Research Data Management for Collaborative and Cross-Jurisdictional Research
- Isabella Vizzacchero
- 4 days ago
- 3 min read
Interdisciplinary and cross-jurisdictional research initiatives are challenging, regardless of the discipline. It's even more challenging when conducting research in the healthcare and social fields. Although working in teams can drive productivity as individuals contribute to the data management cycle, they must still adhere to approved data collection protocols, participant consent, and privacy regulations.
Implementing agreed-upon best practices, such as file naming, file organization, and folder structures, helps facilitate a smoother research process, from data inception to publication, while capturing and maintaining metadata according to prescribed standards.
Digital tools like myLaminin’s consolidated RDM platform help researchers structure a robust framework that supports the collection, analysis, storage, and sharing of data across the entire RDM data lifecycle. By seamlessly facilitating these activities, research practitioners and teams integrate safe data management principles into their core framework, reducing the risk of data loss through the active research phase when colleagues join or leave the project. This article analyzes how an RDM platform and collaboration interact, and recommends key features that researchers should prioritize when seeking a data management platform.
Centralized Team Workspace
Cross-jurisdictional and multi-site research requires environments that can integrate team collaboration, data collection, and reporting. Research teams benefit from platforms with a single shared workspace to ensure consistency among team members, regardless of their geographic location. A centralized workspace fosters transparency and efficiency, and simplifies the administration of role-based access controls to data. This also enables collaboration on documents such as research protocols, papers, or analytical activities. This allows the team to streamline communication, clearly track progress, and identify potential gaps within data.
Support Spanning the Data Management Lifecycle
A truly robust RDM platform requires end-to-end support, spanning the entire research lifecycle, starting from the project initiation stage through to publication.
For researchers, this means selecting tools that embed RDM management principles directly into their workspace, as opposed to attempting to enforce these controls across fragmented services, platforms, and sites. For research teams, this significantly reduces the risk of regulatory non-compliance through the active research phase prior to publication, and safeguards against data breaches and IP theft. Therefore, research teams must address their active research data management requirements very early in their planning as part of their Data Management Plans (DMPs). This includes support for team and agreement management, data collection, data repository management with consideration for national and Indigenous data sovereignty, and audit trails. It will save the team time and precious resources.
myLaminin’s platform ensures that every aspect of the data lifecycle is supported by its consolidated features; from project initiation, team and agreement management, data collection with PHI/PII data management capabilities, support for Institutional Research Ethics Boards (IRBs/REBs), and a robust data repository management with both on-prem or cloud storage options and support for Indigenous data sovereignty principles, to the final dataset publication. This is a priority for teams where the shared responsibility contributes to increased opportunity for error, signalling a need for coordination.
Integrated Standardization
One of the biggest challenges of multi-collaborator research projects is establishing a common language among the team; although each individual contributes distinct strengths to the project, they also bring different naming conventions, documentation styles, and personal approaches that challenge the uniformity and consistency of data collection and dissemination.
Standardization resolves this by ensuring that data is shared in a consistent way, facilitating enhanced teamwork. By integrating standardization principles that maintain consistency in how data is captured, documented, and shared, researchers can ensure that all collaborators share the same context, enabling seamless cross-functional collaboration. This is not only important when working within a team, but also across teams and institutions where studies and datasets are being shared and potentially reused.
myLaminin offers research teams built-in standardization through the platform by providing shared project spaces, standardized templates, key metadata standards, and reconciling different inputs by promoting consistent labelling, tracking, and schema mapping. Altogether, these features structure and share datasets in a uniform way, facilitating seamless cross-institutional collaboration.
Conclusion
Effective collaboration is crucial in driving the successful completion of a research endeavour, increasing a team’s reliance on a robust data management infrastructure that integrates key RDM practices. By employing a consolidated RDM platform that embeds governance and compliance standards across the data lifecycle, research teams and institutions can focus on breakthroughs, not bureaucracy.
Sources
__________________________________
Isabella Vizzacchero (article author) is a myLaminin intern, and studying Management and Organizational Studies (BMOS) at Western University.
