User’s guide

The code is based on the ads package, that allows to perform queries through a python code. Objects in pinnacle include the Article class from ads.

We aim at exploring the publication metrics, relations and networks for researchers at an institute, in order to design strategies to increase impact and productivity.

The process basically consists on creating four datasets:

• pub_auth_all

• pub_auth_top

• pub_inst_all

• pub_inst_top

along with entries with data for the institute and the authors:

• history

• staff

Installation

To install the latest stable version from the Python Package Index:

pip install pinnacle_pub

To install the development version, download the last version of the code from the GitHub page, and run:

pip install .

API usage

The analysis of a set of authors can be made easily with the following steps.

First, load the package:

from pinnacle import pinnacle

For the configuration, edit a configuration file following the template in the set directory, and load it with the Parser.

ini = 'my_institute.ini'
config = Parser(ini)
df = pinnacle.inst_adsentries(config)

The set of the names of the researchers can be loaded with different tools, from Excell spreadsheets, CSV files, or entered manually as lists.

DF = staff.download_inst(staff_staff)
staff.reduce_article_list(DF)
staff.eliminate_repeated('bibcode')
staff.journal_quality()
staff.load_history(nstaff, int(columns[1]))
staff.save_inst()

It not necesary to make this every time, since pickle files are saved. After the first run, the dataset can be loaded simply using:

staff.load_inst()

The number of papers per author per year can be obtained in a dataframe or an XLSX file with:

staff.save_table()

Finally, plotting tools are available with the pub_dataviz class, that inherites the data from a inst_adsentries class:

viz = pub_dataviz.pub_dataviz(df)

There are several plots that can be obtained. The full set is produced with:

viz.plot_all()

Or, alternatiely, individual plots using the function in the class.