NPTEL MOOC on Science Communication: Research Productivity and Data Analytics using Open Source Software
Offered by IIT Delhi Central Library | 20 January to 11 April 2025
About the Course
Language for course content : English
Duration : 12 weeks
Credit Points : 3
Start Date : 20 January 2025
End Date : 11 April 2025
Enrollment Ends : 27 Jan 2025
Exam Date : 03 May 2025 (Note: This exam date is subject to change based on seat availability).
Language for course content : English
Duration : 12 weeks
Credit Points : 3
Start Date : 20 January 2025
End Date : 11 April 2025
Enrollment Ends : 27 Jan 2025
Exam Date : 03 May 2025 (Note: This exam date is subject to change based on seat availability).
ABOUT THE COURSE:
Scientists, researchers, academicians and students are involved in extensive research and publication work to produce new knowledge or a new interpretation of the existing knowledge base. The quality and quantity of this knowledge is popularly 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 aiming to do such analysis. 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, etc., will also be covered.
After successfully completing 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 discipline
INDUSTRY SUPPORT: CFTIs/HEIs/Universities R&D organizations Ranking & Accreditation Agencies/Customers Publishing Industry Policy-making and evaluation organizations Library and Information Centres & Departments.
Scientists, researchers, academicians and students are involved in extensive research and publication work to produce new knowledge or a new interpretation of the existing knowledge base. The quality and quantity of this knowledge is popularly 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 aiming to do such analysis. 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, etc., will also be covered.
After successfully completing 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 discipline
INDUSTRY SUPPORT: CFTIs/HEIs/Universities R&D organizations Ranking & Accreditation Agencies/Customers Publishing Industry Policy-making and evaluation organizations Library and Information Centres & Departments.
Course Layout
Week 1: Science CommunicationWeek 2: Academic Visibility and Research ImpactWeek 3 : Data Sources and ExtractionWeek 4: Working with R: Installation of R and RStudio, Basic Operations, data types, etc.Week 5: Introduction and application of bibliometrics and laws of scientometrics in mapping of science communicationsWeek 6: Descriptive Analysis: Publication and Citation related metricsWeek 7: Science Mapping: Co-citation, bibliographic coupling, co-authorship, PageRank, etc.Week 8: Data VisualizationWeek 9: Text Mining: Topic Modelling of research productivityWeek 10: Best Practices in Academic Rankings in reference to Times Higher Education (THE), QS, Sanghai (ARWU) and NIRF RankingWeek 11: Ethical Guidelines, Academic Integrity in Science CommunicationWeek 12: Case Studies
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