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The Giles Ecosystem – Storage, Text Extraction, and OCR of Documents

Description
In the digital humanities, there is a constant need to turn images and PDF files into plain text to apply analyses such as topic modelling, named entity recognition, and other techniques. However, although there exist different solutions to extract text embedded in PDF files or run OCR on images, they…

In the digital humanities, there is a constant need to turn images and PDF files into plain text to apply analyses such as topic modelling, named entity recognition, and other techniques. However, although there exist different solutions to extract text embedded in PDF files or run OCR on images, they typically require additional training (for example, scholars have to learn how to use the command line) or are difficult to automate without programming skills. The Giles Ecosystem is a distributed system based on Apache Kafka that allows users to upload documents for text and image extraction. The system components are implemented using Java and the Spring Framework and are available under an Open Source license on GitHub (https://github.com/diging/).
ContributorsLessios-Damerow, Julia (Contributor) / Peirson, Erick (Contributor) / Laubichler, Manfred (Contributor) / ASU-SFI Center for Biosocial Complex Systems (Contributor)
Created2017-09-28
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Differential Increase in Prevalence Estimates of Inadequate Sleep Among Black and White Americans

Description

Background: The National Health Interview Survey (NHIS) was used to ascertain whether increases in inadequate sleep differentially affected black and white Americans. We tested the hypothesis that prevalence estimates of inadequate sleep were consistently greater among blacks, and that temporal changes have affected these two strata differentially.

Methods: NHIS is an ongoing cross-sectional…

Background: The National Health Interview Survey (NHIS) was used to ascertain whether increases in inadequate sleep differentially affected black and white Americans. We tested the hypothesis that prevalence estimates of inadequate sleep were consistently greater among blacks, and that temporal changes have affected these two strata differentially.

Methods: NHIS is an ongoing cross-sectional study of non-institutionalized US adults (≥18 years) providing socio-demographic, health risk, and medical factors. Sleep duration was coded as very short sleep [VSS] (<5 h), short sleep [SS] (5–6 h), or long sleep [LS] (>8 h), referenced to 7–8 h sleepers. Analyses adjusted for NHIS’ complex sampling design using SAS-callable SUDAAN.

Results: Among whites, the prevalence of VSS increased by 53 % (1.5 % to 2.3 %) from 1977 to 2009 and the prevalence of SS increased by 32 % (19.3 % to 25.4 %); prevalence of LS decreased by 30 % (11.2 % to 7.8 %). Among blacks, the prevalence of VSS increased by 21 % (3.3 % to 4.0 %) and the prevalence of SS increased by 37 % (24.6 % to 33.7 %); prevalence of LS decreased by 42 % (16.1 % to 9.4 %). Adjusted multinomial regression analysis showed that odds of reporting inadequate sleep for whites were: VSS (OR = 1.40, 95 % CI = 1.13-1.74, p < 0.001), SS (OR = 1.34, 95 % CI = 1.25-1.44, p < 0.001), and LS (OR = 0.94, 95 % CI = 0.85-1.05, NS). For blacks, estimates were: VSS (OR = 0.83, 95 % CI = 0.60-1.40, NS), SS (OR = 1.21, 95 % CI = 1.05-1.50, p < 0.001), and LS (OR = 0.84, 95 % CI = 0.64-1.08, NS).

Conclusions: Blacks and whites are characteristically different regarding the prevalence of inadequate sleep over the years. Temporal changes in estimates of inadequate sleep seem dependent upon individuals’ race/ethnicity.
ContributorsGirardin, Jean-Louis (Author) / Grandner, Michael A. (Author) / Youngstedt, Shawn (Author) / Williams, Natasha J. (Author) / Zizi, Ferdinand (Author) / Sarpong, Daniel F. (Author) / Ogedegbe, Gbenga G. (Author) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor)
Created2015-11-26