Research Data Management and the Data Lifecycle

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You and the Research Data Lifeycle

  1. Take another close look at the research data lifecycle. Does the cycle track how you have dealt with your data – or how you envision doing so? Do you think these steps actually form a cycle? Are there steps that you hadn’t fully considered? Do you think there are steps missing? If you now can see how your research data might have a “lifecycle,” will that change anything about your workflow going forward?
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    1. Answers to these questions may vary depending upon how scholars conceive of the research process and how they collect and analyze data. One aspect you might question is the arrow from “long-term management” to “discovery and planning”. Do you see one research project tightly linked to the next in the way this arrow suggests? One aspect you might not have thought about before is long-term management of your data; had you considered the possibility of (someone) taking care of your data for years or decades? A core concern with the RDL model, particularly in the context of qualitative research, is that it has gaps and glosses over details. You can probably think of multiple steps you will take between any two steps in the model. Also, there is not always a neat distinction between planning, initial, and final data collection. You (will) likely also shift between planning your research (including data collection), carrying it out, and writing. And qualitative researchers often go back and forth between data and theory. The lifecycle doesn’t reflect any of these “back-and-forths”. When you’re planning your research, you should.