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Abstract : |
Traditionally, the scale and scope of an experimental study was determined by the ability of the research team to generate data. Over the past few years, we have been experiencing an unprecedented increase in the ability of small teams of experimental scientists to generate data. This has led to a shift in the balance between the di erent components of an experimental study. Today, it is not uncommon to nd a study whose scale and scope have been determined by the ability of the team to manage the data rather than to generate it. Whether the discipline is experimental computer science [4], genetics, earth and space sciences, soil sciences, or high-energy physics, scientists are faced in their daily work with an experiment and data management bottleneck. Unfortunately, an experimental scientist can not nd ready o-the-shelf management tools that o er both the functionality required by a scienti c environment and an interface that feels natural and intuitive to the non-expert. While no special expertise is needed to manage a collection of images stored as les on a PC or as pictures in a paper notebook, existing database systems (DBMSs) that o er the desired functionality require expertise that most teams of experimental scientists do not have and can not a ord to pay for. This poses a challenge to the database community todevelop management tools that can be tailored by atypical scienti c team to e ectively manage their unique experimental environment., |
