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Research Data Management: Home



The DMPTool is a free resource that helps researchers create data management plans (DMPs) necessary to meet institutional and funding agency requirements. It provides customized templates for creating DMPs that guide researchers in addressing data-related requirements. 

Dryad is a curated digital repository that makes a wide variety of data types accessible, reusable, and citable. Researchers can submit their datasets independently or as part of a manuscript submission for its partner journals.

Click here for more info on publishing your data with Dryad

Create and manage long-term, globally unique identifiers for your data and sources, ensuring their future discoverability.



This guide details ways to organize, manage, and share your data to increase impact and ensure research reproducibility, and help meet journal and funder data sharing requirements. Research data management has become an important skill required of researchers as funding agencies and journal publishers increasingly implement data sharing policies.

What is research data?

Research data are the materials gathered in the course of the research process, from which results are produced and conclusions drawn. Data may take many forms depending on discipline, and may include numeric, qualitative, and visual (e.g., recordings, photographs) materials. Funding agencies and journal publishers will often provide their own definitions, for example the US Office of Management and Budget (circular A-110) defines research data as the "the recorded factual material commonly accepted in the scientific community as necessary to validate research findings". Researchers may also want to consider ancillary information, such as protocols, methods, scripts, and algorithms to manage and share along with their primary research data.

What is research data management?

Data management concerns the active management of data to maintain and extend its value over time.  It includes effectively organizing data for access, documenting context for reproducibility, and securely preserving the physical integrity of the work.

Research Data Management Lifecycle

Research Data Management Life Cycle (source: UCSC

The FAIR Data Principles

One of the grand challenges of data-intensive science is to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of, task-appropriate scientific data and their associated algorithms and workflows. The FAIR Data Principles are a set of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable.

From: FORCE11/The FAIR Data Principles 

F1. (meta)data are assigned a globally unique and eternally persistent identifier.
F2. data are described with rich metadata.
F3. (meta)data are registered or indexed in a searchable resource.
F4. metadata specify the data identifier.

A1  (meta)data are retrievable by their identifier using a standardized communications protocol.
A1.1 the protocol is open, free, and universally implementable.
A1.2 the protocol allows for an authentication and authorization procedure, where necessary.
A2 metadata are accessible, even when the data are no longer available.

I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2. (meta)data use vocabularies that follow FAIR principles.
I3. (meta)data include qualified references to other (meta)data.

R1. meta(data) have a plurality of accurate and relevant attributes.
R1.1. (meta)data are released with a clear and accessible data usage license.
R1.2. (meta)data are associated with their provenance.
R1.3. (meta)data meet domain-relevant community standards.

Data Curation Librarian

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Wasila Dahdul
Science Library
Office 249

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