jkitchin

@article{kitchin-2016-autom-data,
  abstract =	 "In the current scientific publishing landscape, there is a
                  need for an authoring workflow that easily integrates data and
                  code into manuscripts and that enables the data and code to be
                  published in reusable form. Automated embedding of data and
                  code into published output will enable superior communication
                  and data archiving. In this work, we demonstrate a proof of
                  concept for a workflow, org-mode, which successfully provides
                  this authoring capability and workflow integration. We
                  illustrate this concept in a series of examples for potential

jkitchin

@article{kitchin-2016-autom-data,
  abstract =	 "In the current scientific publishing landscape, there is a
                  need for an authoring workflow that easily integrates data and
                  code into manuscripts and that enables the data and code to be
                  published in reusable form. Automated embedding of data and
                  code into published output will enable superior communication
                  and data archiving. In this work, we demonstrate a proof of
                  concept for a workflow, org-mode, which successfully provides
                  this authoring capability and workflow integration. We
                  illustrate this concept in a series of examples for potential

jkitchin

@article{kitchin-2015-examp,
  author =	 {Kitchin, John R.},
  title =	 {Examples of Effective Data Sharing in Scientific Publishing},
  journal =	 {ACS Catalysis},
  volume =	 {5},
  number =	 {6},
  pages =	 {3894-3899},
  year =	 2015,
  doi =		 {10.1021/acscatal.5b00538},
  url =		 { http://dx.doi.org/10.1021/acscatal.5b00538 },

jkitchin

@article{kitchin-2015-examp,
  author =	 {Kitchin, John R.},
  title =	 {Examples of Effective Data Sharing in Scientific Publishing},
  journal =	 {ACS Catalysis},
  volume =	 {5},
  number =	 {6},
  pages =	 {3894-3899},
  year =	 2015,
  doi =		 {10.1021/acscatal.5b00538},
  url =		 { http://dx.doi.org/10.1021/acscatal.5b00538 },

jkitchin

@article{deshpande-2016-quant-uncer,
  author =	 {Siddharth Deshpande and John R. Kitchin and Venkatasubramanian
                  Viswanathan },
  title =	 {Quantifying Uncertainty in Activity Volcano Relationships for
                  Oxygen Reduction Reaction},
  tweeted = 	 {Thu Aug 25 13:15:11 2016},
  journal =	 {ACS Catalysis},
  volume =	 0,
  number =	 {ja},
  pages =	 {null},

jkitchin

@article{deshpande-2016-quant-uncer,
  author =	 {Siddharth Deshpande and John R. Kitchin and Venkatasubramanian
                  Viswanathan },
  title =	 {Quantifying Uncertainty in Activity Volcano Relationships for
                  Oxygen Reduction Reaction},
  tweeted = 	 {Thu Aug 25 13:21:16 2016},
  journal =	 {ACS Catalysis},
  volume =	 6,
  number =	 {8},
  pages =	 {5251-5259},

jkitchin

@article{kitchin-2016-high-throug,
  author =	 {Kitchin, John R. and Gellman, Andrew J.},
  title =	 {High-Throughput Methods Using Composition and Structure Spread
                  Libraries},
  tweeted = 	 {Thu Aug 25 14:50:07 2016},
  journal =	 {AIChE Journal},
  pages =	 {n/a--n/a},
  year =	 2016,
  doi =		 {10.1002/aic.15294},
  url =		 {http://dx.doi.org/10.1002/aic.15294},

jkitchin

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 60, 500)
plt.figure(figsize=(4, 2))
plt.plot(np.exp(-0.1 * x) * np.cos(x),
         np.exp(-0.1 * x) * np.sin(x))

jkitchin

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 60, 500)
plt.figure(figsize=(4, 2))
plt.plot(np.exp(-0.1 * x) * np.cos(x),
         np.exp(-0.1 * x) * np.sin(x))

jkitchin

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 60, 500)
plt.figure(figsize=(4, 2))
plt.plot(np.exp(-0.1 * x) * np.cos(x),
         np.exp(-0.1 * x) * np.sin(x))