Here we provide further details on the replications, the estimation of standardized effect sizes and complementary replicability indicators, the implementation of the prediction markets and surveys, the comparison of prediction market beliefs, survey beliefs, and replication outcomes, the comparison of reproducibility indicators to experimental economics and the psychological sciences, and additional results and data for the individual studies and markets. The code used for the estimation of replication power, standardized effect sizes, all complementary replication indicators, and all results is posted at OSF (https://osf.io/pfdyw/). Replications Inclusion criteriaWe replicated 21 experimental studies in the social sciences published between 2010 and 2015 in Nature and Science. We included all studies that fulfilled our inclusion criteria for:(i) the journal and time period, (ii) the type of experiment, (iii) the subjects included in the experiment, (iv) the equipment and materials needed to implement the experiment, and (v) the results reported in the experiment. We did not exclude studies that had already been subject to a replication, as this could affect the representativity of the included studies. We define and discuss the five inclusion criteria below. Journal and time period: We included experimental studies published in Nature andScience between 2010 and 2015. The reason for focusing on these two journals is that they are typically considered the two most prestigious general science journals. Articles published in these journals are considered exciting, innovative, and important, which is also reflected in their high impact factors. * Number of observations; number of individuals provided in parenthesis. † Replicated; significant effect (p < 0.05) in the same direction as in original study. ‡ Statistical power to detect 50% of the original effect size r. § Relative standardized effect size. * Belief about the probability of replicating in stage 1 (90% power to detect 75% of the original effect size).† Predicted added probability of replicating in stage 2 (90% power to detect 50% of the original effect size) compared to stage 1. * Mean number of tokens (points) invested per transaction. † Mean number of shares bought or sold per transaction.

The Internet is an important source of health information. Thus, the frequency of Internet searches may provide information regarding infectious disease activity. As an example, we examined the relationship between searches for influenza and actual influenza occurrence. Using search queries from the Yahoo! search engine ( http://search.yahoo.com ) from March 2004 through May 2008, we counted daily unique queries originating in the United States that contained influenza-related search terms. Counts were divided by the total number of searches, and the resulting daily fraction of searches was averaged over the week. We estimated linear models, using searches with 1-10-week lead times as explanatory variables to predict the percentage of cultures positive for influenza and deaths attributable to pneumonia and influenza in the United States. With use of the frequency of searches, our models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P < .001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P < .001). Search-term surveillance may provide an additional tool for disease surveillance.

Concerns about a lack of reproducibility of statistically significant results have recently been raised in many fields, and it has been argued that this lack comes at substantial economic costs. We here report the results from prediction markets set up to quantify the reproducibility of 44 studies published in prominent psychology journals and replicated in the Reproducibility Project: Psychology. The prediction markets predict the outcomes of the replications well and outperform a survey of market participants' individual forecasts. This shows that prediction markets are a promising tool for assessing the reproducibility of published scientific results. The prediction markets also allow us to estimate probabilities for the hypotheses being true at different testing stages, which provides valuable information regarding the temporal dynamics of scientific discovery. We find that the hypotheses being tested in psychology typically have low prior probabilities of being true (median, 9%) and that a "statistically significant" finding needs to be confirmed in a well-powered replication to have a high probability of being true. We argue that prediction markets could be used to obtain speedy information about reproducibility at low cost and could potentially even be used to determine which studies to replicate to optimally allocate limited resources into replications.reproducibility | replications | prediction markets T he process of scientific discovery centers on empirical testing of research hypotheses. A standard tool to interpret results in statistical hypothesis testing is the P value. A result associated with a P value below a predefined significance level (typically 0.05) is considered "statistically significant" and interpreted as evidence in favor of a hypothesis. However, concerns about the reproducibility of statistically significant results have recently been raised in many fields including medicine (1-3), neuroscience (4), genetics (5, 6), psychology (7-11), and economics (12, 13). For example, an industrial laboratory could only reproduce 6 out of 53 key findings from "landmark" studies in preclinical oncology (2) and it has been argued that the costs associated with irreproducible preclinical research alone are about US$28 billion a year in the United States (3). The mismatch between the interpretation of statistically significant findings and a lack of reproducibility threatens to undermine the validity of statistical hypothesis testing as it is currently practiced in many research fields (14).The problem with inference based on P values is that a P value provides only partial information about the probability of a tested hypothesis being true (14,15). This probability also depends on the statistical power to detect a true positive effect and the prior probability that the hypothesis is true (14). Lower statistical power increases the probability that a statistically significant effect is a false positive (4, 14). Statistically significant results from small studies are therefore more likely to be fals...

Many behavioral effects of nicotine result from activation of nigrostriatal and mesolimbic dopaminergic systems. Nicotine regulates dopamine release not only by stimulation of nicotinic acetylcholine receptors (nAChRs) on dopamine cell bodies within the substantia nigra and ventral tegmental area (SN/VTA), but also on presynaptic nAChRs located on striatal terminals. The nAChR subtype(s) present on both cell bodies and terminals is still a matter of controversy. The purpose of this study was to use double-labeling in situ hybridization to identify nAChR subunit mRNAs expressed within dopamine neurons of the SN/VTA, by using a digoxigenin-labeled riboprobe for tyrosine hydroxylase as the dopamine cell marker and (35)S-labeled riboprobes for nAChR subunits. The results reveal a heterogeneous population of nAChR subunit mRNAs within midbrain dopamine neurons. Within the SN, almost all dopamine neurons express alpha2, alpha4, alpha5, alpha6, beta2, and beta3 nAChR mRNAs, with more than half also expressing alpha3 and alpha7 mRNAs. In contrast, less than 10% express beta4 mRNA. Within the VTA, a similar pattern of nAChR subunit mRNA expression is observed except that most subunits are expressed in a slightly lower percentage of dopamine neurons than in the SN. Within the SN, alpha4, beta2, alpha7, and beta4 mRNAs are also expressed in a significant number of nondopaminergic neurons, whereas within the VTA this only occurs for beta4. The heterogeneity in the expression of nAChR subunits within the SN/VTA may indicate the formation of a variety of different nAChR subtypes on cell bodies and terminals of the nigrostriatal and mesolimbic pathways.

We tested the hypothesis that combined xenogenic (from mini-pig) adipose-derived mesenchymal stem cell (ADMSC) and ADMSC-derived exosome therapy could reduce brain-infarct zone (BIZ) and enhance neurological recovery in rat after acute ischemic stroke (AIS) induced by 50-min left middle cerebral artery occlusion. Adult-male Sprague-Dawley rats (n = 60) were divided equally into group 1 (sham-control), group 2 (AIS), group 3 [AIS-ADMSC (1.2×106 cells)], group 4 [AIS-exosome (100μg)], and group 5 (AIS-exosome-ADMSC). All therapies were provided intravenously at 3h after AIS procedure. BIZ determined by histopathology (by day-60) and brain MRI (by day-28) were highest in group 2, lowest in group 1, higher in groups 3 and 4 than in group 5, but they showed no difference between groups 3 and 4 (all p < 0.0001). By day-28, sensorimotor functional results exhibited an opposite pattern to BIZ among the five groups (p < 0.005). Protein expressions of inflammatory (inducible nitric oxide synthase/tumor necrosis factor-α/nuclear factor-κB/interleukin-1β/matrix metalloproteinase-9/plasminogen activator inhibitor-1/RANTES), oxidative-stress (NOX-1/NOX-2/oxidized protein), apoptotic (caspase-3/ Poly-ADP-ribose polymerase), and fibrotic (Smad3/transforming growth factor-β) biomarkers, and cellular expressions of brain-damaged (γ-H2AX+/ XRCC1-CD90+/p53BP1-CD90+), inflammatory (CD11+/CD68+/glial fibrillary acid protein+) and brain-edema (aquaporin-4+) markers showed a similar pattern of BIZ among the groups (all n < 0.0001). In conclusion, xenogenic ADMSC/ADMSC-derived exosome therapy was safe and offered the additional benefit of reducing BIZ and improving neurological function in rat AIS.