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      Open collaborative writing with Manubot

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          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.

          Author summary

          Traditionally, scholarly manuscripts have been written in private by a predefined team of collaborators. But now the internet enables realtime open science, where project communication occurs online in a public venue and anyone is able to contribute. Dispersed teams of online contributors require new tools to jointly prepare manuscripts. Existing tools fail to scale beyond tens of authors and struggle to support iterative refinement of proposed changes. Therefore, we created a system called Manubot for writing manuscripts based on collaborative version control. Manubot adopts the workflow from open source software development, which has enabled hundreds of contributors to simultaneously develop complex codebases such as Python and Linux, and applies it to open collaborative writing. Manubot also addresses other shortcomings of current publishing tools. Specifically, all changes to a manuscript are tracked, enabling transparency and better attribution of credit. Manubot automates many tasks, including creating the bibliography and deploying the manuscript as a webpage. Manubot webpages preserve old versions and provide a simple yet interactive interface for reading. As such, Manubot is a suitable foundation for next-generation preprints. Manuscript readers have ample opportunity to not only provide public peer review but also to contribute improvements, before and after journal publication.

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          How open science helps researchers succeed

          Open access, open data, open source and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices. DOI: http://dx.doi.org/10.7554/eLife.16800.001
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            The case for open computer programs.

            Scientific communication relies on evidence that cannot be entirely included in publications, but the rise of computational science has added a new layer of inaccessibility. Although it is now accepted that data should be made available on request, the current regulations regarding the availability of software are inconsistent. We argue that, with some exceptions, anything less than the release of source programs is intolerable for results that depend on computation. The vagaries of hardware, software and natural language will always ensure that exact reproducibility remains uncertain, but withholding code increases the chances that efforts to reproduce results will fail.
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              Systematic integration of biomedical knowledge prioritizes drugs for repurposing

              The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Software
                Role: SoftwareRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                24 June 2019
                June 2019
                : 15
                : 6
                : e1007128
                Affiliations
                [1 ] Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
                [2 ] Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, United States of America
                [3 ] Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
                [4 ] Bioinformatics Core Facility, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
                [5 ] Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
                [6 ] Morgridge Institute for Research, Madison, Wisconsin, United States of America
                Hebrew University of Jerusalem, ISRAEL
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-3012-7446
                http://orcid.org/0000-0002-4655-3773
                http://orcid.org/0000-0003-3928-5050
                http://orcid.org/0000-0002-0144-0564
                http://orcid.org/0000-0001-8713-9213
                http://orcid.org/0000-0002-5324-9833
                Article
                PCOMPBIOL-D-18-01884
                10.1371/journal.pcbi.1007128
                6611653
                31233491
                86c9815d-4268-4994-9ac3-9dd6e6fef2b4
                © 2019 Himmelstein et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 November 2018
                : 24 May 2019
                Page count
                Figures: 3, Tables: 2, Pages: 21
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000879, Alfred P. Sloan Foundation;
                Award ID: G-2018-11163
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000936, Gordon and Betty Moore Foundation;
                Award ID: GBMF4552
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100004917, Cancer Prevention and Research Institute of Texas;
                Award ID: RP150596
                Award Recipient :
                DSH, DH, VR, and CSG were supported by Grant G-2018-11163 from the Alfred P. Sloan Foundation and Grant GBMF4552 from the Gordon and Betty Moore Foundation. VSM was supported by Grant RP150596 from the Cancer Prevention and Research Institute of Texas. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Research and Analysis Methods
                Research Assessment
                Citation Analysis
                Computer and Information Sciences
                Data Management
                Metadata
                Research and Analysis Methods
                Research Assessment
                Peer Review
                Computer and Information Sciences
                Computer Applications
                Web-Based Applications
                Computer and Information Sciences
                Computer Software
                Open Source Software
                Science Policy
                Open Science
                Open Source Software
                Science Policy
                Open Science
                Computer and Information Sciences
                Computer Networks
                Internet
                Research and Analysis Methods
                Scientific Publishing
                Custom metadata

                Quantitative & Systems biology
                Quantitative & Systems biology

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