ABSTRACT
Using Provenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456 12.7 Current and Future Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459
Today data are being collected by a vast number of instruments in every discipline of science. In addition to raw data, new products are created every day as a result of processing existing data and running simulations in order to understand observed data. As the sizes of the datasets grow into the petascale range, and as data are being shared among and across scientific communities, the importance of diligently recoding the meaning of data and the way they were produced increases dramatically.
