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How Data Digitalization Increases the Efficiency of Your Research
ANN-CLAIRE FOETSCH Academia de laboratorio
Who in the laboratory is not consistently confronted with the time-consuming task of documenting laboratory processes? Relying on handwritten protocols on loose-leaf paper, kept in a paper-based lab book, poses a risk to data reproducibility. Complete traceability of the protocols carried out, together with seamless documentation of laboratory workflows, are both of critical importance for an efficient and productive laboratory environment.
There are few things more frustrating to scientists than carrying out a seemingly perfect experiment, only to find that colleagues or other research groups do not achieve similar results. The question of data reproducibility has gained importance over the past years. In light of the concern about a possible “reproducibility crisis”  and its impact on the quality and integrity of research data, academia and industry have mounted efforts to address these challenges together and find ways to improve reproducibility.
Repeatability vs. reproducibility
Whereas repeatability of data pertains to the ability to obtain the same results each time an experiment is performed (of course within the limits of a reasonable standard error), reproducibility of data refers to the question whether other scientists, who possibly work halfway across the globe, can repeat the experiment and obtain similar results .
Digitalization of data in the paperless laboratory
Today, more and more scientists save their experimental data in the form of an electronic laboratory notebook (ELN) or laboratory information management system (LIMS) instead of using a classic paper-based lab book. The switch to the digital format alone, however, will not solve the “reproducibility crisis”. The planning of experiments and data management are critical components of good science, and so are the handling of the data obtained and how we document. The question is whether the complete digitalization of data can contribute to a long-term solution to problems with reproducibility. Internal studies have shown that a structured, clear, and guided data set can contribute to enhanced traceability and transparency of protocols, while at the same time allowing scientists to optimize processes. We at Eppendorf would like to share further insight and design new concepts for data digitalization together with scientists.
If you are interested in collaborating on this vision please contact us.