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Despite the long-standing calls for increased levels of interdisciplinary research as a way to address society’s grand challenges, most science is still disciplinary. To understand the slow rate of convergence to more interdisciplinary research, we examine 154,021 researchers who received a PhD in a biomedical field between 1970 and 2013, measuring the interdisciplinarity of their articles using the disciplinary composition of references. We provide a range of evidence that interdisciplinary research is impactful, but that those who conduct it face early career impediments. The researchers who are initially the most interdisciplinary tend to stop publishing earlier in their careers—it takes about 8 y for half of the researchers in the top percentile in terms of initial interdisciplinarity to stop publishing, compared to more than 20 y for moderately interdisciplinary researchers (10th to 75th percentiles). Moreover, perhaps in response to career challenges, initially interdisciplinary researchers on average decrease their interdisciplinarity over time. These forces reduce the stock of interdisciplinary researchers who can train future cohorts. Indeed, new graduates tend to be less interdisciplinary than the stock of active researchers. We show that interdisciplinarity does increase over time despite these dampening forces because initially disciplinary researchers become more interdisciplinary as their careers progress. 

Government spending on artificial intelligence (AI) has surged across the world. Quantifying the return on research investments is notoriously difficult, especially in newly emerging economic sectors. Here, we propose a novel way to describe and analyze where AI ideas are being used and how they spread—by tracing the people and academic communities involved in AI research as they transition from government-funded research labs to private sector companies, carrying cutting-edge “AI know-how” with them. Linking existing university administrative data with state employment records allows several quantifiable inferences about the value of AI research to be drawn from these academia-to-industry migrations. Here we describe a pilot implementation of this system, which is being developed in the State of Ohio. It offers a template for governments and policy makers all over the world. Importantly, the metrics discussed below offer a way to measure the economic impact of scientific research in general, with implications for critical and emerging technologies that go far beyond AI. 

Using the EconLit dissertation database and large-scale algorithmic methods that identify author demographics from names, we investigate the connection between the gender of economics dissertators and dissertation topics. Despite stagnation in the share of women among economics PhDs in recent years, there has been a remarkable rise in gender-related dissertations in economics over time and in many subfields. Women economists are significantly more likely to write gender-related dissertations and bring gender-related topics into a wide range of fields within economics. Men in economics have also substantially increased their interest in gender-related topics. 

Using dissertation research topics found in the EconLit database and large-scale algorithmic methods that identify author demographics based on names, we explore the link between race and ethnicity and fields of economic research. We find that underrepresented racial and ethnic minority (URM) researchers are more likely to write dissertations in some unexpected subfields of economics but limited evidence that they are more likely to write dissertations on racial topics once we include basic controls. These descriptive results may be due to limitations in the data, intrinsic motivations, or external constraints. 

Abstract There is a well-documented gap between the observed number of works produced by women and by men in science, with clear consequences for the retention and promotion of women 1 . The gap might be a result of productivity differences 2–5 , or it might be owing to women’s contributions not being acknowledged 6,7 . Here we find that at least part of this gap is the result of unacknowledged contributions: women in research teams are significantly less likely than men to be credited with authorship. The findings are consistent across three very different sources of data. Analysis of the first source—large-scale administrative data on research teams, team scientific output and attribution of credit—show that women are significantly less likely to be named on a given article or patent produced by their team relative to their male peers. The gender gap in attribution is present across most scientific fields and almost all career stages. The second source—an extensive survey of authors—similarly shows that women’s scientific contributions are systematically less likely to be recognized. The third source—qualitative responses—suggests that the reason that women are less likely to be credited is because their work is often not known, is not appreciated or is ignored. At least some of the observed gender gap in scientific output may be owing not to differences in scientific contribution, but rather to differences in attribution.