File(s) under permanent embargo
Data Integration Protocol In Ten-steps (DIPIT) : a new standard for medical researchers
journal contribution
posted on 2014-10-01, 00:00 authored by Joanna Frith Dipnall, Michael BerkMichael Berk, Felice JackaFelice Jacka, Lana WilliamsLana Williams, Seetal DoddSeetal Dodd, Julie PascoJulie PascoThe exponential increase in data, computing power and the availability of readily accessible analytical software has allowed organisations around the world to leverage the benefits of integrating multiple heterogeneous data files for enterprise-level planning and decision making. Benefits from effective data integration to the health and medical research community include more trustworthy research, higher service quality, improved personnel efficiency, reduction of redundant tasks, facilitation of auditing and more timely, relevant and specific information. The costs of poor quality processes elevate the risk of erroneous outcomes, an erosion of confidence in the data and the organisations using these data. To date there are no documented set of standards for best practice integration of heterogeneous data files for research purposes. Therefore, the aim of this paper is to describe a set of clear protocol for data file integration (Data Integration Protocol In Ten-steps; DIPIT) translational to any field of research.
History
Journal
MethodsVolume
69Issue
3Pagination
237 - 246Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
eISSN
1095-9130Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2014, ElsevierUsage metrics
Categories
No categories selectedKeywords
data aggregationdata integrationdata linkagedata miningmergingstandardscience & technologylife sciences & biomedicinebiochemical research methodsbiochemistry & molecular biologybiomedical data integrationmultiple data sourcesmissing datarecord linkagehealth datainformationservicesoutcomestrialsclaimsHEALTH
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC