CloudMET 2017 helps establish PhenoMeNal as an effective data analysis and training platform

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It’s been a bit more than a month since the Scientific School on Cloud-based Metabolomics Data Analysis and Collaboration (CloudMET) 2017 wrapped up. This event distinguished itself from the various metabolomics data analysis schools as its organizers – CRS4 and the rest of the PhenoMeNal consortium, with great help from the University of Cagliari – designed a training program that merged state-of-the-art data analysis techniques with notions about how to put these into practice with cloud computing technologies. The PhenoMeNal platform was central to this latter point, as it makes cloud computing infrastructure more easily accessible to everyone, and it provided the foundation on which many of the practical sessions of the course were built.

The weeks that have passed since the conclusion of the school have given us a chance to go through the feedback from the course participants. The course attracted academics and professionals spanning six different countries. We were pleased to see that most of them felt that PhenoMeNal helped them reduce the time to complete the analyses required for the course (57%), and that this effect could be reflected in their own work after the school with an improvement in the quality of their research (65%). Moreover, it seems that giving PhenoMeNal and cloud technologies a try had a profound effect on participants. An impressive 70% of participants said that they are likely to adopt PhenoMeNal for their own work, and 61% go as far as saying that the existence of PhenoMeNal is an important factor that is encouraging them to adopt cloud technologies for their own work. Interestingly, many participants referred that they would have preferred to spend more time on the practical components of the course, using PhenoMeNal to perform data analysis, and less time attending lectures.

Overall, at CloudMET the PhenoMeNal platform proved that it is an effective data analysis and training platform. By taking care of the technological details, it allowed trainers more time to focus on the data analysis questions that are more important for metabolomics research. We were happy to see that the participant feedback confirms this view!

Author: Luca Pireddu


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