Difference between revisions of "Template:Article of the week"

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(Updated article of the week text.)
(Updated article of the week text)
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Udesky EnviroHealth2019 18.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Fairchild FrontPubHealth2018 6.jpg|240px]]</div>
'''"[[Journal:Wrangling environmental exposure data: Guidance for getting the best information from your laboratory measurements|Wrangling environmental exposure data: Guidance for getting the best information from your laboratory measurements]]"'''
'''"[[Journal:Epidemiological data challenges: Planning for a more robust future through data standards|Epidemiological data challenges: Planning for a more robust future through data standards]]"'''


[[Environmental health]] and exposure researchers can improve the quality and interpretation of their chemical measurement data, avoid spurious results, and improve analytical protocols for new chemicals by closely examining lab and field [[quality control]] (QC) data. Reporting QC data along with chemical measurements in biological and environmental [[Sample (material)|samples]] allows readers to evaluate data quality and appropriate uses of the data (e.g., for comparison to other exposure studies, association with health outcomes, use in regulatory decision-making). However many studies do not adequately describe or interpret QC assessments in publications, leaving readers uncertain about the level of confidence in the reported data. One potential barrier to both QC implementation and reporting is that guidance on how to integrate and interpret QC assessments is often fragmented and difficult to find, with no centralized repository or summary. In addition, existing documents are typically written for regulatory scientists rather than environmental health researchers, who may have little or no experience in analytical chemistry. ('''[[Journal:Wrangling environmental exposure data: Guidance for getting the best information from your laboratory measurements|Full article...]]''')<br />
Accessible [[Epidemiology|epidemiological]] data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: (1) interfaces, (2) data formatting, and (3) reporting. ('''[[Journal:Epidemiological data challenges: Planning for a more robust future through data standards|Full article...]]''')<br />
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Revision as of 00:02, 17 June 2021

Fig3 Fairchild FrontPubHealth2018 6.jpg

"Epidemiological data challenges: Planning for a more robust future through data standards"

Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: (1) interfaces, (2) data formatting, and (3) reporting. (Full article...)

Recently featured:

▪ Wrangling environmental exposure data: Guidance for getting the best information from your laboratory measurements
▪ One tool to find them all: A case of data integration and querying in a distributed LIMS platform
▪ What is the "source" of open-source hardware?
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