Sarah Bratt
Syracuse University
23 Papers
42 Citations
Sarah Bratt is an academic researcher from Syracuse University. The author has contributed to research in topics: Computer science & Metadata. The author has an hindex of 6, co-authored 17 publications.
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Papers
Building predictive models of emotion with functional near-infrared spectroscopy
TL;DR: The capability of discriminating between affective states on the valence and arousal dimensions using functional near-infrared spectroscopy (fNIRS), a practical non-invasive device that benefits from its ability to localize activation in functional brain regions with spatial resolution superior to the Electroencephalograph (EEG).
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fNIRS: A new modality for brain activity-based biometric authentication
Abdul Serwadda,Vir V. Phoha,Sujit Poudel,Leanne M. Hirshfield,Danushka Bandara,Sarah Bratt,Mark R. Costa +6 more
- 17 Dec 2015
TL;DR: Functional Near-Infrared Spectroscopy (fNIRS) is evaluated as an alternative approach to brain activity-based user authentication and has significant promise as a biometric authentication modality.
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Big data, big metadata and quantitative study of science: A workflow model for big scientometrics
Sarah Bratt,Jeff Hemsley,Jian Qin,Mark R. Costa +3 more
- 01 Jan 2017
TL;DR: In this paper, a methodological framework for big data analytics in scientific data repositories is proposed, which contains conceptual and computational workflows intercepting through collaborative documentation, and discusses the challenges in big metadata analytics due to the messiness, lack of structures suitable for analytics and heterogeneity in such big metadata.
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Assigning credit to scientific datasets using article citation networks
TL;DR: A network flow measure, called DataRank, aimed at solving the gap between publication and dataset flow rates, and it is shown that DataRank is better at predicting this usage compared to alternatives while offering additional interpretable outcomes.
Data to knowledge in action: A longitudinal analysis of GenBank metadata
Jeff Hemsley,Jian Qin,Sarah Bratt +2 more
- 01 Oct 2020
TL;DR: Key among the findings is that data production team size is positively related to the number of patents each year, which highlights the important role of data production in the diffusion of knowledge as measured by patents.
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