Literature DB >> 31233491

Open collaborative writing with Manubot.

Daniel S Himmelstein1, Vincent Rubinetti1, David R Slochower2, Dongbo Hu1, Venkat S Malladi3,4, Casey S Greene1, Anthony Gitter5,6.   

Abstract

Open, collaborative research is a powerful paradigm that can immensely strengthen the scientific process by integrating broad and diverse expertise. However, traditional research and multi-author writing processes break down at scale. We present new software named Manubot, available at https://manubot.org, to address the challenges of open scholarly writing. Manubot adopts the contribution workflow used by many large-scale open source software projects to enable collaborative authoring of scholarly manuscripts. With Manubot, manuscripts are written in Markdown and stored in a Git repository to precisely track changes over time. By hosting manuscript repositories publicly, such as on GitHub, multiple authors can simultaneously propose and review changes. A cloud service automatically evaluates proposed changes to catch errors. Publication with Manubot is continuous: When a manuscript's source changes, the rendered outputs are rebuilt and republished to a web page. Manubot automates bibliographic tasks by implementing citation by identifier, where users cite persistent identifiers (e.g. DOIs, PubMed IDs, ISBNs, URLs), whose metadata is then retrieved and converted to a user-specified style. Manubot modernizes publishing to align with the ideals of open science by making it transparent, reproducible, immediate, versioned, collaborative, and free of charge.

Entities:  

Mesh:

Year:  2019        PMID: 31233491      PMCID: PMC6611653          DOI: 10.1371/journal.pcbi.1007128

Source DB:  PubMed          Journal:  PLoS Comput Biol        ISSN: 1553-734X            Impact factor:   4.475


  37 in total

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Journal:  PLoS Comput Biol       Date:  2016-07-14       Impact factor: 4.475

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8.  Plasmids for Independently Tunable, Low-Noise Expression of Two Genes.

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Journal:  J R Soc Interface       Date:  2018-04       Impact factor: 4.293

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3.  Plasmids for Independently Tunable, Low-Noise Expression of Two Genes.

Authors:  João P N Silva; Soraia Vidigal Lopes; Diogo J Grilo; Zach Hensel
Journal:  mSphere       Date:  2019-05-29       Impact factor: 4.389

4.  Scaling tree-based automated machine learning to biomedical big data with a feature set selector.

Authors:  Trang T Le; Weixuan Fu; Jason H Moore
Journal:  Bioinformatics       Date:  2020-01-01       Impact factor: 6.937

5.  Associations between blood type and COVID-19 infection, intubation, and death.

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6.  An Open-Publishing Response to the COVID-19 Infodemic.

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7.  Pulmonary acini exhibit complex changes during postnatal rat lung development.

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8.  An Open-Publishing Response to the COVID-19 Infodemic.

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10.  Ten quick tips for deep learning in biology.

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Journal:  PLoS Comput Biol       Date:  2022-03-24       Impact factor: 4.475

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