Most of this conference will be looking at how scientists communicate with others. My talk will look at how scientists are forging new forums to share their scientific know-how and acquire a whole new range of knowledge that will enable them to take advantage of emergent open-science content (open data, open source software, open access publications, and open reviews). By leveraging the social multipliers of networked collaboration, new communities-of-purpose will add real value to shared content, and real reasons to share more often. In the geoscience community, The Federation of Earth Science Information partners is designed to build, test, and finally implement novel modes of communication and forums for sharing. Across disciplines and around the planet, the Research Data Alliance is hoping to build and share data stewardship information. What does open-science look like, and how will it transform the geosciences? These are the questions science is tackling today. Some day soon, perhaps science will actually know what science knows.
The first paradigm is experimental science. The second paradigm is theoretical science, and the third, computational science. The forth is data-intensive science. This data-intensive science paradigm is also a feature of the emerging datafullness of the object of study. Satellites and sensorwebs, CCTVs and Streetviews, MRIs and CAT scans, Facebook and YouTube– what we study is no longer data poor, but increasingly data-full. The question is no longer one of how to scrape up enough data to create a study, but rather how to winnow the emerging data deluge. Sociologists can no more ignore the data available from online social networks than meteorologists can ignore an emerging Mid-Atlantic tropical depression.
In his talk at the IEEE eScience meeting, Jeff Dozier also mentioned that earth sciences are entering a new task horizon. In the1800-1900s, the earth sciences were discipline oriented sciences. From the 1980s+ we saw the development of earth system science. Emerging now: earth knowledge in service of policy to address planetary risks, such as climate change.
The eScience challenges are many here. The increase in observational data make it possible to refine the resolution of climate models, which push the limits of available HPC resources. The data processing algorithms designed for science must be made robust enough to sustain resource and environmental enforcement decisions. New venues for communication between scientists, data providers, and policy decision makers need to be supported and used. This is a real opportunity for organizations such as the ESIP Federation to become active forums for problem solving.