Browsing by Subject "Data analytics"

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    Cell-culture Data Pipeline Python package (CDPpy) for processing and analyzing cell culture datasets
    (2024-08-05) Lu, Yen-An; Fukae, Yudai; Hu, Wei-Shou; Zhang, Qi; qizh@umn.edu; Zhang, Qi; University of Minnesota Decision Discovery and Optimization Lab; University of Minnesota Cellular Engineering Lab
    CDPpy (Cell-culture Data Pipeline Python package) is an open-source library designed for the analysis of fed-batch cell culture data from multiple experiments and cell lines. The package features the functions of a data processing pipeline and visualization toolbox. The processing pipeline reads raw data from Excel files following a fixed template, derives variables such as cumulative substrate consumption and various specific rates, and exports the processed dataset into an Excel file. The specific rates show changing cellular activities over time in culture, providing insights for process optimization. The visualization toolbox enables users to analyze process profiles across experimental runs and cell lines, aiding in future experimental design. In this repository, we include the source code for the package, an instruction for package setup, and a Jupyter notebook that provides step-by-step guidelines for data processing and visualization using an example dataset. The updated version will be announced in the GitHub repository: https://github.com/ddolab/CDPpy in the future.
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    Implementing data analytics as an organizational innovation in colleges and universities
    (2014-11) Foss, Lisa Helmin
    This study explores the question, How are individual adoption and organizational implementation of innovations in higher education related to the context of the organization, the characteristics of the innovation, and the attitudes of adopters? The study uses data collected from a survey of deans and department chairs from U.S. higher education institutions to examine the implementation of data analytics, or the extensive use of data, statistical analysis, data mining and modeling to drive organizational decisions, as an example of an organizational innovation. The findings indicate that individual adoption is associated with the adopter's perception of the usefulness of data analytics in practice and its legitimacy in solving organizational challenges. The usefulness of data analytics is related to the innovation characteristics of usability and functionality, which are in turn related to an organizational context that includes institutional and professional support for adoption, academic leaders engaged in implementation, data and information integrated into existing operations, and an organizational culture that is data-driven. Legitimacy is related to the functionality of data analytics and the existence of a data-driven culture but also the discipline of the adopter and institution type. The findings also indicate that organizational implementation of data analytics is associated with the alignment of data analytics to its organizational culture, the pressure exerted by the external environment, and the organization's dissatisfaction with current external methods or practices in use.