Resourcing

Data management activities require staff time and technical resources. These costs should be considered during project planning and included in funding applications where appropriate. Data producers should consult funder guidance and institutional research support teams when preparing budgets to ensure that data management costs are included appropriately.

Typical data management cost areas may include:

• people time for data organisation, documentation and quality checks
• transcription, anonymisation and data cleaning
• specialist software or secure computing environments
• storage and backup during active research
• preparation of data for deposit and reuse
• access management and any data deposit fees where applicable.

Many funders allow data producers to request resources to support collection-level data management activities. For example, time spent preparing documentation, organising files and preparing data for sharing can often be included in grant budgets.

However, long-term preservation and access services are often provided by national or institutional infrastructure services, such as responsible repositories. In many cases these services are supported through central funding and are not directly charged to individual projects. Some repositories and Trusted Research Environments may apply onboarding, curation or access-related charges, particularly for large, complex or sensitive datasets. Data producers should check repository cost models early and factor any applicable charges into project budgets where required.

On average, it is recommended that two to three weeks be costed into a typical two-year research grant application to prepare data for sharing. However, owing to the disparate nature of research and data creation, we cannot provide advice on the exact costs likely to be incurred in data preparation. There are two approaches to costing data management.

Approach 1

Estimate costs for all data-related activities throughout the project, for the entire data lifecycle, from data creation and analysis to storage, sharing, and long-term preservation.

Approach 2

Focus only on the resources required to preserve and share your data beyond the original research team.

Data management costing tool

This simple activity-based costing tool can be used if taking Approach 2 (above) to costing data management in the social sciences.

The tool lists a series of data management activities and topics considered to be pertinent for data sharing. Comments and suggestions are given for each topic to help you decide whether you need additional resources for a particular data management activity with indicative costs.

Research institutions play a key role in supporting data management and sharing. Most universities and research organisations maintain policies and services that influence how data should be handled.

Data producers and their teams should familiarise themselves with institutional guidance on:

• research data management and retention
• data protection and information governance
• ethics approval processes
• intellectual property and data ownership
• storage, security and backup provision.

Institutions may also provide data management planning tools and templates, training and advisory services, IT infrastructure for secure storage and collaboration and research governance and compliance support.

Aligning collection-level data management plans with institutional policies helps ensure consistency, compliance and access to available support.

In many cases, data producers must meet both institutional and funder requirements. These should be considered together during planning to avoid conflicts or duplication.

Where differences exist, data producers should seek guidance from institutional research offices or data support services to clarify expectations and responsibilities.