 | |  Microsoft Amalga Life Sciences 2009Microsoft Amalga Life Sciences 2009 allows research and development organizations to aggregate data from disparate systems, both within their own institutions and from partner organizations, helping them move faster, with more agility, and with purpose and direction supported by validated facts. This allows researchers to address many data challenges from a single system and transforms the way they do research.
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| Accelerates research velocity | Amalga Life Sciences helps research organizations gain control over the speed and direction of their research activities and gain new insights into the data they already have by providing: | The ability to see all data across many departments, disciplines, systems, and data types; to sort or filter it by any criteria or data element; and to drill up or down at any level so that researchers can see data in ways relevant to their work. Data provenance provided by metadata (“the data about data”) to help researchers interpret and re-use information over time.
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| | Enables organizational agilityAmalga Life Sciences is built for directed exploration, empowering end users to create their own customized views, derive relevance and meaning, and discover rich relationships between data. The system offers: Modeling and analysis of data built upon formal logic structures give researchers the flexibility to view their results in different ways. Researchers can form new methodologies, generate hypotheses, and validate theories. The ability to work with data easily, using a simple, user-friendly interface. The look and feel of Amalga Life Sciences enables anyone who is familiar with Microsoft Office applications to begin exploring data with minimal training.
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| Provides actionable knowledge | Using Amalga Life Sciences, researchers can generate and maintain a rich, experimental context and relevance for subject matter experts, reducing the enormous complexity of life science data and transforming it into actionable knowledge. Amalga Life Sciences provides: | Flexible data fusion that allows heterogeneous experimental data and results to come together in a central place, storing both relational and abstract data in an innovative way that builds a complex network of relationships and concepts to promote semantic querying and reasoning. Identification of novel relationships between data through targeted queries, semantic discovery, and inference abilities. Visibility into these connections enables researchers to bring together data, discoveries, and people in new ways to more deeply investigate what they already know and discover what they do not know.
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