Data Analysis: using the 4 C’s framework to highlight researcher options

Background: Data analysis includes iterative steps that result in four sequential categories of collected data: raw, cleaned, processed, and analyzed. As there are numerous and varied resources used at each stage of data collection, it is a challenge to identify what approach to take to satisfy a researcher’s specific needs. Many research institutions provide support for these resources, and biomedical librarians are ideally situated to provide guidance to researchers on institutional resource identification, evaluation, and selection.

Description: While seeking out data analysis resource options at a large health sciences research university, I determined that the choices could be organized into four broad categories for analyzing bioinformatics data. I named this framework the “Four C’s” as it covers: (1) paying someone else to analyze the data (Core labs), (2) working with another researcher (Collaboration), and analyzing the data yourself either by (3) learning to program (Coding) or (4) using out-of-the-box software (Commercially licensed tools). I then developed a class and libguide on the Four C’s to highlight the pros and cons of each and to instruct researchers on where to find relevant university resources. To assist with these third and fourth stages of the data analysis process, also covered are institutional resources supporting computational needs and rigor and reproducibility considerations.

Conclusion: This poster presents the Four C’s framework for identification of institutional resources for data analysis. Although the examples shared are specific to bioinformatics data analysis at a fortunately resource-rich university, the framework can be used as a tool for librarians at an institution of any type or size to provide support and help identify resources specific to their own researchers.