diff --git a/answers.md b/answers.md
index d125e37..032db5b 100644
--- a/answers.md
+++ b/answers.md
@@ -46,7 +46,7 @@ Introduce approaches (to be even developed in future works) for correcting data
Clarify what measure you used to identify "smaller journals" - what is a "small journal"? What is the threshold used to differentiate it from a "big journal"? What is a "medium journal"?
-*By saying this we do not refer to any previous classification made by other researchers. We defined as "smaller" those journals havng less than 10 DOIs specified in their metadata.*
+*We mean those journals having a mean ad median number of articles lower than the median of the total sample. By saying this we do not refer to any previous classification made by other researchers.*
Including, in the analysis, also the publication type of the citing entity could be beneficial for the discussion of the results, I believe, since you can show if there are different behaviours depending on the kind of the citing entities.
diff --git a/data_viz.ipynb b/data_viz.ipynb
index e7dc58e..721f4ab 100644
--- a/data_viz.ipynb
+++ b/data_viz.ipynb
@@ -1,24 +1,8 @@
{
"cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "'''\n",
- "MIT License (MIT)\n",
- "Copyright © 2022 Silvio Peroni, Alessandro Bertozzi, Davide Brembilla, Chiara Catizone, Constance Dami, Umut Kuçuk, Chiara Manca, Giulia Venditti\n",
- "\n",
- "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
- "\n",
- "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
- "\n",
- "THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n",
- "'''"
- ]
- },
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -52,6 +36,7 @@
"
ref-undefined | \n",
" ref-num | \n",
" year | \n",
+ " type | \n",
" country | \n",
" subject | \n",
" \n",
@@ -59,107 +44,112 @@
" \n",
" \n",
" 0 | \n",
- " 2372-0352 | \n",
- " 10.3934/environsci.2020019 | \n",
- " 1 | \n",
+ " 1392-1215 2029-5731 | \n",
+ " 10.5755/j01.eie.26.6.25849 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
- " 52 | \n",
- " 24 | \n",
- " 76 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
" 2020.0 | \n",
- " US | \n",
- " G | \n",
+ " journal-article | \n",
+ " LT | \n",
+ " STM | \n",
"
\n",
" \n",
" 1 | \n",
- " 2372-0352 | \n",
- " 10.3934/environsci.2020024 | \n",
- " 1 | \n",
+ " 1392-1215 2029-5731 | \n",
+ " 10.5755/j01.eie.26.6.27484 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
- " 11 | \n",
- " 8 | \n",
- " 19 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
" 2020.0 | \n",
- " US | \n",
- " G | \n",
+ " journal-article | \n",
+ " LT | \n",
+ " STM | \n",
"
\n",
" \n",
" 2 | \n",
- " 2372-0352 | \n",
- " 10.3934/environsci.2020026 | \n",
- " 1 | \n",
+ " 1392-1215 2029-5731 | \n",
+ " 10.5755/j01.eie.26.5.26012 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
- " 26 | \n",
- " 23 | \n",
- " 49 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
" 2020.0 | \n",
- " US | \n",
- " G | \n",
+ " journal-article | \n",
+ " LT | \n",
+ " STM | \n",
"
\n",
" \n",
" 3 | \n",
- " 2372-0352 | \n",
- " 10.3934/environsci.2015.3.565 | \n",
- " 1 | \n",
+ " 1392-1215 2029-5731 | \n",
+ " 10.5755/j01.eie.26.5.26002 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
- " 37 | \n",
" 0 | \n",
- " 37 | \n",
- " 2015.0 | \n",
- " US | \n",
- " G | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2020.0 | \n",
+ " journal-article | \n",
+ " LT | \n",
+ " STM | \n",
"
\n",
" \n",
" 4 | \n",
- " 2372-0352 | \n",
- " 10.3934/environsci.2021009 | \n",
- " 1 | \n",
+ " 1392-1215 2029-5731 | \n",
+ " 10.5755/j01.eie.24.4.21482 | \n",
" 1 | \n",
" 1 | \n",
- " 21 | \n",
" 0 | \n",
- " 19 | \n",
- " 40 | \n",
- " 2021.0 | \n",
- " US | \n",
- " G | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2018.0 | \n",
+ " journal-article | \n",
+ " LT | \n",
+ " STM | \n",
"
\n",
" \n",
"\n",
""
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- " issn doi doi-num on-crossref reference \\\n",
- "0 2372-0352 10.3934/environsci.2020019 1 1 1 \n",
- "1 2372-0352 10.3934/environsci.2020024 1 1 1 \n",
- "2 2372-0352 10.3934/environsci.2020026 1 1 1 \n",
- "3 2372-0352 10.3934/environsci.2015.3.565 1 1 1 \n",
- "4 2372-0352 10.3934/environsci.2021009 1 1 1 \n",
+ " issn doi doi-num on-crossref \\\n",
+ "0 1392-1215 2029-5731 10.5755/j01.eie.26.6.25849 1 1 \n",
+ "1 1392-1215 2029-5731 10.5755/j01.eie.26.6.27484 1 1 \n",
+ "2 1392-1215 2029-5731 10.5755/j01.eie.26.5.26012 1 1 \n",
+ "3 1392-1215 2029-5731 10.5755/j01.eie.26.5.26002 1 1 \n",
+ "4 1392-1215 2029-5731 10.5755/j01.eie.24.4.21482 1 1 \n",
"\n",
- " asserted-by-cr asserted-by-pub ref-undefined ref-num year country \\\n",
- "0 0 52 24 76 2020.0 US \n",
- "1 0 11 8 19 2020.0 US \n",
- "2 0 26 23 49 2020.0 US \n",
- "3 0 37 0 37 2015.0 US \n",
- "4 21 0 19 40 2021.0 US \n",
+ " reference asserted-by-cr asserted-by-pub ref-undefined ref-num year \\\n",
+ "0 0 0 0 0 0 2020.0 \n",
+ "1 0 0 0 0 0 2020.0 \n",
+ "2 0 0 0 0 0 2020.0 \n",
+ "3 0 0 0 0 0 2020.0 \n",
+ "4 0 0 0 0 0 2018.0 \n",
"\n",
- " subject \n",
- "0 G \n",
- "1 G \n",
- "2 G \n",
- "3 G \n",
- "4 G "
+ " type country subject \n",
+ "0 journal-article LT STM \n",
+ "1 journal-article LT STM \n",
+ "2 journal-article LT STM \n",
+ "3 journal-article LT STM \n",
+ "4 journal-article LT STM "
]
},
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
@@ -171,8 +161,8 @@
"import country_converter as coco\n",
"import pycountry_convert as pc\n",
"\n",
- "frame = pd.read_pickle('/Users/giuliavenditti/Desktop/data.pkl')\n",
- "frame.head()"
+ "frame = pd.read_pickle('data.pkl')\n",
+ "frame.head()\n"
]
},
{
@@ -184,7 +174,7 @@
},
{
"cell_type": "code",
- "execution_count": 74,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -197,10 +187,10 @@
{
"customdata": [
[
- "Percentage articles not on cross"
+ "Articles not on Crossref"
],
[
- "Percentage articles on cross"
+ "Articles on Crossref"
]
],
"domain": {
@@ -215,8 +205,8 @@
},
"hovertemplate": "ref=%{customdata[0]}
val=%{value}",
"labels": [
- "Percentage articles not on cross",
- "Percentage articles on cross"
+ "Articles not on Crossref",
+ "Articles on Crossref"
],
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"marker": {
@@ -229,8 +219,8 @@
"showlegend": true,
"type": "pie",
"values": [
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+ 384820,
+ 5444827
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}
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@@ -614,10 +604,11 @@
],
"scatter": [
{
- "fillpattern": {
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+ "marker": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
+ }
},
"type": "scatter"
}
@@ -1062,8 +1053,8 @@
"font": {
"size": 20
},
- "text": "Percentage of DOIs registered on Crossref",
- "x": 0.5,
+ "text": "DOAJ articles indexed on Crossref",
+ "x": 0.45,
"xanchor": "center",
"y": 0.9
},
@@ -1081,25 +1072,26 @@
"new = pd.DataFrame()\n",
"noton = totdoi - oncross\n",
"new['val'] = [noton, oncross]\n",
- "new['ref'] = ['Percentage articles not on cross', 'Percentage articles on cross']\n",
- "fig = px.pie(new, values='val', names='ref', color='ref', color_discrete_map={\n",
- " 'Percentage articles not on cross':'#34B67A',\n",
- " 'Percentage articles on cross':'#471D6C'}, width=800,\n",
- "title =\"Percentage of DOIs registered on Crossref\")\n",
+ "new['ref'] = ['Articles not on Crossref', 'Articles on Crossref']\n",
+ "fig = px.pie(new, values='val', names='ref',color='ref', color_discrete_map={\n",
+ " 'Articles not on Crossref':'#34B67A',\n",
+ " 'Articles on Crossref':'#471D6C'}, width=800,\n",
+ "title =\"DOAJ articles indexed on Crossref\")\n",
"\n",
"fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
+ " title = {'xanchor': 'center', 'y':0.9,'x':0.45},\n",
")\n",
"\n",
- "fig.show()"
+ "fig.show()\n",
+ "fig.write_image(\"images/fig1.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 64,
"metadata": {},
"outputs": [
{
@@ -1111,25183 +1103,12819 @@
"data": [
{
"alignmentgroup": "True",
- "hovertemplate": "% DOIs on Crossref=%{y}",
+ "box": {
+ "visible": false
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+ "hovertemplate": "Percentage of DOAJ DOIs on Crossref=%{y}",
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- "notched": false,
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+ "points": "all",
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- " 'Asia' : '#34B67A',\n",
- " 'Europe' : '#471D6C',\n",
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- " 'South America' : '#25848E',\n",
- " 'Oceania' : '#A6DA35',\n",
- " 'North America' : '#FBE625'\n",
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- " labels={ \"country\": \"Country\", \"perc_cr\": \"% DOIs on Crossref\", \"continent\": \"Continent\" },\n",
- " title=\"Percentage of DOIs registered on Crossref - by country.\")\n",
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- "\n",
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- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
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- "frame4 =frame4.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame4['perc_cr'] = (frame4['on-crossref']/frame4['doi-num'])*100\n",
- "frame4['country-name'] = coco.convert(names=frame4.index, to=\"name\")\n",
- "frame4 = frame4[(frame4.perc_cr < 80) & (frame4.perc_cr > 0)]\n",
- "continent_name = []\n",
- "for x in frame4.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame4['continent'] = continent_name\n",
- "frame4 = frame4.sort_values(by=['doi-num'])\n",
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- "fig = px.bar(frame4,x=\"country-name\",y='perc_cr', width=800, color=\"continent\", color_discrete_map={\n",
- " 'Asia' : '#34B67A',\n",
- " 'Europe' : '#471D6C',\n",
- " 'Africa' : '#D4E129',\n",
- " 'South America' : '#25848E',\n",
- " 'Oceania' : '#A6DA35',\n",
- " 'North America' : '#FBE625'\n",
- "},\n",
- " labels={ \"country\": \"Country\", \"perc_cr\": \"% DOIs on Crossref\", \"continent\": \"Continent\" },\n",
- " title=\"Percentage of DOIs registered on Crossref - by country.\")\n",
- "\n",
- "\n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
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- "for x in frame5.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
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- " 'Asia' : '#34B67A',\n",
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- " projection=\"natural earth\", labels={ \"iso_alpha\": \"Country code\", \"perc_cr\": \"% DOIs on Crossref\", \"doi-num\" : \"Number of dois\", \"continent\": \"Continent\" },\n",
- " title=\"Percentage of DOIs registered on Crossref - by country.\")\n",
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- "frame8 = frame.copy()\n",
- "frame8 = frame8.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame8 = frame8[(frame8.year >= 2000)&(frame8.year < 2022)]\n",
- "frame8 = frame8.groupby('year').sum()\n",
- "frame8['perc_cr'] = (frame8['on-crossref']/frame8['doi-num'])*100\n",
- "# fig = px.bar(frame,x=frame.index,y='perc_cr')\n",
- "fig = px.scatter(frame8,x=frame8.index, y='perc_cr', size='doi-num',size_max=60,width=800,\n",
- " labels={ \"year\": \"Year\", \"perc_cr\": \"% DOIs on Crossref\", \"doi-num\": \"Number of DOIs\" },\n",
- " title=\"Percentage of DOIs registered on Crossref - by year\")\n",
- "fig.update_yaxes(range=[50, 110])\n",
- "fig.update_traces(marker_color='#471D6C') \n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
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- "Dal grafico possiamo dedurre che gli articoli pubblicati dopo il 2000, anno di fondazione di Crossref, hanno più probabilità di essere presenti sulla piattaforma. Interessante notare il calo avito tra il 2010 e il 2015."
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- " continent_name.append(country_continent_name)\n",
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- " labels={\"country-name\": \"Country\", \"perc_ref\": \"% of references on Crossref\", \"continent\":\"Contintent\", \"doi-num\": \"Number of DOIs\" },\n",
- " title=\"Percentage of references registered on Crossref - by country.\")\n",
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- "frame14 = frame.copy()\n",
- "frame14 = frame14.groupby('country').sum()\n",
- "frame14 =frame14.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame14['perc_ref'] = (frame14['reference']/frame14['on-crossref'])*100\n",
- "frame14['iso_alpha'] = coco.convert(names=frame14.index, to='ISO3')\n",
- "frame14['country-name'] = coco.convert(names=frame14.index, to='name')\n",
- "continent_name = []\n",
- "for x in frame14.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame14['continent'] = continent_name\n",
- "frame14 = frame14[frame14['perc_ref'] > 0]\n",
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- "fig = px.scatter_geo(frame14, locations=\"iso_alpha\", width=800, color=\"continent\", color_discrete_map={\n",
- " 'Asia' : '#34B67A',\n",
- " 'Europe' : '#471D6C',\n",
- " 'Africa' : '#D4E129',\n",
- " 'South America' : '#25848E',\n",
- " 'Oceania' : '#A6DA35',\n",
- " 'North America' : '#FBE625'\n",
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- " hover_name=\"ref-num\", size=\"perc_ref\", size_max=15,\n",
- " projection=\"natural earth\", \n",
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- " labels={ \"iso_alpha\": \"Country code\", \"perc_ref\": \"% of references on Crossref\", \"doi-num\" : \"Number of dois\", \"continent\": \"Continent\", \"country-name\": \"Country name\" },\n",
- " title=\"Percentage of references registered on Crossref - by country.\")\n",
- "\n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
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- " font_size=10,\n",
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- " labels={ \"subject\": \"Research field\", \"perc_ref\": \"% of references on Crossref\" },\n",
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"x": 0.5,
"xanchor": "center",
"y": 0.9
},
+ "violinmode": "group",
"width": 800,
"xaxis": {
"anchor": "y",
"domain": [
0,
1
- ],
- "title": {
- "text": "Research field"
- }
+ ]
},
"yaxis": {
"anchor": "x",
@@ -27132,8 +14759,12 @@
0,
1
],
+ "range": [
+ 50,
+ 100
+ ],
"title": {
- "text": "avg of value"
+ "text": "Percentage of DOAJ DOIs on Crossref"
}
}
}
@@ -27144,26 +14775,16 @@
}
],
"source": [
- "frame20 = frame.copy()\n",
- "frame20 = frame20.groupby('subject').sum()\n",
- "frame20 =frame20.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame20['perc_ref_nodoi'] = (frame20['ref-undefined']/frame20['ref-num'])*100\n",
- "frame20['perc_ref_doi'] = 100 - frame20['perc_ref_nodoi']\n",
- "fig = px.histogram(frame20, x=frame20.index, y= ['perc_ref_doi', 'perc_ref_nodoi'], barmode='stack', histfunc='avg', width=800,\n",
- "color_discrete_sequence= ['#471D6C', '#34B67A'] ,\n",
- " labels={ \"subject\": \"Research field\", \"perc_ref_nodoi\": \"% reference without DOI\", \"perc_ref_doi\": \"% reference with DOI\"},\n",
- " title=\"References DOI overview - by research field\")\n",
- "\n",
- "newnames = {'perc_ref_doi': \"% of DOIs' references with DOI\", 'perc_ref_nodoi': \"% of reference without DOI\"}\n",
- "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
- " legendgroup = newnames[t.name],\n",
- " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
- " )\n",
- " )\n",
- "\n",
- "for idx in range(len(fig.data)):\n",
- " fig.data[idx].x = ['General works','Philosophy, Psychology, Religion','Auxiliary science of History', 'World History', 'History of the Americs', 'Local History of the Americas', 'Geography, Anthropology, Recreation', 'Social Sciences', 'Political Science', 'Law', 'Education', 'Music', 'Fine Arts', 'Language and Literature', 'Science', 'Medicine', 'Agriculture', 'Technology', 'Military Science', 'Naval Science', 'Bibliography, Library Science']\n",
- "\n",
+ "frame3 = frame.copy()\n",
+ "frame3['year'] = frame3['year'].apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame3 = frame3[(frame3.year >= 1950)&(frame3.year < 2022)]\n",
+ "frame3 = frame3.groupby('issn').sum()\n",
+ "frame3['perc_cr'] = (frame3['on-crossref']/frame3['doi-num'])*100\n",
+ "fig = px.violin(frame3, y=\"perc_cr\", points='all', width=800,\n",
+ "labels={ \"perc_cr\": \"Percentage of DOAJ DOIs on Crossref\" },\n",
+ " title=\"Distribution of DOAJ DOIs registered on Crossref.\")\n",
+ "fig.update_traces(marker_color='#471D6C') \n",
+ "fig.update_yaxes(range=[50, 100])\n",
"fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
@@ -27175,7 +14796,7 @@
},
{
"cell_type": "code",
- "execution_count": 99,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -27186,508 +14807,168 @@
},
"data": [
{
- "hovertemplate": "variable=% of DOIs' references with DOI
value=%{x}
Country=%{y}",
- "legendgroup": "% of DOIs' references with DOI",
+ "alignmentgroup": "True",
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Country=%{x}
DOAJ articles on Crossref=%{y}",
+ "legendgroup": "Africa",
"marker": {
- "color": "#471D6C",
- "symbol": "circle"
+ "color": "#D4E129",
+ "pattern": {
+ "shape": ""
+ }
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- "name": "% of DOIs' references with DOI",
- "orientation": "h",
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value=%{x}
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Country=%{x}
DOAJ articles on Crossref=%{y}",
+ "legendgroup": "North America",
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Country=%{x}
DOAJ articles on Crossref=%{y}",
+ "legendgroup": "Europe",
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- "Spain",
- "Ethiopia",
- "Finland",
- "France",
- "United Kingdom",
- "Georgia",
- "Ghana",
- "Gambia",
+ "Belarus",
+ "Austria",
"Greece",
- "Guatemala",
- "Hong Kong",
- "Honduras",
- "Croatia",
- "Hungary",
- "Indonesia",
- "Ireland",
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- "Rwanda",
- "Saudi Arabia",
- "Sweden",
- "Singapore",
- "Slovenia",
- "Slovakia",
- "South Sudan",
- "Syria",
+ "Portugal",
+ "Italy"
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+ "hovertemplate": "Continent=Asia
Country=%{x}
DOAJ articles on Crossref=%{y}",
+ "legendgroup": "Asia",
+ "marker": {
+ "color": "#34B67A",
+ "pattern": {
+ "shape": ""
+ }
+ },
+ "name": "Asia",
+ "offsetgroup": "Asia",
+ "orientation": "v",
+ "showlegend": true,
+ "textposition": "auto",
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+ "x": [
"Thailand",
- "Turkmenistan",
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- "Ukraine",
- "Uganda",
- "United States",
- "Uruguay",
- "Venezuela",
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+ "China",
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+ "yaxis": "y"
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+ "alignmentgroup": "True",
+ "hovertemplate": "Continent=South America
Country=%{x}
DOAJ articles on Crossref=%{y}",
+ "legendgroup": "South America",
+ "marker": {
+ "color": "#25848E",
+ "pattern": {
+ "shape": ""
+ }
+ },
+ "name": "South America",
+ "offsetgroup": "South America",
+ "orientation": "v",
+ "showlegend": true,
+ "textposition": "auto",
+ "type": "bar",
+ "x": [
+ "Ecuador"
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+ "xaxis": "x",
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}
],
"layout": {
+ "barmode": "relative",
"font": {
"family": "sans-serif",
"size": 10
},
"legend": {
"title": {
- "text": "variable"
+ "text": "Continent"
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@@ -28063,10 +15344,11 @@
],
"scatter": [
{
- "fillpattern": {
- "fillmode": "overlay",
- "size": 10,
- "solidity": 0.2
+ "marker": {
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+ "ticks": ""
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},
"type": "scatter"
}
@@ -28511,7 +15793,7 @@
"font": {
"size": 20
},
- "text": "References DOI overview - by country",
+ "text": "DOAJ articles indexed on Crossref - by Country
The plot is showing Countries with less than 80% of articles on Crossref",
"x": 0.5,
"xanchor": "center",
"y": 0.9
@@ -28524,7 +15806,7 @@
1
],
"title": {
- "text": "value"
+ "text": "Country"
}
},
"yaxis": {
@@ -28534,7 +15816,7 @@
1
],
"title": {
- "text": "Country"
+ "text": "DOAJ articles on Crossref"
}
}
}
@@ -28545,22 +15827,31 @@
}
],
"source": [
- "frame21 = frame.copy()\n",
- "frame21 = frame21.groupby('country').sum()\n",
- "frame21 =frame21.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame21['country-name'] = coco.convert(names=frame21.index, to='name')\n",
- "frame21['perc_ref_nodoi'] = (frame21['ref-undefined']/frame21['ref-num'])*100\n",
- "frame21['perc_ref_doi'] = 100 - frame21['perc_ref_nodoi']\n",
- "fig = px.scatter(frame21, x= ['perc_ref_doi', 'perc_ref_nodoi'], y=\"country-name\", width=800, color_discrete_sequence= ['#471D6C', '#34B67A'],\n",
- "labels={\"country-name\": \"Country\", 'perc_ref_doi':'% References with DOI', 'perc_ref_nodoi': '% References without DOI'},\n",
- " title=\"References DOI overview - by country\")\n",
+ "frame4 = frame.copy()\n",
+ "frame4 = frame4.groupby('country').sum()\n",
+ "frame4 =frame4.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame4['perc_cr'] = (frame4['on-crossref']/frame4['doi-num'])*100\n",
+ "frame4['country-name'] = coco.convert(names=frame4.index, to=\"name\")\n",
+ "frame4 = frame4[(frame4.perc_cr < 80) & (frame4.perc_cr > 0)]\n",
+ "continent_name = []\n",
+ "for x in frame4.index:\n",
+ " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
+ " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
+ " continent_name.append(country_continent_name)\n",
+ "frame4['continent'] = continent_name\n",
+ "frame4 = frame4.sort_values(by=['doi-num'])\n",
+ "\n",
+ "fig = px.bar(frame4,x=\"country-name\",y='perc_cr', width=800, color=\"continent\", color_discrete_map={\n",
+ " 'Asia' : '#34B67A',\n",
+ " 'Europe' : '#471D6C',\n",
+ " 'Africa' : '#D4E129',\n",
+ " 'South America' : '#25848E',\n",
+ " 'Oceania' : '#A6DA35',\n",
+ " 'North America' : '#FBE625'\n",
+ "},\n",
+ " labels={ \"country\": \"Country\", \"perc_cr\": \"DOAJ articles on Crossref\", \"continent\": \"Continent\", \"country-name\": \"Country\" },\n",
+ " title=\"DOAJ articles indexed on Crossref - by Country
The plot is showing Countries with less than 80% of articles on Crossref\")\n",
"\n",
- "newnames = {'perc_ref_doi': \"% of DOIs' references with DOI\", 'perc_ref_nodoi': \"% of reference without DOI\"}\n",
- "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
- " legendgroup = newnames[t.name],\n",
- " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
- " )\n",
- " )\n",
"\n",
"fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
@@ -28568,13 +15859,13 @@
" font_size=10,\n",
" title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
")\n",
- "\n",
- "fig.show()"
+ "fig.show()\n",
+ "fig.write_image(\"images/fig2_boh.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 106,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -28585,325 +15876,65 @@
},
"data": [
{
- "geo": "geo",
- "hovertemplate": "%{hovertext}
Legend=% of references with doi
% of references with doi=%{marker.size}
Country code=%{location}",
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+ "hovertemplate": "Research field=%{x}
Percentage of articles indexed on Crossref=%{y}",
+ "legendgroup": "Social, Science and Humanities (SSH)",
+ "marker": {
+ "color": "#471D6C",
+ "pattern": {
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+ "name": "Social, Science and Humanities (SSH)",
+ "offsetgroup": "SSH",
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+ "textposition": "auto",
+ "type": "bar",
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- "TTO",
- "TWN",
- "UKR",
- "UGA",
- "USA",
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- "VNM",
- "YEM",
- "ZAF"
+ "xaxis": "x",
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+ 85.78301556563233
],
+ "yaxis": "y"
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+ {
+ "alignmentgroup": "True",
+ "hovertemplate": "Research field=%{x}
Percentage of articles indexed on Crossref=%{y}",
+ "legendgroup": "Science, Technology and Medicine (STM)",
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- "sizemode": "area",
- "sizeref": 0.25,
- "symbol": "circle"
+ "color": "#34B67A",
+ "pattern": {
+ "shape": ""
+ }
},
- "mode": "markers",
- "name": "% of references with doi",
+ "name": "Science, Technology and Medicine (STM)",
+ "offsetgroup": "STM",
+ "orientation": "v",
"showlegend": true,
- "type": "scattergeo"
+ "textposition": "auto",
+ "type": "bar",
+ "x": [
+ "STM"
+ ],
+ "xaxis": "x",
+ "y": [
+ 95.74967301807851
+ ],
+ "yaxis": "y"
}
],
"layout": {
+ "barmode": "stack",
"font": {
"family": "sans-serif",
"size": 10
},
- "geo": {
- "center": {},
- "domain": {
- "x": [
- 0,
- 1
- ],
- "y": [
- 0,
- 1
- ]
- },
- "projection": {
- "type": "natural earth"
- }
- },
"legend": {
- "itemsizing": "constant",
"title": {
- "text": "Legend"
+ "text": "Research field"
},
"tracegroupgap": 0
},
@@ -29728,12 +16759,37 @@
"font": {
"size": 20
},
- "text": "References DOI overview - by country",
+ "text": "Articles indexed on Crossref - by research field",
"x": 0.5,
"xanchor": "center",
"y": 0.9
},
- "width": 800
+ "width": 800,
+ "xaxis": {
+ "anchor": "y",
+ "categoryarray": [
+ "SSH",
+ "STM"
+ ],
+ "categoryorder": "array",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Research field"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Percentage of articles indexed on Crossref"
+ }
+ }
}
}
},
@@ -29742,41 +16798,44 @@
}
],
"source": [
- "frame22 = frame.copy()\n",
- "frame22 = frame22.groupby('country').sum()\n",
- "frame22 =frame22.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame22['iso_alpha'] = coco.convert(names=frame22.index, to='ISO3')\n",
- "frame22['country-name'] = coco.convert(names=frame22.index, to='name')\n",
- "frame22['perc_ref_nodoi'] = (frame22['ref-undefined']/frame22['ref-num'])*100\n",
- "frame22['perc_ref_doi'] = 100 - frame22['perc_ref_nodoi']\n",
- "continent_name = []\n",
- "for x in frame22.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame22['continent'] = continent_name\n",
- "frame22 = frame22[frame22['perc_ref_doi'] > 0]\n",
- "frame22['color'] = \"% of references with doi\"\n",
- "\n",
- "fig1 = px.scatter_geo(frame22, locations=\"iso_alpha\", width=800, \n",
- " hover_name='ref-num', size='perc_ref_doi', color=\"color\", color_discrete_map={\"% of references with doi\": \"#471D6C\"},\n",
- " projection=\"natural earth\", labels={ \"iso_alpha\": \"Country code\", 'perc_ref_doi': \"% of references with doi\", \"color\": \"Legend\" },\n",
- "title=\"References DOI overview - by country\")\n",
+ "frame6 = frame.copy()\n",
+ "frame6 = frame6.groupby('subject').sum()\n",
+ "frame6 = frame6.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame6['perc_cr'] = (frame6['on-crossref']/frame6['doi-num'])*100\n",
"\n",
+ "fig = px.bar(frame6, x=frame6.index, y= 'perc_cr', barmode='stack', width=800, color =frame6.index, color_discrete_sequence= ['#471D6C', '#34B67A'],\n",
+ " labels={ \"subject\": \"Research field\", \"perc_cr\": \"Percentage of articles indexed on Crossref\"},\n",
+ " title=\"Articles indexed on Crossref - by research field\")\n",
"\n",
- "fig1.update_layout(\n",
+ "# fig.data[0].x[0] = 'Social, Science and Humanities (SSH)'\n",
+ "# fig.data[0].x[1] = 'Science, Technology and Medicine (STM)'\n",
+ "lables = {'SSH': \"Social, Science and Humanities (SSH)\", 'STM': \"Science, Technology and Medicine (STM)\"}\n",
+ "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
+ " legendgroup = newnames[t.name],\n",
+ " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
+ " )\n",
+ " )\n",
+ "fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
- ")\n",
+ " title = {'xanchor': 'center', 'y':0.9,'x':0.5})\n",
+ "fig.show()\n",
"\n",
- "fig1.show()"
+ "fig.write_image(\"images/fig2.png\")\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2) Reference on crossref doi"
]
},
{
"cell_type": "code",
- "execution_count": 107,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
@@ -29787,298 +16846,14 @@
},
"data": [
{
- "geo": "geo",
- "hovertemplate": "%{hovertext}
Legend=% of references without doi
% of references without doi=%{marker.size}
Country code=%{location}",
- "hovertext": [
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- "RUS",
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- "SWE",
- "SGP",
- "SVN",
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- "TUR",
- "TTO",
- "TWN",
- "UKR",
- "UGA",
- "USA",
- "VEN",
- "VNM",
- "ZAF"
+ "customdata": [
+ [
+ "Articles without reference list"
+ ],
+ [
+ "Articles with reference list"
+ ]
],
- "marker": {
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- "sizemode": "area",
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- "symbol": "circle"
- },
- "mode": "markers",
- "name": "% of references without doi",
- "showlegend": true,
- "type": "scattergeo"
- }
- ],
- "layout": {
- "font": {
- "family": "sans-serif",
- "size": 10
- },
- "geo": {
- "center": {},
"domain": {
"x": [
0,
@@ -30089,15 +16864,33 @@
1
]
},
- "projection": {
- "type": "natural earth"
- }
+ "hovertemplate": "ref=%{customdata[0]}
val=%{value}",
+ "labels": [
+ "Articles without reference list",
+ "Articles with reference list"
+ ],
+ "legendgroup": "",
+ "marker": {
+ "colors": [
+ "#34B67A",
+ "#471D6C"
+ ]
+ },
+ "name": "",
+ "showlegend": true,
+ "type": "pie",
+ "values": [
+ 1681262,
+ 3762483
+ ]
+ }
+ ],
+ "layout": {
+ "font": {
+ "family": "sans-serif",
+ "size": 10
},
"legend": {
- "itemsizing": "constant",
- "title": {
- "text": "Legend"
- },
"tracegroupgap": 0
},
"template": {
@@ -30921,8 +17714,8 @@
"font": {
"size": 20
},
- "text": "References DOI overview - by country",
- "x": 0.5,
+ "text": "DOAJ articles on Crossref with reference list specified",
+ "x": 0.45,
"xanchor": "center",
"y": 0.9
},
@@ -30935,39 +17728,35 @@
}
],
"source": [
- "frame23 = frame.copy()\n",
- "frame23 = frame23.groupby('country').sum()\n",
- "frame23 =frame23.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame23['iso_alpha'] = coco.convert(names=frame23.index, to='ISO3')\n",
- "frame23['country-name'] = coco.convert(names=frame23.index, to='name')\n",
- "frame23['perc_ref_nodoi'] = (frame23['ref-undefined']/frame23['ref-num'])*100\n",
- "continent_name = []\n",
- "for x in frame23.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame23['continent'] = continent_name\n",
- "frame23 = frame23[frame23['perc_ref_nodoi'] > 0]\n",
- "frame23['color'] = \"% of references without doi\"\n",
- "frame23.head()\n",
- "fig2 = px.scatter_geo(frame23, locations=\"iso_alpha\", width=800, color=\"color\", color_discrete_map={\"% of references without doi\": \"#34B67A\"},\n",
- " hover_name='ref-num', size='perc_ref_nodoi',\n",
- " projection=\"natural earth\", labels={ \"iso_alpha\": \"Country code\", 'perc_ref_nodoi': \"% of references without doi\", \"color\": \"Legend\" },\n",
- "title=\"References DOI overview - by country\")\n",
+ "f = frame.copy()\n",
+ "f = f[f['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
+ "\n",
+ "new = pd.DataFrame()\n",
+ "noton = len(f) - f['reference'].sum() \n",
+ "new['val'] = [noton, frame['reference'].sum() ]\n",
+ "new['ref'] = ['Articles without reference list', 'Articles with reference list']\n",
+ "\n",
+ "\n",
+ "fig = px.pie(new, values='val', names='ref', color='ref', color_discrete_map={\n",
+ " 'Articles without reference list':'#34B67A',\n",
+ " 'Articles with reference list':'#471D6C'}, width=800,\n",
+ "title =\"DOAJ articles on Crossref with reference list specified\")\n",
"\n",
- "fig2.update_layout(\n",
+ "fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
+ " title = {'xanchor': 'center', 'y':0.9,'x':0.45},\n",
")\n",
"\n",
- "fig2.show()"
+ "fig.show()\n",
+ "\n",
+ "fig.write_image(\"images/fig3.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 110,
+ "execution_count": 24,
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"font": {
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"size": 10
@@ -33609,7 +31623,7 @@
"font": {
"size": 20
},
- "text": "References DOI overview - by year.",
+ "text": "Comparison between DOAJ articles on Crossref
and relative reference list presence over years.",
"x": 0.5,
"xanchor": "center",
"y": 0.9
@@ -33632,7 +31646,7 @@
1
],
"title": {
- "text": "avg of value"
+ "text": "value"
}
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@@ -33643,38 +31657,36 @@
}
],
"source": [
- "frame24 = frame.copy()\n",
- "frame24 = frame24.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame24 = frame24[(frame24.year >= 1950)&(frame24.year < 2022)]\n",
- "frame24 = frame24.groupby('year').sum()\n",
- "frame24['perc_ref_nodoi'] = (frame24['ref-undefined']/frame24['ref-num'])*100\n",
- "frame24['perc_ref_doi'] = 100 - frame24['perc_ref_nodoi']\n",
- "fig = px.histogram(frame24, x=frame24.index, y= ['perc_ref_doi', 'perc_ref_nodoi'], histfunc='avg', color_discrete_map={\n",
- " 'perc_ref_doi':'#34B67A',\n",
- " 'perc_ref_nodoi':'#471D6C'}, width=800,\n",
- " labels={'year':'Year'},\n",
- "title=\"References DOI overview - by year.\")\n",
+ "frame9 = frame.copy()\n",
+ "frame9 = frame9.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame9 = frame9[(frame9.year >= 1950)&(frame9.year < 2022)]\n",
+ "frame9 = frame9.groupby('year').sum()\n",
+ "frame9['perc_ref'] = (frame9['reference']/frame9['on-crossref'])*100\n",
+ "frame9['perc_cr'] = (frame9['on-crossref']/frame9['doi-num'])*100\n",
+ "fig = px.line(frame9,x=frame9.index,y=['perc_ref', 'perc_cr'],width=800,\n",
+ " labels={ \"year\": \"Year\" }, color_discrete_sequence= ['#471D6C', '#34B67A'] ,\n",
+ " title=\"Comparison between DOAJ articles on Crossref
and relative reference list presence over years.\")\n",
"\n",
- "newnames = {'perc_ref_doi': \"% of DOIs' references with DOI\", 'perc_ref_nodoi': \"% of reference without DOI\"}\n",
+ "newnames = {'perc_ref': \"Percentage of references\", 'perc_cr': \"Percentage of DOAJ articles\"}\n",
"fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
" legendgroup = newnames[t.name],\n",
" hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
" )\n",
" )\n",
- "\n",
"fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
" title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
")\n",
+ "fig.show()\n",
"\n",
- "fig.show()"
+ "fig.write_image(\"images/fig4.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 104,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
@@ -33685,339 +31697,64 @@
},
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- "hovertemplate": "variable=% of reference without DOI
Year=%{x}
value=%{y}",
- "legendgroup": "% of reference without DOI",
- "line": {
- "color": "#471D6C",
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+ "alignmentgroup": "True",
+ "bingroup": "x",
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+ "hovertemplate": "variable=Percentage of articles with reference list
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avg of value=%{y}",
+ "legendgroup": "Percentage of articles with reference list",
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+ "color": "#471D6C",
+ "pattern": {
+ "shape": ""
+ }
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- "mode": "lines",
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+ "name": "Percentage of articles with reference list",
+ "offsetgroup": "perc_ref_doi",
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Year=%{x}
value=%{y}",
- "legendgroup": "% of DOIs' references with DOI",
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avg of value=%{y}",
+ "legendgroup": "Percentage of articles without reference list",
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- "mode": "lines",
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+ "name": "Percentage of articles without reference list",
+ "offsetgroup": "perc_ref_nodoi",
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@@ -34400,10 +32137,11 @@
],
"scatter": [
{
- "fillpattern": {
- "fillmode": "overlay",
- "size": 10,
- "solidity": 0.2
+ "marker": {
+ "colorbar": {
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+ "ticks": ""
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"type": "scatter"
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@@ -34848,7 +32586,7 @@
"font": {
"size": 20
},
- "text": "References DOI overview - by year.",
+ "text": "Crossref references list overview - by research field",
"x": 0.5,
"xanchor": "center",
"y": 0.9
@@ -34861,7 +32599,7 @@
1
],
"title": {
- "text": "Year"
+ "text": "Research field"
}
},
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@@ -34871,7 +32609,7 @@
1
],
"title": {
- "text": "value"
+ "text": "avg of value"
}
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}
@@ -34882,19 +32620,17 @@
}
],
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- "frame25 = frame.copy()\n",
- "frame25 = frame25.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame25 = frame25[(frame25.year >= 1950)&(frame25.year < 2022)]\n",
- "frame25 = frame25.groupby('year').sum()\n",
- "frame25['perc_ref_nodoi'] = (frame25['ref-undefined']/frame25['ref-num'])*100\n",
- "frame25['perc_ref_doi'] = 100 - frame25['perc_ref_nodoi']\n",
- "fig = px.line(frame25,x=frame25.index,y=['perc_ref_nodoi', 'perc_ref_doi'], color_discrete_map={\n",
- " 'perc_ref_doi':'#34B67A',\n",
- " 'perc_ref_nodoi':'#471D6C'}, width=800,\n",
- " labels={'year':'Year'},\n",
- " title=\"References DOI overview - by year.\")\n",
+ "frame10 = frame.copy()\n",
+ "frame10 = frame10.groupby('subject').sum()\n",
+ "frame10 =frame10.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame10['perc_ref_nodoi'] = (frame10['ref-undefined']/frame10['ref-num'])*100\n",
+ "frame10['perc_ref_doi'] = 100 - frame10['perc_ref_nodoi']\n",
+ "fig = px.histogram(frame10, x=frame10.index, y= ['perc_ref_doi', 'perc_ref_nodoi'], barmode='stack', histfunc='avg', width=800,\n",
+ "color_discrete_sequence= ['#471D6C', '#34B67A'] ,\n",
+ " labels={ \"subject\": \"Research field\", \"perc_ref_nodoi\": \"% reference without DOI\", \"perc_ref_doi\": \"% reference with DOI\"},\n",
+ " title=\"Crossref references list overview - by research field\")\n",
"\n",
- "newnames = {'perc_ref_doi': \"% of DOIs' references with DOI\", 'perc_ref_nodoi': \"% of reference without DOI\"}\n",
+ "newnames = {'perc_ref_doi': \"Percentage of articles with reference list\", 'perc_ref_nodoi': \"Percentage of articles without reference list\"}\n",
"fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
" legendgroup = newnames[t.name],\n",
" hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
@@ -34907,19 +32643,14 @@
" font_size=10,\n",
" title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
")\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 4) asserted-by crossref / asserted-by publisher"
+ "fig.show()\n",
+ "\n",
+ "fig.write_image(\"images/fig5.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 105,
+ "execution_count": 50,
"metadata": {},
"outputs": [
{
@@ -34930,52 +32661,167 @@
},
"data": [
{
- "customdata": [
+ "alignmentgroup": "True",
+ "hovertemplate": "Country=%{x}
Percentage of references on Crossref=%{y}
Number of references=%{marker.color}",
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+ "textposition": "auto",
+ "type": "bar",
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+ "Argentina",
+ "Bosnia and Herzegovina",
+ "Bangladesh",
+ "Chile",
+ "Costa Rica",
+ "Denmark",
+ "Ecuador",
+ "Estonia",
+ "Ethiopia",
+ "Finland",
+ "Guatemala",
+ "Croatia",
+ "Hungary",
+ "Indonesia",
+ "Iraq",
+ "Iceland",
+ "Kenya",
+ "Morocco",
+ "Macedonia",
+ "Mexico",
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+ "yaxis": "y"
+ }
+ ],
+ "layout": {
+ "barmode": "relative",
+ "coloraxis": {
+ "colorbar": {
+ "title": {
+ "text": "Number of references"
+ }
+ },
+ "colorscale": [
[
- "Asserted by Crossref"
+ 0,
+ "#440154"
],
[
- "Asserted by citing publisher"
+ 0.05,
+ "#414084"
],
[
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val=%{value}",
- "labels": [
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- "legendgroup": "",
- "marker": {
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- "type": "pie",
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]
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@@ -35355,10 +33201,11 @@
],
"scatter": [
{
- "fillpattern": {
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- "size": 10,
- "solidity": 0.2
+ "marker": {
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+ "ticks": ""
+ }
},
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}
@@ -35803,12 +33650,32 @@
"font": {
"size": 20
},
- "text": "Percentage of DOIs registered on Crossref",
+ "text": "Crossref references list overview - by country
The plot is showing Countries with less than 20% of reference list on Crossref",
"x": 0.5,
"xanchor": "center",
"y": 0.9
},
- "width": 800
+ "width": 800,
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Country"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Percentage of references on Crossref"
+ }
+ }
}
}
},
@@ -35817,35 +33684,44 @@
}
],
"source": [
- "totdoi = len(frame)\n",
- "oncross = frame['on-crossref'].sum()\n",
- "ass_cross = frame['asserted-by-cr'].sum()\n",
- "ass_pub = frame['asserted-by-pub'].sum()\n",
- "und = frame['ref-undefined'].sum()\n",
- "ass_cross, ass_pub\n",
+ "frame12 = frame.copy()\n",
+ "frame12 = frame12.groupby('country').sum()\n",
+ "frame12 = frame12.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame12['perc_ref'] = (frame12['reference']/frame12['on-crossref'])*100\n",
+ "frame12['country-name'] = coco.convert(names=frame12.index, to=\"name\")\n",
+ "frame12 = frame12[ (frame12.perc_ref < 20) & (frame12.perc_ref > 0)]\n",
+ "continent_name = []\n",
+ "for x in frame12.index:\n",
+ " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
+ " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
+ " continent_name.append(country_continent_name)\n",
+ "frame12['continent'] = continent_name\n",
"\n",
+ "fig = px.bar(frame12,x='country-name',y='perc_ref', width=800, color=\"ref-num\", color_continuous_scale=[[0,\"#440154\"],[0.05,\"#414084\"],[0.2,\"#25848E\"], [0.5,\"#66CA5C\"],[1,\"#FBE625\"]], \n",
+ " labels={\"country-name\": \"Country\", \"perc_ref\": \"Percentage of references on Crossref\", \"continent\":\"Contintent\", \"ref-num\": \"Number of references\" },\n",
+ " title=\"Crossref references list overview - by country
The plot is showing Countries with less than 20% of reference list on Crossref\")\n",
"\n",
- "new = pd.DataFrame()\n",
- "new['val'] = [ass_cross, ass_pub, und]\n",
- "new['ref'] = ['Asserted by Crossref', 'Asserted by citing publisher', 'Missing/undefined doi']\n",
- "fig = px.pie(new, values='val', names='ref', color='ref', color_discrete_map={\n",
- " 'Asserted by Crossref':'#34B67A',\n",
- " 'Asserted by citing publisher':'#471D6C',\n",
- " 'Missing/undefined doi':'#D4E129'}, width=800,\n",
- "title =\"Percentage of DOIs registered on Crossref\")\n",
"fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
" title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
")\n",
+ "fig.show()\n",
"\n",
- "fig.show()"
+ "fig.write_image(\"images/fig6.png\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3) Reference that have doi specified"
]
},
{
"cell_type": "code",
- "execution_count": 116,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
@@ -35856,211 +33732,51 @@
},
"data": [
{
- "alignmentgroup": "True",
- "hovertemplate": "variable=% of references DOI asserted by Crossref
Research field=%{x}
value=%{y}",
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value=%{y}",
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Research field=%{x}
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- "legendgroup": "% of reference without DOI",
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- "yaxis": "y"
+ "type": "pie",
+ "values": [
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"layout": {
- "barmode": "relative",
"font": {
"family": "sans-serif",
"size": 10
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"legend": {
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"tracegroupgap": 0
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@@ -36884,32 +34600,12 @@
"font": {
"size": 20
},
- "text": "Reference assertion overviwe - by research field.",
+ "text": "References DOI overview",
"x": 0.5,
"xanchor": "center",
"y": 0.9
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- "width": 800,
- "xaxis": {
- "anchor": "y",
- "domain": [
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- "yaxis": {
- "anchor": "x",
- "domain": [
- 0,
- 1
- ],
- "title": {
- "text": "value"
- }
- }
+ "width": 800
}
}
},
@@ -36918,40 +34614,35 @@
}
],
"source": [
- "frame26 = frame.copy()\n",
- "frame26 = frame26.groupby('subject').sum()\n",
- "frame26 =frame26.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame26['perc_asserted_cr'] = (frame26['asserted-by-cr']/frame26['ref-num'])*100\n",
- "frame26['perc_asserted_pub'] = (frame26['asserted-by-pub']/frame26['ref-num'])*100\n",
- "frame26['perc_ref_nodoi'] = (frame26['ref-undefined']/frame26['ref-num'])*100\n",
- "fig = px.bar(frame26, x=frame26.index, y= ['perc_asserted_cr','perc_asserted_pub','perc_ref_nodoi'],color_discrete_map={\n",
- " 'perc_asserted_cr':'#34B67A',\n",
- " 'perc_ref_nodoi':'#471D6C',\n",
- " 'perc_asserted_pub':'#D4E129'}, width=800,\n",
- "labels={ \"subject\": \"Research field\"},\n",
- "title = \"Reference assertion overviwe - by research field.\")\n",
+ "f = frame.copy()\n",
+ "f = f[f['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
"\n",
- "newnames = {'perc_asserted_pub': '% references DOI asserted by publishers', 'perc_asserted_cr': \"% of references DOI asserted by Crossref\", 'perc_ref_nodoi': \"% of reference without DOI\"}\n",
- "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
- " legendgroup = newnames[t.name],\n",
- " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
- " )\n",
- " )\n",
+ "ref_defined = f['ref-num']-f['ref-undefined']\n",
+ "ref_defined = ref_defined.sum()\n",
+ "ref_undefined = f['ref-undefined'].sum()\n",
+ "\n",
+ "new = pd.DataFrame()\n",
+ "new['val'] = [ref_defined, ref_undefined]\n",
+ "new['ref'] = ['Reference with DOI', 'Reference without DOI']\n",
+ "\n",
+ "fig = px.pie(new, values='val', names='ref', color='ref', color_discrete_map={\n",
+ " 'Reference with DOI':'#34B67A',\n",
+ " 'Reference without DOI':'#471D6C'}, width=800,\n",
+ "title =\"References DOI overview\")\n",
"\n",
- "for idx in range(len(fig.data)):\n",
- " fig.data[idx].x = ['General works','Philosophy, Psychology, Religion','Auxiliary science of History', 'World History', 'History of the Americs', 'Local History of the Americas', 'Geography, Anthropology, Recreation', 'Social Sciences', 'Political Science', 'Law', 'Education', 'Music', 'Fine Arts', 'Language and Literature', 'Science', 'Medicine', 'Agriculture', 'Technology', 'Military Science', 'Naval Science', 'Bibliography, Library Science']\n",
"fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
" title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
")\n",
+ "\n",
"fig.show()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 57,
"metadata": {},
"outputs": [
{
@@ -36962,185 +34653,334 @@
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value=%{x}
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"font": {
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- "text": "Reference assertion overviwe - by research field.",
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- "frame = frame.groupby('subject').sum()\n",
- "frame =frame.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame['perc_asserted_cr'] = (frame['asserted-by-cr']/frame['ref-num'])*100\n",
- "frame['perc_asserted_pub'] = (frame['asserted-by-pub']/frame['ref-num'])*100\n",
- "frame['perc_ref_nodoi'] = (frame['ref-undefined']/frame['ref-num'])*100\n",
- "fig = px.scatter(frame, x= ['perc_asserted_cr','perc_asserted_pub','perc_ref_nodoi'], y=frame.index,\n",
- "labels={ \"subject\": \"Research field\"},\n",
- "title = \"Reference assertion overviwe - by research field.\")\n",
+ "frame14 = frame.copy()\n",
+ "frame14 = frame14[frame14['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
+ "frame14 = frame14.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame14 = frame14[(frame14.year >= 1950)&(frame14.year < 2022)]\n",
+ "frame14 = frame14.groupby('year').sum()\n",
+ "frame14['perc_ref_nodoi'] = (frame14['ref-undefined']/frame14['ref-num'])*100\n",
+ "frame14['perc_ref_doi'] = 100 - frame14['perc_ref_nodoi']\n",
+ "fig = px.line(frame14,x=frame14.index,y=['perc_ref_nodoi', 'perc_ref_doi'], color_discrete_map={\n",
+ " 'perc_ref_doi':'#34B67A',\n",
+ " 'perc_ref_nodoi':'#471D6C'}, width=800,\n",
+ " labels={'year':'Year'},\n",
+ " title=\"References DOI overview over years.\")\n",
"\n",
- "for idx in range(len(fig.data)):\n",
- " fig.data[idx].y = ['General works','Philosophy, Psychology, Religion','Auxiliary science of History', 'World History', 'History of the Americs', 'Local History of the Americas', 'Geography, Anthropology, Recreation', 'Social Sciences', 'Political Science', 'Law', 'Education', 'Music', 'Fine Arts', 'Language and Literature', 'Science', 'Medicine', 'Agriculture', 'Technology', 'Military Science', 'Naval Science', 'Bibliography, Library Science']\n",
+ "newnames = {'perc_ref_doi': \"Percentage of reference lists with DOI\", 'perc_ref_nodoi': \"Percentage of reference lists without DOI\"}\n",
+ "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
+ " legendgroup = newnames[t.name],\n",
+ " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
+ " )\n",
+ " )\n",
"\n",
"fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
+ " title = {'xanchor': 'center', 'y':0.9,'x':0.45},\n",
")\n",
- "fig.show()"
+ "fig.show()\n",
+ "fig.write_image(\"images/fig8.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 117,
+ "execution_count": 58,
"metadata": {},
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- 0,
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+ "Africa",
+ "Asia",
+ "Europe",
+ "North America",
+ "Oceania",
+ "South America"
],
"xaxis": "x",
"y": [
- "United Arab Emirates",
- "Albania",
- "Armenia",
- "Angola",
- "Argentina",
- "Austria",
- "Australia",
- "Azerbaijan",
- "Bosnia and Herzegovina",
- "Bangladesh",
- "Belgium",
- "Bulgaria",
- "Bolivia",
- "Brazil",
- "Bahamas",
- "Belarus",
- "Canada",
- "Switzerland",
- "Chile",
- "China",
- "Colombia",
- "Costa Rica",
- "Cuba",
- "Cyprus",
- "Czech Republic",
- "Germany",
- "Denmark",
- "Dominican Republic",
- "Algeria",
- "Ecuador",
- "Estonia",
- "Egypt",
- "Spain",
- "Ethiopia",
- "Finland",
- "France",
- "United Kingdom",
- "Georgia",
- "Ghana",
- "Gambia",
- "Greece",
- "Guatemala",
- "Hong Kong",
- "Honduras",
- "Croatia",
- "Hungary",
- "Indonesia",
- "Ireland",
- "Israel",
- "India",
- "Iraq",
- "Iran",
- "Iceland",
- "Italy",
- "Jordan",
- "Japan",
- "Kenya",
- "Kyrgyz Republic",
- "South Korea",
- "Kazakhstan",
- "Sri Lanka",
- "Lithuania",
- "Luxembourg",
- "Latvia",
- "Libya",
- "Morocco",
- "Moldova",
- "Montenegro",
- "Macedonia",
- "Mali",
- "Mongolia",
- "Mexico",
- "Malaysia",
- "Nigeria",
- "Nicaragua",
- "Netherlands",
- "Norway",
- "Nepal",
- "New Zealand",
- "Oman",
- "Panama",
- "Peru",
- "Philippines",
- "Pakistan",
- "Poland",
- "Puerto Rico",
- "Portugal",
- "Paraguay",
- "Qatar",
- "Romania",
- "Serbia",
- "Russia",
- "Rwanda",
- "Saudi Arabia",
- "Sweden",
- "Singapore",
- "Slovenia",
- "Slovakia",
- "South Sudan",
- "Syria",
- "Thailand",
- "Turkmenistan",
- "Tunisia",
- "Turkey",
- "Trinidad and Tobago",
- "Taiwan",
- "Ukraine",
- "Uganda",
- "United States",
- "Uruguay",
- "Venezuela",
- "Vietnam",
- "Yemen",
- "South Africa"
+ 54.35988899293244,
+ 59.26160472083973,
+ 81.44281845888804,
+ 83.15857750350172,
+ 85.9341981612876,
+ 48.642637739302984
],
"yaxis": "y"
}
],
"layout": {
+ "barmode": "relative",
"font": {
"family": "sans-serif",
"size": 10
@@ -39616,11 +36797,12 @@
"font": {
"size": 20
},
- "text": "Percentage of DOIs registered on Crossref - by country.",
+ "text": "Refeerences DOI overview - by continent",
"x": 0.5,
"xanchor": "center",
"y": 0.9
},
+ "width": 800,
"xaxis": {
"anchor": "y",
"domain": [
@@ -39628,7 +36810,7 @@
1
],
"title": {
- "text": "value"
+ "text": "Continent"
}
},
"yaxis": {
@@ -39638,7 +36820,7 @@
1
],
"title": {
- "text": "Country"
+ "text": "value"
}
}
}
@@ -39649,38 +36831,55 @@
}
],
"source": [
- "frame = frame.groupby('country').sum()\n",
- "frame =frame.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame['perc_asserted_cr'] = (frame['asserted-by-cr']/frame['ref-num'])*100\n",
- "frame['perc_asserted_pub'] = (frame['asserted-by-pub']/frame['ref-num'])*100\n",
- "frame['perc_ref_nodoi'] = (frame['ref-undefined']/frame['ref-num'])*100\n",
- "frame['country-name'] = coco.convert(names=frame.index, to='name')\n",
- "continent_name = []\n",
+ "frame15 = frame.copy()\n",
+ "frame15 = frame15[frame15['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
+ "frame15 = frame15.groupby('country').sum()\n",
"\n",
- "for x in frame.index:\n",
+ "frame15['country-name'] = coco.convert(names=frame15.index, to='name')\n",
+ "continent_name = []\n",
+ "for x in frame15.index:\n",
" country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
" country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
" continent_name.append(country_continent_name)\n",
- "frame['continent'] = continent_name\n",
+ "frame15['continent'] = continent_name\n",
+ "frame15 = frame15.groupby('continent').sum()\n",
+ "frame15 =frame15.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame15['perc_ref_nodoi'] = (frame15['ref-undefined']/frame15['ref-num'])*100\n",
+ "frame15['perc_ref_doi'] = 100 - frame15['perc_ref_nodoi']\n",
"\n",
- "fig = px.scatter(frame, x= ['perc_asserted_cr','perc_asserted_pub','perc_ref_nodoi'], y=\"country-name\", \n",
- " labels={ \"country-name\": \"Country\", \"perc_cr\": \"% DOIs on Crossref\" },\n",
- " title=\"Percentage of DOIs registered on Crossref - by country.\")\n",
+ "fig = px.bar(frame15, x =frame15.index, y=['perc_ref_nodoi', 'perc_ref_doi'], color_discrete_map={\n",
+ " 'perc_ref_nodoi':'#34B67A',\n",
+ " 'perc_ref_doi':'#471D6C'}, width=800,\n",
+ " labels={ 'continent' : \"Continent\"},\n",
+ " title=\"Refeerences DOI overview - by continent\")\n",
"\n",
- "# fig.update_traces(marker_color='continent', marker_line_color='rgb(0, 224, 198)',\n",
- "# marker_line_width=1.5, opacity=0.8)\n",
+ "newnames = {'perc_ref_doi': \"Percentage of reference lists with DOI\", 'perc_ref_nodoi': \"Percentage of reference lists without DOI\"}\n",
+ "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
+ " legendgroup = newnames[t.name],\n",
+ " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
+ " )\n",
+ " )\n",
+ " \n",
"fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
" title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
")\n",
- "fig.show()"
+ "fig.show()\n",
+ "fig.write_image(\"images/fig9.png\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4) asserted-by crossref / asserted-by publisher"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 74,
"metadata": {},
"outputs": [
{
@@ -39691,295 +36890,17 @@
},
"data": [
{
- "geo": "geo",
- "hovertemplate": "%{hovertext}
Legend=% of references dois asserted by Crossref
perc_asserted_cr=%{marker.size}
Country code=%{location}",
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- "locations": [
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- "DZA",
- "ECU",
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- "ETH",
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- "FRA",
- "GBR",
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- "GRC",
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- "IRQ",
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- "TUR",
- "TTO",
- "TWN",
- "UKR",
- "UGA",
- "USA",
- "VEN",
- "VNM",
- "ZAF"
+ "customdata": [
+ [
+ "Asserted by Crossref"
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+ [
+ "Asserted by citing publisher"
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],
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- "sizemode": "area",
- "sizeref": 0.1943731279418057,
- "symbol": "circle"
- },
- "mode": "markers",
- "name": "% of references dois asserted by Crossref",
- "showlegend": true,
- "type": "scattergeo"
- }
- ],
- "layout": {
- "font": {
- "family": "sans-serif",
- "size": 10
- },
- "geo": {
- "center": {},
"domain": {
"x": [
0,
@@ -39990,15 +36911,36 @@
1
]
},
- "projection": {
- "type": "natural earth"
- }
+ "hovertemplate": "ref=%{customdata[0]}
val=%{value}",
+ "labels": [
+ "Asserted by Crossref",
+ "Asserted by citing publisher",
+ "Missing/undefined doi"
+ ],
+ "legendgroup": "",
+ "marker": {
+ "colors": [
+ "#34B67A",
+ "#471D6C",
+ "#D4E129"
+ ]
+ },
+ "name": "",
+ "showlegend": true,
+ "type": "pie",
+ "values": [
+ 42022592,
+ 86803771,
+ 33952118
+ ]
+ }
+ ],
+ "layout": {
+ "font": {
+ "family": "sans-serif",
+ "size": 10
},
"legend": {
- "itemsizing": "constant",
- "title": {
- "text": "Legend"
- },
"tracegroupgap": 0
},
"template": {
@@ -40822,7 +37764,7 @@
"font": {
"size": 20
},
- "text": "References DOI assertion overview - by country",
+ "text": "Percentage of DOIs registered on Crossref",
"x": 0.5,
"xanchor": "center",
"y": 0.9
@@ -40836,41 +37778,36 @@
}
],
"source": [
- "frame22a = frame.copy()\n",
- "frame22a = frame22a.groupby('country').sum()\n",
- "frame22a =frame22a.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame22a['iso_alpha'] = coco.convert(names=frame22a.index, to='ISO3')\n",
- "frame22a['country-name'] = coco.convert(names=frame22a.index, to='name')\n",
- "\n",
- "frame22a['perc_asserted_cr'] = (frame22a['asserted-by-cr']/frame22a['ref-num'])*100\n",
- "continent_name = []\n",
- "for x in frame22a.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame22a['continent'] = continent_name\n",
- "frame22a = frame22a[frame22a['perc_asserted_cr'] > 0]\n",
- "frame22a['color'] = \"% of references dois asserted by Crossref\"\n",
- "\n",
- "fig1 = px.scatter_geo(frame22a, locations=\"iso_alpha\", width=800, \n",
- " hover_name='ref-num', size='perc_asserted_cr', color=\"color\", color_discrete_map={\"% of references dois asserted by Crossref\": \"#471D6C\"},\n",
- " projection=\"natural earth\", labels={ \"iso_alpha\": \"Country code\", 'perc_ref_doi': \"% of references dois asserted by Crossref\", \"color\": \"Legend\" },\n",
- "title=\"References DOI assertion overview - by country\")\n",
+ "f = frame.copy()\n",
+ "f = f[f['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
"\n",
+ "ass_cross = f['asserted-by-cr'].sum()\n",
+ "ass_pub = f['asserted-by-pub'].sum()\n",
+ "und = f['ref-undefined'].sum()\n",
+ "ass_cross, ass_pub\n",
"\n",
- "fig1.update_layout(\n",
+ "new = pd.DataFrame()\n",
+ "new['val'] = [ass_cross, ass_pub, und]\n",
+ "new['ref'] = ['Asserted by Crossref', 'Asserted by citing publisher', 'Missing/undefined doi']\n",
+ "fig = px.pie(new, values='val', names='ref', color='ref', color_discrete_map={\n",
+ " 'Asserted by Crossref':'#34B67A',\n",
+ " 'Asserted by citing publisher':'#471D6C',\n",
+ " 'Missing/undefined doi':'#D4E129'}, width=800,\n",
+ "title =\"Percentage of DOIs registered on Crossref\")\n",
+ "fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
" title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
")\n",
"\n",
- "fig1.show()\n"
+ "fig.show()\n",
+ "fig.write_image(\"images/fig10.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 61,
"metadata": {},
"outputs": [
{
@@ -40881,295 +37818,120 @@
},
"data": [
{
- "geo": "geo",
- "hovertemplate": "%{hovertext}
Legend=% of references doi asserted by publisher
% of references doi asserted by publisher=%{marker.size}
Country code=%{location}",
- "hovertext": [
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+ "alignmentgroup": "True",
+ "hovertemplate": "variable=Percentage of references DOI asserted by Crossref
Continent=%{x}
value=%{y}",
+ "legendgroup": "Percentage of references DOI asserted by Crossref",
+ "marker": {
+ "color": "#34B67A",
+ "pattern": {
+ "shape": ""
+ }
+ },
+ "name": "Percentage of references DOI asserted by Crossref",
+ "offsetgroup": "perc_asserted_cr",
+ "orientation": "v",
+ "showlegend": true,
+ "textposition": "auto",
+ "type": "bar",
+ "x": [
+ "Africa",
+ "Asia",
+ "Europe",
+ "North America",
+ "Oceania",
+ "South America"
],
- "legendgroup": "% of references doi asserted by publisher",
- "locations": [
- "ALB",
- "ARM",
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- "ARG",
- "AUT",
- "AUS",
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- "TUR",
- "TTO",
- "TWN",
- "UKR",
- "UGA",
- "USA",
- "VEN",
- "YEM",
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@@ -41994,18 +38756,86 @@
"font": {
"size": 20
},
- "text": "References DOI assertion overview - by country",
+ "text": "Reference assertion overview - by continent.",
"x": 0.5,
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+ "source": [
+ "frame17 = frame.copy()\n",
+ "frame17 = frame17[frame17['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
+ "frame17 = frame17.groupby('country').sum()\n",
+ "frame17['country-name'] = coco.convert(names=frame17.index, to='name')\n",
+ "continent_name = []\n",
+ "for x in frame17.index:\n",
+ " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
+ " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
+ " continent_name.append(country_continent_name)\n",
+ "frame17['continent'] = continent_name\n",
+ "frame17 = frame17.groupby('continent').sum()\n",
+ "frame17 =frame17.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame17['perc_asserted_cr'] = (frame17['asserted-by-cr']/frame17['ref-num'])*100\n",
+ "frame17['perc_asserted_pub'] = (frame17['asserted-by-pub']/frame17['ref-num'])*100\n",
+ "frame17['perc_ref_nodoi'] = (frame17['ref-undefined']/frame17['ref-num'])*100\n",
+ "frame17.head()\n",
+ "fig = px.bar(frame17, x=frame17.index, y= ['perc_asserted_cr','perc_asserted_pub','perc_ref_nodoi'],color_discrete_map={\n",
+ " 'perc_asserted_cr':'#34B67A',\n",
+ " 'perc_ref_nodoi':'#471D6C',\n",
+ " 'perc_asserted_pub':'#D4E129'}, width=800,\n",
+ "labels={ 'continent' : \"Continent\"},\n",
+ "title = \"Reference assertion overview - by continent.\")\n",
+ "\n",
+ "newnames = {'perc_asserted_pub': 'Percentage of references DOI asserted by publishers', 'perc_asserted_cr': \"Percentage of references DOI asserted by Crossref\", 'perc_ref_nodoi': \"Percentage of reference without DOI\"}\n",
+ "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
+ " legendgroup = newnames[t.name],\n",
+ " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
+ " )\n",
+ " )\n",
+ "\n",
+ "fig.update_layout(\n",
+ " font_family=\"sans-serif\",\n",
+ " title_font_size=20,\n",
+ " font_size=10,\n",
+ " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
+ ")\n",
+ "fig.show()\n",
+ " \n",
+ "fig.write_image(\"images/fig11.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
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- "symbol": "circle"
+ "color": "#471D6C",
+ "pattern": {
+ "shape": ""
+ }
},
- "mode": "markers",
- "name": "% of references doi asserted by publisher",
+ "name": "Percentage of reference without DOI",
+ "offsetgroup": "perc_ref_nodoi",
+ "orientation": "v",
"showlegend": true,
- "type": "scattergeo"
+ "textposition": "auto",
+ "type": "bar",
+ "x": [
+ "SSH",
+ "STM"
+ ],
+ "xaxis": "x",
+ "y": [
+ 47.44970925940249,
+ 18.31049721032224
+ ],
+ "yaxis": "y"
}
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"layout": {
+ "barmode": "relative",
"font": {
"family": "sans-serif",
"size": 10
},
- "geo": {
- "center": {},
- "domain": {
- "x": [
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- 1
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- "y": [
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"legend": {
+ "title": {
+ "text": "variable"
+ },
"tracegroupgap": 0
},
"template": {
@@ -43402,12 +39758,32 @@
"font": {
"size": 20
},
- "text": "References DOI assertion overview - by country",
+ "text": "Reference assertion overviwe - by research field.",
"x": 0.5,
"xanchor": "center",
"y": 0.9
},
- "width": 800
+ "width": 800,
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Research field"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "value"
+ }
+ }
}
}
},
@@ -43416,51 +39792,39 @@
}
],
"source": [
- "frame23a = frame.copy()\n",
- "frame23a = frame23a.groupby('country').sum()\n",
- "frame23a =frame23a.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame23a['iso_alpha'] = coco.convert(names=frame23a.index, to='ISO3')\n",
- "frame23a['country-name'] = coco.convert(names=frame23a.index, to='name')\n",
- "frame23a['perc_asserted_pub'] = (frame23a['asserted-by-pub']/frame23a['ref-num'])*100\n",
- "continent_name = []\n",
- "for x in frame23a.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame23a['continent'] = continent_name\n",
- "frame23a = frame23a[frame23a['perc_asserted_pub'] > 0]\n",
- "frame23a['color'] = \"% of references doi asserted by publisher\"\n",
- "frame23a.head()\n",
- "fig2 = px.scatter_geo(frame23a, locations=\"iso_alpha\", width=800, color=\"color\", color_discrete_map={\"% of references doi asserted by publisher\": \"#34B67A\"},\n",
- " hover_name='ref-num', size='perc_asserted_pub',\n",
- " projection=\"natural earth\", labels={ \"iso_alpha\": \"Country code\", 'perc_asserted_pub': \"% of references doi asserted by publisher\", \"color\": \"Legend\" },\n",
- "title=\"References DOI assertion overview - by country\")\n",
- "\n",
- "fig2.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
- ")\n",
- "\n",
- "fig2.show()\n",
- "fig=px.scatter_geo( title=\"References DOI assertion overview - by country\", width=800)\n",
- "fig.add_traces(fig1._data)\n",
- "fig.add_traces(fig2._data)\n",
+ "frame18 = frame.copy()\n",
+ "frame18 = frame18[frame18['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
+ "frame18 = frame18.groupby('subject').sum()\n",
+ "frame18 =frame18.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame18['perc_asserted_cr'] = (frame18['asserted-by-cr']/frame18['ref-num'])*100\n",
+ "frame18['perc_asserted_pub'] = (frame18['asserted-by-pub']/frame18['ref-num'])*100\n",
+ "frame18['perc_ref_nodoi'] = (frame18['ref-undefined']/frame18['ref-num'])*100\n",
+ "fig = px.bar(frame18, x=frame18.index, y= ['perc_asserted_cr','perc_asserted_pub','perc_ref_nodoi'],color_discrete_map={\n",
+ " 'perc_asserted_cr':'#34B67A',\n",
+ " 'perc_ref_nodoi':'#471D6C',\n",
+ " 'perc_asserted_pub':'#D4E129'}, width=800,\n",
+ "labels={ \"subject\": \"Research field\"},\n",
+ "title = \"Reference assertion overviwe - by research field.\")\n",
"\n",
+ "newnames = {'perc_asserted_pub': 'Percentage of references DOI asserted by publishers', 'perc_asserted_cr': \"Percentage of references DOI asserted by Crossref\", 'perc_ref_nodoi': \"Percentage of reference without DOI\"}\n",
+ "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
+ " legendgroup = newnames[t.name],\n",
+ " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
+ " )\n",
+ " )\n",
"fig.update_layout(\n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
" title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
")\n",
- "\n",
- "fig.show()"
+ "fig.show()\n",
+ "fig.write_image(\"images/fig12.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 63,
"metadata": {},
"outputs": [
{
@@ -43472,106 +39836,505 @@
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{
"alignmentgroup": "True",
- "hovertemplate": "variable=% of references DOI asserted by Crossref
Continent=%{x}
value=%{y}",
- "legendgroup": "% of references DOI asserted by Crossref",
+ "bingroup": "x",
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+ "hovertemplate": "variable=Percentage of references DOI asserted by Crossref
Year=%{x}
avg of value=%{y}",
+ "legendgroup": "Percentage of references DOI asserted by Crossref",
"marker": {
"color": "#34B67A",
"pattern": {
"shape": ""
}
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- "name": "% of references DOI asserted by Crossref",
+ "name": "Percentage of references DOI asserted by Crossref",
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value=%{y}",
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value=%{y}",
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avg of value=%{y}",
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}
@@ -44405,618 +41168,25696 @@
}
}
},
- "title": {
- "font": {
- "size": 20
+ "title": {
+ "font": {
+ "size": 20
+ },
+ "text": "Reference assertion overviwe - by year.",
+ "x": 0.5,
+ "xanchor": "center",
+ "y": 0.9
+ },
+ "width": 800,
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Year"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "avg of value"
+ }
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "frame20 = frame.copy()\n",
+ "frame20 = frame20[frame20['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
+ "frame20 = frame20.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frame20 = frame20[(frame20.year >= 1950)&(frame20.year < 2022)]\n",
+ "frame20 = frame20.groupby('year').sum()\n",
+ "frame20['perc_asserted_cr'] = (frame20['asserted-by-cr']/frame20['ref-num'])*100\n",
+ "frame20['perc_asserted_pub'] = (frame20['asserted-by-pub']/frame20['ref-num'])*100\n",
+ "frame20['perc_ref_nodoi'] = (frame20['ref-undefined']/frame20['ref-num'])*100\n",
+ "fig = px.histogram(frame20, x=frame20.index, y= ['perc_asserted_cr','perc_asserted_pub','perc_ref_nodoi'], histfunc='avg', color_discrete_map={\n",
+ " 'perc_asserted_cr':'#34B67A',\n",
+ " 'perc_ref_nodoi':'#471D6C',\n",
+ " 'perc_asserted_pub':'#D4E129'}, width=800,\n",
+ "labels={ \"year\": \"Year\"},\n",
+ "title = \"Reference assertion overviwe - by year.\")\n",
+ "\n",
+ "newnames = {'perc_asserted_pub': 'Percentage of references DOI asserted by publishers', 'perc_asserted_cr': \"Percentage of references DOI asserted by Crossref\", 'perc_ref_nodoi': \"Percentage of reference without DOI\"}\n",
+ "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
+ " legendgroup = newnames[t.name],\n",
+ " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
+ " )\n",
+ " )\n",
+ "\n",
+ "fig.update_layout(\n",
+ " font_family=\"sans-serif\",\n",
+ " title_font_size=20,\n",
+ " font_size=10,\n",
+ " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
+ ")\n",
+ "fig.show()\n",
+ "\n",
+ "fig.write_image(\"images/fig13.png\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Combined viz"
+ ]
+ },
+ {
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- "title": {
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- "frame27 = frame.copy()\n",
- "frame27 = frame27.groupby('country').sum()\n",
- "frame27['country-name'] = coco.convert(names=frame27.index, to='name')\n",
- "continent_name = []\n",
- "for x in frame27.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame27['continent'] = continent_name\n",
- "frame27 = frame27.groupby('continent').sum()\n",
- "frame27 =frame27.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame27['perc_asserted_cr'] = (frame27['asserted-by-cr']/frame27['ref-num'])*100\n",
- "frame27['perc_asserted_pub'] = (frame27['asserted-by-pub']/frame27['ref-num'])*100\n",
- "frame27['perc_ref_nodoi'] = (frame27['ref-undefined']/frame27['ref-num'])*100\n",
- "frame27.head()\n",
- "fig = px.bar(frame27, x=frame27.index, y= ['perc_asserted_cr','perc_asserted_pub','perc_ref_nodoi'],color_discrete_map={\n",
- " 'perc_asserted_cr':'#34B67A',\n",
- " 'perc_ref_nodoi':'#471D6C',\n",
- " 'perc_asserted_pub':'#D4E129'}, width=800,\n",
- "labels={ 'continent' : \"Continent\"},\n",
- "title = \"Reference assertion overviwe - by continent.\")\n",
- "\n",
- "newnames = {'perc_asserted_pub': '% references DOI asserted by publishers', 'perc_asserted_cr': \"% of references DOI asserted by Crossref\", 'perc_ref_nodoi': \"% of reference without DOI\"}\n",
- "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
- " legendgroup = newnames[t.name],\n",
- " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
- " )\n",
- " )\n",
- "\n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
- ")\n",
- "fig.show()\n",
- "\n",
- "import os\n",
- "\n",
- "if not os.path.exists(\"images\"):\n",
- " os.mkdir(\"images\")\n",
- " \n",
- "fig.write_image(\"images/pec_ass_continent.png\")"
- ]
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@@ -45838,31 +67679,17 @@
"font": {
"size": 20
},
- "text": "Reference assertion overviwe - by year.",
+ "text": "Distribution of DOIs registered on Crossref by journal
compared to distribution of references",
"x": 0.5,
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},
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- "title": {
- "text": "Year"
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0,
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@@ -45872,41 +67699,33 @@
}
],
"source": [
- "frame28 = frame.copy()\n",
- "frame28 = frame28.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame28 = frame28[(frame28.year >= 1950)&(frame28.year < 2022)]\n",
- "frame28 = frame28.groupby('year').sum()\n",
- "frame28['perc_asserted_cr'] = (frame28['asserted-by-cr']/frame28['ref-num'])*100\n",
- "frame28['perc_asserted_pub'] = (frame28['asserted-by-pub']/frame28['ref-num'])*100\n",
- "frame28['perc_ref_nodoi'] = (frame28['ref-undefined']/frame28['ref-num'])*100\n",
- "fig = px.histogram(frame28, x=frame28.index, y= ['perc_asserted_cr','perc_asserted_pub','perc_ref_nodoi'], histfunc='avg', color_discrete_map={\n",
- " 'perc_asserted_cr':'#34B67A',\n",
- " 'perc_ref_nodoi':'#471D6C',\n",
- " 'perc_asserted_pub':'#D4E129'}, width=800,\n",
- "labels={ \"year\": \"Year\"},\n",
- "title = \"Reference assertion overviwe - by year.\")\n",
+ "frameA = frame.copy()\n",
+ "frameA['year'] = frameA['year'].apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
+ "frameA = frameA[(frameA.year >= 1950)&(frameA.year < 2022)]\n",
+ "frameA = frameA.groupby('issn').sum()\n",
+ "frameA['perc_ref'] = (frameA['reference']/frameA['on-crossref'])*100\n",
+ "frameA['perc_cr'] = (frameA['on-crossref']/frameA['doi-num'])*100\n",
"\n",
- "newnames = {'perc_asserted_pub': '% references DOI asserted by publishers', 'perc_asserted_cr': \"% of references DOI asserted by Crossref\", 'perc_ref_nodoi': \"% of reference without DOI\"}\n",
- "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
- " legendgroup = newnames[t.name],\n",
- " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
- " )\n",
- " )\n",
+ "fig = go.Figure()\n",
+ "fig.add_trace(go.Violin(y=frameA['perc_cr'], points=\"all\", box_visible=True, name=\"DOAJ articles indexed on Crossref\", marker_color='#471D6C' ))\n",
+ "fig.add_trace(go.Violin(y=frameA['perc_ref'], points=\"all\", box_visible=True, name=\"Crossref reference list\", marker_color='#34B67A' ))\n",
"\n",
+ "fig.update_yaxes(range=[0, 100])\n",
"fig.update_layout(\n",
+ " width = 800, \n",
" font_family=\"sans-serif\",\n",
" title_font_size=20,\n",
" font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
+ " title = {'text': 'Distribution of DOIs registered on Crossref by journal
compared to distribution of references', \n",
+ " 'xanchor': 'center', 'y':0.9,'x':0.5},\n",
")\n",
- "fig.show()"
+ "fig.show()\n",
+ "\n",
+ "fig.write_image(\"images/fig7.png\")"
]
}
],
"metadata": {
- "interpreter": {
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"display_name": "Python 3.9.4 64-bit",
"language": "python",
@@ -45924,7 +67743,12 @@
"pygments_lexer": "ipython3",
"version": "3.9.4"
},
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+ "orig_nbformat": 4,
+ "vscode": {
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diff --git a/data_viz_copy.ipynb b/data_viz_copy.ipynb
deleted file mode 100644
index 261388e..0000000
--- a/data_viz_copy.ipynb
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-{
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- "'''\n",
- "MIT License (MIT)\n",
- "Copyright © 2022 Silvio Peroni, Alessandro Bertozzi, Davide Brembilla, Chiara Catizone, Constance Dami, Umut Kuçuk, Chiara Manca, Giulia Venditti\n",
- "\n",
- "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
- "\n",
- "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
- "\n",
- "THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n",
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- " doi-num on-crossref reference asserted-by-cr \\\n",
- "count 12424.000000 12424.000000 12424.000000 1.242400e+04 \n",
- "mean 469.028010 438.097231 302.791452 3.381850e+03 \n",
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- "mean 6.985912e+03 2.732133e+03 1.309990e+04 87.594361 \n",
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- "frame3 = frame3.groupby('country').sum()\n",
- "frame3 =frame3.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
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- "continent_name = []\n",
- "for x in frame3.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
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- "fig = px.scatter(frame3,x=\"perc_cr\",y=\"country-name\", width=800, color=\"continent\", color_discrete_map={\n",
- " 'Asia' : '#34B67A',\n",
- " 'Europe' : '#471D6C',\n",
- " 'Africa' : '#D4E129',\n",
- " 'South America' : '#25848E',\n",
- " 'Oceania' : '#A6DA35',\n",
- " 'North America' : '#FBE625'\n",
- "},\n",
- " labels={ \"country\": \"Country\", \"perc_cr\": \"% DOIs on Crossref\", \"continent\": \"Continent\" },\n",
- " title=\"Percentage of DOIs registered on Crossref - by country.\")\n",
- "\n",
- "\n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
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- "frame4 =frame4.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame4['perc_cr'] = (frame4['on-crossref']/frame4['doi-num'])*100\n",
- "frame4['country-name'] = coco.convert(names=frame4.index, to=\"name\")\n",
- "frame4 = frame4[(frame4.perc_cr < 80) & (frame4.perc_cr > 0)]\n",
- "continent_name = []\n",
- "for x in frame4.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame4['continent'] = continent_name\n",
- "frame4 = frame4.sort_values(by=['doi-num'])\n",
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- "fig = px.bar(frame4,x=\"country-name\",y='perc_cr', width=800, color=\"continent\", color_discrete_map={\n",
- " 'Asia' : '#34B67A',\n",
- " 'Europe' : '#471D6C',\n",
- " 'Africa' : '#D4E129',\n",
- " 'South America' : '#25848E',\n",
- " 'Oceania' : '#A6DA35',\n",
- " 'North America' : '#FBE625'\n",
- "},\n",
- " labels={ \"country\": \"Country\", \"perc_cr\": \"% DOIs on Crossref\", \"continent\": \"Continent\" },\n",
- " title=\"Percentage of DOIs registered on Crossref - by country.\")\n",
- "\n",
- "\n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
- ")\n",
- "fig.show()"
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- "source": [
- "frame8 = frame.copy()\n",
- "frame8 = frame8.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame8 = frame8[(frame8.year >= 2000)&(frame8.year < 2022)]\n",
- "frame8 = frame8.groupby('year').sum()\n",
- "frame8['perc_cr'] = (frame8['on-crossref']/frame8['doi-num'])*100\n",
- "# fig = px.bar(frame,x=frame.index,y='perc_cr')\n",
- "fig = px.scatter(frame8,x=frame8.index, y='perc_cr', size='doi-num',size_max=60,width=800,\n",
- " labels={ \"year\": \"Year\", \"perc_cr\": \"% DOIs on Crossref\", \"doi-num\": \"Number of DOIs\" },\n",
- " title=\"Percentage of DOIs registered on Crossref - by year\")\n",
- "fig.update_yaxes(range=[50, 110])\n",
- "fig.update_traces(marker_color='#471D6C') \n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
- ")\n",
- "fig.show()"
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- "Dal grafico possiamo dedurre che gli articoli pubblicati dopo il 2000, anno di fondazione di Crossref, hanno più probabilità di essere presenti sulla piattaforma. Interessante notare il calo avito tra il 2010 e il 2015."
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- "frame13a['perc_ref'] = (frame13a['reference']/frame13a['on-crossref'])*100\n",
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- " continent_name.append(country_continent_name)\n",
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- " labels={\"country-name\": \"Country\", \"perc_ref\": \"% of references on Crossref\", \"continent\":\"Contintent\", \"doi-num\": \"Number of DOIs\" },\n",
- " title=\"Percentage of references registered on Crossref - by country.\")\n",
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- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
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- "frame14 = frame.copy()\n",
- "frame14 = frame14.groupby('country').sum()\n",
- "frame14 =frame14.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame14['perc_ref'] = (frame14['reference']/frame14['on-crossref'])*100\n",
- "frame14['iso_alpha'] = coco.convert(names=frame14.index, to='ISO3')\n",
- "frame14['country-name'] = coco.convert(names=frame14.index, to='name')\n",
- "continent_name = []\n",
- "for x in frame14.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame14['continent'] = continent_name\n",
- "frame14 = frame14[frame14['perc_ref'] > 0]\n",
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- "fig = px.scatter_geo(frame14, locations=\"iso_alpha\", width=800, color=\"continent\", color_discrete_map={\n",
- " 'Asia' : '#34B67A',\n",
- " 'Europe' : '#471D6C',\n",
- " 'Africa' : '#D4E129',\n",
- " 'South America' : '#25848E',\n",
- " 'Oceania' : '#A6DA35',\n",
- " 'North America' : '#FBE625'\n",
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- " hover_name=\"ref-num\", size=\"perc_ref\", size_max=15,\n",
- " projection=\"natural earth\", \n",
- " hover_data=[\"country-name\"],\n",
- " labels={ \"iso_alpha\": \"Country code\", \"perc_ref\": \"% of references on Crossref\", \"doi-num\" : \"Number of dois\", \"continent\": \"Continent\", \"country-name\": \"Country name\" },\n",
- " title=\"Percentage of references registered on Crossref - by country.\")\n",
- "\n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
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- " labels={ \"year\": \"Year\" }, color_discrete_sequence= ['#471D6C', '#34B67A'] ,\n",
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- "frame20['perc_ref_nodoi'] = (frame20['ref-undefined']/frame20['ref-num'])*100\n",
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- " labels={ \"subject\": \"Research field\", \"perc_ref_nodoi\": \"% reference without DOI\", \"perc_ref_doi\": \"% reference with DOI\"},\n",
- " title=\"References DOI overview - by research field\")\n",
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- "newnames = {'perc_ref_doi': \"% of DOIs' references with DOI\", 'perc_ref_nodoi': \"% of reference without DOI\"}\n",
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- "frame24 = frame24.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame24 = frame24[(frame24.year >= 1950)&(frame24.year < 2022)]\n",
- "frame24 = frame24.groupby('year').sum()\n",
- "frame24['perc_ref_nodoi'] = (frame24['ref-undefined']/frame24['ref-num'])*100\n",
- "frame24['perc_ref_doi'] = 100 - frame24['perc_ref_nodoi']\n",
- "fig = px.histogram(frame24, x=frame24.index, y= ['perc_ref_doi', 'perc_ref_nodoi'], histfunc='avg', color_discrete_map={\n",
- " 'perc_ref_doi':'#34B67A',\n",
- " 'perc_ref_nodoi':'#471D6C'}, width=800,\n",
- " labels={'year':'Year'},\n",
- "title=\"References DOI overview - by year.\")\n",
- "\n",
- "newnames = {'perc_ref_doi': \"% of DOIs' references with DOI\", 'perc_ref_nodoi': \"% of reference without DOI\"}\n",
- "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
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- "labels={ \"subject\": \"Research field\"},\n",
- "title = \"Reference assertion overviwe - by research field.\")\n",
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- "frame = frame.groupby('country').sum()\n",
- "frame =frame.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame['perc_asserted_cr'] = (frame['asserted-by-cr']/frame['ref-num'])*100\n",
- "frame['perc_asserted_pub'] = (frame['asserted-by-pub']/frame['ref-num'])*100\n",
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- "for x in frame.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame['continent'] = continent_name\n",
- "\n",
- "fig = px.scatter(frame, x= ['perc_asserted_cr','perc_asserted_pub','perc_ref_nodoi'], y=\"country-name\", \n",
- " labels={ \"country-name\": \"Country\", \"perc_cr\": \"% DOIs on Crossref\" },\n",
- " title=\"Percentage of DOIs registered on Crossref - by country.\")\n",
- "\n",
- "# fig.update_traces(marker_color='continent', marker_line_color='rgb(0, 224, 198)',\n",
- "# marker_line_width=1.5, opacity=0.8)\n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
- ")\n",
- "fig.show()"
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- " hover_name='ref-num', size='perc_asserted_cr', color=\"color\", color_discrete_map={\"% of references dois asserted by Crossref\": \"#471D6C\"},\n",
- " projection=\"natural earth\", labels={ \"iso_alpha\": \"Country code\", 'perc_ref_doi': \"% of references dois asserted by Crossref\", \"color\": \"Legend\" },\n",
- "title=\"References DOI assertion overview - by country\")\n",
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- " font_family=\"sans-serif\",\n",
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- "frame27 = frame.copy()\n",
- "frame27 = frame27.groupby('country').sum()\n",
- "frame27['country-name'] = coco.convert(names=frame27.index, to='name')\n",
- "continent_name = []\n",
- "for x in frame27.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame27['continent'] = continent_name\n",
- "frame27 = frame27.groupby('continent').sum()\n",
- "frame27 =frame27.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame27['perc_asserted_cr'] = (frame27['asserted-by-cr']/frame27['ref-num'])*100\n",
- "frame27['perc_asserted_pub'] = (frame27['asserted-by-pub']/frame27['ref-num'])*100\n",
- "frame27['perc_ref_nodoi'] = (frame27['ref-undefined']/frame27['ref-num'])*100\n",
- "frame27.head()\n",
- "fig = px.bar(frame27, x=frame27.index, y= ['perc_asserted_cr','perc_asserted_pub','perc_ref_nodoi'],color_discrete_map={\n",
- " 'perc_asserted_cr':'#34B67A',\n",
- " 'perc_ref_nodoi':'#471D6C',\n",
- " 'perc_asserted_pub':'#D4E129'}, width=800,\n",
- "labels={ 'continent' : \"Continent\"},\n",
- "title = \"Reference assertion overviwe - by continent.\")\n",
- "\n",
- "newnames = {'perc_asserted_pub': '% references DOI asserted by publishers', 'perc_asserted_cr': \"% of references DOI asserted by Crossref\", 'perc_ref_nodoi': \"% of reference without DOI\"}\n",
- "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
- " legendgroup = newnames[t.name],\n",
- " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
- " )\n",
- " )\n",
- "\n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
- ")\n",
- "fig.show()\n",
- "\n",
- "import os\n",
- "\n",
- "if not os.path.exists(\"images\"):\n",
- " os.mkdir(\"images\")\n",
- " \n",
- "fig.write_image(\"images/pec_ass_continent.png\")"
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- "frame28['perc_asserted_cr'] = (frame28['asserted-by-cr']/frame28['ref-num'])*100\n",
- "frame28['perc_asserted_pub'] = (frame28['asserted-by-pub']/frame28['ref-num'])*100\n",
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- "labels={ \"year\": \"Year\"},\n",
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- "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
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diff --git a/data_viz_new.ipynb b/data_viz_new.ipynb
deleted file mode 100644
index 5319a7e..0000000
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- "import plotly.express as px\n",
- "import country_converter as coco\n",
- "import pycountry_convert as pc\n",
- "\n",
- "frame = pd.read_pickle('data.pkl')\n",
- "frame.head()\n"
- ]
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The plot is showing Countries with less than 80% of articles on Crossref\")\n",
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The plot is showing Countries with less than 20% of reference list on Crossref",
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- "frame12 = frame.copy()\n",
- "frame12 = frame12.groupby('country').sum()\n",
- "frame12 = frame12.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
- "frame12['perc_ref'] = (frame12['reference']/frame12['on-crossref'])*100\n",
- "frame12['country-name'] = coco.convert(names=frame12.index, to=\"name\")\n",
- "frame12 = frame12[ (frame12.perc_ref < 20) & (frame12.perc_ref > 0)]\n",
- "continent_name = []\n",
- "for x in frame12.index:\n",
- " country_continent_code = pc.country_alpha2_to_continent_code(x)\n",
- " country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)\n",
- " continent_name.append(country_continent_name)\n",
- "frame12['continent'] = continent_name\n",
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- "fig = px.bar(frame12,x='country-name',y='perc_ref', width=800, color=\"ref-num\", color_continuous_scale=[[0,\"#440154\"],[0.05,\"#414084\"],[0.2,\"#25848E\"], [0.5,\"#66CA5C\"],[1,\"#FBE625\"]], \n",
- " labels={\"country-name\": \"Country\", \"perc_ref\": \"Percentage of references on Crossref\", \"continent\":\"Contintent\", \"ref-num\": \"Number of references\" },\n",
- " title=\"Crossref references list overview - by country
Countries with less than 20% of reference list on Crossref\")\n",
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- " font_family=\"sans-serif\",\n",
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- "fig.write_image(\"images/fig6.png\")"
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- "source": [
- "# 3) Reference that have doi specified"
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- "cell_type": "code",
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- "frame14 = frame14[frame14['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
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- "frame14['perc_ref_nodoi'] = (frame14['ref-undefined']/frame14['ref-num'])*100\n",
- "frame14['perc_ref_doi'] = 100 - frame14['perc_ref_nodoi']\n",
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- " 'perc_ref_nodoi':'#471D6C'}, width=800,\n",
- " labels={'year':'Year'},\n",
- " title=\"References DOI overview over years.\")\n",
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- "frame20 = frame20[frame20['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
- "frame20 = frame20.apply(lambda x: pd.to_numeric(x, errors='coerce'))\n",
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- "frame20 = frame20.groupby('year').sum()\n",
- "frame20['perc_asserted_cr'] = (frame20['asserted-by-cr']/frame20['ref-num'])*100\n",
- "frame20['perc_asserted_pub'] = (frame20['asserted-by-pub']/frame20['ref-num'])*100\n",
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- " 'perc_asserted_cr':'#34B67A',\n",
- " 'perc_ref_nodoi':'#471D6C',\n",
- " 'perc_asserted_pub':'#D4E129'}, width=800,\n",
- "labels={ \"year\": \"Year\"},\n",
- "title = \"Reference assertion overviwe - by year.\")\n",
- "\n",
- "newnames = {'perc_asserted_pub': 'Percentage of references DOI asserted by publishers', 'perc_asserted_cr': \"Percentage of references DOI asserted by Crossref\", 'perc_ref_nodoi': \"Percentage of reference without DOI\"}\n",
- "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
- " legendgroup = newnames[t.name],\n",
- " hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
- " )\n",
- " )\n",
- "\n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
- ")\n",
- "fig.show()\n",
- "\n",
- "fig.write_image(\"images/fig13.png\")"
- ]
- },
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- ]
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- "metadata": {},
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- "f = frame.copy()\n",
- "f = f[f['type'].isin(['journal-article', 'book', 'book-chapter', 'proceedings-article', 'dataset', 'posted-content', 'report'])]\n",
- "\n",
- "new = pd.DataFrame()\n",
- "\n",
- "ass_cross = f['asserted-by-cr'].sum()\n",
- "ass_pub = f['asserted-by-pub'].sum()\n",
- "und = f['ref-undefined'].sum()\n",
- "ref_defined = f['ref-num']-f['ref-undefined']\n",
- "ref_defined = ref_defined.sum()\n",
- "\n",
- "new['val'] = [ und, ref_defined, ass_cross, ass_pub ]\n",
- "new['ref'] = ['Missing/undefined doi', 'Articles with DOI reference list','Asserted by Crossref', 'Asserted by Publisher']\n",
- "new['parent'] = ['', '','Articles with DOI reference list', 'Articles with DOI reference list']\n",
- "\n",
- "fig = px.sunburst(\n",
- " new,\n",
- " names='ref',\n",
- " parents='parent',\n",
- " values='val',\n",
- " branchvalues= \"total\",\n",
- " title=\"Percentage of reference lists registered on Crossref and DOIs assertation\",\n",
- " color='ref', \n",
- " color_discrete_map = {'Missing/undefined doi':'#34B67A','Articles with DOI reference list':'#471D6C', 'Asserted by Crossref':'#D4E129'} )\n",
- "\n",
- "fig.update_layout(\n",
- " font_family=\"sans-serif\",\n",
- " title_font_size=20,\n",
- " font_size=10,\n",
- " title = {'xanchor': 'center', 'y':0.9,'x':0.5},\n",
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