The Language of (Non)Replicable Social Science.

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 informatics of ADNI.

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.

Children Sustain Cooperation in a Threshold Public-Goods Game Even When Seeing Others' Outcomes.

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.

Exploration, Distributed Attention, and Development of Category Learning.

Category learning is a crucial aspect of cognition that involves organizing entities into equivalence classes. Whereas adults tend to focus on category-relevant features, young children often distribute attention between relevant and irrelevant ones. The reasons for children's distributed attention are not fully understood. In two category-learning experiments with adults and with children aged 4, 5, and 6 (N = 201), we examined potential drivers of distributed attention, including (a) immature filtering of distractors and (b) the general tendency for exploration or broad information sampling. By eliminating distractor competition, we reduced filtering demands. Despite identifying the features critical for accurate categorization, children, regardless of their categorization performance, continued sampling more information than was necessary. These results indicate that the tendency to sample information extensively contributes to distributed attention in young children. We identify candidate drivers of this tendency that need to be examined in future research.

Electroencephalogram Decoding Reveals Distinct Processes for Directing Spatial Attention and Encoding Into Working Memory.

Past work reveals a tight relationship between spatial attention and storage in visual working memory. But is spatially attending an item tantamount to working memory encoding? Here, we tracked electroencephalography (EEG) signatures of spatial attention and working memory encoding while independently manipulating the number of memory items and the spatial extent of attention in two studies of adults (N = 39; N = 33). Neural measures of spatial attention tracked the position and size of the attended area independent of the number of individuated items encoded into working memory. At the same time, multivariate decoding of the number of items stored in working memory was insensitive to variations in the breadth and position of spatial attention. Finally, representational similarity analyses provided converging evidence for a pure load signal that is insensitive to the spatial extent of the stored items. Thus, although spatial attention is a persistent partner of visual working memory, it is functionally dissociable from the selection and maintenance of individuated representations in working memory.

Why Twitter Sometimes Rewards What Most People Disapprove of: The Case of Cross-Party Political Relations.

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.

Does Physiological Arousal Increase Social Transmission of Information? Two Replications of Berger (2011).

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.

Does Valuing Happiness Lead to Well-Being?

Happiness has become one of the most important life goals worldwide. However, does valuing happiness lead to better well-being? This study investigates the effect of valuing happiness on well-being using a population-based longitudinal survey of Dutch adults (N = 8,331) from 2019 to 2023. Random-intercept cross-lagged panel models indicated that those who valued happiness generally exhibited higher well-being as manifested by life satisfaction, more positive affect, and less negative affect. However, increases in valuing happiness did not result in changes in life satisfaction 1 year later and had mixed emotional consequences (i.e., increasing both positive and negative affect). Additional analyses using fixed-effects models indicated that valuing happiness had contemporaneous positive effects on well-being. These findings indicate that endorsing happiness goals may have immediate psychological benefits but may not necessarily translate into long-term positive outcomes.

The Double-Edged Sword of Social Sharing: Social Sharing Predicts Increased Emotion Differentiation When Rumination Is Low but Decreased Emotion Differentiation When Rumination Is High.

Laypeople believe that sharing their emotional experiences with others will improve their understanding of those experiences, but no clear empirical evidence supports this belief. To address this gap, we used data from four daily life studies (N = 659; student and community samples) to explore the association between social sharing and subsequent emotion differentiation, which involves labeling emotions with a high degree of complexity. Contrary to our expectations, we found that social sharing of emotional experiences was linked to greater subsequent emotion differentiation on occasions when people ruminated less than usual about these experiences. In contrast, on occasions when people ruminated more than usual about their experiences, social sharing of these experiences was linked to lower emotion differentiation. These effects held when we controlled for levels of negative emotion. Our findings suggest that putting feelings into words through sharing may only enable emotional precision when that sharing occurs without dwelling or perseverating.

Patent data-driven analysis of literature associations with changing innovation trends.

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.

Consolidation Enhances Sequential Multistep Anticipation but Diminishes Access to Perceptual Features.

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.

An Overview of Public Retinal Optical Coherence Tomography Datasets: Access, Annotations, and Beyond.

In ophthalmology, Optical Coherence Tomography (OCT) has become a daily used tool in the diagnostics and therapeutic planning of various diseases. Publicly available datasets play a crucial role in advancing research by providing access to diverse imaging data for algorithm development. The accessibility, data format, annotations, and metadata are not consistent across OCT datasets, making it challenging to efficiently use the available resources. This article provides a comprehensive analysis of different OCT datasets, with particular attention to dataset properties, disease representation, accessibility, and aims to create a catalog of all publicly available OCT datasets. The goal is to improve accessibility to OCT data, increase openness about the availability, and give important new perspectives on the state of OCT imaging resources. Our findings reveal the need for improved data-sharing practices and standardized documentation.

Open Data-Driven Decision Making: Observations from COVID-19 Minimal Essential Data, Canton Zürich, Switzerland.

This research delves into the relationship between open data and decision making, drawing insights from the utilization of Minimal Essential Data (MED) during the COVID-19 pandemic in Canton Zürich, Switzerland. It examines observations and lessons learned, emphasizing the pivotal role of open data in response to an emergency. Through a case study approach, this article sheds light on the practical implications and observed strategies in publishing open data from digital public health surveillance software. While it is arguable that open data has the potential to enhance data quality in the context of a public health emergency, our findings indicate that it is firstly essential that a Minimal Essential Dataset (MED) is defined and publicly published, an open data publication platform and procedure are secondly in place (ideally following FAIR data principles), and thirdly, that regular data quality reviews are conducted.

Enhancing the design of voting advice applications with BERT language model.

The relevance and importance of voting advice applications (VAAs) are demonstrated by their popularity among potential voters. On average, around 30% of voters take into account the recommendations of these applications during elections. The comparison between potential voters' and parties' positions is made on the basis of VAA policy statements on which users are asked to express opinions. VAA designers devote substantial time and effort to analyzing domestic and international politics to formulate policy statements and select those to be included in the application. This procedure involves manually reading and evaluating a large volume of publicly available data, primarily party manifestos. A problematic part of the work is the limited time frame. This study proposes a system to assist VAA designers in formulating, revising, and selecting policy statements. Using pre-trained language models and machine learning methods to process politics-related textual data, the system produces a set of suggestions corresponding to relevant VAA statements. Experiments were conducted using party manifestos and YouTube comments from Japan, combined with VAA policy statements from six Japanese and two European VAAs. The technical approaches used in the system are based on the BERT language model, which is known for its capability to capture the context of words in the documents. Although the output of the system does not completely eliminate the need for manual human assessment, it provides valuable suggestions for updating VAA policy statements on an objective, i.e., bias-free, basis.

People Place Larger Bets When Risky Choices Provide a Postbet Option to Cash Out.

After a risky choice, decision makers must frequently wait out a delay period before the outcome of their choice becomes known. In contemporary sports-betting apps, decision makers can "cash out" of their bet during this delay period by accepting a discounted immediate payout. An important open question is how availability of a postchoice cash-out option alters choice. We investigated this question using a novel gambling task that incorporated a cash-out option during the delay between bet and outcome. Across two experiments (N = 240 adults, recruited via Prolific), cash-out availability increased participants' bet amounts by up to 35%. Participants who were more likely to cash out when odds deteriorated were less likely to cash out when odds improved. Furthermore, the effect of cash-out availability on bet amounts was positively correlated with individual differences in cash-out propensity for bets with deteriorating odds only. These results suggest that cash-out availability may promote larger bets by allowing bettors to avoid losing their entire stake.

Code sharing in ecology and evolution increases citation rates but remains uncommon.

Biologists increasingly rely on computer code to collect and analyze their data, reinforcing the importance of published code for transparency, reproducibility, training, and a basis for further work. Here, we conduct a literature review estimating temporal trends in code sharing in ecology and evolution publications since 2010, and test for an influence of code sharing on citation rate. We find that code is rarely published (only 6% of papers), with little improvement over time. We also found there may be incentives to publish code: Publications that share code have tended to be low-impact initially, but accumulate citations faster, compensating for this deficit. Studies that additionally meet other Open Science criteria, open-access publication, or data sharing, have still higher citation rates, with publications meeting all three criteria (code sharing, data sharing, and open access publication) tending to have the most citations and highest rate of citation accumulation.

Impact of new antifungal medications on onychomycosis prescriptions and costs in Japan: A nationwide claims database study.

Onychomycosis, a fungal nail infection, is a common dermatological condition in Japan, with a prevalence of approximately 5%-10%. Despite the introduction of new antifungal medications and updated treatment guidelines published in 2019, data on real-world prescription trends and the associated medical costs are limited. This study aimed to investigate the prescription patterns and medical costs of topical and oral antifungal medications for onychomycosis in Japan from fiscal years 2014 to 2021 using the National Database of Health Insurance Claims and Specific Health Checkups of Japan Open Data. We analyzed the annual prescription volumes and medical costs of four antifungal medications: efinaconazole, luliconazole, fosravuconazole, and terbinafine. The prescription volume of efinaconazole, a topical medication launched in 2014, rapidly increased and dominated the market share. Fosravuconazole, an oral medication introduced in 2018, showed an increasing trend, coinciding with a decline in efinaconazole prescriptions. Terbinafine, a well-established oral medication, experienced a substantial decrease in prescription volume. The sex- and age-adjusted prescription volume per 100 000 population was higher among older adults, particularly for efinaconazole. The total medical costs for onychomycosis treatment more than doubled in fiscal year 2015 compared with that for 2014, mainly driven by efinaconazole prescriptions, and exceeded 30 billion Japanese yen in fiscal years 2019-2021. The costs slightly decreased in fiscal years 2020 and 2021, possibly due to the introduction of fosravuconazole. The predominance of topical prescriptions, especially in older adults, raises concerns regarding adherence to the Japanese guidelines that recommend oral antifungals as the first-line treatment for onychomycosis. The substantial increase in medical costs also highlights the economic burden of onychomycosis and the need for cost-effective treatment strategies. This study provides valuable insights into the real-world prescription trends and medical costs of onychomycosis treatment in Japan, suggesting an opportunity to assess potential gaps between guideline recommendations and clinical practice.

Regional Variability in MRI Scans with Different Magnetic Field Strengths in Japan: Implications for Healthcare Preparedness for Alzheimer's Disease Treatment.

(1) Background: The 2023 approval of lecanemab for early-stage Alzheimer's disease (AD) highlighted the need for routine 1.5T or 3.0T MRI scans to monitor amyloid-related imaging abnormalities (ARIAs). Regional disparities in MRI scan frequency, MRI scanner availability, and scanner magnetic field strengths could affect readiness for anti-amyloid therapy and lead to inconsistencies in ARIA detection nationwide. (2) Methods: We assessed regional variance in MRI scan frequency and field strength across Japan using the National Database (NDB) Open Data website, which summarizes Japanese public health insurance claims from the fiscal years (FYs) 2015 to 2021. We employed a mixed-effects model with prefecture-level random intercepts and slopes over time, subsequently categorizing prefectures into clusters based on MRI usage. (3) Results: 1.5T MRI was the most common magnetic field strength, remaining stable from FY2015 to FY2021. 3.0T MRI usage slightly increased, although the COVID-19 pandemic in FY2020 led to a maximum reduction of 5%. Prefecture-level variance was higher for 3.0T MRIs, with more frequent usage in western Japan. (4) Conclusions: This study highlights prefecture-level variance in MRI usage across Japan. The insights gained could be instrumental in improving healthcare preparedness for anti-amyloid treatment and patient management.