Today's databases are faced with a moving target. There is so much more information available, and the type of data being collated is changing. Decisions have to be made: how should a database be restructured to accommodate the new information? What do we do with the old data? Are they compatible with the new results, or must they be hived off and archived in some way? Take protein structure databases. Not long ago it was enough to submit a set of atomic coordinates that described a protein's structure. Now, such databases are expected to store 'meta-data' as well—how the protein was produced and purified, and how its structure was solved. And the rise in high-throughput projects will make yet greater demands.
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