The volumes and diversity of information in the discovery, development, and business processes within the chemical and life sciences industries require new approaches for analysis. Traditional list- or spreadsheet-based methods are easily overwhelmed by large amounts of data. Furthermore, generating strong hypotheses and, just as importantly, ruling out weak ones, requires integration across different experimental and informational sources. We have developed a framework for this integration, including common conceptual data models for multiple data types and linked visualizations that provide an overview of the entire data set, a measure of how each data record is related to every other record, …
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The volumes and diversity of information in the discovery, development, and business processes within the chemical and life sciences industries require new approaches for analysis. Traditional list- or spreadsheet-based methods are easily overwhelmed by large amounts of data. Furthermore, generating strong hypotheses and, just as importantly, ruling out weak ones, requires integration across different experimental and informational sources. We have developed a framework for this integration, including common conceptual data models for multiple data types and linked visualizations that provide an overview of the entire data set, a measure of how each data record is related to every other record, and an assessment of the associations within the data set.
Chemical Data Analysis in the Large: The Challenge of the Automation Age, Proceedings of the Beilstein-Institut International Workshop, May 22-26, 2000 Bozen, Italy , ; Beilstein-Institut,Frankfurt am Main,Germany.
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Saffer, Jeffrey D.; Albright, Cory L.; Calapristi, Augustin J.; Chen, Guang; Crow, Vernon L.; Decker, Scott D. et al.Visualization and Integrated Data Mining of Disparate Information,
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