Managing research data at the start of a project

Plan and start managing data early in the project to save time and comply with existing guidelines throughout the project period.

Things to do within the first six months

  • Write a data management plan (DMP)
  • Prepare agreements with partner institutions for transferring data. Check whether you need an agreement her.
  • If the project will handle personal data, submit the notification to Sikt. Data collection can begin after Sikt approves the project.
  • Apply early and have the necessary ethical approvals in place. For projects handling personal data: Once the notification to Sikt is submitted, Sikt will guide you regarding this for all projects handling personal data.

The DMP and notification to Sikt will contain overlapping information. They can be prepared in parallel once the research plan is ready with a clear overview of work packages, activities and responsibilities.

Data Management Plan

  • 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 DMP in a nutshell:

    • DMP is a living document.
    • Describes how data in a research project are collected, stored, documented, analyzed and archived, and reasons (if any) for not making the data openly available.
    • Provides an overview of the data, including research aims, rationale and overview of work packages
    • Documents the roles and responsibilities.
    • Data security measures.
    • Ethical considerations
    • DMPs should be version-controlled. Version control allows tracking of changes in plans and reasons underlying key decisions at different stages of the project.

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

    • Identifying challenges at an early stage. The challenges can be related to e.g. information security, handling of personal data, roles and responsibilities.
    • Tracking research data throughout the whole project.
    • Compliance with FAIR principles, ethical and other guidelines throughout the project.
    • Ensuring continuity in research in case someone leaves/joins the project mid-way.
    • Saving time and extra work later on (e.g., when data is to be archived and published and administrative documents or reports are to be prepared).
    • Identifying costs and planning how to cover them. These extra/unforeseen costs can be related to, e.g., storage of large data volumes, for accessing high-performance computation facilities, hiring someone for transcribing and anonymizing data.
    • Increasing data quality and making research data FAIR (Findable, Accessible, Interoperable and Reusable) - Wilkinson et al. 2016.

    Major 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. Moreover, 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:

    Externally-funded projects should use a DMP template recommended by the funder. In accordance with recommendations from major funders, we recommend the template from Sikt if your project handles personal data and is not funded by ERC. However, more important than the choice of template is the content of the DMP. A DMP is supposed to work as a guide for how data are to be managed in a project.

    • Start writing when your research plan is ready with a clear overview of research activities, work packages and publication plans.
    • Summarize the following in tables (Send an e-mail to research-data@nord.no for suggested templates):
      • Work packages that will collect sensitive information (on ground of security, personal data, IPR etc.)
        • If sensitive information will be collected and handled, prepare a separate table summarizing who (/which institution in a collaborative projects) will have access to the sensitive content, who will have access to desensitized content (e.g., anonymized data) and whether the data will be transferred within or beyond EU/EEA. This table will help you and research administration assess the need for entering into specific agreements with collaborating institutes.
      • For each work package, which file types (documents, audios, videos etc.), formats (plain text, spreadsheets, jpg etc.) will be used? Try to provide an estimate of data volume at the very start of the project, especially if you need additional storage or high-performance computation. This estimate can be updated at later stages as the project progresses.
      • Where will the data be stored during the project period?
      • Where will the data be archived?
    • The abovementioned tables will help you identify issues that need special attention in your research project: Protection of personal data, IPR, other ethical issues.
    • Make a DMP detailed and informative by detailing key considerations and decisions. Specify how you address any issues linked to personal data, IPR and ethics.
    • Refer to the relevant rules and guidelines in DMPs. Find out more about how to write a DMP here, including links to some guidelines.

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.