This poster presents a comparison of the data lifecycles of 32 researchers as articulated by researchers themselves. Similarities and differences between the stages within these data lifecycles are noted and implications for data service providers are discussed.A critical element of providing data services is developing a thorough understanding of the nature of the data being produced by researchers. Data lifecycle models are being developed by organizations providing data services as a means to communicate with researchers and other stakeholders who would make use of these services. The test of an effective data lifecycle model is its ability to resonate and connect with the researcher. In other words, does the researcher see her data set (and by extension her needs for her data) represented in the data lifecycle model provided by service organization?Conducting a side by side comparison of the 32 data lifecycle tables presented in DCPs demonstrates the complexity and challenge of developing and communicating data services in ways that resonate across or even within fields of research. An array of terminologies and classifications are employed, the number of stages varies widely and activities such as processing data may be carried out over multiple stages. The results described in this poster demonstrate some limitations of data lifecycle models and emphasize the importance of building strong communication channels with individual researchers.Published DCPs are available through the Data Curation Profiles Directory: http://docs.lib.purdue.edu/dcp/.
展开▼
