Skip to Main Content

Data Management Services

This guide will assist Clemson researchers in managing their data, and includes information on creating Data Management Plans for funding agencies.

Documenting Your Data

In order for your data to be used properly by you, your colleagues, and other researchers in the future, they must be documented.  Data documentation (also known as metadata) enables you to understand your data in detail and will enable other researchers to find, use and properly cite your data.

It is critical 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.

Researchers can choose among various metadata standards, often tailored to a particular file format or discipline.  Following are some general guidelines for aspects of your project and data that you should document, regardless of your discipline.  At a minimum, store this documentation in a readme.txt file or the equivalent together with the data. 

Common Metadata:

Name of the dataset or research project that produced it

Names and addresses of the organization or people who created the data

Number used to identify the data, even if it is just an internal project reference number

Keywords or phrases describing the subject or content of the data

Organizations or agencies who funded the research

Any known intellectual property rights held for the data

Access information
Where and how your data can be accessed by other researchers

Language(s) of the intellectual content of the resource, when applicable

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

Where the data relates to a physical location, record information about its spatial coverage

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

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', '')

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')

Date/time stamp for each file, and use a separate ID for each version

To test if your file has changed over time

Thanks to MIT Libraries for sharing their content.

Metadata Standards

Some metadata standards are very general and can be applied to a variety of situations, while others are more discipline-specific.

README.txt Guidance

Keep documentation in one place

Download an Example Readme.txt (plain text file) template that can be adapted for your data. Update the template frequently throughout the project. The file will contain all the metadata related to your research.