scistatR.bib

@article{begley2012drug,
	Author = {Begley, C Glenn and Ellis, Lee M},
	Journal = {Nature},
	Number = {7391},
	Pages = {531--533},
	Publisher = {Nature Publishing Group},
	Title = {Drug development: Raise standards for preclinical cancer research},
	Volume = {483},
	Year = {2012}}
	
@book{CowpertwaitMet,
	Author = {Cowpertwait, Paul S. P. and Metcalfe, Andrew V.},
	Date = {2009-05-28},
	Ean = {9780387886985},
	Publisher = {Springer New York},
	Timestamp = {2016-07-01},
	Title = {Introductory Time Series with R},
	Year = {2009}}

@article{edwards_roy_2017,
title={Academic Research in the 21st Century: Maintaining Scientific Integrity in a Climate of Perverse Incentives and Hypercompetition},
volume={34},
DOI={10.1089/ees.2016.0223},
number={1},
journal={Environmental Engineering Science},
author={Edwards, Marc A. and Roy, Siddhartha},
year={2017},
pages={51-61}
},

@article{green_kirby_suls_1996,
title={The effects of caffeine on blood pressure and heart rate: A review},
volume={18},
DOI={10.1007/bf02883398},
number={3},
journal={Annals of Behavioral Medicine},
author={Green, Peter J. and Kirby, Robert and Suls, Jerry},
year={1996},
pages={201-216}
},

@book{Hyndman-exp,
	Author = {Hyndman, R. J. and Koehler, A. B. and Ord, J. K. and Snyder, R. D.},
	Date = {2008, 07, 4},
	Date-Modified = {2017-08-18 01:08:58 +0000},
	Ean = {9783540719168},
	Isbn = {3540719164},
	Pagetotal = {360},
	Publisher = {Springer},
	Title = {Forecasting with Exponential Smoothing: The State Space Approach},
	Year = {2008},
	Bdsk-Url-1 = {http://www.exponentialsmoothing.net/}}

@article{lazerEtAl_GFT_2014,
	Author = {Lazer, D. and Kennedy, R. and King, G. and Vespignani, A.},
	Date-Modified = {2017-08-18 01:08:58 +0000},
	Journal = {Science},
	Number = {6176},
	Pages = {1203-1205},
	Title = {The Parable of Google Flu: Traps in Big Data Analysis},
	Volume = {343},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1126/science.1248506}}
	
	@misc{lovelave_cheshire_2014,
title={Introduction to visualising spatial data in R},
url={https://github.com/Robinlovelace/Creating-maps-in-R},
journal={GitHub},
author={Lovelave, R and Cheshire, J},
year={2014}
},

@book{daagur_2011,
place={Cambridge},
edition={3},
title={Data analysis and graphics using R},
publisher={Cambridge University Press},
author={Maindonald, John and Braun, John},
year={2011}
}

@article{prinz2011believe,
	Author = {Prinz, Florian and Schlange, Thomas and Asadullah, Khusru},
	Journal = {Nature reviews Drug discovery},
	Number = {9},
	Pages = {712--712},
	Publisher = {Nature Publishing Group},
	Title = {Believe it or not: how much can we rely on published data on potential drug targets?},
	Volume = {10},
	Year = {2011}}

@article{stark_saltelli_2018,
title={Cargo-cult statistics and scientific crisis},
volume={15},
DOI={10.1111/j.1740-9713.2018.01174.x},
number={4},
journal={Significance},
author={Stark, Philip B. and Saltelli, Andrea},
year={2018},
pages={40-43}
},

@book{taleb_2004,
place={London},
edition={2},
title={Fooled By Randomness: The Hidden Role Of Chance In Life And In The Markets.},
publisher={Penguin books},
author={Taleb, Nassim Nicholas},
year={2004}
}

@article{tukey_1997,
	Author = {Tukey, J. W.},
	Date-Modified = {2017-08-18 01:08:58 +0000},
	Journal = {Journal of Statistical Planning and Inference},
	Number = {1},
	Pages = {21-28},
	Title = {More honest foundations for data analysis},
	Volume = {57},
	Year = {1997},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/s0378-3758(96)00032-8}}

@book{Wood-2017,
	Author = {Wood, S. N.},
	Date = {2017, 05, 30},
	Isbn = {9781498728331},
	Edition = {2},	
	Pagetotal = {410},
	Publisher = {Chapman and Hall/CRC},
	Title = {Generalized Additive Models. An Introduction with R},
	Year = {2017}}

@article{Zhou2019,
	Abstract = {Does the human mind resemble the machine-learning systems that mirror its performance? Convolutional neural networks (CNNs) have achieved human-level benchmarks in classifying novel images. These advances support technologies such as autonomous vehicles and machine diagnosis; but beyond this, they serve as candidate models for human vision itself. However, unlike humans, CNNs are ``fooled''by adversarial examples---nonsense patterns that machines recognize as familiar objects, or seemingly irrelevant image perturbations that nevertheless alter the machine's classification. Such bizarre behaviors challenge the promise of these new advances; but do human and machine judgments fundamentally diverge? Here, we show that human and machine classification of adversarial images are robustly related: In 8 experiments on 5 prominent and diverse adversarial imagesets, human subjects correctly anticipated the machine's preferred label over relevant foils---even for images described as ``totally unrecognizable to human eyes''. Human intuition may be a surprisingly reliable guide to machine (mis)classification---with consequences for minds and machines alike.},
	Author = {Zhou, Zhenglong and Firestone, Chaz},
	Da = {2019/03/22},
	Date-Added = {2019-06-22 09:30:40 +0000},
	Date-Modified = {2019-06-22 09:30:40 +0000},
	Doi = {10.1038/s41467-019-08931-6},
	Id = {Zhou2019},
	Isbn = {2041-1723},
	Journal = {Nature Communications},
	Number = {1},
	Pages = {1334},
	Title = {Humans can decipher adversarial images},
	Ty = {JOUR},
	Url = {https://doi.org/10.1038/s41467-019-08931-6},
	Volume = {10},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.1038/s41467-019-08931-6}}