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# Rule 3. Conduct science with data reuse in mind.   Data re-use is most possible when: 1) data; 2) metadata (information describing the data); and 3) information about the process of generating those data, are all provided. In trying to follow the Rules listed here, you will do best if you plan in advance for ways to provide all three kinds of information.Keeping in mind Rule 2 (permanent identifiers) and Rule 5 (linking data to publications) will help with data and metadata, and remembering Rules 4 (publishing workflow) and 6 (publishing software) will keep you on track for documenting process.  There are information and computer scientists who make careers out of studying the ``provenance" of information, which formally refers to the sum of all of the processes and people (or institutions or other agents) and documents (including data) that were involved in generating or otherwise influencing or delivering a piece of information [W3CProvenance Group]( http://www.w3.org/2005/Incubator/prov/wiki/What\_Is\_Provenance\#A\_Working\_Definition\_of\_Provenance).  Perfect documentation of provenance is rarely, if ever, attained in scientific work today. But, in carrying out your work, you can consider what level of re-use you expect, and plan accordingly. Do you want your work to be fully **reproducible**? If so, then formalized the provenance is best (e.g. working pipeline analysis code, a machine to run it on, and raw data). Do you just want your work to be **inspectable**? If so, then intermediate data products and/or schematic code may be sufficient. Do you want your data to be **usable** in a wide range of applications? If so, consider adopting standard formats and metadata standards early on. At the very least, keeping careful track of versions of data and code, with associated dates, will be appreciated by those looking back from the future.