Nearly every popular programming language comes with one or more package managers. The software packages distributed by such package managers form large software ecosystems. These packaging ecosystems contain a large number of package releases that are updated regularly and that have many dependencies to other package releases. While packaging ecosystems are extremely useful for their respective communities of developers, they face challenges related to their scale, complexity, and rate of evolution. Typical problems are backward incompatible package updates, and the risk of (transitively) depending on packages that have become obsolete or inactive.This manuscript uses the libraries.io dataset to carry out a quantitative empirical analysis of the similarities and differences between the evolution of package dependency networks for seven packaging ecosystems of varying sizes and ages: Cargo for Rust, CPAN for Perl, CRAN for R, npm for JavaScript, NuGet for the .NET platform, Packagist for PHP, and RubyGems for Ruby.We propose novel metrics to capture the growth, changeability, resuability and fragility of these dependency networks, and use these metrics to analyse and compare their evolution. We observe that the dependency networks tend to grow over time, both in size and in number of package updates, while a minority of packages are responsible for most of the package updates. The majority of packages depend on other packages, but only a small proportion of packages accounts for most of the reverse dependencies. We observe a high proportion of "fragile" packages due to a high and increasing number of transitive dependencies. These findings are instrumental for assessing the quality of a package dependency network, and improving it through dependency management tools and imposed policies.

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Kell, L. T., Mosqueira, I., Grosjean, P., Fromentin, J-M., Garcia, D., Hillary, R., Jardim, E., Mardle, S., Pastoors, M. A., Poos, J. J., Scott, F., and Scott, R. D. 2007. FLR: an open-source framework for the evaluation and development of management strategies. – ICES Journal of Marine Science, 64: 640–646. The FLR framework (Fisheries Library for R) is a development effort directed towards the evaluation of fisheries management strategies. The overall goal is to develop a common framework to facilitate collaboration within and across disciplines (e.g. biological, ecological, statistical, mathematical, economic, and social) and, in particular, to ensure that new modelling methods and software are more easily validated and evaluated, as well as becoming widely available once developed. Specifically, the framework details how to implement and link a variety of fishery, biological, and economic software packages so that alternative management strategies and procedures can be evaluated for their robustness to uncertainty before implementation. The design of the framework, including the adoption of object-orientated programming, its feasibility to be extended to new processes, and its application to new management approaches (e.g. ecosystem affects of fishing), is discussed. The importance of open source for promoting transparency and allowing technology transfer between disciplines and researchers is stressed.

Identifying and counting zooplankton are labour-intensive and time-consuming processes that are still performed manually. However, a new system, known as ZOOSCAN, has been designed for counting zooplankton net samples. We describe image-processing and the results of (semi)-automatic identification of taxa with various machine-learning methods. Each scan contains between 1500 and 2000 individuals <0.5 mm. We used two training sets of about 1000 objects each divided into 8 (simplified) and 29 groups (detailed), respectively. The new discriminant vector forest algorithm, which is one of the most efficient methods, discriminates between the organisms in the detailed training set with an accuracy of 75% at a speed of 2000 items per second. A supplementary algorithm tags objects that the method classified with low accuracy (suspect items), such that they could be checked by taxonomists. This complementary and interactive semi-automatic process combines both computer speed and the ability to detect variations in proportions and grey levels with the human skills to discriminate animals on the basis of small details, such as presence/absence or number of appendages. After this checking process, total accuracy increases to between 80% and 85%. We discuss the potential of the system as a standard for identification, enumeration, and size frequency distribution of net-collected zooplankton.

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