Dataset Information

Understanding scholar-trajectories across scientific periodicals.

ABSTRACT: Despite the rapid growth in the number of scientific publications, our understanding of author publication trajectories remains limited. Here we propose an embedding-based framework for tracking author trajectories in a geometric space that leverages the information encoded in the publication sequences, namely the list of the consecutive publication venues for each scholar. Using the publication histories of approximately 30,000 social media researchers, we obtain a knowledge space that broadly captures essential information about periodicals as well as complex (inter-)disciplinary structures of science. Based on this space, we study academic success through the prism of movement across scientific periodicals. We use a measure from human mobility, the radius of gyration, to characterize individual scholars' trajectories. Results show that author mobility across periodicals negatively correlates with citations, suggesting that successful scholars tend to publish in a relatively proximal range of periodicals. Overall, our framework discovers intricate structures in large-scale sequential data and provides new ways to explore mobility and trajectory patterns.

SUBMITTER: Fan Y

PROVIDER: S-EPMC10912201 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Understanding scholar-trajectories across scientific periodicals.

Fan Yangliu Y Blok Anders A Lehmann Sune S

Scientific reports 20240304 1

Despite the rapid growth in the number of scientific publications, our understanding of author publication trajectories remains limited. Here we propose an embedding-based framework for tracking author trajectories in a geometric space that leverages the information encoded in the publication sequences, namely the list of the consecutive publication venues for each scholar. Using the publication histories of approximately 30,000 social media researchers, we obtain a knowledge space that broadly ...[more]

PMID: 38438413

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Project description:Understanding how multiple conditions develop over time is of growing interest, but there is currently limited methodological development on the topic, especially in understanding how multimorbidity (the co-existence of at least two chronic conditions) develops longitudinally and in which order diseases occur. We aim to describe how a longitudinal method, sequence analysis, can be used to understand the sequencing of common chronic diseases that lead to multimorbidity and the socio-demographic factors and health outcomes associated with typical disease trajectories. We use the Scottish Longitudinal Study (SLS) linking the Scottish census 2001 to disease registries, hospitalisation and mortality records. SLS participants aged 40-74 years at baseline were followed over a 10-year period (2001-2011) for the onset of three commonly occurring diseases: diabetes, cardiovascular disease (CVD), and cancer. We focused on participants who transitioned to at least two of these conditions over the follow-up period (N = 6300). We use sequence analysis with optimal matching and hierarchical cluster analysis to understand the process of disease sequencing and to distinguish typical multimorbidity trajectories. Socio-demographic differences between specific disease trajectories were evaluated using multinomial logistic regression. Poisson and Cox regressions were used to assess differences in hospitalisation and mortality outcomes between typical trajectories. Individuals who transitioned to multimorbidity over 10 years were more likely to be older and living in more deprived areas than the rest of the population. We found seven typical trajectories: later fast transition to multimorbidity, CVD start with slow transition to multimorbidity, cancer start with slow transition to multimorbidity, diabetes start with slow transition to multimorbidity, fast transition to both diabetes and CVD, fast transition to multimorbidity and death, fast transition to both cancer and CVD. Those who quickly transitioned to multimorbidity and death were the most vulnerable, typically older, less educated, and more likely to live in more deprived areas. They also experienced higher number of hospitalisations and overnight stays while still alive. Sequence analysis can strengthen our understanding of typical disease trajectories when considering a few key diseases. This may have implications for more active clinical review of patients beginning quick transition trajectories.

Project description:BackgroundResearch suggests four modal trajectories of psychological symptoms after traumatic injury: Resilient, Chronic, Delayed Onset, Recovery. However, most studies focus on symptoms of psychiatric disorders (e.g., posttraumatic stress disorder, depression), which are limited by heterogeneity and symptom overlap. We examined trajectories of cross-cutting posttraumatic symptom dimensions following traumatic injury and predictors of trajectory membership.MethodsIn this longitudinal study of 427 predominantly Hispanic/Latino traumatic injury survivors, posttraumatic psychopathology symptoms were assessed during hospitalization and approximately one and five months post-trauma. Using latent class growth analysis, we estimated trajectories of several posttraumatic symptom dimensions: re-experiencing, avoidance, anxious arousal, numbing, dysphoric arousal, loss, and threat. We then examined sociodemographic and trauma-related characteristics (measured during hospitalization) as predictors of trajectory membership for each dimension.ResultsFour trajectories (Resilient, Chronic, Delayed Onset, Recovery) emerged for all dimensions except loss and threat, which manifested three trajectories (Resilient, Chronic, Delayed Onset). Across dimensions, membership in the Chronic (vs. Resilient) trajectory was consistently predicted by unemployment (7 of 7 dimensions), followed by older age (3/7), female sex (3/7), and assaultive trauma (2/7). For several dimensions, unemployment also distinguished between participants who presented with similar symptom levels days after trauma, but then diverged over time.LimitationsMeasures of posttraumatic symptom dimension constructs differed across assessments.ConclusionsThis study provides evidence of distinct trajectories across transdiagnostic symptom dimensions after traumatic injury. Employment status emerged as the most important predictor of trajectory membership. Research is needed to better understand the etiologies and consequences of these posttraumatic symptom dimension trajectories.

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Understanding scholar-trajectories across scientific periodicals.

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Understanding scholar-trajectories across scientific periodicals.

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