PeerJ Sections: New layout for community interaction and editorial curation
Have you seen the new PeerJ Section pages? You can now find them quickly through the top navigation of our site.
What are PeerJ Sections?
To encourage more community interaction through our journal platform and to help our readers find papers of most relevance to their research interests, PeerJ, the journal, is now divided into separate Sections by field.
Each Section is headed by our newly appointed Section Editors, experienced Academic Editors who now lead on the editorial oversight and community curation of their Section. More on our Section Editors can be found here.
Within each Section, there is now more featured comment and discussion about our individual research papers. Academic Editors will be regularly identifying and highlighting the most important new PeerJ publications in their field.
At PeerJ we are always looking to share more about the process and decision-making behind the published science. You will find expert commentary from our team of Academic Editors and you can follow the links to the peer review history of the article.
In addition to the curation aspects of our Sections, Section Editors have oversight of their section to ensure the journal maintains a fair peer review process and the highest standards of scientific practice in their fields.
Ultimately, we see the introduction of PeerJ Sections as an editorial reboot of our innovative megajournal – encouraging wider community interaction whilst also supporting our rigorous editorial standards.
Find all the PeerJ Sections here:
You can also filter articles by recent publications, citations, ratings, and pageviews. Or check out the latest analytics on citations and downloads across the entire Section.
You can now also subscribe to the content of a particular Section. Learn more about why we’ve introduced Sections & Meet the Section Editors.
And let us know what you think on Twitter @thePeerJ or in the comment section below.
New sections look v nice. Seems a good move. But one thing that’s missing is a section on statistics/methods. PeerJ has been quite popular for authors interested in reproducible/replicable science – the availability of peer review and open data make it an obvious choice. here are some examples of papers that might be hard to fit into an existing section
Schneck, Andreas
TI Examining publication bias-a simulation-based evaluation of statistical
tests on publication bias
DI 10.7717/peerj.4115
AF Marce-Nogue, Jordi
de Esteban-Trivigno, Soledad
Pueschel, Thomas A.
Fortuny, Josep
TI The intervals method: a new approach to analyse finite element outputs
using multivariate statistics
DI 10.7717/peerj.3793
AF Amrhein, Valentin
Korner-Nievergelt, Franzi
Roth, Tobias
TI The earth is flat (p > 0.05): significance thresholds and the crisis of
unreplicable research
AU de Torrente, L
Zimmerman, S
Taylor, D
Hasegawa, Y
Wells, CA
Mar, JC
TI path Var: a new method for pathway-based interpretation of gene
expression variability
DI 10.7717/peerj.3334
AF Rodrigues, Nicolas
Dufresnes, Christophe
TI Using conventional F-statistics to study unconventional sex-chromosome
differentiation
DI 10.7717/peerj.3207
AU Sinclair, L
Ijaz, UZ
Jensen, LJ
TI Seqenv: linking sequences to environments through text mining
DI 10.7717/peerj.2690
AF Bishop, Dorothy V. M.
Thompson, Paul A.
TI Problems in using p-curve analysis and text-mining to detect rate of
p-hacking and evidential value
DI 10.7717/peerj.1715
AF Borowiec, Marek L.
TI AMAS: a fast tool for alignment manipulation and computing of summary
statistics
SO PEERJ
DI 10.7717/peerj.1660
AF Andrew, Rose L.
Albert, Arianne Y. K.
Renaut, Sebastien
Rennison, Diana J.
Bock, Dan G.
Vines, Tim
TI Assessing the reproducibility of discriminant function analyses
DI 10.7717/peerj.1137
AF Lakens, Daniel
TI On the challenges of drawing conclusions from p-values just below 0.05
DI 10.7717/peerj.1142
AF Fisher, Aaron
Anderson, G. Brooke
Peng, Roger
Leek, Jeff
TI A randomized trial in a massive online open course shows people don’t
know what a statistically significant relationship looks like, but they
can learn
DI 10.7717/peerj.589
AF Gil, Manuel
TI Fast and accurate estimation of the covariance between pairwise maximum
likelihood distances
SO PEERJ
DI 10.7717/peerj.583
AF Wang, Xiaoshan
TI Modified generalized method of moments for a robust estimation of
polytomous logistic model
SO PEERJ
DI 10.7717/peerj.467