Managing Complex Change in Clinical Study Metadata

In highly functional metadata-driven software, the interrelationships within the metadata become complex, and maintenance becomes challenging. We describe an approach to metadata management that uses a knowledge-base subschema to store centralized information about metadata dependencies and use cases involving specific types of metadata modification. Our system borrows ideas from production-rule systems in that some of this information is a high-level specification that is interpreted and executed dynamically by a middleware engine. Our approach is implemented in TrialDB, a generic clinical study data management system. We review approaches that have been used for metadata management in other contexts and describe the features, capabilities, and limitations of our system

ALFRED: An Allele Frequency Database for Microevolutionary Studies

Many kinds of microevolutionary studies require data on multiple polymorphisms in multiple populations. Increasingly, and especially for human populations, multiple research groups collect relevant data and those data are dispersed widely in the literature. ALFRED has been designed to hold data from many sources and make them available over the web. Data are assembled from multiple sources, curated, and entered into the database. Multiple links to other resources are also established by the curators. A variety of search options are available and additional geographic based interfaces are being developed. The database can serve the human anthropologic genetic community by identifying what loci are already typed on many populations thereby helping to focus efforts on a common set of markers. The database can also serve as a model for databases handling similar DNA polymorphism data for other species

Metadata-Driven Creation of Data Marts From an EAV-Modeled Clinical Research Database

Generic clinical study data management systems can record data on an arbitrary number of parameters in an arbitrary number of clinical studies without requiring modification of the database schema. They achieve this by using an Entity-Attribute-Value (EAV) model for clinical data. While very flexible for creating transaction-oriented systems for data entry and browsing of individual forms, EAV-modeled data is unsuitable for direct analytical processing, which is the focus of data marts. For this purpose, such data must be extracted and restructured appropriately. This paper describes how such a process, which is non-trivial and highly error prone if performed using non-systematic approaches, can be automated by judicious use of the study metadata—the descriptions of measured parameters and their higher-level grouping. The metadata, in addition to driving the process, is exported along with the data, in order to facilitate its human interpretation