cover letter

writing effective use cases pdf github

You are being redirected to your identity provider in order to authenticate.

If your browser does not redirect you back, please click here to continue.

writing effective use cases pdf github

Writing Effective Use-cases, by A. Cookburn

Use cases? Uh … isn’t it one of those UML diagrams with stickmen, boxes, and bubbles? There is more to it, and “Writing Effective Use-cases” by A. Cockburn is the place to start.

Bookcover

I am always unsure about how much requirement I need, especially when building prototypes? So, reading about use-cases, I came across it on M. Fowler’s post on his “Use Cases” . It still costs about 30 EUR on Amazon , but I went for it anyway. Several years ago, I had read Software Requirements by K. Wiegers , I felt I needed a refresher.

Here come 300 pages on use-cases only. Part I details the various sections they include, whereas Part II discusses frequently asked questions. Part III concludes with a list of “reminders for the busy”. UML diagrams only show up in appendix.

A. Cockburn is another contributor to the Agile Manifesto and created the Crystal family of agile methodology. He authored several books on both use-cases and agile methods.

I found this book very practical as it focuses on textual use-cases as opposed to graphical views as in UML, for instance. We dive straight in with the use case document and its different sections: Scope, main scenario, extensions, etc. Alistair helps with the writing style and provides two templates for the so-called casual and fully-dressed formats. On the downside, I feel the process to collect and refine use-cases is somewhat less clear, but I am guessing this goes beyond the intended scope.

I finally give it 3 stars. It is a good and practical book and a reference I will definitely keep in mind. On the downside, I still do not get how far to go with the requirements.

Due to a planned power outage on Friday, 1/14, between 8am-1pm PST, some services may be impacted.

Internet Archive Audio

writing effective use cases pdf github

writing effective use cases pdf github

writing effective use cases pdf github

writing effective use cases pdf github

writing effective use cases pdf github

Search the history of over 797 billion web pages on the Internet.

Mobile Apps

Browser Extensions

Archive-it subscription.

Save Page Now

Capture a web page as it appears now for use as a trusted citation in the future.

Please enter a valid web address

Writing effective use cases

Item preview, share or embed this item, flag this item for.

[WorldCat (this item)]

plus-circle Add Review comment Reviews

274 Previews

8 Favorites

Better World Books

DOWNLOAD OPTIONS

No suitable files to display here.

14 day loan required to access EPUB and PDF files.

IN COLLECTIONS

Uploaded by LannetteF on February 25, 2010

SIMILAR ITEMS (based on metadata)

Visual Paradigm logo

How to Write Effective Use Cases?

Compatible edition(s): Enterprise , Professional , Standard

The fundamental goal of each software project is to build and deliver the right product for target users.

But: What is a 'right product'?

The right product is a product that the customers want, need, and desire. Unfortunately, no one knows at upfront what they want and need, including the customers themselves.

In this tutorial, we will introduce a systematic approach that helps you identify customers' needs. It involves an upfront recognition of business goals to be satisfied, and gradually a discovery of requirements based around the goals.

Let's start from use cases - a tool proven to be effective in use case management.

Use case and use case diagram

What is use case.

A use case describes a specific business goal to be satisfied by the system to be built. Graphically, it is an oval with a name, which looks simple but is yet the most commonly used tool in managing business goals or project goals.

What is use case diagram?

A use case diagram is a kind of Unified Modeling Language (UML) diagram created for requirement elicitation. Use case diagram provides a graphical overview of goals (modeled by use cases) users (represented by actors) want to achieve by using the system. Use cases in a use case diagram can be organized and arranged according to their relevance, level of abstraction and impacts to users. They can be connected to show their dependency, inclusion and extension relationships.

use case diagram example

The main purpose of modeling use case with use case diagram is to establish a solid foundation of the system by identifying what the users want. Based on the result, you can move forward to study how to fulfill those user needs.

In the next section, we will talk about user story, a tool widely used in managing requirements.

What is user story?

Anyone who has experience in software development would probably have suffered from communication issues with stakeholders. User story is a great way of opening discussion with stakeholders for ensuring the development team knows what stakeholders want. User stories created by the product owner capture "who", "what" and "why" of a requirement simply and concisely, which is typically written in natural language in a non-technical format. Agile development has entered into the mainstream of development approach hand-in-hand with user stories for requirement discovery.

Discovering user stories with use cases

It is important to note that use cases alone represent goals but not the actual requirements to be supported. Nevertheness, use cases provide a great starting point to the discovery of requirements. Here are the benefits:

To summarize: Use cases can be effective when you use it as a tool for requirements discovery and management.

Drawing Use Case Diagram in Visual Paradigm

In the coming sections, we demonstrate how you can develop a use case model and write user stories with Visual Paradigm. We will make use of a hotel reservation system as an example.

Let's start by drawing a use case diagram.

New project window

Writing user stories

In this section, we will carry on by writing user stories within the use case 'Make Reservation'. We will make use of a user story map to manage our user story.

Part I - Forming the 'backbone' of story map from use cases

User story map provides a leveled structure in managing user stories. The top-level is known as the backbone. It captures the high-level activities a user will accomplish.

Visual Paradigm bridges the gap from use case to user stories through a feature called "send to". You can send use cases created in use case diagram(s) to a user story map in forming the backbone. Let's try.

Send use case to product backlog

Open user story map

Part II - Breaking down user activities into epics

To facilitate the management of requirements and work, you can break down the user activities into epics. This can be done by splitting a user activity into multiple function units. Let's have a try.

Create epic

Part III - Writing user stories

Create user stories to capture the requirements under each epic. Let's try.

Create user story

Detailing a user story

User stories have to be detailed to a level that provides sufficient information for the software team to determine the tasks to perform to support users' needs and to perform an accurate estimation of time and effort. In this section, we will show you how to detail the behavior of a user story.

Open user story

Add system response

Something More - Creating Scenario-Based Wireframe

Wireframe is a sketch of user interface. It helps you represent the screen and screen flow of the system to be developed, early in requirements gathering. You can associate wireframes to steps in scenario. This section will show you how to make use of the wireframe tool to add a wireframe to a step.

Select a step

Resize wireframe

Readers of this tutorial also read

Testimonals

I am extremely pleased in both its ease of use as well as its breadth of features. Of particular value to me is the Textual Analysis tool.

Prof. William Burrows

University of Washington, Seattle

Turn every software project into a successful one.

We use cookies to offer you a better experience. By visiting our website, you agree to the use of cookies as described in our Cookie Policy .

© 2022 by Visual Paradigm. All rights reserved.

writing effective use cases pdf github

writing effective use cases pdf github

Get Textbooks on Google Play

Rent and save from the world's largest eBookstore. Read, highlight, and take notes, across web, tablet, and phone.

Go to Google Play Now »

From inside the book

What people are saying  -   write a review, user ratings, librarything review.

A great little guide on use cases from the grandaddy of the method. Highly recommended. Read full review

Review: Writing Effective Use Cases

Very usefully - though definitely not entertainment. Read full review

Other editions - View all

Common terms and phrases, references to this book, about the author  (2001).

Alistair Cockburn is a recognized expert on use cases. He is consulting fellow at Humans and Technology, where he is responsible for helping clients succeed with object-oriented projects. He has more than twenty years of experience leading projects in hardware and software development in insurance, retail, and e-commerce companies and in large organizations such as the Central Bank of Norway and IBM.

Bibliographic information

QR code for Writing Effective Use Cases

writing effective use cases pdf github

Here are 77 public repositories matching this topic...

Davidtakac / prognoza.

Libre weather app with widgets and offline support

4lessandrodev / types-ddd

This package provide utils files and interfaces to assistant build a complex application with domain driving design.

ivanpaulovich / ddd-tdd-rich-domain-model-dojo-kata

DDD patterns implemented following TDD

serradura / from-fat-controllers-to-use-cases

Rails (API) app that shows different kinds of architecture (one per commit), and in the last one, how to use the Micro::Case gem to handle the application business logic.

T8RIN / CookHelper

🍕 CookHelper, a Food Social Network App in Jetpack Compose and Hilt based on modern Android tech-stacks, MVVM+ clean architecture and Material You design system.

miztiik / aws-real-time-use-cases

Repo of quick starts for real time AWS use cases 🎓

serradura / todo-bcdd

wikistat / Apprentissage

Science des Données Saison 3: Apprentissage Automatique / Statistique pour l'Intelligence Artificielle

wikistat / AI-Frameworks

Science des Données Saison 5: Technologies pour l'apprentissage automatique / statistique de données massives et l'Intelligence Artificielle

wikistat / Exploration

Science des Données Saison 2: Exploration statistique multidimensionnelle, ACP, AFC, AFD, Classification non supervisée

magnetis / caze

A DSL to define use cases

T8RIN / PapriCoin

💸 📈 PapriCoin demonstrates Jetpack Compose usage to build modern app based on Clean Architecture and newest Tech-Stack.

kareemAboelatta / Social-media

Is a new version of code for my (Social media app) with Clean Architecture

MianSarimHameed / CalorieTracker

The Carbs, Protein, Fat & Calories Tracker App.

w3c / dpub-pwp-ucr

Use Cases and Requirements for (Packaged) Web Publications

padogrid / padogrid

A comprehensive addon tool for managing data grid environments by distributed workspaces and online use case bundles

gushakov / cargo-clean

Revisiting Cargo tracking example with Clean DDD approach

callebdev / Valorant

Valorant agents app built with Kotlin and Jetpack Compose

Webncyclopaedia / react-best-practices

Лучшие практики реакта

adamhalesworth / skiff

A lightweight Mediator implementation for Dart projects.

Improve this page

Add a description, image, and links to the use-cases topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the use-cases topic, visit your repo's landing page and select "manage topics."

Writing Effective Use Cases [pdf]

writing effective use cases pdf github

no comments yet

Be the first to share what you think!

About Community

Subreddit Icon

Ranked by Size

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

paper cover thumbnail

Writing effective use cases

Profile image of alistair cockburn

There are still no trusted guides about how to write (or review) use cases, even though it is now nearly a decade since use cases have become the "norm" for writing functional requirements for object-oriented software systems, and ...

Related Papers

Gilles Perrouin

Software systems have become essential to many human activities and have proliferated thanks to various hardware innovations such as mobile computing (laptops, personal digital assistants, mobile phones) and networks (DSL, WIFI, GSM, etc.) enabling interactions between users and computer systems in virtually any place. This situation has created both a great complexity for such distributed systems to be designed and great expectations (mainly concerned with quality, time and induced costs of the software) from the users of these systems, requiring improvements in software engineering methods in order to meet these challenges. On the one hand, Model Driven Engineering (MDE), by allowing the description of software systems through abstractions and deriving useful system artifacts, harnesses inherent complex- ity of software systems and reduces time-to-market via model transformations. On the other hand, software product lines foster software reuse by proposing to develop applications based on a set of common assets belonging to a particular domain. Thus, when product line assets are carefully designed, both quality and time-to-market requirements can be achieved. Development methods that have resulted from the product line paradigm generally focus on defining common and variable assets to be reused by product line members. However, they hardly address the development of applications from the product line assets in a systematic way. Furthermore, those considering it propose automated but rather inflexible approaches that unnecessarily ex- clude products which, although addressable by product line assets, have not been explicitly envisioned during product line definition. If in some domains — in particular, those including hardware constraints and/or critical features — it is possible to fully determine the products that are part of the software product line, in the other cases, an initial set of products can only be considered assuming that the customers’ requests will be met by this set. We believe that this assumption is false in general and this thesis examines the research question which consists in proposing a set of models and a product line development method to offer more flexibility while deriving products in order to seamlessly address customers’ requests. The domain we consider is that of web e-bartering systems. This thesis strives to propose a trade-off between automated and unsupported product deriva- tion by providing a model-driven product line development method that allows developers to define product line members by transforming a coherent and layered set of product line models. Moreover, constraints on the possible transformations have to be specified in order to determine which products cannot be derived both for functional and technical reasons. The first part of this thesis introduces the foundational concepts of our FIDJI method. In par- ticular, it describes the notion of architectural framework as a set of models defining product line assets at analysis and design levels and which is instantiated in order to obtain product line members thanks to model transformations. This part then describes key methodological principles driving the choice of architectural framework models and how flexibility in product derivation can be achieved and controlled by constraints defined over the set of architectural framework models. The second part of this thesis is devoted to requirements elicitation, analysis and design phases of the method. For requirements elicitation, a specific product line template is defined to allow for the description of a software product line in an informal manner via use case variants and data dictionaries. The analysis phase refines requirements elicitation by allowing the precise description of domain concepts in terms of UML models as well as functionalities in terms of use cases completed by OCL expressions. Variability is ensured through the use of state variables in OCL expressions which enable a wide variety of scenarios to be implemented in the product. Constraints indirectly define product line boundaries by preventing certain instantiations from being made. The design phase focuses on the architectural design of the architectural frame- work and describes it in terms of interacting components structured via architectural styles. Analysis and design models are supported by UML profiles defining the constructs offered by the FIDJI method, their usage conditions as well as traceability and consistency rules ensuring model correctness. The methodological process for both analysis and design consists in writing a transformation program, validated over the aforementioned constraints, that will instantiate the architectural framework to obtain a viable product line member. The last part of the thesis deals with the practical application of the method. A case study belonging to the e-commerce domain illustrates the FIDJI method in detail and a simple archi- tectural framework is defined for this purpose. In particular, we show how the transformation program is created from predefined transformation operations dedicated to FIDJI models and the rationale and usage of constraints controlling the instantiation of the architectural framework.

writing effective use cases pdf github

shashank nawathe

Pierre-Yves Schobbens

Paul Grefen

When I started to realize it was time for me to leave an exuberant and enjoyable university life behind, PricewaterhouseCoopers gave me the opportunity to do an interesting graduation project with them. As a student in computer science, my focus had already shifted to industrial engineering. PricewaterhouseCoopers added a focus on Accountancy to this. Looking back, I can say that the biggest challenge of this graduation project was to bring the three domains, computer science, industrial engineering, and accountancy together.

Tgk Vasista

Didar Zowghi

Use-case modelling provides a means of specifying external features of a system during requirements elicitation. In principle, use cases can be used to size the system about to be built but, for that, a standard format for their documentation is required. Furthermore, gathering use-case metrics requires a software development process that produces complete use-case descriptions in a repeatable way. Here, we set out the requirements for such a standardization so that use cases can be metricated. Once accomplished, it is possible to evaluate the important research questions of whether use-case attributes such as size and complexity can be controlled and whether use-case metrics are sufficiently rigorous for estimating effort. Finally, we note that this added rigour applied to use cases should improve the consistency and quality of communication between client and developer, helping to ensure that the right system is built.

Ramon Nuñez

Lucy A . D . Lockwood

Carlos Yosimar Ruiz Vasquez

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Royer burgos urquizo

Animesh Pathak , Thiago Colucci , Thanassis Parathyras , Giorgos Veranis

The S-Cube Book

Manuel Carro

Carl-Fredrik Sørensen

Yvonne Howard

Tajebe Tsega

IEEE-SEM Journal

2014 9th International Conference on the Quality of Information and Communications Technology

Estrela Cruz

RELATED TOPICS

Open collaborative writing with Manubot

A DOI-citable version of this manuscript is available in PLOS Computational Biology at https://doi.org/10.1371/journal.pcbi.1007128 . This version of the manuscript contains changes subsequent to the journal publication.

This manuscript ( permalink ) was automatically generated from greenelab/[email protected] on May 25, 2020.

✉ — correspondence preferred via GitHub Issues . Otherwise, address correspondence to [email protected] and [email protected] .

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 webpage. 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.

Introduction

The internet enables science to be shared in real-time at a low cost to a global audience. This development has decreased the barriers to making science open, while supporting new massively collaborative models of research [ 1 ] . However, the scientific community requires tools whose workflows encourage openness [ 2 ] . Manuscripts are the cornerstone of scholarly communication, but drafting and publishing manuscripts has traditionally relied on proprietary or offline tools that do not support open scholarly writing , in which anyone is able to contribute and the contribution history is preserved and public. We introduce Manubot , a new tool and infrastructure for authoring scholarly manuscripts in the open, and report how it was instrumental for the collaborative project that led to its creation.

Based on our experience leading a recent open review [ 3 ] , we discuss the advantages and challenges of open collaborative writing, a form of crowdsourcing [ 4 ] . Our review manuscript [ 5 ] was code-named the Deep Review and surveyed deep learning’s role in biology and precision medicine, a research area undergoing explosive growth. We initiated the Deep Review in August 2016 by creating a GitHub repository ( https://github.com/greenelab/deep-review ) to coordinate and manage contributions. GitHub is a platform designed for collaborative software development that is adaptable for collaborative writing. From the start, we made the GitHub repository public under a Creative Commons Attribution License ( CC BY 4.0 ). We encouraged anyone interested to contribute by proposing changes or additions. Although we invited some specific experts to participate, most authors discovered the manuscript organically through conferences or social media, deciding to contribute without solicitation. In total, the Deep Review attracted 36 authors, who were not determined in advance, from 20 different institutions in less than two years.

The Deep Review and other studies that subsequently adopted the Manubot platform were unequivocal successes bolstered by the collaborative approach. However, inviting wide authorship brought many technical and social challenges such as how to fairly distribute credit, coordinate the scientific content, and collaboratively manage extensive reference lists. The manuscript writing process we developed using the Markdown language, the GitHub platform, and our new Manubot tool for automating manuscript generation addresses these challenges.

Manubot supports citations by adding a persistent identifier like a Digital Object Identifier (DOI) or PubMed Identifier (PMID) directly in the text so that large groups of authors do not have to coordinate reference lists. When text is changed, Manubot automatically updates the manuscript’s webpage so that all authors can read and edit from the latest version. Because manuscripts are created from GitHub repositories, Manubot supports a workflow where all edits are reviewed and discussed, ensuring that the collaborative text has a cohesive style and message and that authors receive precise credit for their work. These and other features support an open collaborative writing process that is not feasible with other writing platforms.

Collaborative writing platforms

There are many existing collaborative writing platforms (Table 1 ) [ 6 ] . In general, platforms with “what you see is what you get” (WYSIWYG) editors, such as Microsoft Word or Google Docs, require the least technical expertise to use. On the flip side, WYSIWYG platforms can be difficult to customize and incorporate into automated computational workflows. Traditionally, LaTeX has been used for these needs, since documents are written in plain text and the system is open source and extensible. Rendering LaTeX documents requires specialized software, but webapps like Overleaf now enable collaborative authoring of LaTeX documents. Nonetheless, LaTeX-based systems are limited in that PDF (or similar) is the only fully supported output format. Alternatively, Authorea is a collaborative writing webapp whose primary output format is HTML. Authorea allows authors to write in Markdown, a limited subset of LaTeX, or their WYSIWYG HTML editor.

Existing platforms work well for editing text and are widely used for scholarly writing. However, they often lack features that are important for open collaborative writing, such as versatile version control and multiple permission levels. For example, Manubot is the only platform listed in Table 1 that offers the ability to address thematically related changes together and enables multiple authors to iteratively refine proposed changes.

Manubot contribution workflow

Manubot’s collaborative writing workflow adopts standard software development strategies that enable any contributor to edit any part of the manuscript but enforce discussion and review of all proposed changes. The GitHub platform supports organizing and editing the manuscript. Manubot projects use GitHub issues for organization, opening a new issue for each discussion topic. For example, in a review manuscript like the Deep Review, this includes each primary paper under consideration. Within a paper’s issue, contributors summarize the research, discuss it (sometimes with participation from the original authors), and assess its relevance to the review. In a primary research article, issues can instead track progress on specific figures or subsections of text being drafted. Issues serve as an open to-do list and a forum for debating the main messages of the manuscript.

GitHub and the underlying Git version control system [ 7 , 8 ] also structure the writing process. The official version of the manuscript is forked by individual contributors, creating a copy they can freely modify. A contributor then adds and revises files, grouping these changes into commits . When the changes are ready to be reviewed, the series of commits are submitted as a pull request through GitHub, which notifies other authors of the pending changes. GitHub’s review interface allows anyone to comment on the changes, globally or at specific lines, asking questions or requesting modifications [ 9 ] . Conversations during review can reference other pull requests, issues, or authors, linking the relevant people and content (Figure 1 ). Reviewing batches of revisions that focus on a single theme is more efficient than independently discussing isolated comments and edits and helps maintain consistent content and tone across different authors and reviewers. Once all requested modifications are made, the manuscript maintainers, a subset of authors with elevated GitHub permissions, formally approve the pull request and merge the changes into the official version. The process of writing and revising material can be orchestrated through GitHub with a web browser (as shown in S1 Video ) or through a local text editor.

The Deep Review issue and pull request on protein-protein interactions demonstrate this process in practice. A new contributor identified a relevant research topic that was missing from the review manuscript with examples of how the literature would be summarized, critiqued, and integrated into the review. A maintainer confirmed that this was a desirable topic and referred to related open issues. The contributor made the pull request, and two maintainers and another participant made recommendations. After four rounds of reviews and pull request edits, a maintainer merged the changes.

We found that this workflow was an effective compromise between fully unrestricted editing and a more heavily-structured approach that limited the authors or the sections they could edit. In addition, authors are associated with their commits, which makes it easy for contributors to receive credit for their work. Figure 2 and the GitHub contributors page summarize all edits and commits from each author, providing aggregated information that is not available on most other collaborative writing platforms. Because the Manubot writing process tracks the complete history through Git commits, it enables detailed retrospective contribution analysis. These pull request and contribution tracking examples both come from Deep Review, the largest Manubot project to date, but illustrate the general principles of transparency and collaboration that are shared by all open Manubot manuscripts.

GitHub issues can also be used for formal peer review by independent or journal-selected reviewers. A reviewer conducting open peer review can create issues using their own GitHub account, as one reviewer did for this manuscript. Alternatively, a reviewer can post feedback with a pseudonymous GitHub account or have a trusted third party such as a journal editor post their comments anonymously. Authors can elect to respond to reviews in the GitHub issues or a public response letter , creating open peer review.

Although we developed Manubot with collaborative writing in mind, it can also be helpful for individuals preparing scholarly documents. Authors may choose to make their changes directly to the master branch, forgoing pull requests and reviews. This workflow retains many of Manubot’s benefits, such as transparent history, automation, and allowing outside contributors to propose changes. In cases where outside contributions are unwanted, authors can disable pull requests on GitHub. It is also possible to use Manubot on a private GitHub repository. Private manuscripts require some additional customization to disable GitHub Pages and may require a paid continuous integration plan. See the existing manuscripts for examples of the range of contribution workflows and Manubot use cases.

Manubot features

Manubot is a system for writing scholarly manuscripts via GitHub. For each manuscript, there is a corresponding Git repository. The master branch of the repository contains all of the necessary inputs to build the manuscript. Specifically, a content directory contains one or more Markdown files that define the body of the manuscript as well as a metadata file to set information such as the title, authors, keywords, and language. Figures can be hosted in the content/images subdirectory or elsewhere and specified by URL. Repositories contain scripts and other files that define how to build and deploy the manuscript. Many of these operations are delegated to the manubot Python package or other dependencies such as Pandoc, which converts between document formats, and Travis CI, which builds the manuscript in the cloud. Manubot pieces together many existing standards and technologies to encapsulate a manuscript in a repository and automatically generate outputs.

With Manubot, manuscripts are written as plain-text Markdown files. The Markdown standard itself provides limited yet crucial formatting syntax, including the ability to embed images and format text via bold, italics, hyperlinks, headers, inline code, codeblocks, blockquotes, and numbered or bulleted lists. In addition, Manubot relies on extensions from Pandoc Markdown to enable citations, tables, captions, and equations specified using the popular TeX math syntax. Markdown with Pandoc extensions supports most formatting options required for scholarly writing [ 12 ] but currently lacks the ability to cross-reference and automatically number figures, tables, and equations. For this functionality, Manubot includes the pandoc-xnos suite of Pandoc filters. A list of formatting options officially supported by Manubot, at the time of writing, is viewable as raw Markdown and the corresponding rendered HTML .

By virtue of its readable syntax, Markdown is well suited for version control using Git. Markdown treats a single line break between text as a space and requires two-or-more consecutive line breaks to denote a new paragraph. For optimal tracking of Markdown files with Git, we recommend placing each sentence on its own line. This convention allows Git to display diffs on a per sentence basis, avoids unnecessary reflows associated with line wrapping, and supports easy rearrangement of sentences.

Citation-by-identifier

Manubot includes an additional layer of citation processing, currently unique to the system. All citations point to a standard identifier, for which Manubot automatically retrieves bibliographic metadata such as the title, authors, and publication date. Table 2 presents the supported identifiers and example citations before and after Manubot processing. Authors can optionally define citation tags to provide short readable alternatives to the citation identifiers. Citation metadata is exported to the Citation Style Language (CSL) JSON Data Items format, an open standard that is widely supported by reference managers [ 13 , 14 ] . However, sometimes external resources provide Manubot with invalid CSL Data, which can cause errors with downstream citation processors, such as pandoc-citeproc . Therefore, Manubot removes invalid fields according to the CSL Data specification . In cases where automatic retrieval of metadata fails or produces incorrect references — which is most common for URL citations — users can manually provide the correct metadata using common reference formats. Manual metadata also supports references without standard identifiers, such as print-only newspaper articles.

Manubot formats bibliographies according to a CSL style specification. Styles define how references are constructed from bibliographic metadata, controlling layout details such as the maximum number of authors to list per reference. Manubot’s default style emphasizes titles and electronic (rather than print) identifiers and applies numeric-style citations [ 23 ] . Alternatively, users can also choose from thousands of predefined styles or build their own [ 24 ] . As a result, adopting the specific bibliographic format required by a journal usually just requires specifying the style’s source URL in the Manubot configuration.

Format conversion

Manubot uses Pandoc to convert manuscripts from Markdown to HTML, PDF, and optionally DOCX outputs. Pandoc also supports Journal Article Tag Suite (JATS), a standard format for scholarly articles that is used by publishers, archives, and text miners [ 25 , 26 , 27 ] . Pandoc’s JATS support provides an avenue to integrate Manubot with the larger JATS ecosystem. In the future, journals may accept submissions in JATS. For now, Manubot’s DOCX output is usually sufficient for journal submissions that require an editable source document. Otherwise, authors generally use the PDF output for preprint and initial journal submissions. The primary Manubot output is HTML intended to be viewed in a web browser. Accordingly, manuscripts natively support JavaScript and can thus include any web-based interactive visualization, such as those produced using Vega-Lite , Bokeh , or Plotly [ 28 , 29 ] .

Interactive features and appearance

Manubot comes with several “plugins” that can be included in manuscripts exported as HTML. These plugins add special interactive features that enhance the user experience of viewing and reading manuscripts (Figure 3 ). For example, with the “tooltips” plugin enabled, when the user hovers over a link to a reference or figure, a preview of that item pops up above the link, along with controls to navigate between other mentions of that item elsewhere in the document. The build process can also accommodate different “themes”, which change the general aesthetics and appearance of the exported document (e.g. from a contemporary sans-serif style to a more traditional serif style). The architecture of the plugins and themes is designed to provide authors with enough flexibility to suit their particular needs and preferences.

writing effective use cases pdf github

The Manubot “front-end” (layout, look, controls, behavior, etc.) was developed in line with current best practices and user expectations of the modern web. The plugins use standard technology built in to most major web browsers, allowing them to be relatively lightweight, modular, and easy to configure.

Continuous publication

Manubot performs continuous publication: Every update to a manuscript’s source is automatically reflected in the online outputs. The approach uses continuous integration (CI) [ 30 , 31 , 32 ] , specifically via Travis CI , to monitor changes. When changes occur, the CI service attempts to generate an updated manuscript. If this process is error free, the CI service timestamps the manuscript and uploads the output files to the GitHub repository. Because the HTML manuscript is hosted using GitHub Pages , the CI service automatically deploys the new manuscript version when it pushes the updated outputs to GitHub. Using CI to build the manuscript automatically catches many common errors, such as misspelled citations, invalid formatting, or misconfigured software dependencies.

To illustrate, the source GitHub repository for this article is https://github.com/greenelab/meta-review . When this repository changes, Travis CI rebuilds the manuscript. If successful, the output is deployed back to GitHub (to dedicated output and gh-pages branches). As a result, https://greenelab.github.io/meta-review stays up to date with the latest HTML manuscript. Furthermore, versioned URLs, such as https://greenelab.github.io/meta-review/v/4b6396bcefd1b9c7ddf39c1d3f0b3eab2dd63f31/ , provide access to previous manuscript versions.

Timestamping

The idea of the “priority of discovery” is important to science, and Vale and Hyman discuss the importance of both disclosure and validation [ 33 ] . In their framework, disclosure occurs when a scientific output is released to the world. However, for a manuscript that is shared as it is written, being able to establish priority could be challenging. Manubot supports OpenTimestamps to timestamp the HTML and PDF outputs on the Bitcoin blockchain. This procedure allows one to retrospectively prove that a manuscript version existed prior to its blockchain-verifiable timestamp [ 17 , 34 , 35 , 36 , 37 ] . Timestamps protect against attempts to rewrite a manuscript’s history and ensure accurate histories, potentially alleviating certain authorship or priority disputes. Because all Bitcoin transactions compete for limited space on the blockchain, the fees required to send a single transaction can be high. OpenTimestamps minimizes fees by encoding many timestamps into a single Bitcoin transaction, enabling the service to be free of charge [ 38 ] . Since transactions can take up to a few days to be made, Manubot initially stores incomplete timestamps and upgrades them in future continuous deployment builds. We find that this asynchronous design with timestamps precise to the day is suitable for the purposes of scientific writing.

Reproducible manuscripts

Manubot and its dependencies are free of charge and largely open source. It does rely on gratis services from two proprietary platforms: GitHub and Travis CI. Fortunately, lock-in to these services is minimal, and several substitutes already exist. Manubot provides a substantial step towards end-to-end document reproducibility, where every figure or piece of data in a manuscript can be traced back to its origin [ 39 ] and is well-suited for preserving provenance. For example, figures can be specified using versioned URLs that refer to the code that created them. In addition, manuscripts can be templated, so that numerical values or tables are inserted directly from the repository that created them. The Figure 2 caption provides examples of templates. Phrases such as “755 Git commits” are written as {{total_commits}} Git commits so that the commit count can be automatically updated.

Getting started

An example repository at https://github.com/manubot/rootstock , referred to as Rootstock, demonstrates Manubot’s features and serves as a template for users to write their own manuscripts with Manubot. The current setup process includes cloning the Rootstock repository, rebranding it to the user’s manuscript, and configuring continuous integration. The setup process is complex but must only be performed once per manuscript. Incorporating new Manubot features into an existing manuscript is also possible by pulling the latest commits from Rootstock, which sometimes involves resolving Git conflicts.

Contributing to a manuscript is less technical and can be performed entirely through GitHub’s web interface, as discussed in the contribution workflow section and demonstrated in S1 Video . Interested readers can practice editing a demo manuscript at https://github.com/manubot/try-manubot .

At the 2019 Pacific Symposium on Biocomputing , we led a working group where 17 conference participants contributed to a different demo manuscript . Based on this experience, we believe most computational scholars have the expertise to contribute to a Manubot manuscript. Proficiency with Manubot requires familiarity with Markdown, Git, GitHub, and continuous integration. While these tools do present a barrier to entry, they are also highly applicable outside of Manubot and increasingly part of the standard curriculum for computational scholars. For example, Markdown is used for documenting Jupyter and R Markdown notebooks.

Existing manuscripts

Since its creation to facilitate the Deep Review, Manubot has been used to write a variety of scholarly documents. The Sci-Hub Coverage Study — performed openly on GitHub from its inception — investigated Sci-Hub’s repository of pirated articles [ 40 ] . Sci-Hub reviewed the initial preprint from this study in a series of tweets, pointing out a major error in one of the analyses. Within hours, the authors used Markdown’s strikethrough formatting in Manubot to cross-out the errant sentences ( commit , versioned manuscript ), thereby alerting readers to the mistake and preventing further propagation of misinformation. One month later, a larger set of revisions explained the error in more detail and was included in a second version of the preprint. As such, continuous publication via Manubot helped the authors address the error without delay, while retaining a public version history of the process. This Sci-Hub Coverage Study preprint was the most viewed 2017 PeerJ Preprint , while the Deep Review was the most viewed 2017 bioRxiv preprint [ 41 ] . Hence, in Manubot’s first year, two of the most popular preprints were written using its collaborative, open, and review-driven authoring process.

Additional research studies are being authored using Manubot, spanning the fields of regulatory genomics [ 42 ] , synthetic biology [ 43 ] , climate science , visual perception [ 44 ] , machine learning [ 45 ] , computational toolkits [ 46 ] , and data visualization . Manubot is also being used for documents beyond traditional journal publications, such as research tips , quality standards [ 47 ] , grant proposals , progress reports , undergraduate research reports [ 48 ] , literature reviews , and lab notebooks. Finally, manuscripts written with other authoring systems have been successfully ported to Manubot, including the Bitcoin Whitepaper [ 49 ] and Project Rephetio manuscript [ 50 ] .

Citation utilities

The manubot Python package provides easy access to Manubot’s citation-by-identifier infrastructure, whose functionality extends beyond just Manubot manuscripts. For example, the Kipoi model zoo for genomics [ 51 ] uses Manubot’s Python interface to retrieve model authors from persistent identifiers. In addition, the manubot cite command line utility takes a list of citations and returns either a rendered bibliography or CSL Data Items (i.e. JSON-formatted reference metadata). For example, the following command outputs a Markdown reference list for the two specified articles according to the bibliographic style of PeerJ :

Pandoc brands itself as a “universal document converter”, and can convert from any of 32 input formats to any of 51 output formats as of version 2.7. Thanks to its versatility and active development since 2006, Pandoc enjoys a large userbase across many disciplines and applications. Its filter interface enables adding custom functionality with community-developed programs. We are prototyping a Manubot-based citation-by-identifier filter. This filter would allow Pandoc users to cite persistent identifiers as part of their existing Pandoc workflows, without requiring them to adopt other aspects of Manubot. It could help popularize citation-by-identifier at an influential scale.

Future enhancements

Manubot is still under active development, and we envision major changes in its design and dependencies going forward. Currently, manuscript repositories must contain a large number of files that do not directly contain manuscript content. While this enables a high-degree of customization, it also increases complexity. Therefore, we are investigating whether configuration files with sensible defaults could enable bare-bones repositories that contain manuscript content and little else.

In addition to simplifying the usage, we’re also looking into simplifying the setup. Presently, setup is complex because users must do advanced command-line operations to clone the Rootstock repository and configure Travis CI. Although we provide detailed instructions, users often struggle to replicate the long list of setup commands in an appropriate computational environment. One priority will be to automate setup to a higher degree. However, this may require switching the services Manubot uses for continuous integration (e.g. from Travis CI to GitHub Actions, CircleCI, Drone, or GitLab CI), environment management (e.g. from Conda to Docker), and repository hosting (e.g. from GitHub to GitLab). In addition to simplifying setup, such migrations may also present the opportunity to decrease dependency on proprietary services and address other Manubot shortcomings, such as the current inability to view rendered manuscripts produced by pull request builds.

Upgrading a Manubot instance is an opt-in procedure. Therefore, when we introduce fundamental changes, existing manuscripts continue to function. However, large Rootstock changes can make upgrading existing manuscripts difficult. We are happy to provide users pro bono assistance to upgrade or troubleshoot manuscripts. Users can open an issue at the Rootstock repository for help.

One strategy to grow Manubot usage is to identify a specific user group or use case for which Manubot can be widely adopted. For example, a journal may decide to build their publishing workflow around Manubot, such that submissions would consist of a Manubot repository. This application would be most suitable for journals that currently use GitHub for submissions and publishing, such as the Journal of Open Source Software [ 52 ] . Manubot could also gain traction as the primary tool used to write collaborative manuscripts within certain communities. For example, open research projects built from voluntary contributions by geographically-distributed individuals could adopt Manubot. Likewise, Manubot may excel at enabling collaborative translation of existing manuscripts into other languages. Another application could be collaborative development of online lessons, documentation, or tutorials. Projects like Software Carpentry already host each lesson in a separate GitHub repository and may benefit from Manubot-generated permalinks to historical versions.

Manubot does not impose any restrictions on authorship. It allows authors to adhere to the author inclusion and ordering conventions of their field, which vary considerably across disciplines [ 53 ] . Some Manubot projects create a table in their GitHub repository to track contributors who did not commit text to the manuscript. This provides a transparent way to record contributions such as experimental research that generated data for the manuscript and discuss whether they meet that project’s authorship criteria. Contribution transparency helps prevent ghostwriting [ 54 ] and is especially important in collaborative writing [ 55 ] . Although we recommend authors provide their ORCID and GitHub username, Manubot also supports pseudonyms, pseudonymous GitHub usernames, and authors without an ORCID or GitHub account.

To determine authorship for the Deep Review, we followed the International Committee of Medical Journal Editors (ICMJE) guidelines and used GitHub to track contributions. ICMJE recommends authors substantially contribute to, draft, approve, and agree to be accountable for the manuscript. We acknowledged other contributors who did not meet all four criteria, including contributors who provided text but did not review and approve the complete manuscript. Although these criteria provided a straightforward, equitable way to determine who would be an author, they did not produce a traditionally ordered author list. In biomedical journals, the convention is that the first and last authors made the most substantial contributions to the manuscript. This convention can be difficult to reconcile in a collaborative effort. Using Git, we could quantify the number of commits each author made or the number of sentences an author wrote or edited, but these metrics discount intellectual contributions such as discussing primary literature and reviewing pull requests. Therefore, we concluded that it is not possible to construct an objective system to compare and weight the different types of contributions and produce an ordered author list [ 56 ] .

To address this issue, we generalized the concept of “co-first” authorship, in which two or more authors are denoted as making equal contributions to a paper. We defined four types of contributions [ 5 ] , from major to minor, and reviewed the GitHub discussions and commits to assign authors to these categories. A randomized algorithm then arbitrarily ordered authors within each contribution category, and we combined the category-specific author lists to produce a traditional ordering. The randomization procedure was shared with the authors in advance (pre-registered) and run in a deterministic manner. Given the same author contributions, it always produced the same ordered author list. We annotated the author list to indicate that author order was partly randomized and emphasize that the order did not indicate one author contributed more than another from the same category. The Deep Review author ordering procedure illustrates authorship possibilities when all contributions are publicly tracked and recorded that would be difficult with a traditional collaborative writing platform.

Papers with hundreds or thousands of authors are on the rise, such as the article describing the experiments and data that led to the discovery of the Higgs Boson [ 57 ] (5000 authors) and the report of the Drosophila genome [ 58 ] (1000 authors). Yet the number of people that participated in writing those papers, as opposed to generating and analyzing the data, is not always clear and is likely to be far below the number of authors [ 59 , 60 ] . Manubot provides the scientists involved in large collaborations the opportunity to actively participate, through a public forum, in the writing process.

Collaborative review manuscripts

The open scholarly writing Manubot enables has particular benefits for review articles, which present the state of the art in a scientific field [ 61 ] . Literature reviews are typically written in private by an invited team of colleagues. In contrast, broadly opening the process to anyone engaged in the topic — such that planning, organizing, writing, and editing occur collaboratively in a public forum where anyone is welcome to participate — can maximize a review’s value. Open drafting of reviews is especially helpful for capturing state-of-the-art knowledge about rapidly advancing research topics at the intersection of existing disciplines where contributors bring diverse opinions and expertise.

Writing review articles in a public forum allows review authors to engage with the original researchers to clarify their methods and results and present them accurately, as exemplified here . Additionally, discussing manuscripts in the open generates valuable pre-publication peer review of preprints [ 22 ] or post-publication peer review [ 16 , 62 , 63 ] . Because incentives to provide public peer review of existing literature [ 64 ] are lacking, open collaborative reviews — where authorship is open to anyone who makes a valid contribution — could help spur more post-publication peer review.

Additional collaborative writing projects

The Deep Review was not the first scholarly manuscript written online via an open collaborative process. This type of manuscript is also known as a Massively Open Online Paper [ 65 ] . In 2013, two dozen mathematicians created the 600-page Homotopy Type Theory book, writing collaboratively in LaTeX on GitHub [ 66 , 67 ] . Two technical books on cryptocurrency — Mastering Bitcoin and Mastering Ethereum — written on GitHub in AsciiDoc format have engaged hundreds of contributors. Both Homotopy Type Theory and Mastering Bitcoin continue to be maintained years after their initial publication. A 2017 perspective on the future of peer review was written collaboratively on Overleaf, with contributions from 32 authors [ 68 ] . While debate was raging over tightening the default threshold for statistical significance, nearly 150 scientists contributed to a Google Doc discussion that was condensed into a traditional journal commentary [ 69 , 70 ] . The greatest success to date of open collaborative writing is arguably Wikipedia, whose English version contains over 5.5 million articles. Wikipedia scaled encyclopedias far beyond any privately-written alternative. These examples illustrate how open collaborative writing can scale scholarly manuscripts where diverse opinion and expertise are paramount beyond what would otherwise be possible.

Open writing also presents new opportunities for distributing scholarly communication. Though it is still valuable to have versioned drafts of a manuscript with digital identifiers, journal publication may not be the terminal endpoint for collaborative manuscripts. After releasing the first version of the Deep Review [ 10 ] , 14 new contributors updated the manuscript (Figure 2 ). Existing authors continue to discuss new literature, creating a living document . Manubot provides an ideal platform for perpetual reviews [ 71 , 72 ] .

Concepts for the future of scholarly publishing extend beyond collaborative writing [ 73 , 74 , 75 ] . Pandoc Scholar [ 12 ] and Bookdown [ 76 ] , which has been used for open writing [ 77 ] , both enhance traditional Markdown to better support publishing. Dokieli is a clientside editor for HTML articles, which aims for decentralization by adhering to web standards that allow articles and reader annotations to be stored by any participating server [ 78 ] . Texture is also a browser-based editor, but uses JATS as the primary storage format.

Several projects in addition to Manubot provide infrastructure for citation-by-identifier. For example, the knitcitations package enables citation by DOI or URL in R Markdown documents. Zotero has developed bibliographic metadata extractors, called translators , for over 500 resources. Citation.js converts bibliographic references or identifiers in a variety of formats to CSL JSON [ 79 ] , and is used by pwcite , a Pandoc filter that enables citing Wikidata IDs.

Examples of continuous integration to automate manuscript generation include gh-publisher and jekyll-travis , which was used to produce a continuously published webpage for the Opening Science book [ 80 , 81 ] . Binder [ 11 ] , Distill journal articles [ 82 ] , Idyll [ 83 ] , and Stencila [ 84 , 85 ] support manuscripts with interactive graphics and close integration with the underlying code. As an open source project, Manubot can be extended to adopt best practices from these other emerging platforms.

Several other open science efforts are GitHub-based like our collaborative writing process. ReScience [ 86 ] as well as titles from Open Journals , such as the Journal of Open Source Software [ 52 ] , rely on GitHub for peer review and hosting. Distill uses GitHub for transparent peer review and post-publication peer review [ 87 ] . GitHub is increasingly used for resource curation [ 88 ] , and collaborative scholarly reviews combine literature curation with discussion and interpretation.

Limitations

There are potential limitations of our GitHub-based approach. Because the Deep Review pertained to a computational topic, most of the authors had computational backgrounds, including previous experience with version control workflows and GitHub. In other disciplines, collaborative writing via GitHub and Manubot could present a steeper barrier to entry and deter participants. In addition, Git carefully tracks all revisions to the manuscript text but not the surrounding conversations that take place through GitHub issues and pull requests. These discussions must be archived to ensure that important decisions about the manuscript are preserved and authors receive credit for intellectual contributions that are not directly reflected in the manuscript’s text. GitHub supports programmatic access to issues, pull requests, and reviews so tracking these conversations is feasible in the future.

In the Deep Review, we established contributor guidelines that discussed norms in the areas of text contribution, peer review, and authorship, which we identified in advance as potential areas of disagreement. Our contributor guidelines required verifiable participation for authorship: either directly attributable changes to the text or participation in the discussion on GitHub. These guidelines did not discuss broader community norms that may have improved inclusiveness. It is also important to consider how the move to an open contribution model affects under-represented minority members of the scientific community [ 19 ] . Recent work has identified clear social norms and processes as helpful to maintaining a collaborative culture [ 89 ] . Conferences and open source projects have used codes of conduct to establish these norms (e.g.  Contributor Covenant ) [ 90 ] . We would encourage the maintainers of similar projects to consider broader codes of conduct for project participants that build on social as well as academic norms.

Manubot in the context of open science

Science is undergoing a transition towards openness. The internet provides a global information commons, where scholarship can be publicly shared at a minimal cost. For example, open access publishing provides an economic model that encourages maximal dissemination and reuse of scholarly articles [ 18 , 91 , 92 ] . More broadly, open licensing solves legal barriers to content reuse, enabling any type of scholarly output to become part of the commons [ 93 , 94 ] . The opportunity to reuse data and code for new investigations, as well as a push for increased reproducibility, has begot a movement to make all research outputs public, unless there are bona fide privacy or security concerns [ 95 , 96 , 97 ] . New tools and services make it increasingly feasible to publicly share the unabridged methods of a study, especially for computational research, which consists solely of software and data.

Greater openness in both research methods and publishing creates an opportunity to redefine peer review and the role journals play in communicating science [ 68 ] . At the extreme is real-time open science, whereby studies are performed entirely in the open from their inception [ 98 ] . Many such research projects have now been completed, benefiting from the associated early-stage peer review, additional opportunity for online collaboration, and increased visibility [ 50 , 99 ] .

Manubot is an ideal authoring protocol for real-time open science, especially for projects that are already using an open source software workflow to manage their research. While Manubot does require technical expertise, the benefits are manyfold. Specifically, Manubot demonstrates a system for publishing that is transparent, reproducible, immediate, permissionless, versioned, automated, collaborative, open, linked, provenanced, decentralized, hackable, interactive, annotated, and free of charge. These attributes empower integrating Manubot with an ecosystem of other community-driven tools to make science as open and collaborative as possible.

Code and data availability

The source code and data for this manuscript are available at https://github.com/greenelab/meta-review and archived via Software Heritage identifier swh:1:dir:da789e842d0af90f0fa50de522f0c4caae95e4e3 . Source code for Manubot resides in the following repositories:

Supporting Information

S1 Video: Editing a manuscript on GitHub. This screen recording demonstrates how to propose edits to a Manubot manuscript via GitHub. In the video [ 100 ] , a contributor creates a pull request to add a sentence to the try-manubot manuscript. The contributor then revises the proposed change to add a citation, after which it is accepted, merged, and automatically deployed.

Acknowledgments

We would like to thank the authors of the Deep Review who helped us test collaborative writing with Manubot. The authors who responded favorably to being acknowledged are Paul-Michael Agapow, Amr M. Alexandari, Brett K. Beaulieu-Jones, Anne E. Carpenter, Travers Ching, Evan M. Cofer, Dave DeCaprio, Brian T. Do, Enrico Ferrero, David J. Harris, Michael M. Hoffman, Alexandr A. Kalinin, Anshul Kundaje, Jack Lanchantin, Christopher A. Lavender, Benjamin J. Lengerich, Zhiyong Lu, Yifan Peng, Yanjun Qi, Gail L. Rosen, Avanti Shrikumar, Srinivas C. Turaga, Gregory P. Way, Laura K. Wiley, Stephen Woloszynek, Wei Xie, Jinbo Xu, and Michael Zietz. In addition, we thank Ogun Adebali, Evan M. Cofer, and Robert Gieseke for contributing to the Rootstock manuscript. We are grateful for additional Manubot discussion and testing by Alexander Dunkel, Ansel Halliburton, Benjamin J. Heil, Zach Hensel, Alexandra J. Lee, YoSon Park, Achintya Rao, and other GitHub users. We thank John MacFarlane and Nikolay Yakimov for assistance with Pandoc and the global Binder team for advice on Binder. Finally, we thank C. Titus Brown and the other anonymous reviewers for their help improving this manuscript.

1. Reinventing Discovery Michael Nielsen Princeton University Press (2011-01-31) https://doi.org/gfx2dm DOI: 10.1515/9781400839452 · ISBN: 9781400839452

2. Open Science by Design: Realizing a Vision for 21st Century Research National Academies of Sciences, Engineering, and Medicine National Academies Press (2018-08-09) https://doi.org/gfxzc4 DOI: 10.17226/25116 · PMID: 30212065 · ISBN: 9780309476249

3. TechBlog: “Manubot” powers a crowdsourced “deep-learning” review Jeffrey Perkel Naturejobs (2018-02-20) http://blogs.nature.com/naturejobs/2018/02/20/techblog-manubot-powers-a-crowdsourced-deep-learning-review/

4. Crowdsourcing in biomedicine: challenges and opportunities Ritu Khare, Benjamin M Good, Robert Leaman, Andrew I Su, Zhiyong Lu Briefings in bioinformatics (2016-01) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4719068/ DOI: 10.1093/bib/bbv021 · PMID: 25888696 · PMCID: PMC4719068

5. Opportunities and obstacles for deep learning in biology and medicine Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, … Casey S. Greene Journal of The Royal Society Interface (2018-04) https://doi.org/gddkhn DOI: 10.1098/rsif.2017.0387 · PMID: 29618526 · PMCID: PMC5938574

6. Scientific writing: the online cooperative Jeffrey M. Perkel Nature (2014-10-01) https://doi.org/gbqsnd DOI: 10.1038/514127a · PMID: 25279924

7. A Quick Introduction to Version Control with Git and GitHub John D. Blischak, Emily R. Davenport, Greg Wilson PLOS Computational Biology (2016-01-19) https://doi.org/gbqsnf DOI: 10.1371/journal.pcbi.1004668 · PMID: 26785377 · PMCID: PMC4718703

8. Ten Simple Rules for Taking Advantage of Git and GitHub Yasset Perez-Riverol, Laurent Gatto, Rui Wang, Timo Sachsenberg, Julian Uszkoreit, Felipe da Veiga Leprevost, Christian Fufezan, Tobias Ternent, Stephen J. Eglen, Daniel S. Katz, … Juan Antonio Vizcaíno PLOS Computational Biology (2016-07-14) https://doi.org/gbrb39 DOI: 10.1371/journal.pcbi.1004947 · PMID: 27415786 · PMCID: PMC4945047

9. Opportunities And Obstacles For Deep Learning In Biology And Medicine Johnny Israeli Towards Data Science (2017-05-31) https://towardsdatascience.com/opportunities-and-obstacles-for-deep-learning-in-biology-and-medicine-6ec914fe18c2

10. Opportunities And Obstacles For Deep Learning In Biology And Medicine Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M Hoffman, … Casey S. Greene bioRxiv (2017-05-28) https://doi.org/gbpvh5 DOI: 10.1101/142760

11. Binder 2.0 - Reproducible, interactive, sharable environments for science at scale Project Jupyter, Matthias Bussonnier, Jessica Forde, Jeremy Freeman, Brian Granger, Tim Head, Chris Holdgraf, Kyle Kelley, Gladys Nalvarte, Andrew Osheroff, … Carol Willing Proceedings of the 17th Python in Science Conference (2018) https://doi.org/gfwcm6 DOI: 10.25080/majora-4af1f417-011

12. Formatting Open Science: agilely creating multiple document formats for academic manuscripts with Pandoc Scholar Albert Krewinkel, Robert Winkler PeerJ Computer Science (2017-05-08) https://doi.org/gbrb4c DOI: 10.7717/peerj-cs.112

13. Reference Management Martin Fenner, Kaja Scheliga, Sönke Bartling Opening Science (2013-12-17) https://doi.org/gbxtc8 DOI: 10.1007/978-3-319-00026-8_8

14. Comparison of Select Reference Management Tools Yingting Zhang Medical Reference Services Quarterly (2012-01) https://doi.org/hpv DOI: 10.1080/02763869.2012.641841 · PMID: 22289095

15. Twenty-Five Shades of Greycite: Semantics for referencing and preservation Phillip Lord, Lindsay Marshall arXiv (2013-04-26) https://arxiv.org/abs/1304.7151v1

16. Reviewing post-publication peer review. Paul Knoepfler Trends in genetics : TIG (2015-04-04) https://www.ncbi.nlm.nih.gov/pubmed/25851694 DOI: 10.1016/j.tig.2015.03.006 · PMID: 25851694 · PMCID: PMC4472664

17. Decentralized Trusted Timestamping using the Crypto Currency Bitcoin Bela Gipp, Norman Meuschke, André Gernandt arXiv (2015-02-13) https://arxiv.org/abs/1502.04015v1

18. Open access Peter Suber MIT Press (2012) ISBN: 9780262517638

19. Open science and open science ~/ (2017-06-05) https://lgatto.github.io/open-and-open/

20. Plan S: Accelerating the transition to full and immediate Open Access to scientific publications cOAlition S (2018-09-04) https://www.wikidata.org/wiki/Q56458321

21. Paywall: The Business of Scholarship (2018-10) https://paywallthemovie.com/paywall

22. Journal clubs in the time of preprints Prachee Avasthi, Alice Soragni, Joshua N Bembenek eLife (2018-06-11) https://doi.org/gdm89h DOI: 10.7554/elife.38532 · PMID: 29889024 · PMCID: PMC5995539

23. On author versus numeric citation styles Daniel Himmelstein Satoshi Village (2018-03-12) https://blog.dhimmel.com/citation-styles/

24. TechBlog: Create the perfect bibliography with the CSL Editor Jeffrey Perkel Naturejobs (2017-05-03) http://blogs.nature.com/naturejobs/2017/05/03/techblog-create-the-perfect-bibliography-with-the-csl-editor/

25. ANSI/NISO Z39.96-2019, JATS: Journal Article Tag Suite, version 1.2 National Information Standards Organization NISO (2019-02-08) https://www.niso.org/publications/z3996-2019-jats

26. Journal Article Tag Suite 1.0: National Information Standards Organization standard of journal extensible markup language Sun Huh Science Editing (2014-08-18) https://doi.org/gbxtdk DOI: 10.6087/kcse.2014.1.99

27. NISO Z39.96-201x, JATS: Journal Article Tag Suite Mark H. Needleman Serials Review (2012-09) https://doi.org/gbxtdj DOI: 10.1080/00987913.2012.10765464

28. Data visualization tools drive interactivity and reproducibility in online publishing Jeffrey M. Perkel Nature (2018-02-01) https://doi.org/gfw6g3 DOI: 10.1038/d41586-018-01322-9 · PMID: 29388968

29. Vega-Lite: A Grammar of Interactive Graphics Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, Jeffrey Heer IEEE Transactions on Visualization and Computer Graphics (2017-01) https://doi.org/f92f32 DOI: 10.1109/tvcg.2016.2599030 · PMID: 27875150

30. Collaborative software development made easy Andrew Silver Nature (2017-10) https://doi.org/cdvr DOI: 10.1038/550143a · PMID: 28980652

31. Reproducibility of computational workflows is automated using continuous analysis Brett K Beaulieu-Jones, Casey S Greene Nature Biotechnology (2017-03-13) https://doi.org/f9ttx6 DOI: 10.1038/nbt.3780 · PMID: 28288103 · PMCID: PMC6103790

32. Developing a modern data workflow for evolving data Glenda M Yenni, Erica M Christensen, Ellen K Bledsoe, Sarah R Supp, Renata M Diaz, Ethan P White, SK Morgan Ernest bioRxiv (2018-06-12) https://doi.org/gdqbzn DOI: 10.1101/344804

33. Priority of discovery in the life sciences Ronald D Vale, Anthony A Hyman eLife (2016-06-16) https://doi.org/gcx6gx DOI: 10.7554/elife.16931 · PMID: 27310529 · PMCID: PMC4911212

34. Proof of prespecified endpoints in medical research with the bitcoin blockchain – The Grey Literature https://blog.bgcarlisle.com/2014/08/25/proof-of-prespecified-endpoints-in-medical-research-with-the-bitcoin-blockchain/

35. The most interesting case of scientific irreproducibility? Daniel Himmelstein Satoshi Village (2017-03-08) https://blog.dhimmel.com/irreproducible-timestamps/

36. Bitcoin for the biological literature Douglas Heaven Nature (2019-02) https://doi.org/gft5gp DOI: 10.1038/d41586-019-00447-9 · PMID: 30718888

37. Bitcoin: A Peer-to-Peer Electronic Cash System Satoshi Nakamoto Manubot (2019-11-20) https://git.dhimmel.com/bitcoin-whitepaper/

38. OpenTimestamps: Scalable, Trust-Minimized, Distributed Timestamping with Bitcoin Peter Todd Peter Todd (2016-09-15) https://petertodd.org/2016/opentimestamps-announcement

39. eLife supports development of open technology stack for publishing reproducible manuscripts online Emily Packer eLife Press Pack (2017-09-07) https://elifesciences.org/for-the-press/e6038800/elife-supports-development-of-open-technology-stack-for-publishing-reproducible-manuscripts-online

40. Sci-Hub provides access to nearly all scholarly literature Daniel S Himmelstein, Ariel Rodriguez Romero, Jacob G Levernier, Thomas Anthony Munro, Stephen Reid McLaughlin, Bastian Greshake Tzovaras, Casey S Greene eLife (2018-03-01) https://doi.org/ckcj DOI: 10.7554/elife.32822 · PMID: 29424689 · PMCID: PMC5832410

41. 2017 in news: The science events that shaped the year Ewen Callaway, Davide Castelvecchi, David Cyranoski, Elizabeth Gibney, Heidi Ledford, Jane J. Lee, Lauren Morello, Nicky Phillips, Quirin Schiermeier, Jeff Tollefson, … Alexandra Witze Nature (2017-12-21) https://doi.org/chnh DOI: 10.1038/d41586-017-08493-x · PMID: 29293246

42. GimmeMotifs: an analysis framework for transcription factor motif analysis Niklas Bruse, Simon J. van Heeringen bioRxiv (2018-11-20) https://doi.org/gfxrkc DOI: 10.1101/474403

43. Plasmids for Independently Tunable, Low-Noise Expression of Two Genes João P. N. Silva, Soraia Vidigal Lopes, Diogo J. Grilo, Zach Hensel mSphere (2019-05-29) https://doi.org/gf3vhf DOI: 10.1128/msphere.00340-19 · PMID: 31142623

44. Illusions et hallucinations visuelles : une porte sur la perception Laurent Perrinet The Conversation (2019-06-06) https://theconversation.com/illusions-et-hallucinations-visuelles-une-porte-sur-la-perception-117389

45. Scaling tree-based automated machine learning to biomedical big data with a feature set selector Trang T Le, Weixuan Fu, Jason H Moore Bioinformatics (2019-06-04) https://doi.org/gf3tds DOI: 10.1093/bioinformatics/btz470 · PMID: 31165141

46. Genotyping structural variants in pangenome graphs using the vg toolkit Glenn Hickey, David Heller, Jean Monlong, Jonas Andreas Sibbesen, Jouni Siren, Jordan Eizenga, Eric Dawson, Erik Garrison, Adam Novak, Benedict Paten bioRxiv (2019-06-01) https://doi.org/gf3jfm DOI: 10.1101/654566

47. A set of common software quality assurance baseline criteria for research projects Pablo Orviz, Álvaro López García, Doina Cristina Duma, Giacinto Donvito, Mario David, Jorge Gomes (2017) https://digital.csic.es/handle/10261/160086

48. Vagelos Report Summer 2017 Michael Zietz Figshare (2017-08-25) https://doi.org/gbr3pf DOI: 10.6084/m9.figshare.5346577

49. How I used the Manubot to reproduce the Bitcoin Whitepaper Daniel Himmelstein Steem (2017-09-20) https://busy.org/@dhimmel/how-i-used-the-manubot-to-reproduce-the-bitcoin-whitepaper

50. Systematic integration of biomedical knowledge prioritizes drugs for repurposing Daniel Scott Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini eLife (2017-09-22) https://doi.org/cdfk DOI: 10.7554/elife.26726 · PMID: 28936969 · PMCID: PMC5640425

51. The Kipoi repository accelerates community exchange and reuse of predictive models for genomics Žiga Avsec, Roman Kreuzhuber, Johnny Israeli, Nancy Xu, Jun Cheng, Avanti Shrikumar, Abhimanyu Banerjee, Daniel S. Kim, Thorsten Beier, Lara Urban, … Julien Gagneur Nature Biotechnology (2019-05-28) https://doi.org/gf3fmq DOI: 10.1038/s41587-019-0140-0 · PMID: 31138913

52. Journal of Open Source Software (JOSS): design and first-year review Arfon M. Smith, Kyle E. Niemeyer, Daniel S. Katz, Lorena A. Barba, George Githinji, Melissa Gymrek, Kathryn D. Huff, Christopher R. Madan, Abigail Cabunoc Mayes, Kevin M. Moerman, … Jacob T. Vanderplas PeerJ Computer Science (2018-02-12) https://doi.org/gc5sjf DOI: 10.7717/peerj-cs.147

53. A Systematic Review of Research on the Meaning, Ethics and Practices of Authorship across Scholarly Disciplines Ana Marušić, Lana Bošnjak, Ana Jerončić PLoS ONE (2011-09-08) https://doi.org/fp5drd DOI: 10.1371/journal.pone.0023477 · PMID: 21931600 · PMCID: PMC3169533

54. What Should Be Done To Tackle Ghostwriting in the Medical Literature? Peter C Gøtzsche, Jerome P Kassirer, Karen L Woolley, Elizabeth Wager, Adam Jacobs, Art Gertel, Cindy Hamilton PLoS Medicine (2009-02-03) https://doi.org/bnzbx7 DOI: 10.1371/journal.pmed.1000023 · PMID: 19192943 · PMCID: PMC2634793

55. Ten simple rules for collaboratively writing a multi-authored paper Marieke A. Frassl, David P. Hamilton, Blaize A. Denfeld, Elvira de Eyto, Stephanie E. Hampton, Philipp S. Keller, Sapna Sharma, Abigail S. L. Lewis, Gesa A. Weyhenmeyer, Catherine M. O’Reilly, … Núria Catalán PLOS Computational Biology (2018-11-15) https://doi.org/gfj8wf DOI: 10.1371/journal.pcbi.1006508 · PMID: 30439938 · PMCID: PMC6237291

56. Revisiting authorship, and JOSS software publications C. Titus Brown Living in an Ivory Basement (2019-01-16) http://ivory.idyll.org/blog/2019-authorship-revisiting.html

57. Combined Measurement of the Higgs Boson Mass in pp Collisions at sqrt[s]=7 and 8 TeV with the ATLAS and CMS Experiments G. Aad, B. Abbott, J. Abdallah, O. Abdinov, R. Aben, M. Abolins, O. S. AbouZeid, H. Abramowicz, H. Abreu, R. Abreu, … Physical Review Letters (2015-05-14) https://doi.org/f3nr73 DOI: 10.1103/physrevlett.114.191803 · PMID: 26024162

58. Drosophila Muller F Elements Maintain a Distinct Set of Genomic Properties Over 40 Million Years of Evolution Wilson Leung, Christopher D. Shaffer, Laura K. Reed, Sheryl T. Smith, William Barshop, William Dirkes, Matthew Dothager, Paul Lee, Jeannette Wong, David Xiong, … Sarah C. R. Elgin G3: Genes|Genomes|Genetics (2015-03-04) https://doi.org/gfw5vf DOI: 10.1534/g3.114.015966 · PMID: 25740935 · PMCID: PMC4426361

59. Fruit-fly paper has 1,000 authors Chris Woolston Nature (2015-05-13) https://doi.org/gckfqm DOI: 10.1038/521263f

60. Physics paper sets record with more than 5,000 authors Davide Castelvecchi Nature (2015-05-15) https://doi.org/4sn DOI: 10.1038/nature.2015.17567

61. Ten Simple Rules for Writing a Literature Review Marco Pautasso PLoS Computational Biology (2013-07-18) https://doi.org/gcx9dm DOI: 10.1371/journal.pcbi.1003149 · PMID: 23874189 · PMCID: PMC3715443

62. A Stronger Post-Publication Culture Is Needed for Better Science Hilda Bastian PLoS Medicine (2014-12-30) https://doi.org/gdm9cj DOI: 10.1371/journal.pmed.1001772 · PMID: 25548904 · PMCID: PMC4280106

63. Post-Publication Peer Review: Opening Up Scientific Conversation Jane Hunter Frontiers in Computational Neuroscience (2012) https://doi.org/gdm9cm DOI: 10.3389/fncom.2012.00063 · PMID: 22969719 · PMCID: PMC3431010

64. Post-publication peer review, in all its guises, is here to stay Michael Markie Insights the UKSG journal (2015-07-07) https://doi.org/gdm9ck DOI: 10.1629/uksg.245

65. Introducing Massively Open Online Papers (MOOPs) Jonathan Tennant, Natalia Z Bielczyk, Bastian Greshake Tzovaras, Paola Masuzzo, Tobias Steiner (2019-07-02) https://doi.org/gf7k53 DOI: 10.31222/osf.io/et8ak

66. The HoTT Book Homotopy Type Theory (2013-03-12) https://homotopytypetheory.org/book/

67. Mathematics and Computation | The HoTT book http://math.andrej.com/2013/06/20/the-hott-book/

68. A multi-disciplinary perspective on emergent and future innovations in peer review Jonathan P. Tennant, Jonathan M. Dugan, Daniel Graziotin, Damien C. Jacques, François Waldner, Daniel Mietchen, Yehia Elkhatib, Lauren B. Collister, Christina K. Pikas, Tom Crick, … Julien Colomb F1000Research (2017-11-01) https://doi.org/gc5tcv DOI: 10.12688/f1000research.12037.2 · PMID: 29188015 · PMCID: PMC5686505

69. Nearly 100 scientists spent 2 months on Google Docs to redefine the p-value. Here’s what they came up with Jop Vrieze Science (2018-01-18) https://doi.org/gc5tct DOI: 10.1126/science.aat0471

70. Justify your alpha Daniel Lakens, Federico G. Adolfi, Casper J. Albers, Farid Anvari, Matthew A. J. Apps, Shlomo E. Argamon, Thom Baguley, Raymond B. Becker, Stephen D. Benning, Daniel E. Bradford, … Rolf A. Zwaan Nature Human Behaviour (2018-02-26) https://doi.org/gcz8f3 DOI: 10.1038/s41562-018-0311-x

71. A proposal for regularly updated review/survey articles: “Perpetual Reviews” David L. Mobley, Daniel M. Zuckerman arXiv (2015-02-03) https://arxiv.org/abs/1502.01329v2

72. Why we need the Living Journal of Computational Molecular Science David L. Mobley, Michael R. Shirts, Daniel M. Zuckerman Living Journal of Computational Molecular Science (2017-08-22) https://www.livecomsjournal.org/article/2031-why-we-need-the-living-journal-of-computational-molecular-science DOI: 10.33011/livecoms.1.1.2031

73. The “Paper” of the Future Alyssa Goodman, Josh Peek, Alberto Accomazzi, Chris Beaumont, Christine L. Borgman, How-Huan Hope Chen, Merce Crosas, Christopher Erdmann, August Muench, Alberto Pepe, Curtis Wong Authorea https://doi.org/gf3c59 DOI: 10.22541/au.148769949.92783646

74. The arXiv of the future will not look like the arXiv Alberto Pepe, Matteo Cantiello, Josh Nicholson Authorea https://doi.org/gdqbz3 DOI: 10.22541/au.149693987.70506124

75. TechBlog: C. Titus Brown: Predicting the paper of the future C. Titus Brown Naturejobs (2017-06-01) http://blogs.nature.com/naturejobs/2017/06/01/techblog-c-titus-brown-predicting-the-paper-of-the-future/

76. bookdown Yihui Xie Chapman &Hall/CRC The R Series (2016-12-21) https://doi.org/gdqbz2 DOI: 10.1201/9781315204963

77. Orchestrating a community-developed computational workshop and accompanying training materials Sean Davis, Marcel Ramos, Lori Shepherd, Nitesh Turaga, Ludwig Geistlinger, Martin T. Morgan, Benjamin Haibe-Kains, Levi Waldron F1000Research (2018-10-17) https://doi.org/gfzrwh DOI: 10.12688/f1000research.16516.1 · PMID: 30473781 · PMCID: PMC6234736

78. Decentralised Authoring, Annotations and Notifications for a Read-Write Web with dokieli Sarven Capadisli, Amy Guy, Ruben Verborgh, Christoph Lange, Sören Auer, Tim Berners-Lee (2017-03-18) https://csarven.ca/dokieli-rww

79. Citation.js: a format-independent, modular bibliography tool for the browser and command line Lars G. Willighagen PeerJ Computer Science (2019-08-12) https://doi.org/gf7k5g DOI: 10.7717/peerj-cs.214

80. Continuous Publishing Gobbledygook http://blog.martinfenner.org/2014/03/10/continuous-publishing/ DOI: /2014/03/10/continuous-publishing

81. Opening Science Sönke Bartling, Sascha Friesike (editors) Springer International Publishing (2014) https://doi.org/gdqbzz DOI: 10.1007/978-3-319-00026-8

82. The Building Blocks of Interpretability Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, Alexander Mordvintsev Distill (2018-03-06) https://doi.org/gdvhz5 DOI: 10.23915/distill.00010

83. Announcing idyll.pub Matthew Conlen, Andrew Osheroff Idyll (2018-06-26) https://idyll.pub/post/announcing-idyll-pub-0a3eff0661df3446a915700d/

84. Stencila – an office suite for reproducible research Michael Aufreiter, Aleksandra Pawlik, Nokome Bentley eLife Labs (2018-07-02) https://elifesciences.org/labs/c496b8bb/stencila-an-office-suite-for-reproducible-research

85. Introducing eLife’s first computationally reproducible article Giuliano Maciocci, Michael Aufreiter, Nokome Bentley eLife Labs (2019-02-20) https://elifesciences.org/labs/ad58f08d/introducing-elife-s-first-computationally-reproducible-article

86. Sustainable computational science: the ReScience initiative Nicolas P. Rougier, Konrad Hinsen, Frédéric Alexandre, Thomas Arildsen, Lorena A. Barba, Fabien C. Y. Benureau, C. Titus Brown, Pierre de Buyl, Ozan Caglayan, Andrew P. Davison, … Tiziano Zito PeerJ Computer Science (2017-12-18) https://doi.org/gcx5kf DOI: 10.7717/peerj-cs.142

87. Distill Update 2018 Distill Editors Distill (2018-08-14) https://doi.org/gfbzs9 DOI: 10.23915/distill.00013

88. The appropriation of GitHub for curation Yu Wu, Na Wang, Jessica Kropczynski, John M. Carroll PeerJ Computer Science (2017-10-09) https://doi.org/gb3bxk DOI: 10.7717/peerj-cs.134

89. Innovating Collaborative Content Creation: The Role of Altruism and Wiki Technology Christian Wagner, Pattarawan Prasarnphanich 2007 40th Annual Hawaii International Conference on System Sciences (HICSS’07) (2007) https://doi.org/b6vqgx DOI: 10.1109/hicss.2007.277

90. Code of conduct in open source projects Parastou Tourani, Bram Adams, Alexander Serebrenik 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER) (2017-02) https://doi.org/gfzbs6 DOI: 10.1109/saner.2017.7884606

91. The academic, economic and societal impacts of Open Access: an evidence-based review Jonathan P. Tennant, François Waldner, Damien C. Jacques, Paola Masuzzo, Lauren B. Collister, Chris. H. J. Hartgerink F1000Research (2016-09-21) https://doi.org/gbqrbc DOI: 10.12688/f1000research.8460.3 · PMID: 27158456 · PMCID: PMC4837983

92. How open science helps researchers succeed Erin C McKiernan, Philip E Bourne, C Titus Brown, Stuart Buck, Amye Kenall, Jennifer Lin, Damon McDougall, Brian A Nosek, Karthik Ram, Courtney K Soderberg, … Tal Yarkoni eLife (2016-07-07) https://doi.org/gbqsng DOI: 10.7554/elife.16800 · PMID: 27387362 · PMCID: PMC4973366

93. The Legal Framework for Reproducible Scientific Research: Licensing and Copyright Victoria Stodden Computing in Science & Engineering (2009-01) https://doi.org/b7tskf DOI: 10.1109/mcse.2009.19

94. Legal confusion threatens to slow data science Simon Oxenham Nature (2016-08-03) https://doi.org/bndt DOI: 10.1038/536016a · PMID: 27488781

95. Enhancing reproducibility for computational methods V. Stodden, M. McNutt, D. H. Bailey, E. Deelman, Y. Gil, B. Hanson, M. A. Heroux, J. P. A. Ioannidis, M. Taufer Science (2016-12-08) https://doi.org/gbr42b DOI: 10.1126/science.aah6168 · PMID: 27940837

96. The case for open computer programs Darrel C. Ince, Leslie Hatton, John Graham-Cumming Nature (2012-02-22) https://doi.org/hqg DOI: 10.1038/nature10836 · PMID: 22358837

97. The Open Knowledge Foundation: Open Data Means Better Science Jennifer C. Molloy PLoS Biology (2011-12-06) https://doi.org/g3b DOI: 10.1371/journal.pbio.1001195 · PMID: 22162946 · PMCID: PMC3232214

98. This revolution will be digitized: online tools for radical collaboration C. Patil, V. Siegel Disease Models & Mechanisms (2009-04-30) https://doi.org/fvjhcj DOI: 10.1242/dmm.003285 · PMID: 19407323 · PMCID: PMC2675795

99. Publishing the research process Daniel Mietchen, Ross Mounce, Lyubomir Penev Research Ideas and Outcomes (2015-12-17) https://doi.org/f3mn7d DOI: 10.3897/rio.1.e7547

100. How to edit a manuscript on GitHub with Manubot David Slochower, Daniel Himmelstein Figshare (2019) https://doi.org/gfzb6b DOI: 10.6084/m9.figshare.7946192.v2

IMAGES

  1. Use Cases Relevant Reading Writing Effective Use Cases

    writing effective use cases pdf github

  2. Writing Effective Use Cases Alistair Cockburn Pdf

    writing effective use cases pdf github

  3. Writing Effective Use Cases

    writing effective use cases pdf github

  4. Writing Effective Use Cases

    writing effective use cases pdf github

  5. Writing Effective Use Cases Traduccion

    writing effective use cases pdf github

  6. Writing Effective Use Cases by Alistair Cockburn PDF Download

    writing effective use cases pdf github

VIDEO

  1. 5 Steps to Writing Effective Essays #shorts #ielts

  2. Use RouterOS as intelligent management router for enterprises

  3. How to write effective Test Cases Example

  4. Docs-as-Code Tools I use as a Technical Writer

  5. Effective Writing

  6. Eating African Food Mukbang #funny #viralvideo

COMMENTS

  1. PDF Books-3/Writing Effective Use Cases.pdf at master

    Books-3/Writing Effective Use Cases.pdf. Go to file. GianGC1998 First commit. Latest commit 4957470 on Aug 18, 2018 History. 1 contributor. 834 KB.

  2. PDF Books-1/Writing Effective Use Cases.pdf at master

    Books-1/Writing Effective Use Cases.pdf Go to file Go to fileT Go to lineL Copy path Copy permalink This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Cannot retrieve contributors at this time 834 KB Download Open with Desktop Download

  3. PDF Use Case Template

    Use Case Template (adapted by Pressman and Maxim, Software Engineering: A Practitioner's Approach, pp. 151-152, from Cockburn, A., Writing Effective Use-Cases, Addison-Wesley, 2001) Use Case: <use-case-title> Primary Actor: <primary-actor> Goal in Context: <the aim of the primary-actor in a particular circumstance>

  4. PDF WS14-EiSE-06-Requirements Egineering-Use Cases

    A use case is a set of scenarios tied together by a common user goal. Examples are: (a) "sign up for the exam", (b) "make a bank transfer". Use Cases | 4 Use cases can not be used for all kinds of requirements! Use cases are text stories, widely used to discover and record requirements.

  5. PDF UT II.1

    Other Use Cases Actor Goal (Use Case) Level & Types Use Case Summary Customer Build a House Context Includes Customer buying building supplies from HBS - includes buying, delivery, return. User Login to System Internal Provides appropriate permissions to use functions of the system. Note that the User includes "everybody"

  6. Writing Effective Use-cases, by A. Cookburn

    Use cases? Uh … isn't it one of those UML diagrams with stickmen, boxes, and bubbles? There is more to it, and "Writing Effective Use-cases" by A. Cockburn is the place to start. I am always unsure about how much requirement I need, especially when building prototypes? So, reading about use-cases, I came across it on M. Fowler's post on his "Use Cases". It still costs about 30 ...

  7. Writing effective use cases : Cockburn, Alistair

    Writing effective use cases by Cockburn, Alistair. Publication date 2001 Topics Application software, Use cases (Systems engineering) Publisher Boston : Addison-Wesley Collection inlibrary; printdisabled; internetarchivebooks; americana ... 14 day loan required to access EPUB and PDF files.

  8. How to Write Effective Use Cases?

    To create a Use Case Diagram, select Diagram > New from the toolbar. In the New Diagram window, select Use Case Diagram and click Next. Keep "Blank" selected and click Next. Enter System Use Cases as diagram name and click OK. Press on Actor in the diagram toolbar. Drag it onto the diagram to create an actor and name it Customer.

  9. Writing Effective Use Cases

    Writing Effective Use Cases - Alistair Cockburn - Google Books Sign in Try the new Google Books Books Add to my library Write review Get print book No eBook available Amazon.com Barnes&Noble.com...

  10. PDF Writing

    for use cases, is contained in "Your use case is not my use case" on page 20. Writing Effective Use Cases is a technique guide, describing the nuts and bolts of use case writing. Although you can use the techniques on almost any project, the templates and writing standards must be selected according to the needs of each individual project.

  11. Writing Effective Use Cases 1st Edition

    Writing Effective Use Cases 1st Edition by Alistair Cockburn (Author) 201 ratings See all formats and editions Kindle $33.99 Read with Our Free App Paperback $12.32 - $37.01 96 Used from $2.25 12 New from $33.04 1 Collectible from $29.99 There is a newer edition of this item: Computer Science Department Recommended Bundle $90.27

  12. [PDF] Writing Effective Use Cases

    Object technology expert Alistair Cockburn borrows from his extensive experience in this realm, and expands on the classic treatments of use cases to provide software developers with a "nuts-and-bolts" tutorial for writing use cases. From the Publisher: Writing use cases as a means of capturing the behavioral requirements of software systems and business processes is a practice that is quickly ...

  13. use-cases · GitHub Topics · GitHub

    Rails (API) app that shows different kinds of architecture (one per commit), and in the last one, how to use the Micro::Case gem to handle the application business logic. ruby rails architecture clean-architecture ruby-on-rails interactors use-cases service-objects concerns fat-controller fat-model-skinny-controller domain-objects.

  14. Writing Effective Use Cases [pdf] : programming

    Writing Effective Use Cases [pdf] Close. 2. Posted by 15 years ago. Archived. Writing Effective Use Cases [pdf] cs.otago.ac.nz/cosc46... 1 comment. share. save. hide. report. 57% Upvoted. This thread is archived. New comments cannot be posted and votes cannot be cast. Sort by: best. no comments yet.

  15. (PDF) Writing effective use cases

    Download Free PDF Writing effective use cases alistair cockburn Abstract There are still no trusted guides about how to write (or review) use cases, even though it is now nearly a decade since use cases have become the "norm" for writing functional requirements for object-oriented software systems, and ... Continue Reading Download Free PDF

  16. Open collaborative writing with Manubot

    GitHub is a platform designed for collaborative software development that is adaptable for collaborative writing. From the start, we made the GitHub repository public under a Creative Commons Attribution License (CC BY 4.0). We encouraged anyone interested to contribute by proposing changes or additions.