Research Center

Knowledge does not arise from the simple accumulation of facts. Rather, it is a complex, dynamic system, and its emergent outcomes - including scientific consensus - are unpredictable.

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The application of scientific techniques to the process of science itself -- a field known as the “science of science” or SciSci -- reveals patterns that can be used to fuel more efficient and faster discoveries. In a new paper for Science, a team of authors including Knowledge Lab director and CI Senior Fellow James Evans reviews recent SciSci achievements and discusses how researchers, funding agencies, universities, and other entities can apply the findings to drive innovation and progress.

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What does the field of science look like?

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To build a sustainable career, many scientists have to balance safe research that will likely be published with ambitious, riskier experiments. A new paper from CI Senior Fellows James Evans and Andrey Rzhetsky, with UCLA assistant professor of sociology Jacob Foster, finds that these cultural pressures handicap scientific innovation, encouraging incremental research to the detriment of bolder discoveries. 

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A new computational model developed by Computation Institute scientists could help improve the allocation of U.S. biomedical research resources. The tool, called the Research Opportunity Index (ROI), measures disparities between resources dedicated to a disease and its relative burden on society. 

The CI’s 2014-15 Inside the Discovery Cloud speaker series focused on collaboration, presenting pairs of speakers who are working together to unlock new knowledge through computation. Attendees heard about how new computational approaches are changing medicine, biology, social science, public policy, and more, and discover opportunities for new collaborations and student research projects. View the videos of these stimulating talks.

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The web of science seems to be immeasurably large, with researchers around the world churning out papers in hundreds of different fields. So when scholars try to describe and explain how scientists weave new threads into the fabric of knowledge, they typically stick to very small patches . But in a massive new analysis of nearly 20 million biomedical journal articles, Knowledge Lab researchers constructed the most complete picture yet of the network of biomedical science -- and in doing so, found that it was surprisingly compact.

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Andrey Rzhetsky, professor of medicine and human genetics, isn’t a computer scientist by trade. But the messy complexity of biomedicine is a problem that fairly cries out for analysis by computation. It was also the perfect springboard for him to discuss the overarching theme in his work in his talk for the Visualization Speaker Series, “Adventures in Analysis of Large Biomedical Datasets”: getting data for complex networks, combining data sets, and drawing from them some “non-obvious conclusions.”

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Text mining is often discussed in the context of humanities research or marketing, where an enormous pool of text can be computationally sifted for new insight or targeted advertising.

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The thesaurus is an essential tool for writers, a helpful reference when they need help expanding their vocabulary.