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Resolving Complex Research Data Management Issues in Biomedical Laboratories: Qualitative Study of an Industry-Academia Collaboration

机译:解决生物医学实验室中的复杂研究数据管理问题:行业-学术界合作的定性研究

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摘要

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.
机译:本文介绍了行业和学术界之间的分布式协作工作,以在学术生物医学实验室中系统化数据管理。在生物医学实验室中创建的研究数据的异质性和数量性使得信息管理变得困难并且研究没有成果。在四年的时间内,使用人种学观察,半结构化访谈,基于网络的调查,进度报告,电话会议摘要和面对面的小组讨论等数据收集方法对一项这样的协作努力进行了评估。使用定性的数据分析方法对数据进行分析,以:1)利用传统的信息管理实践对生物医学研究人员面临的具体问题进行特征描述; 2)确定干预区域,以引入一种名为Labmatrix的新研究信息管理系统,最后进行3)评估和划定重要领域可以优化生物医学实验室实施过程结果的一般协作(干预)特征。结果强调了最终用户的毅力,以人为中心的互操作性评估以及展示实验室成员和行业人员的努力和时间的投资回报对于实施过程成功的重要性。此外,还有一个内在的学习组件,它与信息管理系统的实现过程相关联。可以通过使用支持人与认知互操作性的信息系统来减轻在复杂环境(例如生物医学实验室)中的技术转让经验。此类信息学功能还可以促进成功的协作,并有希望促进科学生产力。

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