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Biomedical research is a prominent case of knowledge work, often driven by data and information. A major limiting factor in biomedical research is access to information when and where it is needed, namely on the job. Biomedical research is very data-driven and even the amount of data that one researcher generates can be overwhelming. Consequently, researchers are prone to develop a psychological state called information overload, which hampers creative thinking. In order to facilitate optimal innovation strategies, research organizations are advised to implement assistance systems, which provide opportunities for digital data management in experimental laboratories directly at the work bench. Assistance systems have the potential to improve efficiency, quality, and reliability at the same time, while supporting researchers with the “dull-side” of keeping records and entering data.This article provides a detailed technical overview of recent innovative solutions for the specific problems of experimental work in biomedical research listed below: 1) automation of standardized, repetitive methodological routines; 2) establishment of ubiquitous computing environments to facilitate access to and storage of digital information at various locations in wet labs; 3) replacement of paper-bound notebooks with electronic laboratory notebooks, which are enterprise software applications; 4) integration of office and lab work space into single lab benches with tabletop systems; 5) electronic guidance through complex pipetting experiments, which are automatically recorded; 6) helping researchers to remain focused on hands-on activities with augmented reality provided by smartglasses and; 7) voice assistance as a tool to keep hands free, in order to improve processes and increase efficiency .Since none of the reviewed innovations have become mainstream in research organizations yet, they were identified as disruptive technologies. This article will give a broad overview of those technologies and their characteristics and attempts to gauge their potential for future deployment in research laboratories.
Schneikart et al. (Fri,) studied this question.
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