International researchers recently launched an online, open-access database that pools emerging data from a wide range of fields — ranging from economic modeling to weather surveillance to genomic medicine — to improve collaboration on epidemiological research.
A single epidemiological study can require tens of thousands of clinical observations from around the world. The Clinical Epidemiology Database (ClinEpiDB) streamlines the data gathering process through the same computational framework that was developed 20 years ago for the Eukaryotic Pathogen Database (EuPathDB).
“It is increasingly possible to generate spectacularly valuable, large-scale datasets, but how to store and manage this information so that people can make sensible use of it is arguably the overriding challenge of our day,” David Roos, a researcher at the University of Pennsylvania who co-developed ClinEpiDB, said. “The EuPathDB project has demonstrably helped translate the promise of infectious-disease genomics into practice, and with ClinEpiDB we are providing a resource to help get the information from large patient studies into the hands of those who can do the most good with it, while also protecting the confidentiality of study participants.”
While it is common for scientific journals to encourage or require scientists to make study data public, ClinEpiDB has established a standardized process to make the complex clinical data more accessible. Featuring an intuitive interface, the database allows scientists to use point-and-click filtering, simple queries, advance search strategies, and exploratory statistical analysis tools.
“Establishing formal definitions of and relationships between data variables is one key to the success of this initiative,” Christian Stoeckert, a researcher at Penn’s Perelman School of Medicine and co-developer of ClinEpiDB, said. “EuPathDB uses an OBO Foundry based ontology, aiding integration across datasets and establishing common, user-friendly terms for study details.”
The database’s first dataset came from the Program for Resistance, Immunology, Surveillance and Modeling of Malaria project, known as PRISM. The dataset includes more than 40,000 clinical observations of 1,400 study participants.
“The goal of PRISM project is to improve our understanding of malaria, and measure the impact of population-level control interventions,” Grant Dorsey, a professor at the University of California, said. “This study represents seven years of work to date, from scores of researchers, with contributions from many hundreds of Ugandan kids at risk for malaria, as well as their families. It is exciting that ClinEpiDB makes it easy for anyone to browse and analyze the data and to quickly test parameters that may be associated with increased or decreased risk of serious malaria.”