interplay across stakeholders’ interests.  For example, a concern among some researchers about “vexatious requests for data [p. 38]” was seen as being mediated through developing and publishing DMPs.  Furthermore, DMPs were seen as a method of communicating a timeframe for exclusive use of data by researchers prior to it being shared.  The expectation of funders to publish DMPs was seen as a transparency factor, keeping everyone informed of the agreements around the rights and responsibilities of a project’s data.

Other stakeholders can be seen also to have rights and responsibilities communicated in DMPs.  For example, a university has a right to know the demands on research data management infrastructure that the data across all locally based projects cumulatively have on a campus’ resources, including data curation services, storage, network capacity, and computational power.   On the flip side, a campus has the responsibility to support data management infrastructure that will facilitate high quality research, something to be gleaned from its researchers’ DMPs.

As Canadian institutions look to introduce DMPs as a policy tool, a wider discussion should take into account the relationships to be expressed in such plans.  We should expect to get full value out of this tool.

The Value of Data Management Plans

A big news item coming out of the Digital Infrastructure Summit held in Ottawa on January 28-29, 2014 was the announcement that Canada’s federal research councils will introduce policy changes over the next 24 months that will require applicants to include data management plans in their funding proposals. This announcement came quickly on the heels of a Fall 2013 consultation conducted by these same councils on Capitalizing on Big Data. Within the background material prepared for this study, these councils were challenged to adopt “agency-based and focused data stewardship plans (p. 8)” of which data management plans (DMPs) were seen as integral.  The push toward this policy change will now likely face some opposition, although momentum currently seems to be with those promoting policies in support of a Canadian data stewardship culture.

Some research councils in other countries have already implemented DMPs. For example, a guideline among the data principles of the Research Councils of the United Kingdom (RCUK) specifically encourages its members to develop data management plans:

Institutional and project specific data management policies and plans should be in accordance with relevant standards and community best practice. Data with acknowledged long-term value should be preserved and remain accessible and usable for future research.

Provided as an umbrella framework, each of the seven research councils of RCUK is independently responsible for its data policies.  For example, the Economic and Social Research Council (ESRC) describes its reasons for requiring data management plans as:

We believe that a structured approach to data management results in better quality data that is ready to deposit for further sharing.

This single sentence is very revealing about the expected returns on DMPs.  To begin, a DMP is seen to contribute structure to the handling of data within a project.  An outcome of this approach is believed to be higher quality data.  Furthermore, the data will be better prepared for deposit with an organization that will make the data available for others.

On the surface, data management plans appear to be a very straightforward policy tool. They simply lengthen current funding applications by another page or two. However, the purposes they fulfill and the processes they embody will enrich the production and custodial care of research data.  The ESRC anticipation of higher quality data for sharing also implies collaboration with data curation services and with data repositories.  Ultimately, a DMP should engage researchers in conversations with those providing such services.  In this context, a DMP becomes a document of relationships that should be shared, edited, and monitored among those contributing to a project.  From this viewpoint, a DMP functions as a dynamic document of agreements.

To serve the multiple purposes just described, DMPs should be designed for easy digital exchange across a variety of applications.  The best way to approach this in today’s complex world of  information technology is through a metadata standard describing a data model of elements constituting a DMP.   CASRAI, a community-based standards body for research administrative information, is well positioned to do this.  In fact, the U.K. chapter of CASRAI has already begun work on a set of elements for a DMP data model.  In conjunction with this, it would be helpful if the Standards and Interoperability Committee of Research Data Canada would develop a fundamental flowchart representing the interplay of purposes, uses, and relationships expressed in a DMP.  This would be both informative for the CASRAI working group developing specifications for DMPs as well as helpful in validating the completeness of a DMP data model.