Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry–academia collaboration

Sahiti Myneni, Vimla L. Patel, G. Steven Bova, Jian Wang, Christopher F. Ackerman, Cynthia A. Berlinicke, Steve H. Chen, Mikael Lindvall, Donald J. Zack
2016 Computer Methods and Programs in Biomedicine  
This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semistructured interviews, web-based surveys,
more » ... s reports, conference call summaries, and faceto-face group discussions. Data were analyzed using qualitative methods of data analysis to 1) characterize specific problems faced by biomedical researchers with traditional information management practices, 2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to 3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity.
doi:10.1016/j.cmpb.2015.11.001 pmid:26652980 pmcid:PMC4778387 fatcat:pd7keyhl4nhlxhf2sejivusdvq