<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bramer, Lisa M</style></author><author><style face="normal" font="default" size="100%">Dixon, Holly M</style></author><author><style face="normal" font="default" size="100%">Degnan, David J</style></author><author><style face="normal" font="default" size="100%">Rohlman, Diana</style></author><author><style face="normal" font="default" size="100%">Herbstman, Julie B</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Waters, Katrina M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration.</style></title><secondary-title><style face="normal" font="default" size="100%">Pac Symp Biocomput</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Pac Symp Biocomput</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Silicones</style></keyword><keyword><style  face="normal" font="default" size="100%">Wearable Electronic Devices</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">170-186</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kevin A Hobbie</style></author><author><style face="normal" font="default" size="100%">Elena S Peterson</style></author><author><style face="normal" font="default" size="100%">Michael L Barton</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integration of data systems and technology improves research and collaboration for a superfund research center.</style></title><secondary-title><style face="normal" font="default" size="100%">J Lab Autom</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Lab Autom</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biostatistics</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemistry Techniques, Analytical</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Cooperative Behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Health</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Integrated Advanced Information Management Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Oregon</style></keyword><keyword><style  face="normal" font="default" size="100%">Polycyclic Hydrocarbons, Aromatic</style></keyword><keyword><style  face="normal" font="default" size="100%">Universities</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2012</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">275-83</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large collaborative centers are a common model for accomplishing integrated environmental health research. These centers often include various types of scientific domains (e.g., chemistry, biology, bioinformatics) that are integrated to solve some of the nation&#039;s key economic or public health concerns. The Superfund Research Center (SRP) at Oregon State University (OSU) is one such center established in 2008 to study the emerging health risks of polycyclic aromatic hydrocarbons while using new technologies both in the field and laboratory. With outside collaboration at remote institutions, success for the center as a whole depends on the ability to effectively integrate data across all research projects and support cores. Therefore, the OSU SRP center developed a system that integrates environmental monitoring data with analytical chemistry data and downstream bioinformatics and statistics to enable complete &quot;source-to-outcome&quot; data modeling and information management. This article describes the development of this integrated information management system that includes commercial software for operational laboratory management and sample management in addition to open-source custom-built software for bioinformatics and experimental data management.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">&lt;p&gt;http://www.ncbi.nlm.nih.gov/pubmed/22651935?dopt=Abstract&lt;/p&gt;
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