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https://github.com/connectedsystems/wosis

Wosis - a python package developed to support bibliometric analysis of Web of Science data from querying to visualization.
https://github.com/connectedsystems/wosis

bibliometric-analysis python science science-data visualization web-of-science

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Wosis - a python package developed to support bibliometric analysis of Web of Science data from querying to visualization.

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README 

        
# Wosis



A python package developed to support analysis of Web of Science data from querying to visualization.



[![DOI](https://zenodo.org/badge/155658135.svg)](https://zenodo.org/badge/latestdoi/155658135)



This package is under development and is currently intended for limited use.



Currently it simplifies the process of:



* Getting publication data from the Web of Science collection

* Creating plots that indicate publication trends

* Identifying topics of interest



See the [included tutorial](https://github.com/ConnectedSystems/wosis/tree/master/tutorial) for a more complete introductory guide.



Examples of its use can be found by looking at the notebooks within the

[sd-prac-bibanalysis](https://github.com/ConnectedSystems/sd-prac-bibanalysis) repository.



Key dependencies include:



* WOS Client, a SOAP-based client for Web of Science, developed by E. Bacis [@enricobacis](https://github.com/enricobacis)

* wos_parser, a parser for Web of Science XML data, developed by T. Achakulvisut [@titipata](https://github.com/titipata)

* Metaknowledge, a Python library for bibliometric research, developed at [Networks Lab](https://github.com/networks-lab/metaknowledge)

* Pandas, the de facto standard library for data analysis in Python.



For the moment it is probably best to install by:



```bash

$ pip install git+https://github.com/titipata/wos_parser.git@master

$ git clone https://github.com/ConnectedSystems/wosis.git

$ cd wosis

$ pip install -e .

```



Alternatively, via `pip`



```bash

$ pip install git+https://github.com/titipata/wos_parser.git@master

$ pip install git+https://github.com/ConnectedSystems/wosis.git@master

```



# Getting Started



You will need access to the Premium API for Clarivate's Web of Science. This is given as a username and password.



It is advised that this information be placed in a `.yml` file in the following format:



```yaml

wos:

  user: username

  password: password

```



This is to keep your secret information out of the code. Remember not to share this file with others.



The configuration file can then be loaded like so:



```python

import wosis



path_to_your_config_file = "config.yml"

wos_config = wosis.load_config(path_to_your_config_file)

```



This just returns a dictionary of the username and passwords.



Then build a query using a list of desired and undesired terms and the subject areas to search. The format follows the standard given by Web of Science, as seen [here](http://ipscience-help.thomsonreuters.com/wosWebServicesLite/WebServiceOperationsGroup/WebServiceOperations/g2/user_query.html).



```python

search_terms = {

    "inclusive_kw": ("some", "keywords", "of", "interest"),

    "exclusive_kw": ("I", "do not", "want", "these", "keywords"),

    "exclusive_jo": ('PSYCHOL*', ),  # journals to exclude

    "subject_area": ("ENVIRONMENTAL SCIENCES", ),  # Note the trailing comma for single item lists!

}



# Build a list of queries to send

topics = [wosis.build_query(search_terms), ]

```



The queries can then be sent to the Web of Science servers. The results will be dumped to a text file labelled with a `query_id` inside a temporary directory (`tmp`). Be warned that this temporary data store is up to you to manage. Please take care to remove the data once your analysis is complete.



```python

overwrite = False  # do not overwrite existing data store if it exists

id_to_query = wosis.query(topics, overwrite, wos_config)

```



`id_to_query` will be a Python dictionary which maps a generated query id to the query that was sent. Because we sent a single query in this example, we are interested in the first `query_id`.



```python

import pandas as pd

import metaknowledge as mk



query_id = list(id_to_query.keys())[0]  # Get the first query_id

RC = mk.RecordCollection(f"tmp/{query_id}.txt")  # Load the results

```



Wosis provides convenient plotting methods.



```python

import wosis.analysis.plotting as wos_plot



wos_plot.plot_kw_trend(RC, title='Plot of the number of keywords over time', save_plot_fn='figs/num_kw_per_pub.png')

```



Specific analysis can be accomplished by using Metaknowledge and Pandas.



See the [included tutorial](https://github.com/ConnectedSystems/wosis/tree/master/tutorial) for a more complete introductory guide.



# Related Works



* [revtools](http://revtools.net/), an R package for exploratory analysis of bibliographic data developed by M. Westgate (https://doi.org/10.1101/262881)

* [Science Concierge](https://github.com/titipata/science_concierge), Python package for content based recommendation by T. Achakulvisut et al. (http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0158423)