Scientific Publishing in a Connected, Mobile World
Speaker: Mark Abbott
New tools for content development and new distribution channels create opportunities for the scientific community, opening new venues for collaboration, review, and self-publication. However, publishing is at the heart of the culture of science, and several centuries of experience with publishing in journals will not simply vanish. Issues of peer review, reproducibility, integrity, and scientific context will need to be addressed before these new tools take hold. Open access is but one part of this conversation.
How to Collaborate with the Crowd: a Method for “Publishing” Ongoing Work
Speaker: Jeff Dozier
The typical model for interdisciplinary research starts with a small-group partnership, typically with colleagues who have known each other for a while. They learn to articulate problems across disciplinary boundaries and discover shared interests. They successfully seek funding, and work together for several years. This model works, but can be cumbersome. An alternative model is to express a sequence of processes and data that integrate to create a suite of data products, and to identify insertion points where expertise from another perspective might be able to contribute to a better solution.
When Provenance Gets Real: Implications of Ubiquitous Provenance for Scientific Collaboration and Publishing
Speaker: James Frew
We expect (or hope?) that the impending standardization of data models, ontologies, and services for information provenance will make scientific collaboration easier and scientific publishing more transparent. We propose a panel of active producers and users of provenance who will address scenarios such as:
Data Journal Challenge for the Fourth Paradigm-Trust through Data on Environmental Studies and Projects
Speaker: Shuichi Iwata, The Graduate School of Project Design
Landscapes on recent big data issues to bridge environmental studies and social expectations are reviewed to design an e-Journal with data files and models. Data parts are keys to give semantics to original scientific papers, and also double keys for computational models. Structured data with explicit descriptions about their metadata can be managed and their traceability can be realized systematically, step by step. However, almost all available data are unstructured, fragmented, and contain ambiguities and uncertainties. Balances between data quality and freshness/costs/coverage are discussed so as to draw a road map for a data journal, referring to two preliminary case studies on materials data and data due to nuclear reactor accidents and problems.