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Research data across the lifecycle

An overview of considerations, resources and tools for working with data in your research projects

The lifecycle of research data

There are numerous models of the lifecycle of research data, with many commonalities.  The following one, from The Lifecycle of Social Science Research Data: Improved Discovery through better Metadata and Search Tools, informs the arrangement of this subject guide:

a research data lifecycle model

It is, by necessity, somewhat generic and generalizing, and based in the quantitative social sciences.

  • In the Idea stage, the researcher conceptualizes the possible research project in which data may be collected/generated.  If external funding for undertaking the project is pursued, the researcher may well have to write a data management plan as part of the funding application, and it may help to be aware of the following stages of the research data lifecycle for doing that.

  • In the Search & Discovery stage, the researcher seeks to find existing data sources, to a) possibly be utilized in the research project, and b) avoid duplication of substantially similar data collection that already occurred.  (This stage of the lifecycle, incidentally, also applies to the student researcher who has to do nothing more or less than find existing data for a paper.)   To aid with the discovery of one commonly sought form of research data, the online databases of the AU Library that contain downloadable numeric data are identified with this icon: Downloadable Data icon
    If you need help with identifying an appropriate data source for your research, ask a librarian!

  • In the Design & Collection stage, the researcher develops and undertakes the data collection process.  This may involve the creation of data gathering methods and tools, such as survey instruments, scientific labs, and so on, depending on the scholarly discpline.

  • In the Analysis & Processing stage, the manipulation and analyses of the collected data takes place.  In this and the previous stage, at AU, the CTRL Research Support Group, and the Mathematics/Statistics department's software support and statistical consulting center can be of assistance.  This is all too often the end of the lifecycle for research data, which then eventually can become lost, unidentifiable, or unusable - unless the researcher enables its proceeding to the following stages:

  • In the data Publication (in the less formal sense) stage, research data is made publicly available - this can happen, for example, through the researcher's personal or departmental web site, by submitting it to a journal publisher who allows or requires submission of the underlying digital data with a manuscript, or via a university data portal.  For more on this, see the data sharing & visualization section of this guide.  Note that here, immediate and medium-term access and exposure is intended and implied, not long term preservation ... that happens ...

  • In the Archiving stage, where data is deposited into a data archive or repository with the goal of long-term preservation; this is increasingly required by research funding bodies.  Examples include the AU Digital Repository, or, for data in the social and allied sciences, ICPSR's Data Archive.  These archives quite often also fulfill the data sharing/publishing role from the previous lifecycle stage, but possibly with less functionality, interactivity, or exposure for the data.

Metadata, in the form of machine- and human-readable description and documentation of the data's format, purpose, content, collection method, etc., forms the bridge that allows the lifecycle to close, helping to make the research data discoverable in the future.