Data management plan

Effective data management

Data management establishes the foundation for research, ensuring structured data management for transparency, efficient reuse, and compliance with research organization requirements and ethical guidelines.

Foundation of research data

You provide a solid basis for research through structured management of research data. You offer transparency and traceability of collected data, enhancing the reliability of the research.

Efficient (re)use of data

  • You facilitate efficient reuse of data for future analyses or follow-up research.
  • You minimize the loss of valuable information through structured storage and documentation.

Meeting the requirements of research organizations

  • You comply to the requirements and standards of research organizations and funding agencies, enhancing the credibility of the research.
  • You facilitate compliance with ethical guidelines, legislation, and codes of conduct related to data management.

(*)Research shows that publications in which the underlying data are made available in any form (via appendices, URLs or contact information) are cited more often on average than publications in which the underlying data are not made available.

Components

A data management plan is dynamic. In the initial phase of your research, it is not always possible to fill in all the details; adjust it based on significant changes in your research that affect data management. Update the plan at least annually, keep old versions, and document from when each plan is valid. The data management plan is part of the research project documents; store these documents in Research Drive so that each project member has access to the latest version of the data management plan.

In general, the data management plan consists of the eight components listed below. The online THUAS training on research data management provides more detailed explanations for each component.

Project data

Generic information about your research such as the purpose and details of the researcher(s), collaboration partners and subsidy providers.

Data collection

Data characteristics such as data types, data formats, the expected size of the data and the methods and techniques you will use to collect and organise your data such as directory structure, naming, standards and norms, and versioning.

Documentation and metadata

What data about your data and accompanying documentation are needed to help others understand and (re)use your data; think of using a metadata standard and a README.txt file.

Ethical and legal aspects

Your approach to issues that require extra caution, including data protection, privacy, copyright and intellectual property rights.
For more information view the pages about Research integrity and Ethics Committee and legislation. 

Storage and backup

Answer questions about storing, sharing with other researchers and data backup during the research process; for example, the storage space for both storing and backing up the data, the frequency of backing up and a (recovery) plan in case of data loss or a data leak. Research Drive serves as the secure data storage repository within The Hague University of Applied Sciences.

Selection of data retention

Describe and justify your decision on whether or not to retain your data (or part of it) for the long term and whether there are legal grounds to destroy (part of) the data immediately after completion of your research.

Data sharing

Describe your intended audience for the data that you will retain and how you will make the data findable for your intended audience, the data repository that you will use for this purpose, the conditions under which your intended audience may access the data and when the data will be available. Use the minimum requirements that may also have been imposed by your subsidy provider: use of persistent identifier, public availability of information, access protocols, data licences and guarantees for sustainable availability. View the research support page about archiving and sharing research data.

Responsibilities and resources

Indicate who is responsible for the data management plan and the activities you have described in the previous sections and what financial and other resources such as specialist knowledge and hardware or software you will need to realise these activities.

Templates

Various templates are available for drawing up a data management plan. The subsidy provider of your research may have its own template.

The template of the Dutch Research Council (NWO) can be downloaded at the bottom of this page. The NWO template also applies for THUAS research projects without external funding. The template for ZonMw can be downloaded here.

Templates from other subsidy providers can be found in the tools en templates listed below.

  • DMPonline: online tool for writing, editing, sharing and saving a data management plan, the use of which is also prescribed or recommended by a number of subsidy providers (when creating an account, choose your subsidy provider as the organisation; if you are not using a subsidy provider for your research, choose 'DMPonline – Tutorials' as the organisation).
  • ERC Data Management Plan Template: European Research Council template.

Costs

Good data management involves costs, in hours and money. For an overview of the potential costs per activity in the research process view the UK Data Service Data management costing tool and checklist.

FAIR data

Ultimately, your data management efforts are about making your research results verifiable and the underlying data reusable. For this, the data must be FAIR: Findable, Accessible, Interoperable, Reusable. In other words, your data must be findable, accessible, exchangeable, reusable and sustainably stored.

Data management paragraph

When submitting a subsidy application, the subsidy provider may request that a number of data management issues are already mentioned in the application. This is called the data management paragraph. We have listed the questions that can be addressed in a data management paragraph with advice on how to answer them.

What is research software? 

Software is a set of instructions that tell the computer what to do. Various types of software are used in research, but not all software used in research is considered research software. Research software is software developed during the research process or for research purposes. It helps solve scientific problems or supports the functioning of research instruments. 

Software Management Plan (SMP) 

A Software Management Plan (SMP) is a document similar to a Data Management Plan, where you describe how you will manage the research software you intend to develop. Among other things, you outline why it is important and necessary to develop this software and how other researchers can benefit from it. To make the software reproducible and reusable, consider technical aspects such as programming languages and operating systems, as well as metadata and documentation. Core elements of an SMP include: the purpose of the research software, version control, data repository, user-level documentation, deployment and development, software licensing, and software citation. 

If you need help with your Software Management Plan, you can use the guide written by NWO and the Dutch eScience Center. If you want to know everything about Research Software, you can consult the training materials provided by the Netherlands eScience Center. 

Publishing and reusing research software   

The Open Science movement encourages researchers to make their research software reproducible and reusable. If research software already exists that could be applied to your own research, it is more sustainable to extend this software to meet the needs of the research project, rather than developing new research software. 

Preregistration of research projects 

Preregistration is one of the emerging Open Science practices. By preregistration, we mean publicly sharing a read-only version of a research plan on designated platforms. This research plan is written before data collection and includes hypotheses, data collection procedures, variables, and the plan of analysis. The goal of preregistration is to limit bias and dubious research practices, such as HARKing (hypothesizing after results are known). Preregistration enhances the quality of research and its results. 

There are several platforms where you can register your research, among others: 

- Open Science Framework (recommended) 

- AsPredicted.org 

You can read more about preregistration on the Center for Open Science website. 

Help

As a researcher, you are responsible for good data management. Do you need assistance with the data management paragraph or plan? A team consisting of staff members from different departments, each with his/her own specialism, is waiting for you at The Hague University of Applied Sciences: Research Team, Subsidy Support Office, IT, Privacy Officer and the Library. Ask your question via [email protected].