Sciento Py User Manual

User Manual:

Open the PDF directly: View PDF PDF.
Page Count: 10

ScientoPy v 1.3.5, Installation and User Manual
Juan Pablo Ruiz Rosero
jpabloruiz@unicauca.edu.co
Contents
1 Installation 2
2 Download the bibliometric dataset 2
2.1 Download the dataset from Scopus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.2 Download the dataset from WoS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3 Running the ScientoPy scripts 3
3.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.2 Extract the top topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.3 Analyze custom topics inside a criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.3.1 Asterisk (*) wildcard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.3.2 Parametric plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.4 Finding trending topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.5 Analysis based on the previous results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.6 Output files and directories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4 ScientoPy graph types 8
4.1 Time line graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.2 Horizontal bars graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.3 Parametric graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.4 Parametric2 graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.5 Word cloud graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1
1 Installation
1. For Windows download and install the Python 3 latest version (for example Python 3.6.5) from:
https://www.python.org/downloads/.
2. For Debian or Ubuntu run these commands to install Python3:
sudo apt-get install python3 python3-tk python3-pip
3. To use wordCloud in Windows, install Microsoft Visual C++ Redistributable para Visual Studio 2017 according
to these instructions: https://www.scivision.co/python-windows-visual-c++-14-required/
4. Install the unidecode, numpy, scipy, matplotlib, and wordcloud Python libraries. For Windows, enter in the
command line (Windows + R, cmd, and Enter), and run the installation script:
python3 -m pip install --user unidecode numpy scipy matplotlib wordcloud
2 Download the bibliometric dataset
This section describes how to download the proper dataset from Scopus and WoS. Define a search criteria that will
be used for Scopus and WoS. For this guide and for the example dataset we are using: "Internet of thing" AND
"Gateway"
2.1 Download the dataset from Scopus
1. Make your search with the defined search criteria for Article title, Abstract, Keywords.
2. Select all the results and click on Export:
3. Select as method of export CSV (Excel), and select the Customize export Citation information, Biblio-
graphical information, Abstract and Keywords, then click on Export:
2
4. Save the file on the folder /ScientoPy/dataIn
2.2 Download the dataset from WoS
1. Make your search with the defined search criteria for Topic.
2. Select Save in Other File Formats
3. Select the number of records to download, on Record Contented select Full Record and Cited References,
on File Format select Tab-delimited (Win, UTF-8), and click on Send.
4. Save the file on the folder /ScientoPy/dataIn
3 Running the ScientoPy scripts
This section describes the ScientoPy scripts to preprocess and analyze the bibliometric dataset.
3
3.1 Preprocessing
First we need to preprocess the downloaded dataset. This preprocess merge all the downloaded files from one folder
to a single file. Also, this process remove the duplicated files. To preprocess the example dataset ("Internet of thing"
AND "Gateway" located in dataInExample) run this command inside ScientoPy folder:
python3 preProcess.py dataInExample
Then, inside the folder ScientoPy/dataPre you will find the following files:
•papersPreprocessed.tsv: this file contains the information of all papers after the pre-process. This file will
be used by the others scripts as the input data.
•PreprocessedBrief.tsv: this file briefs the pre-process statics results, such as duplicated papers removed, types
of documents, and others.
To find more options of the preprocessing script you can run:
python3 preProcess.py -h
3.2 Extract the top topics
With this script you can extract the top topics of a selected criterion. The ScientoPy criterion are described on Table
1:
Table 1: ScientoPy criterion description
Criteron Description
author Authors last name and first name initial
sourceTitle Publication or journal name
subject Research areas, only from WoS documents
authorKeywords Author keywords
indexKeywords Keywords generated by the index, from WoS {Keyword Plus}, and
from Scopus {Indexed keywords}
bothKeywords AuthorKeywords and indexKeywords are used for this search
abstract Document abstract, for use with pre-defined topics and asterisk wild-
card
documentType Type of document
dataBase Database where the document was extracted (WoS or Scopus)
country Country extracted from authors affiliations
institution Institution extracted from authors affiliations
institutionWithCountry Institution with country extracted from authors affiliations
For example, to find the top author keywords you can run this script:
python3 scientoPy.py authorKeywords
4
This will generate a list with the top 10 topics on the selected criterion (in this case authorKeywords), with the
number of documents per topic, and the h-index associated to each one. Also, this script graphs the evolution of
each topic per year, and saves the quantitative results on the folder ScientoPy/results.
This script have more options like, save the plot on a file, or increase the number of topic results. For more information
you can run:
python3 scientoPy.py -h
3.3 Analyze custom topics inside a criterion
If you want to make an analysis of custom topics, such as the two selected countries papers evolution, you can use
the scientoPy.py script, with the option -t, to specify the topics:
python3 scientoPy.py country -t "United States; Brazil"
You can analyze any topic in any criterion. Put the topics on the -t argument. Divide the topics with the ;. Also,
you can integrate two or more topics in one, by dividing it with ,. This is very useful for abbreviations and plural
singulars, for example:
python3 scientoPy.py authorKeywords -t \
"WSN, Wireless sensor network, Wireless sensor networks; RFID, RADIO FREQUENCY IDENTIFICATION"
Note: The command is very long, for that reason the command was divided by \. If you have problems in Windows,
remove the "änd put the command in one single line.
3.3.1 Asterisk (*) wildcard
You can use the asterisk wildcard to find phrases or words which starts or ends with the letters that you have inserted.
For example, if you want to find "device", "devices", and "device integration", enter the following command:
python3 scientoPy.py authorKeywords -t "device*"
ScientoPy will print the topics found for the previous search:
Topics found for device*:
"devices;device management;Device Interactions;Device objectification;Device;Device integration"
You can use this information, to analyze each specific topic found, like this:
python3 scientoPy.py authorKeywords -t \
"devices;device management;Device Interactions;Device objectification;Device;Device integration"
3.3.2 Parametric plot
Also, you can see the results with a parametric graphic (add --parametirc). This option plot the accumulative
documents, average documents per year (ADY), and h-Index of the selected topic, for example:
5
python3 scientoPy.py authorKeywords -t \
"WSN, Wireless sensor network, Wireless sensor networks; RFID, RADIO FREQUENCY IDENTIFICATION" \
--parametric
This script have more options like, save the plot on a file, or others. For more information you can run:
python3 scientoPy.py -h
3.4 Finding trending topics
This script finds the top trending topics based on the higher average growth rate (AGR) over the others. The AGR
is calculated on two years periods, using the following Equation (3.4):
AGR =
Ye
P
i=Ys
Pi−Pi−1
(Ye−Ys) + 1 ,
where:
AGR = Average growth rate;
Ys= Start year;
Ye= End year;
Pi= Number of publications on year i.
To find the top trending topics on author keywords criterion, you can run the following script:
python3 scientoPy.py authorKeywords --trend --windowWidth 2 --startYear 2006 --endYear 2017 \
-l 10 -s 3 --parametric --agrForGraph --graphTitle "Internet of things trending topics"
This script will find the top 200 topics, then it calculates the AGR for the last 2 years (--windowWidth 2). Finally,
the 200 top topics are sorted from the highest AGR in the last 2 year period to the lower. The first 3 AGR topics are
filtered (they correspond to the keyword Internet of things), and the next 10 topics are garph in a parametric plot.
3.5 Analysis based on the previous results
ScientoPy generates an output file with all the output documents from the last run script. For example if we run the
command:
python3 scientoPy.py country -t "Canada" --noPlot
ScientoPy will create a documents output file (results/papersPreprocessed.tsv) with all documents that have
authors with affiliation in Canada. This output file can be used by ScientoPy to perform an analysis based on this,
in that way if we run the following command with the option -r or --previousResults after the previous one to
analyze based on the previous results:
python3 scientoPy.py authorKeywords -r --bar
we will obtain the top author keywords from papers where the author affiliation correspond to Canada. Also, we can
run the following command to know which are the countries that have more common documents with Canada:
6
python3 scientoPy.py country -r --bar
Note: the ScientoPy documents output file is only generated when the -r or --previousResults is not used. In
that way, if we run many times a ScientoPy command with this option, the documents output file will not overwritten.
3.6 Output files and directories
After run some ScientoPy commands or after run all the example commands by executing the script exampleGenerateGraphs.sh
you will find the following folder and files structure:
ScientoPy
dataInExample
dataPre
papersPreprocessed.tsv
PreprocessedBrief.tsv
graphs
Manual
results
AuthorKeywords.tsv
AuthorKeywords_extended.tsv
papersPreprocessed.tsv
These folders and output files are described bellow:
•dataInExample: contains Scopus and WoS example data set for the search criteria "Internet of things" AND
"Gateway" downloaded in 27 November 2017. This is the input example for preprocess script.
•dataPre: output folder for the preprocess results, and input folder for scientoPy script.
•papersPreprocessed.tsv: preprocesed papers data with all input documents merged, filtered, and duplication
removed. This is the input file that scientoPy script uses.
•PreprocessedBrief.tsv: preproceses brief table that shows the preprocess results related to total papers found
per data base, the omitted papers, the duplicated papers count per data base, and the total number of papers
per paper type (Conference paper, article, review...)
•graphs: graphs output folder for preprocess and scientoPy scripts
•Manual: folder with the pdf manual and example paper with scientoPy commands highlighted used for graph
and tables generation.
•results: output folder for scientoPy result output files
•AuthorKeywords.tsv: scientoPy output file for the selected criterion (in this case authorKeywords) that
shows the top topics or the custom topics with the total number of documents, the Average Growth Rate
(AGR), the Average Documents per Year (ADY), the h-index, and the documents per each year.
•AuthorKeywords_extended.tsv: scientoPy output file for the selected criterion (in this case author-
Keywords) that show the top or custom topics with the documents related to each one.
•papersPreprocessed.tsv: inside the results folder, this file contains the output papers from the last scientoPy
used script. This is used as an input for scientoPy script when it use the option -r or --previousResults
7
4 ScientoPy graph types
ScientoPy has 5 different ways to graph the results described on Table 2.
Table 2: ScientoPy output graphs types
Graph type Argument Description
Time line No argument Graphs the number of documents of each topic vs the publication
year
Horizontal bars --bar Graphs the total number of documents of each topic in horizontal
bars
Word cloud --wordCloud Generate a word cloud based on the topic total number of publications
Parametric --parametric Graphs two plots, one with the accumulative number of documents
vs the publication year, and other with the average papers per year
vs the h-index
Parametric2 --parametric2 Graphs the total number of documents vs average papers per year
Below are showed some examples of these graphs types, with the command used.
4.1 Time line graph
Command:
python3 scientoPy.py authorKeywords --startYear 2010 --endYear 2016 \
--graphTitle "Internet of things top author keywords"
4.2 Horizontal bars graph
Command:
python3 scientoPy.py authorKeywords --startYear 2010 --endYear 2016 --bar \
--graphTitle "Internet of things top author keywords"
8
4.3 Parametric graph
Command:
python3 scientoPy.py authorKeywords --startYear 2010 --endYear 2016 --parametric \
--graphTitle "Internet of things top author keywords"
4.4 Parametric2 graph
Command:
python3 scientoPy.py authorKeywords --startYear 2010 --endYear 2016 --parametric2 \
--graphTitle "Internet of things top author keywords"
9
4.5 Word cloud graph
Command:
python3 scientoPy.py authorKeywords --startYear 2010 --endYear 2016 -l 500 --wordCloud
10

Navigation menu