Data Management Plans in Practice

Lukas Lammers

Universität zu Köln

14 October 2026

Overview

In this session, we will cover:

  • Common opinions about DMPs — why they are often seen as bureaucratic and unhelpful
  • Exchange your own experiences — about writing or working with DMPs in your research projects
  • Explore additional software — You could use to create a DMP
  • Identify core elements of DMPs — to integrate them efficiently into your research workflow

Note

Goal: Learn why DMPs are relvevant and how they can support your work.

1. Initial Questions

About your experience with Data Management Plans (DMPs):

  • Who has written a DMP before?
  • Who would be able to show me an up-to-date DMP for their current project?
  • Who has no experience with DMPs at all?

2. Definition

Definition 1:

“A Data Management Plan (DMP) provides a structured framework for handling research data in a scientific project. It describes how the data used will be managed both during the project and after its completion. Many third-party funding agencies (DFG, FWF, SNF, Horizon Europe, Volkswagen Foundation) require information on research data management as part of a grant application […]. In particular, a DMP allows you to document the current status and specific details throughout the entire research data lifecycle. This makes it helpful for administrative purposes and for maintaining an overview.” forschungsdaten.info, translated by L. Lammers with DeepL

Definition 2:

“As a planning tool, the data management plan forms the foundation of professional research data management. It provides a systematic overview of the research project in question and outlines how the generated research data will be handled during each phase of its lifecycle, from collection to dissemination. […]” fdm.uni-koeln.de, translated by L. Lammers with DeepL

Definition 3:

“A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed. The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; this may lead to data being well-managed in the present, and prepared for preservation in the future.” Wikipedia

3. My experiences with DMPS

Part I

  • SPP 2143 “Entangled Africa: Inner African relations between the rainforest and the Mediterranean, around 6000–500 years ago”

  • 13 projects from 2018 to 2026

  • Interdisciplinary approach: methods from archaeology, natural sciences, linguistics and many other fields come together

  • The projects overlap spatially and temporally

Overview map of research areas in Entangled Africa

3.1 The first Survey pt. I

  • At the start of Entangled Africa a survey was conducted to get an overview about RDM needs

  • Are there already data management plans?

  • Finding: Very heterogeneous level of knowledge in research data management.

The survey contained about 40 questions

3.2 The first Survey pt. II

Checked: My project does not have a formalized data management plan. (4/10)

Following the free text answers:

  • “I’m not sure what a data management plan is.?”

  • “best practice”

3.3 The first Survey pt. III

Checked: My project has a data management plan for the duration of the project. (3/10)

Following the free text answers:

  • “Data management is planned via [names of three repositories].”

  • “In accordance with the data management guidelines of the [institution name]”

  • “USB stick. First copy on another USB stick stored separately. Second and third copies on separate [network drives] at the [data center]. Fourth to sixth copies on external hard drives at the institute. Seventh to tenth copies on external hard drives at my home. – Daily backups to at least three of the above storage locations. Regular backup to TSM ([data center]).”

3.4 The first phase of Entangled Africa

  • Survey results became irrelevant:

  • Many projects changed original plans

  • Workshops and active support in RDM by FAIR.rdm

  • Primarily phase of acquiring data: Management of data was done “on the fly”

A lot of work went into raising awareness for good RDM

3.5 The second phase of Entangled Africa

  • 2021 - 2026

  • None of the original DMP plans were implemented

  • Keeping track became difficult (13 projects, 16 repositories in use)

  • RDMO was considered

The Research Data Management Organiser (RDMO) was funded by the DFG.

4. The perfect DMP: RDMO

  • RDMO is a web-based tool for creating and managing data management plans
  • it was funded by the DFG
  • it is open source and can be self-hosted
  • basically a repository (or infrastructure) for DMPs
  • the University of Cologne has no RDMO instance

4.1 Text+ and GRO

5. Practical exercise with GRO

https://plan.goettingen-research-online.de/

  • Log in to GRO! (Federated Login via your university account)
  • Create a new project for your current research project
  • Pick either Text+ questionaire or the DFG linguistics template for spoken corpus or text corpus
  • Fill in the DMP template with your own information (not too detailed, just a rough overview about the contents and questions)
  • YOu have 20 minutes

5.1 Practical exercise with GRO: Results

  • What was your experience with the DMP template?
  • What advantages do you see?
  • Are there any disadvantages?
  • Would you consider to using it for your own research project?

6. My experiences with DMPs

Part II

  • To use RDMO in Entangled Africa, we would have had to create a new instance of RDMO for the SPP 2143
  • Far too much work!
  • instead we used living documents, simple word files
  • Structure: Project Metadata, Project data history, Record of published and to pubish data, current agreements
  • Fast, easy, see the most important things quickly

Intern DMP of Project 02

6.1 Lessons learned

  • This worked well and the project was a success in general
  • But a few problems occured:
  • A lot of data is unpublished
  • Organization of raw data in some projects is not documented and it’s nearly impossible for people from outside to work or even understand the data
  • The researches know this issue (method to protect the data?)
  • One project lost huge amounts of data (hard drives got lost).
  • A DMP can help to avoid these situations

7. Key Aspects of DMPs

  • RDMO is often praised as the perfect tool for creating DMPs
  • But a good DMP does not depend on the format, the content is more important!
  • You can find many templates and guiding questions online
  • Uni Köln: fdm.uni-koeln.de
  • forschungsdaten.info: forschungsdaten.info
  • key aspects can be derived from common rdm principles

8. FAIR Data Principles

  • Do you remember the FAIR principles?
The acronym FAIR stands for
  • Findable
  • Accessible
  • Interoperable
  • Reusable
  • originally they apply to metadata as technical guidelines but today they are also used as a general framework for good research data management
  • original article: Wilkinson et al. (2016)

9. Research Data Lifecycle

  • Do you remember what the research data lifecycle is?
  • The decisions on how to prepare and conduct every step are a complete DMP

the research data lifecycle

10. Choice for a Repository

The choice for a repository can shape your DMP very much, e.g.:

  • Which file formats are accepted?
  • How is personal data handled?
  • How is the data licensed?
  • Is long term archiving possible?
  • etc.

11. Conclusion

  • you can spend a lot of time filling out endless questionaires
  • a DMP should support your work, not be a bureaucratic burden
  • a DMP should be a living document, not a static one
  • start with basic descions using common data management principles:
  • most important: wich kind of data will be generated and where will it be stored?

References

  • Slide “3. My experiences with DMPS”: Simon Kellers, DAI Geoserver
  • Slide “6.1 Lessons learned”, DCH Demerit Badges: RDM Demerit Badges, CC-BY 4.0 Elisabeth Mollenhauer (badges) and Felix Rau (text), DCH Uni Köln
  • Slide “9. Research Data Lifecycle”: SZF TU Berlin, CC BY 4.0, TU Berlin SZF