YML is a research project that aims to provide tools for using global computing middleware such as GRID, peer to peer, and metacomputing environments. The YML software architecture enables the definition of parallel applications, independently of the underlying middleware used. Parallel applications are defined using a workflow language called YvetteML.
LabKey Server is open source software that helps scientists manage, analyze, and share complex datasets. It supports tandem mass spectrometry, flow cytometry, assays for neutralizing antibodies, Luminex, observational studies, and secure, Web-based collaboration. The software is modular, configurable, and customizable. It can be installed in your institution on any modern hardware and operating system. It is designed to integrate with your existing systems, instruments, and work flows, and to be readily adapted by skilled programmers to novel methods of inquiry. The project is under active development by a team of professional software engineers and a community of active contributors. New versions are released about four times per year.
INIshell is a graphical INI file generator. A set of constraints regarding the sections, keys, and values that might be present in the INI file are defined in an XML file. INIshell reads this XML file and dynamically generates a GUI allowing the end user to edit the INI file. Its flexibility allows any application to describe its configuration options and easily offer a GUI to the end user.
GenFoo is a general Fokker-Planck solver for models of arbitrary dimensionality. It contains three backend solvers, a delta-f Monte Carlo, a standard Monte Carlo, and a Finite Element solver. The key property of the GenFoo package is that physics are separated from numerics by runtime loading of the Fokker-Planck coefficients, which enable solutions of a large class of Fokker-Planck models.
Erudite is an application for training and testing back propogation neural networks using the ANNeML (Artifical Neural Network Markup Language) XML format. It supports testing and training neural nets with CSV files and has support for randomized training sets, optional adapting learning rate, sigmoid or hyperbolic tangent transfer functions, optional bias and weight adjustment locking, and more.