DOI: 10.63565/journal.scipinion
Publication Date: August, 2025
Iteration: Volume 1, issue 2 (Summer 2025)
Where Scientific Truth Emerges Through Expert Consensus
The SciPinion Collective Wisdom Hub serves as a central repository for objective scientific knowledge derived from our global community of verified experts. Our mission is to introduce clarity and certainty to complex scientific questions, instilling universal trust in science through transparency, objectivity, and integrity.
Welcome to the Summer 2025 issue of the SciPinion Collective Wisdom Hub. This issue examines critical aspects of the research ecosystem through two major community polls and introduces five new statistical resources. Journal editors share their perspectives on peer review quality and timeliness, while our expert community weighs in on research and development funding, publishing practices, and peer review processes. Additionally, we’re pleased to present a suite of statistical calculators—LOGIT-PB, LIN-M, LIN-N, Desired Validity Coefficient, and Sample Size for Margin of Error—designed to support researchers in study design and statistical analysis.
Publications in this Issue:
SciPoll 817: Journal Editor Poll: Peer Review Quality and Timeliness
August, 2025
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10.63565/scipinion.scipoll.817
This SciPoll surveyed journal editors across multiple disciplines to gather their perspectives on the challenges facing peer review. Editors from journals including PLOS Computational Biology, Molecular Biology and Evolution, Saudi Pharmaceutical Journal, Drug Target Insights, and others shared insights about reviewer quality, manuscript handling, and the sustainability of traditional peer review models.
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August, 2025
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10.63565/scipinion.resource.logit-pb
The Power and Bias Calculator for Logistic Regression (LOGIT-PB) helps users interactively examine the sensitivity of power and bias in simple logistic regression models to errors in exposure estimates. Using simulated data, it provides insights into how different factors influence the outcomes of logistic regression with classical additive measurement errors in the exposure estimates.
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August, 2025
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10.63565/scipinion.resource.lin-m
The Simple Linear Regression (SLR) Repeated Measures of Exposures Calculator (LIN-M) with Measurement Error helps users interactively optimize for the number of repeat measures of exposure (m) required to achieve a desired minimum detectable effect (MDE) size in a linear regression model for a defined number of subjects in a study.
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August, 2025
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10.63565/scipinion.resource.lin-n
The Simple Linear Regression (SLR) Repeated Measures of Exposures Calculator (LIN-N) with Measurement Error helps users interactively optimize for the number of subjects (sample size) required to achieve a desired minimum detectable effect (MDE) size in a linear regression model given a defined number of samples (M) per subject in a study.
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Desired Validity Coefficient Calculator
August, 2025
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10.63565/scipinion.resource.dvc
This calculator using the Fleiss Formula helps users determine the number of repeated measurements needed to achieve a desired validity coefficient for a given intraclass correlation coefficient (ICC). This tool is useful for researchers and practitioners who need to ensure their measurements are reliable and valid.
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Sample Size for Margin of Error Calculator
July, 2025
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10.63565/scipinion.resource.sample-moe
This calculator helps researchers determine the necessary sample size to achieve their desired precision, which is critical for planning studies and ensuring statistically valid results. Adequate sample size calculation is also essential for justifying the study design to peer reviewers and ensuring the reliability and validity of the findings.
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SciPoll 779: Research & Development Funding, Publishing, and Peer Review
June, 2025
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10.63565/scipinion.scipoll.779
This comprehensive survey examines the dramatic shift in U.S. R&D funding from 70% government/30% industry in the 1960s to 70% industry/30% government today. The 128 expert responses explore sustainability concerns, implications for basic research, impacts on scientific independence, and the potential consequences for fields without immediate commercial value. Experts discuss whether this trend threatens foundational research and public-interest science.
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