Lagring av forskningsdata - Ansatt

Research Data – From Planning to Reuse

Research Data – From Planning to Reuse

Here you will find information on managing research data throughout the research lifecycle – from planning, collection, and storage to processing, completion, archiving, and reuse.
Published
Latest update

Transparency, verifiability, and the possibility of reuse are important research ethics principles that are particularly relevant for research data. In accordance with OsloMet’s Research Data Policy, research data must be managed in line with the principles of FAIR data management and, in most cases, deposited in a repository that facilitates accessibility.

Are you a student with questions about research data? See the information on the page Storing and processing data in a bachelor’s and master's thesis (student.oslomet.no).

  • Planning

    • Personal data. As with the rest of the research process, it is important to plan early for the lifecycle of your research data. Will you be processing personal data in your research project? If so, you must complete and submit the notification form to Sikt (sikt.no) and remember that medical and health-related projects require prior approval from the Regional Committees for Medical and Health Research Ethics (REK). On OsloMet’s pages about privacy and information security, you will find comprehensive guidance on how to use and process personal data in research.
    • Data Classification. It is important to understand the classification of your research data, as this determines the level of protection required and which ICT systems you can use for storing and processing your data (see the Storage Guide) in research.
    • Data Management Plans. A data management plan describes how research data will be handled throughout a project and how openly they can be shared after publication and project completion. This is a useful tool that helps structure and streamline workflows and ensures quality in data management throughout the research process. More information on why and how to write a data management plan is available on our dedicated page on data management plans.
    • Secure Storage. If you need a secure storage area, this must be planned in advance, as most solutions require an application. Information about the storage solutions offered by OsloMet and their features can be found under the Storage Guide and Storage in Research.
    • Research Collaboration. Collaboration with others is a broader topic covered under agreements and other legal matters, but it is mentioned here so you can plan whether you need an agreement and what type of agreement may be required for the use of research data. See our templates for agreements related to the use of personal data.
    • Find previous relevant research and data for reuse. An essential part of preparing for a research process is mapping previous research on the topic you intend to explore. Does it make sense to reuse existing data? Identify existing research data that can be reused and further developed in new research projects. On this website, we have gathered information about available repository solutions, which can be a good starting point. You can also find information on discipline-specific archives for long-term storage or visit SIKT’s own page “Find Data” (sikt.no).

     

     

     

     

  • Conducting the Research

    Structuring Your Research Data

    How data is collected, organized, documented, and secured affects both the quality and security of your research, as well as ensuring that you and others can understand the dataset in the future. The foundation for this should be a solid data management plan, which you should update throughout the project to reflect how data is handled and organized—for example, folder structures, file names, versions, formats, access permissions, and any explanatory files (ReadMe).

    Collecting Research Data

    Different disciplines have different data sources or preferred methods for data collection. In addition, there are numerous tools and methods for collecting various types of data from different sources. Below is a list of tools offered by OsloMet for data collection and the types of data they are suitable for:

    Processing and Analyzing Research Data

    • Transcription. If you have audio files that need transcription, you will find an overview of recommended tools and useful tips here.
    • Analysis Tools. Different disciplines have different tools and preferred methods for processing and analyzing research data. For an overview of the tools OsloMet provides licenses for, go to the Software Center, which you can access from the menu on your OsloMet laptop. For some software, research support and courses are available. If you don't find your suitable software in SoftwareCenter, you can send a request to IT for special software licenses (pureservice.com). 
    • Physical Research Data. See OsloMet’s dedicated page for procedures if you have physical (non-digital) research data, such as physical samples or paper-based research data.

  • Completion

    After Project End

    After the project has ended, research data must be archived securely and, whenever possible, shared openly. Projects must also be closed in the systems where they were created if they use technical infrastructure such as TSD, EduCloud, Mime, etc.

    The main principle for deciding how research data should be preserved for the future is:as open as possible – as closed as necessary.Key considerations include:

    • What can be shared openly?
    • What can be shared with restricted access?
    • What can be shared at a later time?
    • What cannot be shared at all?

    Another important consideration is determining which documentation from the project must be made available so that others can understand how the research data were produced and how the studies can be verified and reproduced.

    Other Considerations

    Deletion and Anonymization

    It is possible to preserve parts of the dataset in ways that do not compromise privacy. Anonymized data cannot be traced back to an individual and thus fall outside personal data regulations.A dataset is considered anonymized when the linkage key has been deleted and, in the case of transcripts, when they have been rewritten so that identifying background variables (e.g., gender, age, residence) do not appear.Remember that audio recordings of voices (including distorted voices) and video material are considered personal data and must be deleted according to the information provided to participants and in the notification form submitted to the Kanalregister.

    Preparing for Archiving

    Additional Documentation

    As mentioned, not all datasets can be anonymized, which means they cannot be archived openly. However, you may still have material that qualifies as research data and is worth archiving in an open research data repository. Examples include:

    • The questionnaire given to respondents
    • Interview guide
    • Notes without personal data or identifying background variables
    • ReadMe file
    • Codebook
    • Code/scripts used, e.g., as a Jupyter Notebook file

    Good Metadata

    By publishing good metadata (metadata = data about data) and detailed information about the dataset, you enable proper citation and reuse of the data, while minimizing the risk of misinterpretation or misunderstanding.

    If you have a dataset that cannot be archived openly, you can still publish metadata describing the project, methodology, data collection method, data types, geographic information, and time/date of collection. Supplementary metadata and contact information should be included in a ReadMe file attached to the dataset.

  • Archiving

    Archiving is the final step in managing research data, but to ensure technical, legal, and ethical requirements are met, you should have planned this stage earlier in the research process. Guidance on choosing a suitable repository and how to archive can be found on OsloMet’s dedicated page on archiving research data.

    The University Library offers advice and support to researchers through several services related to project completion and archiving, as well as through specific courses (oslomet.no).

  • Reuse

    Reuse of research data means using data that others have already collected and compiled and/or data that is openly available.

    National Solutions

    Services for Searching

    Find datasets by searching in these search engines: