16 Wrapping It Up

Up until this point, we have mostly focused on a simplistic workflow for one team, one project. However, it is becoming more common for projects to include multi-site collaborations (e.g., to have a a grant shared with multiple research institutions), and for teams to have for teams to have multiple projects. Both of these add complexity to data management which I will briefly address.

16.0.1 Multi-site collaborations

Multi-site collaborations require additional planning around roles and responsibilities, workflows, and standards. Jumping into multi-site collaborations with out spending time cross-team planning often leads to unfortunate data security and quality concerns. Before a project begins, consider documenting expectations in a collaboration agreement. Review everything in a typical data management checklist but come to an agreement on decisions. The following issues should be addressed (Briney 2015; Schmitt and Burchinal 2011).

• How will teams maintain consistency in procedures across sites? (e.g. shared SOPs, shared style guides, oversight of practices)
  • If each site is handling their own data tracking, capture, entry, and cleaning procedures, it is imperative that these processes are standardized to allow for datasets to be integrated.
• How will teams handle data ownership?
• What are the roles and responsibilities across sites?
• What tools will be used to allow for multi-site data tracking, collection, entry, storage, and sharing?

Documents such as RACI charts (see Chapter 7) can be helpful in laying out expectations for team collaborations (Figure 16.1). In these charts, each site is assigned to a task as either responsible, accountable, consulted, or informed. Assigning levels of responsibility to each site allows collaborators to clearly see what is expected from them in a project. Then within sites tasks can then be further assigned to specific roles.

Figure 16.1: Example of a RACI chart for a multi-site project collaboration

16.0.2 Multi-project sites

Similar to multi-site projects, organizing multiple projects within a team requires additional coordination. As your center grows, the sophistication of your center should grow with it (Van den Eynden et al. 2011).

1. Centralize resources
   • Create templates, SOPs, and style guides that can be used across projects. Utilize your team wiki to post shared resources in a central location where team members can easily access them. Centralizing resources reduces duplication of efforts, and also improves standardization, allowing you to more easily compare data across projects.
2. Encourage team science
   • As you receive more grants, the “lone cowboy” model of just having one person manage everything becomes even less feasible (J. H. Reynolds et al. 2014). Embrace the idea that it takes a team of people, skilled in many different areas (e.g., project management expertise, data expertise, content expertise), to do quality research. With more than one grant, it is potentially more feasible to hire people to fill specialized roles, and to fund them across multiple grants.
3. Create oversight roles
   • Along with creating standards that are applied across projects, it becomes important to assign someone to oversee fidelity to those standards. Create a hierarchy that includes both oversight and mentoring to prevent internal drift.
4. Create support systems
   • If your team is large enough, and you have multiple people working on data management across different projects, it may be helpful to create an internal data core. This internal group of data people can meet on a regular schedule to share knowledge and resources, develop and modify shared documentation, and develop internal data trainings for staff, increasing capacity for your center.

16.0.3 Data management pays off in dividends

Collecting data is a bit like cooking a good meal. If you clean as you go, when you are full and sleepy you will have much less to do
- Felicia LeClere (2010)

Starting data management before a project begins, and implementing quality practices throughout the research life cycle will make measurable differences in your work. Slow science is often used to describe an antithesis to the increasingly fast pace of academic research (Frith 2020). Instead suggesting that science should be a slower, more methodical process. Similarly, if we hurry a research project along without spending time putting quality processes into place, we increase the possibility that we end up putting research into the world that we cannot trust. Yet, while this slow process may be difficult to reconcile early on, remember that data management gets easier the more you do it. Once you have templates, protocols, style guides in place, those documents and processes can be reused as often as you want, easing any prior development burdens.

There is no one-size-fits-all approach to data management (Bergmann 2023; J. H. Reynolds et al. 2014). Projects are nuanced. There is no way to teach or anticipate every way in which a specific project’s data needs to be managed. Instead, use the “buffet approach” and implement what works best for your project and your team (Bergmann 2023). What matters is that those practices are implemented consistently within your project, and that ultimately they produce quality data products that are accepted in the field. Similarly, while maybe all of the practices mentioned in this book work for your project, it is unlikely that your team has the bandwidth to do it all. Instead, implement “good enough” practices that allow you to achieve the quality outcomes you desire (Borghi and Van Gulick 2022; Wilson et al. 2017). You don’t have to create all the documentation or use the most sophisticated data cleaning methods. You simply need to use methods that are good enough to reach your goals. Just don’t forget that data management practices should be periodically reviewed to ensure you are keeping up with changing requirements, technologies, standards, or team/project needs.

As the awareness of the necessity of good data management continues to grow in our field, we can only hope that systemic changes will continue to happen that make data management easier for education researchers. Integrating data management content into required college coursework would improve data management practices for all the researchers who are out there “winging it” because they learned data management through informal methods. As Wilson et al. suggested in their 2017 paper, with requirements for data management and sharing expanding, “it is unfair as well as counterproductive to insist that researchers do things without teaching them how” (Wilson et al. 2017, 19). Similarly, developing shared standards for the field of education would do wonders in both easing the burden on researchers who are having to make on the fly data management decisions and also in improving the consistency of quality and usability of publicly shared data products.