Data Management @ ODU

Data Management, also called Research Data Management or RDM, is a term "used to describe the processes researchers and institutions use for organizing, securing, archiving, and sharing research data throughout the research lifecycle” (OCLC, 2017, p. 6: PDF – Links to an external source and may not be accessible).

Managing your data well and using best practices outlined in this guide can help make your research process more efficient, reduce redundancies, and decrease the chance of misplaced or lost data.

Other benefits include:

• Compliance with funder requirements
• Your data will be more secure
• Your research findings will be transparent
• Collaboration and data sharing will be easier
• Your data will be preserved so that others can use it
• Your data can support the FAIR and CARE data principles

Research data, is defined in ODU Policy #5350: Research and Scholarly Digital Data Management Policy as:

"...digitally recorded information that are necessary to support or validate a research project's observations, findings, or outputs."

This policy is maintained by the ODU Research and Scholarly Data Governance Committee (RSDGC) and establishes general digital data management standards and responsibilities for Old Dominion University researchers.

Research is often described in terms of a lifecycle. According to the Harvard Data Lifecycle, the data lifecycle has seven components. Although called the Biomedical Data Lifecycle, the data management elements are broadly applicable. It should be recognized that research is an iterative process and it is not always the case that a researcher will move from one element to the next sequentially.

The elements of this data lifecycle include:

• Plan & Design: Plan processes from onboarding to project closure and data resources
• Collect & Create: Organization and integration of data sets and collection processes
• Analyze & Collaborate: Processing and analyzing data should be collaborative and documented
• Store & Manage: Each stage of the [Biomedical] Data Lifecycle revolves around the management of data storage
• Evaluate & Archive: Identify essential research records and evaluate for retention
• Share & Disseminate: Establishing and supporting the reach and impact of your data
• Publish & Reuse: Ensuring the broad utility of your research data efforts for other researchers

There are various versions of the research (data) lifecycle. For other examples and description, please see: 

• Digital Curation Centre: Curation Lifecycle Model
• Princeton Research Lifecyle Guide
• ICPSR Guide to Social Science Data Preparation and Archiving: Best Practice Throughout the Data Life Cycle: 6th Edition

One goal of FAIR is to optimize the reuse of data. Metadata is a key component

F - Findable

A - Accessible

I - Interoperable

R - Reusable

Download FAIR Overview pdf (PDF – Links to an external source and may not be accessible)

According to the Global Indigenous Data Alliance (GIDA), "the current movement toward open data and open science does not fully engage with Indigenous Peoples rights and interests," (GIDA, n.d., CARE Principles for Indigenous Data Governance). In response, "the CARE Principles for Indigenous Data Governance were drafted at the International Data Week and Research Data Alliance Plenary co-hosted event “Indigenous Data Sovereignty Principles for the Governance of Indigenous Data Workshop,” 8 November 2018, Gaborone, Botswana" (GIDA, n.d., CARE Principles for Indigenous Data Governance Acknowledgements).

The CARE data principles aim to be complementary to the FAIR data principles and are outlined as follows:

C - Collective Benefit

A - Authority to Control

R - Responsibility

E - Ethics

Download CARE Principles for Indigenous Data Governance pdf (PDF – Links to an external source and may not be accessible)