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Indian Institute of Technology Delhi

Science Communication: Research Productivity and Data Analytics using Open Source Software

Indian Institute of Technology Delhi and NPTEL via Swayam

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Overview

ABOUT THE COURSE: Scientists, researchers, academicians and students are involved in extensive research and publication work to produce new knowledge or in the interpretation of the existing knowledge base. The quality and quantity of this knowledge are popularly 4 evaluated by using various quantitative metrics known as mapping tools and technologies. Many organisations, policy makers, and government agencies regularly conduct such analyses for various reasons like ranking, funding, evaluations, project awards, rewards, etc. The evaluation process includes extracting large-scale research data, pre-processing and analysing. It requires both mathematical and computer skills to do effective analysis and presentation. This course has been developed, keeping all these parameters in mind, targeting people working or interested in the areas of Mapping Science/Scientometrics/Humanities & Social Sciences/Library & Information Science, Information Systems & Services professionals and the practitioners who are involved and aimed to do so such analysis in future. The course will introduce the concepts of various assessment metrics of research output, data extraction, pre-processing, different visualisation tools, ethics of analysis, software for extraction, refining and analysis of data, etc.The course also includes various case studies on quantitative assessment of Institutions, Authors, Journals, Domains, and Countries. Prior knowledge of mathematics, statistics, or programming is optional to take advantage of the contents of the course as it starts with the basics and helps understand the advanced concepts with easily understandable day-to-day examples. Some of the practical aspects of each concept covered in the course will be delivered in the RStudio. Other software like VOSviewer, Citespace, OpenRefine, etc., will also be covered.After the successful completion of the course, the learners will be able to understand the concept as above. They will also be able to conduct and publish the assessment studies in reputed journals, conferences or as research reports or manuscripts in any other form. The course aims to give learners the skills necessary to utilise open-source environments like R to evaluate and map the scientific knowledge generated by researchers.INTENDED AUDIENCE: Faculty, Researchers, Post Graduate Students, Administrators, Policy Makers, Information Professionals, Library Science Professionals, etc.PREREQUISITES: Undergraduate in any disciplineINDUSTRY SUPPORT: CFTIs/HEIs/Universities R&D organizations Ranking & Accreditation Agencies/Customers Publishing Industry Policy-making and evaluation organizations Library and Information Centres & Departments

Syllabus

Week 1: Introduction to Science Communication, Research Productivity, Data Analytics, Open Source Software Bibliometrics, Scientometrics, Research Metrics, Impact Factor, H Index, G Index, i10 Index, H-5 Index, JCR, SJR, Eigenfactor, SNIP, IPP and Cite Score
Week 2:
Data Sources and Extraction: PubMed, Scopus, Web of Science, Google Scholar. Boolean Search, API, Software for Extraction
Week 3:
Pre-processing of Data, Data Cleaning
Week 4:
Theoretical and Empirical Laws: Bradford, Lotka, Zipf's Laws
Week 5:
Installation of R and RStudio: Basic operations, packages, functions
Week 6:
Data Visualization: Heatmap, Bubble chart, WordCloud and other different charts
Week 7:
Science Mapping: Co-citation, bibliographic coupling, co-word, co-authorship, Network Analysis: Eigenvector, PageRank, Clustering
Week 8:
Performance Analysis: Publication and Citation related metrics
Week 9:
Text Mining /Topic Modelling in Bibliometrics, Social media metrics, altmetrics
Week 10:
Ethical Guidelines, Journal Selection, Theme, Review
Week 11:
Academic Rankings: NIRF, THE, ARWU, QS
Week 12:
Case Studies

Taught by

Prof. Nabi Hasan, Prof. Mohit Garg

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