Andrew Sallans and Sherry Lake. "Data Management Assessment and Planning Tools," In Research Data Management: Practical Strategies for Information Professionals (West Lafayette, IN: Purdue University Press, 2014. 436 pages. ISBN 9781557536648):87-107.
Sallans and Lake start off by discussing some of the challenges of data management. Data management has gotten a lot of attention lately. Funding agencies are driving a lot of this attention, and are making recommendations regarding the existence of a data management plan, but their requirements are often vague and can result in researchers simply doing the minimum to meet funding requirements. In this chapter, Sallans and Lake describe several efforts to assess specific data management plans.
The University of Virginia began their efforts in this area by conducting data management interviews. They hoped to identify "common research data problems and needs," identify the types of data being created, identify communities and researchers under pressure from grant requirements, identify partnerships for "institutional repository data deposit," and "develop opportunities to provide data management recommendations and training" (p. 91). However, they needed to use the information they learned in their interviews to develop recommendations and weigh assessment factors. This led to the development of the DMVitals Tool, an Excel spreadsheet designed to collect information and produce reports. The authors provide guidance on how this tool can be used at other institutions.
The DMPTool was subsequently developed through a collaborative effort with many other institutions. While the tool was not completed at the time of publication, it shows promise in helping researchers manage their data and improve their research data management practices.
Thursday, July 3, 2014
Wednesday, July 2, 2014
"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.
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.
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