New directions in science emerge from disconnection and discord

Academic mentees thrive in big groups, but survive in small groups

Mentoring is a key component of scientific achievements, contributing to overall measures of career success for mentees and mentors. Within the scientific community, possessing a large research group is often perceived as an indicator of exceptional mentorship and high-quality research. However, such large, competitive groups may also escalate dropout rates, particularly among early-career researchers. Overly high dropout rates of young researchers may lead to severe postdoc shortage and loss of top-tier academics in contemporary academia. In this context, we collect longitudinal genealogical data on mentor-mentee relations and their publications, and analyse the influence of a mentor's group size on the future academic longevity and performance of their mentees. Our findings indicate that mentees trained in larger groups tend to exhibit superior academic performance compared with those from smaller groups, provided they remain in academia post graduation. However, we also observe two surprising patterns: academic survival rate is significantly lower for (1) mentees from larger groups and for (2) mentees with more productive mentors. The trend is verified in institutions of different prestige levels. These findings highlight a negative correlation between a mentor's success and the academic survival rate of their mentees, prompting a rethinking of effective mentorship and offering actionable insights for career advancement.

Limited diffusion of scientific knowledge forecasts collapse

Market bubbles emerge when asset prices are driven unsustainably higher than asset values, and shifts in belief burst them. We demonstrate an analogous phenomenon in the case of biomedical knowledge, when promising research receives inflated attention. We introduce a diffusion index that quantifies whether research areas have been amplified within social and scientific bubbles, or have diffused and become evaluated more broadly. We illustrate the utility of our diffusion approach in tracking the trajectories of cardiac stem cell research (a bubble that collapsed) and cancer immunotherapy (which showed sustained growth). We then trace the diffusion of 28,504 subfields in biomedicine comprising nearly 1.9 M papers and more than 80 M citations to demonstrate that limited diffusion of biomedical knowledge anticipates abrupt decreases in popularity. Our analysis emphasizes that restricted diffusion, implying a socio-epistemic bubble, leads to dramatic collapses in relevance and attention accorded to scientific knowledge.

Evolution of parasitological knowledge: can the past inform the future?

The growth of scientific knowledge is often likened to the evolution and diversification of life: new disciplines branch off older ones, and subsequently prosper or decline in a manner reminiscent of the expansion or extinction of diverse lineages of organisms. Based on a parallel between evolutionary diversification and knowledge growth, I examine the expansion of subdisciplines within 'ecological and evolutionary parasitology'. Bibliometric data are used to map the rise and fall of subdisciplines over time, capturing historical trends over the past several decades. This historical overview is followed by a critical consideration of its practical applications for decision-making, ranging from rational funding allocation among subdisciplines to whether the collective planning of future research directions is a desirable option.

Does the use of unusual combinations of datasets contribute to greater scientific impact?

Scientific datasets play a crucial role in contemporary data-driven research, as they allow for the progress of science by facilitating the discovery of new patterns and phenomena. This mounting demand for empirical research raises important questions on how strategic data utilization in research projects can stimulate scientific advancement. In this study, we examine the hypothesis inspired by the recombination theory, which suggests that innovative combinations of existing knowledge, including the use of unusual combinations of datasets, can lead to high-impact discoveries. Focusing on social science, we investigate the scientific outcomes of such atypical data combinations in more than 30,000 publications that leverage over 5,000 datasets curated within one of the largest social science databases, Interuniversity Consortium for Political and Social Research. This study offers four important insights. First, combining datasets, particularly those infrequently paired, significantly contributes to both scientific and broader impacts (e.g., dissemination to the general public). Second, infrequently paired datasets maintain a strong association with citation even after controlling for the atypicality of dataset topics. In contrast, the atypicality of dataset topics has a much smaller positive impact on citation counts. Third, smaller and less experienced research teams tend to use atypical combinations of datasets in research more frequently than their larger and more experienced counterparts. Last, despite the benefits of data combination, papers that amalgamate data remain infrequent. This finding suggests that the unconventional combination of datasets is an underutilized but powerful strategy correlated with the scientific impact and broader dissemination of scientific discoveries.

Productive scientists are associated with lower disruption in scientific publishing

While scientific researchers often aim for high productivity, prioritizing the quantity of publications may come at the cost of time and effort dedicated to individual research. It is thus important to examine the relationship between productivity and disruption for individual researchers. Here, we show that with the increase in the number of published papers, the average citation per paper will be higher yet the mean disruption of papers will be lower. In addition, we find that the disruption of scientists' papers may decrease when they are highly productive in a given year. The disruption of papers in each year is not determined by the total number of papers published in the author's career, but rather by the productivity of that particular year. Besides, more productive authors also tend to give references to recent and high-impact research. Our findings highlight the potential risks of pursuing productivity and aim to encourage more thoughtful career planning among scientists.

Parsing 20 Years of Public Data by AI Maps Trends in Proteomics and Forecasts Technology

The trends of the last 20 years in biotechnology were revealed using artificial intelligence and natural language processing (NLP) of publicly available data. Implementing this "science-of-science" approach, we capture convergent trends in the field of proteomics in both technology development and application across the phylogenetic tree of life. With major gaps in our knowledge about protein composition, structure, and location over time, we report trends in persistent, popular approaches and emerging technologies across 94 ideas from a corpus of 29 journals in PubMed over two decades. New metrics for clusters of these ideas reveal the progression and popularity of emerging approaches like single-cell, spatial, compositional, and chemical proteomics designed to better capture protein-level chemistry and biology. This analysis of the proteomics literature with advanced analytic tools quantifies the Rate of Rise for a next generation of technologies to better define, quantify, and visualize the multiple dimensions of the proteome that will transform our ability to measure and understand proteins in the coming decade.

A simulation-based analysis of the impact of rhetorical citations in science

Authors of scientific papers are usually encouraged to cite works that meaningfully influenced their research (substantive citations) and avoid citing works that had no meaningful influence (rhetorical citations). Rhetorical citations are assumed to degrade incentives for good work and benefit prominent papers and researchers. Here, we explore if rhetorical citations have some plausibly positive effects for science and disproportionately benefit the less prominent papers and researchers. We developed a set of agent-based models where agents can cite substantively and rhetorically. Agents first choose papers to read based on their expected quality, become influenced by those that are sufficiently good, and substantively cite them. Next, agents fill any remaining slots in their reference lists with rhetorical citations that support their narrative, regardless of whether they were actually influential. We then turned agents' ability to cite rhetorically on-and-off to measure its effects. Enabling rhetorical citing increased the correlation between paper quality and citations, increased citation churn, and reduced citation inequality. This occurred because rhetorical citing redistributed some citations from a stable set of elite-quality papers to a more dynamic set with high-to-moderate quality and high rhetorical value. Increasing the size of reference lists, often seen as an undesirable trend, amplified the effects. Overall, rhetorical citing may help deconcentrate attention and make it easier to displace established ideas.

Identify novel elements of knowledge with word embedding

As novelty is a core value in science, a reliable approach to measuring the novelty of scientific documents is critical. Previous novelty measures however had a few limitations. First, the majority of previous measures are based on recombinant novelty concept, attempting to identify a novel combination of knowledge elements, but insufficient effort has been made to identify a novel element itself (element novelty). Second, most previous measures are not validated, and it is unclear what aspect of newness is measured. Third, some of the previous measures can be computed only in certain scientific fields for technical constraints. This study thus aims to provide a validated and field-universal approach to computing element novelty. We drew on machine learning to develop a word embedding model, which allows us to extract semantic information from text data. Our validation analyses suggest that our word embedding model does convey semantic information. Based on the trained word embedding, we quantified the element novelty of a document by measuring its distance from the rest of the document universe. We then carried out a questionnaire survey to obtain self-reported novelty scores from 800 scientists. We found that our element novelty measure is significantly correlated with self-reported novelty in terms of discovering and identifying new phenomena, substances, molecules, etc. and that this correlation is observed across different scientific fields.

A construction and empirical research of the journal disruption index based on open citation data

For many years, the journal evaluation system has been centered on impact indicators, resulting in evaluation results that do not reflect the academic innovation of journals. To solve this issue, this study attempts to construct the Journal Disruption Index (JDI) from the perspective of measuring the disruption of each journal article. In the actual study, we measured the disruption of articles of 22 selected virology journals based on the OpenCitations Index of Crossref open DOI-to-DOI citations (COCI) first. Then we calculated the JDI of 22 virology journals based on the absolute disruption index (D Z ) of the articles. Finally, we conducted an empirical study on the differences and correlations between the impact indicators and disruption indicators as well as the evaluation effect of the disruption index. The results of the study show: (1) There are large differences in the ranking of journals based on disruption indicators and impact indicators. Among the 22 journals, 12 are ranked higher by JDI than Cumulative Impact Factor for 5 years (CIF5), the Journal Index for PR6 (JIPR6) and average Percentile in Subject Area (aPSA). The ranking difference of 17 journals between the two kinds of indicators is greater than or equal to 5. (2) There is a medium correlation between disruption indicators and impact indicators at the level of journals and papers. JDI is moderately correlated with CIF5, JIPR6 and aPSA, with correlation coefficients of 0.486, 0.471 and - 0.448, respectively. D Z was also moderately correlated with Cumulative Citation (CC), Percentile Ranking with 6 Classifications (PR6) and Percentile in Subject Area (PSA) with correlation coefficients of 0.593, 0.575 and - 0.593, respectively. (3) Compared with traditional impact indicators, the results of journal disruption evaluation are more consistent with the evaluation results of experts' peer review. JDI reflects the innovation level of journals to a certain extent, which is helpful to promote the evaluation of innovation in sci-tech journals.

Effect of the topic-combination novelty on the disruption and impact of scientific articles: Evidence from PubMed

Novelty, disruption and impact are essential concepts for understanding the originality and importance of scientific discoveries. By drawing on a large-scale corpus consisting of nearly 0.9 million PubMed papers published between 1970 and 2009 and their citations before 2018 in the Web of Science, we found that the topic-combination novelty has different effects on the impact and disruption of scientific papers, that is, an inverted U-shaped effect on the impact and a positive effect on disruption. One of our contributions is that we have significantly improved the reliability of topic-combination novelty by applying MeSH terms of PubMed to the measurement of novelty. Another contribution is that we have explained how a novel combination of MeSH terms of an article contributes to citations and citation networks, that is, the middle-level novelty is more likely to achieve large citation counts. In contrast, high topic-combination novelty relates to the discontinuity in the focal paper’s citation network.

Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs

The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes.

Flat teams drive scientific innovation

With teams growing in all areas of scientific and scholarly research, we explore the relationship between team structure and the character of knowledge they produce. Drawing on 89,575 self-reports of team member research activity underlying scientific publications, we show how individual activities cohere into broad roles of 1) leadership through the direction and presentation of research and 2) support through data collection, analysis, and discussion. The hidden hierarchy of a scientific team is characterized by its lead (or L) ratio of members playing leadership roles to total team size. The L ratio is validated through correlation with imputed contributions to the specific paper and to science as a whole, which we use to effectively extrapolate the L ratio for 16,397,750 papers where roles are not explicit. We find that, relative to flat, egalitarian teams, tall, hierarchical teams produce less novelty and more often develop existing ideas, increase productivity for those on top and decrease it for those beneath, and increase short-term citations but decrease long-term influence. These effects hold within person—the same person on the same-sized team produces science much more likely to disruptively innovate if they work on a flat, high-L-ratio team. These results suggest the critical role flat teams play for sustainable scientific advance and the training and advancement of scientists.