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With distributed systems being developed as a part of the cloud age. With focus on performance associated with an application dependent on one DB instance, there is a need to bring down the dependency to a database to generate unique numbers for creating a private key when lots of data needs to be stored in the database tables. This library is intended to provide a way of generating unique numbers on the fly in a JAVA based distributed system. This is not a new concept but this library helps in keeping you focused on the business logic that you need to develop for your applications rather than spending time on how this is done. Typically applications powered by RDBMS can leverage the benefit as instant scalability might not be available for these setups when the load to the system spikes.

The Systems Biology Simulation Core Library provides an efficient and exhaustive Java™ implementation of methods to interpret the content of models encoded in the Systems Biology Markup Language (SBML) and its numerical solution. This library is based on the JSBML project. It can be used on every operating system for which a Java Virtual Machine is available. Version 2.0 and beyond support simulation in three frameworks: constraint-based analysis, stochastic simulation, and ordinary differential equation systems. SBSCL supports SED-ML and COMBINE archives in OMEX format.

Samply.Common.MDRFaces is a Java Server Faces library which eases the user interface design of web applications where the respective data model relies on well defined data elements. Especially in case of systems for electronic data capturing, where the necessary data model is not known beforehand and can even vary over time, the user interface has to be easily adjustable. This often means the user instead of the developer designs the various forms for data entry and therefore an easy to use mechanism has to be provided. By using Samply.MDRFaces the developer can focus on the implementation of that mechanism, e.g. some editor component, but does not have to cope with the visualization of every single data element as for that is taken care of automatically.

Protempa supports the construction of software systems for querying clinical databases, summarizing patient data, detecting clinically significant data patterns in groups of patients or individual patients, and extract, transform and load (ETL). Its special feature, temporal abstraction, supports queries and summarization of data in terms of temporal patterns. Temporal patterns enable robust querying and extraction of clinical history. These patterns include frequencies (at least two, three, etc. of some event or observation); sequences (chemotherapy followed by surgery within 180 days, a hospital encounter followed by another hospital encounter within 30 days); and overlaps (falling platelet counts in the setting of HELLP syndrome).