references.bib

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@proceedings{49385,
title	= {Google Dataset Search by the Numbers},
editor	= {Omar Benjelloun and Shiyu Chen and Natasha Noy},
year	= {2020},
URL	= {https://arxiv.org/abs/2006.06894},
booktitle	= {International Semantic Web Conference (ISWC-2020), In-Use Track}
}


@misc{googledataset,
    title = {Google Search Central Dataset},
    url = {https://developers.google.com/search/docs/advanced/structured-data/dataset},
    howpublished = {https://developers.google.com/search/docs/advanced/structured-data/dataset}
}

@article{holdgraf_rapid_2016,
	title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility},
	volume = {7},
	issn = {2041-1723},
	url = {http://www.nature.com/doifinder/10.1038/ncomms13654},
	doi = {10.1038/ncomms13654},
	number = {May},
	journal = {Nature Communications},
	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.},
	year = {2016},
	pages = {13654},
	file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf}
}

@article{holdgraf_encoding_2017,
	title = {Encoding and decoding models in cognitive electrophysiology},
	volume = {11},
	issn = {16625137},
	doi = {10.3389/fnsys.2017.00061},
	abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.},
	journal = {Frontiers in Systems Neuroscience},
	author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.},
	year = {2017},
	keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials}
}

@book{ruby,
  title     = {The Ruby Programming Language},
  author    = {Flanagan, David and Matsumoto, Yukihiro},
  year      = {2008},
  publisher = {O'Reilly Media}
}