Published March 15, 2022 | Version Published (post-print)
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How green is my Valley? Measuring open access friendliness of Indian Institutes of Technology (IITs) through data carpentry

  • 1. University of Kalyani

Description

This research study aims to develop a distributed weightage based ranking framework for
measuring Open Access (OA) support/friendliness (Open Access Friendliness Indicator – OAFI) of
a given Indian institute. It applies data carpentry tools and methods for gathering and extracting OA
data from an array of diverse sources available against ODbL. The ranking framework has four
primary areas (OA publications share, OA license share, OA citations share and OA altmetric share)
and a total of ten factors under these selected areas. The distributions of weightage have been set on
the basis of SWOC analysis of open access scenario in India and the product is a 100-point scale for
measuring OA friendliness of a given institute. The framework has been tested with the publications
data (1,59,107), citations data (21,69,395), and altmetric data (24,308) of the top Indian Institutes of
Technology (16 IITs) that are listed in the top 100 of the overall category of the National
Institutional Ranking Framework (NIRF), 2020. The final ranked list of the selected 16 IITs shows
that in general older IITs are much ahead in terms of numbers (publications, citations, altmetric) but
newer IITs are tenanted all top five positions in OAFI framework as factor formulae are ratio
dependent. It also shows that Indian Institute of Technology Gandhinagar (established in 2008)
occupied the top most position with leading scores in area I (OA publications share) and area III
(OA citations share) and Indian Institute of Technology Bhubaneswarhas obtained the highest value
in area II – OA license share, and Indian Institute of Technology Ropar has topped the list in area
IV – OA altmetric score share.

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Additional details

Related works

Is part of
Dataset: 10.5281/zenodo.6511151 (DOI)

Subjects

Open access
https://lccn.loc.gov/n88005993
Information science Statistical methods
https://lccn.loc.gov/sh2007006260