1.1. Research data definition

Research data are the foundation of scientific knowledge. They are fundamental for researchers to answer their research question or to test a hypothesis. The type of research data collected during a project depends on the research method.

Research data come in many types, e.g. text documents, spreadsheets, 3D models, software, audio and video recordings, photographs, reports, questionnaires and literature review records.

Research data are valuable, therefore proper data management is crucial.

 

1.2. RDM definition

RDM refers to the way you handle your research data throughout the research life cycle. More specifically, RDM concerns how you:

  • Create data and plan for its use;
  • Organise, structure, and name data;
  • Keep data (make it secure, provide access, store and back it up);
  • Find information resources;
  • Share with collaborators;
  • Publish data and get cited.

“Research data management concerns the organization of data, from its entry to the research cycle through to the dissemination and archiving of valuable results.”
(from: Whyte, A., Tedds, J. (2011). ‘Making the Case for Research Data Management).

For more information about national and UM requirements for RDM:

 

1.3. Motives for RDM

As a researcher, you benefit from RDM because:

  • It increases efficiency;
  • Increases data quality;
  • It makes your research reproducible;
  • It will eventually save you time;
  • It keeps your data safe, minimises the risk of data loss or unauthorized access;
  • It is a step in making your data FAIR (Findable, Accessible, Interoperable, Reusable).

 

1.4. Steps in the Research Data Life Cycle

Steps to be taken at different stages of the research cycle to ensure successful data curation and preservation upon project completion:

RDM-research-life-cycle

Proposal Planning & Writing

  • Determine funding opportunities
  • Write a (funder) proposal
  • Review existing datasets
  • Determine whether the project will create a new dataset (or combine existing)
  • Investigate archiving challenges, consent, confidentiality and licenses
  • Make an estimate of RDM costs
  • Identify potential users of your data
  • Contact your faculty data steward or library support for advice on a suitable archive

Project Start-up

  • Create a data management plan (DMP)
  • The what, why, and how of data management planning:

 

 

  • Make decisions about documentation forms (protocol, naming convention, version control, loggings) and content of the project
  • Content pre-test & tests of materials and methods

Data Collection

  • Follow best practices
  • Organise files, backups & storage
  • Think about access control and security

Data Analysis

  • Manage file versions
  • Document analysis and file manipulations

Data Sharing

  • ‘FAIRification’ of your data
  • Determine file formats
  • Contact archive for advice
  • Document (more) and clean up data

End of Project 

  • Write a paper
  • Submit report findings
  • Deposit data in data archive (repository)

[based on University of Virginia Library]

 

1.5. The FAIR principles

The FAIR principles are a set of guiding principles for improving the findability, accessibility, interoperability and reusability of data. They have become a well-known concept as the science community is moving towards transparent, reproducible and Open Science.

For more information on the FAIR principles (and their 15 subprinciples):

  • Check out FAIR at UM
  • Watch the video below with an explanation of the FAIR principles
  • Watch the video about the adoption of the FAIR principles in terms of 6 RDM processes:

 

1.6. Motives for FAIR data

  • It makes research more effective
  • It facilitates new research
  • It improves the visibility of your work, resulting in citations, increased impact and esteem and expansion of your network
  • You comply with funder, publisher and institutional requirements