Data Management Plans
Research Data Services > Data Management > Data Management Plans
Data Management Plans will facilitate collaboration, data sharing, and data publishing. Planning at the beginning of your research process will assist you in overcoming a number of obstacles in the course of your research. For example, if you have a pre-defined naming schema for your variables and file names, it will be much easier to find the right data and the right version in the future. If you have estimated the amount of data you will collect, you will be able to request grant funds for curating and managing them.
Planning is particularly important in longitudinal studies, studies that involve surveys, projects that result in multiple data files, including images and video, and Big Data.
A number of agencies now request data management plans to accompany proposals.
Starting a Project? See this Managing Your Data: Project Checklist
A data management plan ought to address
- How the data will be collected
- The type or format of data collected
- The size of the data
- How the data will be described (i.e will you be using codebooks, logs, specific metadata standards, ontologies, etc.)
- Where the data will be stored, backed up and secured if necessary
- How the data will be analyzed
- How the data will be shared and preserved, or reasons not to do so, including who will have permissions to use the data.
UC Davis researchers have access to the DMPTool, a service of the California Digital Library, with their Kerberos login. The tool contains templates from multiple federal and private funding agencies. The tool permits the user to create a document for submission to a funding agency, and can accommodate different versions as funding requirements change.
DMPtool also provides guidance for writing data management plans here: https://dmptool.org/general_guidance
Many funding agencies have published their own guides. These may inform your situated data management requirements:
- CIESIN: Geospatial Electronic Records: Resources on managing and preserving geospatial data and related electronic records.
- Digital Curation Centre (DCC): Includes resources, events, and training opportunities.
- ICPSR Guide to Social Science Data Preparation and Archiving: Outlines best practices throughout the research process, including applying for a research grant, collecting data, and preparing data for deposit in a public archive.
- Oak Ridge National Laboratory: Data Management for Data Providers: Offers practical methods to successfully share and archive data.
- UK Data Archive: Create & Manage Data: Provides best practice strategies and methods for creating, preparing, and storing shareable datasets. See also Managing and Sharing Data: A Best Practice Guide for Researchers [pdf].
Contact us for further assistance at firstname.lastname@example.org.
Working with and planning for data management with geospatial data has unique challenges. Here are some points from UC Davis Geospatial Data Specialist Michele Tobias (email@example.com). Michele Tobias is available for questions and consultations.
Most GIS programs will allow you to create basic metadata that will reside along side the spatial and attribute data you create. Several government agencies and standards bodies have developed metadata standards for geospatial data. You should select a standard to follow based on what information you need to convey to potential users and who those users will likely be. Funding bodies may also set requirements that should be considered.
Description of Data Creation
Spatial analysis can generate a large number of intermediate files. Document the analysis workflow you follow as you perform it, noting which files and processes were used to generate each subsequent file. Some researchers write out a list of steps, while others use a flowchart, or a software system like ArcGIS’ Model Builder.
Sharing & Preservation
The files we work with for analysis may not necessarily be the ideal format for sharing or storing geospatial data. For example, when sending a shapefile, it can be easy to forget to include one of the multiple files required to properly use the data. Consider storing and sharing data in open formats (i.e. a format that doesn’t need a specific software to open it) to make your data accessible by the largest number of people.