Grid-Enabled Measures

Abstract

Scientists are taking advantage of the Internet and collaborative web technology to accelerate discovery in a massively connected, participative environment—a phenomenon referred to by some as Science 2.0. As a new way of doing science, this phenomenon has the potential to push science forward in a more efficient manner than was previously possible. The Grid-Enabled Measures (GEM) database has been conceptualized as an instantiation of Science 2.0 principles by the National Cancer Institute (NCI) with two overarching goals: (1) promote the use of standardized measures, which are tied to theoretically based constructs; and (2) facilitate the ability to share harmonized data resulting from the use of standardized measures. The first is accomplished by creating an online venue where a virtual community of researchers can collaborate together and come to consensus on measures by rating, commenting on, and viewing meta-data about the measures and associated constructs. The second is accomplished by connecting the constructs and measures to an ontological framework with data standards and common data elements such as the NCI Enterprise Vocabulary System (EVS) and the cancer Data Standards Repository (caDSR). This paper will describe the web 2.0 principles on which the GEM database is based, describe its functionality, and discuss some of the important issues involved with creating the GEM database, such as the role of mutually agreed-on ontologies (i.e., knowledge categories and the relationships among these categories—for data sharing).

Introduction

The digital and information age has transformed how we access information and interact with others; a transformation that has implications for the conduct of health science. For example, the NIH was one of the first scientific agencies to use networked information technologies (a prototype cyberinfrastructure) to coordinate input from literally hundreds of laboratories from around the word to document the more than 3 billion base pairs comprising the human genome.¹ This technology-mediated effort virtually defined the concept of “team science”² in an era of distributed computing. Likewise, current efforts to extract research value from the terabytes of data existing within electronic medical records hold the promise of reducing healthcare costs through comparative effectiveness studies,³ reducing health disparities by informing policy decisions at the systems level,⁴ and accelerating discovery by closing the gap in translational research.5, 6

A limiting factor in taking full advantage of these networked information technologies, however, has been the organizational challenge of deriving agreement from scientific communities on the common terms, measurements, and data elements that will make up the content and structure of the interconnected data systems.7, 8, 9 This paper describes one solution to that problem: the Grid-Enabled Measures (GEM) database. Whereas other systems have used consensus panels⁷ and psychometric analytic techniques¹⁰ to select common measures for very specific purposes, the GEM database is distinct in that it uses “web 2.0” functionality to solicit, comment, vet, and select measures from the behavioral and population science communities in open and transparent ways. It is an example of what some National Science Foundation (NSF) grantees have referred to as “Science 2.0.”11, 12