Though Arguments for Open Science are Aplenty, Institutional Barriers to Its Implementation Remain

Author: Pablo Markin
Published Online: 2017-09-29

As a latest Montreal-based initiative in neuroscience and the European “Horizon 2020” program show, despite efforts promoting it, Open Science continues to be exposed to budgeting and resources shortfalls.


As Giusppe Valiate reports, from 2016, based at Canada’s McGill University, the Montreal Neurological Institute and Hospital (MNIH) has been applying Open Science principles to its artificial intelligence research. As part of implementing Open Access in various fields of scientific inquiry, Open Science does not suffer from a lack of definitions, schools of thoughts or academic articles proffering arguments in its favor as Benedikt Fecher and Sascha Friesike discuss in detail in their book chapter published in 2014. Perhaps due to the heteroclite nature of this phenomenon, as Open Science can refer to its technological infrastructure, knowledge creation accessibility, alternative impact metrics, knowledge access democratization, and collaborative research practices, its application in the research and scientific community continues to be divergent. Moreover, as far as academic journals are concerned, this term largely refers to Open Access.

Thus, what the MNIH initiative primarily boils down to is making its empirical, clinical and research data, such as brain imaging, biological sample and cellular data, available in Open Access. This contribution to Open Science is aimed at promoting drug discovery and development, e.g., via the facilitation of medicine tests, as part of the drive to openly share research data. At the same time, given that this field of research demands large-scale data sets, technical infrastructure for their storage and corresponding financial resources, this Open Science project also seeks to encourage a transition to Open Access, as an effort to cut costs. Similarly, Canadian researchers and scholars express increasing resistance to subscription-based journals of large publishers, such as by refusing to review their manuscripts and creating rival Open Access journals, e.g., the Journal of Machine Learning Research.

By Pablo Markin

The full-length original  blog article and additional resources can be found at OpenScience blog.

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