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Name of Submitter:
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Miriah Meyer
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Title of proposed idea:
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Data Analysis for the Individual: Addressing the Long Tail of Biology
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What is the major obstacle/challenge in the biomedical research field? What is needed to overcome this obstacle/challenge?
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It is a well-accepted fact that the challenge of big-data in biology is no longer in generating data, but in making sense of what it all means. Most data analysis approaches, whether computational or visual, are designed to address the questions and needs of the largest number of scientists possible -- the plethora of genome browsers or tools like Cytoscape are examples of these. While these general purpose tools are used by many biologists, they rarely address the more specific analysis needs of individual investigators, questions which form a very long tail of scientific inquiry, rich with potential for impact. It is these specific questions that differentiate the research of scientist from another, and they are the key to discovery and insight. Currently, biologists must either make due with broadly available general purpose tools that are ill-equiped to support more specific analysis, or they must develop custom tools in-house. To fully realize the impact of answering long tail questions we need tools that target specific, complex analysis needs, and we need processes and methods to create these tools in a scalable way. |
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What emerging scientific opportunity is ripe for investment by the Common Fund?
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Amazon has transformed how we buy and what we buy by offering an incredibly diverse set of products to a globally distributed customer base. No longer are we limited to just what the big-box store down the street carries -- you can choose just about any niche product to buy, any where in the world. How can we build an infrastructure that will support biological data analysis in the same way? The opportunity here is to develop multidisciplinary teams of biologists and computer scientists that focus on:
1) reaching a shared, scientific goal through the design and development of specific analysis approaches;
2) translating the lessons learned along the way into more generalizable methods, techniques, tools, and processes;
3) leveraging across teams to build an infrastructure from the bottom up. |
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What are the potential Common Fund investments that could accelerate scientific progress in this field?
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What is needed is a blend of investments, starting with small-scale investments early on that support individual teams and explore the space of analysis need, followed by more broad support that leverages the existing teams to grow the individual contributions into a more generalized approach. |
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If a Common Fund program on this topic achieved its objectives, what would be the impact?
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We can accelerate science and increase the impact of individual investigators by providing data analysis tools that support answering the questions that matter as efficiently, and effectively, as possible. |
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