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ones like this!” This sort of relevancy feedback can help the user define his or her question, while helping the computer develop more accurate search capabilities.

Currently, the process of feature extraction is limited to database creation. In next-generation databases, feature extraction and imaging data analysis would be an ongoing process. The structure of the relational database would therefore change over time, to reflect evolving scientific understanding.


  1. The neuroscience community must define standards for acquiring imaging data and demand that instrument vendors accommodate those standards. Those standards would anticipate the needs of basic science, including:

    1. sharing and searching heterogeneous imaging data;

    2. metadata standards native to instrumentation and specific to neuroscience aims; and

    3. community benchmarks, or ground truth datasets for assessing and stimulating algorithm performance.

  1. Scientists must get over their data sharing issues and adopt an open-source model rather than a competitive one.

  2. Although the technologies already exist for next-generation databases, the databases themselves do not. Perhaps the biggest reason for this is the lack of interdisciplinary action between people with deep knowledge in a scientific field and people with deep informatics knowledge. Because the problems with current databases have obvious solutions, they fail to interest people in informatics. And because universities reward active research over interdisciplinary expertise, few scientists within those domains have the expertise. In order to create the kind of next-generation databases described here, there must be more interaction between these two groups.

    1. Research is needed into how to pool, evaluate, weight, and use heterogeneous image data.

    2. A plug-in model for database query is desirable, i.e., native support for image processing in the database that has an open modular architecture.

    3. Agile exploratory tools that incorporate image analysis and machine learning must be imagined and implemented for imaging databases.

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