Simplifying Resource Distribution By Linking Data Aggregation To Budgets and Grants With Software
Laboratory research is a compilation of data production spread across various projects. Each project typically has its own slice of a larger departmental budget, or is funded by a specific grant. So long as the projects are separately funded on paper, they’re held to creating a data set that’s unique from other overlapping projects within the same laboratory. This picture becomes much more complicated when financial issues are introduced to equipment, reagent, and personnel sharing that exists between labs with similar purposes.1 How should labs budget their activities when many of the items in the budget overlap with the items on the budget of similar groups? Likewise, how should groups with similar projects differentiate what is solely theirs with what is shared when it comes to equipment and data? Robust data aggregation software in the laboratory can answer most of these issues definitively, but maintaining the right perspective on research is a necessity.
Shared Goals, Different Experiments
In both private and public sector laboratory research, research groups that perform similar functions and investigate similar phenomena are often grouped together geographically so that they can make use of each other’s equipment and personnel. These groupings create many informal collaborations and transactions when the members of the groups reach out across the aisle to ask for help when a group’s resources are insufficient for whatever reason. Informal collaborations can sprawl and create problems when it comes time to make a new laboratory budget, however.
When projects draw to a close, thanking the other group is customary– but totally insufficient when it comes to tracking resource consumption and aggregating project data so that it’s easily accessible for everyone who contributed. Projects may have generated their data using another group’s capital, but the scientific results won’t reflect it, which makes making decisions about which projects are worth continuing and which projects are too expensive extremely difficult. Clarifying the relationship between each project, the resources it uses, and the data that it generates will be increasingly necessary as science becomes more collaborative.
The traditional congenial laboratory environment dictates that helping one’s neighbors is obligatory, and most research personnel have little care for the project funding status of the items they are sharing. Aside from frustrating grant administrators and project managers trying to keep track of which group should be paying for which items in the larger lab budget, these micro-collaborations typically produce data that should be aggregated in the same location but are often separate, slowing the pace of research.
In these labs, the following conditions are normal:
- Lab spaces are shared
- Ownerships of equipment are linked to specific budgets
- Equipment is shared, sometimes extensively
- Reagents are and linked to specific budgets and may be shared extensively
- Data produced by sharing of equipment is shared
- New proposals for funding are produced using shared data and shared equipment
Additional questions and concerns then arise when laboratory groups need to justify their budgets by applying for new grants or funding.2
Many Projects, Many Budgets
In the budgeting or grant funding processes, it isn’t acceptable to merely state that another group’s equipment will be used because it’s shared– doing so may ignite a war over the limits of fiefdoms in particularly resource scarce laboratory environments.3 Likewise, by underestimating the genuine costs of research because a project was able to use the reagents and personnel of another group, funding is far more likely to get cut.
As a result, using shared equipment is often written into budgets and grant proposals as areas of expense, but this isn’t reflected for the groups who technically own the equipment.4 This inaccuracy is a source of added costs and also added confusion in accounting for each of the separate groups within a laboratory.
These issues are entirely avoidable if all of the laboratory groups have a software suite which helps them keep track of which funds are used for which project and which costs are in effect subsidized by other groups as part of a collaborative research environment.
Bridging The Gap Between Resources And Data
Tracking which resources are being used and linking their use to the intended budget isn’t the only place where software provide benefits, however. As a data aggregation system, the right laboratory software can create models which identify groups whose resource consumption and data output are the most efficient, which could be used as an argument for greater funding. Administrators will finally have an answer for research groups who beg for more funding on the basis of having to support other groups via their equipment and reagents. With such relationships explicitly linked to data output and trackable experimentation, each experiment’s true funding and actual costs is easy to trace.
Likewise, powerful laboratory software means that proposals for new projects won’t have to fudge their numbers when it comes to identifying the resources that they’ll use– proposals can identify multiple budgets and point to the data streams generated by those other projects and make a clearer case for how the new project will contribute to the whole.
Science Cloud is the laboratory information software which your lab can use to aggregate data and link the generation of that data to individual grants and budgets within your organization. Using Science Cloud, laboratory budgeting will be faster, and finding savings caused by overlapping budget items in similar projects will be easier than ever. Contact us today to find out how we can help you use our software to make your laboratory group’s accounting processes more streamlined and accurate than ever before.
- “A game-theory modeling approach to utility and strength of interactions dynamics in biomedical research social networks.” January 2017, https://casmodeling.springeropen.com/articles/10.1186/s40294-017-0044-0. ↩
- “Sponsorship, authorship and accountability.” September 2001, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC81460/. ↩
- “7 Lessons On Startup Funding From a Research Scientist.” January 2012, http://onstartups.com/tabid/3339/bid/76387/7-Lessons-On-Startup-Funding-From-a-Research-Scientist.aspx. ↩
- “A perspective on laboratory utilization management from Canada.” January 2014, https://www.sciencedirect.com/science/article/pii/S0009898113003689. ↩