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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. The difference between the number of men and women listed as authors on scientific papers and inventors on patents is at least partly attributable to unacknowledged contributions by women scientists.

Theories of scientific and technological change view discovery and invention as endogenous processes 1 , 2 , wherein previous accumulated knowledge enables future progress by allowing researchers to, in Newton’s words, ‘stand on the shoulders of giants’ 3 , 4 , 5 , 6 – 7 . Recent decades have witnessed exponential growth in the volume of new scientific and technological knowledge, thereby creating conditions that should be ripe for major advances 8 , 9 . Yet contrary to this view, studies suggest that progress is slowing in several major fields 10 , 11 . Here, we analyse these claims at scale across six decades, using data on 45 million papers and 3.9 million patents from six large-scale datasets, together with a new quantitative metric—the CD index 12 —that characterizes how papers and patents change networks of citations in science and technology. We find that papers and patents are increasingly less likely to break with the past in ways that push science and technology in new directions. This pattern holds universally across fields and is robust across multiple different citation- and text-based metrics 1 , 13 , 14 , 15 , 16 – 17 . Subsequently, we link this decline in disruptiveness to a narrowing in the use of previous knowledge, allowing us to reconcile the patterns we observe with the ‘shoulders of giants’ view. We find that the observed declines are unlikely to be driven by changes in the quality of published science, citation practices or field-specific factors. Overall, our results suggest that slowing rates of disruption may reflect a fundamental shift in the nature of science and technology. A decline in disruptive science and technology over time is reported, representing a substantive shift in science and technology, which is attributed in part to the reliance on a narrower set of existing knowledge.

As global deaths from COVID-19 continue to rise, the world’s governments, institutions, and agencies are still working toward an understanding of who is most at risk of death. In this study, data on all recorded COVID-19 deaths in Sweden up to May 7, 2020 are linked to high-quality and accurate individual-level background data from administrative registers of the total population. By means of individual-level survival analysis we demonstrate that being male, having less individual income, lower education, not being married all independently predict a higher risk of death from COVID-19 and from all other causes of death. Being an immigrant from a low- or middle-income country predicts higher risk of death from COVID-19 but not for all other causes of death. The main message of this work is that the interaction of the virus causing COVID-19 and its social environment exerts an unequal burden on the most disadvantaged members of society. Better understanding of who is at highest risk of death from COVID-19 is important for public health planning. Here, the authors demonstrate an unequal mortality burden associated with socially disadvantaged groups in Sweden.

One of the most universal trends in science and technology today is the growth of large teams in all areas, as solitary researchers and small teams diminish in prevalence 1 – 3 . Increases in team size have been attributed to the specialization of scientific activities 3 , improvements in communication technology 4 , 5 , or the complexity of modern problems that require interdisciplinary solutions 6 – 8 . This shift in team size raises the question of whether and how the character of the science and technology produced by large teams differs from that of small teams. Here we analyse more than 65 million papers, patents and software products that span the period 1954–2014, and demonstrate that across this period smaller teams have tended to disrupt science and technology with new ideas and opportunities, whereas larger teams have tended to develop existing ones. Work from larger teams builds on more-recent and popular developments, and attention to their work comes immediately. By contrast, contributions by smaller teams search more deeply into the past, are viewed as disruptive to science and technology and succeed further into the future—if at all. Observed differences between small and large teams are magnified for higher-impact work, with small teams known for disruptive work and large teams for developing work. Differences in topic and research design account for a small part of the relationship between team size and disruption; most of the effect occurs at the level of the individual, as people move between smaller and larger teams. These results demonstrate that both small and large teams are essential to a flourishing ecology of science and technology, and suggest that, to achieve this, science policies should aim to support a diversity of team sizes. Analyses of the output produced by large versus small teams of researchers and innovators demonstrate that their work differs systematically in the extent to which it disrupts or develops existing science and technology.

Social capital—the strength of an individual’s social network and community—has been identified as a potential determinant of outcomes ranging from education to health 1 – 8 . However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers 9 , we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date 10 , 11 . Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality 12 – 14 . To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org . Analyses of data on 21 billion friendships from Facebook in the United States reveal associations between social capital and economic mobility.

Many households in the United States face issues of incomplete plumbing and poor water quality. Prior scholarship on this issue has focused on one dimension of water hardship at a time, leaving the full picture incomplete. Here we begin to complete this picture by documenting incomplete plumbing and poor drinking water quality for the entire United States, as well as poor wastewater quality for the 39 states and territories where data is reliable. In doing so, we find evidence of a regionally-clustered, socially unequal household water crisis. Using data from the American Community Survey and the Environmental Protection Agency, we show there are 489,836 households lacking complete plumbing, 1,165 community water systems in Safe Drinking Water Act Serious Violation, and 9,457 Clean Water Act permittees in Significant Noncompliance. Further, elevated levels of water hardship are associated with rurality, poverty, indigeneity, education, and age—representing a nationwide environmental injustice. Proper water and sanitation access remains an issue for many in the United States. Here the authors estimate and map the full scope of water hardship, including both incomplete plumbing and water quality across the country.

To help humanity solve fundamental problems of cooperation, scientists need to reconceive artificial intelligence as deeply social. To help humanity solve fundamental problems of cooperation, scientists need to reconceive artificial intelligence as deeply social.

Participatory action research (PAR) is an approach to research that prioritizes the value of experiential knowledge for tackling problems caused by unequal and harmful social systems, and for envisioning and implementing alternatives. PAR involves the participation and leadership of those people experiencing issues, who take action to produce emancipatory social change, through conducting systematic research to generate new knowledge. This Primer sets out key considerations for the design of a PAR project. The core of the Primer introduces six building blocks for PAR project design: building relationships; establishing working practices; establishing a common understanding of the issue; observing, gathering and generating materials; collaborative analysis; and planning and taking action. We discuss key challenges faced by PAR projects, namely, mismatches with institutional research infrastructure; risks of co-option; power inequalities; and the decentralizing of control. To counter such challenges, PAR researchers may build PAR-friendly networks of people and infrastructures; cultivate a critical community to hold them to account; use critical reflexivity; redistribute powers; and learn to trust the process. PAR’s societal contribution and methodological development, we argue, can best be advanced by engaging with contemporary social movements that demand the redressing of inequities and the recognition of situated expertise. Participatory action research (PAR) involves the participation and leadership of people experiencing issues, who take action to produce emancipatory social change, through conducting systematic research to generate new knowledge. In this Primer, Cornish et al. set out key considerations for the design of a PAR project and discuss ways to overcome the challenges faced by PAR projects.

Research on climate change mitigation tends to focus on supply-side technology solutions. A better understanding of demand-side solutions is missing. We propose a transdisciplinary approach to identify demand-side climate solutions, investigate their mitigation potential, detail policy measures and assess their implications for well-being.

Low levels of social interaction across class lines have generated widespread concern 1 – 4 and are associated with worse outcomes, such as lower rates of upward income mobility 4 – 7 . Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper 7 . We show that about half of the social disconnection across socioeconomic lines—measured as the difference in the share of high-socioeconomic status (SES) friends between people with low and high SES—is explained by differences in exposure to people with high SES in groups such as schools and religious organizations. The other half is explained by friending bias—the tendency for people with low SES to befriend people with high SES at lower rates even conditional on exposure. Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces. Distinguishing exposure from friending bias is helpful for identifying interventions to increase cross-SES friendships (economic connectedness). Using fluctuations in the share of students with high SES across high school cohorts, we show that increases in high-SES exposure lead low-SES people to form more friendships with high-SES people in schools that exhibit low levels of friending bias. Thus, socioeconomic integration can increase economic connectedness in communities in which friending bias is low. By contrast, when friending bias is high, increasing cross-SES interactions among existing members may be necessary to increase economic connectedness. To support such efforts, we release privacy-protected statistics on economic connectedness, exposure and friending bias for each ZIP (postal) code, high school and college in the United States at https://www.socialcapital.org . Social disconnection across socioeconomic lines is explained by both differences in exposure to people with high socioeconomic status and friending bias—the tendency for people to befriend peers with similar socioeconomic status even conditional on exposure.