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.

1 Response

  1. Dorothy Bishop says:

    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
    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
    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
    DI 10.7717/peerj.583

    AF Wang, Xiaoshan
    TI Modified generalized method of moments for a robust estimation of
    polytomous logistic model
    DI 10.7717/peerj.467