Towards a holistic understanding of society: bridging social sciences, statistics and computational sciences

This study examines the evolution of collaboration behaviors among Italian academic scholars over a decade, focusing on four macro sectors: two in economics and statistics, and two in political and social sciences. By distinguishing between formal and informal co-authorship relationships, the research seeks to understand their respective impacts on scholarly performance. Formal collaborations are sourced from international bibliographic databases such as Scopus, which provide comprehensive records of co-authored publications. In contrast, informal collaborations are assessed via a specially designed survey using an egonetwork approach, targeting individuals identified through the formal collaboration data. Objectives The study aims to understand how both formal and informal collaborations affect academic performance [1]. It also seeks to demonstrate the capabilities of MaScoNet, a web-based tool developed specifically for this research and soon to be available to the scientific community, designed to capture detailed insights into collaboration patterns through structured questionnaires. Methods MaScoNet administers a survey divided into two sections. The first section focuses on the nature of coauthorship relationships, gathering detailed information about the types of collaborations that have led to scientific outputs between respondents and their co-authors. Each co-author is addressed on a separate page with specific questions tailored to their collaborative interactions. The second section explores the extent of informal relationships maintained by respondents with colleagues who have not co-authored any publications with them. Additionally, MaScoNet offers a feature in the formal section that allows researchers to input a list of scientific articles co-authored by the respondent and each identified co-author. This enables the creation of a two-mode network analysis, where one set of nodes represents the scientific articles and the other represents the authors. Respondents can associate multiple articles with each co-author, thereby enhancing the understanding of collaborative structures and their impact on academic performance. When collecting data, both focal individuals (egos) and their associated network members (alters) must be identified. Alters may be identified either through name-generator questions—where participants list their collaborators—or via researcher pre-selection. MaScoNet offers the flexibility to choose the most suitable approach, thereby addressing potential biases such as recall bias or researcher subjectivity. Discussion/Conclusions The combined use of bibliographic data and survey-based network information through MaScoNet provides a comprehensive perspective on academic collaborations, enhancing the understanding of their impact on scholarly performance.