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Khushi Shah

Publications and source records attributed to Khushi Shah.

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Assessing the impact of Open Research Information Infrastructures using NLP driven full-text Scientometrics: A case study of the LXCat open-access platform

Open research information (ORI) play a central role in shaping how scientific knowledge is produced, disseminated, validated, and reused across the research lifecycle. While the visibility of such ORI infrastructures is often assessed through citation-based metrics, in this study, we present a full-text, natural language processing (NLP) driven scientometric framework to systematically quantify the impact of ORI infrastructures beyond citation counts, using the LXCat platform for low temperature plasma (LTP) research as a representative case study. The modeling of LTPs and interpretation of LTP experiments rely heavily on accurate data, much of which is hosted on LXCat, a community-driven, open-access platform central to the LTP research ecosystem. To investigate the scholarly impact of the LXCat platform over the past decade, we analyzed a curated corpus of full-text research articles citing three foundational LXCat publications. We present a comprehensive pipeline that integrates chemical entity recognition, dataset and solver mention extraction, affiliation based geographic mapping and topic modeling to extract fine-grained patterns of data usage that reflect implicit research priorities, data practices, differential reliance on specific databases, evolving modes of data reuse and coupling within scientific workflows, and thematic evolution. Importantly, our proposed methodology is domain-agnostic and transferable to other ORI contexts, and highlights the utility of NLP in quantifying the role of scientific data infrastructures and offers a data-driven reflection on how open-access platforms like LXCat contribute to shaping research directions. This work presents a scalable scientometric framework that has the potential to support evidence based evaluation of ORI platforms and to inform infrastructure design, governance, sustainability, and policy for future development.

physics.plasm-ph

Proposed DBMS for OTT platforms in line with new age requirements

Database management has become an enormous tool for on-demand content distribution services, proffering required information and providing custom services to the user. Also plays a major role for the platforms to manage their data in such a way that data redundancy is minimized. This paper emphasizes improving the user experience for the platform by efficiently managing data. Keeping in mind all the new age requirements, especially after COVID-19 the sudden surge in subscription has led the stakeholders to try new things to lead the OTT market. Collection of shows being the root of the tree here, this paper improvises the currently existing branches via various tables and suggests some new features on how the data collected can be utilized for introducing new and much-required query results for the consumer.

cs.DB