The Data and Analytics Core offers advanced computational and informatics support for studies involving large datasets and complex experiments with multiple data types. By providing these services, the core aims to enhance the understanding of the causes of metabolic diseases and, in turn, help inform novel therapeutic solutions.
The Data and Analytics Core collaborates with various institutional centers, including the Center for Computational Biology and Bioinformatics (CCBB), the Center for Medical Genomics (CMG), the Center for Proteome Analysis and the Regenstrief Institute. The core also partners with the Ruth Lilly Medical Library’s research data services team to support managing, sharing and preserving research data at the IU School of Medicine. Connecting with these established centers and services enables the Data and Analytics Core to aid in the interpretation of vast datasets, test hypotheses with real-world electronic health records from the Indiana Network for Patient Care and to offer support and training to researchers in the field of diabetes and metabolic research.
Services
The Data and Analytics Core focuses on supporting investigators with the tools, knowledge and collaborative opportunities needed to advance research, from understanding complex datasets to translating findings into real-world applications. The core is committed to:
-
Facilitate the understanding and interpretation of investigator-generated “omics” datasets and translate this complex information to actionable experimentation.
-
Facilitate the understanding and analysis of publicly available datasets that can be utilized to support the work of Indiana Diabetes Research Center investigators.
-
Advance the use of Electronic Health Record (EHR) and other real-world health data to translate findings from model and preclinical systems to humans. The Core works closely with the Regenstrief Institute to facilitate access to and analysis of EHR data from healthcare organizations across Indiana.
-
Expand investigator knowledge of omics and informatics-based approaches to enable the expansion of systems-level experiments and to ensure that data sharing and preservation is in line with the new National Institutes of Health (NIH) policy on Data Management and Sharing.
