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2. Socially Networked Science
After data, coding, software, and collaboration standards evolve then social networks will begin to evolve. The uniform scientific creole will enable distributed, modular, asynchroneous research. It will be easy for information from data sources to be leveraged for data science tools. Ad-hoc (pidgin) workflows will transition to become more interoperable and cross-disciplinary collaborations will require lower cost to access and a higher an exponentially higher return on investment.
I hope this is a fun & happy time. Data science is going to thrive; a lot of scientific knowledge will be produced. Science is going to learn what to do with ALL of the information. Data scientists are going to be able to hammer on beautiful datasets available from the previous disruption; we are going to gain an enormous grasp on nature during this time. Value and veracity will hold a general value at this point. Reproducible science will thrive here. Currently, I believe that most discussions about data verification, valuation, and validation in research science are moot until a large enough community can provide input. The masses will decide where the ideas go.
During this time, users, codes, and datasets will be evaluated on the web based metrics like downloads, forks, and social networks. The scientific research process is going to change, it is going to increase in velocity, information will be available beyond control
