This guide focuses specifically on some of the decisions you may need to make regarding the materials you have created or used in your research process, including drawings and photographs, tables and charts, lab notes and datasets, interviews and newscasts, software and digital artworks. It describes in non-legal language the basics of a few important terms, including “fair use,” “public domain,” “Creative Commons,” and “patent” as they may apply to these materials. Failure to consider the implications of different copyright and patent approaches for your own work can limit the impact of your work. Failure to adequately review, vet, and seek permission to use others’ work can, in a worst-case scenario, prevent your work from getting published or (in rare cases) lead to legal actions.
How researchers structure their data varies by disciplines and research questions. Still, there are general guidelines for structuring data that make it more likely to be usable in the future. The following questions should be considered for any project that gathers data. These questions should be considered first at the planning stage, again as data is being gathered and stored, and once more prior to final deposit into a digital archive or repository.
1. What are the data organization standards for your field? For example, there are often standards for labeling data fields that will make your data machinereadable. There may also be specific variables and coding guidelines that you can use that will make your work interoperable with other datasets. Lastly, there may be accepted hierarchies and directory structures in your discipline that you can build upon.
2. What are the data export options in the software you are using? If using proprietary and/or highly specialized software to analyze large data sets, export the data in a format that is likely to be supported in the future, and that will be accessible from other software programs. This usually means choosing an open format that is not proprietary. Remember that you may not have access to the same software in the future, and not all software upgrades can read old file types.
3. What forms of the data will be needed for future access? Consider the various forms the data may take, and the scale of the data involved. You may need to preserve not only the underlying raw data, but also the resulting analyses you have created from it.