- Large domain-specific datasets should be deposited in a public repository ( e.g. GenBank, Biological Magnetic Resonance Data Bank, Cambridge Structural Database, ChemSpider, EMDataBank, Crystallography Open Database, PeptideAtlas, Protein DataBank, PubChem, etc) and an accession number or access address provided in the published article.
- Where suitable domain-specific repositories do not exist, authors may deposit in Dryad, Dataverse, the Open Science Framework, or an institutional repository and provide the access information with the manuscript. Alternately, authors may choose to deposit non-standard data (including figures, posters, rich media) on Figshare or PeerJ Preprints, for example. In all cases, the DOI reference (where applicable) should be provided in the article.
- Any supporting data sets for which there are no suitable repositories may be made available as publishable Supplemental Information files by PeerJ Chemistry journals.
- Data should be provided in an appropriate, machine-readable format. Note: formats such as PDF, Powerpoint, and images of tables etc. are not considered suitable for raw data sharing.
- Where novel research compounds are used, their chemical identity must be disclosed. Rigorous evidence for both the purity and identity of the materials must be provided so that the reliability of the experimental results can be assessed.
- Publications using commercial antibodies should report the supplying company and code number for all antibodies used. We recommend reporting using the following format:
- In accordance with the principles in Sharing Publication-Related Data and Materials (National Academies Press, 2003), research using proprietary data must also evaluate a piece of comparable public data if the authors cannot or do not make the proprietary data available.
- For software papers, 'materials' are taken to mean the source code and/or relevant software components required to run the software and reproduce the reported results. The software should be open source, made available under an appropriate license, and deposited in an appropriate archive. Data used to validate a software tool is subject to the same sharing requirements as any data in PeerJ publications.
PeerJ will consider timely and well-targeted literature reviews of fields with broad cross-disciplinary interest within the journal's scope. While we do not impose a hard limit, we recommend a maximum of 8,000-12,000 words in order to keep the review focused. The review should include a rationale for why it is needed, describe who it is intended for, and include a description of the procedures used to ensure that it is comprehensive and unbiased (for example, the search strategies that were employed). Gaps in the literature, future avenues of research and opportunities for cross-disciplinary collaborations should be clearly identified.
Since, by their nature, literature reviews rely heavily on the published work of others, it is especially important to avoid inadvertent plagiarism by copying and pasting sections of text from the original source. In addition, it is very important when quoting or paraphrasing, to correctly acknowledge your sources.
We recommend that your review is structured following the guidelines for Literature Review Articles in standard sections.
PeerJ welcomes articles describing chemoinformatics software tools. These articles should present new software tools (or significant new functionality in existing software) of particular interest to the bioinformatics and/or computational biology communities. The described tools should provide new computational or analytical functionality for researchers.
The functionality of the software should, where appropriate, be validated using real-world chemical data and/or compared to existing tools. If available as a package (e.g. Python, R, Matlab, Octave), it should be accompanied by a minimal script that downloads the data (if not included in the package), loads it, and performs the analysis to reproduce the results (tables, plots, visualizations etc.) in the manuscript. Documentation & comments must be clear and sufficient to allow a typical user to perform the analysis described in the article. Ideally, the tools should be usable in a workflow that encourages reproducible research practices.
The software must be released under an open source license (e.g. MIT, GPL) and be widely available (i.e. hosted in a public repository such as Github or Bitbucket, or an institutional repository). Use of a version control system (e.g. Git, Subversion etc.) is strongly encouraged, as is adherence to language-specific packaging practices where appropriate. We also recommend the inclusion of appropriate unit tests. The software must be free to noncommercial users, and must be accessible without requiring any personally identifiable information. Software and validation data sets must comply with PeerJ Data and Materials Sharing policies. Reliance on a proprietary software such as Matlab does not preclude the publication but in general, a fully open source method is to be preferred.
In general, it is expected that work involving animal experiments will be submitted to PeerJ - the Journal of Life and Environmental Science, but if the intended audience is the chemistry community and the animal experiments are a minor part of the overall work then it will be considered for publication in PeerJ Chemistry journals, so long as it complies with the usual ethical requirements.
In general, it is expected that work involving human subjects will be submitted to PeerJ - the Journal of Life and Environmental Science, but if the intended audience is the chemistry community and the human experiments are a minor part of the overall work then it will be considered for publication in PeerJ Chemistry journals, so long as it complies with the usual ethical requirements.
These policies are made available under the Creative Commons CC BY 4.0 license and can be copied for reuse with attribution.