Research life cycle: 1.Planning: Data management plan

A data management plan is usually an elaboration of a data management section. In this plan you indicate whether existing data is to be used or whether it is a new data collection. In addition it indicates how the data collection will be made available according to the FAIR principles (see separate tab). The plan provides an overview of all aspects of data management, during and after the research, and helps to structuralize the data collection process.

The sooner you think about data management, the more value a DMP has. Thinking this through before the start of the research reduces the chance of unpleasant surprises during the research. This saves time, work and money.

The DMP is a dynamic document that usually requires regular updating. During the research it may become apparent that it is necessary to arrange matters differently, and questions that are irrelevant at the start of a research may be topical at a later stage.

The size of a DMP should be proportionate to the size and nature of the research project. One page may suffice for a short study of six months, whereas a large scale project encompassing several years will require a more detailed DMP.

Examples of detailed DMPs:

• Lucie Vermeulen. PhD candidate at Wageningen University, Environmental Systems Analysis Group
• Fieke Schoots and Peter Verhaar. Leiden University, Italian Department, VIDI research group Splitting and clustering grammatical information
• ZonMw
• via DCC. University of Leeds, Social Science (2 DMP's)

1. Tool
    A DMP helps you think about all aspects of collecting and
    managing your research data, explains and refers to
    resources.
2. Legitimation
    With a DMP you can show the various parties involved in the
    research that you are handling the funds and/or data that have
    been made available for the research project well.
3. Budget
    By thinking about data management in the application phase
    of the research project, you gain insight into whether costs
    need to be estimated.
4. Quality
    A completed DMP on its own does not guarantee quality. But
    thinking about the questions in a DMP and actually following
    up on the plan contributes to a high quality dataset.
5. Support:
    A detailed DMP helps you to get a good idea of the necessary
    support, e.g. library, ICT, legal matters, etc.

There are many templates available that can serve as a guide for writing a data management plan; not all questions need apply to your research project. The most important are:

• DANS
• Maastricht University
• 4TU
• DMP-online
  Templates from ZonMw, NWO, Hartstichting, Horizon 2020 and DCC can be downloaded via 'Funder requirements'. If you want to use the DMPonline tool, you need to register for free once.

The Digital Curation Center has compiled a list of questions for writing a DMP:

• What data are you going to collect?
• How are the data collected?
• What documentation and metadata are added to the data?
• How are ethical issues dealt with?
• How are copyright and intellectual property rights handled?
• How are the data stored and backed up during the research?
• How is data access and security regulated?
• Which data should be retained, shared and/or saved?
• How is the long-term storage of the data regulated?
• How are data shared?
• Are restrictions required on data sharing?
• Who is responsible for data management?
• Which resources are needed to implement the DMP?

Detailed information and tips per question can be found in the complete checklist:

One of the questions that needs to be addressed in a DMP relates to the selection criteria you use to determine which part of the collected data has to be preserved after the cessation of the research project. Data Archiving and Networked Services (DANS) has compiled a list of essential points that are important when selecting data for archiving: