Science aspires to be cumulative. Reproducibility efforts strengthen science by testing the reliability of published findings, promoting self-correction, and informing policy-making1. Computational reproductions, whereby independent researchers reproduce the results of published studies, are an essential diagnostic tool2-10. Such efforts should have greater visibility11-16. However, little social science reproduction and robustness has been conducted at scale10,13,17-23. Here we reproduced original analyses and conducted robustness checks of 110 articles that were published in leading economics and political science journals with mandatory data and code sharing policies17,18. We found that more than 85% of published claims were computationally reproducible. In robustness checks, our reanalyses showed that 72% of statistically significant estimates remain significant and in the same direction, and the median reproduced effect size is nearly the same as the originally published effect size (that is, 99% of the published effect size). Additionally, 6 independent research teams examined 12 pre-specified hypotheses about determinants of robustness. Research teams with more experience found lower levels of robustness, and robustness did not correlate with author characteristics or data availability.

Electronic coherences resulting from molecular photoionization underlie the process of attosecond charge migration, widely investigated as a possible path towards controlled charge-directed reactivity1-4. However, photoionization often creates entangled ions and photoelectrons. This entanglement compromises the ability to explore coherent ultrafast electron dynamics within ions or of their accompanying photoelectrons5-8. Here we present experiments and calculations in which hydrogen molecules are ionized by the combination of a phase-locked pair of isolated attosecond laser pulses and a few-cycle near-infrared (NIR) laser pulse. The electronic coherence in the dissociating H2+ ion is influenced by ion-photoelectron entanglement. We demonstrate experimental control over the degree of entanglement by varying the delay between the two attosecond pulses and the delay between these pulses and the few-cycle NIR pulse. Our work demonstrates the importance of proper consideration of the role of quantum entanglement for the optimal observation of electronic coherences in attosecond experiments.

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science1-3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts' choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen's d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses4. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

Ensuring safe and effective use of artificial intelligence (AI) requires understanding and anticipating its performance on new tasks, from advanced scientific challenges to transformed workplace activities1-3. So far, benchmarking has guided progress in AI but has offered limited explanatory and predictive power for general-purpose AI systems4-8, attributed to limited transferability across specific tasks9-11. Here we introduce general scales for AI evaluation that elicit demand profiles explaining what capabilities common AI benchmarks truly measure, extract ability profiles quantifying the general strengths and limits of AI systems and robustly predict AI performance for new task instances. Our fully automated methodology builds on 18 rubrics, capturing a broad range of cognitive and intellectual demands, which place different task instances on the same general scales, illustrated on 15 large language models (LLMs) and 63 tasks. Both the demand and the ability profiles on these scales bring new insights such as construct validity through benchmark sensitivity and specificity and explain conflicting claims about whether AI has reasoning capabilities. Ultimately, high predictive power at the instance level becomes possible using the general scales, providing superior estimates over strong black-box baseline predictors, especially in out-of-distribution settings (new tasks and benchmarks). The scales, rubrics, battery, techniques and results presented here constitute a solid foundation for a science of AI evaluation, underpinning the reliable deployment of AI in the years ahead.

Contrail cirrus clouds are a main contributor to the climate forcing from aviation1. Yet, the number of contrail ice crystals forming behind aircraft with modern lean-burn engines is unknown. Theory spans a four orders of magnitude range in ice crystal numbers2,3-rendering related climate effects unpredictable. Here we show that lean-burn combustion reduces soot particle number emissions by three orders of magnitude compared with conventional rich-quench-lean engines4,5-but does not significantly decrease volatile particles or contrail ice crystal numbers-both can exceed 1015 particles per kg of burned fuel. Our findings arise from in-flight observations behind an A321neo aircraft with lean-burn engines, thus providing real-world confirmation of some laboratory work6 and narrowing the range of theoretical expectations. Our results indicate that the tested lean-burn engine configurations alone are unlikely to reduce the warming effect of contrails, suggesting that modifications of fuel composition and lubrication oil venting architecture may be required. We show that contrail ice particle numbers in the low-soot regime can be reduced by using low-sulfur fuels and that organic fuel constituents and lubrication oil vapours can increase contrail ice particle numbers. Future research should explore how reductions in volatile particles, apart from soot, affect contrail ice formation.

Pursuing replicability - independent evidence for previous claims - is important for creating generalizable knowledge1,2. Here we attempted replications of 274 claims of positive results from 164 quantitative papers published from 2009 to 2018 in 54 journals in the social and behavioural sciences. Replications were high powered on average to detect the original effect size (median of 99.6%), used original materials when relevant and available, and were peer reviewed in advance through a standardized internal protocol. Replications showed statistically significant results in the original pattern for 151 of 274 claims (55.1% (95% confidence interval (CI) 49.2-60.9%)) and for 80.8 of 164 papers (49.3% (95% CI 43.8-54.7%)), weighed for replicating multiple claims per paper. We observed modest variation in replication rates across disciplines (42.5-63.1%), although some estimates had high uncertainty. The median Pearson's r effect size was 0.25 (95% CI 0.21-0.27) for original studies and 0.10 (95% CI 0.09-0.13) for replication studies, an 82.4% (95% CI 67.8-88.2%) reduction in shared variance. Thirteen methods for evaluating replication success provided estimates ranging from 28.6% to 74.8% (median of 49.3%). Some decline in effect size and significance is expected based on power to detect original effects and regression to the mean because we replicated only positive results. We observe that challenges for replicability extend across social-behavioural sciences, illustrating the importance of identifying conditions that promote or inhibit replicability3,4.

Published claims should be reproducible, yielding the same result when the same analysis is applied to the same data1,2. Here we assess reproducibility in a stratified random sample of 600 papers published from 2009 to 2018 in 62 journals spanning the social and behavioural sciences. The authors of 144 (24.0%, 95% confidence interval (CI) = 20.8-27.6%) papers made data available to assess reproducibility and, for 38 others, we obtained source data to reconstruct the dataset. We assessed 143 out of the 182 available datasets and found that 76.6 (53.6%, 95% CI = 45.8-60.7%) papers were rated as precisely reproducible and 105.0 (73.5%, 95% CI = 66.4-80.0%) were rated as at least approximately reproducible (within 15% of the original effects or within 0.05 of original P values) after inverse weighting each of the 551 claims by the number of claims per paper. We observed higher reproducibility for papers from political science and economics compared with other fields, for more recent papers compared with older papers and for papers from journals that require data sharing. Implementation of measures to verify that research is reproducible is needed to support trustworthiness in the complex enterprise of knowledge production3,4.

Light olefins-ethylene, propylene and butylene (C2=-C4=)-are essential building blocks in the chemicals industry and are traditionally produced by thermal or catalytic cracking of hydrocarbon feedstocks. Directly converting syngas (CO and H2) into light olefins under mild conditions is attractive but challenging1-4. Prismatic cobalt carbide (Co2C) and associated hydrophobic modifications have shown potential for selective light-olefin synthesis under mild conditions5,6. Here we show another hydrophilic-promotion strategy in which a set of hydroxy promoters, exemplified by hydroxyapatite (Ca5(PO4)3(OH), HAP), fumed silica (SiO2(F)) and amorphous boehmite (AlO(OH), AB), is physically mixed with a Co2MnO4 precursor, inducing synergistic cobalt-manganese (Co-Mn) oxides and Co2C for syngas conversion. The induced anorthic Co-Mn oxides may serve as active phase for adsorbed-hydrogen-assisted CO dissociation to CHx/CHxO intermediates, whereas induced Co2C or the Co2C-oxide interface may mediate C-C coupling of these intermediates to form light olefins. This design achieved 70-82% CO conversion with light-olefins selectivity of more than 60% at 250-260 °C, 0.1 MPa with H2/CO ratios of 1-2, giving light-olefins carbon utilization efficiency up to 13%, among the highest reported for syngas to light olefins. This simple hydrophilic strategy for facilitating CO activation may provide useful insights for improving industrial Fischer-Tropsch processes.