Managing research data at the start of a project

Data Management Plan

Do you wonder what a data management plan (DMP) is, why a DMP is useful, and what the typical content in a DMP is? Here you can find guidance on these questions.

  • A DMP is a document that describes how data in a research project are managed. It states e.g. how data will be collected, stored, documented, analysed and, if possible, shared. A DMP is a "living document" that needs to be updated over the course of a research project.

    A DMP is first and foremost a tool for helping researchers organize and structure their data, contributing to the adoption of best practices in managing research data.

  • A good data management plan facilitates the workflow in a project, and enables project participants to understand and work with the data more efficiently. Also, a DMP helps with:

    • Identifying challenges and issues at an early stage. These can be related to e.g. information security, handling of personal data, roles and responsibilities.
    • Tracking research data throughout the whole project.
    • Facilitating data management in case someone leaves/joins the project underway.
    • Saving time and extra work later on (e.g., when data is to be archived and published).
    • Identifying costs and planning how to cover them.
    • Increasing data quality and making research data FAIR (Findable, Accessible, Interoperable and Reusable) - Wilkinson et al. 2016.

    Several research funding agencies (e.g., The Research Council of Norway and the European Research Council) require that the projects funded by them have a DMP. Also, research projects conducted by researchers at Nord University shall have a DMP, according to Nord's guidelines on research data management.

  • The content and scope of a DMP will vary according to the needs of a project, but the following is typically included in a DMP. It is common to write a DMP by filling out a template. It is also common to start with a simple DMP and improve it as your project progress forward.

    • Data description and collection or re-use of existing data e.g., How will new data be collected or produced and/or how will existing data be re-used? What type of data will be collected or produced?
    • Documentation and data quality e.g., What metadata and documentation will accompany data? What data quality control measures will be used?
    • Storage and backup during the research process e.g., How will data and metadata be stored and backed up during the research process? How will data security and protection of (personal) data be taken care of during the research?
    • Legal and ethical requirements, codes of conduct e.g., What legislation is applicable to the project? Which ethical guidelines and codes of conduct will be followed? How will other legal issues, such as intellectual property rights and ownership, be managed?
    • Data sharing and long-term preservation e.g., How and when will data be shared? Are there possible restrictions to data sharing or embargo reasons? What methods or software tools will be needed to access and use the data? How will the project employ a unique and persistent identifier (such as a DOI) to each dataset?
    • Data management responsibilities and resources e.g., Who will be responsible for the data? What resources will be dedicated to data management?

    Source: Science Europe.

    If you are a researcher at Nord and have questions regarding DMPs, please contact us at research-data@nord.no.

  • When elaborating a DMP, it may be useful to consult how others have done it. On these websites, you can find examples of published data management plans:

    • LIBER has a Data Management Plan Catalogue with an assessment of DMPs from various disciplines in terms of quality and completeness. The DMPs themselves are archived in a special collection in the general purpose repository Zenodo.
    • DMPonline has publicly available DMPs from several disciplines and based on different templates.
    • Digital Curation Centre has several examples of DMPs, organized by research funding agencies.

  • Several templates for DMPs are available. Here are some examples:

    Generally, we recommend the template from Sikt. However, more important than the choice of template is the content of the DMP. A data management plan is supposed to work as a guide for how data are to be managed in a project.

    It is worth noting that some research funders may require a specific DMP template. For instance, Horizon Europe has its own tem​plate for DMPs. In these situations, the template indicated by the funder must be used.

Handling personal information in research projects

If your research project will include personal data, routines for privacy in research must be followed. Personal data is any information that can be linked to a person. Personal data can be, for example, a national identification number, name, address, e-mail or IP address. Follow this link for guidance on how to manage research projects that include personal information.

Research ethics​

All research carried out at Nord University must be based on applicable ethical guidelines, which include respect for the rights of research participants. Research ethics guidelines for Nord university can be found on this link.

Searching and citing existing research data

Similar to searching and citing scientific publications, you can also search and cite research data. Given the growing availability of open research data, it is likely that your research project can benefit from already-existing research data. Here are a few examples of multi-disciplinary search engines for research data:

It is important to experiment with these search engines and to try out several search terms, similar to what you would do when performing a literature review. While these search engines include open metadata, you will need to go to the individual data archives (e.g., Zenodo, Figshare, Dryad, or DataverseNO) to access the data.

Alternatively, you can start your data search in the archive register re3data.org. Here, you can browse through an extensive list of data archives. You can organize your search, for example, by the archive’s subject area, content types, and countries. Starting your data search in re3data.org is relevant, for instance, when you want to restrict it to selected archives.

Once you have found data that you wish to use in your research project, it is essential to cite your data sources properly. See more information on how to cite research data here.

​Costs of data management

An important aspect of data management planning is to estimate the resources necessary to implement good data practices in a project (e.g., costs for data collection, data documentation, and data preservation).

Consult this guide from UK Data Service for estimating the costs of data management. Most research funders consider such costs in a project budget.

Useful online resources when you are searching for existing data:

  • Our World in Data: contains data on a range of topics with a focus on the world's current challenges such as poverty, disease, hunger, climate change, war and inequality.

  • Eurostat: provides statistics and data about Europe within topics such as economy and finance, population and social conditions, industry, trade and services.
  • Sikt: has extensive collections of data about people and society. Here you will also find links to other data resources such as the European Social Survey and statistics on education and research.