A Canadian Context for Research Data Management

6 The RDM Maturity Assessment Model in Canada (MAMIC)

Jane Fry; Jennifer Abel; Dylanne Dearborn; Alison Farrell; and Chantal Ripp

Learning Outcomes

By the end of this chapter you should be able to:

  1. Explain what a maturity assessment model is.
  2. Understand the value of completing a Research Data Management maturity assessment.
  3. Understand why and how a made-in-Canada maturity assessment model was developed.
  4. Be able to use the Maturity Assessment Model in Canada to assess Research Data Management service maturity at a Canadian research institution.
  5. Be able to support evidence-based decision making with the results gathered from the completed Maturity Assessment Model in Canada.


By now you know that Research Data Management (RDM) involves a range of practices and services such as data management planning, curation, discovery, and preservation. So research institutions — universities, colleges, hospitals — thinking about RDM should consider all services, resources, and personnel that support RDM for every research project, particularly when an institution is formalizing services, as many Canadian institutions were at the time of writing (spring 2022) in response to the Tri-Agency Research Data Management Policy.

But how can people at a research institution determine whether all areas of the research data lifecycle are being supported and who is responsible for what? To support Canadian research institutions in undertaking this important step, the authors of this chapter came together in the summer of 2021 to develop the RDM Maturity Assessment Model in Canada, or MAMIC (Fry et al., 2021), to help RDM partners understand the services and resources available to support data management at their institution.

In this chapter, we’ll examine why Canadian institutions may want to conduct an RDM maturity assessment for their institution, in particular, looking at the institutional RDM strategy requirement which was implemented by Canada’s three federal research funding agencies (the agencies); the development of the MAMIC; and how to complete the MAMIC and use its results. Finally, we’ll highlight the importance of community efforts in creating the tool.

Access the MAMIC here: English, French

The Need: How to Assess an Institution’s RDM Services

In the spring of 2021, the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Social Sciences and Humanities Research Council of Canada (SSHRC) released their long-anticipated Tri-Agency RDM Policy. This policy supports Canadian research excellence by ensuring that researchers engage in sound RDM and data stewardship practices, and that their institutions support them in these practices. The Agencies expect high standards of excellence — that research is performed ethically, funds are used wisely, experiments and studies are replicable, and research results are as accessible as possible (Government of Canada, 2021). To demonstrate this, institutions must create and publish an RDM strategy that sets out their commitment to RDM principles and how they will support their researchers in adopting them (see section 3.1 of the policy).

Since one type of strategy will not fit all situations, each institution should consider its particular circumstances, such as its size, research intensity, and existing RDM capacity. But how does an institution determine what its RDM capacity is or what its strategy should be? To help determine this, in 2018, the Digital Research Alliance of Canada (formerly the Portage Network) released an Institutional RDM Strategy Development Template, which was updated in November 2021. The template outlines a five-stage process to inform and shape the creation of an RDM strategy that meets local needs and resource capacities. We’ll focus on the second stage of the process, which encourages institutions to assess their state of RDM using assessment models and tools.

What Is a Maturity Assessment Model? And Why Does Canada Need One?

Maturity assessment models and tools evaluate an institution’s maturity and readiness for RDM service provision and help determine the level of sophistication of a service or product. A common feature of these models is the use of a scale to represent an organization’s maturity in specific capabilities — in other words, how reliably the organization performs the process (Rans & Whyte, 2017). The maturity rubric allows a user to quantify capabilities and enable continuous process improvement.

Internationally, RDM is well-established for enabling research excellence, and several maturity models have already been developed. However, when Canadian institutions began using these models to evaluate the state of RDM on their campuses, they found that these tools did not align with the Canadian RDM landscape; for example, Canadian institutions are not required to have RDM policies, unlike institutions in some other countries.

In 2021, after the release of the Tri-Agency RDM Policy, members of the national RDM community began informally discussing how institutions should go about creating their RDM strategies. The National Training Expert Group (NTEG)[1], a group of information professionals, researchers, and practitioners, decided to create a series of webinars and workshops to be presented in October 2021 to bring representatives of different institutions together to discuss their strategy development work. While planning for the fall series, several members noted that there was no Canadian maturity assessment model that institutions could use in the second stage of their strategy development. NTEG decided that a Canadian-focused maturity assessment model tool could be a useful starting place for discussions about institutional RDM capacity with strategies in alignment with the Tri-Agency RDM Policy. In April 2021, a smaller group set to work on developing what would become the first version of the MAMIC, in time for the October workshop. We — the authors of this chapter — along with Shahira Khair of the University of Victoria, were the members of that group.

How the MAMIC Was Created

Environmental Scan of Maturity Assessment Models

As a first step, we examined several international assessment tools. While the available models were excellent, they included sections not applicable to Canada, and there were gaps — things we needed to include in the Canadian model, one of them being the requirement by the agencies for an RDM Institutional Strategy. After our review, we focused on aspects of the three most popular models to help develop the MAMIC — the Research Infrastructure Self Evaluation Framework (RISE) tool, published in 2017 by the Digital Curation Centre (DCC); the Evaluate your RDM Offering tool by SPARC Europe; and the Data Management Framework of the Australian National Data Service (ANDS)[2]. We based our Canadian model primarily on RISE, with elements inspired by the SPARC model and the ANDS frameworks.

Overview of the MAMIC

In our Canadian tool, we detail the reason for, intention of, and definition of the MAMIC, and provide a section on how to complete it. There are four tables to be filled out by the research partners:

  • Institutional Policies and Processes
  • IT Infrastructure
  • Support Services
  • Financial Support

Each table has five columns:

  • element being assessed
  • definition of that element
  • its maturity level (how advanced the institution’s RDM is)
  • its scale (who may access the service or support)
  • any required explanation as to the rating given to that element

Below each table, there’s space for the date of completion as well as the name(s) and role(s) of the person(s) filling it out, since users need to know who those research partners are in order to address questions or concerns about how the table was completed. The MAMIC is also intended to be used in the future, so it’s important to know who filled in the previous version.

We also wanted to ensure that the terms used were well defined, so we included a page of definitions of maturity and scale levels specific to each table, along with hints to help fill them out.

Initial Version of the MAMIC

After receiving feedback from members of the RDM community, a draft was completed, the MAMIC was translated into French (where it is the MEMAC, or Modèle d’évaluation de la maturité de la GDR au Canada) and introduced to the attendees at the Institutional Strategies Workshop in late October 2021.

Note: There are certain areas not covered in this initial version, so changes will need to be made in future versions. For example, a future version should contain considerations for Indigenous data sovereignty. Another idea is to explore different ways to present the tool, such as developing an online tool that could allow users to produce different types of charts, as the SPARC tool does.

These revisions will be useful for those who plan to do this type of assessment on a regular basis as part of reviewing and revising their institutional RDM strategy or as part of ongoing service improvements. It may also be useful to apply the MAMIC at a national scale to highlight and address gaps and to showcase where institutions may be able to rely on national resources.

Using the MAMIC

The MAMIC can be used to determine whether RDM resources and services exist, as well as who is responsible for these different supports so that institutions can support researchers in effective data management and also be aware of what is needed to supplement their current offerings. Using the model involves coordination between interested parties across campus, such as the library, research office, ethics office, and IT department.

Categories and Measures

Before the work begins, research partners filling in the MAMIC should discuss the process so everyone understands how scales and measures will be applied and to ensure that key decisions about how to do the work are documented. Each category (Institutional Policies and Processes, IT Infrastructure, Support Services, and Financial Support) can be assessed in its own table — see Appendix 2 for an example of a completed Institutional Policies and Processes category — using three different measures:

Measure 1: Maturity Level of the element at the institution is rated on a 5-level scale ranging from “does not exist” OR “do not know” to “robust and focuses on continuous evaluation.” Note: the first level of this scale is 0 not 1 because sometimes an institution cannot provide a service or support, or does not feel they need it. The category “does not exist” is not meant to indicate a level of maturity, but rather an acknowledgement that this element is not available to researchers at an institution.

Measure 2: Scale is used to identify who can access the service or support. The element may not be applicable to certain users or is not available to all populations. This allows an institution to see whether its services are being offered in an equitable and appropriate manner or if there are accessibility issues.

Measure 3: Comments are perhaps the most important measure because this section identifies specific strengths and weaknesses and provides an avenue for discussion. This is also a place where regional, national, consortial, or other tools that complement the institution’s RDM maturity can be noted. It can be difficult to determine the maturity level or scale of an element if there are multiple initiatives within that element (e.g., multiple units offering similar data management services), so the comments section can be used for explanations of such instances.

Filling in the MAMIC

The data collected in the MAMIC are for that particular institution’s use only; none will be collected by any other organization, and those who fill in the MAMIC are the ones to decide how to collect and use their data.

While the MAMIC can be completed by an individual, we recommend that a group of interested research partners be involved. These people should come from the areas being assessed. For example, IT Infrastructure should be assessed by representatives of IT; Financial Support should be assessed by representatives of the areas which provide RDM services and support (e.g., libraries, IT, research services).

After the MAMIC was made public, the RDM community shared four examples of the MAMIC completion process with us, and each looked similar. Three of the institutions had a small working group composed of librarians, IT representatives, research office members, and either a researcher or an industry partner. At one of the institutions, however, the data librarian filled in the entire document, reaching out to colleagues in the office of research services to help fill in gaps. This method was less effective and a tremendous amount of work, by comparison. In each case, the results of the MAMIC were taken to a larger RDM committee for discussion.

Benefits of the MAMIC

When developing RDM strategies and supports, partners must reflect on the state and scope of RDM services and supports at their institution and on future needs and desires. A maturity assessment model, like the MAMIC, can help identify gaps, strengths, weaknesses, challenges, and opportunities that exist in the research data landscape. This helps the institution decide where resources and efforts should be directed so they can have supports in place to ensure the success of researchers.

Effective use of this tool creates a complete and representative assessment, but this process requires collaboration and input from a variety of research partners; so a benefit of using the MAMIC is that these types of opportunities for discussion can open the door for relationship building. This supports the institutional RDM landscape and presents opportunities for dialogue, collegiality, and partnership outside of RDM. For example, it may open up lines of communication between IT services and the library’s internal IT unit to allow for greater integration of library services and IT resources.

Bringing together research partners by using a shared tool can illustrate the complexity of RDM and the breadth of efforts across an institution. This can help break down silos and distinguish areas of expertise within the institution, draw connections and interactions, and highlight areas for collaboration and discussions about the institutional strategy and priorities, resource allocation, or budget considerations. Using the same tool over time can also be helpful for benchmarking, to track institutional developments and progress.

On a larger scale, the MAMIC may facilitate conversations between Canadian institutions. Noting where external resources are available or are being developed can help institutions decide where to invest locally. Also, identifying gaps across institutions may offer an opportunity to forge new national initiatives. This can reduce the duplication of effort to solve each gap at an institutional level, which can be time consuming, costly, and require dedicated staff support.


This chapter has presented the MAMIC in two ways: as a tool that RDM practitioners and institutions can use in current or future RDM work, and as a useful example of how Canadian RDM community members can create tools to help everyone work more effectively and efficiently. We identified a need and set out to fill it using the skills and techniques we use elsewhere in our work: conducting environmental scans and literature reviews, developing materials for user groups, gathering user feedback, and working as a team. We also used the resources and people available to us — in particular, the national RDM Network of Experts community and the RDM team at the Digital Research Alliance of Canada — to help develop and disseminate the tool.


Reflective Questions

Choose a category of the MAMIC to reflect on, and then complete the following:

  • Consider what research partners should be involved in order to get an accurate picture of RDM supports offered in this category at an institution. How would you encourage participation from them?
  • List four ways the MAMIC can help assess the level of RDM support at an institution.


Key Takeaways

  • A maturity assessment model is a tool to determine the level of sophistication of a service or product.
  • Maturity assessment models specific to RDM have been developed by different international organizations and have been used for years to assess RDM support services.
  • The MAMIC was developed to reflect the needs of Canadian institutions that are creating institutional RDM strategies.
  • Completing the MAMIC allows research partners to engage in discussion and evaluation about the state of RDM at their institution, to understand the breadth of RDM offerings and support, and to collaborate across divisions.
  • There are a variety of ways in which completing the MAMIC could be used to help in institutional decisions and discussions around RDM. This can enable research partners to move their institution forward by making evidence-based decisions about how RDM services and resources could develop in the future.

Additional Readings and Resources

Australian Research Data Commons (ARDC). https://ardc.edu.au/

Digital Research Alliance of Canada – Putting the Policy into Practice Webinar Series, October 2021

Digital Research Alliance of Canada – Research Data Management. https://alliancecan.ca/services/research-data-management

Fry, J., Doiron, J., Létourneau, D., Perrier, L., Perry, C., & Watkins, W. (2017, January 31). Research data management training landscape in Canada: A white paper [R]. http://dx.doi.org/10.14288/1.0372048

Institutional RDM Strategy Template Revision Working Group. (2021). Institutional research data management strategy development template (3.0). Zenodo. https://doi.org/10.5281/zenodo.5745906

Jacob, B., Whyte, A., Meyer, A., D’haenens, S., Hartmann, N. K., & Weiß, N. (2019, October 2). Using RISE, an international perspective [lightning talk].15th International Digital Curation Conference (IDCC), Dublin, Ireland. https://doi.org/10.5281/zenodo.3565440

Jones, S., Pryor, G., & Whyte, A. (2012). Developing research data management capability: The view from a national support service. In: R. Moore, K. Ashley, & S. Ross (Eds.), Proceedings of the 9th International Conference on Preservation of Digital Objects (iPRES), (pp. 142-149). Toronto: University of Toronto Faculty of Information. https://phaidra.univie.ac.at/detail/o:293775

Jones, S., Rans, J., Sisu, D., & Whyte, A. (2014). Reshaping the DCC institutional engagement programme. International Journal of Digital Curation, 9(2), 47-64. https://doi.org/10.2218/ijdc.v9i2.334

Kouper, I., Fear, K., Ishida, M., Kollen, C., & Williams, S. C. (2017). Research data services maturity in academic libraries [B]. http://dx.doi.org/10.14288/1.0343479

National Research Data Management Network of Experts. https://alliancecan.ca/services/research-data-management/network-experts

Perry, C., Fry, J., & Doiron, J. (2017, June 1). Portaging the landscape: Developing and delivering a national RDM training infrastructure in Canada [presentation]. IASSIST 2017.  https://doi.org/10.5281/zenodo.4551708

SPARC Europe. How open are you? https://sparceurope.org/what-we-do/open-access/sparc-europe-open-access-resources/open-research-checklist-institutions/

UC3. (September 12, 2016). Building a user-friendly RDM maturity modelhttps://uc3.cdlib.org/2016/09/12/building-a-user-friendly-rdm-maturity-model/


Reference List

ANDS. (2018, March 23). Creating a data management framework. https://web.archive.org/web/20220309174711/https://www.ands.org.au/__data/assets/pdf_file/0005/737276/Creatinga-data-management-framework.pdf

Fry, J., Dearborn, D., Farrell, A., Khair, S., & Ripp, C. (2021, November 30). RDM maturity assessment model in Canada (MAMIC) (1.0). Zenodo. https://doi.org/10.5281/zenodo.5745493

Government of Canada. (2021, March 15). Tri-Agency research data management policy. https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html

Rans, J., & Whyte, A. (2017). Using RISE, the research infrastructure self evaluation framework v.1.1. Edinburgh: Digital Curation Centre. www.dcc.ac.uk/guidance/how-guides/RISE

SPARC Europe. Evaluate your RDM offering. https://sparceurope.org/evaluate-your-rdm-offering/

  1. NTEG is part of the Network of Experts that is affiliated with the Digital Research Alliance of Canada.
  2. Since the time of the development of the MAMIC, ANDS has been folded into the Australian Research Data Commons (ARDC), https://ardc.edu.au/

About the authors

Jane Fry is the Data Services Librarian at Carleton’s MacOdrum Library where Research Data Management is one of her responsibilities. She is also the lead on Carleton’s Institutional RDM Strategy Working Group. As well, she chaired the Training Expert Group under Portage for four years and remains a member of that group today.


Jennifer Abel is the Research Data Management Specialist in the University of Calgary’s Research Services Office, and is coordinating the development of UCalgary’s RDM Strategy. She previously worked as the Training Coordinator for both the Portage Network and the Digital Research Alliance of Canada’s RDM team. She holds a PhD in Linguistics and an MLIS from the University of British Columbia.


Dylanne Dearborn is the Research Data Management Coordinator and the Research Data Librarian for Sciences & Engineering at the University of Toronto, in the Map and Data Library. She chaired the Research Intelligence Expert Group with Portage for three years and remains a member of that group today.


Alison Farrell is a Research Data Management and Public Services Librarian at the Health Sciences Library at Memorial University of Newfoundland and is a member of the RDM Institutional Strategy working group at Memorial. She was the co-chair for the Portage Institutional Strategies working group, whose mandate was to provide educational materials on developing institutional strategies.


Chantal Ripp is a Research Librarian in the Interdisciplinary Data Team at the University of Ottawa.  She is a member of the University’s RDM Advisory Group responsible for developing an institutional strategy. She is also a member of a number of other local and national committees, including the DLI Professional Development Committee and the Digital Research Alliance Data Management Plan Expert Group (DMPEG).



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Research Data Management in the Canadian Context Copyright © 2023 by Jane Fry; Jennifer Abel; Dylanne Dearborn; Alison Farrell; and Chantal Ripp is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.

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