Social network analysis of the CaRE2 health equity center: Team science in full display.

Cancer health disparities that exist in the Black or African American and Hispanic or Latino/x communities are scientific challenges, yet there are limited team science approaches to mitigate these challenges. This article's purpose is to evaluate the team science collaborations of the National Institutes of Health-funded Florida-California Cancer Research, Education & Engagement (CaRE2 ) Center partnership underscoring the inclusion of multidisciplinary team members and future under-represented minority (URM) cancer researchers. To understand our collaborative efforts, we conducted a social network analysis (SNA) of the CaRE2 Center partnership among University of Florida, Florida A&M University, and University of Southern California with data collected via the dimensions.ai application programming interface. We downloaded metadata for all publications associated with dimensions.ai IDs. The CaRE2 collaboration network increased over time as evidenced by accruing more external collaborators and more publishing of collaborative works. Degree centrality of key personnel was stable in each wave of the networks. CaRE2 key personnel averaged a total of 60.8 collaborators in 2018-2019 (SD = 57.4, minimum = 3, maximum = 221), and 65.8 collaborators in 2020-2021 (SD = 56.06, minimum = 4, maximum = 222). Betweenness was largely stable across all groups and waves. We observed a steady decline in transitivity, the probability that a pair of CaRE2 co-authors shared a third co-author, from 0.74 in 2018 to 0.47 in 2022. The SNA findings suggest that the CaRE2 Center partnership's publications show growth in team science collaborations with the inclusion of multidisciplinary team members from the three partner institutions and future URM cancer researchers who were mentored as trainees and early-stage investigators.

Discovering new pathways toward integration between health and sustainable development goals with natural language processing and network science.

BACKGROUND: Research on health and sustainable development is growing at a pace such that conventional literature review methods appear increasingly unable to synthesize all relevant evidence. This paper employs a novel combination of natural language processing (NLP) and network science techniques to address this problem and to answer two questions: (1) how is health thematically interconnected with the Sustainable Development Goals (SDGs) in global science? (2) What specific themes have emerged in research at the intersection between SDG 3 ("Good health and well-being") and other sustainability goals? METHODS: After a descriptive analysis of the integration between SDGs in twenty years of global science (2001-2020) as indexed by dimensions.ai, we analyze abstracts of articles that are simultaneously relevant to SDG 3 and at least one other SDG (N = 27,928). We use the top2vec algorithm to discover topics in this corpus and measure semantic closeness between these topics. We then use network science methods to describe the network of substantive relationships between the topics and identify 'zipper themes', actionable domains of research and policy to co-advance health and other sustainability goals simultaneously. RESULTS: We observe a clear increase in scientific research integrating SDG 3 and other SDGs since 2001, both in absolute and relative terms, especially on topics relevant to interconnections between health and SDGs 2 ("Zero hunger"), 4 ("Quality education"), and 11 ("Sustainable cities and communities"). We distill a network of 197 topics from literature on health and sustainable development, with 19 distinct network communities - areas of growing integration with potential to further bridge health and sustainability science and policy. Literature focused explicitly on the SDGs is highly central in this network, while topical overlaps between SDG 3 and the environmental SDGs (12-15) are under-developed. CONCLUSION: Our analysis demonstrates the feasibility and promise of NLP and network science for synthesizing large amounts of health-related scientific literature and for suggesting novel research and policy domains to co-advance multiple SDGs. Many of the 'zipper themes' identified by our method resonate with the One Health perspective that human, animal, and plant health are closely interdependent. This and similar perspectives will help meet the challenge of 'rewiring' sustainability research to co-advance goals in health and sustainability.

Taking the problem of colliders seriously in the study of crime: A research note.

Objectives: We provide a brief overview of collider bias and its implications for criminological research. Methods: Owing to the nature of the topics studied, as well as the common data sources used to carry out much of this research, work in the field may often become vulnerable to a specific methodological problem known as collider bias. Collider bias occurs when exposure variables and outcomes independently cause a third variable, and this variable is included in statistical models. Colliders represent somewhat of a paradox in that there is scholarship discussing the issue, yet it has managed to remain a relatively cryptic threat compared to other sources of bias. Results: We argue that, far from being an obscure concern, colliders almost certainly have pervasive impact in criminal justice and criminology. Conclusion: We close by offering a general set of strategies for addressing the challenges posed by collider bias. While there is no panacea, there are better practices, many of which are underutilized in the disciplines that study crime and its attendant topics.

Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals.

The United Nations' (UN) Sustainable Development Goals (SDGs) are heterogeneous and interdependent, comprising 169 targets and 231 indicators of sustainable development in such diverse areas as health, the environment, and human rights. Existing efforts to map relationships among SDGs are either theoretical investigations of sustainability concepts, or empirical analyses of development indicators and policy simulations. We present an alternative approach, which describes and quantifies the complex network of SDG interdependencies by applying computational methods to policy and scientific documents. Methods of Natural Language Processing are used to measure overlaps in international policy discourse around SDGs, as represented by the corpus of all existing UN progress reports about each goal (N = 85 reports). We then examine if SDG interdependencies emerging from UN discourse are reflected in patterns of integration and collaboration in SDG-related science, by analyzing data on all scientific articles addressing relevant SDGs in the past two decades (N = 779,901 articles). Results identify a strong discursive divide between environmental goals and all other SDGs, and unexpected interdependencies between SDGs in different areas. While UN discourse partially aligns with integration patterns in SDG-related science, important differences are also observed between priorities emerging in UN and global scientific discourse. We discuss implications and insights for scientific research and policy on sustainable development after COVID-19.

Great minds think alike, or do they often differ? Research topic overlap and the formation of scientific teams.

Over the last century scientific research has become an increasingly collaborative endeavor. Commentators have pointed to different factors which contribute to this trend, including the specialization of science and growing need for diversity of interest and expertise areas in a scientific team. Very few studies, however, have precisely evaluated how the diversity of interest topics between researchers is related to the emergence of collaboration. Existing theoretical arguments suggest a curvilinear relationship between topic similarity and collaboration: too little similarity can complicate communication and agreement, yet too much overlap can increase competition and limit the potential for synergy. We test this idea using data on six years of publications across all disciplines at a large U.S. research university (approximately 14,300 articles, 12,500 collaborations, and 3,400 authors). Employing topic modelling and network statistical models, we analyze the relationship between topic overlap and the likelihood of coauthorship between two researchers while controlling for potential confounders. We find an inverted-U relationship in which the probability of collaboration initially increases with topic similarity, then rapidly declines after peaking at a similarity "sweet spot". Collaboration is most likely at low-to-moderate levels of topic overlap, which are substantially lower than the average self-similarity of scientists or research groups. These findings - which we replicate for different units of analysis (individuals and groups), genders of collaborators, disciplines, and collaboration types (intra- and interdisciplinary) - support the notion that researchers seek collaborators to augment their scientific and technical human capital. We discuss implications for theories of scientific collaboration and research policy.

Marital Strain, Support, and Alcohol Use: Results from a Twin Design Statistically Controlling for Genetic Confounding.

Background: Marriage is one of the most frequently examined sources of social support and has been shown to protect against alcohol use and abuse. This study examines the relationship between perceived marital strain and support, and alcohol use controlling for additive genetic influence. Methods: Data from monozygotic (MZ) (n = 320) and dizygotic (DZ) (n = 464) twin pairs from the second wave of the National Survey of Midlife Development in the United States (MIDUS II) were used to test whether past year marital strain and support were associated with recent alcohol use. Generalized linear mixed models (GLMM) were estimated, allowing us to control for additive genetic and shared environmental influences as variance components. Results: Marital strain and support had positive, statistically significant associations with alcohol use. However, only the relationship between marital strain and alcohol use remained after controlling for variance in alcohol use attributed to genetics. Conclusions: After accounting for genetics, midlife adults still appear to cope with marital strain via alcohol use. However, this coping is unlikely to result in heavy episodic drinking or alcohol use disorder without other compounding factors.