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Research Data Management

When you are doing research, good data management practices and transparency are essential. This toolbox provides practical information and guidelines for both PhD students and researchers when working with research data.

Costs & Data Management

Many research funders encourage applicants to include data management and sharing costs in research proposals. Some funders will provide advice on costs related to data management. Some remarks on costs are provided here:

  • The Data Management Plan should describe the activities that incur costs and provide justification for the allocation of resources (example: acquisition of a programmer who will write software needed to capture the data).
  • No expenditure can be ‘double funded’, i.e. a service that is centrally supported by indirect costs must not be included as a direct cost as well (example: computers that are already provided to employees and paid for by the university may not be included).
  • The budget and justification should broadly indicate where RDM costs will be incurred, where possible. E.g. data capture and cleaning, data curation and preservation, data sharing.
  • Include budget for long-term storage if data are expected to be deposited in a repository not funded by the university or external funders (VU repositories are: DataverseNL, ArchStor, DarkStor).

A practical costing tool is available from the UK Data Archive. Based on this costing tool, Utrecht University has developed a guide to calculate the costs of data management. Similarly, in collaboration with RDM experts from several Dutch universities, the Landelijk Coördinatiepunt Research Data Management (LCRDM) has made an RDM costs table that includes budgetting for technical applications. You can use those guides as well to estimate the costs needed specifically for RDM.

Most material costs of the storage solutions offered by the VU are covered centrally, but if you need to specify the costs for your project, look at the following sections in the LibGuide:

Examples to put in a data management plan:

Data Stage Dataset Type of data Costs
Raw data Interviews Audio files
  • Audio equipment rental
  • Location rental costs
  • Data storage & backup
Processed data Transcription of interviews Word files
  • Personnel costs: hiring research assistants for manual entry
  • Data storage & backup
  Analysis software R script
  • Personnel costs: programmer to write a programme to mine the data
Analysed data Regression graphic Photoshop files
  • Software costs
  Project Website HTML, Java
  • Hosting fee
  • Personnel to build initial website