Effective data management must take into account:
- The type of data you collect
- How to store the data safely
- How you will approach version management
- Who has access to the data and what data others can/can't modify
- How to document the data so that others can understand and reuse them
Data management planning involves thinking about the story of your research data, from its creation to '...and they lived happily ever after'. In drawing up the research design, you think about the data you want to collect, how you are going to store them, and how you are going to share them with your current and future colleagues. Watch an introductory video on the what, why and how of data management planning here.
One way to think systematically about the data collection process is to complete a data management plan (DMP). You record the story of your research data. Such a plan with accompanying activities promotes the integrity and increases the impact of your research(*) and you meet the requirements of legislation and codes of conduct, subsidy providers and publishers.
For example, the Netherlands Code of Conduct for Research Integrity requires you, as a researcher, to be transparent in what data you use and to organise and describe your data in such a way that they can be verified and reused. Responsible data management is also increasingly being included as a requirement in subsidy applications. Subsidy providers ask you, as a researcher, to keep and manage your data carefully.
(*)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.
Broadly speaking, a data management plan consists of the following components:
- 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 care, including data protection, privacy, copyright and intellectual property rights.
- Storage and backup: answer questions about storing, sharing with other researchers and backing up your data 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.
- 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 target audience, the data repository that you will use for this purpose, the conditions under which your target audience may access the data and when the data will be available. Observe 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.
- 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.
A data management plan is a living document. At the beginning of the research process, you will not be able to fill in everything in detail. Also, during the course of your research, it may be necessary to do things differently than you originally intended. Create a new version of your data management plan every time there are significant changes in your research that affect your data management. It is advisable to update your data management plan at least once a year.
Please note that old versions of your data management plan must be retained and you must clearly document from when which plan applies.
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.
If your subsidy provider does not prescribe its own template or if a subsidy provider is not applicable, you can use the general templates and tools 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.
- 4TU.ResearchData template: template of 4TU.ResearchData, a joint data management initiative of the Dutch technical universities.
Good data management involves costs, in hours and in money. This overview shows the possible costs per activity of the research process. The overview is based on the UK Data Service Data management costing tool and checklist. In addition to this overview, you can also use the interactive tool designed by TU Delft to estimate the costs of data management.
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.
- Datamangementplan (DMP)
- Ten components of a good data management plan
- Ten tips for writing a data management plan
- Checklist for a Data Management Plan, Digital Curation Centre in Edinburgh, available at https://www.dcc.ac.uk/resources/data-management-plans
- To support disciplines in setting up discipline-specific DMP templates, Science Europe published a guidance note in January 2018
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.
As a researcher, you are responsible for good data management. But are you experiencing any trouble? Or does your data management paragraph or plan need a check? Then a data steward with 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 Library. Ask your question via email@example.com.