Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry-academia collaboration.
Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry-academia collaboration.
复制标题
解决生物医学实验室中复杂的研究数据管理问题：产学界合作的定性研究。
DOI：
10.1016/j.cmpb.2015.11.001
复制
发表时间：
2016
影响因子：
6.1
通讯作者：
Zack,DonaldJ
中科院分区：
文献类型：
--
作者：
Myneni,Sahiti;Patel,VimlaL;Bova,GSteven;Wang,Jian;Ackerman,ChristopherF;Berlinicke,CynthiaA;Chen,SteveH;Lindvall,Mikael;Zack,DonaldJ
本文介绍了一个分布式的合作努力，工业界和学术界之间的系统化数据管理的学术生物医学实验室。生物医学实验室中产生的研究数据的异质性和海量性使得信息管理困难，研究效率低下。一个这样的合作努力进行了为期四年的评估，使用数据收集方法，包括人种学观察，半结构化访谈，基于网络的调查，进度报告，电话会议摘要，面对面的小组讨论。使用数据分析的定性方法对数据进行分析，以（1）描述生物医学研究人员在传统信息管理实践中面临的具体问题，（2）确定干预领域，以引入新的研究信息管理系统Labmatrix，最后，（3）评估和描述重要的一般合作（干预）的特点，可以优化生物医学实验室的实施过程的结果。结果强调了最终用户的毅力，以人为中心的互操作性评估，并展示了实验室成员和行业人员的努力和时间的投资回报的重要性，成功的实施过程。此外，还有一个与信息管理系统的实施过程相关的内在学习组成部分。在生物医学实验室等复杂环境中的技术转让经验可以通过使用支持人类和认知互操作性的信息系统来简化。这种信息学特征也有助于成功的合作，并有望提高科学生产力。
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, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-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.