<?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%">Lisa M Bramer</style></author><author><style face="normal" font="default" size="100%">Holly Dixon</style></author><author><style face="normal" font="default" size="100%">David J Degnan</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data to Accompany: Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2023</style></date></pub-dates></dates><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%">Lisa M Bramer</style></author><author><style face="normal" font="default" size="100%">Holly Dixon</style></author><author><style face="normal" font="default" size="100%">David J Degnan</style></author><author><style face="normal" font="default" size="100%">Diana Rohlman</style></author><author><style face="normal" font="default" size="100%">Julie Herbstman</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</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%">bioRxiv</style></secondary-title><alt-title><style face="normal" font="default" size="100%">bioRxiv</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Oct 02</style></date></pub-dates></dates><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%">Gosline, Sara J C</style></author><author><style face="normal" font="default" size="100%">Kim, Doo Nam</style></author><author><style face="normal" font="default" size="100%">Pande, Paritosh</style></author><author><style face="normal" font="default" size="100%">Thomas, Dennis G</style></author><author><style face="normal" font="default" size="100%">Truong, Lisa</style></author><author><style face="normal" font="default" size="100%">Peter D Hoffman</style></author><author><style face="normal" font="default" size="100%">Michael L Barton</style></author><author><style face="normal" font="default" size="100%">Loftus, Joseph</style></author><author><style face="normal" font="default" size="100%">Moran, Addy</style></author><author><style face="normal" font="default" size="100%">Hampton, Shawn</style></author><author><style face="normal" font="default" size="100%">Dowson, Scott</style></author><author><style face="normal" font="default" size="100%">Franklin, Lyndsey</style></author><author><style face="normal" font="default" size="100%">David J Degnan</style></author><author><style face="normal" font="default" size="100%">Anderson, Lindsey</style></author><author><style face="normal" font="default" size="100%">Thessen, Anne</style></author><author><style face="normal" font="default" size="100%">Robyn L Tanguay</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Superfund Research Program Analytics Portal: linking environmental chemical exposure to biological phenotypes.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Data</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Data</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Exposure</style></keyword><keyword><style  face="normal" font="default" size="100%">Hazardous Substances</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Northwestern United States</style></keyword><keyword><style  face="normal" font="default" size="100%">Polycyclic Aromatic Hydrocarbons</style></keyword><keyword><style  face="normal" font="default" size="100%">Zebrafish</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Mar 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">151</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The OSU/PNNL Superfund Research Program (SRP) represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons (PAHs) at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental toxicity model that provides quantitative measurements of the exposure to chemicals. Toward this end, we have linked over 150 PAHs found at Superfund sites to the effect of these same chemicals in zebrafish, creating a rich dataset that links environmental exposure to biological response. To quantify this response, we have implemented a dose-response modelling pipeline to calculate benchmark dose parameters which enable potency comparison across over 500 chemicals and 12 of the phenotypes measured in zebrafish. We provide a rich dataset for download and analysis as well as a web portal that provides public access to this dataset via an interactive web site designed to support exploration and re-use of these data by the scientific community at http://srp.pnnl.gov .&lt;/p&gt;
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