Algorithms, code, and software tools are crucial to the discovery process in every field of research, from bioinformatics to digital humanities. At F1000Research, we believe that the researchers and software engineers who develop these tools should receive full credit for their work. We also believe that this work should be recognized as part of their research productivity and rewarded as much as traditional research papers. This is where publishing your software tool article comes in.
Software tool articles on F1000Research allow you to describe new software you have created to support or conduct research in any field. These articles explore:
- Why you developed the software
- Details of the code, method, and analysis used
- Examples of data input sets
- Examples of outputs, and how to interpret these
- Tips on how to maximize the tool’s potential
We also welcome articles describing tools created from existing software, web tools, apps, containers, packages, and workflows.
F1000Research is a fully open access publishing platform, offering rapid publication of articles and other research outputs without editorial bias. All articles benefit from transparent post-publication peer review, and editorial guidance on making source data openly available.
F1000Research advocates for transparency and reproducibility in research, and our unique publishing model supports this at every stage. Articles can be published in as few as 14 days, with post-publication peer review creating an open dialogue between authors and their research community. This generates feedback which can be used to improve the article and develop the author's skills.
Our data and software sharing policy is designed to ensure that research is reproducible. For software tool articles this means the tools themselves should be openly accessible and clearly linked to any appropriate data and results.
Why publish your software tool article on F1000Research?
Receive full credit and recognition for your work by publishing your software tool article as a separate citable item.
Bring your data to life with interactive figures in the body of your article, powered by Plot.ly or via an iFrame. Interactive figures can help tell the story of your research by allowing readers to play with different visualizations, zoom in, filter results, and explore the data in detail for themselves.
LaTex authors can submit through the F1000Research Overleaf template.
Embed Code Ocean capsules for in-article code integration and reanalysis. Code Ocean makes it quick and easy for others to re-run your analysis, and even edit your code to see how results differ by changing parameters. Code Ocean supports the reproducibility of science without users needing to install anything on their computer – it all happens within the article.
Code syntax highlighting
Benefit from proper support for code syntax highlighting, so that your code is fully readable in the body of your article.
Increase the visibility, reach and impact of your research and software through indexing in Scopus and PubMed.
Keep your software tool article up to date by easily publishing new versions whenever you need to share the latest developments in your work. Versions are all individually citable and clearly linked, making it easy for readers to navigate and cite the version they want.
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Aaron T. L. Lun, David J. McCarthy, John C. Marioni
This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project.
It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection.
Karoline Faust, Jeroen Raes
This article presents the Cytoscape app version of association network inference tool CoNet.
Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly.