Open access links (NeuromatchAcademy#853)

* Fix a few minor link issues * Provide substitutions for open/closed access logos * make open access logo background transparent * move _static to correct place * Add open/closed access badges and provide preprint/postprint links
neurovium · Apr 11, 2022 · c53db9f · c53db9f
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diff --git a/book/_config.yml b/book/_config.yml
@@ -30,6 +30,12 @@ parse:
     - amsmath
     - dollarmath
     - html_image
+    - substitution
+sphinx:
+  config:
+    myst_substitutions:
+     open_access: "<img alt='Open Access publication' src='../static/Open_Access_logo.png' height=0.8em class='no-scaled-link inline-icon'>"
+     closed_access: "<img alt='Closed Access publication' src='../static/Closed_Access_logo.png' height=0.8em class='no-scaled-link inline-icon'>"
 
 #######################################################################################
 # HTML-specific settings

diff --git a/book/_static/custom.css b/book/_static/custom.css
@@ -0,0 +1,3 @@
+.inline-icon {
+    vertical-align: baseline;
+}
diff --git a/tutorials/W1D1_ModelTypes/further_reading.md b/tutorials/W1D1_ModelTypes/further_reading.md
@@ -1,58 +1,58 @@
 # Suggested further readings 
 
-Bassett, D. S., Zurn, P., and Gold, J. I. (2018). On the nature and use of models in network neuroscience. Nature Reviews Neuroscience.  doi: [10.1038/s41583-018-0038-8](https://doi.org/10.1038/s41583-018-0038-8).
+Bassett, D. S., Zurn, P., and Gold, J. I. (2018). On the nature and use of models in network neuroscience. Nature Reviews Neuroscience.  doi: [10.1038/s41583-018-0038-8](https://doi.org/10.1038/s41583-018-0038-8) {{ closed_access }} (postprint: [europepmc.org/articles/pmc6466618?pdf=render](https://europepmc.org/articles/pmc6466618?pdf=render) {{ open_access }}).
 
 Bennett, M. R., and Hacker, P. M. S. (2003). Philosophical Foundations of Neuroscience, Wiley-Blackwell.
 
-Blohm, G., Kording, K. P., and Schrater, P. R. (2020). A how-to-model guide for Neuroscience. Eneuro, 7(1). doi : [10.1523/ENEURO.0352-19.2019](https://doi.org/10.1523/ENEURO.0352-19.2019).
+Blohm, G., Kording, K. P., and Schrater, P. R. (2020). A how-to-model guide for Neuroscience. Eneuro, 7(1). doi: [10.1523/ENEURO.0352-19.2019](https://doi.org/10.1523/ENEURO.0352-19.2019) {{ open_access }}.
 
 Burgess, J. (1998). Occam’s razor and scientific method. In The Philosophy of Mathematics Today (pp. 195–214). Clarendon Press, Oxford. 
 
 Chandrasekhar, S. (2013). Truth and beauty. University of Chicago Press.
 
-Chater, N., and Oaksford, M. (1999). Ten years of the rational analysis of cognition. Trends in cognitive sciences, 3(2), 57-65. doi: [10.1016/S1364-6613(98)01273-X](https://doi.org/10.1016/S1364-6613(98)01273-X). 
+Chater, N., and Oaksford, M. (1999). Ten years of the rational analysis of cognition. Trends in cognitive sciences, 3(2), 57-65. doi: [10.1016/S1364-6613(98)01273-X](https://doi.org/10.1016/S1364-6613(98)01273-X) {{ closed_access }}. 
 
-Churchland, P. S., and Sejnowski, T. J. (1990). Neural representation and neural computation. Philosophical Perspectives, 4, 343-382. doi: [10.2307/2214198](https://doi.org/10.2307/2214198).
+Churchland, P. S., and Sejnowski, T. J. (1990). Neural representation and neural computation. Philosophical Perspectives, 4, 343-382. doi: [10.2307/2214198](https://doi.org/10.2307/2214198) {{ closed_access }} (preprint: [papers.cnl.salk.edu/PDFs/Neural%20Representation%20and%20Neural%20Computation%201990-3325.pdf](http://papers.cnl.salk.edu/PDFs/Neural%20Representation%20and%20Neural%20Computation%201990-3325.pdf) {{ open_access }}).
 
-Churchland, P. S., and Sejnowski, T. J. (1988). Perspectives on cognitive neuroscience. Science, 242(4879), 741-745. doi: [10.1126/science.3055294](https://doi.org/10.1126/science.3055294).
+Churchland, P. S., and Sejnowski, T. J. (1988). Perspectives on cognitive neuroscience. Science, 242(4879), 741-745. doi: [10.1126/science.3055294](https://doi.org/10.1126/science.3055294) {{ closed_access }}.
 
-Cichy, R. M., and Kaiser, D. (2019). Deep neural networks as scientific models. Trends in cognitive sciences, 23(4), 305-317. doi: [10.1016/j.tics.2019.01.009](https://doi.org/10.1016/j.tics.2019.01.009).
+Cichy, R. M., and Kaiser, D. (2019). Deep neural networks as scientific models. Trends in cognitive sciences, 23(4), 305-317. doi: [10.1016/j.tics.2019.01.009](https://doi.org/10.1016/j.tics.2019.01.009) {{ open_access }}.
 
 Dayan, P. (2005). Theoretical Neuroscience: Computational And Mathematical Modeling of Neural Systems. MIT Press.
 
-Feldman, J. (2016). The simplicity principle in perception and cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 7(5), 330-340. doi: [10.1002/wcs.1406](https://doi.org/10.1002/wcs.1406).
+Feldman, J. (2016). The simplicity principle in perception and cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 7(5), 330-340. doi: [10.1002/wcs.1406](https://doi.org/10.1002/wcs.1406) {{ closed_access }} (postprint: [europepmc.org/articles/pmc5125387?pdf=render](https://europepmc.org/articles/pmc5125387?pdf=render) {{ open_access }}).
 
 Gillett, C. (2016). Reduction and Emergence in Science and Philosophy. Cambridge University Press.
 
-Goldstein, R. E. (2018). Point of View: Are theoretical results ‘Results’?. Elife, 7, e40018. doi: [10.7554/elife.40018](https://doi.org/10.7554/elife.40018).
+Goldstein, R. E. (2018). Point of View: Are theoretical results ‘Results’?. Elife, 7, e40018. doi: [10.7554/elife.40018](https://doi.org/10.7554/elife.40018) {{ open_access }}.
 
-Jonas, E., and Kording, K. P. (2017). Could a neuroscientist understand a microprocessor?. PLoS computational biology, 13(1), e1005268. doi: [10.1371/journal.pcbi.1005268](https://doi.org/10.1371/journal.pcbi.1005268).
+Jonas, E., and Kording, K. P. (2017). Could a neuroscientist understand a microprocessor?. PLoS computational biology, 13(1), e1005268. doi: [10.1371/journal.pcbi.1005268](https://doi.org/10.1371/journal.pcbi.1005268) {{ open_access }}.
 
 Josephson, J. R., and Josephson, S. G. (Eds.). (1996). Abductive inference: Computation, philosophy, technology. Cambridge University Press.
 
-Kaplan, D. M. (2011). Explanation and description in computational neuroscience. Synthese, 183(3), 339-373. doi: [https://doi.org/10.1007/s11229-011-9970-0](10.1007/s11229-011-9970-0). 
+Kaplan, D. M. (2011). Explanation and description in computational neuroscience. Synthese, 183(3), 339-373. doi: [10.1007/s11229-011-9970-0](https://doi.org/10.1007/s11229-011-9970-0) {{ closed_access }}.
 
-Kording, K., Blohm, G., Schrater, P., and Kay, K. (2018). Appreciating diversity of goals in computational neuroscience. doi: [10.31219/osf.io/3vy69](https://doi.org/10.31219/osf.io/3vy69).
+Kording, K., Blohm, G., Schrater, P., and Kay, K. (2018). Appreciating diversity of goals in computational neuroscience. doi: [10.31219/osf.io/3vy69](https://doi.org/10.31219/osf.io/3vy69) {{ open_access }}.
 
-Lee, M. D., Criss, A. H., Devezer, B., Donkin, C., Etz, A., Leite, F. P., ..., and Vandekerckhove, J. (2019). Robust modeling in cognitive science. Computational Brain & Behavior, 2(3), 141-153. doi: [10.31234/osf.io/dmfhk](https://doi.org/10.31234/osf.io/dmfhk).
+Lee, M. D., Criss, A. H., Devezer, B., Donkin, C., Etz, A., Leite, F. P., ..., and Vandekerckhove, J. (2019). Robust modeling in cognitive science. Computational Brain & Behavior, 2(3), 141-153. doi: [10.31234/osf.io/dmfhk](https://doi.org/10.31234/osf.io/dmfhk) {{ open_access }}.
 
-Lombrozo, T. (2012). Explanation and Abductive Inference. Oxford Handbooks Online. doi: [10.1093/oxfordhb/9780199734689.013.0014](https://doi.org/10.1093/oxfordhb/9780199734689.013.0014).
+Lombrozo, T. (2012). Explanation and Abductive Inference. Oxford Handbooks Online. doi: [10.1093/oxfordhb/9780199734689.013.0014](https://doi.org/10.1093/oxfordhb/9780199734689.013.0014) {{ closed_access }}.
 
 Marr, D., and Poggio, T. (1976). From Understanding Computation to Understanding Neural  Circuitry. Artificial Intelligence Laboratory. A.I. Memo. Massachusetts Institute of Technology. AIM-357. Retrieved from [dspace.mit.edu/handle/1721.1/5782](https://dspace.mit.edu/handle/1721.1/5782).
 
-Parker, W. S. (2012). Computer simulation and philosophy of science. Metascience, Vol. 21, pp. 111–114. doi: [10.1007/s11016-011-9567-8](https://doi.org/10.1007/s11016-011-9567-8).
+Parker, W. S. (2012). Computer simulation and philosophy of science. Metascience, Vol. 21, pp. 111–114. doi: [10.1007/s11016-011-9567-8](https://doi.org/10.1007/s11016-011-9567-8) {{ closed_access }}.
 
 Pearl, J., and Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books. 
 
-Russell, B. (1917). Mysticism and logic, and other essays. doi: [10.5962/bhl.title.19230](https://doi.org/10.5962/bhl.title.19230).
+Russell, B. (1917). Mysticism and logic, and other essays. doi: [10.5962/bhl.title.19230](https://doi.org/10.5962/bhl.title.19230) {{ closed_access }} (postprint: [archive.org/download/mysticismlogicot00russiala/mysticismlogicot00russiala_bw.pdf](https://archive.org/download/mysticismlogicot00russiala/mysticismlogicot00russiala_bw.pdf) {{ open_access }}).
 
-Schrater, P., Kording, K., & Blohm, G. (2019). Modeling in Neuroscience as a Decision Process. OSF Preprints. Retrieved from https://osf.io/w56vt	 
+Schrater, P., Kording, K., & Blohm, G. (2019). Modeling in Neuroscience as a Decision Process. OSF Preprints. Retrieved from <https://osf.io/w56vt>
 
 Simon, H. A. (1969). The sciences of the artificial MIT Press. Cambridge, MA.
 
 Trappenberg, T. (2009). Fundamentals of computational neuroscience. OUP Oxford.
 
-Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. Elife, 8, e49547. doi: [10.7554/eLife.49547](https://doi.org/10.7554/eLife.49547)
+Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. Elife, 8, e49547. doi: [10.7554/eLife.49547](https://doi.org/10.7554/eLife.49547) {{ open_access }}
 
 
 

diff --git a/tutorials/W1D3_ModelFitting/further_reading.md b/tutorials/W1D3_ModelFitting/further_reading.md
@@ -2,9 +2,9 @@
 
 ### Generic tips on model fitting in neuroscience: 
 
-Palminteri, S., Wyart, V., and Koechlin, E. (2017). The importance of falsification in computational cognitive modeling. Trends in cognitive sciences, 21(6), 425-433. doi: [10.1016/j.tics.2017.03.011](https://doi.org/10.1016/j.tics.2017.03.011).
+Palminteri, S., Wyart, V., and Koechlin, E. (2017). The importance of falsification in computational cognitive modeling. Trends in cognitive sciences, 21(6), 425-433. doi: [10.1016/j.tics.2017.03.011](https://doi.org/10.1016/j.tics.2017.03.011) {{ closed_access }} (preprint: [www.biorxiv.org/content/biorxiv/early/2016/11/14/079798.full.pdf](https://www.biorxiv.org/content/biorxiv/early/2016/11/14/079798.full.pdf) {{ open_access }}).
 
-Wilson, R. C., and Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. Elife, 8, e49547. doi: [10.7554/eLife.49547](https://doi.org/10.7554/eLife.49547).
+Wilson, R. C., and Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. Elife, 8, e49547. doi: [10.7554/eLife.49547](https://doi.org/10.7554/eLife.49547) {{ open_access }}.
 
 ### On linear regression: 
 
@@ -14,7 +14,7 @@ MacKay, D. J. (2003). Information theory, inference and learning algorithms. Cam
 
 ### On model selection: 
 
-Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics surveys, 4, 40-79. doi: [10.1214/09-SS054](https://doi.org/10.1214/09-SS054).
+Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics surveys, 4, 40-79. doi: [10.1214/09-SS054](https://doi.org/10.1214/09-SS054) {{ open_access }}.
 
 MacKay, D. J. (2003). Information theory, inference and learning algorithms. Cambridge university press. Chapter 28 (Model selection and Occam’s razor) . Freely available at [www.inference.org.uk/itprnn](https://www.inference.org.uk/itprnn/book.pdf).
 
@@ -26,4 +26,4 @@ Boyd, S., Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge
 
 ### Research example developed in outro:
 
-Wei, K., & Kording, K. (2009). Relevance of error: what drives motor adaptation?. Journal of neurophysiology, 101(2), 655-664. doi: [10.1152/jn.90545.2008](https://doi.org/10.1152/jn.90545.2008).
+Wei, K., & Kording, K. (2009). Relevance of error: what drives motor adaptation?. Journal of neurophysiology, 101(2), 655-664. doi: [10.1152/jn.90545.2008](https://doi.org/10.1152/jn.90545.2008) {{ open_access }}.