| 1. |
- Ebersole, Charles R., et al.
(author)
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Many Labs 5: Testing Pre-Data-Collection Peer Review as an Intervention to Increase Replicability
- 2020
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In: Advances in Methods and Practices in Psychological Science. - : Sage. - 2515-2467 .- 2515-2459. ; 3:3, s. 309-331
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Journal article (peer-reviewed)abstract
- Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect (p < .05). Commenters suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these RP:P studies failed to replicate the original effects. We revised the replication protocols and received formal peer review prior to conducting new replication studies. We administered the RP:P and revised protocols in multiple laboratories (median number of laboratories per original study = 6.5, range = 3-9; median total sample = 1,279.5, range = 276-3,512) for high-powered tests of each original finding with both protocols. Overall, following the preregistered analysis plan, we found that the revised protocols produced effect sizes similar to those of the RP:P protocols (Delta r = .002 or .014, depending on analytic approach). The median effect size for the revised protocols (r = .05) was similar to that of the RP:P protocols (r = .04) and the original RP:P replications (r = .11), and smaller than that of the original studies (r = .37). Analysis of the cumulative evidence across the original studies and the corresponding three replication attempts provided very precise estimates of the 10 tested effects and indicated that their effect sizes (median r = .07, range = .00-.15) were 78% smaller, on average, than the original effect sizes (median r = .37, range = .19-.50).
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| 2. |
- Han, Huijong, et al.
(author)
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The XBI BioLab for life science experiments at the European XFEL
- 2021
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In: Journal of applied crystallography. - 0021-8898 .- 1600-5767. ; 54, s. 7-21
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Journal article (peer-reviewed)abstract
- The science of X-ray free-electron lasers (XFELs) critically depends on the performance of the X-ray laser and on the quality of the samples placed into the X-ray beam. The stability of biological samples is limited and key biomolecular transformations occur on short timescales. Experiments in biology require a support laboratory in the immediate vicinity of the beamlines. The XBI BioLab of the European XFEL (XBI denotes XFEL Biology Infrastructure) is an integrated user facility connected to the beamlines for supporting a wide range of biological experiments. The laboratory was financed and built by a collaboration between the European XFEL and the XBI User Consortium, whose members come from Finland, Germany, the Slovak Republic, Sweden and the USA, with observers from Denmark and the Russian Federation. Arranged around a central wet laboratory, the XBI BioLab provides facilities for sample preparation and scoring, laboratories for growing prokaryotic and eukaryotic cells, a Bio Safety Level 2 laboratory, sample purification and characterization facilities, a crystallization laboratory, an anaerobic laboratory, an aerosol laboratory, a vacuum laboratory for injector tests, and laboratories for optical microscopy, atomic force microscopy and electron microscopy. Here, an overview of the XBI facility is given and some of the results of the first user experiments are highlighted.
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| 3. |
- Huber, Dominik, et al.
(author)
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PFASST with dynamic resource management for large-scale applications
- 2023
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Conference paper (other academic/artistic)abstract
- Dynamic resource management is a fairly recent development made to improve the flexibility of job scheduling. MPI-based approach [1] makes this possible by treating resources on a set granularity and performs set operations whenever changes are made. Due to the extreme resource requirements, using dynamic resources will likely be compulsory for parallel-in-time methods. Development towards this has been made by prototyping an interface for dynamic resource management and then incorporating it into LibPFASST to provide proof of concept. The intent of this poster is to motivate further investigation and exploration of dynamic resources for large-scale applications such as weather/climate simulations, molecular dynamics, graph design and more.
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| 4. |
- Western, Benedikte, et al.
(author)
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Dropout from exercise trials among cancer survivors—An individual patient data meta-analysis from the POLARIS study
- 2024
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In: Scandinavian Journal of Medicine and Science in Sports. - Chichester, West Sussex : Wiley-Blackwell Publishing Inc.. - 0905-7188 .- 1600-0838. ; 34:2, s. 1-10
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Journal article (peer-reviewed)abstract
- Introduction: The number of randomized controlled trials (RCTs) investigating the effects of exercise among cancer survivors has increased in recent years; however, participants dropping out of the trials are rarely described. The objective of the present study was to assess which combinations of participant and exercise program characteristics were associated with dropout from the exercise arms of RCTs among cancer survivors. Methods: This study used data collected in the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study, an international database of RCTs investigating the effects of exercise among cancer survivors. Thirty-four exercise trials, with a total of 2467 patients without metastatic disease randomized to an exercise arm were included. Harmonized studies included a pre and a posttest, and participants were classified as dropouts when missing all assessments at the post-intervention test. Subgroups were identified with a conditional inference tree. Results: Overall, 9.6% of the participants dropped out. Five subgroups were identified in the conditional inference tree based on four significant associations with dropout. Most dropout was observed for participants with BMI >28.4 kg/m2, performing supervised resistance or unsupervised mixed exercise (19.8% dropout) or had low-medium education and performed aerobic or supervised mixed exercise (13.5%). The lowest dropout was found for participants with BMI >28.4 kg/m2 and high education performing aerobic or supervised mixed exercise (5.1%), and participants with BMI ≤28.4 kg/m2 exercising during (5.2%) or post (9.5%) treatment. Conclusions: There are several systematic differences between cancer survivors completing and dropping out from exercise trials, possibly affecting the external validity of exercise effects. © 2024 The Authors. Scandinavian Journal of Medicine & Science In Sports published by John Wiley & Sons Ltd.
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