Inheritance patterns in citation networks reveal scientific memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and we validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.Comment: 8 two-column pages, 5 figures; accepted for publication in Physical Review

Science mapping analysis characterizes 235 biases in biomedical research

OBJECTIVE: Many different types of bias have been described. Some biases may tend to coexist or be associated with specific research settings, fields, and types of studies. We aimed to map systematically the terminology of bias across biomedical research. STUDY DESIGN AND SETTING: We used advanced text-mining and clustering techniques to evaluate 17,265,924 items from PubMed (1958-2008). We considered 235 bias terms and 103 other terms that appear commonly in articles dealing with bias. RESULTS: Forty bias terms were used in the title or abstract of more than 100 articles each. Pseudo-inclusion clustering identified 252 clusters of terms. The clusters were organized into macroscopic maps that cover a continuum of research fields. The resulting maps highlight which types of biases tend to co-occur and may need to be considered together and what biases are commonly encountered and discussed in specific fields. Most of the common bias terms have had continuous use over time since their introduction, and some (in particular confounding, selection bias, response bias, and publication bias) show increased usage through time. CONCLUSION: This systematic mapping offers a dynamic classification of biases in biomedical investigation and related fields and can offer insights for the multifaceted aspects of bias.J Clin Epidemio

Streams of Media Issues, Monitoring World Food Security. Paper presented at the United Nations

Consultable sur Internet : http://pulseweb.veilledynamique.com/static/files/wp1.pd

Streams of Media Issues, Monitoring World Food Security. Paper presented at the United Nations

Consultable sur Internet : http://pulseweb.veilledynamique.com/static/files/wp1.pd

Stepping on the cracks—transcending the certainties of big data analytics

Every aspect of modern life is dominated by decision-making and the availability of data. We constantly access, process and evaluate data as we navigate complex and uncertain problem spaces. Communication and Information Technologies (ICTs) have developed to a point where it is possible for very large data sets, measured in Exabyte, to be stored across many servers and gathered by many different people and organizations, for multiple purposes. At the same time, research into Artificial Intelligence has progressed to a point where human decision-making can be supported, or even replaced, by intelligent agents and robotics. We recognize that many routine jobs that were once carried out by people can now be done faster and more flexibly using robotics, and software robotics has now moved beyond the factory and into administrative processes. The possibilities for such systems are enormous and can deliver many benefits to business, governments and ordinary citizens. However, there is also a downside to be considered. Is there still a role for human experience and intuition? How can we ensure that the benefits of analytics and AI continue to outweigh threats? How should we approach management of BI and AI on an on-going basis? This paper advocates an open systems approach in which B&AI may be incorporated with tools that support complex methods of inquiry