Managing Your Data

What does data management involve?

There are many aspects to managing your data and it can be a bit overwhelming to think of them as a whole. This section explains the different issues related to data management and provides links to resources and additional information.

Data management is the systematic organization and planning for data throughout the research lifecycle which includes:

►gathering and processing data

►analyzing data and publishing research results

►planning for long-term access and storage

Data Creation

During a project, researchers may create data or obtain it from standardized repositories or data providers. Data could be in the form of spreadsheets, database dumps, images, audio/video files, or text files generated from observations, surveys, experiments, interviews, or from any other source that informs and supports research conclusions. More complex data structures may include relational databases, geographical information systems, and data pipelines.

Data formats, structure, and sizes will vary across research projects and disciplines, as will their management requirements. There are several aspects of the organization and planning process that you need consider when putting together a data management plan:

Research data lifecycle and management requirements

The graph below illustrates a simplified research data lifecycle. The phases marked in green constitute active data management stages and those in red include publishing and long term archiving.

At any point during active data management (the green phases), a researcher may need short-term storage to host their data, guidance and technical resources to integrate multiple data sources, to set up a GIS server, consulting to organize data workflows, or to implement mechanisms to provide remote access to collaborators. During the red stages, researchers consider publishing and archiving options for their data in stand-alone web resources or in institutional repositories that meet the functionalities, scope, and preservation requirements of their field. Throughout the entire process, licensing and attribution and issues related with the presence of sensitive data have to be considered.

Learn more about the Resources at UT for managing your data throughout the lifecycle.

Data Lifecycle Flowchart

*graph modified from the Data Documentation Initiative

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