Open peer review (OPR), as with other elements of open science and open research, is on the rise. It aims to bring greater transparency and participation to formal and informal peer review processes. But what is meant by `open peer review', and what advantages and disadvantages does it have over standard forms of review? How do authors or reviewers approach OPR? And what pitfalls and opportunities should you look out for? Here, we propose ten considerations for OPR, drawing on discussions with authors, reviewers, editors, publishers and librarians, and provide a pragmatic, hands-on introduction to these issues. We cover basic principles and summarise best practices, indicating how to use OPR to achieve best value and mutual benefits for all stakeholders and the wider research community.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6073088 | PMC |
| http://dx.doi.org/10.12688/f1000research.15334.1 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Effects of using microalgae in poultry diets on the production and quality of meat and eggs: a review.
Br Poult Sci
January 2025
LEAF- Linking Landscape, Environment, Agriculture and Food Research Center, Associate Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa, Portugal.
1. This review was conducted to examine the nutritional composition of microalgae and their effects as a feed ingredient in poultry diets, delving into their influence on the production and quality of meat and eggs. Data collection focused on peer-reviewed scientific articles, with no limitation on the temporal horizon.
View Article and Find Full Text PDFEvaluating the Impact of Changes in AI-derived Case Scores over Time on Digital Breast Tomosynthesis Screening Outcomes.
Radiol Artif Intell
January 2025
Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104.
Purpose To evaluate the change in DBT-AI (digital breast tomosynthesis-artificial intelligence) case scores over sequential screens. Materials and Methods This retrospective review included 21,108 female patients (mean age, 58.1 ± [SD] 11.
View Article and Find Full Text PDFAccuracy of Fully Automated and Human-assisted AI-based CT Quantification of Pleural Effusion Changes after Thoracentesis.
Radiol Artif Intell
January 2025
From the Department of Radiology (E.J.H., S.K., H.K., D. K., S.H.Y.) and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak- ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine (E.J.H., H.K., S.H.Y.), Seoul, Korea; Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine (S-J.Y., Seoul, Korea).
Quantifying pleural effusion change on chest CT is important for evaluating disease severity and treatment response. The purpose of this study was to assess the accuracy of artificial intelligence (AI)-based volume quantification of pleural effusion change on CT images, using the volume of drained fluid as the reference standard. Seventy-nine participants (mean age, 65 ± [SD] 13 years; 47 male) undergoing thoracentesis were prospectively enrolled from October 2021 to September 2023.
View Article and Find Full Text PDFNNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High- Resolution Short Echo Time MR Spectroscopy Datasets.
Radiol Artif Intell
January 2025
From the Department of Radiation Oncology (A.S.G., V.H., H.S.) and Department of Radiology and Imaging Sciences (B.D.W.), Emory University School of Medicine, 1701 Uppergate Dr, C5008 Winship Cancer Institute, Atlanta, GA 30322; Department of Radiology, University of Miami {School of Medicine?}, Miami, Fla (S.S., A.A.M.); Department of {Radiology?} Northwestern University {Feinberg School of Medicine?}, Chicago, Ill (L.A.D.C.); Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, Ga (Y.L.); Department of Psychology, Emory University, Atlanta, Ga (M.T.); and Department of Radiology, Duke University Medical Center, Durham, NC (B.J.S.).
Purpose To develop and evaluate the performance of NNFit, a self-supervised deep-learning method for quantification of high-resolution short echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computational bottleneck of conventional spectral quantification methods in the clinical workflow. Materials and Methods This retrospective study included 89 short-TE whole-brain EPSI/GRAPPA scans from clinical trials for glioblastoma (Trial 1, May 2014-October 2018) and major-depressive-disorder (Trial 2, 2022- 2023). The training dataset included 685k spectra from 20 participants (60 scans) in Trial 1.
View Article and Find Full Text PDFPosttraining Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.
Radiol Artif Intell
January 2025
From the Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15,81377 Munich, Germany (T.W., J.D., M.I.); Department of Statistics, LMU Munich, Munich, Germany (T.W., D.R.); and Munich Center for Machine Learning, Munich, Germany (T.W., J.D., D.R., M.I.).
Purpose To investigate whether the computational effort of 3D CT-based multiorgan segmentation with TotalSegmentator can be reduced via Tucker decomposition-based network compression. Materials and Methods In this retrospective study, Tucker decomposition was applied to the convolutional kernels of the TotalSegmentator model, an nnU-Net model trained on a comprehensive CT dataset for automatic segmentation of 117 anatomic structures. The proposed approach reduced the floating-point operations (FLOPs) and memory required during inference, offering an adjustable trade-off between computational efficiency and segmentation quality.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!