A Fictional Study Case on Research Data Management

 

Luise Leader is a research assistant at the Institute for Squirrel Research. She has successfully acquired the project "Population Dynamics of Sciurus vulgaris."

While talking to her friend Carlo Cientifico, she finds out how research data management, RDM for short, supports academic work and adherence to good academic practice.

 

Follow Luise on Her First Steps in RDM

Step One: Planning

  • Planning the investigation design
  • Data management plan, DMP for short – formats, storage locations, naming files, collaborative platforms, et cetera
  • Defining responsibilities
  • Localizing existing data
  • Clarifying authorship and data ownership
  • Agreeing access conditions, preparing consent procedures
  • Creating an initial DMP, using a DMP tool if necessary

Luise is pleased to see that the aspects relevant to her RDM can all be found in a DMP template.

Step Two: Survey

  • Carrying out experiments, measurements, simulations, observations
  • Recording and creating additional information – so-called metadata – that describe the data
  • Entering, digitizing, transcribing, translating data
  • Checking, validating, and cleaning up data – quality assurance
  • Backing up and managing data

Luise and her colleague collect their data and describe it with the predefined metadata. They can be certain that their data is secure thanks to their elaborate backup concept.

Step Three: Analysis

  • Interpreting data
  • Enabling data exchange during the project
  • Saving data and preparing data preservation
  • Preparing data for use in publications
  • Are the data understandable for my colleagues or do I need to add more information?
  • If necessary, make use of the time stamp service

Thanks to consistent data descriptions and the use of a collaborative platform, it is easier for Luise and her colleague to analyze the data.

Step Four: Archiving

  • Upholding good academic practice principles – ten years' storage obligation
  • Selecting data for archiving
  • Migrating data into suitable or open source formats
  • Selecting a suitable archive option – guaranteed storage time
  • Checking data documentation or enhance metadata
  • Assigning ePIC persistent identifiers to link data and text publications if necessary


Luise looks into several different archiving possibilities and finds that some common ones do not guarantee storage for ten years or more.

Step Five: Access

  • Sharing, distributing, publishing data
    • As a data publication for a text publication
    •  As an independent data publication in a repository
  • Making data known and findable – catalogs
  • Making data citable – assigning a direct object identifier
  • Allocating licenses for subsequent use
  • Controlling access where appropriate

The project is coming to an end and Luise would like to publish part of her data as a FAIR independent data publication so that it can be reused by other researchers for their research.

Step Six: Subsequent Use

  • Carrying out further investigations using the data
  • Putting data into new contexts, cross-disciplinary use of data
  • Big data applications
  • Reviewing, reviewing, and discussing research results
  • Use in practical teaching
  • Citing research data

Luise is pleased that her data will be reused by her colleague Remy Reuse in a new project and that her work will be cited.