http://nopr.niscpr.res.in/handle/123456789/61834 2026-10-10T06:44:00Z http://nopr.niscpr.res.in/handle/123456789/61839 Title: A study of ‘calf-path’ in file naming in institutional repositories in India Authors: Jabeen, Husna; Harinarayana, N. S. Abstract: This study examined the file naming practices in 39 institutional repositories. There is evidence that calf-path exists in file naming among the curators of institutional repositories in India. The study showed that no standard or logic seems to have been followed by repositories in the naming of the files, except by the National Digital Library of India (NDLI) and the CSIR-National Institute of Science Communication and Policy Research (CSIR-NIScPR). The study also examined the composition of the filenames, which shows that the author names (11.2%), titles (11.9%), journal title with volume and issue numbers (21.1%) form the basis for the formation of filenames. It is suggested that digital repository managers have to give more attention to name files in the institutional repository in the interest of uniformity and consistency. Page(s): 1-9 2023-05-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/61838 Title: The scope of open peer review in the scholarly publishing ecosystem Authors: Majumdar, Sandip Abstract: This study explores the selective corpus of existing literature on Open Peer Review (OPR) to understand and map the extent of adoption of OPR in the scholarly communication, the reflection of different aspects of human emotion embedded in the open peer review reports and authors’ response as well, the influence of OPR reports on citation status of articles, and application of Blockchain, Artificial Intelligence and similar technologies in improving the operational viability as well as acceptability of OPR among the scholarly community. The study finds the emergence of various OPR adoption policies and levels of adoption together with emerging models of scientific publishing. Clearly, there is a lack of uniform OPR adoption policy. It also highlights the association of different sets of human emotional traits with OPR reports. The experimentation with the possibility of treating preprint servers and open access repositories as a manuscript marketplace for the eventual selection of articles for open peer review and journal publication is noticed. More research on the influence of human behavioural aspects on OPR practice and the application of emergent technologies in OPR would be required before finally settling down on a stable roadmap for OPR. Page(s): 10-21 2023-05-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/61837 Title: Collaborative authorship patterns in computer science publications Authors: Kumari, Priti; Kumar, Rajeev Abstract: Based on the analysis of data we observe that the share of single-authored papers was significantly high in theoretical computer science, while collaborative efforts dominate computer science system research like PL, AI, ML, etc. Collaborative authorship is higher in journals over conferences. Further, values of collaborative indicators are also high for journals except for the Machine Learning (ML) subfield. In addition, the author distribution patterns are different for conferences and journals. The findings also exhibited diversity in authorship trends across sub-fields of CS research. Our results show collaboration trends in conferences and journals of major CS subfields. Such collaborative patterns benefit the funding agency, policymakers, scientific community, and researchers to plan and execute their research. Page(s): 22-32 2023-05-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/61836 Title: Automatic extraction of significant terms from the title and abstract of scientific papers using the machine learning algorithm: A multiple module approach Authors: Mukherjee, Bhaskar; Majhi, Debasis Abstract: Keyword extraction is the task of identifying important terms or phrase that are most representative of the source document. Although the process of automatic extraction of keywords from title is an old method, it was mainly for extraction from a single web document. Our approach differs from previous research works on keyword extraction in several aspects. For those who are non-expert of the scientific fields, understating scientific research trends is difficult. The purpose of this study is to develop an automatic method of obtaining overviews of a scientific field for non-experts by capturing research trends. This empirical study excavates significant term extraction using Natural Language Processing (NLP) tools. More than 15000 titles saved in a .csv file was our dataset and scripts written in Python were our process to compare how far significant terms of scientific title corpus are similar or different to the terms available in the abstract of that same scientific article corpus. A light-weight unsupervised title extractor, Yet Another Keyword Extractor (YAKE) was used to extract the results. Based on our analysis, it can be concluded that these algorithms can be used for other fields too by the non-experts of that subject field to perform automatic extraction of significant words and understanding trends. Our algorithm could be a solution to reduce the labour-intensive manual indexing process. Page(s): 33-40 2023-05-01T00:00:00Z