The evolution of interdisciplinarity in physics research

Science, being a social enterprise, is subject to fragmentation into groups that focus on specialized areas or topics. Often new advances occur through cross-fertilization of ideas between sub-fields that otherwise have little overlap as they study dissimilar phenomena using different techniques. Thus to explore the nature and dynamics of scientific progress one needs to consider the large-scale organization and interactions between different subject areas. Here, we study the relationships between the sub-fields of Physics using the Physics and Astronomy Classification Scheme (PACS) codes employed for self-categorization of articles published over the past 25 years (1985-2009). We observe a clear trend towards increasing interactions between the different sub-fields. The network of sub-fields also exhibits core-periphery organization, the nucleus being dominated by Condensed Matter and General Physics. However, over time Interdisciplinary Physics is steadily increasing its share in the network core, reflecting a shift in the overall trend of Physics research.Comment: Published version, 10 pages, 8 figures + Supplementary Informatio

The evolution of knowledge within and across fields in modern physics

9 pages, 4 figuresThe exchange of knowledge across different areas and disciplines plays a key role in the process of knowledge creation, and can stimulate innovation and the emergence of new fields. We develop here a quantitative framework to extract significant dependencies among scientific disciplines and turn them into a time-varying network whose nodes are the different fields, while the weighted links represent the flow of knowledge from one field to another at a given period of time. Drawing on a comprehensive data set on scientific production in modern physics and on the patterns of citations between articles published in the various fields in the last 30 years, we are then able to map, over time, how the ideas developed in a given field in a certain time period have influenced later discoveries in the same field or in other fields. The analysis of knowledge flows internal to each field displays a remarkable variety of temporal behaviours, with some fields of physics showing to be more self-referential than others. The temporal networks of knowledge exchanges across fields reveal cases of one field continuously absorbing knowledge from another field in the entire observed period, pairs of fields mutually influencing each other, but also cases of evolution from absorbing to mutual or even to back-nurture behaviors

Latent Geometry for Complementarity-Driven Networks

Networks of interdisciplinary teams, biological interactions as well as food webs are examples of networks that are shaped by complementarity principles: connections in these networks are preferentially established between nodes with complementary properties. We propose a geometric framework for complementarity-driven networks. In doing so we first argue that traditional geometric representations, e.g., embeddings of networks into latent metric spaces, are not applicable to complementarity-driven networks due to the contradiction between the triangle inequality in latent metric spaces and the non-transitivity of complementarity. We then propose the cross-geometric representation for these complementarity-driven networks and demonstrate that this representation (i) follows naturally from the complementarity rule, (ii) is consistent with the metric property of the latent space, (iii) reproduces structural properties of real complementarity-driven networks, if the latent space is the hyperbolic disk, and (iv) allows for prediction of missing links in complementarity-driven networks with accuracy surpassing existing similarity-based methods. The proposed framework challenges social network analysis intuition and tools that are routinely applied to complementarity-driven networks and offers new avenues towards descriptive and prescriptive analysis of systems in science of science and biomedicine

Evolution of interdisciplinarity in biodiversity science

The study of biodiversity has grown exponentially in the last thirty years in response to demands for greater understanding of the function and importance of Earth's biodiversity and finding solutions to conserve it. Here, we test the hypothesis that biodiversity science has become more interdisciplinary over time. To do so, we analyze 97,945 peer‐reviewed articles over a twenty‐two‐year time period (1990–2012) with a continuous time dynamic model, which classifies articles into concepts (i.e., topics and ideas) based on word co‐occurrences. Using the model output, we then quantify different aspects of interdisciplinarity: concept diversity, that is, the diversity of topics and ideas across subdisciplines in biodiversity science, subdiscipline diversity, that is, the diversity of subdisciplines across concepts, and network structure, which captures interactions between concepts and subdisciplines. We found that, on average, concept and subdiscipline diversity in biodiversity science were either stable or declining, patterns which were driven by the persistence of rare concepts and subdisciplines and a decline in the diversity of common concepts and subdisciplines, respectively. Moreover, our results provide evidence that conceptual homogenization, that is, decreases in temporal β concept diversity, underlies the observed trends in interdisciplinarity. Together, our results reveal that biodiversity science is undergoing a dynamic phase as a scientific discipline that is consolidating around a core set of concepts. Our results suggest that progress toward addressing the biodiversity crisis via greater interdisciplinarity during the study period may have been slowed by extrinsic factors, such as the failure to invest in research spanning across concepts and disciplines. However, recent initiatives such as the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services (IPBES) may attract broader support for biodiversity‐related issues and hence interdisciplinary approaches to address scientific, political, and societal challenges in the coming years

Knowledge and Social Relatedness Shape Research Portfolio Diversification

Scientific discovery is shaped by scientists' choices and thus by their career patterns. The increasing knowledge required to work at the frontier of science makes it harder for an individual to embark on unexplored paths. Yet collaborations can reduce learning costs -- albeit at the expense of increased coordination costs. In this article, we use data on the publication histories of a very large sample of physicists to measure the effects of knowledge and social relatedness on their diversification strategies. Using bipartite networks, we compute a measure of topics similarity and a measure of social proximity. We find that scientists' strategies are not random, and that they are significantly affected by both. Knowledge relatedness across topics explains $\approx 10\%$ of logistic regression deviances and social relatedness as much as $\approx 30\%$, suggesting that science is an eminently social enterprise: when scientists move out of their core specialization, they do so through collaborations. Interestingly, we also find a significant negative interaction between knowledge and social relatedness, suggesting that the farther scientists move from their specialization, the more they rely on collaborations. Our results provide a starting point for broader quantitative analyses of scientific diversification strategies, which could also be extended to the domain of technological innovation -- offering insights from a comparative and policy perspective.Comment: Typos corrected; references added; section S2 added; results unchange

Biomedical Convergence Facilitated by the Emergence of Technological and Informatic Capabilities

We analyzed Medical Subject Headings (MeSH) from 21.6 million research articles indexed by PubMed to map this vast space of entities and their relations, providing insights into the origins and future of biomedical convergence. Detailed analysis of MeSH co-occurrence networks identifies three robust knowledge clusters: the vast universe of microscopic biological entities and structures; systems, disease and diagnostics; and emergent biological and social phenomena underlying the complex problems driving the health, behavioral and brain science frontiers. These domains integrated from the 1990s onward by way of technological and informatic capabilities that introduced highly controllable, scalable and permutable research processes and invaluable imaging techniques for illuminating fundamental structure-function-behavior questions. Article-level analysis confirms a positive relationship between team size and topical diversity, and shows convergence to be increasing in prominence but with recent saturation. Together, our results invite additional policy support for cross-disciplinary team assembly to harness transdisciplinary convergence.Comment: 12 pages, 4 figures; 8 pages of Supplementary Informatio