Abstract This study explores the impact of social networks on film education practices, leveraging a systematic literature review and qualitative data analysis to understand how these digital platforms influence learning environments, creative processes, and professional development within film education. The research synthesises insights from existing literature and interviews with film students and educators, highlighting the accessibility of educational content, interactivity, user... This study explores the impact of social networks on film education practices, leveraging a systematic literature review and qualitative data analysis to understand how these digital platforms influence learning environments, creative processes, and professional development within film education. The research synthesises insights from existing literature and interviews with film students and educators, highlighting the accessibility of educational content, interactivity, user perception, digital literacy, algorithmic influence, and integration with traditional methods. Findings reveal that social networks significantly enhance learning outcomes by providing diverse resources, fostering interactive and engaging learning experiences, and supporting professional growth. However, challenges related to digital literacy, privacy, and algorithmic biases must be addressed to optimise their educational use. The study underscores the importance of a balanced approach that combines online and offline learning to maximise educational benefits. Implications for educators and policymakers include strategically incorporating social networks into curricula and developing policies to support their integration while addressing associated challenges. Read More Read Less

Abstract Open research data repositories are crucial in preserving, sharing, and managing research outputs. The paper compares two leading data repositories: Dataverse and Zenodo. The study highlights their strengths and differences by examining their practices, norms, functionalities, and impacts. The key comparison areas include metadata standards, community engagement, computational reproducibility, and overall impact on research visibility and discoverability. The findings show... Open research data repositories are crucial in preserving, sharing, and managing research outputs. The paper compares two leading data repositories: Dataverse and Zenodo. The study highlights their strengths and differences by examining their practices, norms, functionalities, and impacts. The key comparison areas include metadata standards, community engagement, computational reproducibility, and overall impact on research visibility and discoverability. The findings show that the Dataverse is highly user-friendly and supports reproducibility well, Zenodo stands out for its broad scope and effective community engagement. Both repositories are essential to the research ecosystem, meeting diverse needs and promoting open science. The study emphasizes the need for ongoing exploration of repository practices to support collaborative research. Read More Read Less

Abstract This paper provides an in-depth analysis of the design and implementation of a voice-controlled virtual assistant developed using Python. It integrates several powerful libraries to enable Jarvis to perform various tasks based on voice commands and demonstrates the integration of multiple Python modules to create a functional and interactive virtual assistant. Voice assistance showcases the potential of voice-activated systems to... This paper provides an in-depth analysis of the design and implementation of a voice-controlled virtual assistant developed using Python. It integrates several powerful libraries to enable Jarvis to perform various tasks based on voice commands and demonstrates the integration of multiple Python modules to create a functional and interactive virtual assistant. Voice assistance showcases the potential of voice-activated systems to enhance user interaction with computers, making daily tasks more efficient and accessible. The detailed implementation and functionalities of the project highlight Python’s capabilities in developing versatile and user-friendly applications. Future enhancements could further expand Jarvi’s functionalities, improve speech recognition accuracy, and incorporate machine learning techniques to make the Assistant more intelligent and responsive to user needs. Read More Read Less