Data Management Life Cycle

This section will be the most detailed of your plan. The data management life cycle covers production, management and archiving of your data. It is best for data management to commence at the beginning of a project and to become an integral part of doing the project managing the research. The life cycle should include details on each step, from data storage to publication.

Data storage

Your data management plan should include information on how your data will be stored during the project lifetime. Information to include in the plan may include types of storage and databases, database schemas, expected data volumes, storage management, and data backup procedures. The ODM (Horsburgh et al., 2008) is a data schema for time-series data.

Data curation

A data management plan should include information on quality control and assurance. Many examples of quality assurance plans are available from different organizations. For example, proposers may wish to consult EPA's extensive information on their quality assurance projects plans (www.epa.gov/QUALITY/qapps.html), or DataOne's Best Practices (www.dataone.org/dataonepedia)

Long term Archiving and Data Publication

This facet of the data management plan addresses NSF EAR's policy that: "Investigators...to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing." (www.nsf.gov/geo/ear/2010EAR_data_policy_9_28_10.pdf). After the funding period ends, you must detail how you will archive your data and make it available for further use. For compatible data, this need may be met by using CUAHSI–HIS services or software for data publication and archiving (see Options for Data Publication).

Example Language

If your project generates standard time series data, your data management life cycle section might include information such as:

Storage: Project data will be stored before publication as Excel files on University servers. The servers are secure and are backed up nightly. Taped back ups are stored off site.

Curation: Graduate students will perform quality control on the data when they are downloaded from the data logger following protocols documented at http://www.******.edu. The measurement equipment will be checked daily by a graduate student and any problems will be noted in the record. Suspect or outlier data will be flagged by the research assistant and checked by the PI. Missing data will be noted as -9999 along with a reason code.

Archiving and Publication: This project will submit all time series data to CUAHSI for publication and archiving. In this way, all data will be discoverable easily via the web and CUAHSI will maintain the data sets past the end date of the project.