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prediction is included as keyword or extra keyword in 0 datasets, 0 tools and 5 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Invasion biology and the success of social collaboration networks, with application to wikipedia||Mangel M.
|Israel Journal of Ecology and Evolution||English||2013||We adapt methods from the stochastic theory of invasions - for which a key question is whether a propagule will grow to an established population or fail - To show how monitoring early participation in a social collaboration network allows prediction of success. Social collaboration networks have become ubiquitous and can now be found in widely diverse situations. However, there are currently no methods to predict whether a social collaboration network will succeed or not, where success is defined as growing to a specified number of active participants before falling to zero active participants. We illustrate a suitable methodology with Wikipedia. In general, wikis are web-based software that allows collaborative efforts in which all viewers of a page can edit its contents online, thus encouraging cooperative efforts on text and hypertext. The English language Wikipedia is one of the most spectacular successes, but not all wikis succeed and there have been some major failures. Using these new methods, we derive detailed predictions for the English language Wikipedia and in summary for more than 250 other language Wikipedias. We thus show how ideas from population biology can inform aspects of technology in new and insightful ways.||0||0|
|The ToxBank data warehouse: Supporting the replacement of in vivo repeated dose systemic toxicity testing||Kohonen P.
|Molecular Informatics||English||2013||The aim of the SEURAT-1 (Safety Evaluation Ultimately Replacing Animal Testing-1) research cluster, comprised of seven EU FP7 Health projects co-financed by Cosmetics Europe, is to generate a proof-of-concept to show how the latest technologies, systems toxicology and toxicogenomics can be combined to deliver a test replacement for repeated dose systemic toxicity testing on animals. The SEURAT-1 strategy is to adopt a mode-of-action framework to describe repeated dose toxicity, combining in vitro and in silico methods to derive predictions of in vivo toxicity responses. ToxBank is the cross-cluster infrastructure project whose activities include the development of a data warehouse to provide a web-accessible shared repository of research data and protocols, a physical compounds repository, reference or "gold compounds" for use across the cluster (available via wiki.toxbank.net), and a reference resource for biomaterials. Core technologies used in the data warehouse include the ISA-Tab universal data exchange format, REpresentational State Transfer (REST) web services, the W3C Resource Description Framework (RDF) and the OpenTox standards. We describe the design of the data warehouse based on cluster requirements, the implementation based on open standards, and finally the underlying concepts and initial results of a data analysis utilizing public data related to the gold compounds. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.||0||0|
|Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data||Márton Mestyán
|English||2012||Use of socially generated "big data" to access information about collective states of the minds in human societies becomes a new paradigm in the emerging field of computational social science. One of the natural application of this would be prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging between "real time monitoring" and "early predicting" remains as a big challenge. Here, we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie could be predicted well in advance by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.||0||0|
|Seeking health information online: does Wikipedia matter?||Michaël R. Laurent
Tim J. Vickers
|Journal of the American Medical Informatics Association : JAMIA||English||2009||OBJECTIVE To determine the significance of the English Wikipedia as a source of online health information. DESIGN The authors measured Wikipedia's ranking on general Internet search engines by entering keywords from MedlinePlus, NHS Direct Online, and the National Organization of Rare Diseases as queries into search engine optimization software. We assessed whether article quality influenced this ranking. The authors tested whether traffic to Wikipedia coincided with epidemiological trends and news of emerging health concerns, and how it compares to MedlinePlus. MEASUREMENTS Cumulative incidence and average position of Wikipedia compared to other Web sites among the first 20 results on general Internet search engines (Google, Google UK, Yahoo, and MSN, and page view statistics for selected Wikipedia articles and MedlinePlus pages. RESULTS Wikipedia ranked among the first ten results in 71-85% of search engines and keywords tested. Wikipedia surpassed MedlinePlus and NHS Direct Online (except for queries from the latter on Google UK), and ranked higher with quality articles. Wikipedia ranked highest for rare diseases, although its incidence in several categories decreased. Page views increased parallel to the occurrence of 20 seasonal disorders and news of three emerging health concerns. Wikipedia articles were viewed more often than MedlinePlus Topic (p = 0.001) but for MedlinePlus Encyclopedia pages, the trend was not significant (p = 0.07-0.10). CONCLUSIONS Based on its search engine ranking and page view statistics, the English Wikipedia is a prominent source of online health information compared to the other online health information providers studied.||55||1|
|The end of print: Digitization and its consequence - Revolutionary changes in scholarly and social communication and in scientific research||Davidson L.A.||International Journal of Toxicology||English||2005||The transformation from print to digital media for scientific communication, driven in part by the growth of the Internet and the tremendous explosion in the amount of information now available to everybody, is creating fundamental changes in institutions such as publishers, libraries, and universities that primarily exist for the creation, management, and distribution of information and knowledge. Scientific, technological, and medical journals are the first publications to be completely transformed from print to digital format but monographs are beginning to appear in digital format as well and soon all communication and publishing of scientific information will be entirely electronic. In fact, this change is affecting all components of the scientific enterprise, from personal correspondence and laboratory methods to peer reviewing and the quality assessment of scientific research. Along with these radical and rapid changes in information presentation and distribution are coincident changes in the expectations of both the public and other scientists, with both groups demanding ever more rapid, open, and global access to scientific information than has been available in the past. The consequence of this revolution in the mechanics of communications technology is threatening the very existence of a number of highly regarded institutions such as intellectual property, commercial publishers, scientific societies, and academic libraries and might soon begin to threaten even the traditional university. Copyright||0||0|