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PANDA is a comprehensive and flexib tool for quantitative proteomics data analysis, which is developed based on our solid foundations in quantitative proteomics for years. Several novelties have been implemented in it. First, we implement the advantage algorithms of LFQuant (Proteomics 2012, 12, (23-24), 3475-84) and SILVER (Bioinformatics 2014, 30, (4), 586-7) into PANDA. Second, we consider the state-of-art concept of quantification reliability in this quantitative workflow. On the levels...

Java-based scientific graphics with support for Java, Groovy, MATLAB, Python, the R statistical environment, Scala and SciLab.

The Dataverse Network is a Java EE5 app that enables researchers to share data on-line. It provides data citation standards, facilitates preservation, distribution and replication of data, and includes statistical analysis. More at: http://thedata.org

TOPS provides the benchtop scientist with a free toolset to analyze, filter and visualize data from functional genomic gene-gene and gene-drug interaction screens with a flexible interface to accommodate various different technologies and analysis algorithms in addition to those already provided here.

GISBox by Citiviz is a ready-to-use virtual appliance containing an assortment of powerful, free and open source GIS, Data Mining and Analysis, Modelling and Programming software and packages. The pre-configured and tested GISBox allows you to try and use in few minutes a variety of software used by data scientists, engineers, physicists, mathematicians, geomatics engineers, and geographers from around the globe, without installing anything more than once the Oracle VirtualBox™ software...

A simple tool to help you create an R interface for your Java project. Simply annotate the constructors and methods you want to make available and JAnnotateR will produce a working package, complete with documentation generated from javadocs.

The Vyper Dynamic Server framework is a pure-java library for use in the creation of backend / server platforms. VDS is specifically tailored for financial applications, and suited for many high-throughput, asynchronous and distributed environments.

For software developers in domains involving statistical analysis, the "R + Java = Inf" project analyses and enhances the interoperability between the R and Java programming languages. The deliverables are source code, documentation and benchmarks.