Today’s research knowledge can be harvested and data analyzed faster than has been possible in all previous generations combined. As a result, Open Research practices and outputs face a number of tensions between initial intentions and unforeseen consequences. For example, the FAIR Data Principles propose that research data should be Findable, Accessible, Interoperable, and Reusable — but nothing has prepared us for the use and misuse of personal data. Even if they start out ethically approved and safe in the researcher’s toolkit, they can later be sold to a third party in exchange for analytical services, enabling machines to identify disease states from a picture, classify your intelligence and demographic profile in four “likes” or less, or traffic organs and direct market to those that need them on social media.
And so our questions about Open Research are also changing — from “why” to “how” — amidst growing awareness that the required skill sets, both technical and social, are not yet part of the standard training programs for researchers. Consider, for example, the questions and challenges that early career researchers face as they critique a distinguished professor’s work while conducting an open peer review. How do they balance the need for research integrity and rigorous review without career-ending consequences? How do we protect reviewers who review in good faith only to be raked through the coals on social media, while the perpetrators are funded and their work is published.
So, if you actually want to practice Open Research, how do you learn about it? How do you balance effort with effect? How do you discover and validate the standards that are being adopted by your communities?
