Summary of Lessons Learned

Below is a summary of the key lessons shared throughout the chapters. We offer them as a guide and a source of inspiration as you embark on your own journey to synergize research and build community within your own projects. While we did not have the benefit of these insights when we began, we share them with the hope that they will serve you well. Happy coordinating. 

Chapter 2: Starting a Coordination Center – From Proposal to Funded Initiative

• Prior to writing the proposal, read! There is literature on the functions and roles of research coordination centers across disciplines. Reading this literature will be helpful in framing your scope of work, goals, and specific aims. 
• Be inspired. Embrace the intellectual challenge; not only of research and data coordination, but also of building both community and infrastructure to sustain it.  
• Reduce the administrative burden of research teams. Coordination centers, if implemented well, can lessen the administrative burden on research teams. 
• Develop and sustain authentic partnerships. Initiating and fostering authentic partnerships is critical to successful coordination center proposals and subsequent implementation. 
• Develop compelling specific aims. Communicating an exciting, well-developed, compelling scope of work and specific aims aligned with the funding agency priorities will go a long way in motivating reviewers to fund a proposal.

Chapter 3: Establishing Communication and Maintaining Infrastructure 

• Start with relationships. Infrastructure is essential, but relationships make the system work.
• Make communication a practice, not a product. The way we speak to one another is a reflection of how we work together.
• Invite feedback early and respond to it visibly. Trust is built when people see that their voice matters.
• Design communication with care. Every document, email, meeting, and tool is an opportunity to affirm belonging and reduce barriers.

Chapter 4: Working Together – Setting Up Common Measures 

• Invest time to build trust. Do not underestimate the importance of taking time to build trust with members of the community with whom you are working.
• Identify common measures early. Start the process of identifying and selecting common measures EARLY, before research community members begin to collect data.
• Be transparent about data collection and use. Be clear about who will collect common data, how it will be collected, who will have access to it, and how it will be used.
• Design easy to use processes. Make using common measures easy by using templates and shared online resources.
• Be accountable and responsive. Be accountable and responsive to the research community you are collaborating with; respond to inquiries promptly, provide reminders for action items, and share updates regularly.

Chapter 5: Tracking Research Activities and Demonstrating Collective Progress

• Prioritize building relationships with research managers. Research managers are invaluable members of research teams and building relationships with them should be prioritized.
• Provide training opportunities to research team members. Research team members have varying levels of experience and comfort working with data. Additional training may be needed to ensure clear expectations and requirements.
• Acknowledge needs and provide resources. Acknowledging research team member needs, making adjustments to accommodate work styles, and creating supportive resources will build trust with the research community.
• Provide onboarding to align expectations. Routine onboarding with new research team members is necessary to ensure clear expectations.
• Be proactive about checking in with research managers. Research studies may evolve throughout the grant, so it is important to proactively check in and inquire and stay informed about changes to study.
• Create tracking materials to showcase project outcomes. Take time to consider the types of information that will showcase project outcomes and use backward design to create tracking materials that allow data to be easily compiled.
• Consider what information is needed for reporting. Carefully consider what information will be needed for creating reports and presentations that will be shared with collaborators (e.g., funders, program officers, steering committee members, institutional leaders) and what will be useful for demonstrating collective progress with community members. Some data, such as information on the career stage of participants, may not be publicly available or found in data shared with your coordination center. Create plans to obtain this information if needed.

Chapter 6: Building a Community of Practice Among Research Managers Supporting Mentorship Research 

• Involve all community members in decision making. Implementing an iterative needs assessment approach can enhance buy-in by creating an inclusive space where participants feel their voices are heard and honored.
• Manage both content and process. The “what” or content that was discussed was shaped by the community. The topics we selected were generated by the community. In addition, we attended to “how” the community interacted with one another. By attending to content and process, not only were topics covered across domain, practice, and community elements, but special attention was paid to the relationships among research managers.
• Assign facilitators for community meetings. To support the infrastructure of a CoP (e.g., polling, logistical tasks including slide deck creation, topic selection, guiding the discussion), at least one facilitator is recommended.
• Consider logistics and address evolving needs. Facilitators polled research managers regarding the frequency and duration of meetings. In this way CoP members were the ones to make decisions regarding logistics. As the grant began the sunsetting process, research managers requested meetings with less frequency and shorter duration.
• Encourage peer mentorship. Peer mentorship is a powerful way to learn, connect, and grow. Encouraging peer mentorship among research managers allows them to support each other in skill-building and problem solving.
• Consider collecting research data on your CoP. Anecdotal evidence from our CoP highlighted the potential effectiveness of bringing together individuals from geographically dispersed, large-scale research consortiums—demonstrating the collective impact collaboration can achieve. This evidence will add to the limited research that exists on CoPs (Abedini et al., 2021).
• Tend to building relationships in the community. Focusing on building relationships among our research managers ultimately led to them supporting each other.

Chapter 7: Cleaning, Consolidating, and Sharing Data

• Standardize survey design and data collection methods when feasible. Extra care should be taken to ensure that common measures used across surveys follow the same prompts, scale items, and scoring. Different survey versions, formats, response scales, and variable definitions across research teams created challenges for data consolidation and comparison. While perfect standardization can be difficult, especially in multi-study consortia, encouraging research teams to use consistent survey instruments and response options from the outset can significantly improve the ease of data cleaning and analysis. Even partial alignment in key measures can significantly reduce ambiguity, enhance data quality, and improve the interpretability of results.
• Consider intervention and data collection timeline alignment early on and how it will impact data cleaning and consolidation. Aligning time points across studies would ideally facilitate smoother data merging and longitudinal comparisons. However, in many Request for Applications (RFA)-driven consortia, differences in study designs, funding timelines, and objectives make perfect alignment nearly impossible. Nonetheless, early communication about timing and efforts to harmonize time points where possible can mitigate complexity in later data consolidation.
• Implement a robust system for tracking participants to minimize duplicates. Issues with duplicates arose from blank or partial responses by the same participants, as well as double-counting due to overlap across cohorts and research studies. This duplication occurred because research teams shared raw data before completing their internal cleaning, as their studies were still ongoing during the timeframe of the NRMN Coordination Center’s requests for data. To improve data integrity, reduce duplication, and accurately identify unique individuals, implementing a more robust system for tracking participants is essential.
• Develop a strategy for reviewing and resolving data issues early. Lack of standardized coding for missing data and inconsistent documentation of multiple survey versions limited usability. Without knowing which measures appeared on each survey version, distinguishing truly missing data was difficult. Establishing structured processes and tools early to track survey versions, variable inclusion, and missing data classification can improve data quality and streamline analysis.
• Consider user accessibility. We anticipate that users with limited data analysis experience may face challenges navigating the dataset. Greater focus on clear documentation, comprehensive metadata, and user-friendly tools will make the data more accessible for a broad range of users.

Chapter 8: Sunsetting a Coordination Center 

• Dedicate sufficient time and effort for the adjournment phase. Do not underestimate the time it takes to support transitions.
• Structure the process of “coming apart.” Normalize the final phase of relationships and create shared ways to experience them together.
• Start preparing for transition early. Do not wait until the end to start preparing for the ending.
• Be accountable to the community you are charged to serve. Communicate, uplift, and honor the work that has been accomplished by the research community.
• Provide ways to stay connected during and after transition. Check in with your community after the official end date.