Many societal challenges are threshold dilemmas requiring people to cooperate to reach a threshold before group benefits can be reaped. Yet receiving feedback about others\' outcomes relative to one\'s own (relative feedback) can undermine cooperation by focusing group members\' attention on outperforming each other. We investigated the impact of relative feedback compared to individual feedback (only seeing one\'s own outcome) on cooperation in children from Germany and India (6- to 10-year-olds, N = 240). Using a threshold public-goods game with real water as a resource, we show that, although feedback had an effect, most groups sustained cooperation at high levels in both feedback conditions until the end of the game. Analyses of children\'s communication (14,374 codable utterances) revealed more references to social comparisons and more verbal efforts to coordinate in the relative-feedback condition. Thresholds can mitigate the most adverse effects of social comparisons by focusing attention on a common goal.

Patents are essential for transferring scientific discoveries to meaningful products that benefit societies. While the academic community focuses on the number of citations to rank scholarly works according to their \"scientific merit,\" the number of citations is unrelated to the relevance for patentable innovation. To explore associations between patents and scholarly works in publicly available patent data, we propose to utilize statistical methods that are commonly used in biology to determine gene-disease associations. We illustrate their usage on patents related to biotechnological trends of high relevance for food safety and ecology, namely the CRISPR-based gene editing technology (>60,000 patents) and cyanobacterial biotechnology (>33,000 patents). Innovation trends are found through their unexpected large changes of patent numbers in a time-series analysis. From the total set of scholarly works referenced by all investigated patents (~254,000 publications), we identified ~1,000 scholarly works that are statistical significantly over-represented in the references of patents from changing innovation trends that concern immunology, agricultural plant genomics, and biotechnological engineering methods. The detected associations are consistent with the technical requirements of the respective innovations. In summary, the presented data-driven analysis workflow can identify scholarly works that were required for changes in innovation trends, and, therefore, is of interest for researches that would like to evaluate the relevance of publications beyond the number of citations.

Using publicly available data from 299 preregistered replications from the social sciences, we found that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the article characteristics, study design and results, author information, and replication effort. To understand why, we analyzed the textual differences between replicable and nonreplicable studies. Our findings suggest that the language in replicable studies is transparent and confident, written in a detailed and complex manner, and generally exhibits markers of truthful communication, possibly demonstrating the researchers\' confidence in the study. Nonreplicable studies, however, are vaguely written and have markers of persuasion techniques, such as the use of positivity and clout. Thus, our findings allude to the possibility that authors of nonreplicable studies are more likely to make an effort, through their writing, to persuade readers of their (possibly weaker) results.

The Alzheimer\'s Disease Neuroimaging Initiative (ADNI) has revolutionized the landscape of Alzheimer\'s research through its Informatics Core, which has facilitated unprecedented data standardization and sharing. Over 20 years, ADNI established a robust informatics framework, enabling the validation of biomarkers and supporting global research efforts. The Informatics Core, centered at the Laboratory of Neuro Imaging (LONI), provides a comprehensive data hub that ensures data quality, accessibility, and security, fostering over 5600 publications and significant scientific advancements. By embracing open data sharing principles, ADNI set a gold standard in data transparency, allowing over 26,000 investigators from 169 countries to access and download a wealth of multimodal data. This collaborative approach not only accelerated biomarker discovery and drug development and advanced our understanding of Alzheimer\'s disease but also has served as a model for other research initiatives, demonstrating the transformative potential of carefully designed informatics models and shared data in driving global scientific progress. HIGHLIGHTS: Accelerating biomarker discovery and drug development for Alzheimer\'s disease. Alzheimer\'s Disease Neuroimaging Initiative\'s (ADNI\'s) open data sharing drives scientific progress. Data exploration and coupled analytics to data archives.

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Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the extensive amounts of patient data. The data serve as a foundation upon which algorithms learn and make predictions. Thus, the importance of data cannot be underestimated, and clinically corresponding datasets are required. Many researchers face a lack of medical data due to limited access, privacy concerns, or the absence of available annotations. One of the most widely used diagnostic tools in ophthalmology is Optical Coherence Tomography (OCT). Addressing the data availability issue is crucial for enhancing AI applications in the field of OCT diagnostics. This review aims to provide a comprehensive analysis of all publicly accessible retinal OCT datasets. Our main objective is to compile a list of OCT datasets and their properties, which can serve as an accessible reference, facilitating data curation for medical image analysis tasks. For this review, we searched through the Zenodo repository, Mendeley Data repository, MEDLINE database, and Google Dataset search engine. We systematically evaluated all the identified datasets and found 23 open-access datasets containing OCT images, which significantly vary in terms of size, scope, and ground-truth labels. Our findings indicate the need for improvement in data-sharing practices and standardized documentation. Enhancing the availability and quality of OCT datasets will support the development of AI algorithms and ultimately improve diagnostic capabilities in ophthalmology. By providing a comprehensive list of accessible OCT datasets, this review aims to facilitate better utilization and development of AI in medical image analysis.

Recent evidence has shown that social-media platforms like Twitter (now X) reward politically divisive content, even though most people disapprove of interparty conflict and negativity. We document this discrepancy and provide the first evidence explaining it, using tweets by U.S. Senators and American adults\' responses to them. Studies 1a and 1b examined 6,135 such tweets, finding that dismissing tweets received more Likes and Retweets than tweets that engaged constructively with opponents. In contrast, Studies 2a and 2b (N = 856; 1,968 observations) revealed that the broader public, if anything, prefers politicians\' engaging tweets. Studies 3 (N = 323; 4,571 observations) and 4 (N = 261; 2,610 observations) supported two distinct explanations for this disconnect. First, users who frequently react to politicians\' tweets are an influential yet unrepresentative minority, rewarding dismissing posts because, unlike most people, they prefer them. Second, the silent majority admit that they too would reward dismissing posts more, despite disapproving of them. These findings help explain why popular online content sometimes distorts true public opinion.

Across four studies (N = 816 U.S. adults), we uncovered a gender stereotype about dual pathways to social hierarchy: Men were associated with power, and women were associated with status. We detected this pattern both explicitly and implicitly in perceptions of individuals drawn from Forbes magazine\'s powerful people lists in undergraduate and online samples. We examined social-cognitive implications, including prominent people\'s degree of recognition by individuals and society, and the formation of men\'s and women\'s self-concepts. We found that power (status) ratings predicted greater recognition of men (women) and lesser recognition of women (men). In terms of the self-concept, we found that women internalized the stereotype associating women with status more than power implicitly and explicitly. Although men explicitly reported having less status and more power than women, men implicitly associated the self with status as much as power. No gender differences emerged in the desires for power and status.

People share information for many reasons. For example, Berger (2011, N = 40) found that undergraduate participants manipulated to have higher physiological arousal were more likely to share a news article with others via email than people who had low arousal. Berger\'s research is widely cited as evidence of the causal role of arousal in sharing information and has been used to explain why information that induces high-arousal emotions is shared more than information that induces low-arousal emotions. We conducted two replications (N = 111, N = 160) of Berger\'s study, using the same arousal manipulation but updating the sharing measure to reflect the rise of information sharing through social media. Both studies failed to find an impact of incidental physiological arousal on undergraduate participants\' willingness to share news articles on social media. Our studies cast doubt on the idea that incidental physiological arousal-in the absence of other factors-impacts people\'s decisions to share information on social networking sites.

Many experiences unfold predictably over time. Memory for these temporal regularities enables anticipation of events multiple steps into the future. Because temporally predictable events repeat over days, weeks, and years, we must maintain-and potentially transform-memories of temporal structure to support adaptive behavior. We explored how individuals build durable models of temporal regularities to guide multistep anticipation. Healthy young adults (Experiment 1: N = 99, age range = 18-40 years; Experiment 2: N = 204, age range = 19-40 years) learned sequences of scene images that were predictable at the category level and contained incidental perceptual details. Individuals then anticipated upcoming scene categories multiple steps into the future, immediately and at a delay. Consolidation increased the efficiency of anticipation, particularly for events further in the future, but diminished access to perceptual features. Further, maintaining a link-based model of the sequence after consolidation improved anticipation accuracy. Consolidation may therefore promote efficient and durable models of temporal structure, thus facilitating anticipation of future events.