Research lifecycle

"Research data management concerns the organisation of data, from its entry to the research cycle through to the dissemination and archiving of valuable results. It aims to ensure reliable verification of results, and permits new and innovative research built on existing information." - DCC(1)

    Main points

Research data have a longer lifecycle than the period in which they were created. One way to look at it is by using a research lifecycle. The meaning of the data varies with every phase of the research cycle they are in.The image to the right depicts such a cycle.


A research lifecycle is intended to help illustrate how the various phases in the life of research data tie in with each other and how the choices you make in one phase influence the data quality in the other. A lifecycle helps to shift the short-term perspective to a long-term one: what is the intended purpose for these research data? How do you make sure that the choices you make when you collect data are robust enough to enable reuse and long-term storage? 

There are many lifecycles in circulation and all are tailored to a user group’s needs. The image to the left is the lifecycle of environmental sensing research, in which the scientific output derived from it is depicted. 

Data archives focussing on long-term storage sometimes use the term 
digital curation lifecycle.(4) It provides a detailed description of the steps an archive takes after entering a dataset. The lifecycle comprises activities focusing on the primary goal of a data archive: long-term storage and availability.



Essentials 4 Data Support

In Essentials 4 Data Support we distinguish a planning phase (Chapter II)research phase (Chapter III) en user phase (Chapter IV) (mouse over the images to read the corresponding text):


   An in-depth look 

  • There are quite a few data lifecycles in circulation. Staff of the University of Bath wrote an article(5) in which they compared the various models. What are the recurring elements in each lifecycle?

   Sources and additional reading

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  1. Whyte, A.; Tedds, J. (2011). Making the Case for Research Data Management. DCC Briefing Papers. Edinburgh: Digital Curation Centre. Retrieved from
  2. University of Bath. Project Research360. Retrieved from
  3. Pepe, A., e.a. (2009). From Artifacts to Aggregations: Modeling Scientific Life Cycles on the Semantic Web. Retrieved from
  4. DCC. What is digital curation. Retrieved from 
  5. Ball, A. (2012). Review of Data Management Lifecycle Models. University of Bath. Retrieved from

Additional reading

  • Lenhardt, C.; Ahalt, S.; Blanton, B.; Christopherson, L.; Idaszak, R. (2013). Data Management Lifecycle and Software Lifecycle Management in the Context of Conducting Science. Retrieved from figshare
    The article emphasizes the fact that not only data have lifecycles but the accompanying software has one as well: "Much of what science software does is work with data".

   Your additions

What is your experience with research lifecycles? Do you prefer a particular lifecycle? If so, why?
Do you want to comment on this section? Lease let us know by posting a comment.