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Research Data Management: Introduction

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. Hanken students and researchers are responsible for complying with good data management practices that include Hanken'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 principal investigators ought to familiarize students and research staff with good data management practices. See Instructions for supervisors.

Follow the stages and checklist outlined in Data management process at Hanken. These concrete stages with descriptions, as well as must-dos, in each stage offer students and researchers, respectively, comprehensive and thorough guides that you can trace the whole data management process easily, clearly, and closely.

E-forms and templates you (may) need can be found:

FAIR data principles

FAIR is an acronym that data is Findable, Accessible, Inter-operable, and Re-useable. These are all essential elements in making data truly open.

To be Findable:

F1. (meta)data are assigned a globally unique and persistent identifier
F2. data are described with rich metadata (defined by R1 below)
F3. metadata clearly and explicitly include the identifier of the data it describes
F4. (meta)data are registered or indexed in a searchable resource

To be Accessible:

A1. (meta)data are retrievable by their identifier using a standardised communications protocol
A1.1 the protocol is open, free, and universally implementable
A1.2 the protocol allows for an authentication and authorisation procedure, where necessary
A2. metadata are accessible, even when the data are no longer available

To be Interoperable:

I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation
I2. (meta)data use vocabularies that follow FAIR principles
I3. (meta)data include qualified references to other (meta)data

To be Reusable:

R1. meta(data) are richly described with a plurality of accurate and relevant attributes
R1.1. (meta)data are released with a clear and accessible data usage license
R1.2. (meta)data are associated with detailed provenance
R1.3. (meta)data meet domain-relevant community standards

See Guidelines on FAIR Data Management in Horizon 2020.

Why manage your data?

Benefits of managing research data include:

  • Making it easier to find, understand, cite and reuse your data to increase the impact of your research.
  • Enabling the sharing of data within and across disciplines, facilitating collaboration.
  • Promoting new discoveries.
  • Meeting funder requirements.
  • Complying with journal data policies.
  • Ensuring data integrity.
  • Archiving and preserving your data in the long term.
  • Saving time and resources.
  • Supporting open science.
  • Well-managed and documented data make it easier to write up research results for publication.

Help and support

Help and support for research data management is a network of staff from Hanken's Data Protection Officer, the library, and IT Services. Questions about data management, ethical and legal matters concerning data security, data protection and IPR issues, assistance with data management plans, data storage, and data sharing and preservation, please contact Hanken's Data Protection Officer or

We also provide training in creating data management plans and in data management and data protection throughout your research lifecycle. Training is offered both as part of studies and as staff training. More information, see Courses and workshops.