Quantifying the impact of weak, strong, and super ties in scientific careers

Scientists are frequently faced with the important decision to start or terminate a creative partnership. This process can be influenced by strategic motivations, as early career researchers are pursuers, whereas senior researchers are typically attractors, of new collaborative opportunities. Focusing on the longitudinal aspects of scientific collaboration, we analyzed 473 collaboration profiles using an ego-centric perspective which accounts for researcher-specific characteristics and provides insight into a range of topics, from career achievement and sustainability to team dynamics and efficiency. From more than 166,000 collaboration records, we quantify the frequency distributions of collaboration duration and tie-strength, showing that collaboration networks are dominated by weak ties characterized by high turnover rates. We use analytic extreme-value thresholds to identify a new class of indispensable `super ties', the strongest of which commonly exhibit >50% publication overlap with the central scientist. The prevalence of super ties suggests that they arise from career strategies based upon cost, risk, and reward sharing and complementary skill matching. We then use a combination of descriptive and panel regression methods to compare the subset of publications coauthored with a super tie to the subset without one, controlling for pertinent features such as career age, prestige, team size, and prior group experience. We find that super ties contribute to above-average productivity and a 17% citation increase per publication, thus identifying these partnerships - the analog of life partners - as a major factor in science career development.Comment: 13 pages, 5 figures, 1 Tabl

The Extraction of Community Structures from Publication Networks to Support Ethnographic Observations of Field Differences in Scientific Communication

The scientific community of researchers in a research specialty is an important unit of analysis for understanding the field specific shaping of scientific communication practices. These scientific communities are, however, a challenging unit of analysis to capture and compare because they overlap, have fuzzy boundaries, and evolve over time. We describe a network analytic approach that reveals the complexities of these communities through examination of their publication networks in combination with insights from ethnographic field studies. We suggest that the structures revealed indicate overlapping sub- communities within a research specialty and we provide evidence that they differ in disciplinary orientation and research practices. By mapping the community structures of scientific fields we aim to increase confidence about the domain of validity of ethnographic observations as well as of collaborative patterns extracted from publication networks thereby enabling the systematic study of field differences. The network analytic methods presented include methods to optimize the delineation of a bibliographic data set in order to adequately represent a research specialty, and methods to extract community structures from this data. We demonstrate the application of these methods in a case study of two research specialties in the physical and chemical sciences.Comment: Accepted for publication in JASIS

An analysis of the semantic shifts of citations

The semantic shifts in natural language is a well established phenomenon and have been studied for many years. Similarly, the meanings of scientific publications may also change as time goes by. In other words, the same publication may be cited in distinct contexts. To investigate whether the meanings of citations have changed in different scenarios, which is also called in the semantic shifts in citations, we followed the same ideas of how researchers studied semantic shifts in language. To be more specific, we combined the temporal referencing model and the Word2Vec model to explore the semantic shifts of scientific citations in two aspects: their usages over time and their usages across different domains. By observing how citations themselves changed over time and comparing the closest neighbors of citations, we concluded that the semantics of scientific publications did shift in terms of cosine distances

Citation and peer review of data: moving towards formal data publication

This paper discusses many of the issues associated with formally publishing data in academia, focusing primarily on the structures that need to be put in place for peer review and formal citation of datasets. Data publication is becoming increasingly important to the scientific community, as it will provide a mechanism for those who create data to receive academic credit for their work and will allow the conclusions arising from an analysis to be more readily verifiable, thus promoting transparency in the scientific process. Peer review of data will also provide a mechanism for ensuring the quality of datasets, and we provide suggestions on the types of activities one expects to see in the peer review of data. A simple taxonomy of data publication methodologies is presented and evaluated, and the paper concludes with a discussion of dataset granularity, transience and semantics, along with a recommended human-readable citation syntax

The Interdependence of Scientists in the Era of Team Science: An Exploratory Study Using Temporal Network Analysis

How is the rise in team science and the emergence of the research group as the fundamental unit of organization of science affecting scientists’ opportunities to collaborate? Are the majority of scientists becoming dependent on a select subset of their peers to organize the intergroup collaborations that are becoming the norm in science? This dissertation set out to explore the evolving nature of scientists’ interdependence in team-based research environments. The research was motivated by the desire to reconcile emerging views on the organization of scientific collaboration with the theoretical and methodological tendencies to think about and study scientists as autonomous actors who negotiate collaboration in a dyadic manner. Complex Adaptive Social Systems served as the framework for understanding the dynamics involved in the formation of collaborative relationships. Temporal network analysis at the mesoscopic level was used to study the collaboration dynamics of a specific research community, in this case the genomic research community emerging around GenBank, the international nucleotide sequence databank. The investigation into the dynamics of the mesoscopic layer of a scientific collaboration networked revealed the following—(1) there is a prominent half-life to collaborative relationships; (2) the half-life can be used to construct weighted decay networks for extracting the group structure influencing collaboration; (3) scientists across all levels of status are becoming increasingly interdependent, with the qualification that interdependence is highly asymmetrical, and (4) the group structure is increasingly influential on the collaborative interactions of scientists. The results from this study advance theoretical and empirical understanding of scientific collaboration in team-based research environments and methodological approaches to studying temporal networks at the mesoscopic level. The findings also have implications for policy researchers interested in the career cycles of scientists and the maintenance and building of scientific capacity in research areas of national interest

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed