Browsing by Subject "Data sharing"

Now showing 1 - 3 of 3
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    A meta-review of transparency and reproducibility-related reporting practices in published meta-analyses on clinical psychological interventions (2000–2020)
    (Springer, 2022-02) López Nicolás, Rubén; López López, José Antonio; Rubio Aparicio, María; Sánchez Meca, Julio; Psicología Básica y Metodología
    Meta-analysis is a powerful and important tool to synthesize the literature about a research topic. Like other kinds of research,meta-analyses must be reproducible to be compliant with the principles of the scientific method. Furthermore, reproducible meta-analyses can be easily updated with new data and reanalysed applying new and more refined analysis techniques. We attemptedto empirically assess the prevalence of transparency and reproducibility-related reporting practices in published meta-analysesfrom clinical psychology by examining a random sample of 100 meta-analyses. Our purpose was to identify the key points thatcould be improved, with the aim of providing some recommendations for carrying out reproducible meta-analyses. We conduct-ed a meta-review of meta-analyses of psychological interventions published between 2000 and 2020. We searched PubMed,PsycInfo and Web of Science databases. A structured coding form to assess transparency indicators was created based onprevious studies and existing meta-analysis guidelines. We found major issues concerning: completely reproducible searchprocedures report, specification of the exact method to compute effect sizes, choice of weighting factors and estimators, lackof availability of the raw statistics used to compute the effect size and of interoperability of available data, and practically totalabsence of analysis script code sharing. Based on our findings, we conclude with recommendations intended to improve thetransparency, openness, and reproducibility-related reporting practices of meta-analyses in clinical psychology and related areas.
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    Automatic detection of large extended data-race-free regions with conflict isolation
    (Institute of Electrical and Electronics Engineers, 2018-03) Jimborean, Alexandra; Ekemark, Per; Waern, Jonatan; Kaxiras, Stefanos; Ros, Alberto; Ingeniería y Tecnología de Computadores
    Data-race-free (DRF) parallel programming becomes a standard as newly adopted memory models of mainstream programming languages such as C++ or Java impose data-race-freedom as a requirement. We propose compiler techniques that automatically delineate extended data-race-free (xDRF) regions, namely regions of code that provide the same guarantees as the synchronization-free regions (in the context of DRF codes). xDRF regions stretch across synchronization boundaries, function calls and loop back-edges and preserve the data-race-free semantics, thus increasing the optimization opportunities exposed to the compiler and to the underlying architecture. We further enlarge xDRF regions with a conflict isolation (CI) technique, delineating what we call xDRF-CI regions while preserving the same properties as xDRF regions. Our compiler (1) precisely analyzes the threads’ memory accessing behavior and data sharing in shared-memory, general-purpose parallel applications, (2) isolates data-sharing and (3) marks the limits of xDRF-CI code regions. The contribution of this work consists in a simple but effective method to alleviate the drawbacks of the compiler’s conservative nature in order to be competitive with (and even surpass) an expert in delineating xDRF regions manually. We evaluate the potential of our technique by employing xDRF and xDRF-CI region classification in a state-of-the-art, dual-mode cache coherence protocol. We show that xDRF regions reduce the coherence bookkeeping and enable optimizations for performance (6.4%) and energy efficiency (12.2%) compared to a standard directory-based coherence protocol. Enhancing the xDRF analysis with the conflict isolation technique improves performance by 7.1% and energy efficiency by 15.9%.
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    Reproducibility of Published Meta-Analyses on Clinical-Psychological Interventions
    (SAGE Publications, 2024-02-05) López Nicolás, Rubén; Lakens, Daniel; López López, José Antonio; Rubio Aparicio, María; Sandoval Lentisco, Alejandro; López-Ibáñez, Carmen; Blázquez-Rincón, Desirée; Sánchez Meca, Julio; Psicología Básica y Metodología; Facultad de Psicología y Logopedia
    Meta-analysis is one of the most useful research approaches, the relevance of which relies on its credibility. Reproducibility of scientific results could be considered as the minimal threshold of this credibility. We assessed the reproducibility of a sample of meta-analyses published between 2000 and 2020. From a random sample of 100 articles reporting results of meta-analyses of interventions in clinical psychology, 217 meta-analyses were selected. We first tried to retrieve the original data by recovering a data file, recoding the data from document files, or requesting it from original authors. Second, through a multistage workflow, we tried to reproduce the main results of each meta-analysis. The original data were retrieved for 67% (146/217) of meta-analyses. Although this rate showed an improvement over the years, in only 5% of these cases was it possible to retrieve a data file ready for reuse. Of these 146, 52 showed a discrepancy larger than 5% in the main results in the first stage. For 10 meta-analyses, this discrepancy was solved after fixing a coding error of our data-retrieval process, and for 15 of them, it was considered approximately reproduced in a qualitative assessment. In the remaining meta-analyses (18%, 27/146), different issues were identified in an in-depth review, such as reporting inconsistencies, lack of data, or transcription errors. Nevertheless, the numerical discrepancies were mostly minor and had little or no impact on the conclusions. Overall, one of the biggest threats to the reproducibility of meta-analysis is related to data availability and current data-sharing practices in meta-analysis.