"The Use of Life Cycle Models in Developing and Supporting Data Services," by Jake Carlson

Jake Carlson. "The Use of Life Cycle Models in Developing and Supporting Data Services," In Research Data Management: Practical Strategies for Information Professionals (West Lafayette, IN: Purdue University Press, 2014. 436 pages. ISBN 9781557536648): 63-86.

In the third chapter of this book, author Jake Carlson, Associate Professor of Library Science and data services specialist at Purdue University Libraries, introduces the reader to the concept of life cycle modelling for research data management. Life cycle models are commonly adopted by organizations which are trying to promote best practices around managing, organizing, and preserving research data. Similar to biological organisms, research data progresses through a cycle of transformations in format, application, use, and purpose.

Organizations have come to recognize that research data can be used to create new products and generate new areas of research, but often researchers aren't thinking beyond their own original uses for their data. Research data life cycle models can be used to illustrate the overall research process from start to finish, and demonstrate how data can be re-used by others. Data life cycle models are really a subset of the research life cycle. There are three types of life cycle models: individual, organizational, and community. They are an effective tool for designing and carrying out a research project, and help researchers articulate and diagram activities in the research project.

Organizational life cycle models are used by organizations that offer services or assistance to researchers, such as libraries, data repositories, scholarly societies, publishers, etc. One example of an organizational life cycle model is that used by ICPSR.

Carlson makes the point that in models, the action is orderly and linear; whereas in real life, there are many variables that can change the direction of a project. He concludes by stating that effective data services depend on an in-depth understanding of the needs of the researchers they're meant to serve.