Dataset Information

A transparent and transferable framework for tracking quality information in large datasets.

ABSTRACT: The ability to evaluate the validity of data is essential to any investigation, and manual "eyes on" assessments of data quality have dominated in the past. Yet, as the size of collected data continues to increase, so does the effort required to assess their quality. This challenge is of particular concern for networks that automate their data collection, and has resulted in the automation of many quality assurance and quality control analyses. Unfortunately, the interpretation of the resulting data quality flags can become quite challenging with large data sets. We have developed a framework to summarize data quality information and facilitate interpretation by the user. Our framework consists of first compiling data quality information and then presenting it through 2 separate mechanisms; a quality report and a quality summary. The quality report presents the results of specific quality analyses as they relate to individual observations, while the quality summary takes a spatial or temporal aggregate of each quality analysis and provides a summary of the results. Included in the quality summary is a final quality flag, which further condenses data quality information to assess whether a data product is valid or not. This framework has the added flexibility to allow "eyes on" information on data quality to be incorporated for many data types. Furthermore, this framework can aid problem tracking and resolution, should sensor or system malfunctions arise.

SUBMITTER: Smith DE

PROVIDER: S-EPMC4224478 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A transparent and transferable framework for tracking quality information in large datasets.

Smith Derek E DE Metzger Stefan S Taylor Jeffrey R JR

PloS one 20141107 11

The ability to evaluate the validity of data is essential to any investigation, and manual "eyes on" assessments of data quality have dominated in the past. Yet, as the size of collected data continues to increase, so does the effort required to assess their quality. This challenge is of particular concern for networks that automate their data collection, and has resulted in the automation of many quality assurance and quality control analyses. Unfortunately, the interpretation of the resulting ...[more]

PMID: 25379884

Similar Datasets

Project description:Today's health care environment encourages health care consumers to take an active role in managing their health. As digital natives, young educated adults do much of their health information management through the Internet and consider it a valid source of health advice. However, the quality of information on health websites is highly variable and dynamic. Little is known about the understandings and perceptions that young educated adults have garnered on the quality of information on health websites used for health care-related purposes.To fill this gap, the aim of this study was to develop a conceptual framework of health website information quality with quality dimensions (ie, criteria) and associated quality drivers (ie, attributes) specified in the context of young educated adults' use of health websites for health care-related purposes. This aim was achieved by (1) identifying information quality dimensions of health websites from the perspective of young educated adults; (2) identifying the importance ratings of these quality dimensions; and (3) constructing a framework of health website information quality with quality dimensions and associated drivers specified in the context of young educated adults' use of health websites for health care-related purposes.The study employed both qualitative and quantitative methods. Methods included semistructured group interviews and an individual quality assessment exercise grounded in visiting various websites and responding to Likert scale questions regarding the importance ratings of information quality dimensions and open-ended questions with specifying website quality drivers. Study participants included junior and senior undergraduate and graduate students in business, allied health, and public health majors. Qualitative, open-coding procedures were used to develop the conceptual framework reflecting the participants' means of assessing information quality on health websites.Five dimensions of information quality for health websites were identified: Completeness of information, Understandability of information, Relevance of information, Depth of information, and Accuracy of information. Completeness of information and Understandability of information were rated as the two most important quality dimensions by the study participants. Results indicated that these five information quality dimensions for health websites were supported by the following main driver themes: Content, Design, Links, Consumer resources, Search functionality, Supporting references, User focus, Content FAQ, Open access, Policy statements, and Site performance.This study contributes to the literature by developing a health website information quality conceptual framework with quality dimensions and associated drivers specified for a young educated adult population. The detailed quality drivers supporting the corresponding quality dimensions provide a rich picture of young educated adults' perceptions on health website information quality. This framework can be used to guide the development of health websites, as well as the foundation for a means to evaluate health information from existing health websites with young educated adults as the target audience.

Dataset Information

A transparent and transferable framework for tracking quality information in large datasets.

Publications

A transparent and transferable framework for tracking quality information in large datasets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets