Skip Navigation

Columbia University

Maintenance notice: We are currently addressing issues with broken links due to recent major website changes. We apologize for any inconvenience and appreciate your patience. Please contact brittany.trottier@niehs.nih.gov for assistance.

Superfund Research Program

Data Management Core

Project Leader: Diane Levy
Grant Number: P42ES010349
Funding Period: 2006-2017

Project-Specific Links

Connect with the Grant Recipients

Visit the grantee's eNewsletter page Visit the grantee's Twitter page Visit the grantee's Video page

Project Summary (2012-2017)

Modern epidemiological research is collaborative and data intensive. Sophisticated data collection, storage and quality control techniques are available and capable of greatly enhancing the efficiency and scientific precision of such projects. However, advanced expertise in utilizing these resources does not typically fall within the skill set of the individual investigator. As a result, hiring proficient personnel and centralizing the data management resources and making them available to a set of investigators with similar needs achieves great benefits and efficiencies. In addition, when the programmers and data managers are involved in work in a specialized area for a period of time, they gain unique familiarity with the methods and needs of the investigators. The data management core collaborates with Joseph Graziano, Habibul Ahsan, and Mary Gamble, as well with the Trace Metals Laboratory Core, the Biogeochemistry Laboratory Core and junior investigators on a regular basis. Standard operating procedures for data entry, storage and quality control have been developed; validation routines are regularly programmed for systems causing data cleaning efforts to become less cumbersome and more streamlined. Ultimately the process of creating required datasets for analyses has been greatly expedited. The Core communicates directly with project statisticians thereby gaining a clear understanding of specific dataset requirements for each project. Data quality has been greatly enhanced and the time to completion from the beginning of the data entry process through to the submission of transcripts has been substantially reduced. A variety of program modules, database templates and a network infrastructure have been developed and are immediately available to be incorporated into solutions to address the specific aims of this proposal.

Back
to Top