Somalia and the Pirates. ESF Working Paper No. 33, 18 December 2009

Piracy is defined by The Hague Centre for Strategic Studies as an "act of boarding or attempting to board any ship with the apparent intent to commit theft or any other crime and with the apparent intent or capability to use force in furtherance of that act." And it is estimated that from 1995 to 2009, around 730 persons were killed or are presumed dead, approximately 3,850 seafarers were held hostage, around 230 were kidnapped and ransomed, nearly 800 were seriously injured and hundreds more were threatened with guns and knives. (See paper by Rob de Wijk). In November 2009, CEPS held a European Security Forum seminar, in collaboration with the Institute for Strategic Studies, the Centre for the Democratic Control of Armed Forces and the Geneva Centre for Security Policy, to focus on the issue of Somalia and the Pirates, chaired by Francois Heisbourg. Four eminent specialists in this field: David Anderson, Rob de Wijk, Steven Haines and Jonathon Stevenson looked at the links with Somalia, and the historical, legal, political and security dimensions of the troubling success of piracy in today’s world. Their conclusions and recommendations for future action are brought together in this ESF 33 Working Paper

Validation of ocean model syntheses against hydrography using a new web application

Results are presented from a new web application called OceanDIVA - Ocean Data Intercomparison and Visualization Application. This tool reads hydrographic profiles and ocean model output and presents the data on either depth levels or isotherms for viewing in Google Earth, or as probability density functions (PDFs) of regional model-data misfits. As part of the CLIVAR Global Synthesis and Observations Panel, an intercomparison of water mass properties of various ocean syntheses has been undertaken using OceanDIVA. Analysis of model-data misfits reveals significant differences between the water mass properties of the syntheses, such as the ability to capture mode water properties

An ECOOP web portal for visualising and comparing distributed coastal oceanography model and in situ data

As part of a large European coastal operational oceanography project (ECOOP), we have developed a web portal for the display and comparison of model and in situ marine data. The distributed model and in situ datasets are accessed via an Open Geospatial Consortium Web Map Service (WMS) and Web Feature Service (WFS) respectively. These services were developed independently and readily integrated for the purposes of the ECOOP project, illustrating the ease of interoperability resulting from adherence to international standards. The key feature of the portal is the ability to display co-plotted timeseries of the in situ and model data and the quantification of misfits between the two. By using standards-based web technology we allow the user to quickly and easily explore over twenty model data feeds and compare these with dozens of in situ data feeds without being concerned with the low level details of differing file formats or the physical location of the data. Scientific and operational benefits to this work include model validation, quality control of observations, data assimilation and decision support in near real time. In these areas it is essential to be able to bring different data streams together from often disparate locations

The Environmental Data Abstraction Library (EDAL): a modular approach to processing and visualising large environmental data

The Environmental Data Abstraction Library provides a modular data management library for bringing new and diverse datatypes together for visualisation within numerous software packages, including the ncWMS viewing service, which already has very wide international uptake. The structure of EDAL is presented along with examples of its use to compare satellite, model and in situ data types within the same visualisation framework. We emphasize the value of this capability for cross calibration of datasets and evaluation of model products against observations, including preparation for data assimilation

Dissecting Trait Heterogeneity: a Comparison of Three Clustering Methods Applied to Genotypic Data

Background: Trait heterogeneity, which exists when a trait has been defined with insufficient specificity such that it is actually two or more distinct traits, has been implicated as a confounding factor in traditional statistical genetics of complex hu man disease. In the absence of de tailed phenotypic data collected consistently in combination with genetic data, unsupervised computational methodologies offer the potential for discovering underlying trait heteroge neity. The performance of three such methods – Bayesian Classification, Hyperg raph-Based Clustering, and Fuzzy k -Modes Clustering – appropriate for categorical data were comp ared. Also tested was the ability of these methods to detect trait heterogeneity in the presence of locus heteroge neity and/or gene-gene interaction , which are two other complicating factors in discovering genetic models of complex human disease. To dete rmine the efficacy of applying the Bayesian Classification method to re al data, the reliability of its intern al clustering metr ics at finding good clusterings was evaluated using permutation testing. Results: Bayesian Classifica tion outperformed the other two method s, with the exception that the Fuzzy k -Modes Clustering performed best on the most comp lex genetic model. Bayesian Classificati on achieved excellent recovery for 75% of the da tasets simulated under the simplest genetic model, while it achieved moderate recovery for 56% of datase ts with a sample size of 500 or more (across all simulated models) and for 86% of datasets with 10 or fewer nonfuncti onal loci (across all si mulated models). Neither Hypergraph Clustering nor Fuzzy k -Modes Clustering achieved good or excellent cluster recovery for a majority of datasets even under a re stricted set of conditions. When usin g the average log of class strength as the internal clustering metric, th e false positive rate was controlled very well, at three percent or less for all three significance levels (0. 01, 0.05, 0.10), and the false negative rate was acceptably low (18 percent) for the least stringent sign ificance level of 0.10. Conclusion: Bayesian Classificati on shows promise as an unsuper vised computational method for dissecting trait hetero geneity in genotypic data. Its control of fa lse positive and false negative rates lends confidence to the validity of its results. Further investigation of how differ ent parameter settings may improve the performance of Bayesian Classification, especi ally under more comp lex genetic models, is ongoing