Topic Definition and Scope
Train-the-Trainer: Foundations of Data Management Plan (DMP) Instruction
This module provides trainers with the pedagogical framework needed to teach effective DMP creation. The lesson plan covers the core requirements and overarching purpose of a DMP, preparing trainers to confidently explain these concepts to peers and researchers. Special emphasis is placed on identifying common drafting pitfalls and resolving complex legal, ethical, and privacy dilemmas. Additionally, the session prepares trainers to introduce diverse DMP templates, facilitate hands-on drafting exercises, and connect researchers with vital institutional support systems.
Summary of Tasks and Actions
This lesson is divided itself in the 3 phases:
1.0 Context and Assets of the Data Management Plan: In this phase the trainer explains the core of a Data Management Plan, and why it is relevant at an institutional, national and international level. The trainer also explains the foundational core of a DMP and its shifting role from a bureaucratic hurdle to an essential asset.
2.0 The when and how of a DMP in the research process at MUMC+: The trainer then delves into the phases of the Research Life Cycle and where a DMP is located at within this cycle. The trainer connects the document directly to the timeline of everyday scientific work.
3.0 Practical Part: Working with the DMP & Data Steward Feedback: Facilitate Participants filling out their own DMP form
4.0 Practical Part: Work with the DMP and send to a Data Steward: The trainer then divides the group into pairs, depending on the number of participants. The participants will now work on their own DMPs and debrief about them in the later section.
Materials/Equipment :
For the participants: Instruct participants to bring their project proposals and other documents that will be helpful to fill out their DMPs. They also have to ideally bring a computer to fill out the form.
References
| [DMP Training Session MUMC+ FHML | Zenodo](https://zenodo.org/records/20344397) |
Additional Resources
Lesson content
Reading Independent Learning
- Learning Objectives: Familiarize participants with the structural components of a standard Data Management Plan (DMP) and prompt initial questions.
- Format: Individual reading and preparation (Optional Pre-Work).
- Materials Provided by Trainer: A standard DMP template sent out via email before the session.
- Trainer Note: Use your own institutional template (e.g., Maastricht University’s FHML template) or download a standard funder template from a national funder like NWO or an international funder like Horizon Europe.
Guided Exercise:
Instruct the participants to complete the following tasks at home before the workshop begins:
- Browse the Template: Review the provided DMP template and identify its main, high-level components (e.g., data collection, storage, ethics, archiving).
- Flag the Friction Points: Highlight any questions, sections, or wording in the template that seem confusing, tricky, or difficult to explain.
- Prepare Questions: Come to the session with at least one question for the trainer regarding how to interpret or complete the template.
2.0 Debriefing:
Time: 5–10 minutes at the very beginning of the live session.
Because this pre-work was optional, do not assume everyone did it. Instead, use the debriefing to bridge the gap between those who prepared and those who didn’t.
- Step 1: The Visual Anchoring: Display the template on the main screen. Run a 2-minute rapid walkthrough pointing out the main components (e.g., “Section 1 is Administrative, Section 2 is Data Collection, Section 3 covers Ethics…“). This catches up anyone who didn’t read it at home.
- Step 2: The Question Harvest: Ask the participants who did review the template to share their thoughts.
- Trainer Prompts to the Room:
- “For those who had a chance to look at the document, which section felt the most intimidating or confusing?”
- “What questions did you write down for me about how this template is structured?”
- Trainer Prompts to the Room:
- Step 3: The Gateway to the Lesson: Use their answers to transition into the main lecture.
Exercise:
Filling out a DMP
Learning Objective: Have participants fill out a DMP complete and it learn how to do it efficiently.
Materials:
- Laptops with active login credentials.
- Participants’ own project proposals or grant applications.
- Access to the institutional DMP template (via the digital tool or an active worksheet). Or in access to another DMP template of their choice.
The exercise:
1.0 Start the DMP using your own DMP or the one provide as an example: Divide the participants into pairs. If they brought their own active project proposals, they will work on their own plans. If they do not have a live project, provide them with a pre-made sample research case study.
2.0 Discuss the form with your peer.
Pairs swap laptops or look at each other’s screens. They must read through their partner’s draft and look for two specific things:
- Completeness: Did they answer every part of the template or did they leave out details (like file formats or specific storage locations)?
- Clarity: Is the language concrete, or is it too vague? (e.g., If they wrote “Data will be saved securely,” push them to change it to “Pseudonymized data will be stored on the institutional secure network drive.”)
3.0 Get ready to debrief the following questions:
- What did you find hard from filling out a DMP?
- Do you have any questions left?
4.0 Wrap up discussion: How do we efficiently do this?
Close the activity by asking the room: “What strategies did you use today to speed up the process without losing quality?”
- Reinforce the habit of recycling text directly from their grant proposals/ethical applications and using available institutional drop-down menus rather than writing every policy description from scratch.
Additional resources
- Ten Simple Rules for Creating a Good Data Management Plan arrow_outward PLoS Comput Biol 11 (10) : e1004525 (2015)
- Data management arrow_outward
The terms4FAIRskills project has created a formalised terminology that describes the competencies, skills and knowledge associated with making and keeping data FAIR.