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Last active December 24, 2015 20:48
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PLoS Time to Publication
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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"PLoS Time to Publication"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In [an earlier IPython Notebook](http://nbviewer.ipython.org/6211587) I took a look at the time to publication in PLoS ONE.\n",
"\n",
"Here I'll take a comparative look at the time to publication in PLoS ONE, Biology, Computational Biology, and Genetics.\n",
"\n",
"[Drop me a line](https://twitter.com/mbgrw) for any feedback."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import gzip\n",
"import cPickle as pickle\n",
"from datetime import date\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_one = pickle.load(gzip.open('plos_one.gzip', 'rb'))\n",
"data_biology = pickle.load(gzip.open('plos_biology.gzip', 'rb'))\n",
"data_comp_biology = pickle.load(gzip.open('plos_comp_biology.gzip', 'rb'))\n",
"data_genetics = pickle.load(gzip.open('plos_genetics.gzip', 'rb'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Overview of Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data we just loaded are dictionaries whose keys are randomly assigned, unique integer identifiers for each editor\n",
"and whose values are the list of articles that these editors have handled."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'number of PLoS ONE editors in data set', len(data_one.keys())\n",
"print 'number of PLoS Biology editors', len(data_biology.keys())\n",
"print 'number of PLoS Computational Biology editors', len(data_comp_biology.keys())\n",
"print 'number of PLoS Genetics editors', len(data_genetics.keys())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"number of PLoS ONE editors in data set 4885\n",
"number of PLoS Biology editors 675\n",
"number of PLoS Computational Biology editors 570\n",
"number of PLoS Genetics editors 626\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'number of PLoS ONE articles in data set', len([1 for ed_i in data_one.keys() for article in data_one[ed_i]])\n",
"print 'number of PLoS Biology articles', len([1 for ed_i in data_biology.keys() for article in data_biology[ed_i]])\n",
"print 'number of PLoS Comp. Biology articles', len([1 for ed_i in data_comp_biology.keys() for article in data_comp_biology[ed_i]])\n",
"print 'number of PLoS Genetics articles', len([1 for ed_i in data_genetics.keys() for article in data_genetics[ed_i]])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"number of PLoS ONE articles in data set 63753\n",
"number of PLoS Biology articles 1754\n",
"number of PLoS Comp. Biology articles 2490\n",
"number of PLoS Genetics articles 3371\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'earliest PLoS ONE publication', min([date(*article['publication_date']) for ed_i in data_one.keys() for article in data_one[ed_i]])\n",
"print 'most recent PLoS ONE publication', max([date(*article['publication_date']) for ed_i in data_one.keys() for article in data_one[ed_i]])\n",
"print ''\n",
"print 'earliest PLoS Biology publication', min([date(*article['publication_date']) for ed_i in data_biology.keys() for article in data_biology[ed_i]])\n",
"print 'most recent PLoS Biology publication', max([date(*article['publication_date']) for ed_i in data_biology.keys() for article in data_biology[ed_i]])\n",
"print ''\n",
"print 'earliest PLoS Comp Biology publication', min([date(*article['publication_date']) for ed_i in data_comp_biology.keys() for article in data_comp_biology[ed_i]])\n",
"print 'most recent PLoS Comp Biology publication', max([date(*article['publication_date']) for ed_i in data_comp_biology.keys() for article in data_comp_biology[ed_i]])\n",
"print ''\n",
"print 'earliest PLoS Genetics publication', min([date(*article['publication_date']) for ed_i in data_genetics.keys() for article in data_genetics[ed_i]])\n",
"print 'most recent PLoS Genetics publication', max([date(*article['publication_date']) for ed_i in data_genetics.keys() for article in data_genetics[ed_i]])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"earliest PLoS ONE publication "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2006-12-01\n",
"most recent PLoS ONE publication 2013-07-31\n",
"\n",
"earliest PLoS Biology publication 2003-08-18\n",
"most recent PLoS Biology publication 2013-10-01\n",
"\n",
"earliest PLoS Comp Biology publication 2005-06-24\n",
"most recent PLoS Comp Biology publication "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2013-09-26\n",
"\n",
"earliest PLoS Genetics publication 2005-06-17\n",
"most recent PLoS Genetics publication 2013-09-26\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For each journal, let's throw all publications into one vat and take a look at the empirical distribution of\n",
"the amount of time elapsed between submission and publication."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# one mislabeled article in PLoS ONE, http://nbviewer.ipython.org/6211587\n",
"# exclude that one mislabeled article whose 'time to publication' is less than zero\n",
"duration_one = [(date(*article['publication_date'])-date(*article['received_date'])).days for ed_i in data_one.keys() for article in data_one[ed_i] if (date(*article['publication_date'])-date(*article['received_date'])).days >= 0]\n",
"duration_biology = [(date(*article['publication_date'])-date(*article['received_date'])).days for ed_i in data_biology.keys() for article in data_biology[ed_i]]\n",
"duration_comp_biology = [(date(*article['publication_date'])-date(*article['received_date'])).days for ed_i in data_comp_biology.keys() for article in data_comp_biology[ed_i]]\n",
"duration_genetics = [(date(*article['publication_date'])-date(*article['received_date'])).days for ed_i in data_genetics.keys() for article in data_genetics[ed_i]]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Distribution of Time to Publication"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n, bins, patches = hist([duration for duration in duration_one], normed=True, bins=range(600))\n",
"title_one = title('PLoS ONE Time to Publication')\n",
"_ = xlabel('time to publication / days')\n",
"_ = ylabel('frequency')\n",
"show()\n",
"\n",
"n, bins, patches = hist([duration for duration in duration_biology], normed=True, bins=range(600))\n",
"title_biology = title('PLoS Biology Time to Publication')\n",
"_ = xlabel('time to publication / days')\n",
"_ = ylabel('frequency')\n",
"show()\n",
"\n",
"n, bins, patches = hist([duration for duration in duration_comp_biology], normed=True, bins=range(600))\n",
"title_biology = title('PLoS Comp. Biology Time to Publication')\n",
"_ = xlabel('time to publication / days')\n",
"_ = ylabel('frequency')\n",
"show()\n",
"\n",
"n, bins, patches = hist([duration for duration in duration_genetics], normed=True, bins=range(600))\n",
"title_biology = title('PLoS Genetics Time to Publication')\n",
"_ = xlabel('time to publication / days')\n",
"_ = ylabel('frequency')\n",
"show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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fH2RkZKCwsNAiTVFRkbQIZWxsLBoaGqDX6x3uu3DhQrz44otyZZ2IiNpBtrGp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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x4bb9110>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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lgl6vl6b1er3FXoetMnV7LPbmA0BYWBgmTJgAAIiPj4e/vz/OnDnjtD9ERORZ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\nwYBCRESy+P/CSY7x4Dj1JQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x4f8bbd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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aGly4cAFvvvmmTQcjIiLHZTGgbN++XXru5OSEzp07w83Nza6VIvvS/dZfv7kW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\nhYiIZPF/C+/ugQqBpQ0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x6163210>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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9nj44TERErnN4YaNGo8HevXsBANXV1Vi+fDmuv/56xRtGrpFrJpT9cnxg734k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\n",
"text": [
"<matplotlib.figure.Figure at 0x494ae10>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"_ = boxplot([duration_one,\n",
" duration_biology,\n",
" duration_comp_biology,\n",
" duration_genetics])\n",
"xticks([1, 2, 3, 4], ['PLoS ONE',\n",
" 'Biology',\n",
" 'Comp. Biology',\n",
" 'Genetics'])\n",
"_ = ylabel('time to publication / days')\n",
"show()\n",
"\n",
"_ = boxplot([duration_one,\n",
" duration_biology,\n",
" duration_comp_biology,\n",
" duration_genetics])\n",
"xticks([1, 2, 3, 4], ['PLoS ONE',\n",
" 'Biology',\n",
" 'Comp. Biology',\n",
" 'Genetics'])\n",
"ylim([0, 600])\n",
"_ = ylabel('time to publication / days')\n",
"show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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puYZLZAsAGC6PEAkg+/7yFwBSG9yenMeuxo6drGIip2a4pCO1HGfQt5wlB3R9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\nkUlMEEREZBITBBERmcQEQUREJv0/zW3/6ecehS0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x3eddd50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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7d+7Exx9/jL59+8LZ2Rk7duzAp59+atSbFxYWYvz48VAqlRg2bBjeffddAEB5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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x716dcd0>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Editorial Influence in Time to Publication"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One interesting observation we made previously is that the time to publication appears to depend somewhat on the handling editor.\n",
"\n",
"Let's see if we observe similar trends in the other PLoS journals."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lists = []\n",
"for a_dict_i, a_dict in enumerate([data_one, data_biology, data_comp_biology, data_genetics]):\n",
" a_list = []\n",
" for key in a_dict.keys():\n",
" _ = []\n",
" for article in a_dict[key]:\n",
" _.append((date(*article['publication_date'])-date(*article['received_date'])).days)\n",
" a_list.append(_)\n",
" lists.append(a_list)\n",
" \n",
"data_one_lists = sorted(lists[0], key=np.median)\n",
"data_biology_lists = sorted(lists[1], key=np.median)\n",
"data_comp_biology_lists = sorted(lists[2], key=np.median)\n",
"data_genetics_lists = sorted(lists[3], key=np.median)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'PLoS ONE data okay', len(data_one_lists) == len(data_one) \n",
"print 'PLoS Biology data okay', len(data_biology_lists) == len(data_biology)\n",
"print 'PLoS Comp. Biology data okay', len(data_comp_biology_lists) == len(data_comp_biology)\n",
"print 'PLoS Genetics okay', len(data_genetics_lists) == len(data_genetics)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"PLoS ONE data okay True\n",
"PLoS Biology data okay True\n",
"PLoS Comp. Biology data okay True\n",
"PLoS Genetics okay True\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"_ = scatter(range(len(data_one_lists)), [np.median(a_list) for a_list in data_one_lists])\n",
"xlabel('editor')\n",
"ylabel('median time to publication / days')\n",
"xlim(-100, len(data_one_lists)+100)\n",
"ylim(0, 600)\n",
"_ = title('PLoS ONE Median Time to Publication')\n",
"show()\n",
"\n",
"_ = scatter(range(len(data_biology_lists)), [np.median(a_list) for a_list in data_biology_lists])\n",
"xlabel('editor')\n",
"ylabel('median time to publication / days')\n",
"xlim(-10, len(data_biology_lists)+10)\n",
"ylim(0, 600)\n",
"_ = title('PLoS Biology Median Time to Publication')\n",
"show()\n",
"\n",
"_ = scatter(range(len(data_comp_biology_lists)), [np.median(a_list) for a_list in data_comp_biology_lists])\n",
"xlabel('editor')\n",
"ylabel('median time to publication / days')\n",
"xlim(-10, len(data_comp_biology_lists)+10)\n",
"ylim(0, 600)\n",
"_ = title('PLoS Comp. Biology Median Time to Publication')\n",
"show()\n",
"\n",
"_ = scatter(range(len(data_genetics_lists)), [np.median(a_list) for a_list in data_genetics_lists])\n",
"xlabel('editor')\n",
"ylabel('median time to publication / days')\n",
"xlim(-10, len(data_genetics_lists)+10)\n",
"ylim(0, 600)\n",
"_ = title('PLoS Genetics Median Time to Publication')\n",
"show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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pT6gJIYSoGvHx8UBdIB24DFwB9Dg4OJhVjkxVKYQQtdRzzz3Hv/+9A3gSOI2h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\ncCBgveehwlQtl52drby9vZVOp1O5ubm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"text": [
"<matplotlib.figure.Figure at 0x7346bd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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CsWPHcHV1pXv37rz33nv079/f4t/8YaNRytMzrqgU/v33Xxo1akRKSopZ+WzDhg1ZsGAB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\nPwBEEmxRSAH4NgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x5994ed0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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8cInVOYfK1WvIaDRy7tw58+tz586VavGDvLw82rRpw4ABAwBITEykV69e+Pn5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\nvQpGr+rR+8mDJeMvWK2j97SxxTI3jip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FICKS4FQEIiIJTkUgIpLgVAQiIglORSAikuBU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\ngYhIglMRiIgkuP8ByWQXiW8wFK0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x55f9c10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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VQ35+fhw/fpyuXbty5MgRCgsL6du3L/v27av0xStCqoaEEA1Rnz5D2LdvGPAX\ncBKw5eGHPdi16+syHV+ppSodHR0BYyPx8ePHSUlJKXHErxBCiKqXk5NDdPRhwANYBfwJTEKrtaqS\n81tsI3jmmWdIT09nwYIFDBw4kPz8/FJHqAkhhKhaf/75J/n5VsCrGLPtQmAmTzzxUZWcX5aqFEKI\nOqywsBBv73ZcuVIALMJYIgArq32cOxdDq1atSj1eVamqoddff53r16+bXl+/fp0333yzTBcWQghR\nOfv37yc5OQvwAb4CHgfycHdvxl133VUl17AYCMLDw3FxcTG9dnFxYefOnVVycSGEEKXLzs5Gq7UD\nxmIcURwFWPPgg31uOyFoRVhsI8jLy6OgoMA03UN+fj45OTlVcnEhhBCla9WqFQZDCjAHGAPkYmUV\nxQcfHK2ya1gMBKNHj+bBBx9k0qRJKIrC6tWrGTNmTJUlQAghRMlefXUuWu14Cgtjga/RaAxMn/4c\nvr6+VXYNi4Fg7ty5BAQE8NNPP6HRaHjttdd49NFHqywBQgghSnb8eCyFhZ9jnGQOFOX/kZQUWaXX\nsBgIAEaMGMGIESOq9MJCCCFKp9frSU29BqwBegL52NltpkePwVV6nRK7j/bp04c//viDxo0b39Ig\nodFoii0/WZOk+6gQoqGYNGkKq1dvAloBWUAGWm0OGRlJNGrUqFznqvTi9XWJBAIhREOgKAr29m7k\n5/cBvsU47bQtWm1PEhNP0rx583Kdr0Kzj6akpJR6Ujc3t3IlQgghRNn98ssv5OcDRAIXAD9gBzY2\nGjw9Pav0WiUGgm7dupXaRzUuLq5KEyKEEKLIjh07gPbAaaAr4A7oWLnyv1hbl6l5t8ykakgIIeoY\nRVFwcmpOVpY6rcQR4AAuLudITb1UoYFklVqYRlEUNmzYwO+//45Wq6VPnz6MHj263IkQQghRNs88\n8w+ysrKBhcBrQBrgzahRj1XZaGJzFksEkyZNIikpidDQUBRFYfPmzTRv3rzWViiTEoEQ4k6WlJRE\nixatgY6AF7AMiAdGsWfPtzz44IMVOm+lSgT79u3j1KlTpig0adIk7r777golRAghROmWLl0K2GCc\narojMBiwxdo6n+Dg4Gq5psVJ5zp27FhsIZrExEQJBEIIUQ2OHz/Ohx8uBe4CUoCfMY4ovsTHH/+r\nWqqFoJSqoWHDhgGQnp5OVFQUvXr1QqPREBUVRc+ePfn111+rJUGWSNWQEOJO1bSpDzrdVYwrkXUG\nzgNX6dRXR9yZAAAgAElEQVTpLk6cqNwkcxWqGnrllVdKPaEQQoiqM3fufHS6S0BTYAHG6aadgGje\neOO1ar22dB8VQohapigKtrYO6PW2FNXYBwMncHPL58qVs5UeO1CpKSbM5xrKz8+noKCAxo0by1xD\nQghRRQoKCrCzs0dRgoATGKuGdEAmGRlXady4caWvUaleQ5mZmabfDQYD27dvZ9++fZVOlBBCCCMb\nGxvuued+Dh06DjwI5AKJzJs3u0qCgCUVqhoKDAzk0KFD1ZEei6REIIS4k8TExDBo0DAuXbqMsVoo\nH43GgdmzX2LBggVVdp1KlQi+/vpr0+8Gg4Ho6OgyXTQ3N5e+ffui1+vJyspi6NChLF68mJSUFEJD\nQ7ly5QrNmzdn48aNuLq6ArBw4ULWrl2LlZUVixYt4uGHHy7TtYQQoj7au3cv/fo9jDEAuAN7AB8U\nZSKHDsXWWDoslgiefvppUxuBVqvFx8eHqVOnlmkK1JycHBwcHNDr9QQFBbFw4UK2bt1K27ZtmTFj\nBkuWLCEuLo6lS5cSHR3N1KlTiYyM5PLlywQFBREbG4utrW3xBEuJQAhxBygsLMTRsSl5edmAAeOa\nxLNvvHsWJ6d+pKdfqrLrVapEsHr16gpf2MHBATA2MhcWFtK0aVN27NhBVFQUAOPHj6d3794sXbqU\n7du3M3r0aKysrPD29sbPz4+oqCiCgoIqfH0hhKiL0tLSCAzsQV5eAcYgYAAOAwqgAY7g6tqkxtJj\nMRDExsby4osv8ueffwJw//33s2zZMjp06GDx5AaDgW7dunHu3DmmTZuGn58fOp0Od3d3ADw8PEhO\nTgbg4sWL9O/f33Ssj49PsRHN5ubNm2f6PTg4uNqGXQshRFVLTk7Gx6cDBQUGIB9wwBgIdmHsMuqD\nVruNtWt/qNR1IiIiiIiIKNO+FgPBqFGjmDVrFj/8YEzUli1bGDVqFIcPH7Z4cq1Wy+HDh7l+/TqD\nBg3il19+KVOiLDEPBEIIUZ/06vUABQUAtoA/cAnIAa4DfzBsWAj//nd0mR62S3PzQ/L8+fNL3Nfi\nXEM2NjaMHTsWGxsbbGxsGDNmTLkHNri4uDB06FD279+Pp6cnV69eBUCn09G0aVPAWAJISEgwHZOY\nmEjLli3LdR0hhKir4uPj8fRszoULFzFOKpeLcVbRloA3YGDLlo1s27at0kGgvCwGgoceeoiPPvqI\n+Ph44uPj+fe//82AAQNISUkpdTnLa9eukZGRARgbjX/88Ue6dOlCSEgIYWFhAISFhRESEgJASEgI\nGzduRK/Xk5iYSExMDL169aqKexRCiFqTl5fHJ598QuvWd3P1aibginFmUWuMVUL5wDmCgvrxxBNP\n1EoaLfYa8vX1LXFuIY1Gw/nz52/73rFjx3jqqadQFIXc3FzGjh3LnDlzinUf9fLyYtOmTabuo++/\n/z5hYWFotVoWLVrEoEGDbntN6TUkhKjLFEUhLi6O4cOf4PjxUxifue1u/GQDAcAhwArQM2XKU3zx\nxefVmqZKTTFR10ggEELUVXq9nmee+Qdr1mzA2PsHjEFAgzEIFGLsGWQA2gCnePPNV3nvvaobOFYS\nCQRCCFED7rmnN0eOHMWY8esxPvFbY5w76BrGBmIroDFwiREjQti6dUuNpK20vNNiG4EQQgjLXnrp\nJY4cOYCxETgXYwBwvPFvCtAMY7VQNpDEf/7zUY0FAUsqN6+pEEI0cJmZmQQFPciRIycoqgKyuvF7\nDkWlgxRAi6urC2fOHMLDw6PW0nyzMpUINm7cyPTp05k+fTqbN2+u7jQJIUSddvr0aUaOHImdnRNO\nTm4cOXIEY2bvS9FI4XyMbQIFgMLAgd34449dpKT8XaeCAJShjWDGjBkcP36cMWPGoCgKmzZtolOn\nTixZsqSm0liMtBEIIWrLb7/9xqOPjiIt7TrGJ30txsZfG4xP/z4YSwPXgEyaNXNj8eKPCQ0NRaut\n3Zr4SjUWd+7cmZiYGNNNGAwG/Pz8OHnyZNWntAwkEAghapqiKPTt258//tiPMaNXq36sMQYCbrxu\nDtwNHMfFJY3k5LhbJs6sLZVuLDZfjay2ViYTQoja0rt3X/744wDGjN8OY+8fayALY0mgAGOp4BSw\nHS+vTOLjj9eZIGCJxcbi1157DX9/fwYMGICiKOzZs4d33nmnJtImhBC1bs6c+URFRWPM/B0oyvjV\nrqEajL2EFKARb7/9Yr3LI8s0juDvv/8mMjISjUZD7969a3UOIKkaEkLUhD/++IO3336bX375A2MA\nsMbY9dMKYyOwAWNwyMPR0YG33nqd1157DSsrq9pLdCkq1Ubw0EMP8fPPP1vcVlMkEAghqtP27dsZ\nPnwUBoMeY+25AWiPcTH5HNSeQHZ2dqxc+QVjx44tcRqeuqRCC9Pk5OSQnZ2NTqcrNrlcVlYWFy5c\nqPpUCiFELUpPT2fZsmW8/fa7FI0K1mOcFygOuAdjMPibESMeYevWjbWX2CpWYiBYvnw5S5cu5dKl\nS3Tv3t203cHBgWnTptVI4oQQojrl5OQQGxvLhAkTiYk5hTFLVPvQFGBsGNYDk4FNgIKDg4avv95Q\nOwmuJharhpYtW8aLL75YU+mxSKqGhBCV8ddffxEaOoHz5y9grOaBovEA1hgHgjXGWCWkxzhNhA3G\ndQOOsmvXNzz88MM1n/BKkknnhBANkqIoHDt2jIiICPbu3cs334RTWJiPMWOHokFhthgDQeaN3w03\n3lNHBtvj49OUnTu/w9/fv+ZvpApUavF6IYSoLy5dusRzzz3Hjz/upaCgAGNmrtz4UXvzNKLo6V8d\nFJaOMfNvB2RgDA7JaLUGjhw5hJ+fX71oEK4oKREIIeq9lJQUBg4cyMGDx29sUbt4am/8qEHB+saP\nLcYqIMON/dSnfxuM7QIGHBwMxMYevWOWzK3UyGK9Xs+KFSuYO3cuYFxLOCoqqmpTKIQQZZSZmcmK\nFSu4555uNG7cBI3GFnd3bw4ePIUxI7fHmLFrgDyMff+VG9sdMGb4WRjr/wsxBg11org8HnywC+vX\nf0pKStIdEwQssVgimDx5Mvb29uzZs4dTp06Rnp5OcHAwBw8erKk0FiMlAiEalvz8fGbMmMGqVRvI\ny8vGmMEbKHqOVUf4qvX+ORgzef2NbVqM1UEZFLUFqO8V0KSJIw8++AAzZsygb9++NXVbNa5SbQT7\n9+/n+PHjBAYGAuDs7IzBYKjaFAohxE0MBgPh4eEMHTqSomUfrSha+tH8XxuMGb26j9oeoA4Iy6Fo\nsfg8PD092bZtK717966Re6nrLAYCa2trCgsLTa9TU1PR6/XVmighRMOSlZVFWFgYn322nFOnzpCf\nX0jx9X3VXjxqPb/69K9m+Jk3tqnrABiffDUacHJqREBAZwIDA7n33nt59NFHady4cY3dW31gMRBM\nnz6dRx99lOTkZObMmcOmTZt44403aiJtQog7WHx8PDNmzCA8/Efy8tQGWzBm4mqmb0fRhG7WGOv4\nNRgbfwswZvpOGOv/1XmAbHjvvTd48803a/J26rUy9Ro6cuQIP/74IwADBw6ka9eu1Z6wkkgbgRD1\n14EDB3jrrbfYs2cfer06g6daxaPO7a9W4YDxSb8RxkzfgPGpv5CiKSAU1MFgVlZaXnjhWRYvXlyT\nt1RvVHpAmU6nIzExEYPBYOpL261bt6pNZRlJIBCi/sjOzmbChKf54Yed5Ofn39iqZuBqhq926VTr\n+3MpGtXbAuNqXwWojbxWVnb4+jZnxIjHmDhxYr0d4FXTKtVYPGvWLMLCwmjXrl2xpdZ++eWXqkuh\nEOKOYDAYOHr0KOvWrWPp0v/eGNSltjGaz+YJxozfgaJePGqffnuKqoPUGT8NeHq25Ny5GJycnGru\nhhoIiyWCtm3bcvLkyTqz0o6UCISofampqSxbtoxt27aRlJSETpd1o6oHijJ8tfeO2tCbR9FKXh4Y\ne/mYT+ymfq/VeX/ycXNzpkePHsyaNYv+/fvX1O3dkSpVIrjnnntIT0/Hw8OjyhMmhKg/9u/fz6hR\nE/j77wSKGnRVarWOGgDsb2xXM/hcjJO3KRif+q9jLA2ovXwKcHCwZ/Lkp5kyZQr+/v539JQOdY3F\nEsGBAwd49NFH8ff3x87OzniQRsO2bdtqJIE3kxKBENUnLy+PyMhIjh07Rk5ODhEREURF/UVKShoG\ng/lEBAaKFnCHojp+dXBXPsYMX/1dHQCmTuVQCNhib+/I7NkvMnv2G5LxV7NKlQieeuopXn/9dfz9\n/U1tBPIHE6J+UxSFhIQEFi1axObNW0hKSqWoCgeKeuNA0ZKM6vvGJ3jj++q8PeZdPfMxlgjUXkHq\nhG96bGw0BAcH8dFHH9G1a1fJS+oIiyWC3r17ExkZWaGTJyQkMG7cOFJTU8nPz+eZZ57h//7v/0hJ\nSSE0NJQrV67QvHlzNm7ciKurKwALFy5k7dq1WFlZsWjRolvm/ZYSgRDll5mZyZdffslXX33F4cMn\nyc3No3hmb55hq4O41EFb6vZ8jBm9Om0zFDX+qt05tRRvHNbj6NiYF154nnfffbfOrufbEFSq++jM\nmTNxcHDgkUceMVUNQdm6j165cgWdToe/vz+ZmZl069aNzZs3s2LFCtq2bcuMGTNYsmQJcXFxLF26\nlOjoaKZOnUpkZCSXL18mKCiI2NjYYg3VEgiEKF1sbCy7du0iOjqaX375lYsXr2AwqFUy6vKL2hv/\nQlH9vlqto2boapBQME7Spnb3NP/+qVVEuRgDhw329ja0a+fNY489xuTJk2ndunV13q4oo0pVDR08\neBCNRsO+ffuKbS9L99FmzZrRrFkzABo3bkxAQAAXL15kx44dphlMx48fT+/evVm6dCnbt29n9OjR\nWFlZ4e3tjZ+fH1FRUQQFBVm8lhANjU6n4//+7//47rvvSU3NoegJ/+YndLUuX63Ht6FoUjb1ad+W\nosFaamav1vOrc/k0xpjh64E82rdvxauvvsrYsWOxsrLC3t5eqnrqKYuBICIiokouFB8fz4EDB1i1\nahU6nQ53d3cAPDw8SE5OBuDixYvFuoj5+PiQmJh4y7nmzZtn+j04OJjg4OAqSaMQdZXBYGDDhg18\n9dVX7N8fzdWraTfeMZ98DYp321RH6tqYvZ+PsV++grHXjhVFg7XyKJrZE7PzqW0DOmxtbXnllZd4\n9913i40rEnVPREREmfPvEgPB2rVrmTBhAosWLSoW5RVFQaPRMHPmzDInKDMzk5EjR7J06VKcnZ3L\nfFxJzAOBEPWZXq8nPj6e3bt3ExERwb59kVy9mkJBgQFF0aMoamZuPhALjBmzWrVTaLZNrdpRM391\nARbzXjzq1968VGDsDuroaEOrVq1p06YNrq6uuLq68sgjjzBgwACp369nbn5Inj9/fon7lhgIsrOz\nAcjIyKhUca+goIAnnniCcePG8dhjjwHg6enJ1atX8fDwQKfT0bRpU8BYAkhISDAdm5iY2GAWhhB3\nPr1ez4IFC1i5ciUXL6ZS1Kfe/GkeijJm80Gc6sAsG7NthRQtuqL23VfPoQ7gyqUo8zfc2GYLWGFl\npeDv34G3336bESNGyBN+A2axsfj333+/pY7+dttuR1EUJk6ciLu7e7GJoF544QVTY/HixYuJi4tj\n2bJlpsbiP//809RYfObMGWxsiv7zS2OxqGsMBgOKoqAoCjt37mTlypXEx8eTl5dHUlIy16/nUdRA\nq2by6u+Gm/5Vu2Sq1T3m9fxqjxwDxgCgTsdcYHYOtdeP+eItGiCXRo0ceeaZiTz99NP4+/vXmdkC\nRM2oVK+hwMBADh06ZHHb7fz+++/069ePgIAAU6li4cKF9OrVy9R91MvLi02bNpm6j77//vuEhYWh\n1WpZtGgRgwYNKvPNCFFdCgsL+fnnn9mzZw8RERGcOHGWzMwMFKW0p+ib++Sr1TVqdQ8Uza+TS9G6\numrVjvni6rk39jdfa1etLioqWVhZWePoaE+nTu3p0aMH3bp1Y8SIETRp0qQqPgZRj1UoEPz555/s\n27ePxYsXM3PmTNMJsrOz+eqrrzh58mT1pbgUEghEdcnNzSU6OprTp0+zfv16jhw5QlpaOsZJM80z\nXXWu/Jv/H6p19lqz/dVqGbXRVu2+aaBoauVCiqZbMD+nDcYAoC7Ers7YmY8xIBTStGkTPvpoIRMn\nTqyyz0HcmSrUfTQ/P5+MjAwKCwvJyMgwbXdwcGDr1q1Vn0ohqoler6ewsJBTp06xaNEioqOjycvL\no6CggKtXU8nOVjNmlfam39UfNQiojabqv+oTuZ6ihll1pK2qMUWTrKmjdNVj1T795l9Std+/cZ4e\nV1cnfHx8CAkJYfTo0bRp0wYXF5cKfyZCmLNYNRQfH4+vr28NJccyKRGIkuTm5vLf//6Xjz9eyt9/\nJ2EwqPXxUDSYSq2uMZ9OwfzpXl39yrzOXq23V7tkqnX2eRQto6in6LlKPcY8wJgHk6KeOnZ2tnh6\nNsHPz4977rmHfv36MWDAAKm/F1Wu0gvT1CUSCBqOzMxM/vjjDxISEtDr9Rw/fpzIyEguXPib7Oxs\nCgv15OdrMJge5s0zd7VqRs381emP1cZTNZMvvsqVMWCoT/VqLx11Lh3zIKHOpa/Omw9FPXturuIp\nWmvXzs6GkJD+fPXVWuzt7RGiplRqZLEQ1SEtLY1PP/2Ub7/9litXrpj+g6alZZCZqaeo4dScea8b\ndSCV+Xvqk7vagwaMT+1qF021m6Vab28+V34O5rNiGqt11F46xZ/iiwKH2oBbgIuLIw4ODri5uTFw\n4ECmTJmCg4MD1tbWNG/eXPrgizpNSgSiRpw5c4YVK1bcmNY4hlsHR5ln8OajWxWzf9XGUrUrpflr\n9Tjzunp1oRQrwBljxq1eS22kNW/YNb+W1uzffDQaK1q2bEa/fv3o06cP/fv3p0OHDlXx0QhRIypV\nIkhKSmL58uUkJCRguFEG12g0rFq1qmpTKeql3NxcfvvtN2JiYtDpdOzevZuzZ8+Tm5uLwWBAr1d7\nvJhX2ZhPcqaheF29+kSvBoNCjBm6Olum2mfeGuNTu1oVoz7VmzfkqudRbrxnzNSLqF8KY/2+Vqvg\n4eFEp0534+fnx6RJk+jRo0fVfFBC1GEWA0FISAgPP/wwgwYNkvUIGrjDhw8zb948fvvtd1JT1cnH\n1OoZNbM3H9lq3girPuGrGbFajaM+0atVNup5sikaPKXFOGpWHTylnjfL7Hpq3/t8irpqGgNBo0Y2\nODk54uzsTGBgIC+//LLpad7Ozg5HR8cq+HSEqL8qNKCsNknVUPVSFIXdu3ezZs0aDh06RFxcErm5\nuWZ7qJm9eVUNFJ/+QH2atzbbT62uMa+nh1unNDZ/ijfv9WNe9WNltg2MT/saHBwc8PT05PHHH+Od\nd96RRc6FMFOpqqGhQ4cSHh7O4MGDqzxhovbpdDr++c9/snPnLjIz8yjKZPXcOrOlmlEXUDTR2c0z\nXarVPTYUNdSqP+pTujrF8c3UxlsADRqNLc7OdtxzT2c8PDxo3rw5/fr144EHHsDT01NKpkJUEYsl\ngsaNG5OdnY2tra1pzh+NRkN6enqNJPBmUiKoOEVRWLJkCXPnvk9GRhbFpylQM3J1WgPzp301w1Wn\nQlAHTDmanUNdwcq8EbgRRb1xzEsRtkAuHh5NmDdvDhMnTsTKysqUsdvZ2UkmL0QVk3EEDdSVK1dY\ntmwZW7Z8zfnzl9Dr1SdztSFVHfCk1rOb19fbmL1XQFGPG3UtWvMBVzdPombe+GvsCtqoUSO6dg1g\n9OjR+Pv707NnT6m6EaIGVXocgU6n48yZM+j1etO2fv36VU3qRKUYDAYiIiLYvXs3Bw4c4OjRY1y9\nmkXRSFfzKhx1EFSh2Xb1Cd6OoqobdVCV+p9GHYilTmOs7qNFo9Hi4tKIe+7pTNu2bfHz82PMmDF4\neXlV520LIaqQxRLBsmXL+OKLL7h06RKBgYFERkZy3333sWfPnppKYzENvUSgKApJSUls3ryZefPe\nJy1NraIzr6Ixr9ox70FjRVFVjTrSVp33Rh01y02v1akONFhZaejVK4A1a9bQtm1bqb4Roh6pVNVQ\nhw4dOHLkCPfddx+HDx/mzJkzzJo1q9YmnmsIgUCv1/Pll1+yfv16jh8/hU6XgaIUmu/BraNsVeaD\nrNQqHvORsLYYgwEUn9/eFrVxt1kzD4KDg5kwYQKtW7emY8eOMjJWiHquUlVDzs7OODg4UFhYSH5+\nPu3bt6+1KajvdIWFhQQHD+D33yNvbLl5eUK1bt98JSq1AVdtkG10Y7v9je2ZFM2Yqa5Pm2vaZm/v\nQIcOrfjoo49uWftBCNEwWAwELVq0ID09nUceeYSHHnqIJk2ayPKRlZSZmUlUVBQ6nY6CggK2bdvG\n7t27b6xkpXbZVJ/W7cxeq0/2YMzQsylal1ZtD8i/sU+O2b75qIuiaLVaeva8n+++20KzZs1q4naF\nEHVcuXoN7d69m9zcXAYPHlxr0+TWl6o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"text": [
"<matplotlib.figure.Figure at 0x55dad10>"
]
}
],
"prompt_number": 11
}
],
"metadata": {}
}
]
}
@Carreau
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Carreau commented Jul 28, 2014

I curious if you still have the data available (or even the code to re-download) to see if there is some structure in the time-to-publication propability distribution. eg, I'm wondering if the "noise" have a 7-day periodicity that could be due to week-ends.

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