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{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# The Future of OA: A large-scale analysis projecting Open Access publication and readership\n\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "\n**Heather Piwowar &#42;<sup>1</sup>, Jason Priem &#42;<sup>1</sup>, Richard Orr<sup>1</sup>** \n\n&#42; shared first authorship \n<sup>1</sup>_Our Research ([email protected])_\n\nPreprint first submitted: October 6, 2019\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "------\n**Summary**\n\n*Will move the Summary ([Section 4.5](#Summary) right now) up to the top here. Is at the bottom right now so it can produc the graphs in it using the code below :)*\n\n\n- delete cache, rerun\n- fix wording of supp info\n\n------"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-1\"></a>\n## 1. Introduction\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The adoption of [open access (OA)](https://en.wikipedia.org/wiki/Open_access) publishing is changing scholarly communication. Predicting the future prevalence of OA is crucial for many stakeholders making decisions now, including:\n\n- libraries deciding which journals to subscribe to and how much they should pay\n\n- institutions and funders deciding what mandates they should adopt, and the implications of existing mandates\n\n- scholarly publishers deciding when to flip their business models to OA\n\n- scholarly societies deciding how best to serve their members.\n\nDespite how useful OA prediction would be, only a few studies have made an attempt to empirically predict open access rates. Lewis (2012) extrapolated the rate at which [gold OA](https://en.wikipedia.org/wiki/Open_access#Gold_OA) would replace subscription-based publishing using a simple log linear extrapolation of gold vs subscription market share. Antelman (2017) used one empirically-derived growth rate for [green OA](https://en.wikipedia.org/wiki/Open_access#Green_OA) and another for all other kinds of OA combined. Both of these studies are based on data collected before 2012, and rely on relatively simple models. Moreover, these studies predict the number of papers that are OA. While this number is important, it is arguably less meaningful than the number of views that are OA, since this latter number describes the prevalence of OA as experienced by actual readers."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "This paper aims to address this gap in the literature. In it, we build a detailed model using data extrapolated from large and up-to-date Unpaywall dataset (https://unpaywall.org/).  We use the model to predict the number of articles that will be OA (including gold, green, hybrid, and bronze OA) over the next five years, and also use data from the Unpaywall browser add-on (https://unpaywall.org/products/extension) to predict the proportion of scholarly article views that will lead readers to OA articles over time.\n\nThis paper aims to provide models of OA growth, taking the following complexities into account:\n\n- some forms of OA include a delay between when a paper is first published and when it is first freely available\n\n- different forms of open access are being adopted at different rates\n\n- wide-sweeping policy changes, technical improvements, or cultural changes may cause disruptions in the growth rates of OA in the future"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-2\"></a>\n## 2. Data"
},
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"source": "%%capture --no-stderr --no-stdout --no-display\n\n# hidden: code to import libraries, set up database connection, other initialization\nimport warnings\nwarnings.filterwarnings('ignore')\n\nimport os\nimport sys\nimport datetime\nimport pandas as pd\nimport numpy as np\nimport scipy\nfrom scipy import signal\nfrom scipy.optimize import curve_fit\nfrom scipy.stats.distributions import t\nfrom matplotlib import pyplot as plt\nimport matplotlib as mpl\nfrom matplotlib import cm\nfrom matplotlib.colors import ListedColormap\nimport seaborn as sns\nfrom sqlalchemy import create_engine\nimport sqlalchemy\nimport psycopg2\nfrom datetime import timedelta\nfrom IPython.display import display, HTML, Markdown\nimport cache_magic\nfrom tabulate import tabulate\n\n# our database connection\nredshift_engine = create_engine(os.getenv(\"DATABASE_URL_REDSHIFT\"))\n\n# graph style\nsns.set(style=\"ticks\")\n\n# long print, wrap\npd.set_option('display.expand_frame_repr', False)\n\n# read from file if available, else from db and save it in a file for next time\n# will also help have data files ready for archiving in zenodo \ndef read_from_file_or_db(varname, query, skip_cache=False):\n filename = \"data/{}.csv\".format(varname)\n my_dataframe = pd.DataFrame()\n try:\n if not skip_cache:\n my_dataframe = pd.read_csv(filename)\n except IOError:\n pass\n if my_dataframe.empty:\n global redshift_engine\n my_dataframe = pd.read_sql_query(sqlalchemy.text(query), redshift_engine)\n my_dataframe.to_csv(filename, index=False) # cache for the future\n\n return my_dataframe.copy()\n\n\n# make figure captions work. use like this: \n# make a code cell, and include\n# register_new_figure(\"my-figure-anchor-name\") \n# before you want to refer to a figure. This is where the link will go to.\n# and then in text markdown to refer to the figure\n# {{figure_link(\"my-figure-anchor-name\")}}\n\nglobal figure_so_far\nglobal figure_numbers\nfigures_so_far = 1\nfigure_numbers = {}\n\n# inspired by https://github.com/l-althueser/nbindex-jupyter/blob/master/nbindex/numbered.py\ndef leave_figure_anchor(anchor_text):\n key = u\"figure-{}\".format(anchor_text)\n \"\"\"\n Adds numbered named object HTML anchors. Link to them in MarkDown using: [to keyword 1](#keyword-1)\n \"\"\"\n return display(HTML('''<div id=\"%s\"></div>\n <script>\n var key = \"%s\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n ''' % (key,key)))\n\ndef register_new_figure(anchor_text):\n global figures_so_far\n global figure_numbers\n if not anchor_text in figure_numbers:\n figure_numbers[anchor_text] = figures_so_far\n leave_figure_anchor(anchor_text)\n figures_so_far += 1\n return figure_numbers[anchor_text]\n\ndef figure_link(anchor_text=None):\n if anchor_text:\n template = \"[Figure {figure_number}](#figure-{anchor_text})\"\n my_return = template.format(figure_number=figure_numbers[anchor_text], \n anchor_text=anchor_text)\n else:\n my_return = figure_numbers\n return my_return\n \n\n# set up colors\noa_status_order = [\"green\", \"gold\", \"hybrid\", \"bronze\", \"closed\"]\noa_status_colors = [\"green\", \"gold\", \"orange\", \"brown\", \"grey\"]\noa_color_lookup = pd.DataFrame(data = {\"name\": oa_status_order, \"color\": oa_status_colors, \"order\": range(0, len(oa_status_order))})\nmy_cmap = sns.color_palette(oa_status_colors)\n\ngraph_type_order = [\"green\", \"gold\", \"hybrid\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]\ngraph_type_colors = [\"green\", \"gold\", \"orange\", \"brown\", \"salmon\", \"gray\"]\ngraph_type_lookup = pd.DataFrame(data = {\"name\": graph_type_order, \"color\": graph_type_colors, \"order\": range(0, len(graph_type_order))})\nmy_cmap_graph_type = sns.color_palette(graph_type_colors)\n\ngraph_type_colors_plus_biorxiv = [\"lawngreen\"] + graph_type_colors\ngraph_type_order_plus_biorxiv = [\"biorxiv\"] + graph_type_order\nplus_biorxiv_labels = [\n \"green (biorxiv)\",\n \"green (other)\",\n \"gold\",\n \"hybrid\",\n \"bronze (immediate)\",\n \"bronze (delayed)\",\n \"closed\"\n]\ngraph_type_plus_biorxiv_lookup = pd.DataFrame(data = {\"name\": graph_type_order_plus_biorxiv, \"color\": graph_type_colors_plus_biorxiv, \"order\": range(0, len(graph_type_colors_plus_biorxiv))})\nmy_cmap_graph_type_plus_biorxiv = sns.color_palette(graph_type_colors_plus_biorxiv)\n",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": "%cache magic is now registered in ipython\n",
"name": "stdout"
}
]
},
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"metadata": {},
"cell_type": "markdown",
"source": "The data in this analysis comes from two sources: (1) the Unpaywall dataset and (2) the access logs of the Unpaywall web browser extension.\n\n<a id=\"section-oa-vocab\"></a>\n<a id=\"section-2-1\"></a>\n### 2.1 OA type: the Unpaywall dataset of OA availability \n\nPredicting levels of open access publication in the future requires detailed, accurate, timely data. This study uses the [Unpaywall](https://unpaywall.org/) dataset to provide this data. Unpaywall is an open source application that  links every research article that has been assigned a Crossref DOI (more than 100 million in total) to the OA URLs where the paper can be read for free. It  is built and maintained by Our Research (formerly Impactstory), a US-based nonprofit organization. Unpaywall gathers data gathered from over 50,000 journals and open-access repositories from all over the world. The full Unpaywall dataset is freely, publicly available (see details: <https://unpaywall.org/user-guides/research>)."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Our definitions of OA type (gold, green, hybrid, bronze, closed) are described in Piwowar et al. (2018). To facilitate prediction, for the purpose of this analysis we subdivided bronze OA into immediate and delayed OA. In summary, these definitions are:\n\n- **<span style=\"color:gold; font-size:100%;\">&#x2588;</span> Gold:** published in a fully-OA journal\n- **<span style=\"color:orange; font-size:100%;\">&#x2588;</span> Hybrid:** published in a toll-access journal, available on the publisher site, with an OA license\n- **<span style=\"color:brown; font-size:100%;\">&#x2588;</span> Bronze:** published in a toll-access journal, available on the publisher site, without an OA license\n - **<span style=\"color:brown; font-size:100%;\">&#x2588;</span> Immediate Bronze:** available as Bronze OA immediately upon publication\n - **<span style=\"color:salmon; font-size:100%;\">&#x2588;</span> Delayed Bronze:** available as Bronze OA after an embargo period\n- **<span style=\"color:green; font-size:100%;\">&#x2588;</span> Green:** published in a toll-access journal and the only fulltext copy available is in an OA repository\n- **<span style=\"color:gray; font-size:100%;\">&#x2588;</span> Closed:** everything else\n\nThis analysis uses all articles with a Crossref article type of \"journal-article\" published between 1950 and the date of the analysis (October 2019), which is 71 million articles. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-2-2\"></a>\n### 2.2 Article views: access logs of the Unpaywall web browser extension\n\n\nPredicting the open access pattern of usage requests requires knowing the relative usage demands of papers based on their age. This study has extracted these pageview patterns from the usage logs of the [Unpaywall browser extension](https://unpaywall.org/products/extension) for Chrome and Firefox."
},
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"cell_type": "code",
"source": "register_new_figure(\"unpaywall_map\");",
"execution_count": 2,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-unpaywall_map\"></div>\n <script>\n var key = \"figure-unpaywall_map\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
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"metadata": {
"variables": {
" print figure_link(\"unpaywall_map\") ": "<a href=\"#figure-unpaywall_map\">Figure 1</a>"
}
},
"cell_type": "markdown",
"source": "This extension is an open-source tool made by the same non-profit as the Unpaywall dataset described above, with the goal of helping people conveniently find free copies of research papers directly from their web browser. The extension has [more than 200,000 active users](http://blog.our-research.org/unpaywall-200k-users/), distributed globally, as shown in {{ print figure_link(\"unpaywall_map\") }}.\n\n<img src=\"https://github.com/Impactstory/future-oa/blob/master/img/unpaywall+extension+users+by+location.jpg?raw=true\"></img>\n\n**{{ print figure_link(\"unpaywall_map\") }}: Map of Unpaywall users in February 2019.**\n\n\nThe Unpaywall browser automatically extension detects when a user is on a scholarly article webpage -- we consider this an access request, or a view. The extension can be disabled, or can be configured to only run upon request, but very few users use these settings. \n\nThe extension received more than 3 million article access requests in July 2019 which we use for most of our analysis. Because readership data is private and potentially sensitive, we are not releasing the Unpaywall usage logs along with the other datasets behind this paper other than as aggregate counts by OA type by year.\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-3\"></a>\n## 3. Approach\n\n<a id=\"section-3-1\"></a>\n### 3.1 Overview \n\n\nThe goal of this analysis is to predict two aspects of OA growth:\n1. Growth in OA articles and their proportion of the literature over time\n2. Growth in OA article views and their proportion of all literature views over time"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We examine the growth in OA articles *by date of observation*, rather than by date of publication. This requires us to calculate the OA lag between publication and availability for different types of OA, which is done in [Section 4.1](#section-4-1).\n\nOnce we have the pattern of OA availability by year, we forecast the OA availability for future years by assuming that it will have the same overall pattern as previous years -- the papers that will be made available next year will have the same age distribution as papers that were made available last year. We allow the absolute number of papers to increase year-over-year: we estimate the future growth multiplier by extrapolating the height of past availability curves. This analysis is presented in [Section 4.2](#section-4-2)."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Next, we turn to predicting the growth of OA article views -- what proportion of what is read is available OA, and how will this change in the future? The Unpaywall browser extension logs give us a relative baseline of what is read right now. By assuming that reading patterns remain relatively unchanged over time (specifically the probability that a reader wants to read a paper given its age and OA type), we use the publication estimates we made in previous sections to calculate the relative number of views by OA type in the past and the future. This is described in [Section 4.3](#section-4-3).\n\nFinally, we look at the impact of extending the model to include a disruptive change, in this case the growth of bioRxiv, in [Section 4.4](#section-4-4).\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-3-2\"></a>\n### 3.2 Glossary\n\nIn addition to the OA types defined in [Section 2.1](#section-oa-vocab), we define additional terms as we use them in this paper, in approximate order they are discussed:\n\n- **Date of publication**: the date an article is published in a journal\n- **Embargo**: the delay that some toll-access journals require between date of publication and when an article can be made Green or Delayed Bronze OA\n- **Self-archiving**: when an author posts their article in an OA repository\n- **OA type**: the OA classification of an article, as defined in [Section 2](#section-oa-vocab). The OA type of an article may change over time (from Closed to Delayed Bronze OA, or from Closed to Green OA) because of embargoes and other self-archiving delays\n- **Date first available OA**: the date an article first becomes an OA type other than \"Closed\"\n- **OA lag**: the length of time between an article's Date of Publication and its Date First Available OA\n<pre></pre>"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "- **OA assessment**: the determination of the OA type of an article at a given point in time\n- **Date of observation**: the point in time for which we make an OA assessment for an article. Explained in [Section 3.3](#section-3-3).\n- **Observation age** of an article: the length of time between an OA assessment observation and the article's date of publication\n<pre></pre>"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "- **View**: someone on the internet visited the publisher webpage of an article, presumably with the hope of reading the article\n- **Date of view**: the date of the view\n- **View age** of an article: the length of time between an article's date of publication and the date of a view\n<pre></pre>"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "- **Articles by age curve**: for a given snapshot, the plot of snapshot age (in years) on the x-axis and number of articles published of that snapshot age on the y-axis\n- **Views by age curve**: the plot of view age (in years) on the x-axis and number of views received by articles of that view age on the y-axis\n- **Views per article by age curve**: the plot of view or snapshot age (in years) on the x-axis and number of views per article (by views of that view age and articles of that snapshot age) on the y-axis\n- **Views per year curve**: the plot of year on the x-axis and the number of views estimated to have been made that year on the y-axis"
},
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"start_time": "2019-10-06T11:22:03.138182Z",
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"cell_type": "markdown",
"source": "<a id=\"section-3-3\"></a>\n### 3.3 Date of Observation"
},
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"cell_type": "code",
"source": "register_new_figure(\"date_of_observation\");",
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-date_of_observation\"></div>\n <script>\n var key = \"figure-date_of_observation\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
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" print figure_link(\"date_of_observation\") ": "<a href=\"#figure-date_of_observation\">Figure 2</a>"
}
},
"cell_type": "markdown",
"source": "In this paper we approach the growth of OA from the Date of Observation of OA assessment, rather than the date of publication. We explain this with the use of {{ print figure_link(\"date_of_observation\") }}.\n"
},
{
"metadata": {
"variables": {
" print figure_link(\"date_of_observation\") ": "<a href=\"#figure-date_of_observation\">Figure 2</a>"
}
},
"cell_type": "markdown",
"source": "<img src=\"https://github.com/Impactstory/future-oa/blob/master/img/date_of_observation_prediction.jpg?raw=true\" style=\"float:right;\"></img>\n\n**{{ print figure_link(\"date_of_observation\") }}: Date of observation.**\n\n\nLet’s imagine two observers, <span style=\"color:blue\">Alice</span> (blue) and <span style=\"color:red\">Bob</span> (red), shown by the two stick figures at the top of {{ print figure_link(\"date_of_observation\") }}.\n\nAlice lives at the end of Year 1--that’s her \"Date Of Observation.\" Looking down, she can see all 8 articles (represented by solid colored dots) published in Year 1, along with their access status: Gold OA, Green OA, or Closed. The Year of Publication for all eight of these articles is Year 1.\n\nAlice likes reading articles, so she decides to read all eight Year 1 articles, one by one.\n\nShe starts with Article A. This article started its life early in the year as Closed. Later that year, though--after an OA Lag of about six months--Article A became Green OA as its author deposited a manuscript (the green circle) in their institutional repository. Now, at Alice’s Date of Observation, it’s open! Excellent. Since Alice is inclined toward organization, she puts Article A article in a stack of Green articles she’s keeping below.\n\nNow let’s look at Bob. Bob lives in Alice’s future, in Year 3 (ie, his “Date of Observation” is Year 3). Like Alice, he’s happy to discover that Article A is open. He puts it in his stack of Green OA articles, which he’s further organized by date of their publication (it goes in the Year 1 stack).\n\nNext, Alice and Bob come to Article B, which is a tricky one. Alice is sad: she can’t read the article, and places it in her Closed stack. Unbeknownst to poor Alice, she is a victim of OA Lag, since Article B will become OA in Year 2. By contrast, Bob, from his comfortable perch in the future, is able to read the article. He places it in his Green Year 1 stack. He now has two articles in this stack, since he’s found two Green OA articles in Year 1.\n\nFinally, Alice and Bob both find Article C is closed, and place it in the closed stack for Year 1. We can model this behavior for a hypothetical reader at each year of observation, giving us their view on the world--and that’s exactly the approach we take in this paper.\n\nNow, let’s say that Bob has decided he’s going to figure out what OA will look like in Year 4. He starts with Gold. This is easy, since Gold article are open immediately upon publication, and publication date is easy to find from article metadata. So, he figures out how many articles were Gold for Alice (1), how many in Year 2 (3), and how many in his own Year 3 (6). Then he computes percentages, and graphs them out using the stacked area chart at the bottom of {{ print figure_link(\"date_of_observation\") }}. From there, it’s easy to extrapolate forward a year.\n\nFor Green, he does the same thing--but he makes sure to account for OA Lag. Bob is trying to draw a picture of the world every year, as it appeared to the denizens of that world. He wants Alice’s world as it appeared to Alice, and the same for Year 2, and so on. So he includes OA Lag in his calculations for Green OA, in addition to publication year. Once he has a good picture from each Date Of Observation, and a good understanding of what the OA Lag looks like, he can once again extrapolate to find Year 4 numbers.\n\nBob is using the same approach we will use in this paper--although in practice, we will find it to be rather more complex, due to varying lengths of OA Lag, additional colors, of OA, and a lack of stick figures. \n"
},
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"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-3-4\"></a>\n### 3.4 Statistical analysis\n\nThe analysis was implemented as an executable python Jupyter notebook using the pandas, scipy, matplotlib, and sqlalchemy libraries. See the [Data and code availability section](#Data-and-code-availability) below for links to the analysis code and raw data."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "*---- delete the text between these lines in the final paper ----*"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Code: Functions"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "See notebook."
},
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef get_data_extrapolated(graph_type, data_type=False, extrap=\"linear\", now_delta_years=0, cumulative=True):\n \n calc_min_year = 1951\n display_min_year = 2010\n now_year = 2019 - now_delta_years\n max_year = 2024\n\n min_y = 0\n max_y = None\n color = graph_type\n if \"bronze\" in graph_type:\n color = \"bronze\"\n \n if isinstance(data_type, pd.DataFrame):\n df_this_color = data_type.loc[(data_type.graph_type==graph_type)]\n elif data_type == \"basic\":\n df_this_color = articles_by_color_by_year.loc[(articles_by_color_by_year.oa_status==color)]\n else:\n df_this_color = articles_by_graph_type_by_year.loc[(unpaywall_graph_type.oa_status==graph_type)]\n\n totals = pd.DataFrame()\n for i, prediction_year in enumerate(range(calc_min_year, now_year)):\n\n if \"published_year\" in df_this_color.columns:\n if cumulative:\n df_this_plot = df_this_color.loc[(df_this_color[\"published_year\"] <= prediction_year)]\n else:\n df_this_plot = df_this_color.loc[(df_this_color[\"published_year\"] == prediction_year)]\n else:\n df_this_plot = df_this_color\n y = [a for a in df_this_plot[\"num_articles\"] if not np.isnan(a)]\n prediction_y = sum(y)\n\n totals = totals.append(pd.DataFrame(data={\"prediction_year\": [prediction_year], \n \"num_articles\": [prediction_y]}))\n\n \n x = totals[\"prediction_year\"]\n y = totals[\"num_articles\"]\n xnew = np.arange(now_year-1, max_year+1, 1)\n if extrap==\"linear\":\n f = scipy.interpolate.interp1d(x, y, fill_value=\"extrapolate\", kind=\"linear\")\n ynew = f(xnew)\n else:\n f = scipy.interpolate.interp1d(x, np.log10(y), fill_value=\"extrapolate\", kind=\"linear\")\n ynew = 10 ** f(xnew)\n \n new_data = pd.DataFrame({\"color\":color, \"graph_type\": graph_type, \"x\":np.append(x[:-1], xnew), \"y\":np.append(y[:-1], ynew)})\n\n return new_data\n\n\ndef graph_data_extrapolated(graph_type, data_type=False, extrap=\"linear\", now_delta_years=0, ax=None, cumulative=True):\n calc_min_year = 1951\n display_min_year = 2000\n now_year = 2019 - now_delta_years\n max_year = 2024\n\n min_y = 0\n max_y = None\n color = graph_type\n if \"bronze\" in graph_type:\n color = \"bronze\"\n \n new_data = get_data_extrapolated(graph_type, data_type, extrap, now_delta_years, cumulative)\n\n year_range = range(display_min_year, now_year)\n \n if not isinstance(data_type, pd.DataFrame) and data_type == \"simple\":\n my_color_lookup = oa_color_lookup.loc[oa_color_lookup[\"name\"]==color]\n else:\n my_color_lookup = graph_type_lookup.loc[graph_type_lookup[\"name\"]==graph_type]\n \n if not ax:\n fig = plt.figure()\n ax = plt.subplot(111)\n\n if not max_y:\n max_y = 5 * max(new_data[\"y\"])\n\n df_actual = new_data.loc[new_data[\"x\"] < now_year]\n x = [int(a) for a in df_actual[\"x\"]]\n y = [int(a) for a in df_actual[\"y\"]]\n df_future = new_data.loc[new_data[\"x\"] >= now_year]\n xnew = [int(a) for a in df_future[\"x\"]]\n ynew = [int(a) for a in df_future[\"y\"]]\n\n ax.plot(x, y, 'o', color=\"black\")\n ax.fill_between(x, y, color=my_color_lookup[\"color\"])\n\n ax.plot(xnew, ynew, 'o', color=\"black\", alpha=0.3)\n ax.fill_between(xnew, ynew, color=my_color_lookup[\"color\"], alpha=0.3)\n if cumulative:\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.0f}'.format(y/(1000*1000.0))))\n ax.set_ylabel(\"articles (millions)\")\n ax.set_xlabel(\"year\")\n else:\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.1f}'.format(y/(1000*1000.0))))\n ax.set_ylabel(\"articles (millions)\")\n ax.set_xlabel(\"year of publication\")\n ax.set_xlim(min(year_range), max_year)\n ax.set_title(graph_type);\n\n return new_data",
"execution_count": 4,
"outputs": []
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# graph! :)\n\ndef graph_available_papers_at_year_of_availability(graph_type, now_delta_years=0, ax=None):\n calc_min_year = 1951\n display_min_year = 2010\n now_year = 2018 - now_delta_years\n max_year = 2024\n\n color = graph_type\n if \"bronze\" in graph_type:\n color = \"bronze\"\n\n if graph_type == \"biorxiv\":\n my_color_lookup = {\"color\": \"limegreen\"}\n else:\n my_color_lookup = graph_type_lookup.loc[graph_type_lookup[\"name\"]==graph_type] \n \n all_papers_per_year = get_papers_by_availability_year_including_future(graph_type, calc_min_year, max_year)\n\n most_recent_year = all_papers_per_year.loc[all_papers_per_year.article_years_from_availability == 0]\n \n x = [int(a) for a in most_recent_year.loc[most_recent_year.prediction_year <= now_year][\"prediction_year\"]]\n xnew = [int(a) for a in most_recent_year.loc[most_recent_year.prediction_year > now_year][\"prediction_year\"]]\n y = [int(a) for a in most_recent_year.loc[most_recent_year.prediction_year <= now_year][\"num_articles\"]]\n ynew = [int(a) for a in most_recent_year.loc[most_recent_year.prediction_year > now_year][\"num_articles\"]]\n\n year_range = range(display_min_year, now_year)\n if not ax:\n fig = plt.figure()\n ax = plt.subplot(111)\n\n max_y = 1.2 * max(ynew)\n\n ax.plot(x, y, 'o', color=\"black\")\n ax.fill_between(x, y, color=my_color_lookup[\"color\"])\n\n ax.plot(xnew, ynew, 'o', color=\"black\", alpha=0.3)\n ax.fill_between(xnew, ynew, color=my_color_lookup[\"color\"], alpha=0.3)\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.2f}'.format(y/(1000*1000.0))))\n ax.set_ylabel(\"total papers (millions)\")\n\n ax.set_xlim(min(year_range), max_year)\n# ax.set_ylim(0, max_y)\n ax.set_xlabel('year of observation')\n title = plt.suptitle(\"OA status by observation year\")\n title.set_position([.5, 1.05])\n all_papers_per_year.reset_index(inplace=True)\n return all_papers_per_year",
"execution_count": 5,
"outputs": []
},
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef graph_available_papers_in_observation_year_by_pubdate(graph_type, data, observation_year, ax=None):\n display_min_year = 2010\n max_year = 2025\n\n x = [int(a) for a in data[\"publication_date\"]]\n y = [int(a) for a in data[\"num_articles\"]]\n\n my_color_lookup = graph_type_lookup.loc[graph_type_lookup[\"name\"]==graph_type]\n if not ax:\n fig = plt.figure()\n ax = plt.subplot(111)\n\n alpha = 1\n# if observation_year > 2018:\n# alpha = 0.3\n ax.bar(x, y, color=my_color_lookup[\"color\"], alpha=alpha, width=1, edgecolor=my_color_lookup[\"color\"])\n\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.1f}'.format(y/(1000*1000.0))))\n ax.set_xlim(display_min_year, max_year+1)\n max_y = 1.2 * data.num_articles.max()\n try:\n ax.set_ylim(0, max_y)\n except:\n pass\n ax.set_xlabel(\"\")\n ax.set_ylabel(\"\")\n ax.spines['top'].set_visible(False)\n ax.spines['right'].set_visible(False)\n \n# ax.set_title(\"{}: {}\".format(graph_type, observation_year)); \n# title = plt.suptitle(\"Availability in {}, by publication date\".format(observation_year))\n# title.set_position([.5, 1.05])\n return \n\n",
"execution_count": 6,
"outputs": []
},
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"start_time": "2019-10-07T02:59:37.108668Z",
"end_time": "2019-10-07T02:59:37.135194Z"
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"cell_type": "code",
"source": "def get_papers_by_availability_year_including_future(graph_type, start_year, end_year):\n start_calc_year = 2009\n last_year_before_extrap = 2017\n offset = 0\n global final_extraps\n \n my_return = pd.DataFrame()\n\n for prediction_year in range(min(start_year, start_calc_year), last_year_before_extrap+1): \n# print prediction_year\n papers_per_year = get_papers_by_availability_year(graph_type, prediction_year, just_this_year=False)\n papers_per_year[\"prediction_year\"] = prediction_year\n my_return = my_return.append(papers_per_year)\n \n if end_year >= last_year_before_extrap:\n scale_df = final_extraps.copy()\n current_year_all = get_papers_by_availability_year(graph_type, last_year_before_extrap, just_this_year=False)\n now_year_new = get_papers_by_availability_year(graph_type, last_year_before_extrap, just_this_year=True)\n for i, prediction_year in enumerate(range(last_year_before_extrap+1, end_year+1)): \n current_year_all[\"article_years_from_availability\"] += 1 \n# print now_year_all.head()\n# print now_year_new.head()\n merged_df = current_year_all.merge(now_year_new, on=\"article_years_from_availability\", suffixes=[\"_all\", \"_new\"], how=\"outer\")\n merged_df = merged_df.fillna(0)\n# print merged_df.head(10)\n scale = float(scale_df.loc[(scale_df.x==prediction_year)&(scale_df.graph_type==graph_type)].y) / int(scale_df.loc[(scale_df.x==last_year_before_extrap)&(scale_df.graph_type==graph_type)].y) \n merged_df[\"num_articles\"] = merged_df[\"num_articles_all\"] + [int(scale * a) for a in merged_df[\"num_articles_new\"]]\n merged_df[\"prediction_year\"] = prediction_year\n current_year_all = pd.DataFrame(merged_df, columns=[\"num_articles\", \n \"article_years_from_availability\", \n \"prediction_year\"])\n my_return = my_return.append(current_year_all)\n\n my_return.reset_index(inplace=True)\n return my_return",
"execution_count": 7,
"outputs": []
},
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"start_time": "2019-10-07T02:59:37.138899Z",
"end_time": "2019-10-07T02:59:37.173473Z"
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# graph! :)\n\ndef graph_views(graph_type, data=None, now_delta_years=0, ax=None):\n calc_min_year = 1951\n display_min_year = 2010\n now_year = 2018 - now_delta_years\n max_year = 2025\n\n color = graph_type\n\n if isinstance(data, pd.DataFrame):\n df_views_by_year = data\n else:\n df_views_by_year = get_predicted_views(graph_type, display_min_year, max_year)\n\n year_range = range(display_min_year, now_year)\n if graph_type == \"biorxiv\":\n my_color_lookup = {\"color\": \"limegreen\"}\n else:\n my_color_lookup = graph_type_lookup.loc[graph_type_lookup[\"name\"]==color]\n \n if not ax:\n fig = plt.figure()\n ax = plt.subplot(111)\n\n \n x = [int(a) for a in df_views_by_year[\"observation_year\"]]\n y = [int(a) for a in df_views_by_year[\"views\"]]\n max_y = 1.2 * max(y)\n\n ax.scatter(x, y, marker='x', s=70, color=my_color_lookup[\"color\"])\n\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.1f}'.format(y/(1000*1000.0))))\n ax.set_ylabel(\"views (millions)\")\n\n ax.set_xlim(min(year_range), max_year+1)\n# ax.set_ylim(0, max_y)\n ax.set_xlabel('view year')\n# title = plt.suptitle(\"Estimated views by access year, by OA type\")\n# title.set_position([.5, 1.05])\n return df_views_by_year",
"execution_count": 8,
"outputs": []
},
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"ExecuteTime": {
"start_time": "2019-10-07T02:59:37.180521Z",
"end_time": "2019-10-07T02:59:37.303468Z"
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"cell_type": "code",
"source": "\n# do calculations\n\ndef get_papers_by_availability_year(graph_type=\"closed\", availability_year=2000, just_this_year=False):\n my_return = pd.DataFrame()\n \n if just_this_year:\n if graph_type == \"closed\":\n rows_published_this_year = articles_by_color_by_year.loc[articles_by_color_by_year[\"published_year\"] == availability_year]\n total_this_year = rows_published_this_year.num_articles.sum()\n \n open_this_year = 0\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\"]:\n temp_papers = get_papers_by_availability_year(prep_graph_type, availability_year, just_this_year=False)\n temp_papers = temp_papers.loc[temp_papers.article_years_from_availability == 0]\n num_articles = temp_papers.num_articles.sum()\n# print prep_graph_type, num_articles\n open_this_year += num_articles\n num_closed = total_this_year - open_this_year\n \n my_return = pd.DataFrame({\n \"article_years_from_availability\": [0],\n \"num_articles\": [num_closed]\n })\n else:\n prev_year_history = get_papers_by_availability_year(graph_type, availability_year-1, just_this_year=False)\n prev_year_history[\"article_years_from_availability\"] += 1\n this_year_history = get_papers_by_availability_year(graph_type, availability_year, just_this_year=False)\n df_merged = this_year_history.merge(prev_year_history, on=\"article_years_from_availability\", how=\"left\")\n df_merged = df_merged.fillna(0)\n df_merged[\"num_articles\"] = df_merged[\"num_articles_x\"] - df_merged[\"num_articles_y\"]\n df_merged[\"num_articles\"][df_merged[\"num_articles\"] < 25] = 0\n df_merged = df_merged.loc[df_merged[\"article_years_from_availability\"] <= 10]\n my_return = pd.DataFrame({\n \"article_years_from_availability\": df_merged[\"article_years_from_availability\"],\n \"num_articles\": df_merged[\"num_articles\"]\n })\n\n else:\n \n if graph_type == \"delayed_bronze\":\n temp_papers = delayed_bronze_after_embargos_age_years.loc[delayed_bronze_after_embargos_age_years[\"prediction_year\"]==availability_year]\n my_return = pd.DataFrame({\n \"article_years_from_availability\": temp_papers[\"article_age_years\"],\n \"num_articles\": temp_papers[\"num_articles\"]\n })\n\n elif graph_type == \"green\":\n\n my_green_oa = green_oa_with_dates_by_availability\n\n my_green_oa = my_green_oa.loc[my_green_oa[\"months_old_at_first_deposit\"] >= -24]\n my_green_oa = my_green_oa.loc[my_green_oa[\"months_old_at_first_deposit\"] <= 12*25]\n my_green_oa = my_green_oa.loc[my_green_oa[\"year_of_first_availability\"] <= availability_year]\n\n my_green_oa_pivot = my_green_oa.pivot_table(\n index='published_year', values=['num_articles'], aggfunc=np.sum)\n my_green_oa_pivot.reset_index(inplace=True)\n my_green_oa_pivot = my_green_oa_pivot.sort_values(by=[\"published_year\"], ascending=False)\n my_green_oa_pivot[\"article_years_from_availability\"] = [(availability_year - a) for a in my_green_oa_pivot[\"published_year\"]]\n my_return = pd.DataFrame({\n \"article_years_from_availability\": my_green_oa_pivot[\"article_years_from_availability\"],\n \"num_articles\": my_green_oa_pivot[\"num_articles\"]\n })\n\n elif graph_type == \"closed\":\n my_return = pd.DataFrame()\n for i, year in enumerate(range(availability_year+1, 1990, -1)):\n closed_rows = get_papers_by_availability_year(graph_type, availability_year - i, just_this_year=True)\n closed_rows[\"article_years_from_availability\"] = i\n my_return = my_return.append(closed_rows)\n \n elif graph_type == \"immediate_bronze\":\n temp_papers = articles_by_color_by_year_with_embargos.loc[(articles_by_color_by_year_with_embargos.oa_status==\"bronze\") &\n (articles_by_color_by_year_with_embargos[\"embargo\"].isnull()) &\n (articles_by_color_by_year_with_embargos.published_year <= availability_year)]\n# temp_papers[\"article_years_from_availability\"] = availability_year - temp_papers[\"published_year\"] \n temp_pivot = temp_papers.pivot_table(\n index='published_year', values=['num_articles'], aggfunc=np.sum)\n temp_pivot.reset_index(inplace=True)\n my_return = pd.DataFrame({\n \"article_years_from_availability\": availability_year - temp_pivot.published_year,\n \"num_articles\": temp_pivot.num_articles\n })\n\n elif graph_type == \"biorxiv\": \n my_return = biorxiv_growth_otherwise_closed.copy()\n my_return = my_return.loc[my_return[\"published_year\"] <= availability_year]\n my_return[\"article_years_from_availability\"] = availability_year - my_return[\"published_year\"]\n else:\n temp_papers = articles_by_color_by_year.loc[(articles_by_color_by_year.oa_status==graph_type) &\n (articles_by_color_by_year.published_year <= availability_year)]\n my_return = pd.DataFrame({\n \"article_years_from_availability\": [availability_year - a for a in temp_papers[\"published_year\"]],\n \"num_articles\": temp_papers[\"num_articles\"]\n })\n\n\n if not my_return.empty:\n my_return = pd.DataFrame(my_return, columns=[\"article_years_from_availability\", \"num_articles\"]) \n my_return = my_return.sort_values(by=\"article_years_from_availability\")\n\n return my_return\n\n\n",
"execution_count": 9,
"outputs": []
},
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"ExecuteTime": {
"start_time": "2019-10-07T02:59:37.309030Z",
"end_time": "2019-10-07T02:59:37.326276Z"
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef get_predicted_views_by_pubdate(graph_type, observation_year):\n\n views_per_article = get_views_per_article(graph_type)\n \n df_views_by_year = pd.DataFrame()\n all_papers_per_year = get_papers_by_availability_year_including_future(graph_type, observation_year, observation_year+1)\n papers_per_year = all_papers_per_year.loc[all_papers_per_year[\"prediction_year\"] == observation_year]\n \n try:\n data_merged_clean = papers_per_year.merge(views_per_article, left_on=[\"article_years_from_availability\"], right_on=[\"article_age_years\"])\n data_merged_clean = data_merged_clean.sort_values(\"article_age_years\")\n# print data_merged_clean.head()\n data_merged_clean[\"views\"] = data_merged_clean[\"views_per_article\"] * data_merged_clean[\"num_articles\"]\n data_merged_clean[\"observation_year\"] = observation_year\n data_merged_clean[\"publication_year\"] = observation_year - data_merged_clean[\"article_age_years\"]\n new_data = pd.DataFrame(data_merged_clean, columns=[\"publication_year\", \"views\", \"article_age_years\", \"observation_year\"])\n df_views_by_year = df_views_by_year.append(new_data)\n except (ValueError, KeyError): # happens when the year is blank\n pass\n \n return df_views_by_year",
"execution_count": 10,
"outputs": []
},
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"trusted": true
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"source": "",
"execution_count": null,
"outputs": []
},
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"ExecuteTime": {
"start_time": "2019-10-07T02:59:37.332446Z",
"end_time": "2019-10-07T02:59:37.359191Z"
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef get_predicted_views(graph_type, now_delta_years=0):\n# calc_min_year = 1951\n calc_min_year = 1995\n display_min_year = 2010\n now_year = 2020 - now_delta_years\n max_year = 2024\n exponential = False\n\n if graph_type == \"biorxiv\":\n exponential = True\n \n views_per_article = get_views_per_article(graph_type)\n \n df_views_by_year = pd.DataFrame()\n \n all_papers_per_year = all_predicted_papers_future\n for prediction_year in range(calc_min_year, max_year+1): \n# for prediction_year in range(calc_min_year, 2019): \n# for prediction_year in range(2017, 2019): \n papers_per_year = all_papers_per_year.loc[all_papers_per_year[\"prediction_year\"] == prediction_year]\n papers_per_year = papers_per_year.loc[papers_per_year[\"graph_type\"] == graph_type]\n# print views_per_article.head()\n try:\n data_merged_clean = papers_per_year.merge(views_per_article, left_on=[\"article_years_from_availability\"], right_on=[\"article_age_years\"])\n data_merged_clean = data_merged_clean.sort_values(\"article_age_years\")\n win = data_merged_clean[\"views_per_article\"] \n sig = data_merged_clean[\"num_articles\"]\n views_by_access_year = signal.convolve(win, sig, mode='same', method=\"direct\")\n y = max(views_by_access_year)\n df_views_by_year = df_views_by_year.append(pd.DataFrame({\"observation_year\":[prediction_year], \"views\": [y]}))\n except (ValueError, KeyError): # happens when the year is blank\n pass\n \n\n return df_views_by_year",
"execution_count": 11,
"outputs": []
},
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"end_time": "2019-10-07T02:59:37.413935Z"
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef get_papers_by_availability_year_total(availability_year):\n all_data = pd.DataFrame()\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n# for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\"]:\n temp_papers = get_papers_by_availability_year_including_future(prep_graph_type, availability_year, availability_year+1)\n temp_papers[\"graph_type\"] = prep_graph_type\n# print prep_graph_type\n# print \"{:,.0f}\".format(temp_papers.num_articles.max()), \"{:,.0f}\".format(temp_papers.num_articles.sum())\n# print \"\\n\"\n all_data = all_data.append(temp_papers)\n return all_data\n\ndef get_views_per_year_total():\n all_data = pd.DataFrame()\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n temp_papers = get_views_per_year(prep_graph_type)\n temp_papers[\"graph_type\"] = prep_graph_type\n# print prep_graph_type\n# print \"{:,.0f}\".format(temp_papers.num_views_per_year.max()), \"{:,.0f}\".format(temp_papers.num_views_per_year.sum())\n# print \"\\n\"\n all_data = all_data.append(temp_papers)\n return all_data\n\n\n\ndef get_views_per_article_total():\n all_data = pd.DataFrame()\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n temp_papers = get_views_per_article(prep_graph_type)\n# print prep_graph_type\n# print \"{:,.0f}\".format(temp_papers.views_per_article.max()), \"{:,.0f}\".format(temp_papers.views_per_article.sum())\n# print \"\\n\"\n temp_papers[\"graph_type\"] = prep_graph_type\n all_data = all_data.append(temp_papers)\n return all_data\n\n\ndef get_predicted_views_total(observation_year):\n all_data = pd.DataFrame()\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n# for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\"]:\n temp_papers = get_predicted_views(prep_graph_type, observation_year)\n temp_papers[\"graph_type\"] = prep_graph_type\n# print prep_graph_type \n all_data = all_data.append(temp_papers)\n return all_data\n\ndef get_predicted_views_by_pubdate_total(observation_year):\n all_data = pd.DataFrame()\n# for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\"]:\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n temp_papers = get_predicted_views_by_pubdate(prep_graph_type, observation_year)\n temp_papers[\"graph_type\"] = prep_graph_type\n# print prep_graph_type\n all_data = all_data.append(temp_papers)\n return all_data\n",
"execution_count": 12,
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"cell_type": "code",
"source": "def get_views_per_year(graph_type):\n if graph_type == \"delayed_bronze\":\n views_per_year = views_by_age_years.loc[(views_by_age_years.oa_status==\"bronze\") &\n (views_by_age_years.delayed_or_immediate==\"delayed\")]\n elif graph_type == \"immediate_bronze\":\n views_per_year = views_by_age_years.loc[(views_by_age_years.oa_status==\"bronze\") &\n (views_by_age_years[\"delayed_or_immediate\"]==\"immediate\")]\n\n else:\n views_per_year = views_by_age_years.loc[(views_by_age_years.oa_status==graph_type)]\n\n views_per_year[\"num_views_one_month\"] = views_per_year[\"num_views\"] # this is just for one month\n views_per_year[\"num_views_per_year\"] = 12.0 * views_per_year[\"num_views_one_month\"]\n del views_per_year[\"num_views\"]\n del views_per_year[\"delayed_or_immediate\"]\n views_per_year = views_per_year.sort_values(by=\"article_age_years\")\n views_per_year = views_per_year.loc[views_per_year[\"article_age_years\"] < 15]\n \n return views_per_year \n\n\ndef get_views_per_article(graph_type):\n if graph_type == \"biorxiv\":\n graph_type = \"green\"\n \n views_per_year = get_views_per_year(graph_type)\n papers_per_year = get_papers_by_availability_year(graph_type, 2018, just_this_year=False)\n papers_per_year[\"article_age_years\"] = papers_per_year[\"article_years_from_availability\"]\n papers_per_year = papers_per_year.loc[(papers_per_year[\"article_age_years\"] <=15 )]\n\n data_merged_clean = papers_per_year.merge(views_per_year, on=[\"article_age_years\"]) \n data_merged_clean[\"views_per_article\"] = data_merged_clean[\"num_views_per_year\"] / data_merged_clean[\"num_articles\"]\n\n views_per_article = pd.DataFrame(data_merged_clean, columns=[\"article_age_years\", \"views_per_article\"])\n views_per_article = views_per_article.sort_values(by=\"article_age_years\")\n\n if graph_type==\"delayed_bronze\":\n # otherwise first one is too high because number articles too low in year 0 for delayed subset\n views_per_article.loc[views_per_article.article_age_years==0, [\"views_per_article\"]] = float(views_per_article.loc[views_per_article.article_age_years==1].views_per_article)\n\n return views_per_article",
"execution_count": 13,
"outputs": []
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"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\ndef plot_area_and_proportion(df, color_type, start_year, end_year, divide_year, \n xlabel=\"year of publication\",\n fancy=None):\n if color_type==\"simple\":\n my_colors = oa_status_colors\n my_color_order = oa_status_order\n color_column = \"color\"\n elif color_type==\"standard\":\n my_colors = graph_type_colors\n my_color_order = graph_type_order\n color_column = \"graph_type\"\n else:\n my_colors = graph_type_colors_plus_biorxiv\n my_color_order = graph_type_order_plus_biorxiv\n color_column = \"graph_type\"\n \n all_data_pivot = df.pivot_table(\n index='x', columns=color_column, values=['y'], aggfunc=np.sum)\\\n .sort_index(axis=1, level=1)\\\n .swaplevel(0, 1, axis=1)\n all_data_pivot.columns = all_data_pivot.columns.levels[0]\n\n fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 3), sharex=True, sharey=False)\n plt.tight_layout(pad=0, w_pad=2, h_pad=1)\n plt.subplots_adjust(hspace=1)\n\n all_data_pivot_graph = all_data_pivot\n ylabel = \"articles (millions)\"\n if fancy==\"cumulative\":\n ylabel = \"cumulative articles (millions)\"\n all_data_pivot_graph = all_data_pivot_graph.cumsum(0)\n elif fancy==\"diff\":\n ylabel = \"newly available articles (millions)\"\n all_data_pivot_graph = all_data_pivot_graph.diff()\n all_data_pivot_graph = all_data_pivot_graph.loc[all_data_pivot_graph.index > 1950]\n all_data_pivot_graph = all_data_pivot_graph.loc[all_data_pivot_graph.index <= end_year]\n \n # print all_data_pivot_graph\n all_data_pivot_actual = all_data_pivot_graph.loc[all_data_pivot_graph.index <= divide_year+1]\n my_plot = all_data_pivot_actual[my_color_order].plot.area(stacked=True, color=my_colors, linewidth=.1, ax=ax1)\n if end_year > divide_year:\n all_data_pivot_projected = all_data_pivot_graph.loc[all_data_pivot_graph.index > divide_year]\n my_plot = all_data_pivot_projected[my_color_order].plot.area(stacked=True, color=my_colors, linewidth=.1, ax=ax1, alpha=0.6)\n ax1.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:.0f}'))\n ax1.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.0f}'.format(y/(1000*1000.0))))\n ax1.set_xlabel(xlabel)\n ax1.set_ylabel(ylabel) \n ax1.set_xlim(start_year, end_year)\n ax1.set_ylim(0, 1.2*max(all_data_pivot_graph.sum(1)))\n# ax1.set_title(\"Number of papers\");\n handles, labels = my_plot.get_legend_handles_labels(); my_plot.legend(reversed(handles[0:len(my_colors)]), reversed(labels[0:len(my_colors)]), loc='upper left'); # reverse to keep order consistent\n\n df_diff_proportional = all_data_pivot_graph.div(all_data_pivot_graph.sum(1), axis=0)\n all_data_pivot_actual = df_diff_proportional.loc[all_data_pivot_graph.index <= divide_year+1]\n my_plot = all_data_pivot_actual[my_color_order].plot.area(stacked=True, color=my_colors, linewidth=.1, ax=ax2)\n if end_year > divide_year:\n all_data_pivot_projected = df_diff_proportional.loc[all_data_pivot_graph.index > divide_year]\n my_plot = all_data_pivot_projected[my_color_order].plot.area(stacked=True, color=my_colors, linewidth=.1, ax=ax2, alpha=0.6)\n my_plot.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))\n ax2.set_xlabel(xlabel)\n ax2.set_ylabel('proportion of articles')\n# ax2.set_title(\"Proportion of papers\");\n ax2.set_xlim(start_year, end_year)\n ax2.set_ylim(0, 1) \n handles, labels = my_plot.get_legend_handles_labels(); my_plot.legend(reversed(handles[0:len(my_colors)]), reversed(labels[0:len(my_colors)]), loc='upper left'); # reverse to keep order consistent\n\n plt.tight_layout(pad=.5, w_pad=4, h_pad=2.0) \n return (all_data_pivot_graph, df_diff_proportional)",
"execution_count": 14,
"outputs": []
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"ExecuteTime": {
"start_time": "2019-10-07T02:59:37.580386Z",
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"cell_type": "code",
"source": "# plot graphs duplicate new\n\ndef get_long_data(graph_type):\n full_range = range(1990, 2020)\n\n totals_bronze = pd.DataFrame()\n for i, prediction_year in enumerate(full_range):\n new_frame = get_papers_by_availability_year(graph_type, prediction_year, just_this_year=True)\n new_frame[\"prediction_year\"] = prediction_year\n new_frame[\"published_year\"] = [int(prediction_year - a) for a in new_frame[\"article_years_from_availability\"]]\n totals_bronze = totals_bronze.append(new_frame)\n\n long_data_for_plot = totals_bronze\n long_data_for_plot = long_data_for_plot.loc[long_data_for_plot[\"article_years_from_availability\"] < 15]\n return long_data_for_plot\n\ndef first_detailed_plots(graph_type):\n my_color_lookup = graph_type_lookup.loc[graph_type_lookup[\"name\"]==graph_type] \n\n long_data_for_plot = get_long_data(graph_type)\n pivot_data_for_plot = long_data_for_plot.pivot_table(\n index='published_year', columns='prediction_year', values=['num_articles'], aggfunc=np.sum)\\\n .sort_index(axis=1, level=1)\\\n .swaplevel(0, 1, axis=1)\n pivot_data_for_plot.columns = pivot_data_for_plot.columns.levels[0]\n pivot_data_for_plot[pivot_data_for_plot < 0] = 0\n pivot_data_for_plot[\"published_year\"] = [int(a) for a in pivot_data_for_plot.index]\n\n years = range(2015, 2018+1)\n\n historical_graphs = False\n color_idx = np.linspace(0, 1, len(years))\n fig, axes = plt.subplots(1, len(years), figsize=(12, 3), sharex=True, sharey=True)\n axes_flatten = axes.flatten()\n axis_index = 0\n max_y_for_this_plot = max(pivot_data_for_plot.max(1))\n\n for i, prediction_year in enumerate(years):\n ax = axes_flatten[axis_index] \n axis_index += 1\n rows = pivot_data_for_plot.copy()\n rows = rows.loc[pd.notnull(rows[prediction_year])]\n x = [int(a) for a in rows.index]\n y = [int(a) for a in rows[prediction_year]]\n ax.bar(x, y, color=my_color_lookup[\"color\"])\n ax.set_ylim(0, 1.2*max_y_for_this_plot) \n ax.set_xlim(2010, 2019)\n if ax.get_legend():\n ax.get_legend().remove() \n ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n ax.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:.0f}'))\n ax.spines['top'].set_visible(False)\n ax.spines['right'].set_visible(False)\n ax.spines['bottom'].set_visible(True)\n ax.spines['left'].set_visible(True) \n ax.set_xlabel(\"year of publication\")\n ax.set_title(\"year first available OA\\n{}\".format(prediction_year))\n\n axes_flatten[0].set_ylabel(\"articles\\nfirst made available\") \n plt.tight_layout(pad=0, w_pad=0, h_pad=0)\n plt.subplots_adjust(hspace=0)\n plt.show()\n \n \n",
"execution_count": 15,
"outputs": []
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"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
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"start_time": "2019-10-07T02:59:37.716365Z",
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"cell_type": "code",
"source": "def make_detailed_plots(graph_type):\n num_subplots = 8\n\n long_data_for_plot = get_long_data(graph_type)\n pivot_data_for_plot = long_data_for_plot.pivot_table(\n index='published_year', columns='prediction_year', values=['num_articles'], aggfunc=np.sum)\\\n .sort_index(axis=1, level=1)\\\n .swaplevel(0, 1, axis=1)\n pivot_data_for_plot.columns = pivot_data_for_plot.columns.levels[0]\n pivot_data_for_plot[pivot_data_for_plot < 0] = 0\n # print pivot_data_for_plot\n\n years = [year for year in pivot_data_for_plot.columns if year > 1990]\n\n for historical_graphs in (False, True):\n color_idx = np.linspace(0, 1, len(years))\n fig, axes = plt.subplots(len(years[-num_subplots:]), 1, figsize=(7, 6), sharex=True, sharey=True)\n axes_flatten = axes.flatten()\n axis_index = 0\n max_y_for_this_plot = max(pivot_data_for_plot.max(1))\n for i, prediction_year in zip(color_idx[-num_subplots:], years[-num_subplots:]):\n ax = axes_flatten[axis_index] \n axis_index += 1\n if historical_graphs:\n pivot_data_for_plot[range(2000, prediction_year+1)].plot.area(stacked=True, alpha=0.4, ax=ax, color=[plt.cm.jet(i) for x in range(2000, prediction_year)])\n try:\n pivot_data_for_plot[range(2000, prediction_year)].plot.area(stacked=True, ax=ax, alpha=.9, color=\"lightgray\")\n ax.set_ylim(0, 3*max_y_for_this_plot) \n except TypeError:\n pass \n else:\n pivot_data_for_plot[prediction_year].plot.area(stacked=False, ax=ax, alpha=.4, color=plt.cm.jet(i))\n ax.set_ylim(0, 1.2*max_y_for_this_plot) \n ax.set_xlim(2009, 2018)\n if ax.get_legend():\n ax.get_legend().remove() \n ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n ax.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:.0f}'))\n ax.spines['top'].set_visible(False)\n ax.spines['right'].set_visible(False)\n ax.spines['bottom'].set_visible(False)\n ax.spines['left'].set_visible(False) \n y_label = \"{} made available during {}:\".format(graph_type, prediction_year) \n ax.set_ylabel(y_label, rotation='horizontal', labelpad=150, verticalalignment=\"center\")\n ax.set_yticks([])\n plt.tight_layout()\n plt.show()\n\n fig, ax1 = plt.subplots(1, 1, figsize=(10, 3))\n pivot_data_for_plot[years].plot.area(stacked=True, ax=ax1, alpha=.4, cmap=plt.cm.jet)\n ax1.set_xlim(2000, 2018)\n legend_handles, legend_labels = ax1.get_legend_handles_labels(); ax1.legend(reversed(legend_handles[-8:]), reversed(legend_labels[-8:]), loc='upper left'); # reverse to keep order consistent\n ax1.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n ax1.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:.0f}'))\n ax1.axvline(x=2015, color='black')\n ax1.set_title(\"Total {} OA available in 2019, by year of availability and publication year\".format(graph_type));\n ax1.set_ylabel(\"number of articles\")\n ax1.set_xlabel(\"published year\")\n \n plt.tight_layout()\n plt.show()\n",
"execution_count": 16,
"outputs": []
},
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"ExecuteTime": {
"start_time": "2019-10-07T02:59:37.813490Z",
"end_time": "2019-10-07T02:59:37.837442Z"
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"trusted": true
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"cell_type": "code",
"source": "\ndef make_zoom_in_plot(graph_type):\n full_range = range(1990, 2020)\n long_data_for_plot = get_long_data(graph_type)\n color_idx = np.linspace(0, 1, len(full_range))\n\n fig, ax1 = plt.subplots(1, 1, figsize=(4, 4))\n data_for_this_plot = long_data_for_plot\n data_for_this_plot = data_for_this_plot.loc[data_for_this_plot[\"published_year\"]==2015]\n total_sum = data_for_this_plot[\"num_articles\"].sum()\n data_for_this_plot = data_for_this_plot.loc[data_for_this_plot[\"num_articles\"]/total_sum>=0.01]\n# print data_for_this_plot\n # data_for_this_plot = data_for_this_plot.drop(columns=[\"article_age_months\"])\n pivot_df = data_for_this_plot.pivot_table(index='published_year', columns='prediction_year', aggfunc=np.sum)\n pivot_df = pivot_df.div(pivot_df.sum(1), axis=0)\n pivot_df.plot.bar(stacked=True, alpha=.4, ax=ax1, colors=[plt.cm.jet(a) for a in list(color_idx[-len(pivot_df.sum(0)):])])\n ax1.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))\n plt.ylabel('proportion of articles')\n plt.title(\"Proportion of {} articles published in 2015\".format(graph_type));\n ax1.set_xlabel(\"\")\n ax1.set_xticks([]) \n legend_handles, legend_labels = ax1.get_legend_handles_labels(); \n cleaned_legend_labels = [a[-5:-1] for a in legend_labels]\n legend_length = len(data_for_this_plot) # just the nonzero ones\n ax1.legend(reversed(legend_handles[-legend_length:]), reversed(cleaned_legend_labels[-legend_length:]), loc='upper left'); # reverse to keep order consistent\n",
"execution_count": 17,
"outputs": []
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"ExecuteTime": {
"start_time": "2019-10-07T02:59:37.844249Z",
"end_time": "2019-10-07T02:59:37.955587Z"
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"trusted": true
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"cell_type": "code",
"source": "# Nonlinear curve fit with confidence interval\ndef curve_fit_with_ci(graph_type, papers_per_year_historical, curve_type, ax=None):\n my_rows = papers_per_year_historical.loc[papers_per_year_historical.article_years_from_availability <= 5]\n my_rows = my_rows.loc[my_rows.prediction_year >= 2000]\n my_rows = my_rows.loc[my_rows.prediction_year < 2018]\n x = my_rows.groupby(\"prediction_year\", as_index=False).sum().prediction_year\n y = my_rows.groupby(\"prediction_year\", as_index=False).sum().num_articles\n\n my_color_lookup = graph_type_plus_biorxiv_lookup.loc[graph_type_plus_biorxiv_lookup[\"name\"]==graph_type]\n my_color = my_color_lookup.iloc[0][\"color\"]\n \n if not ax:\n fig, ax = plt.subplots(1, 1, figsize=(3, 3), sharex=True, sharey=False)\n ax.plot(x, y, 'o', color=my_color)\n ax.set_xlim(2000, 2025)\n ax.set_ylabel(\"articles (millions)\")\n ax.set_title(\"{}\".format(graph_type))\n \n if curve_type == \"no_line\":\n ax.set_xlabel(\"year of observation\")\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.2f}'.format(y/(1000*1000.0)))) \n return\n \n if curve_type == \"linear\":\n initial_guess=None\n def func(x, a, b):\n return a * (x - 2000) + b\n elif curve_type == \"exp\":\n if graph_type == \"biorxiv\":\n initial_guess=(5, 1, 1)\n def func(x, a, b, d):\n return b + a * np.exp((x - 2014)/d)\n else:\n initial_guess=(14287, 21932, 5)\n def func(x, a, b, d):\n return b + a * np.exp((x - 2000)/d)\n elif curve_type == \"negative_exp\":\n initial_guess=(1731700, 22962997, -7)\n def func(x, a, b, d):\n return b - a * np.exp((x - 2000)/d) \n\n pars, pcov = curve_fit(func, x, y, initial_guess)\n\n xfit_extrap = range(2000, 2040+1)\n if curve_type == \"linear\":\n yfit_extrap = [func(a, pars[0], pars[1]) for a in xfit_extrap]\n yfit = [func(a, pars[0], pars[1]) for a in x]\n else:\n yfit_extrap = [func(a, pars[0], pars[1], pars[2]) for a in xfit_extrap]\n yfit = [func(a, pars[0], pars[1], pars[2]) for a in x]\n \n alpha = 0.05 # 95% confidence interval = 100*(1-alpha)\n n = len(y) # number of data points\n p = len(pars) # number of parameters\n dof = max(0, n - p) # number of degrees of freedom\n tval = t.ppf(1.0-alpha/2., dof) # student-t value \n\n residuals = y - yfit\n ss_res = np.sum(residuals**2)\n ss_tot = np.sum((y - np.mean(y))**2)\n r_squared = 1 - (ss_res / ss_tot)\n \n fit_string = \"\"\n for i, p, var in zip(range(n), pars, np.diag(pcov)):\n sigma = var**0.5\n fit_string += ' p{}: {} [{} {}] '.format(i, \n round(p, 3),\n round(p - sigma*tval, 3),\n round(p + sigma*tval, 3))\n fit_string += \"{}\".format(round(r_squared, 3))\n# print \"{} {} {}\".format(graph_type, curve_type, fit_string)\n\n ax.plot(xfit_extrap[0:25], yfit_extrap[0:25], '-', color=my_color)\n ax.set_xlabel(\"r^2={}\".format(round(r_squared, 3)))\n if max(yfit_extrap) > 100000:\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.2f}'.format(y/(1000*1000.0))))\n my_return = pd.DataFrame({\n \"x\": xfit_extrap,\n \"y\": yfit_extrap,\n \"r_squared\": [r_squared for y in yfit_extrap]\n })\n return my_return",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-30T17:10:37.851170Z",
"end_time": "2019-09-30T17:10:37.857306Z"
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"code_folding": []
},
"cell_type": "markdown",
"source": "#### Code: SQL"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "See notebook."
},
{
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"ExecuteTime": {
"start_time": "2019-10-07T02:59:37.962176Z",
"end_time": "2019-10-07T02:59:37.994506Z"
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"trusted": true
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# query for articles_by_color_by_year_with_embargos and articles_by_color_by_year\n\nq = \"\"\"\nselect date_part('year', fixed.published_date)::int as published_year, \nfixed.oa_status,\ndelayed.embargo,\ncount(*) as num_articles\nfrom unpaywall u\nleft join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\njoin unpaywall_updates_view fixed on fixed.doi=u.doi\nwhere genre = 'journal-article' and journal_issn_l not in ('0849-6757', '0931-7597')\nand published_year > '1950-01-01'::timestamp\ngroup by published_year, fixed.oa_status, embargo\norder by published_year asc\n\"\"\"\narticles_by_color_by_year_with_embargos = read_from_file_or_db(\"articles_by_color_by_year_with_embargos\", q)\n\narticles_by_color_by_year = articles_by_color_by_year_with_embargos.drop(columns = [\"embargo\"])\narticles_by_color_by_year = articles_by_color_by_year.groupby(['published_year', 'oa_status']).sum()\narticles_by_color_by_year.reset_index(inplace=True)\n",
"execution_count": 19,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T02:59:38.001357Z",
"end_time": "2019-10-07T02:59:38.020991Z"
},
"trusted": true
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# query for articles_by_graph_type_by_year\n\nq = \"\"\"\nselect date_part('year', fixed.published_date) as published_year, \nfixed.oa_status,\ncase when fixed.oa_status='bronze' and delayed.embargo is not null then 'delayed_bronze' \n when fixed.oa_status='bronze' and delayed.embargo is null then 'immediate_bronze' \n else fixed.oa_status end\n as graph_type,\ncount(*) as num_articles\nfrom unpaywall u\nleft join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\njoin unpaywall_updates_view fixed on fixed.doi=u.doi\nwhere genre = 'journal-article' and journal_issn_l not in ('0849-6757', '0931-7597')\nand published_year > '1950-01-01'::timestamp\nand published_year < '2019-01-01'::timestamp\ngroup by published_year, fixed.oa_status, graph_type\norder by published_year asc\n\"\"\"\narticles_by_graph_type_by_year = read_from_file_or_db(\"articles_by_graph_type_by_year\", q)\n",
"execution_count": 20,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T02:59:38.027580Z",
"end_time": "2019-10-07T02:59:38.042837Z"
},
"code_folding": [],
"trusted": true
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# query for views_by_age_months_no_color_full_year. maybe don't need this one in the final paper?\n\nq = \"\"\"\nselect datediff('days', fixed.published_date, received_at_raw::timestamp)/30 as article_age_months, \ncount(u.doi) as num_views \nfrom papertrail_unpaywall_extracted extracted \njoin unpaywall u on extracted.doi=u.doi \njoin unpaywall_updates_view fixed on fixed.doi=u.doi\nwhere genre = 'journal-article' and journal_issn_l not in ('0849-6757', '0931-7597')\nand fixed.published_date > '1950-01-01'::timestamp\nand extracted.doi not in ('10.1038/nature21360', '10.1038/nature11723')\ngroup by article_age_months\norder by article_age_months asc\n\n\"\"\"\nviews_by_age_months_no_color_full_year = read_from_file_or_db(\"views_by_age_months_no_color_full_year\", q)\n",
"execution_count": 21,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T02:59:38.048644Z",
"end_time": "2019-10-07T02:59:38.068561Z"
},
"code_folding": [],
"trusted": true
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# query for views_by_age_months\n# not used by analysis but here for data dump\n\nq = \"\"\"\nselect datediff('days', fixed.published_date, received_at_raw::timestamp)/30 as article_age_months, \nfixed.oa_status,\ncount(u.doi) as num_views \nfrom papertrail_unpaywall_extracted extracted\njoin unpaywall u on extracted.doi=u.doi \njoin unpaywall_updates_view fixed on fixed.doi=u.doi\nwhere genre = 'journal-article' and journal_issn_l not in ('0849-6757', '0931-7597')\nand fixed.published_date > '1950-01-01'::timestamp\nand fixed.published_date < current_date\nand received_at_raw > '2019-07-01'\nand received_at_raw <= '2019-08-01'\nand extracted.doi != '10.1038/nature21360'\ngroup by article_age_months, fixed.oa_status\norder by article_age_months asc\n\"\"\"\nviews_by_age_months = read_from_file_or_db(\"views_by_age_months\", q)\n\n",
"execution_count": 22,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T02:59:38.074625Z",
"end_time": "2019-10-07T02:59:38.092807Z"
},
"trusted": true
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# query for views_by_age_years\n\nq = \"\"\"\nselect datediff('days', fixed.published_date, received_at_raw::timestamp)/(30*12) as article_age_years, \nfixed.oa_status,\ncase when fixed.oa_status='bronze' and journal_issn_l in (select issn_l from journal_delayed_oa_active) then 'delayed' when fixed.oa_status='bronze' then 'immediate' else null end as delayed_or_immediate,\ncount(u.doi) as num_views \nfrom papertrail_unpaywall_extracted extracted \njoin unpaywall u on extracted.doi=u.doi \njoin unpaywall_updates_view fixed on fixed.doi=u.doi\nwhere genre = 'journal-article' and journal_issn_l not in ('0849-6757', '0931-7597')\nand fixed.published_date > '1950-01-01'::timestamp\nand fixed.published_date < current_date\nand received_at_raw > '2019-07-01'\nand received_at_raw <= '2019-08-01'\nand extracted.doi != '10.1038/nature21360'\ngroup by article_age_years, fixed.oa_status, delayed_or_immediate\norder by article_age_years asc\n\"\"\"\nviews_by_age_years = read_from_file_or_db(\"views_by_age_years\", q)\n",
"execution_count": 23,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T02:59:38.099351Z",
"end_time": "2019-10-07T02:59:38.140028Z"
},
"trusted": true
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\nq = \"\"\"\nselect date_part('year', min_record_timestamp) as year_of_first_availability, \ndatediff('days', fixed.published_date, min_record_timestamp)/30 as months_old_at_first_deposit,\ndate_part('year', fixed.published_date) as published_year,\ncount(*) as num_articles\nfrom unpaywall u\njoin unpaywall_pmh_record_min_timestamp pmh on u.doi=pmh.doi\njoin unpaywall_updates_view fixed on fixed.doi=u.doi\nwhere fixed.oa_status = 'green'\nand genre = 'journal-article' and journal_issn_l not in ('0849-6757', '0931-7597')\nand year_of_first_availability is not null\ngroup by year_of_first_availability, months_old_at_first_deposit, published_year\n\"\"\"\ngreen_oa_with_dates_by_availability = read_from_file_or_db(\"green_oa_with_dates_by_availability\", q)\n",
"execution_count": 24,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T02:59:38.146393Z",
"end_time": "2019-10-07T02:59:47.900983Z"
},
"code_folding": [],
"trusted": true
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# queries delayed_bronze_after_embargos_age_months\n# not used by analysis but here for data dump\n\nmin_prediction_year = 1949\nmax_prediction_year = 2019 + 1\nprediction_year_range = range(min_prediction_year, max_prediction_year)\ndelayed_bronze_after_embargos_age_months = pd.DataFrame()\n\nfor i, prediction_year in enumerate(range(min_prediction_year - 1, max_prediction_year)):\n \n q = \"\"\"\n select \n datediff('days', fixed.published_date, '{prediction_year}-01-01'::timestamp)/30 as article_age_months, \n --datediff('days', fixed.published_date, current_date)/30 as article_age_months_from_now, \n {prediction_year} as prediction_year,\n count(*) as num_articles\n from unpaywall u\n left join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n join unpaywall_updates_view fixed on fixed.doi=u.doi\n where genre = 'journal-article' and journal_issn_l not in ('0849-6757', '0931-7597')\n and fixed.oa_status = 'bronze'\n and delayed.embargo is not null\n and fixed.published_date > '1950-01-01'::timestamp\n and fixed.published_date <= ADD_MONTHS('{prediction_year}-01-01'::timestamp, -embargo::integer)\n group by prediction_year, article_age_months\n order by prediction_year, article_age_months asc\n \"\"\".format(prediction_year=prediction_year)\n\n filename_root = \"delayed_bronze_sql_parts/{varname}_{index}\".format(varname=\"bronze_rows_by_month\", index=i) \n bronze_rows = read_from_file_or_db(filename_root, q)\n \n delayed_bronze_after_embargos_age_months = delayed_bronze_after_embargos_age_months.append(bronze_rows)\ndelayed_bronze_after_embargos_age_months.to_csv(\"data/delayed_bronze_after_embargos_age_months.csv\")\n\n",
"execution_count": 25,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T02:59:47.910308Z",
"end_time": "2019-10-07T02:59:55.117624Z"
},
"cell_style": "center",
"trusted": true
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\n# queries delayed_bronze_after_embargos_age_years\n\nmin_prediction_year = 1949\nmax_prediction_year = 2019 + 1\nprediction_year_range = range(min_prediction_year, max_prediction_year)\ndelayed_bronze_after_embargos_age_years = pd.DataFrame()\n\nfor i, prediction_year in enumerate(range(min_prediction_year - 1, max_prediction_year)):\n \n q = \"\"\" \n select \n datediff('days', fixed.published_date, '{prediction_year}-01-01'::timestamp)/(30*12) as article_age_years, \n {prediction_year} as prediction_year,\n count(*) as num_articles\n from unpaywall u\n left join journal_delayed_oa_active delayed on u.journal_issn_l = delayed.issn_l\n join unpaywall_updates_view fixed on fixed.doi=u.doi\n where genre = 'journal-article' and journal_issn_l not in ('0849-6757', '0931-7597')\n and fixed.oa_status = 'bronze'\n and delayed.embargo is not null\n and fixed.published_date > '1950-01-01'::timestamp\n and fixed.published_date <= ADD_MONTHS('{prediction_year}-01-01'::timestamp, -embargo::integer)\n \n group by prediction_year, article_age_years\n order by prediction_year, article_age_years asc\n \"\"\".format(prediction_year=prediction_year)\n\n filename_root = \"delayed_bronze_sql_parts/{varname}_{index}\".format(varname=\"bronze_rows_by_year\", index=i)\n bronze_rows_by_year = read_from_file_or_db(filename_root, q)\n \n delayed_bronze_after_embargos_age_years = delayed_bronze_after_embargos_age_years.append(bronze_rows_by_year)\ndelayed_bronze_after_embargos_age_years.to_csv(\"data/delayed_bronze_after_embargos_age_years.csv\")\n",
"execution_count": 26,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T02:59:55.124248Z",
"end_time": "2019-10-07T02:59:55.138905Z"
},
"trusted": true
},
"cell_type": "code",
"source": "%%capture --no-stderr --no-stdout --no-display\n\nq = \"\"\"select u.year::numeric as published_year, count(distinct u.doi) as num_articles \nfrom unpaywall u\njoin unpaywall u_biorxiv_record on u_biorxiv_record.doi = replace(u.best_url, 'https://doi.org/', '')\nwhere u.doi not like '10.1101/%' and u.best_url like '%10.1101/%'\nand datediff('days', u_biorxiv_record.published_date::timestamp, u.published_date::timestamp)/(30.0) >= 0\nand u.year >= 2013 and u.year < 2019\ngroup by u.year\norder by u.year desc\n\"\"\"\nbiorxiv_growth_otherwise_closed = read_from_file_or_db(\"biorxiv_growth_otherwise_closed\", q)\n",
"execution_count": 27,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "*---- delete the text to the line above in the final paper ----*"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-4\"></a>\n## 4. Methods and Results"
},
{
"metadata": {
"scrolled": true
},
"cell_type": "markdown",
"source": "<a id=\"section-4-1\"></a>\n### 4.1 Past OA Publication, by date of observation"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-4-1-1\"></a>\n#### 4.1.1 OA lag\n\nFor Gold OA and Hybrid OA understanding OA lag is easy -- there is no lag: papers become OA at the time of publication. \n\nFor Green and Bronze OA the lag is more complicated. Authors often self-archive (upload their paper to a repository) months or years after the official publication date of the paper, typically  because the journal has a policy that authors must wait a certain length of time (the \"embargo period\") before self-archiving. Funder policies that mandate Green OA often allow a delay between publication and availability (notably the National Institutes of Health in the USA allows a 12 month embargo, which is relevant for most of the content in the large PMC repository). Finally, some journals open up their back catalogs once articles reach a certain age, which has been called \"delayed OA\" (Laakso and Björk, 2013) and we consider an important subset of Bronze.\n\nWe explore and model these dynamics below."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-4-1-2\"></a>\n#### 4.1.2. OA lag for Green OA"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T02:59:55.144785Z",
"end_time": "2019-10-07T02:59:55.153871Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"oa_lag_green\");",
"execution_count": 28,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-oa_lag_green\"></div>\n <script>\n var key = \"figure-oa_lag_green\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
" print figure_link(\"oa_lag_green\") ": "<a href=\"#figure-oa_lag_green\">Figure 3</a>"
}
},
"cell_type": "markdown",
"source": "Calculating OA lag requires data on both when an article was first published in its journal and the date it was first made OA. \n\nThe date an article becomes Green OA can be derived from the date it was made available in a repository, which we can get from its matched [OAI-PMH records](https://www.openarchives.org/pmh/) (as harvested by Unpaywall). \n\n{{ print figure_link(\"oa_lag_green\") }} shows four plots: the leftmost plot shows Green OA articles that were first made OA in 2015, the second plot shows Green OA articles that were first made OA in 2016, and so on. Each plot is a histogram of number of articles by date of publication. \n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T02:59:55.170024Z",
"end_time": "2019-10-07T03:00:01.316415Z"
},
"trusted": true
},
"cell_type": "code",
"source": "first_detailed_plots(\"green\")",
"execution_count": 29,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x216 with 4 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"oa_lag_green\")": "<a href=\"#figure-oa_lag_green\">Figure 3</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"oa_lag_green\")}}: OA lag for Green OA.** Each plot shows articles that were first made available during the given year of observation, by year of their publication on the x-axis."
},
{
"metadata": {
"variables": {
" print figure_link(\"oa_lag_green\") ": "<a href=\"#figure-oa_lag_green\">Figure 3</a>"
}
},
"cell_type": "markdown",
"source": "By looking at the first plot in depth, we can see that a few articles are made available *before* they are actually published (articles published in 2016 or 2017) -- these were preprints, submitted before publication. Continuing to look at the first row, we can see the bulk of the articles that became available in 2015 were published in 2015 (lag of zero years) or in 2014 (lag of 1 year). A few were published in 2013 (an OA lag of 2 years), and then a long tail represents the backfilling of older articles. \n\nLooking now at all plots in {{ print figure_link(\"oa_lag_green\") }}, we can see that a similar OA lag pattern (a few preprints are available before publication, most articles become available within a 3 year OA lag, then a long tail) has held for the last four years of Green OA availability (the distribution of the bars are similar in all four graphs). "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We can also see that the relative amount of green OA is growing slightly by year of OA-first-availability (the area under the whole histogram gets higher with subsequent histograms). Green OA appears to be growing. We will explore this further in [Section 4.2](#section-4-2).\n\nMore details on Green OA lag are included in Supplementary Information, [Section 11.1](#section-11-1)."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-4-1-3\"></a>\n#### 4.1.3 OA lag for Bronze Delayed OA"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "There was no recent, complete, publicly-available list of Delayed OA journals, so we derived a list empirically based on the Unpaywall database. We have made our list publicly available: details are in [Section 7.2](#section-7-2).\n\nTo create the list we started by looking at existing compilations of Delayed OA journals, including:\n\n- <https://www.elsevier.com/about/open-science/open-access/open-archive>\n\n- <http://highwire.stanford.edu/cgi/journalinfo#loc>\n\n- <https://www.ncbi.nlm.nih.gov/pmc/journals/?filter=t3&titles=current&search=journals#csvfile>\n\n- <https://en.wikipedia.org/wiki/Category:Delayed_open_access_journals>\n\n- [Delayed open access: An overlooked high‐impact category of openly available scientific literature](https://helda.helsinki.fi/bitstream/10138/157658/3/Laakso_Bj_rk_2013_Delayed_OA.pdf) by Laakso and Björk (2013).\n\nFrom those sources we determined that almost all embargoes for Delayed OA journals are at 6, 18, 24, 36, 48, or 60 months. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Next we used the Unpaywall data to calculate the OA rate of all journals, partitioned by age of their articles. We looked at Bronze OA rates before and after each of these common month cutoffs, highlighting cutoffs where OA was much less than 90% before the cutoff and 90% or higher afterwards. For each cutoff that looked like a Delayed OA candidate, we manually examined the full OA pattern for the journal and made a judgment call about whether it had an OA pattern consistent with a Delayed OA journal (low OA rates for articles until an embargo date, then high OA rates). We finally cross-referenced this empirically derived list with the sources again to see if it was roughly equivalent for journals on both lists -- it is, and the empirically derived list is more comprehensive. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Our resulting list includes 3.6 million articles (4.9% of all articles) published in 546 journals, with the following embargo lengths:\n\nembargo\tlength (months)|number of journals|number of articles\n---|---|---\n6\t|58 | 511,326\n12\t|175| 1,608,597\n18\t|137 | 68,9820\n24\t|42 | 188,949\n36\t|71 | 269,186\n48\t|63 | 316,510\n**Total**\t|**546** | **3,584,388**"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:00:01.398012Z",
"end_time": "2019-10-07T03:00:01.423129Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"oa_lag_delayed_bronze\");\n",
"execution_count": 30,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-oa_lag_delayed_bronze\"></div>\n <script>\n var key = \"figure-oa_lag_delayed_bronze\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
" print figure_link(\"oa_lag_delayed_bronze\") ": "<a href=\"#figure-oa_lag_delayed_bronze\">Figure 4</a>"
}
},
"cell_type": "markdown",
"source": "We used this list to split articles labelled \"Bronze\" by Unpaywall into two categories: \"Delayed Bronze\" for articles published in journals in our Delayed OA list, and \"Immediate Bronze\" for all others.\n\nImmediate Bronze articles have no OA lag: they become available on the publisher site immediately.\n\nWe estimate the OA lag for a Delayed Bronze OA article as the Delayed OA embargo for journal it is published in. From there we can also estimate the date it first became OA by adding the embargo period to the publication date of the article.\n\n{{ print figure_link(\"oa_lag_delayed_bronze\") }} shows four plots: the leftmost plot shows Delayed Bronze OA articles that were first made OA in 2015, the second plot shows Delayed Bronze OA articles that were first made OA in 2016, and so on. Each plot is a histogram of number of articles by date of publication. \n\nThe distribution of Delayed Bronze OA articles by date first made OA is shown in {{ print figure_link(\"oa_lag_delayed_bronze\") }}, as histograms by publication date. Most articles become available after a 1 year lag. Bumps that represent articles that become available at 24, 36, and 48 months are also clearly visible."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:00:01.504656Z",
"end_time": "2019-10-07T03:00:03.565378Z"
},
"trusted": true
},
"cell_type": "code",
"source": "first_detailed_plots(\"delayed_bronze\")",
"execution_count": 31,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x216 with 4 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-30T18:42:41.622135Z",
"end_time": "2019-09-30T18:42:41.637086Z"
},
"variables": {
"print figure_link(\"oa_lag_delayed_bronze\")": "<a href=\"#figure-oa_lag_delayed_bronze\">Figure 4</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"oa_lag_delayed_bronze\")}}: OA lag for Delayed Bronze OA.** Each plot shows articles that were first made available during the given year of observation, by year of their publication on the x-axis."
},
{
"metadata": {
"variables": {
" print figure_link(\"oa_lag_delayed_bronze\") ": "<a href=\"#figure-oa_lag_delayed_bronze\">Figure 4</a>"
}
},
"cell_type": "markdown",
"source": "By looking at the first plot of {{ print figure_link(\"oa_lag_delayed_bronze\") }} in depth, we can see that most articles first made available in Delayed Bronze OA journals were made available with 1 year OA lag, in 2014. A few were made available with a lag of less than one year, 2 years, or 4 years. \n\nWe can also see that the relative amount of Delayed Bronze OA is not growing very much by year of OA-first-availability (the area under the whole histogram gets higher with subsequent histograms is approximately the same for all histograms). Delayed Bronze OA is not growing quickly. We will explore this further in [Section 4.2](#section-4-2).\n\nMore details on Delayed Bronze OA lag are included in Supplementary Information, [Section 11.2](#section-11-2)."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T20:10:53.604913Z",
"end_time": "2019-09-23T20:10:53.610995Z"
}
},
"cell_type": "markdown",
"source": "<a id=\"section-4-1-4\"></a>\n#### 4.1.4 Closed access at date of observation"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We consider an article Closed if it has been published and is not considered OA at the time of observation."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-27T20:09:40.198184Z",
"end_time": "2019-09-27T20:09:40.203439Z"
}
},
"cell_type": "markdown",
"source": "<a id=\"section-4-1-5\"></a>\n#### 4.1.5 Past OA by date of observation and date of publication"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:00:03.649687Z",
"end_time": "2019-10-07T03:00:03.664707Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure('small-multiples-num-papers-past');",
"execution_count": 32,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-small-multiples-num-papers-past\"></div>\n <script>\n var key = \"figure-small-multiples-num-papers-past\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
" print figure_link('small-multiples-num-papers-past')": "<a href=\"#figure-small-multiples-num-papers-past\">Figure 5</a>",
" print figure_link(\"oa_lag_green\") ": "<a href=\"#figure-oa_lag_green\">Figure 3</a>",
" print figure_link(\"oa_lag_delayed_bronze\") ": "<a href=\"#figure-oa_lag_delayed_bronze\">Figure 4</a>"
}
},
"cell_type": "markdown",
"source": "We combine the OA lag data above to describe OA by date of observation for all OA types, in {{ print figure_link('small-multiples-num-papers-past')}}. \n\nEach column is a year of observation, from 2014 to 2018. Each row is a different OA type. Each mini plot is a histogram of all articles available by publication date, for the given observation year and OA type. \n\nThis figure differs from {{ print figure_link(\"oa_lag_green\") }} and {{ print figure_link(\"oa_lag_delayed_bronze\") }} in that it is cumulative over date of first availability: it shows all papers published prior to the year of observation."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:00:03.732292Z",
"end_time": "2019-10-07T03:01:05.099977Z"
},
"trusted": true
},
"cell_type": "code",
"source": "# start here \n\nnow_year = 2018\npapers_per_year_historical = pd.DataFrame()\nfor graph_type in graph_type_order:\n for prediction_year in range(1990, now_year+1): \n papers_per_year = get_papers_by_availability_year(graph_type, prediction_year, just_this_year=True)\n papers_per_year[\"graph_type\"] = graph_type\n papers_per_year[\"prediction_year\"] = prediction_year\n papers_per_year_historical = papers_per_year_historical.append(papers_per_year)\n \npapers_per_year_historical_cumulative = pd.DataFrame()\nfor graph_type in graph_type_order:\n for prediction_year in range(1990, now_year+1): \n papers_per_year = get_papers_by_availability_year(graph_type, prediction_year, just_this_year=False)\n papers_per_year[\"graph_type\"] = graph_type\n papers_per_year[\"prediction_year\"] = prediction_year\n papers_per_year_historical_cumulative = papers_per_year_historical_cumulative.append(papers_per_year) \n",
"execution_count": 33,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:05.107227Z",
"end_time": "2019-10-07T03:01:16.391714Z"
},
"trusted": true
},
"cell_type": "code",
"source": "my_range = range(2014, 2018+1)\n\nfig, axes = plt.subplots(len(graph_type_order)+1, len(my_range), figsize=(12, 6), sharex=True, sharey=False)\naxes_flatten = axes.flatten()\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\ni = 0\nfor observation_year in my_range:\n ax = axes_flatten[i]\n ax.set_axis_off() \n column_label = \"observation year\\n{}\".format(observation_year)\n ax.text(.3, .2, column_label,\n horizontalalignment='center',\n verticalalignment='bottom',\n fontsize=14,\n transform=ax.transAxes)\n i += 1\n\nfor graph_type in graph_type_order[::-1]:\n for observation_year in my_range: \n ax = axes_flatten[i]\n this_data = papers_per_year_historical_cumulative.copy()\n this_data = this_data.loc[this_data.graph_type == graph_type]\n this_data = this_data.loc[this_data.prediction_year == observation_year]\n this_data[\"publication_date\"] = [int(observation_year - a) for a in this_data.article_years_from_availability]\n new_data = graph_available_papers_in_observation_year_by_pubdate(graph_type, this_data, observation_year, ax=ax)\n\n y_max = papers_per_year_historical_cumulative.loc[(papers_per_year_historical_cumulative.graph_type == graph_type) &\n (papers_per_year_historical_cumulative.prediction_year <= max(my_range))][\"num_articles\"].max()\n ax.set_ylim(0, 1.2*y_max)\n \n axis_color = \"silver\"\n ax.spines['bottom'].set_color(axis_color)\n ax.spines['top'].set_color(axis_color) \n ax.spines['right'].set_color(axis_color)\n ax.spines['left'].set_color(axis_color)\n ax.tick_params(axis='x', colors=axis_color)\n ax.tick_params(axis='y', colors=axis_color)\n\n i += 1\n\ni_bottom_left_graph = len(graph_type_order) * len(my_range) \nax_bottom_left = axes_flatten[i_bottom_left_graph]\nax_bottom_left.set_ylabel(\"articles\\n(millions)\");\nax_bottom_left.set_xlabel(\"year of publication\");\naxis_color = \"black\"\nax_bottom_left.spines['bottom'].set_color(axis_color)\nax_bottom_left.spines['top'].set_color(axis_color) \nax_bottom_left.spines['right'].set_color(axis_color)\nax_bottom_left.spines['left'].set_color(axis_color)\nax_bottom_left.tick_params(axis='x', colors=axis_color)\nax_bottom_left.tick_params(axis='y', colors=axis_color)",
"execution_count": 34,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": "<Figure size 864x432 with 35 Axes>"
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"small-multiples-num-papers-past\")": "<a href=\"#figure-small-multiples-num-papers-past\">Figure 5</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"small-multiples-num-papers-past\")}}: Articles by year of observation, 2014-2018.** Each row is an OA Type, each column is a Year of Observation, the x-axis of each graph is the Year of Publication, and the y-axis is the total number of articles (millions) available at the year of observation."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:16.412513Z",
"end_time": "2019-10-07T03:01:16.418872Z"
},
"trusted": true
},
"cell_type": "code",
"source": "first_year_row = 2014",
"execution_count": 35,
"outputs": []
},
{
"metadata": {
"variables": {
" print first_year_row+1": "2015",
" print first_year_row": "2014",
" print first_year_row+4": "2018"
}
},
"cell_type": "markdown",
"source": "We can see that Gold, Hybrid, and Immediate Bronze OA articles all simply accumulate new articles each year, immediately. For example, the {{ print first_year_row+1}} Gold graph is identical to the {{ print first_year_row}} Gold graph beside it, other than the addition of a new, taller rightmost bar showing new papers published and made available in 2015. \n\nIn contrast, Green OA (6th row) and Delayed Bronze OA (2nd row) graphs all have more complicated trends. The graphs for the {{ print first_year_row+1}} observation year differ from the {{ print first_year_row}} graphs beside them in that they have a few new publications in {{ print first_year_row+1}}, but they also boost the {{ print first_year_row}} publication year, and even older years. In fact we can see that when observed in {{ print first_year_row+4}} (the last column of the whole figure) Green OA is higher in all publication years than it was in the observation year {{ print first_year_row}} (the first column in the figure) because of met embargoes and backfilling. A similar trend is visible for Delayed Bronze OA. "
},
{
"metadata": {
"variables": {
"print figure_link(\"small-multiples-num-papers-past\")": "<a href=\"#figure-small-multiples-num-papers-past\">Figure 5</a>",
" print first_year_row": "2014",
" print first_year_row+4": "2018"
}
},
"cell_type": "markdown",
"source": "It is hard to see at the scale of {{print figure_link(\"small-multiples-num-papers-past\")}}, but the Closed access graphs (top row) have the opposite trend -- when observed in 2018 (the last column), *fewer* papers in early bars of the histogram were considered Closed OA compared to an observation made in {{ print first_year_row}} (first column). This is because some of what was \"observed\" as Closed in {{ print first_year_row}} has become Green and Bronze by the observation year of {{ print first_year_row+4}}, and therefore no longer appears in the Closed access histograms."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-06T14:22:14.751975Z",
"end_time": "2019-10-06T14:22:14.928776Z"
}
},
"cell_type": "markdown",
"source": "<a id=\"section-4-1-6\"></a>\n#### 4.1.6 Combined Past OA by date of observation"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:16.584200Z",
"end_time": "2019-10-07T03:01:16.594281Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure('articles_by_oa_historical');",
"execution_count": 36,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-articles_by_oa_historical\"></div>\n <script>\n var key = \"figure-articles_by_oa_historical\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"small-multiples-num-papers-past\")": "<a href=\"#figure-small-multiples-num-papers-past\">Figure 5</a>",
"print figure_link(\"articles_by_oa_historical\")": "<a href=\"#figure-articles_by_oa_historical\">Figure 6</a>"
}
},
"cell_type": "markdown",
"source": "We can now graph all papers by OA availability, by taking the area under each histogram in {{print figure_link(\"small-multiples-num-papers-past\")}}. This gives us {{print figure_link(\"articles_by_oa_historical\")}}, with the absolute number of articles on the left panel and proportion by OA type on the right panel. We can see that the number of OA articles has been growing over time between 2000 and 2018, though slowly. Looking at the last year of observation in the proportion graph and its table below shows 27% of the literature published by 2018 could be observed as OA in 2018."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:16.627018Z",
"end_time": "2019-10-07T03:01:17.815758Z"
},
"trusted": true
},
"cell_type": "code",
"source": "articles_by_obs_year_df = papers_per_year_historical.copy()\narticles_by_obs_year_df = articles_by_obs_year_df.rename(\n columns={\"prediction_year\": \"x\", \"num_articles\": \"y\"})\n(articles_by_obs_historical, articles_by_obs_historical_proportional) = plot_area_and_proportion(\n articles_by_obs_year_df, \n \"standard\", \n 2000, 2018, 2018,\n xlabel=\"year of observation\", \n fancy=\"cumulative\");",
"execution_count": 37,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x216 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"articles_by_observation_year_prediction\")": "<a href=\"#figure-articles_by_observation_year_prediction\">Figure 11</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"articles_by_observation_year_prediction\")}}: Total articles by OA type, by year of observation.** OA type as of year of observation."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-06T14:39:36.975504Z",
"end_time": "2019-10-06T14:39:36.991379Z"
}
},
"cell_type": "markdown",
"source": "Table of percentages for the right panel:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:10:38.739955Z",
"end_time": "2019-10-07T03:10:39.140211Z"
},
"trusted": true
},
"cell_type": "code",
"source": "df = articles_by_obs_historical_proportional.copy()\nrows = df.loc[(df.index==2010) | (df.index==2018)]\nrows[\"all OA\"] = 1 - rows[\"closed\"]\nmy_markdown = tabulate(100*rows[graph_type_order+[\"all OA\"]], tablefmt=\"pipe\", headers=\"keys\", floatfmt=\",.0f\")\ndisplay(Markdown(my_markdown))",
"execution_count": 102,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.Markdown object>",
"text/markdown": "| x | green | gold | hybrid | immediate_bronze | delayed_bronze | closed | all OA |\n|-----:|--------:|-------:|---------:|-------------------:|-----------------:|---------:|---------:|\n| 2010 | 2 | 3 | 1 | 8 | 4 | 83 | 17 |\n| 2018 | 4 | 8 | 3 | 8 | 3 | 73 | 27 |"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-4-2\"></a>\n### Future OA Publication, by date of observation\n\n<a id=\"section-4-2-1\"></a>\n#### 4.2.1 Approach"
},
{
"metadata": {
"variables": {
"print figure_link(\"small-multiples-num-papers-past\")": "<a href=\"#figure-small-multiples-num-papers-past\">Figure 5</a>",
"print figure_link(\"oa_lag_green\")": "<a href=\"#figure-oa_lag_green\">Figure 3</a>",
"print figure_link(\"oa_lag_delayed_bronze\")": "<a href=\"#figure-oa_lag_delayed_bronze\">Figure 4</a>"
}
},
"cell_type": "markdown",
"source": "We wish to project OA availability {{print figure_link(\"small-multiples-num-papers-past\")}} in future years. How can we extrapolate these graphs into the future?\n\nThe model we use is based on observing that the papers that become available each year have a consistent distribution by article age, as seen in {{print figure_link(\"oa_lag_green\")}} for Green OA and {{print figure_link(\"oa_lag_delayed_bronze\")}} for Bronze OA (the histograms within each figure have a similar shape). \n\nIf we then assume that the articles that will become available next year are similar to the articles that became available this year, for a given article age and OA type we can predict the future like this:\n\n```\n total articles available next year = \n\n total articles available this year\n\n +\n\n scaling factor to account for growth\n *\n articles made newly available last year```"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:18.345983Z",
"end_time": "2019-10-07T03:01:18.364365Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure('extrap_linear');\nregister_new_figure('extrap_exp');\nregister_new_figure('extrap_negative_exp');",
"execution_count": 39,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-extrap_linear\"></div>\n <script>\n var key = \"figure-extrap_linear\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-extrap_exp\"></div>\n <script>\n var key = \"figure-extrap_exp\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-extrap_negative_exp\"></div>\n <script>\n var key = \"figure-extrap_negative_exp\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"small-multiples-num-papers-past\")": "<a href=\"#figure-small-multiples-num-papers-past\">Figure 5</a>",
"print figure_link(\"oa_lag_green\")": "<a href=\"#figure-oa_lag_green\">Figure 3</a>",
"print figure_link(\"oa_lag_delayed_bronze\")": "<a href=\"#figure-oa_lag_delayed_bronze\">Figure 4</a>"
}
},
"cell_type": "markdown",
"source": "We have much of what we need already calculated in previous sections: the **total articles available this year** is the observation year 2018 in {{print figure_link(\"small-multiples-num-papers-past\")}}, and the **articles made newly available last year** is the last histogram of {{print figure_link(\"oa_lag_green\")}} for Green OA and {{print figure_link(\"oa_lag_delayed_bronze\")}} for Bronze OA. \n\nAll that remains is to calculate the **scaling factor to account for growth**. We do this next."
},
{
"metadata": {
"variables": {
"print figure_link(\"extrap_linear\")": "<a href=\"#figure-extrap_linear\">Figure 7</a>"
}
},
"cell_type": "markdown",
"source": "<a id=\"section-4-2-2\"></a>\n#### 4.2.2 Scaling factor\n\n{{print figure_link(\"extrap_linear\")}} shows a scatter plot of new articles by OA type, by year of observation. We add a linear best fit line, using the `scipy.optimize.curve_fit()` function. The r<sup>2</sup> value below each graph is the sum of squares between the data and the fit, indicating goodness of fit (close to 1.0 is better). "
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:18.422525Z",
"end_time": "2019-10-07T03:01:19.468427Z"
},
"trusted": true
},
"cell_type": "code",
"source": "naive_data_all = pd.DataFrame()\n\ncurve_type=\"linear\"\nfig, axes = plt.subplots(1, len(graph_type_order), figsize=(12, 2), sharex=True, sharey=False)\naxes_flatten = axes.flatten()\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\nfor i, graph_type in enumerate(graph_type_order):\n curve_type_display = curve_type\n data_for_plot = papers_per_year_historical.loc[papers_per_year_historical.graph_type==graph_type]\n new_data = curve_fit_with_ci(graph_type, data_for_plot, curve_type=curve_type_display, ax=axes_flatten[i])\n new_data[\"curve_type\"] = curve_type\n new_data[\"graph_type\"] = graph_type\n naive_data_all = naive_data_all.append(new_data)\n\nplt.show()",
"execution_count": 40,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x144 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"extrap_linear\")": "<a href=\"#figure-extrap_linear\">Figure 7</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"extrap_linear\")}}: Total articles by year of observation, by OA type, with a linear extrapolation.** "
},
{
"metadata": {
"variables": {
"print figure_link(\"extrap_exp\")": "<a href=\"#figure-extrap_exp\">Figure 8</a>"
}
},
"cell_type": "markdown",
"source": "We can see this isn't a particularly good fit for any of the OA types, so we try fitting with an exponential curve e<sup>x</sup> in {{print figure_link(\"extrap_exp\")}}. This is a better fit for the first four OA types, which we can see both visually and because they have higher r<sup>2</sup> values."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:19.502556Z",
"end_time": "2019-10-07T03:01:20.933553Z"
},
"trusted": true
},
"cell_type": "code",
"source": "curve_type=\"exp\"\nfig, axes = plt.subplots(1, len(graph_type_order), figsize=(12, 2), sharex=True, sharey=False)\naxes_flatten = axes.flatten()\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\nfor i, graph_type in enumerate(graph_type_order):\n curve_type_display = curve_type\n data_for_plot = papers_per_year_historical.loc[papers_per_year_historical.graph_type==graph_type]\n new_data = curve_fit_with_ci(graph_type, data_for_plot, curve_type=curve_type_display, ax=axes_flatten[i])\n new_data[\"curve_type\"] = curve_type\n new_data[\"graph_type\"] = graph_type\n naive_data_all = naive_data_all.append(new_data)\n\nplt.show()",
"execution_count": 41,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x144 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"extrap_exp\")": "<a href=\"#figure-extrap_exp\">Figure 8</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"extrap_exp\")}}: Total articles by year of observation, by OA type, with an exponential extrapolation.** "
},
{
"metadata": {
"variables": {
"print figure_link(\"extrap_negative_exp\")": "<a href=\"#figure-extrap_negative_exp\">Figure 9</a>"
}
},
"cell_type": "markdown",
"source": "The Delayed Bronze and Closed data look like they may actually trend down, so something of the form 1 - e<sup>x</sup> may be a better fit. This is shown in {{print figure_link(\"extrap_negative_exp\")}} for all OA Types and does indeed look like the best fit yet for both Delayed Bronze and Closed."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:20.975538Z",
"end_time": "2019-10-07T03:01:22.520808Z"
},
"trusted": true
},
"cell_type": "code",
"source": "curve_type=\"negative_exp\"\nfig, axes = plt.subplots(1, len(graph_type_order), figsize=(12, 2), sharex=True, sharey=False)\naxes_flatten = axes.flatten()\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\nfor i, graph_type in enumerate(graph_type_order):\n curve_type_display = curve_type\n data_for_plot = papers_per_year_historical.loc[papers_per_year_historical.graph_type==graph_type]\n new_data = curve_fit_with_ci(graph_type, data_for_plot, curve_type=curve_type_display, ax=axes_flatten[i])\n new_data[\"curve_type\"] = curve_type\n new_data[\"graph_type\"] = graph_type\n naive_data_all = naive_data_all.append(new_data)\n\nplt.show()",
"execution_count": 42,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x144 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"extrap_negative_exp\")": "<a href=\"#figure-extrap_negative_exp\">Figure 9</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"extrap_negative_exp\")}}: Total articles by year of observation, by OA type, with an exponential extrapolation fitting 1-exp().** "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We conclude this hunt for the best scaling factors by choosing the extrapolation function with the highest r<sup>2</sup> value for each OA Type. We use the chosen curves to extrapolate through 2025, and use the ratio of the value in 2018 to each subsequent observation year as that year's scaling factor."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:22.539924Z",
"end_time": "2019-10-07T03:01:22.580779Z"
},
"trusted": true
},
"cell_type": "code",
"source": "final_extraps = pd.DataFrame()\nfinal_extraps = final_extraps.append(naive_data_all.loc[(naive_data_all.graph_type == \"delayed_bronze\") & (naive_data_all.curve_type==\"negative_exp\")])\nfinal_extraps = final_extraps.append(naive_data_all.loc[(naive_data_all.graph_type == \"closed\") & (naive_data_all.curve_type==\"negative_exp\")])\nfinal_extraps = final_extraps.append(naive_data_all.loc[(naive_data_all.graph_type != \"delayed_bronze\") &\n (naive_data_all.graph_type != \"closed\") & \n (naive_data_all.curve_type==\"exp\")])",
"execution_count": 43,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:22.587204Z",
"end_time": "2019-10-07T03:01:22.597881Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure('small-multiples-num-papers-future');",
"execution_count": 44,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-small-multiples-num-papers-future\"></div>\n <script>\n var key = \"figure-small-multiples-num-papers-future\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"small-multiples-num-papers-future\")": "<a href=\"#figure-small-multiples-num-papers-future\">Figure 10</a>"
}
},
"cell_type": "markdown",
"source": "<a id=\"section-4-2-3\"></a>\n#### 4.2.3 Future OA by date of observation, by date of publication\n\nWe now have all the information we need to calculate **total articles available next observation year** as described in [Section 4.2.1](#section-4-2-1). We use this approach to calculate total articles available at observation year 2019 based on 2018 data, then apply it again to calculate total articles at observation year 2020, and so on until 2025. The result is shown in {{print figure_link(\"small-multiples-num-papers-future\")}}."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:22.615871Z",
"end_time": "2019-10-07T03:01:52.030068Z"
},
"trusted": true
},
"cell_type": "code",
"source": "def get_all_predicted_papers(my_min, my_max):\n all_predicted_papers = pd.DataFrame()\n for i, graph_type in enumerate(graph_type_order):\n all_data = get_papers_by_availability_year_including_future(graph_type, my_min, my_max)\n all_data[\"graph_type\"] = graph_type\n all_predicted_papers = all_predicted_papers.append(all_data)\n return all_predicted_papers\n\n%cache all_predicted_papers_future = get_all_predicted_papers(1995, 2026)",
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"text": "creating new value for variable 'all_predicted_papers_future'\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:01:52.037708Z",
"end_time": "2019-10-07T03:02:08.502446Z"
},
"trusted": true
},
"cell_type": "code",
"source": "my_range = range(2020, 2025+1)\nfig, axes = plt.subplots(len(graph_type_order)+1, len(my_range), figsize=(12, 6), sharex=True, sharey=False)\naxes_flatten = axes.flatten()\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\ni = 0\nfor observation_year in my_range:\n ax = axes_flatten[i]\n ax.set_axis_off() \n column_label = \"observation year\\n{}\".format(observation_year)\n ax.text(.3, .2, column_label,\n horizontalalignment='center',\n verticalalignment='bottom',\n fontsize=14,\n transform=ax.transAxes)\n i += 1\n\nfor graph_type in graph_type_order[::-1]:\n for observation_year in my_range: \n ax = axes_flatten[i]\n this_data = all_predicted_papers_future.copy()\n this_data = this_data.loc[this_data.graph_type == graph_type]\n this_data = this_data.loc[this_data.prediction_year == observation_year]\n this_data[\"publication_date\"] = [int(observation_year - a) for a in this_data.article_years_from_availability]\n new_data = graph_available_papers_in_observation_year_by_pubdate(graph_type, this_data, observation_year, ax=ax)\n\n y_max = all_predicted_papers_future.loc[(all_predicted_papers_future.graph_type == graph_type) &\n (all_predicted_papers_future.prediction_year <= max(my_range))][\"num_articles\"].max()\n ax.set_ylim(0, 1.2*y_max)\n \n axis_color = \"silver\"\n ax.spines['bottom'].set_color(axis_color)\n ax.spines['top'].set_color(axis_color) \n ax.spines['right'].set_color(axis_color)\n ax.spines['left'].set_color(axis_color)\n ax.tick_params(axis='x', colors=axis_color)\n ax.tick_params(axis='y', colors=axis_color)\n\n i += 1\n\ni_bottom_left_graph = len(graph_type_order) * len(my_range)\nax_bottom_left = axes_flatten[i_bottom_left_graph]\nax_bottom_left.set_ylabel(\"articles\\n(millions)\");\nax_bottom_left.set_xlabel(\"year of publication\");\naxis_color = \"black\"\nax_bottom_left.spines['bottom'].set_color(axis_color)\nax_bottom_left.spines['top'].set_color(axis_color) \nax_bottom_left.spines['right'].set_color(axis_color)\nax_bottom_left.spines['left'].set_color(axis_color)\nax_bottom_left.tick_params(axis='x', colors=axis_color)\nax_bottom_left.tick_params(axis='y', colors=axis_color)\n",
"execution_count": 46,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": "<Figure size 864x432 with 42 Axes>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"variables": {
"print figure_link(\"small-multiples-num-papers-future\")": "<a href=\"#figure-small-multiples-num-papers-future\">Figure 10</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"small-multiples-num-papers-future\")}}: Articles by year of observation, extrapolated into the future.** Each row is an OA Type, each column is a Year of Observation, the x-axis of each graph is the Year of Publication, and the y-axis is the total number of articles (millions) available at the year of observation."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-27T17:03:03.883855Z",
"end_time": "2019-09-27T17:03:03.919284Z"
}
},
"cell_type": "markdown",
"source": "<a id=\"section-4-2-4\"></a>\n#### 4.2.4 Combined Future OA by date of observation"
},
{
"metadata": {
"variables": {
"print figure_link(\"articles_by_observation_year_prediction\")": "<a href=\"#figure-articles_by_observation_year_prediction\">Figure 11</a>"
}
},
"cell_type": "markdown",
"source": "Finally, as in [Section 4.1.6](#section-4-1-6), we can sum the area under the histograms above to calculate total articles by year of observation by OA Type, for past articles as well as future projections. We show this in {{print figure_link(\"articles_by_observation_year_prediction\")}}."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:08.553841Z",
"end_time": "2019-10-07T03:02:09.461319Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"articles_by_observation_year_prediction\");\narticles_by_obs_year_df = all_predicted_papers_future.copy()\narticles_by_obs_year_df = articles_by_obs_year_df.rename(\n columns={\"prediction_year\": \"x\", \"num_articles\": \"y\"})\n(df_articles_absolute, df_articles_proportional) = plot_area_and_proportion(articles_by_obs_year_df, \n \"standard\", \n 2000, 2025, 2018,\n xlabel=\"year of observation\")\n",
"execution_count": 47,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-articles_by_observation_year_prediction\"></div>\n <script>\n var key = \"figure-articles_by_observation_year_prediction\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x216 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-30T22:30:34.622883Z",
"end_time": "2019-09-30T22:30:34.643116Z"
},
"variables": {
"print figure_link(\"articles_by_observation_year_prediction\")": "<a href=\"#figure-articles_by_observation_year_prediction\">Figure 11</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"articles_by_observation_year_prediction\")}}: Total articles by OA type, by year of observation.** OA type as of year of observation."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We project 44% of articles will be OA by 2025: Gold will account for 15% of all articles, Bronze 13%, and Green and Hybrid 7% each. A table showing the proportions of the right panel is below:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:09.478712Z",
"end_time": "2019-10-07T03:02:09.896574Z"
},
"trusted": true
},
"cell_type": "code",
"source": "df = df_articles_proportional.copy()\nrows = df.loc[(df.index==2010) | (df.index==2019) | (df.index==2025)]\nrows[\"all OA\"] = 1 - rows[\"closed\"]\nmy_markdown = tabulate(100*rows[graph_type_order+[\"all OA\"]], tablefmt=\"pipe\", headers=\"keys\", floatfmt=\",.0f\")\ndisplay(Markdown(my_markdown))",
"execution_count": 48,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.Markdown object>",
"text/markdown": "| x | green | gold | hybrid | immediate_bronze | delayed_bronze | closed | all OA |\n|-----:|--------:|-------:|---------:|-------------------:|-----------------:|---------:|---------:|\n| 2010 | 2 | 3 | 2 | 12 | 4 | 78 | 22 |\n| 2019 | 4 | 9 | 4 | 10 | 3 | 69 | 31 |\n| 2025 | 7 | 18 | 7 | 10 | 3 | 56 | 44 |"
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"articles_by_observation_year_prediction\")": "<a href=\"#figure-articles_by_observation_year_prediction\">Figure 11</a>",
"print figure_link(\"articles_by_observation_year_prediction_diff\")": "<a href=\"#figure-articles_by_observation_year_prediction_diff\">Figure 12</a>"
}
},
"cell_type": "markdown",
"source": "\nIf we plot the difference between observation years in {{print figure_link(\"articles_by_observation_year_prediction\")}}, we get the *net change* in articles by OA type, by year of observation. This net change is shown in {{print figure_link(\"articles_by_observation_year_prediction_diff\")}}. "
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:09.944676Z",
"end_time": "2019-10-07T03:02:11.149879Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"articles_by_observation_year_prediction_diff\");\narticles_by_obs_year_df = all_predicted_papers_future.copy()\narticles_by_obs_year_df = articles_by_obs_year_df.rename(\n columns={\"prediction_year\": \"x\", \"num_articles\": \"y\"})\n\n# articles_by_obs_year_df_closed = articles_by_obs_year_df.loc[\n# (articles_by_obs_year_df.graph_type==\"closed\") & \n# (articles_by_obs_year_df.x <= 2025)]\n# print articles_by_obs_year_df_closed\n# plt.plot(articles_by_obs_year_df_closed.groupby(\"x\").y.sum())\n# plt.ylim(0, 2000000)\n\n# plt.plot(articles_by_obs_year_df_closed.groupby(\"x\").y.sum().diff())\n# plt.ylim(0, 2000000)\nnum_articles_diff, num_articles_diff_proportional = plot_area_and_proportion(articles_by_obs_year_df, \n \"standard\", \n 2000, 2025, 2018,\n xlabel=\"year of observation\", \n fancy=\"diff\")\n",
"execution_count": 49,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-articles_by_observation_year_prediction_diff\"></div>\n <script>\n var key = \"figure-articles_by_observation_year_prediction_diff\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x216 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"articles_by_observation_year_prediction_diff\")": "<a href=\"#figure-articles_by_observation_year_prediction_diff\">Figure 12</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"articles_by_observation_year_prediction_diff\")}}: Change in number of articles from previous year of observation, by OA type.** Includes newly published articles, as well as articles that have changed OA type."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "This shows that by 2025 72% of articles that are newly available every year are available as OA, compared to 52% in 2019. About half of the articles that become OA each year are Gold."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:11.165762Z",
"end_time": "2019-10-07T03:02:11.403799Z"
},
"trusted": true
},
"cell_type": "code",
"source": "df = num_articles_diff_proportional.copy()\nrows = df.loc[(df.index==2010) | (df.index==2019) | (df.index==2025)]\nrows[\"all OA\"] = 1 - rows[\"closed\"]\nmy_markdown = tabulate(100*rows[graph_type_order+[\"all OA\"]], tablefmt=\"pipe\", headers=\"keys\", floatfmt=\",.0f\")\ndisplay(Markdown(my_markdown))",
"execution_count": 50,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.Markdown object>",
"text/markdown": "| x | green | gold | hybrid | immediate_bronze | delayed_bronze | closed | all OA |\n|-----:|--------:|-------:|---------:|-------------------:|-----------------:|---------:|---------:|\n| 2010 | 4 | 7 | 3 | 8 | 3 | 74 | 26 |\n| 2019 | 9 | 23 | 8 | 9 | 2 | 48 | 52 |\n| 2025 | 11 | 39 | 13 | 8 | 1 | 28 | 72 |"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-4-3\"></a>\n### 4.3 Past and Future OA Views"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-06T15:25:46.684142Z",
"end_time": "2019-10-06T15:25:46.689473Z"
}
},
"cell_type": "markdown",
"source": "<a id=\"section-4-3-1\"></a>\n#### 4.3.1 Approach"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Now that we have projections of publication trends, we change tack to examine *views* -- when people access the literature, how likely is it the article they want to read is available as OA? How do we think this has changed over the years, and what patterns do we project in the future?\n\nTo answer these questions we will use data from the Unpaywall browser extension, as described in [Section 2.2](#section-2-2) above. This data allows us to make inferences about overall readership trends."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We will estimate views using this general equation:\n\n***\n```\n views = (number of articles) * (views/article)\n```\n***\n\nA key assumption underlying our model is that the **views/article by age of article** distribution curve is stable over time, for each OA type. We calculate this distribution for views made during July 2018, and assume that readers in all other months and years, past and future, will have a similar relative interest in articles based on their age and OA type -- we assume the number of views varies solely based on number of articles available of each age and OA type.\n\nBecause we want to know views over time (rather than just at a single point in time) we treat each of the terms in the above equation as a [signal](https://en.wikipedia.org/wiki/Digital_signal) and use [digital signal processing](https://en.wikipedia.org/wiki/Digital_signal_processing) calculation techniques, which we will describe as we encounter them and in supplementary information.\n\nThe signals for **number of articles** were already calculated in Sections 4.1 and 4.2.\n\nThe signals for **views/article** will be calculated in Section 4.3.2. \n\nWe \"multiply\" these two signals together using signal processing techniques, described in Section 4.3.3, to get total views across time.\n\nWe do these calculations for each OA type individually, and then add them together in Section 4.3.5 to look at relative trends.\n\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-4-3-2\"></a>\n#### 4.3.2 Views per article"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-27T15:14:53.271089Z",
"end_time": "2019-09-27T15:14:53.326383Z"
}
},
"cell_type": "markdown",
"source": "We calculate views per article as you'd expect:\n```\n the average number of views per article = \n\n (the total number of views for articles of that age and OA type) \n\n /\n\n (the number of articles of that age and OA type)\n```"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-27T15:14:53.271089Z",
"end_time": "2019-09-27T15:14:53.326383Z"
}
},
"cell_type": "markdown",
"source": "We can state this more concisely and precisely as follows. For each OA type:\n\n```\nviews per article by age = dot_division( views by age, articles by age )\n \n```\n\nwhere dot_division is the [element-wise Hadamard division](https://en.wikipedia.org/wiki/Hadamard_product_(matrices)) of two signals."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:11.409496Z",
"end_time": "2019-10-07T03:02:11.427939Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"view-by-age-no-color\");",
"execution_count": 51,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-view-by-age-no-color\"></div>\n <script>\n var key = \"figure-view-by-age-no-color\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
" print figure_link(\"view-by-age-no-color\") ": "<a href=\"#figure-view-by-age-no-color\">Figure 13</a>"
}
},
"cell_type": "markdown",
"source": "It is important to look at views by article age, because it is well known that readers are more interested in accessing newly-published articles, and indeed this trend can be seen in the Unpaywall usage logs. {{ print figure_link(\"view-by-age-no-color\") }} shows monthly access requests to the Unpaywall extension made between August 2018 and August 2019, distributed by article age. As expected, the distribution is very skewed and readers are most interested in articles published less than a year ago.\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:11.445560Z",
"end_time": "2019-10-07T03:02:12.439001Z"
},
"code_folding": [],
"trusted": true
},
"cell_type": "code",
"source": "# hidden: code to query and graph \n%matplotlib inline\n\nmy_data = views_by_age_months_no_color_full_year.loc[views_by_age_months_no_color_full_year.article_age_months >= 0]\nmy_data = my_data.loc[my_data.article_age_months <= 12*15]\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 2), sharex=True, sharey=False)\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1, wspace=.3)\n\nmy_plot = my_data.plot.line(x=\"article_age_months\", y=\"num_views\", ax=ax1)\nax1.xaxis.set_major_locator(plt.MaxNLocator(6))\nax1.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.1f}'.format(y/(1000*1000.0))))\nax1.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda x, pos: '{0:,.1f}'.format(x/(12.0))))\n\nax1.spines['top'].set_visible(False)\nax1.spines['right'].set_visible(False)\nax1.spines['left'].set_visible(False)\nax1.set_xlabel('article age (years)')\nax1.set_ylabel('views (millions)')\nax1.get_legend().remove()\n\nmy_plot = my_data.plot.line(x=\"article_age_months\", y=\"num_views\", ax=ax2)\nax2.set_yscale(\"log\")\nax2.set_xlabel('article age (years)')\nax2.set_ylabel('views (log scale)');\nax2.spines['top'].set_visible(False)\nax2.spines['right'].set_visible(False)\nax2.spines['left'].set_visible(False)\nax2.get_legend().remove()\n",
"execution_count": 52,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 576x144 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"view-by-age-no-color\")": "<a href=\"#figure-view-by-age-no-color\">Figure 13</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"view-by-age-no-color\")}}: Distribution of views by article age**: left panel is linear y-axis and y panel is a log plot."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:12.458329Z",
"end_time": "2019-10-07T03:02:12.471981Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"views_by_age_with_color\");",
"execution_count": 53,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-views_by_age_with_color\"></div>\n <script>\n var key = \"figure-views_by_age_with_color\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-27T15:14:58.755833Z",
"end_time": "2019-09-27T15:14:58.765799Z"
},
"variables": {
" print \"{0:,.0f}\".format(views_per_year_total.num_views_one_month.sum()) ": "2,774,403",
" print figure_link(\"views_by_age_with_color\") ": "<a href=\"#figure-views_by_age_with_color\">Figure 14</a>"
}
},
"cell_type": "markdown",
"source": "We now look at this data by OA type. To simplify interactions, for the rest of the analysis we will restrict our view data to that from just one month: July 2019, and to articles less than 15 years old. This accounts for {{ print \"{0:,.0f}\".format(views_per_year_total.num_views_one_month.sum()) }} views, which we then multiply by 12 to approximate a year's worth of views by the Unpaywall extension (not important because the point of the views analysis is growth rather than absolute numbers, but it helps slightly with interpretation). \n\n{{ print figure_link(\"views_by_age_with_color\") }} shows the distribution of views by article age, by OA type."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:12.535658Z",
"end_time": "2019-10-07T03:02:16.750997Z"
},
"trusted": true
},
"cell_type": "code",
"source": "%cache views_per_year_total = get_views_per_year_total() \ndata_now = views_per_year_total.loc[views_per_year_total[\"article_age_years\"] >= 0]\ng = sns.FacetGrid(data_now, col=\"graph_type\", hue=\"graph_type\", col_order=graph_type_order, hue_order=graph_type_order, palette=my_cmap_graph_type)\nkws = dict(linewidth=5)\ng.map(plt.plot, \"article_age_years\", \"num_views_per_year\", **kws);\nfor ax in g.axes.flat: \n ax.set_xlabel(\"article age (years)\")\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.1f}'.format(y/(1000*1000.0))))\ng.axes.flat[0].set_ylabel(\"views per year (millions)\");\n \n",
"execution_count": 54,
"outputs": [
{
"output_type": "stream",
"text": "creating new value for variable 'views_per_year_total'\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 1296x216 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"views_by_age_with_color\")": "<a href=\"#figure-views_by_age_with_color\">Figure 14</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"views_by_age_with_color\")}}: Distribution of views by article age, by OA type**"
},
{
"metadata": {
"variables": {
"print figure_link(\"small-multiples-num-papers-future\")": "<a href=\"#figure-small-multiples-num-papers-future\">Figure 10</a>"
}
},
"cell_type": "markdown",
"source": "Closed access articles receive the most views, which isn't surprising because as we saw in [Section 4.1.6](#section-4-1-6), most articles available in 2018 are Closed access. What happens when we divide these curves to get views *per article*, as is our goal? \n\nA detailed walkthrough is given in Supplementary Information [Section 11.4](#section-11-4). Here we present the results of dividing the above view distribution signals by the article signals we calculated {{print figure_link(\"small-multiples-num-papers-future\")}} for the 2018 observation year:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:16.783270Z",
"end_time": "2019-10-07T03:02:16.793948Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"views-by-article-main\");",
"execution_count": 55,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-views-by-article-main\"></div>\n <script>\n var key = \"figure-views-by-article-main\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:16.800523Z",
"end_time": "2019-10-07T03:02:16.832593Z"
},
"trusted": true
},
"cell_type": "code",
"source": "def get_views_per_article(graph_type):\n if graph_type == \"biorxiv\":\n graph_type = \"green\"\n \n views_per_year = get_views_per_year(graph_type)\n papers_per_year = get_papers_by_availability_year(graph_type, 2018, just_this_year=False)\n papers_per_year[\"article_age_years\"] = papers_per_year[\"article_years_from_availability\"]\n papers_per_year = papers_per_year.loc[(papers_per_year[\"article_age_years\"] <=15 )]\n\n data_merged_clean = papers_per_year.merge(views_per_year, on=[\"article_age_years\"]) \n data_merged_clean[\"views_per_article\"] = data_merged_clean[\"num_views_per_year\"] / data_merged_clean[\"num_articles\"]\n\n views_per_article = pd.DataFrame(data_merged_clean, columns=[\"article_age_years\", \"views_per_article\"])\n views_per_article = views_per_article.sort_values(by=\"article_age_years\")\n\n if graph_type==\"delayed_bronze\":\n # otherwise first one is too high because number articles too low in year 0 for delayed subset\n views_per_article.loc[views_per_article.article_age_years==0, [\"views_per_article\"]] = float(views_per_article.loc[views_per_article.article_age_years==1].views_per_article)\n\n return views_per_article\n\ndef get_views_per_article_total():\n all_data = pd.DataFrame()\n for prep_graph_type in [\"gold\", \"hybrid\", \"green\", \"immediate_bronze\", \"delayed_bronze\", \"closed\"]:\n temp_papers = get_views_per_article(prep_graph_type)\n# print prep_graph_type\n# print \"{:,.0f}\".format(temp_papers.views_per_article.max()), \"{:,.0f}\".format(temp_papers.views_per_article.sum())\n# print \"\\n\"\n temp_papers[\"graph_type\"] = prep_graph_type\n all_data = all_data.append(temp_papers)\n return all_data",
"execution_count": 56,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:16.838417Z",
"end_time": "2019-10-07T03:02:23.201313Z"
},
"trusted": true
},
"cell_type": "code",
"source": "\n%cache views_per_article_total = get_views_per_article_total() \ndata_now = views_per_article_total.loc[views_per_article_total[\"article_age_years\"] >= 0]\ng = sns.FacetGrid(data_now, col=\"graph_type\", hue=\"graph_type\", col_order=graph_type_order, hue_order=graph_type_order, palette=my_cmap_graph_type)\nkws = dict(s=50)\ng.map(plt.scatter, \"article_age_years\", \"views_per_article\", **kws);\ng.map(plt.plot, \"article_age_years\", \"views_per_article\");\nfor ax in g.axes.flat: \n ax.set_xlabel(\"article age (years)\")\ng.axes.flat[0].set_ylabel(\"views per article\");",
"execution_count": 57,
"outputs": [
{
"output_type": "stream",
"text": "creating new value for variable 'views_per_article_total'\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": "<Figure size 1296x216 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"views-by-article-main\")": "<a href=\"#figure-views-by-article-main\">Figure 15</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"views-by-article-main\")}}: Views by article curve, by OA type**"
},
{
"metadata": {
"variables": {
"print figure_link(\"views-by-article-main\")": "<a href=\"#figure-views-by-article-main\">Figure 15</a>"
}
},
"cell_type": "markdown",
"source": "We see in {{print figure_link(\"views-by-article-main\")}} that number of views per article is much higher for Green than other kinds of articles, particularly for articles that are available as Green OA within the first year of publication (age 0). This is consistent with previously-documented download advantages of Green OA articles, and could be caused by various factors including self-selection bias, or the common cause of strong funding support for high-interest medical papers by funders like the NIH.\n\nRelative to Closed access articles, the average number of views per article for Gold, Hybrid, and Delayed Bronze is particularly strong for older articles."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-4-3-3\"></a>\n#### 4.3.3 Calculating Views"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Finally, we are ready to calculate overall views. As a reminder, here is our general approach: \n***\n```\n views = (number of articles) * (views/article)\n```\n***\n\nWe can state this more precisely as follows. For each OA type:\n\n```\n views in a given year = convolution(articles by age for that year, \n views/article by age) \n```\n\nwhere [convolution](https://en.wikipedia.org/wiki/Convolution) is the standard mathematical operation of modifying a signal by another signal, by integrating the product of the two curves after one is reversed and shifted. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "A detailed walkthrough of this convolution (and more information on what convolution means!) is given in Supplementary Information [Section 11.5](#section-11-5). Here we present the results of convolution, which can be seen roughly as multiplication of the articles-by-age estimates we made in [Section 4.2.2](#section-4-2-2) and [Section 4.2.3](#section-4-2-3) with the views/article curves we calculated in [Section 4.3.2](#section-4-3-2). "
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:23.230633Z",
"end_time": "2019-10-07T03:02:23.262057Z"
},
"trusted": true
},
"cell_type": "code",
"source": "def get_predicted_views(graph_type, now_delta_years=0, label_for_graph=None, show_graph=True):\n# calc_min_year = 1951\n calc_min_year = 1995\n display_min_year = 2010\n now_year = 2020 - now_delta_years\n max_year = 2025\n exponential = False\n\n if graph_type == \"biorxiv\":\n exponential = True\n \n views_per_article = get_views_per_article(graph_type)\n \n df_views_by_year = pd.DataFrame()\n all_papers_per_year = get_papers_by_availability_year_including_future(graph_type, calc_min_year, max_year)\n for prediction_year in range(calc_min_year, max_year+1): \n# for prediction_year in range(calc_min_year, 2019): \n# for prediction_year in range(2017, 2019): \n papers_per_year = all_papers_per_year.loc[all_papers_per_year[\"prediction_year\"] == prediction_year]\n# print views_per_article.head()\n try:\n data_merged_clean = papers_per_year.merge(views_per_article, left_on=[\"article_years_from_availability\"], right_on=[\"article_age_years\"])\n data_merged_clean = data_merged_clean.sort_values(\"article_age_years\")\n win = data_merged_clean[\"views_per_article\"] \n sig = data_merged_clean[\"num_articles\"]\n views_by_observation_year = signal.convolve(win, sig, mode='same', method=\"direct\")\n y = max(views_by_observation_year)\n df_views_by_year = df_views_by_year.append(pd.DataFrame({\"observation_year\":[prediction_year], \"views\": [y]}))\n except (ValueError, KeyError): # happens when the year is blank\n pass\n \n\n return df_views_by_year\n\ndef get_predicted_views_total(observation_year):\n all_data = pd.DataFrame()\n for prep_graph_type in graph_type_order:\n temp_papers = get_predicted_views(prep_graph_type, observation_year)\n temp_papers[\"graph_type\"] = prep_graph_type\n all_data = all_data.append(temp_papers)\n return all_data",
"execution_count": 58,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:23.268651Z",
"end_time": "2019-10-07T03:02:23.280853Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"views-small-main\");",
"execution_count": 59,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-views-small-main\"></div>\n <script>\n var key = \"figure-views-small-main\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:02:23.286771Z",
"end_time": "2019-10-07T03:03:02.493738Z"
},
"trusted": true
},
"cell_type": "code",
"source": "observation_year = 2025\n%cache predicted_views_total = get_predicted_views_total(observation_year) \n\ndata_now = predicted_views_total.loc[predicted_views_total[\"observation_year\"] >= 2010]\ng = sns.FacetGrid(data_now, col=\"graph_type\", hue=\"graph_type\", col_order=graph_type_order, hue_order=graph_type_order, palette=my_cmap_graph_type)\nkws = dict(marker=\"x\", s=70)\ng.map(plt.scatter, \"observation_year\", \"views\", **kws);\nfor ax in g.axes.flat: \n ax.set_xlabel(\"observation year\")\n ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.0f}'.format(y/(1000*1000.0))))\ng.axes.flat[0].set_ylabel(\"views (millions)\");",
"execution_count": 60,
"outputs": [
{
"output_type": "stream",
"text": "creating new value for variable 'predicted_views_total'\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 1296x216 with 6 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"views-small\")": "<a href=\"#figure-views-small\">Figure 28</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"views-small\")}}: Views** Views by year."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-06T15:34:46.982910Z",
"end_time": "2019-10-06T15:34:46.995245Z"
}
},
"cell_type": "markdown",
"source": "<a id=\"section-4-3-4\"></a>\n#### 4.3.4 Combined Past and Future Views"
},
{
"metadata": {
"variables": {
"print figure_link(\"views_stacked\")": "<a href=\"#figure-views_stacked\">Figure 17</a>"
}
},
"cell_type": "markdown",
"source": "We can plot these lines stacked on top of each other to see how the OA types change over time, shown in {{print figure_link(\"views_stacked\")}}.\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:02.602423Z",
"end_time": "2019-10-07T03:03:02.614839Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"views_stacked\");",
"execution_count": 61,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-views_stacked\"></div>\n <script>\n var key = \"figure-views_stacked\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:02.622201Z",
"end_time": "2019-10-07T03:03:04.545951Z"
},
"trusted": true
},
"cell_type": "code",
"source": "\n# not cumulative because cumulative views don't mean anything\n\nviews_all_data_pivot = predicted_views_total.pivot_table(\n index='observation_year', columns='graph_type', values=['views'], aggfunc=np.sum)\\\n .sort_index(axis=1, level=1)\\\n .swaplevel(0, 1, axis=1)\nviews_all_data_pivot.columns = views_all_data_pivot.columns.levels[0]\nviews_all_data_pivot\n# all_data_pivot[oa_status_order].plot.area(stacked=True, color=oa_status_colors)\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 3), sharex=True, sharey=False)\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\nviews_all_data_pivot_graph = views_all_data_pivot.copy()\nviews_all_data_pivot_graph = views_all_data_pivot_graph.loc[views_all_data_pivot_graph.index > 1960]\nmy_plot = views_all_data_pivot_graph[graph_type_order].plot.area(stacked=True, linewidth=.1, color=graph_type_colors, ax=ax1)\nax1.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.0f}'.format(y/(1000*1000.0))))\nax1.set_xlabel('year of view')\nax1.set_ylabel('views (millions)')\nax1.set_xlim(2000, 2025)\nax1.set_ylim(0, 1.2*max(views_all_data_pivot_graph.sum(1)))\nax1.set_title(\"Estimated views by year of observation\");\nhandles, labels = my_plot.get_legend_handles_labels(); my_plot.legend(reversed(handles[0:6]), reversed(labels[0:6]), loc='upper left'); # reverse to keep order consistent\n\nviews_df_diff_proportional = views_all_data_pivot_graph.div(views_all_data_pivot_graph.sum(1), axis=0)\nmy_plot = views_df_diff_proportional[graph_type_order].plot.area(stacked=True, linewidth=.1, color=graph_type_colors, ax=ax2)\nmy_plot.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))\nax2.set_xlabel('year of view')\nax2.set_ylabel('proportion of views')\nax2.set_title(\"Proportion of views\");\nax2.set_xlim(2000, 2025)\nax2.set_ylim(0, 1)\nhandles, labels = my_plot.get_legend_handles_labels(); my_plot.legend(reversed(handles[0:6]), reversed(labels[0:6]), loc='upper left'); # reverse to keep order consistent\n\nplt.tight_layout(pad=.5, w_pad=4, h_pad=2.0) \n\n\n",
"execution_count": 62,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x216 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"views_stacked\")": "<a href=\"#figure-views_stacked\">Figure 17</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"views_stacked\")}}: Views, by year of view**"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-04T17:05:00.212835Z",
"end_time": "2019-10-04T17:05:00.222564Z"
}
},
"cell_type": "markdown",
"source": "Here is the raw data for the proportions at a few specific observation years:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:12:29.142295Z",
"end_time": "2019-10-07T03:12:29.415717Z"
},
"trusted": true
},
"cell_type": "code",
"source": "df = views_df_diff_proportional.copy()\nrows = df.loc[(df.index==2000) | (df.index==2010) | (df.index==2019) | (df.index==2025)]\nrows[\"all OA\"] = 1 - rows[\"closed\"]\nmy_markdown = tabulate(100*rows[graph_type_order+[\"all OA\"]], tablefmt=\"pipe\", headers=\"keys\", floatfmt=\",.0f\")\ndisplay(Markdown(my_markdown))",
"execution_count": 103,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.Markdown object>",
"text/markdown": "| observation_year | green | gold | hybrid | immediate_bronze | delayed_bronze | closed | all OA |\n|-------------------:|--------:|-------:|---------:|-------------------:|-----------------:|---------:|---------:|\n| 2000 | 0 | 2 | 1 | 7 | 6 | 84 | 16 |\n| 2010 | 6 | 6 | 3 | 6 | 8 | 71 | 29 |\n| 2019 | 15 | 20 | 7 | 5 | 6 | 48 | 52 |\n| 2025 | 19 | 33 | 10 | 4 | 4 | 30 | 70 |"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Our estimated number of views per year increases steadily over time, and the proportion of views to OA resources goes from 16% in 2000 to 52% in 2019, and to 70% in 2025. This increase is driven primarily by Green and Gold OA: we estimate 19% of views in 2025 will lead to Green OA articles and 33% of views will lead to Gold articles. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-4-4\"></a>\n### 4.4 Extending the model: Growth of bioRxiv"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "An advantage of building a model is that now we can layer on alternate assumptions and see how anticipated disruptions might affect OA in coming years. A comprehensive examination of all the alternative futures is clearly outside the scope of this paper, however an example will be illustrative.\n\nBioRxiv, a preprint server in biology, provides an excellent example.  As described in Abdill and Blekhman (2019), deposits into bioRxiv are growing rapidly. If growth continues at the current rate, biorxiv could prove to be a major disruptor: it is growing extremely quickly, and the vast majority of the deposits which are published have zero OA lag (and so are OA at the time of highest demand)."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We model the growth of bioRxiv and its impact on OA availability by extrapolating from bioRxiv papers that:\n\n- were deposited at or before the date they were published (to simplify the model), and\n- are Closed access other than their Green bioRxiv copy (so that we don't double-count articles made OA as Gold, Hybrid, or Bronze).\n\nThe number of articles that meet these criteria are shown in the table below, by date of publication."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:12:35.780730Z",
"end_time": "2019-10-07T03:12:35.803659Z"
},
"trusted": true
},
"cell_type": "code",
"source": "biorxiv_growth_otherwise_closed",
"execution_count": 104,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 104,
"data": {
"text/plain": " published_year num_articles\n0 2018.0 1191\n1 2017.0 431\n2 2016.0 171\n3 2015.0 77\n4 2014.0 31",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>published_year</th>\n <th>num_articles</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2018.0</td>\n <td>1191</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2017.0</td>\n <td>431</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2016.0</td>\n <td>171</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2015.0</td>\n <td>77</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2014.0</td>\n <td>31</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:04.948885Z",
"end_time": "2019-10-07T03:03:04.958787Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"biorxiv-exp\");",
"execution_count": 65,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-biorxiv-exp\"></div>\n <script>\n var key = \"figure-biorxiv-exp\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"biorxiv-exp\")": "<a href=\"#figure-biorxiv-exp\">Figure 18</a>"
}
},
"cell_type": "markdown",
"source": "This growth has a very strong logarithmic extrapolation fit, as seen in {{print figure_link(\"biorxiv-exp\")}}. "
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:04.987112Z",
"end_time": "2019-10-07T03:03:11.886067Z"
},
"trusted": true
},
"cell_type": "code",
"source": "biorxiv_now_year = 2018\n\n# reset\npapers_per_year_historical = papers_per_year_historical.loc[papers_per_year_historical.graph_type != 'biorxiv']\n\nfor graph_type in [\"biorxiv\"]:\n for prediction_year in range(2000, biorxiv_now_year+1): \n papers_per_year = get_papers_by_availability_year(graph_type, prediction_year, just_this_year=True)\n papers_per_year[\"graph_type\"] = graph_type\n papers_per_year[\"prediction_year\"] = prediction_year\n papers_per_year_historical = papers_per_year_historical.append(papers_per_year)",
"execution_count": 66,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:11.894224Z",
"end_time": "2019-10-07T03:03:12.647960Z"
},
"trusted": true
},
"cell_type": "code",
"source": "fig, ax = plt.subplots(1, 1, figsize=(4, 2), sharex=True, sharey=False)\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n \ndata_for_plot = papers_per_year_historical.loc[papers_per_year_historical.graph_type==\"biorxiv\"]\nnew_data = curve_fit_with_ci(\"biorxiv\", data_for_plot, curve_type=\"exp\", ax=ax)\nnew_data[\"curve_type\"] = \"exp\"\nnew_data[\"graph_type\"] = \"biorxiv\"\nfinal_extraps = final_extraps.loc[final_extraps.graph_type != 'biorxiv']\nfinal_extraps = final_extraps.append(new_data)\nax.set_xlim(2012, 2025)\nax.set_yscale(\"log\")\nax.set_ylabel(\"articles (log scale)\");\n",
"execution_count": 67,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 288x144 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"biorxiv-exp\")": "<a href=\"#figure-biorxiv-exp\">Figure 18</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"biorxiv-exp\")}}: bioRxiv extrapolation**"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:12.667423Z",
"end_time": "2019-10-07T03:03:12.679263Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"articles_by_observation_year_prediction_plus_biorxiv\");",
"execution_count": 68,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-articles_by_observation_year_prediction_plus_biorxiv\"></div>\n <script>\n var key = \"figure-articles_by_observation_year_prediction_plus_biorxiv\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
" print figure_link(\"articles_by_observation_year_prediction_plus_biorxiv\")": "<a href=\"#figure-articles_by_observation_year_prediction_plus_biorxiv\">Figure 19</a>"
}
},
"cell_type": "markdown",
"source": "BioRxiv won't be able to grow exponentially forever -- there are a limited number of papers in Biology. But if we were to imagine bioRxiv continued its current growth rate for another 5 years, we would estimate its impact on the relative proportion of articles available as OA in {{ print figure_link(\"articles_by_observation_year_prediction_plus_biorxiv\")}}."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:12.698440Z",
"end_time": "2019-10-07T03:03:15.412766Z"
},
"trusted": true
},
"cell_type": "code",
"source": "all_predicted_papers_future_plus_biorxiv = all_predicted_papers_future.copy()\n\nbiorxiv_predicted_papers = get_papers_by_availability_year_including_future(\"biorxiv\", 1995, 2026)\nbiorxiv_predicted_papers[\"graph_type\"] = \"biorxiv\"\n\nall_predicted_papers_future_plus_biorxiv = all_predicted_papers_future_plus_biorxiv.append(biorxiv_predicted_papers)\n\n\narticles_by_obs_year_df_plus_biorxiv = all_predicted_papers_future_plus_biorxiv.copy()\narticles_by_obs_year_df_plus_biorxiv = articles_by_obs_year_df_plus_biorxiv.rename(\n columns={\"prediction_year\": \"x\", \"num_articles\": \"y\"})\nplot_area_and_proportion(articles_by_obs_year_df_plus_biorxiv, \n \"standard_plus_biorxiv\", \n 2000, 2025, 2018,\n xlabel=\"year of observation\");",
"execution_count": 69,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x216 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"articles_by_observation_year_prediction_plus_biorxiv\")": "<a href=\"#figure-articles_by_observation_year_prediction_plus_biorxiv\">Figure 19</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"articles_by_observation_year_prediction_plus_biorxiv\")}}: Prediction of articles by OA type by year of observation, including bioRxiv**"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:15.430618Z",
"end_time": "2019-10-07T03:03:15.440838Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"biorxiv-stacked\");",
"execution_count": 70,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-biorxiv-stacked\"></div>\n <script>\n var key = \"figure-biorxiv-stacked\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"views-by-article\") ": "<a href=\"#figure-views-by-article\">Figure 24</a>",
"print figure_link(\"biorxiv-stacked\") ": "<a href=\"#figure-biorxiv-stacked\">Figure 20</a>"
}
},
"cell_type": "markdown",
"source": "This doesn't look like many articles, but let's see how it affects viewership. For simplicity we use the generic green OA access trend derived in {{print figure_link(\"views-by-article\") }}. This results in views as shown in {{print figure_link(\"biorxiv-stacked\") }} -- a notable impact on the total views of all scholarly papers, and obviously the impact would be even greater within the field of biology."
},
{
"metadata": {
"scrolled": true,
"ExecuteTime": {
"start_time": "2019-10-07T03:03:15.498345Z",
"end_time": "2019-10-07T03:03:18.128276Z"
},
"trusted": true
},
"cell_type": "code",
"source": "total_views_including_biorxiv = predicted_views_total.copy()\nbiorxiv_views = get_predicted_views(\"biorxiv\", 2010, 2025)\nbiorxiv_views[\"graph_type\"] = \"biorxiv\"\nbiorxiv_views[\"oa_status\"] = \"biorxiv\"\ntotal_views_including_biorxiv = total_views_including_biorxiv.append(biorxiv_views)",
"execution_count": 71,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:18.136574Z",
"end_time": "2019-10-07T03:03:18.894815Z"
},
"trusted": true
},
"cell_type": "code",
"source": "all_data_pivot_plus_biorxiv = total_views_including_biorxiv.pivot_table(\n index='observation_year', columns='graph_type', values=['views'], aggfunc=np.sum)\\\n .sort_index(axis=1, level=1)\\\n .swaplevel(0, 1, axis=1)\nall_data_pivot_plus_biorxiv.columns = all_data_pivot_plus_biorxiv.columns.levels[0]\n# all_data_pivot_plus_biorxiv[\"biorxiv\"] = all_data_pivot_plus_biorxiv[\"biorxiv\"].fillna(0)\n# all_data_pivot_plus_biorxiv[\"closed\"] -= all_data_pivot_plus_biorxiv[\"biorxiv\"]\nall_data_pivot_plus_biorxiv\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4), sharex=True, sharey=False)\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\nall_data_pivot_plus_biorxiv_graph = all_data_pivot_plus_biorxiv\nall_data_pivot_plus_biorxiv_graph = all_data_pivot_plus_biorxiv_graph.loc[all_data_pivot_plus_biorxiv_graph.index > 1960]\nmy_plot = all_data_pivot_plus_biorxiv_graph[graph_type_order_plus_biorxiv].plot.area(stacked=True, color=graph_type_colors_plus_biorxiv, ax=ax1, linewidth=0.1)\nax1.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: '{0:,.0f}'.format(y/(1000*1000.0))))\nax1.set_xlabel('year of view')\nax1.set_ylabel('views (millions)')\nax1.set_xlim(2010, 2025)\nax1.set_ylim(0, 1.2*max(all_data_pivot_plus_biorxiv_graph.sum(1)))\nax1.set_title(\"Estimated views by year of observation, including biorxiv growth\");\nhandles, labels = my_plot.get_legend_handles_labels(); my_plot.legend(reversed(handles[0:7]), reversed(plus_biorxiv_labels[0:7]), loc='upper left'); # reverse to keep order consistent\n\ndf_diff_proportional_plus_biorxiv = all_data_pivot_plus_biorxiv.div(all_data_pivot_plus_biorxiv.sum(1), axis=0)\nmy_plot = df_diff_proportional_plus_biorxiv[graph_type_order_plus_biorxiv].plot.area(stacked=True, color=graph_type_colors_plus_biorxiv, ax=ax2, linewidth=0.1)\nmy_plot.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))\nax2.set_xlabel('year of view')\nax2.set_ylabel('proportion of views')\nax2.set_title(\"Proportion of views, including biorxiv growth\");\nax2.set_xlim(2010, 2025)\nax2.set_ylim(0, 1)\nhandles, labels = my_plot.get_legend_handles_labels(); my_plot.legend(reversed(handles[0:7]), reversed(plus_biorxiv_labels[0:7]), loc='upper left'); # reverse to keep order consistent\n\nplt.tight_layout(pad=.5, w_pad=4, h_pad=2.0) \nplt.subplots_adjust(hspace=1)\n",
"execution_count": 72,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 864x288 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"variables": {
"print figure_link(\"biorxiv-stacked\")": "<a href=\"#figure-biorxiv-stacked\">Figure 20</a>"
}
},
"cell_type": "markdown",
"source": "**{{print figure_link(\"biorxiv-stacked\")}}: Predicted views, including bioRxiv, by year of view**"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:18.911177Z",
"end_time": "2019-10-07T03:03:19.279504Z"
},
"trusted": true
},
"cell_type": "code",
"source": "df = df_diff_proportional_plus_biorxiv.copy()\nrows = df.loc[(df.index==2010) | (df.index==2019) | (df.index==2025)]\nrows[\"all OA\"] = 1 - rows[\"closed\"]\nmy_markdown = tabulate(100*rows[graph_type_order_plus_biorxiv+[\"all OA\"]], tablefmt=\"pipe\", headers=\"keys\", floatfmt=\",.0f\")\ndisplay(Markdown(my_markdown))",
"execution_count": 73,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.Markdown object>",
"text/markdown": "| observation_year | biorxiv | green | gold | hybrid | immediate_bronze | delayed_bronze | closed | all OA |\n|-------------------:|----------:|--------:|-------:|---------:|-------------------:|-----------------:|---------:|---------:|\n| 2010 | nan | 6 | 6 | 3 | 6 | 8 | 71 | 29 |\n| 2019 | 0 | 15 | 20 | 7 | 5 | 6 | 48 | 52 |\n| 2025 | 4 | 18 | 31 | 10 | 4 | 3 | 29 | 71 |"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "\n\n------------\n*Move this section to the top of the paper*\n\n### Summary\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Understanding the growth of open access (OA) is important for deciding funder policy, subscription allocation, and infrastructure planning. \n\nThis study analyses the number of papers available as OA over time. The models includes both OA embargo data and the relative growth rates of different OA types over time, based on the OA status of 70 million journal articles published between 1950 and 2019.\n\nThe study also looks at article usage data, analyzing the proportion of views to OA articles vs views to articles which are closed access. Signal processing techniques are used to model how these viewership patterns change over time. Viewership data is based on 2.8 million uses of the Unpaywall browser extension in July 2019. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We found that Green, Gold, and Hybrid papers receive more views than their Closed or Bronze counterparts, particularly Green papers made available within a year of publication. We also found that the proportion of Green, Gold, and Hybrid articles is growing most quickly.\n\nIn 2019: \n- 31% of all journal articles are available as OA\n- 52% of article views are to OA articles\n\nGiven existing trends, we estimate that by 2025:\n- 44% of all journal articles will be available as OA\n- 70% of article views will be to OA articles\n\nThe declining relevance of closed access articles is likely to change the landscape of scholarly communication in the years to come."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:19.291886Z",
"end_time": "2019-10-07T03:03:20.512344Z"
},
"trusted": true
},
"cell_type": "code",
"source": "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4), sharex=True, sharey=False)\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\nstart_year = 2000\nend_year = 2025\ndivide_year = 2018\nxlabel=\"year of observation\"\nmy_colors = graph_type_colors\nmy_color_order = graph_type_order\ncolor_column = \"graph_type\"\nfancy = None # \"diff\"\n\n\n \nsummary_views_pivot_actual = views_df_diff_proportional.loc[views_df_diff_proportional.index <= divide_year+1]\nmy_plot = summary_views_pivot_actual[graph_type_order].plot.area(stacked=True, color=my_colors, ax=ax1, linewidth=0.1)\nsummary_views_pivot_projected = views_df_diff_proportional.loc[views_df_diff_proportional.index > divide_year]\nmy_plot = summary_views_pivot_projected[my_color_order].plot.area(stacked=True, color=my_colors, linewidth=.1, ax=ax1, alpha=0.6)\nmy_plot.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))\nax1.set_xlabel('year of view')\nax1.set_ylabel('proportion of views')\nax1.set_xlim(2010, 2025)\nax1.set_ylim(0, 1)\nax1.minorticks_on()\nax1.tick_params(axis='x', which='minor', bottom=False)\nax1.tick_params(which='both', right='on', left='on')\nax1.yaxis.set_minor_locator(plt.MaxNLocator(10))\nax1.xaxis.set_major_locator(plt.MaxNLocator(5))\nax1.set_title(\"Projected views, by OA type\")\nhandles, labels = my_plot.get_legend_handles_labels(); my_plot.legend(reversed(handles[0:6]), reversed(labels[0:6]), loc='upper left'); # reverse to keep order consistent\n\nsummary_articles_pivot_actual = df_articles_proportional.loc[df_articles_proportional.index <= divide_year+1]\nmy_plot = summary_articles_pivot_actual[my_color_order].plot.area(stacked=True, color=my_colors, linewidth=.1, ax=ax2)\nsummary_articles_pivot_projected = df_articles_proportional.loc[df_articles_proportional.index > divide_year]\nmy_plot = summary_articles_pivot_projected[my_color_order].plot.area(stacked=True, color=my_colors, linewidth=.1, ax=ax2, alpha=0.6)\nmy_plot.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))\nax2.set_xlabel(xlabel)\nax2.set_ylabel('proportion of articles')\n# ax2.set_title(\"Proportion of papers\");\nax2.set_xlim(start_year, end_year)\nax2.set_ylim(0, 1) \nax2.minorticks_on()\nax2.tick_params(axis='x', which='minor', bottom=False)\nax2.tick_params(which='both', right='on', left='on')\nax2.yaxis.set_minor_locator(plt.MaxNLocator(10))\nax2.set_title(\"Projected articles, by OA type\")\nhandles, labels = my_plot.get_legend_handles_labels(); my_plot.legend(reversed(handles[0:6]), reversed(labels[0:6]), loc='upper left'); # reverse to keep order consistent\n\n\nplt.tight_layout(pad=.5, w_pad=4, h_pad=2.0) \nplt.subplots_adjust(hspace=1)\n",
"execution_count": 74,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": "<Figure size 864x288 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Move the raw data below to supplementary information (it is the data behind the graphs above):"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Percent of views available as OA type, for certain years:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:13:01.771680Z",
"end_time": "2019-10-07T03:13:02.046487Z"
},
"trusted": true
},
"cell_type": "code",
"source": "\ndf = views_df_diff_proportional\nrows = df.loc[(df.index==2010) | (df.index==2019) | (df.index==2025)]\n# with pd.option_context('display.float_format', '{:,.0f}%'.format):\n# print 100*rows[graph_type_order_plus_biorxiv]\n\nrows[\"all OA\"] = 1 - rows[\"closed\"]\nmy_markdown = tabulate(100*rows[graph_type_order+[\"all OA\"]], tablefmt=\"pipe\", headers=\"keys\", floatfmt=\",.0f\")\ndisplay(Markdown(my_markdown))\n",
"execution_count": 105,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.Markdown object>",
"text/markdown": "| observation_year | green | gold | hybrid | immediate_bronze | delayed_bronze | closed | all OA |\n|-------------------:|--------:|-------:|---------:|-------------------:|-----------------:|---------:|---------:|\n| 2010 | 6 | 6 | 3 | 6 | 8 | 71 | 29 |\n| 2019 | 15 | 20 | 7 | 5 | 6 | 48 | 52 |\n| 2025 | 19 | 33 | 10 | 4 | 4 | 30 | 70 |"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Percent of papers available as OA type, for certain years:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:13:03.416769Z",
"end_time": "2019-10-07T03:13:03.684146Z"
},
"trusted": true
},
"cell_type": "code",
"source": "\ndf = df_articles_proportional.copy()\nrows = df.loc[(df.index==2010) | (df.index==2019) | (df.index==2025)]\nrows[\"all OA\"] = 1 - rows[\"closed\"]\nmy_markdown = tabulate(100*rows[graph_type_order+[\"all OA\"]], tablefmt=\"pipe\", headers=\"keys\", floatfmt=\",.0f\")\ndisplay(Markdown(my_markdown))\n",
"execution_count": 106,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.Markdown object>",
"text/markdown": "| x | green | gold | hybrid | immediate_bronze | delayed_bronze | closed | all OA |\n|-----:|--------:|-------:|---------:|-------------------:|-----------------:|---------:|---------:|\n| 2010 | 2 | 3 | 2 | 12 | 4 | 78 | 22 |\n| 2019 | 4 | 9 | 4 | 10 | 3 | 69 | 31 |\n| 2025 | 7 | 18 | 7 | 10 | 3 | 56 | 44 |"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "---------------"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-5\"></a>\n## 5. Discussion"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We found that Green, Gold, and Hybrid papers receive more views than their Closed or Bronze counterparts, particularly Green papers made available within a year of publication. We also found that the proportion of Green, Gold, and Hybrid articles is growing quickly.\n\nIn 2019: \n\n- 31% of all journal articles are available as OA\n- 52% of article views are to OA articles\n\nGiven existing trends, we estimate that by 2025:\n\n- 44% of all journal articles will be available as OA\n- 70% of article views will be to OA articles"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Our model is conservative. Although the extrapolations assume continued incremental adoption of OA, they do not yet model other disruptive changes that will likely increase the growth of OA in coming years: the adoption of Plan S, a change in embargo periods for existing mandates, a dramatic increase in institutional self-archiving, large scale read and publish agreements, etc.\n\nThis area is ripe for future research in other ways as well: understanding how OA publication and viewership rates vary by discipline, country, and publisher is key. The assumption that the views/article curve is stable over time should be further investigated and relaxed if found to be inadequate. The model could be refined in many other ways as well, for example to use custom viewership patterns for readers within a specific university, or of a specific journal.\n\nOne interesting realization from the modeling we've done is that when the proportion of papers that are OA increases, or when the OA lag decreases, the total number of views increase -- the scholarly literature becomes more heavily viewed and thus more valuable to society. This is intuitive, but could be explored quantitatively in future work."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The study has several limitations. Only journal articles with DOIs are included, which under-represents disciplines and geographical areas which rely heavily on conference papers or articles without DOIs. Illegal repositories (SciHub) or articles posted on academic social networks (ResearchGate and Academia.edu) are not considered, which may undercount articles that are relevant for some uses. The users of the Unpaywall browser extension may not be representative of other readers, and using page views as a proxy for article interest is inexact. Nonetheless, we believe this analysis represents a useful approach for modeling the growth and importance of OA in the future. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The genesis for this study was a steady stream of inquiries from university librarians, asking for OA rates for specific journals to help inform their subscription decisions and negotiations. We realized it would be even more helpful if we could provide OA rates (a) for the future, (b) by date when the OA resource is available, and (c) weighted by the importance of the article to their faculty. The model presented here addresses these issues and will form the basis of information available to librarians and other decision-makers in the future.\n\nThe declining relevance of closed access articles is likely to change the landscape of scholarly communication in the years to come."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## 6. References"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "- Abdill, R.J., and Blekhman, R. (2019). Tracking the popularity and outcomes of all bioRxiv preprints. eLife 8.\n\n- Antelman, K. (2017). Leveraging the growth of open access in library collection decision making. At the Helm: Leading Transformation. Association of College and Research Libraries 411–422.\n\n- Laakso, M., and Björk, B.-C. (2013). Delayed open access: An overlooked high-impact category of openly available scientific literature. Journal of the American Society for Information Science and Technology 64, 1323–1329.\n\n- Lewis, D.W. (2012). The Inevitability of Open Access. College & Research Libraries 73, 493–506.\n\n- Piwowar, H., Priem, J., Larivière, V., Alperin, J.P., Matthias, L., Norlander, B., Farley, A., West, J., and Haustein, S. (2018). The State of OA: a large-scale analysis of the prevalence and impact of Open Access articles. PeerJ 6, e4375.\n\n- Piwowar, H., Priem, J., & Orr, R. (2019). Data From: The Future of OA: A large-scale analysis projecting Open Access publication and readership [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3474007"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-7\"></a>\n## 7. Data and code availability\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T15:32:53.005069Z",
"end_time": "2019-09-23T15:32:53.024401Z"
}
},
"cell_type": "markdown",
"source": "<a id=\"section-7-1\"></a>\n### 7.1 Empirical Gold OA list\n\nThe empirical Gold OA journal list is available in the Zenodo dataset at http://doi.org/10.5281/zenodo.3474007, in the file \"gold_oa_empirical_list.csv\"."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-7-2\"></a>\n### 7.2 Empirical bronze delayed OA list\n\nThe empirical Bronze Delayed OA journal list is available in the Zenodo dataset at http://doi.org/10.5281/zenodo.3474007, in the file \"delayed_bronze_empirical_list.csv\".\n\nThe list of combined delayed OA policies we extracted from various sources is available in the Zenodo dataset at http://doi.org/10.5281/zenodo.3474007, in the file \"delayed_bronze_extracted_policies.csv\".\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-7-3\"></a>\n### 7.3 Study data\n\nAll study data is available in Zenodo, at \n```\nPiwowar H, Priem J, & Orr R. (2019). Data From: The Future of OA: A large-scale analysis projecting Open Access publication and readership [Data set]. Zenodo.``` http://doi.org/10.5281/zenodo.3474007\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-7-4\"></a>\n### 7.4 Analysis notebook\n\nThe Jupyter analysis notebook is available from GitHub at https://github.com/Impactstory/predicting-oa-paper.\n\nAlso, for Jupyter nerds and to help us remember: export using \n```jupyter nbconvert manuscript.ipynb --to html --TemplateExporter.exclude_input=True```\n then push to github, then can be viewed at\nhttps://htmlpreview.github.io/?https://github.com/Impactstory/predicting-oa-paper/blob/master/manuscript.html\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## 8. Competing Interests\n\nThe authors work at [Our Research](https://ourresearch.org/) (formerly Impactstory), a non-profit company that builds tools to make scholarly research more open, connected, and reusable, including Unpaywall."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-23T13:28:12.076835Z",
"end_time": "2019-09-23T13:28:12.086746Z"
}
},
"cell_type": "markdown",
"source": "## 9. Funding\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-09-25T19:36:59.762379Z",
"end_time": "2019-09-25T19:36:59.776277Z"
}
},
"cell_type": "markdown",
"source": "The authors received no funding for this analysis."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## 10. Acknowledgements"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The authors would like to thank Bianca Kramer for extensive and valuable comments on a draft of this article. The author order of JP and HP was determined by coin flip, as is their custom."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-11\"></a>\n## 11. Supplementary Information\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<a id=\"section-11-1\"></a>\n### 11.1 Detailed look at OA Lag of Green OA\n"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:20.978076Z",
"end_time": "2019-10-07T03:03:20.989131Z"
},
"trusted": true
},
"cell_type": "code",
"source": "register_new_figure(\"detailed-green\");",
"execution_count": 77,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div id=\"figure-detailed-green\"></div>\n <script>\n var key = \"figure-detailed-green\"\n $(\"div\").each(function(i){\n if (this.id === key){\n this.innerHTML = '<a name=\"' + key + '\"></a>';\n }\n });\n </script>\n "
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "This data supplements the discussion of Green OA lag in [Section 4.1.2](#section-4-1-2)."
},
{
"metadata": {
"variables": {
"print figure_link(\"detailed-green\")": "<a href=\"#figure-detailed-green\">Figure 21</a>"
}
},
"cell_type": "markdown",
"source": "In {{print figure_link(\"detailed-green\")}} we plot the number of Green OA papers made available each year vs their date of publication. The first plot is a histogram of number of papers made available each year (one row for each year). The second plot is the same, but superimposes the articles made available in previous years. This stacked area represents the total cumulative number of Green OA papers that are available in that year -- if you were in that year and wondering what was available as Green OA that's what you'd find.\n\nThe third plot is a larger version of the availability as of 2018, showing the accumulation of availability. It allows us to appreciate that less than half of papers papers published in, say, 2015, were made available the same year -- most of the papers have been made available in subsequent years. The fourth plot is a slice in isolation, for clarity: the Green OA for articles with a Publication Date of 2015."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2019-10-07T03:03:21.008443Z",
"end_time": "2019-10-07T03:03:33.309698Z"
},
"trusted": true
},
"cell_type": "code",
"source": "make_detailed_plots(\"green\")",
"execution_count": 78,
"outputs": [
{
"output_type": "display_data",
"data": {
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