The data life cycle has eight components:
The data life cycle from the perspective of a researcher. The ‘plan’ component describes the entire life cycle.
Plan for data management as your research proposal (for funding agency, dissertation committee, etc.) is being developed. Revisit your data management plan frequently during the project and make changes as necessary. Consider the following:
It is important to collect data in such a way as to ensure its usability later. Careful consideration of methods and documentation before collection occurs is important.
Perform basic quality assurance and quality control on your data (see here), during data collection, entry, and analysis. Describe any conditions during collection that might affect the quality of the data. Identify values that are estimated, double-check data that are entered by hand (preferably entered by more than one person), and use quality level flags (see here) to indicate potential problems. Check the format of the data to be sure it is consistent across the data set. Perform statistical and graphical summaries (e.g. max/min, average, range) to check for questionable or impossible values and to identify outliers.
Communicate data quality using either coding within the data set that indicates quality, or in the metadata or data documentation. Identify missing values. Check data using similar data sets to identify potential problems. Additional problems with the data may also be identified during analysis and interpretation of the data prior to manuscript preparation.
Comprehensive data documentation (i.e. metadata) is the key to future understanding of data. Without a thorough description of the context of the data file, the context in which the data were collected, the measurements that were made, and the quality of the data, it is unlikely that the data can be easily discovered, understood, or effectively used. Consider the following when documenting your data:
Metadata should be generated in a metadata format commonly used by the most relevant science community. Use metadata editing tools to generate comprehensive descriptions of the data. Comprehensive metadata enables others to discover, understand, and use your data.
Work with a data center or archiving service that is familiar with your area of research. They can provide guidance as to how to prepare formal metadata, how to preserve the data, what file formats to use, and how to provide additional services to future users of your data. Data centers can provide tools that support data discovery, access, and dissemination of data in response to users needs.
If your data fall into any of the following categories, there are additional considerations regarding sharing: Rare, threatened or endangered species; Cultural items returned to their country of origin; Native American and Native Hawaiian human remains and objects; Any research involving human subjects. If you use data from other sources, you should review your rights to use the data and be sure you have the appropriate licenses and permissions.