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Data-intensive research depends on tools that manage multidimensional, heterogeneous datasets. We built OME Remote Objects (OMERO), a software platform that enables access to and use of a wide range of biological data. OMERO uses a server-based middleware application to provide a unified interface for images, matrices and tables. OMERO's design and(More)
To demonstrate a simple low-cost system for tele-echocardiology, focused on paediatric cardiology applications. The system was realized using open-source software and COTS technologies. It is based on the transmission of two simultaneous video streams, obtained by direct digitization of the output of an ultrasound machine and by a netcam showing the(More)
SUMMARY BioBlend.objects is a new component of the BioBlend package, adding an object-oriented interface for the Galaxy REST-based application programming interface. It improves support for metacomputing on Galaxy entities by providing higher-level functionality and allowing users to more easily create programs to explore, query and create Galaxy datasets(More)
The number of domains affected by the big data phenomenon is constantly increasing, both in science and industry, with high-throughput DNA sequencers being among the most massive data producers. Building analysis frameworks that can keep up with such a high production rate, however, is only part of the problem: current challenges include dealing with(More)
Data-intensive research depends on tools that manage multi-dimensional, heterogeneous data sets. We have built OME Remote Objects (OMERO), a software platform that enables access to and use of a wide range of biological data. OMERO uses a server-based middleware application to provide a unified interface for images, matrices, and tables. OMERO’s design and(More)
In this work we describe pyEHR, a new toolkit for building scalable clinical/phenotypic data management systems for biomedical research applications. The toolkit uses openEHR formalisms to guarantee the decoupling of clinical data descriptions from implementation details, and NoSQL technologies, or next-generation SQL ones, to provide scalable storage(More)
Motivation and Objectives Scaling up production in medium and large high-throughput sequencing facilities presents a number of challenges. As the rate of samples to process increases, manually performing and tracking the center’s operations becomes increasingly difficult, costly and error prone, while processing the massive amounts of data poses significant(More)
This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR's formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to(More)
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