Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

Geosciences Roadmap for Research Infrastructures 2025 - 2028 by the Swiss Geosciences Community

This roadmap is the product of a grassroots effort by the Swiss Geosciences community. It is the first of its kind, outlining an integrated approach to research facilities for the Swiss Geosciences. It spans the planning period 2025-2028. Swiss Geoscience is by its nature leading or highly in-volved in research on many of the major national and global challenges facing society such as climate change and meteorological extreme events, environmental pol-lution, mass movements (land- and rock-slides), earth-quakes and seismic hazards, global volcanic hazards, and energy and other natural resources. It is essential to under- stand the fundamentals of the whole Earth system to pro-vide scientific guidelines to politicians, stakeholders and society for these pressing issues. Here, we strive to gain efficiency and synergies through an integrative approach to the Earth sciences. The research activities of indivi- dual branches in geosciences were merged under the roof of the 'Integrated Swiss Geosciences'. The goal is to facilitate multidisciplinary synergies and to bundle efforts for large research infrastructural (RI) requirements, which will re-sult in better use of resources by merging sectorial acti- vities under four pillars. These pillars represent the four key RIs to be developed in a synergistic way to improve our understanding of whole-system processes and me- chanisms governing the geospheres and the interactions among their components. At the same time, the roadmap provides for the required transition to an infrastructure adhering to FAIR (findable, accessible, interoperable, and reusable) data principles by 2028.The geosciences as a whole do not primarily profit from a single large-scale research infrastructure investment, but they see their highest scientific potential for ground-break-ing new findings in joining forces in establishing state-of-the-art RI by bringing together diverse expertise for the benefit of the entire geosciences community. Hence, the recommendation of the geoscientific community to policy makers is to establish an integrative RI to support the ne- cessary breadth of geosciences in their endeavor to ad-dress the Earth system across the breadth of both temporal and spatial scales. It is also imperative to include suffi-cient and adequately qualified personnel in all large RIs. This is best achieved by fostering centers of excellence in atmospheric, environmental, surface processes, and deep Earth projects, under the roof of the 'Integrated Swiss Geosciences'. This will provide support to Swiss geo-sciences to maintain their long standing and internatio- nally well-recognized tradition of observation, monitor-ing, modelling and understanding of geosciences process-es in mountainous environments such as the Alps and beyond

Crowdsourcing hypothesis tests: making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim