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Data management

What is research data management (RDM)?

Research data management (RDM) means organization, description, storage, preservation, and sharing of data collected and used in a research project. This guide is intended to give an overview of the practices and process of managing your research data.

RDM is an integral part of good research practices. Arcada students and researchers are responsible for complying with good data management practices that include Arcada's ethical research guidelines on the management and sharing of research data, data security and data protection in accordance with legislation and research integrity. Departments and supervisors ought to familiarize students and research staff with good data management practices. Follow the stages and checklist outlined in Data management process at Arcada. These instructions and step-by-step guides offer students and researchers support through the whole data management process.

What are research data and why manage your data?

In principle, research data can be any material (either physical or digital) a research project uses and produces as the basis for research findings from the beginning (hypothesis, research questions) to the concluding point (research outputs) of the research.

In RDM, research data are generally understood as digital datasets generated, processed, and used in scientific research, and can include:

  • data collected by various methods (such as surveys, interviews, video recordings, images),
  • data produced during the research (such as analysis results),
  • research sources reused (such as open archived data, commercial databases),
  • source code and software
  • information describing the context, contents and structure of the data (readme files and metadata).

Benefits of managing research data include:

  • Making it easier to find, understand, cite, and reuse your data to increase the impact of your research.
  • Promoting the transparency, validity, reliability, and quality of your research.
  • Enabling the sharing of data within and across disciplines, facilitating collaboration and promoting new discoveries.
  • Meeting funders' requirements and journal data policies.
  • Comply with data protection legislation and agree upon data ownership and rights.
  • Archiving and preserving your data in the long term.
  • Well-managed and documented data make it easier to write up research results for publication.