How Cloud Connectivity Can Combat the Reproducibility Crisis
Infographic Dec 14, 2016
The “Reproducibility Crisis.” That’s the name of a phenomenon that illustrates just how hit-and-miss science can be. When scientists try to repeat the findings of a published paper, studies show only 10% to slightly more than 50% of results can be successfully reproduced. Issues that affect reproducibility can arise at any point in an experiment, from study design to data analysis. Progress and ultimate success in science relies on published results that are accurate and that can stand the test of time.
Technology has great potential to solve the reproducibility crisis. Specifically, connecting instruments with each other and with the cloud—the so-called Internet of Things (IoT)—can help in at three crucial ways. First, storing and managing data on the cloud can prevent data loss. One study shows data is lost at a rate of 17 percent annually; in fact 80 percent of datasets older than 20 years are no longer available (source: Current Biology, 2014). Second, permitting access to data on the cloud can improve attempts by other groups to verify (or invalidate) the data. Finally, cloud-based software capable of analyzing data in real-time can alert researchers to suspicious statistical outliers. For instance, Gilson is working on a cloud-connected pipette that tells users when they are using the wrong pipetting technique; e.g., holding the pipette at an angle instead of vertical.
In our highly connected world, saturated with personal technologies, this online transition is natural and inevitable. Well-designed and well-integrated IoT systems can improve lab efficiency, data reliability and study reproducibility for the collective success of the field.
Discovery vs Targeted ProteomicsInfographic
Currently, modern proteomics research using Mass Spectrometry (MS) is split between discovery and targeted assays. This infographic includes workflows for the two techniques and will examine the advantages and disadvantages of each method. Finally, common applications of each technology will be explored, and we will look to a future where the division between targeted and discovery methods might not even be required.
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