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PLOS ONE time to publication analysis
{
"metadata": {
"name": "plos_one_analysis"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": "I recemtly picked up working with PLOS's API and downloaded over 63,000 articles from PLOS ONE which required about 6 hours, pagination of my requests to the API, and around 2.5 GB of hard disk space.\n\nHere is some analysis I have been doing with this data -- just some fun in the summer while on vacation.\n\nAs you will notice, I am by no means experienced with data analysis and I am grateful for any advice and criticism you may wish to offer.\n\nThis notebook can be found [on GitHub's Gist](https://gist.github.com/waltherg/6211587) where you can leave comments at the bottom of the page.\nOr shoot me a tweet [on Twitter](https://twitter.com/mbgrw).\n\nIf you are interested in the data file I use in this notebook just get in touch."
},
{
"cell_type": "code",
"collapsed": false,
"input": "import sys\nimport cPickle as pickle\nimport gzip\nfrom datetime import date\nimport numpy as np\nfrom matplotlib import pylab as pl\nimport matplotlib",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": "home = !(echo $HOME)\nhome = home[0]\nplos_dat = home+'/private/plos_one/editors_plos_one_dates.dat'",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": "dat_file = gzip.open(plos_dat, 'rb')\ndat = pickle.load(dat_file)\ndat_file.close()",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": "durations = []\n\ndurations_annual = {}\nreview_time_annual = {}\npublication_time_annual = {}\neditors_annual = {}\n\nreceived = []\npublished = []\n\nfor key in dat.keys():\n dur = []\n for pub in dat[key]:\n dt = date(*pub['publication_date'])-date(*pub['received_date'])\n dur.append(dt.days)\n \n if date(*pub['received_date']).year in durations_annual.keys():\n durations_annual[date(*pub['received_date']).year].append(dt.days)\n else:\n durations_annual[date(*pub['received_date']).year] = [dt.days]\n \n dt = date(*pub['accepted_date'])-date(*pub['received_date'])\n if date(*pub['received_date']).year in review_time_annual.keys():\n review_time_annual[date(*pub['received_date']).year].append(dt.days)\n else:\n review_time_annual[date(*pub['received_date']).year] = [dt.days]\n \n dt = date(*pub['publication_date'])-date(*pub['accepted_date'])\n if date(*pub['accepted_date']).year in publication_time_annual.keys():\n publication_time_annual[date(*pub['accepted_date']).year].append(dt.days)\n else:\n publication_time_annual[date(*pub['accepted_date']).year] = [dt.days]\n \n if date(*pub['received_date']).year in editors_annual.keys():\n editors_annual[date(*pub['received_date']).year].append(key)\n else:\n editors_annual[date(*pub['received_date']).year] = [key]\n \n received.append(date(*pub['received_date']))\n published.append(date(*pub['publication_date']))\n \n durations.append(dur)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Let's get a few details about the data we have."
},
{
"cell_type": "code",
"collapsed": false,
"input": "earliest_submission = min(received)\nlatest_submission = max(received)\nearliest_publication = min(published)\nlatest_publication = max(published)\nno_submissions = len(received)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": "print 'number of submissios',no_submissions\nprint 'earliest submission',earliest_submission\nprint 'latest submission',latest_submission\nprint 'earliest publication',earliest_publication\nprint 'latest publication',latest_publication",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "number of submissios 63753\nearliest submission 2006-08-01\nlatest submission 2013-08-02\nearliest publication 2006-12-01\nlatest publication 2013-07-31\n"
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Our data covers 63,753 publications in PLOS ONE -- with the earliest publication dating back to 2006 and the most recent publication from 2013."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Let us take a closer look at the earliest and most recent submissions and publications."
},
{
"cell_type": "code",
"collapsed": false,
"input": "for key in dat.keys():\n for pub in dat[key]:\n \n if date(*pub['publication_date']) == earliest_publication:\n print 'earliest publication',pub['id']\n \n if date(*pub['received_date']) == earliest_submission:\n print 'earliest submission',pub['id']\n \n if date(*pub['publication_date']) == latest_publication:\n print 'latest publication',pub['id']\n \n if date(*pub['received_date']) == latest_submission:\n print 'latest submission',pub['id']",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "earliest submission 10.1371/journal.pone.0000003\nlatest publication 10.1371/journal.pone.0070002\nlatest publication 10.1371/journal.pone.0069696\nlatest publication 10.1371/journal.pone.0069531\nearliest publication 10.1371/journal.pone.0000044\nlatest publication 10.1371/journal.pone.0070022\nlatest publication 10.1371/journal.pone.0069692\nlatest publication"
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 10.1371/journal.pone.0066849\nlatest publication 10.1371/journal.pone.0069684\nlatest publication 10.1371/journal.pone.0069848\nlatest publication 10.1371/journal.pone.0069517\nlatest publication 10.1371/journal.pone.0069574\nlatest publication"
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 10.1371/journal.pone.0070039\nlatest submission 10.1371/journal.pone.0052595\n"
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The earliest submission in our dataset carries a doi ending in **3** and not **1** and the most recent submission in our dataset with the doi **10.1371/journal.pone.0052595** seems to have mislabeled meta data:\n\nThe submission date for this article is August 2, 2013 and its publication date is given as January 28, 2013.\n\nThis seems to be the only oddity in our dataset."
},
{
"cell_type": "code",
"collapsed": false,
"input": "no_annual_publ = {}\nno_annual_recv = {}\n\nfor pub in published:\n if pub.year in no_annual_publ.keys():\n no_annual_publ[pub.year]+=1\n else:\n no_annual_publ[pub.year] = 1\n \nfor pub in received:\n if pub.year in no_annual_recv.keys():\n no_annual_recv[pub.year]+=1\n else:\n no_annual_recv[pub.year] = 1",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": "print 'annual received',no_annual_recv\nprint 'annual published',no_annual_publ",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "annual received {2006: 265, 2007: 1943, 2008: 3532, 2009: 5019, 2010: 9590, 2011: 16966, 2012: 21140, 2013: 5298}\nannual published {2006: 137, 2007: 1170, 2008: 2757, 2009: 4631, 2010: 6498, 2011: 13864, 2012: 17287, 2013: 17409}\n"
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The annual numbers of publications in our dataset follow closely those stated on [Wikipedia](http://en.wikipedia.org/wiki/PLOS_ONE) -- with the exception of the year 2012.\n\nGraphically, this data looks as follows:"
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig, ax = pl.subplots()\nax.plot(no_annual_recv.keys(), no_annual_recv.values(),'o',color='blue')\nax.plot(no_annual_publ.keys(), no_annual_publ.values(),'o',color='red')\nax.ticklabel_format(useOffset=False)\nax.set_xlim(2005,2014)\nax.set_ylim(-500,22000)\nax.set_xlabel('year')\nax.set_ylabel('no. of articles')\npl.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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plqm5uVk0NjaKm2++WdTV1QkhhPjTn/4kVq1apWqms2fPiltvvVXU1tYKIYR4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\nQ0REimDBEBGRIv4fi1TKc0wt1wQAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": "There seems to be a widening gap between the number of articles published (red disks) and received (blue disks) annually. Since all articles in our dataset were published eventually this trend may point to an increase in review time over the years."
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig, ax = pl.subplots()\nda_median = [np.median(durations_annual[key]) for key in durations_annual.keys()]\nax.plot(durations_annual.keys(), da_median, 'o', color='blue')\nax.ticklabel_format(useOffset=False)\nax.set_xlim(2005,2014)\nax.set_xlabel('year')\nax.set_ylabel('median total time to publication / days')\npl.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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QlhaD+PgoR8ciIrIbs0XjyJEj2LZtG/z8/ODu7g6AS26BtoKxfPmXqKxcJ7VV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\nAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": "In the above plot we show the median number of days between submission and publication sorted by the year of submission -- i.e. an article submitted in 2006 and published in 2007 is included in the data point for 2006."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "There seems to be a clear increase in the number of days that authors have to wait before their articles are published. The median for 2013 is relatively low since any article submitted in 2013 and published in the same year would lie at the low end of the distribution of wait times -- those articles that will raise the median time to publication are likely to get published later this year or even next year."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The **total time to publication** we plotted above includes the entire time span between submission and publication of articles. Let us break this down into the time spans between submission to acceptance and acceptance to publication of the articles in our dataset:\n\n* By **review time** we mean the amount of time between submission and acceptance, and\n* by **publication time** we mean the amount of time between acceptance and publication."
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig, ax = pl.subplots()\nrta_median = [np.median(review_time_annual[key]) for key in review_time_annual.keys()]\npta_median = [np.median(publication_time_annual[key]) for key in publication_time_annual.keys()]\nax.plot(publication_time_annual.keys(), pta_median, 'o', color='red', markersize=10)\nax.plot(review_time_annual.keys(), rta_median, 'o', color='blue')\nax.ticklabel_format(useOffset=False)\nax.set_xlim(2005,2014)\nax.set_ylim(20, 120)\nax.set_xlabel('year')\nax.set_ylabel('median time / days')\npl.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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fvn3izJkzt100zJVJp9OJ3NxcIYQQDQ0NYtSoUeKLL75QNJMQQtTV1Rm+njhx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\nSUVORK5CYII=\n"
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": "rta_median",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 13,
"text": "[47.0, 80.0, 83.0, 90.0, 105.0, 110.0, 108.0, 76.0]"
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": "While the median publication time (red disks) is relatively stable, the median review time shows a steady increase -- the **median review time more than doubles between 2006 and 2012** (ignoring 2013 for the same reason as stated above).\n\nEven if one ignored 2006 as a special year for PLOS ONE then the **median review time increased by roughly one month between 2007 and 2012**."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "One can speculate what causes this steady increase in review time. The one possible factor we can look at with our dataset is the ratio of editors to received (and published) articles."
},
{
"cell_type": "code",
"collapsed": false,
"input": "ea = [len(set(editors_annual[key])) for key in editors_annual.keys()]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": "ea",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 15,
"text": "[153, 532, 637, 807, 1517, 2295, 3381, 2087]"
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": "These figures are the numbers of editors that worked on PLOS ONE submissions received in the corresponding year: An article received in 2006 and published in 2007 is included in the editor count for 2006.\n\nFrom these figures we can see that **in 2006 153 editors** worked for PLOS ONE while this figure was **3,381 in 2012**."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Let us now see how the ratio of received (and published) articles to editors scaled over time."
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig, ax = pl.subplots()\ned_recv_ratio = [float(recv)/float(ed) for recv, ed in zip(no_annual_recv.values(), ea)]\nax.plot(editors_annual.keys(), ed_recv_ratio, 'o', color='red')\nax.ticklabel_format(useOffset=False)\nax.set_xlim(2005,2014)\nax.set_xlabel('year')\nax.set_ylabel('no. received articles / editors')\npl.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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eeuopm/PYkqm4uBitWrXC8uXL4e/vj549e+LYsWOIiYlxWKazZ8/Cx8cHR48e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\ngg==\n"
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": "ed_recv_ratio",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 17,
"text": "[1.7320261437908497,\n 3.6522556390977443,\n 5.544740973312402,\n 6.219330855018588,\n 6.321687541199736,\n 7.392592592592592,\n 6.252587991718427,\n 2.5385721130809773]"
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The **ratio of submitted articles to editors** handling those submissions increased from around **1.73 in 2006** to around **6.25 in 2012** with a peak of **7.39 in 2011**. These values would mean that on average one editor handled just over 6 manuscripts per year.\n\nThis increase in the average number of submissions handled by editors may contribute to the observed increase in review time and total time to publication.\n\nMean values are sometimes biased by extreme values in the dataset and we should probably look at the median number of submissions handled by each editor sorted by year."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Since there appears to be some editorial impact, let us look at the spread of the **total time to publication** across the editors in our dataset."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Our *durations* variable holds the total time to publication for all editors in days. If an editor appears on multiple published papers then *durations* contains a list of total times for that editor.\n\nHere is a sample of what this looks like:"
},
{
"cell_type": "code",
"collapsed": false,
"input": "print durations[0]\nprint durations[2]",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[127, 150, 95, 120, 144, 62, 84]\n[140]\n"
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": "**Editor 0** appears on **7 published articles** while **editor 2** appears on **1 published article** -- this one article took a total of 140 days to be published (from the date of submission to the date of publication)."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Let us analyze the median total time per editor. To this end we plot a histogram of the median total time (per editor)."
},
{
"cell_type": "code",
"collapsed": false,
"input": "n, bins, patches = pl.hist([np.median(ed) for ed in durations], normed=True, bins=20)\npl.xlabel('median total time to publication / days')\npl.ylabel('frequency')\npl.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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55pv49re/jT/84Q8BM74rV67gypUrAICvv/4aVqsVkiQFzPiio6MRGRmJTz/9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\n+rXk3QAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": "print sorted([(count, tt) for count, tt in zip(n,bins)], key=lambda x: x[0], reverse=True)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[(0.011719646020428425, 125.2), (0.0082885654420793084, 151.75), (0.0073324890562022575, 98.650000000000006), (0.0037626231960322851, 178.30000000000001), (0.0025443968333824854, 72.099999999999994), (0.0014957969262915218, 204.84999999999999), (0.00076331905001474597, 45.549999999999997), (0.00070934699597329821, 231.40000000000001), (0.00038551467172461982, 257.95000000000005), (0.00022359850960027903, 284.5), (0.00013107498838637046, 311.05000000000001), (0.00010794410808289338, 19.0), (7.71029343449238e-05, 337.60000000000002), (6.1682347475939173e-05, 364.15000000000003), (1.5420586868984759e-05, 470.35000000000002), (1.5420586868984759e-05, 496.90000000000003), (7.7102934344923966e-06, 523.45000000000005), (7.7102934344923796e-06, 390.69999999999999), (7.7102934344923796e-06, 417.25), (7.7102934344923796e-06, 443.80000000000001)]\n"
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": "We observe a peak in the frequency of the median total times at around **125 days**. This most likely median is slightly higher than the median [reported for a subset of our dataset](http://metarabbit.wordpress.com/2013/06/03/how-long-does-plos-one-take-to-accept-a-paper/)."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Let us visualize the spread of the median total times in a scatter plot:"
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.scatter(range(len(durations)), [np.median(ed) for ed in durations])\npl.xlabel('editor')\npl.ylabel('median total time to publication / days')\npl.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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ZgOoggadPH2Dx4iWYMGF8jskwe/Y4tG3bFbGxveHgcB358m1Hnz4HbdqHu7sL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\nD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NNr3QFmjHaBVWopuRrF0HiC5aTWw1kJZ+TXPRUs12YEU7qh/PiRXJbn4cy7hPL+C4OH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\nEuP/AxBkKDKf+clBAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": "It is surprising to see that **for some editors** the **median total time to publication** is greater than **300 days**.\n\nAre these editors that handled one, yet *tough*, submission?"
},
{
"cell_type": "code",
"collapsed": false,
"input": "print [len(ed) for ed in durations if np.median(ed) >= 300]\nprint 'number of editors with median >= 300 and one submission', len([ed for ed in durations if np.median(ed) >= 300 and len(ed)==1])\nprint 'number of editors with median >= 300 and more than one submission', len([ed for ed in durations if np.median(ed) >= 300 and len(ed)>1])",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[1, 2, 1, 1, 5, 3, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 4, 1, 1, 1, 3, 5, 1, 1, 1, 1, 4, 1, 3, 1, 3, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 4, 1, 3, 2, 5, 1, 3, 1, 8, 1, 3]\nnumber of editors with median >= 300 and one submission "
},
{
"output_type": "stream",
"stream": "stdout",
"text": "32\nnumber of editors with median >= 300 and more than one submission "
},
{
"output_type": "stream",
"stream": "stdout",
"text": "21\n"
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": "As the above shows, **32 editors** whose handled submissions had a **median total time to publication above 300 days** have only handled one single submission -- however **21 editors** had handled more than one submission.\n\nOne editor has even handled eight submissions, all of which had consistently long total times to publication:"
},
{
"cell_type": "code",
"collapsed": false,
"input": "print [ed for ed in durations if np.median(ed) >= 300 and len(ed) == 8]",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[[325, 265, 580, 272, 427, 158, 369, 309]]\n"
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": "If we classify editors by how many manuscripts they have handled from submission to publication, then the above figures seem to indicate that outliers (long median total times) occur across multiple classes.\n\nLet us see if general patterns in total time to publication repeat themselves across difference classes.\n\nTo this end we put all observed total times to publication into classes of experience -- we classify our dataset by how many submissions have been handled by the corresponding editors."
},
{
"cell_type": "code",
"collapsed": false,
"input": "classes = {}\nfor ed in durations:\n a_class = len(ed)\n if a_class in classes.keys():\n classes[a_class].append(ed)\n else:\n classes[a_class] = [ed]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": "print 'least number of submissions handled to publication', min(classes.keys())\nprint 'greatest number of submissions handled to publication', max(classes.keys())",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "least number of submissions handled to publication 1\ngreatest number of submissions handled to publication 441\n"
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": "print len(classes[441])",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "1\n"
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Our dataset seems to include one editor that has handled 441 manuscripts from submission to publication.\n\nIs it possible that these are in fact multiple editors with the same name? **A quick check of our data confirms that this particular editor and other editors that have handled a large number of manuscripts appear to be unique names.**"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Let us now visualize the spread of total times to publication for each class in a box plot."
},
{
"cell_type": "code",
"collapsed": false,
"input": "bp_data = []\nfor cl in classes.keys():\n dummy = []\n for ed in classes[cl]:\n for val in ed:\n dummy.append(val)\n bp_data.append(dummy)\n \nfig, ax = pl.subplots()\nax.boxplot(bp_data, positions=classes.keys())\nax.set_xlabel('number of submissions handled')\nax.set_ylabel('total time to publication / days')\nax.set_xticks(range(1,max(classes.keys())+50, 50))\npl.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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VKnTv3h1lZWUWVzBt2jTk5uaitLQUgYGBePXVV5GamoqpU6dizZo16N69u35eSmxsLCZN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\nJARBEIRNkJAQBEEQNkFCQhAEQdjE/weWCaYHF/17OAAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Apart from the aforementioned negative total time (DOI: 10.1371/journal.pone.0052595) that is due to mislabeled meta data, we observe one extreme outlier near 2000 days.\n\nApart from these outliers most total times appear to fall into a range between 0 and 1,000 days. Let us zoom into this time frame:"
},
{
"cell_type": "code",
"collapsed": false,
"input": "bp_data = []\nfor cl in classes.keys():\n dummy = []\n for ed in classes[cl]:\n for val in ed:\n dummy.append(val)\n bp_data.append(dummy)\n \nfig, ax = pl.subplots()\nax.boxplot(bp_data, positions=classes.keys())\nax.set_xlabel('number of submissions handled')\nax.set_ylabel('total time to publication / days')\nax.set_xticks(range(1,max(classes.keys())+50, 50))\nax.set_ylim(0, 1200)\npl.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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YCsjs2bNx7tw57Nq1C1arFenp6V6z9IYbmYbk22/JbHXyJK3AqqpyTz8uI9NL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\nTzMITyLh7T21jFHQobc2hCBTREMFhAkN/D00Pxo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h+Pzzzw3b2rNnD66++mpkZWXh8ssv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\nIoQQwhUpIEIIIVyRAiKEEMKVP1kpAxb5wYxxAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 28
},
{
"cell_type": "markdown",
"metadata": {},
"source": "This boxplot suggests that there is no clear correlation between the number of articles handled and the total time to publication for individual articles.\n\nPut another way, the **experience of editors does not appear to alter the total time to publication of their handled submissions**: less experienced and more experienced editors seem to be largerly indistinguishable from one another.\n\nLet us look at the spread of total times between editors in the same class."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "As a first step, let us look at the one editor that has handled 441 submissions to date."
},
{
"cell_type": "code",
"collapsed": false,
"input": "ed_id = None\ned_durations = None\nfor ed_i, ed in enumerate(durations):\n if len(ed) == 441:\n ed_id = ed_i\n ed_durations = ed\n\nprint ed_id",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "977\n"
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The one editor that has handled 441 submissions is **editor 977** in our dataset."
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.scatter(range(len(durations[ed_id])), durations[ed_id])\npl.xlabel('submission number handled by editor 977')\npl.ylabel('total time to publication / days')\nprint 'median total time for editor 977:',np.median(durations[977])",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "median total time for editor 977: 102.0\n"
},
{
"output_type": "display_data",
"png": 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MGWzfvh0XLlzA/v37MWbMGKSlpRXybQmCIAjFJc9E4e3tDUtLS9SpUwcbN27E9u3b0bp1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MggIqLnn3+eTp06RURE27Zt4/8P1NXV8X7b+kBEtGzZMl5vWVkZv6F2zZo1tGLFCqex3r9/P6Wk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\nD4WOdHqp+31pAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 30
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The median total time to publication for editor 977 is 102 days over the 441 submission they have handled."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "What is the overall pattern in the median total time to publication across all editors irrespective of the number of submissions handled?"
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.scatter(range(len(durations)), sorted([np.median(ed) for ed in durations]))\npl.xlabel('editor')\npl.ylabel('median total time to publication / days')\npl.xlim(-100,len(durations)+100)\npl.ylim(0, 600)\npl.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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bHdTpMlb7CLZu3eqSPTFdRfoIhBCNzahRo9i0adNF/sUL84Jy1aOETAwfPpzN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\nCOHh/h/esBBRaz9mlAAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Here we plot the median total time to publication for each editor in our dataset, sorted by that median value.\n\nThere is still a multitude of potentially important factors in this data, such as the editor's experience and the scientific field their assigned submissions fall into.\n\nLet us take a look at the potential effects due to the editor's experience. Since at least one class of experience (441 handled submissions) contains just one editor, we will restrict the following plots to just those classes that contain at least 50 editors and fewer than 200 editors -- otherwise we would just have too many curves to look at."
},
{
"cell_type": "code",
"collapsed": false,
"input": "for cl in classes.keys():\n dummy = []\n for ed in durations:\n if len(ed) == cl:\n dummy.append(ed)\n if len(dummy) > 50 and len(dummy) < 200:\n pl.scatter(range(len(dummy)), sorted([np.median(ed) for ed in dummy]))\n print 'class',cl\n \npl.xlabel('editor')\npl.ylabel('median total time to publication / days')\npl.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "class 7\nclass 8\nclass 9\nclass 10\nclass"
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 11\nclass 12\nclass 13\nclass 14\nclass 15\nclass"
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 16\nclass 17\nclass 18\nclass 19\nclass 20\nclass 21\nclass"
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 23\n"
},
{
"output_type": "display_data",
"png": 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w55kzZ2yy7++//6a9vZvMNjsvReFfmkzevP/+XgTeLTSC2cWgoJIfgXC3USFC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\n8v8BdkzvmvKUeU4AAAAASUVORK5CYII=\n"
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Visualizing the median total time to publication for this narrow band of editor classes (between 7 and 23 handled submissions) shows at least two things:\n\n* The maximal median values of around 600 days are not captured by this subset of classes, but, and more importantly,\n\n* there is a pattern in median total times that repeats itself across the different classes considered here: all classes appear to vary between the same extrema in median total times."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "This scatter plot seems to corroborate the conclusion we have drawn from the above boxplots: Editorial experience does not appear to impact the total time to publication of the submissions they handle."
}
],
"metadata": {}
}
]
}
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