Scientific data represents a significant portion of the linked open data cloud and scientists stand to benefit from the data fusion capability this will afford. Publishing linked data into the cloud, however, doesn’t ensure the required reusability. Publishing has requirements of provenance

in this seductively simple formulation. Indeed, the book represents reductionism at its best, as the authors guide the reader through the many intermediate levels that link neurons with mental life. In so doing they attack a problem that has persisted for some decades in the neurosciences, since the development

Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ‘‘phantom traffic jams’ ’ even though drivers all like to drive fast

ProtKB/Swiss-Prot section and the automatically annotated UniProtKB/ TrEMBL section, is the preeminent storehouse of pro-tein annotation. The extensive cross-references, functional and feature annotations and literature-based evidence attribution enable scientists to anal-yse proteins and query across databases. The Uni

Simulating how the global Internet data network behaves is an immensely challenging undertaking because of the network's great heterogeneity and rapid change. The heterogeneity ranges from the individual links that carry the network's traffic, to the protocols that interoperate over

data acquisition and scientific studies. Collaborations between computer scientists and other domain scientists have produced networks that can record data at a scale and resolution not previously possible. Taking this progress one step further, wireless sensor networks can potentially advance

How does one create an intelligent machine? This problem has proven difficult. Over the past several decades, scientists have taken one of three approaches: In the first, which is knowledge-based, an intelligent machine in a laboratory is directly programmed to perform a given task. In a second

We develop a parsimonious model of innovating firms rich enough to confront firm-level evidence. It captures the dynamic behavior of individual heterogenous firms, describes the evolution of an industry with simultaneous entry and exit, and delivers a general equilibrium model of technological

the power of class-linkage alone for classification in machine learning benchmark data sets. The results demonstrate that very simple network-classification models perform quite well—well enough that they should be used regularly as baseline classifiers for studies of learning with networked data

and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge. Generalized linear mixed models