Skip to Main Content

Graduate Student Research Support

Guidance to help new scholars navigate the realm of scholarship.

Email this link:

Data Curation Librarian

Profile Photo
Wasila Dahdul
Contact:
Science Library
Office 249
949-824-2185

Digital Scholarship Services

The UCI Libraries Digital Scholarship Services (DSS) fosters the use of digital content and transformative technology in scholarship and academic activities. DSS works with the campus community to publish, promote, and preserve the digital products of research in several areas. DSS can help you with all stages of data management required by funding agencies:

  • Write grant winning Data Management Plans
  • Deposit data into repositories for access and preservation
  • Capture metadata to allow re-use
  • Create permanently resolvable hyperlinks
  • Connect your data with your publications

Learn more!

This page provides a very basic overview of research data management. For more in-depth information on data management plans, managing, sharing and preserving data, and working with sensitive data, please visit the:

Research data management

Whether you are working in the Humanities, Social Sciences, or STEM, you will likely be collecting and analyzing some type of data for your research. Oral histories, interviews, survey results - all of this is data. And there are various steps and standards for collecting and organizing your data. Not only will learning about these processes early help you establish good data maintenance practices, but oftentimes, you'll be ahead of the game when you go to publish something that requires your data also be publicly accessible.

Research data management lifecycle

Research Data Management Life Cycle (source: UCSC)

Data management plans

Data management plans (DMPs) are now a standard part of grant proposals for most funding agencies. DMPs are formal documents that describe how data will be collected and managed during research, and how data will be shared and made accessible after a project is completed. Details include how data will be collected, documented, analyzed, transformed, and stored, and how data will be preserved and shared.

You may have already considered some or all of these issues but writing them down helps formalize the process, identify weaknesses in the plan, and provides a record of what you intend to do.

DMPTool

The DMPTool is a free resource that helps researchers create data management plans (DMP) necessary to meet institutional and funding agency requirements. It provides customized templates for creating DMPs that guide researchers in addressing data-related requirements. This tool also provides links to funding agencies, best practices documentation for creating DMPs, and samples of public DMPs shared by their authors.

This video provides quick overview of the features of the easy to use DMPTool.  You can also use this self-help guide or schedule a consultation with a UCI librarian for additional support.

Managing data

Managing data is an integral part of the research process.  How you manage your data depends on the type of data, how the data is collected, and how the data is used throughout the life cycle of the project.  Effective data management helps you organize your files and data for access and analysis.  It helps ensure the quality of your research and supports the published results of your research. 

More information is available on the following pages:

  • Store: both storage and backup are essential to safeguarding your important data assets.
  • Organize: effective file naming conventions is an investment of time and effort but they do save time / effort in the long run.
  • Track: when creating new versions of files, record the changes made to the files and give the new version a unique name.

Data wrangling

Do you need to clean up your data? Or transform it from one format to another? Do you need to fix inconsistencies?

Data wrangling - also called data cleaning, data remediation, or data munging - refers to a variety of processes designed to transform raw data into more readily used formats. The exact methods differ from project to project depending on the data you’re leveraging and the goal you’re trying to achieve.

Some examples of data wrangling include:

  • Merging multiple data sources into a single dataset for analysis
  • Identifying gaps in data (for example, empty cells in a spreadsheet) and either filling or deleting them
  • Deleting data that’s either unnecessary or irrelevant to the project you’re working on
  • Identifying extreme outliers in data and either explaining the discrepancies or removing them so that analysis can take place

Data wrangling can be a manual or automated process. Plagiarized from Harvard)