It is critical to begin to document your data at the very beginning of your research project, even before data collection begins; doing so will make data documentation easier and reduce the likelihood that you will forget aspects of your data later in the research project.
Following are some general guidelines for aspects of your project and data that you should document, regardless of your discipline. At minimum, store this documentation in a readme.txt file or the equivalent, together with the data. One can also reference a published article which may contain some of this information.
| Title | Name of the dataset or research project that produced it |
| Creator | Names and addresses of the organization or people who created the data |
| Identifier | Number used to identify the data, even if it is just an internal project reference number |
| Subject | Keywords or phrases describing the subject or content of the data |
| Funders | Organizations or agencies who funded the research |
| Rights | Any known intellectual property rights held for the data |
| Access information | Where and how your data can be accessed by other researchers |
| Language | Language(s) of the intellectual content of the resource, when applicable |
| Dates | Key dates associated with the data, including: project start and end date; release date; time period covered by the data; and other dates associated with the data lifespan, e.g., maintenance cycle, update schedule |
| Location | Where the data relates to a physical location, record information about its spatial coverage |
| Methodology | How the data was generated, including equipment or software used, experimental protocol, other things one might include in a lab notebook |
| Data processing | Along the way, record any information on how the data has been altered or processed |
| Sources | Citations to material for data derived from other sources, including details of where the source data is held and how it was accessed |
| List of file names | List of all data files associated with the project, with their names and file extensions (e.g. 'NWPalaceTR.WRL', 'stone.mov') |
| File Formats | Format(s) of the data, e.g. FITS, SPSS, HTML, JPEG, and any software required to read the data |
| File structure | Organization of the data file(s) and the layout of the variables, when applicable |
| Variable list | List of variables in the data files, when applicable |
| Code lists | Explanation of codes or abbreviations used in either the file names or the variables in the data files (e.g. '999 indicates a missing value in the data') |
| Versions | Date/time stamp for each file, and use a separate ID for each version (see file organization) |
| Checksums | To test if your file has changed over time (see data backup) |
Source: from MIT Libraries
Also, see DRYAD's ReadMe guidance and University of Minnesota Library's readme template.
"Metadata" is data about data. It's structured information that describes content and makes it easier to find or use. A metadata record can be embedded in data or stored separately. Any data file in any format can have metadata fields. In social science, this record is called the "codebook" or "data dictionary."
There are many metadata standards and which one is right for your data will depend on the type, scale, and discipline of your research project. The UK's Digital Curation Centre has a list of metadata standards by discipline.
Some examples of metadata standards are:
If it turns out your field doesn't have a metadata standard or if you just need a simpler system to keep track of data within your own lab, consider the general guidelines in the "Documentation" box on the left side of this page.
If you are uncertain about your rights to disseminate data you collected, consult with the UCLA Office of Intellectual Property and Industry Sponsored Research or the UCLA Office of Campus Counsel.