Addressing the gaps and data opportunities mentioned above – and the manifold research questions that may arise within them – requires novel datasets on OC embedded in a collaborative in-frastructure, and a portfolio of research tools and computational models to analyze this data. There are three major building blocks:
1.Datasets: There are today many datasets that can be leveraged for research in the area of OC, including existing and pro-cessed data on Wikipedia, open source software development platforms, OC systems forming around platforms like Open-Data.gov, as well as data on virtual science infrastructures like NanoHub.org, and open innovation networks like Ninesights. It is crucial however that new datasets be made available, possibly through derivation from existing datasets by mining, experiments, novel processing, and existing and new datasets be linked to create context-rich datasets.
2.Computational and analytical tools: There is today a large num-ber of such tools, including metanetwork models, network discovery, dynamic and predictive statistical network analy-sis, genetic computation, network analysis algorithms, agent-based simulations, sequencing analysis and statistical predic-tion, event study tools, and collaboration and visualization tools. It is critical however that such tools be easily integrated and made available on unified digital platforms.
3.Collaborative cyberinfrastructure: Their goal is to serve as a vir-tual living lab for experiments, offering the community to build capacity, to share data and results, and communicate findings seamlessly across different media.