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 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: The first step in (re)using data is to find them.

1. (meta)data are assigned a globally unique and persistent identifier
2. data are described with rich metadata (defined by R1 below)
3. metadata clearly and explicitly include the identifier of the data it describes
4. (meta)data are registered or indexed in a searchable resource

A... Accessible: Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.

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

I... Interoperable: The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.

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

R... Reusable: The ultimate goal of FAIR is to optimize the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

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

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: Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.

• C1 For inclusive development and innovation
• C2 For improved governance and citizen engagement
• C3 For equitable outcomes 

A... Authority to Control: Indigenous Peoples’ rights and interests in Indigenous data must be recognized and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data.

• A1 Recognizing rights and interests
• A2 Data for governance
• A3 Governance of data

R... Responsibility: Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self determination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples.

• R1 For positive relationships
• R2 For expanding capability and capacity
• R3 For Indigenous languages and worldviews

E... Ethics: Indigenous Peoples' rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem.

• E1 For minimizing harm and maximizing benefit
• E2 For justice
• E3 For future use

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