Category: Planning

Why is this a good Data Management Plan?


This blog post reports from a workshop session led by Marjan Grootveld and Ellen Leenarts from DANS. The workshop was part of a larger event “Towards cultural change in data management – data stewardship in practice” organised by TU Delft Library on 24th of May 2018.

This blog post was written by Marjan Grootveld from DANS it was published before on the OpenAIRE blog.

It’s not just colonel Hannibal Smith, who loves it when a plan comes together. Don’t we all? On a more serious note, this also holds for Data Management Plans or DMPs. In a DMP a researcher or research team describes what data goes into a project (reuse) and comes out of it (potential reuse), How the team takes care of the data, and Who is allowed to do What with the data When.

Just like a project plan a DMP undergoes a reviewing process. Often, however, researchers share their draft version and questions with research support staff and data stewards (see the results of this survey by OpenAIRE and the FAIR Data Expert Group). About twenty data stewards shared their review and pre-view experiences in a lively session at the Technical University Delft on May 24th. During the day the organisers and speakers highlighted various aspects of data stewardship with a welcome focus on practice situations, especially in the break-out sessions. (When the presentations are available we will add a link to this blog post.)

In the session called “Why is this a good Data Management Plan?” Marjan Grootveld (DANS, OpenAIRE) and Ellen Leenarts (DANS, EOSC-hub) presented text samples taken from DMPs. By raising their hands – or not! – and subsequent discussion the participants gave their view on the quality of the sample DMP texts. For instance, the majority gave a thumbs-up for “A brief description of each dataset is provided in table 2, including the data source, file formats and estimated volume to plan for storage and sharing”. In contrast, the quote “Both the collected and the generated data, anonymised or fictional, are not envisioned to be made openly accessible.” drew a good laugh and the thumbs went down. Similarly, the information that the length of time for which the data will remain re-usable “may vary for the type of data and <is> difficult to specify at this stage of the project” was found not acceptable; the plan should a least explain why it is difficult, and how and when the project team nevertheless will provide a specific answer. And is it really more difficult than for other projects, whose DMPs do provide this information?

Although it can be hard to be specific in the first version of a DMP, it’s essential to demonstrate that you know what Data Management is about, and that you will deliver FAIR and maximally Open data. Does the DMP, for instance, tell what kind of metadata and documentation will be shared to provide the necessary context for others to interpret the data correctly? Does it distinguish between storing the data during the project and sustainably archiving them afterwards? (Yes, we had a sample text neatly describing the file formats during the data processing stage versus the file formats for sharing and preservation.)

There was consensus in the group on the quality of most of the quotes. Where opinions differed, this had mainly to do with the fact that the quotes were brief and therefore open to more lenient or more picky interpretation. In other cases, a sample text had both positive and negative aspects. For instance, “The source code will be released under an open source licensing scheme, whenever IPR of the partners is not infringed.” was found rather hedging (“whenever”) and unspecific (which licensing scheme?), but the plan to make also source code available is good; too often this seems to be forgotten, when the notion of “data” is understood in a limited way.

The session participants agreed that a plan with many phrases like “where suitable/ where appropriate/ should/ possibly” is too vague and doesn’t inspire much trust. On the other hand, information on who is responsible for particular data management activities is valuable, and so is planning like “The work package leaders will evaluate and update the DMP at least in months 12, 24 and 36”. Reviewers prefer explicit information and commitment to good intentions – which may be something to keep in mind for your “Open A-Team“.


Training for Data Stewards

As discussed in our previous blog post, TU Delft embarked on an ambitious Data Stewardship programme. The programme aims to address disciplinary needs in Research Data Management by appointing a dedicated subject-specific Data Steward at each one of TU Delft’s eight Faculties. Three Faculties have already appointed their Data Stewards.

Data Stewards are subject-specific experts and therefore bring different skills and knowledge to the team. However, this also means that Stewards might have different degrees of understanding about the overall trends and expectations in the field of research data management and Open Science. Therefore, in order to ensure that all Stewards deliver consistent messages and good quality support to the research community, an intense training programme was developed.

External training

In the first instance, all Data Stewards will attend and complete the intense Essentials 4 Data Support, delivered to international communities by the Research Data Netherlands. The main goal of the course is “teaching the basic knowledge and skills (essentials) to enable a data supporter to take the first steps towards supporting researchers in storing, managing, archiving and sharing their research data”.

Internal training

In addition to external training, Data Stewards will also attend a series of two-hour in-house training sessions, delivered by local experts in data management:

  • Introduction to delivering data management workshops
  • Awareness about local support for data archiving:
    • 4TU.Centre for Research Data – why to use it and how to use it?
    • Data Funds – available for researchers to prepare data for deposit
  • Open Access services available at the Library
  • Supporting researchers with Data Management Plans
  • ICT support for data management at TU Delft
  • Selfish benefits of data sharing
  • Presentation skills

Training is scheduled to complete by end of December 2017. Data Stewards will develop and start delivering training for their local research communities from January 2018.

If you have any comments or would like to make suggestions about this training programme, please add your comment below or contact – we would be delighted to hear from you.

Data Stewardship – August 2017 Update

Where is the project at and what is the next milestone?

As of the end of August 2017, four key staff members were appointed for the project:

  • Three dedicated, subject-specific Data Stewards:
    • Jasper van Dijck, Munire van der Kruyk and Robbert Eggermont (joint appointment) at the Faculty of Electrical Engineering, Mathematics and Computer Science
    • Kees den Heijer at the Faculty of Civil Engineering and Geosciences
    • Heather Andrews at the Faculty of Aerospace Engineering
  • Data Stewardship Coordinator:
    • Marta Teperek, based at TU Delft Library

The next key milestone is to appoint the remaining five Data Stewards. The process will be initiated in September 2017 with the aim of appointing Data Stewards in early 2018.