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Paper #195

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23 changes: 23 additions & 0 deletions .github/workflows/draft-pdf.yml
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on: [push]

jobs:
paper:
runs-on: ubuntu-latest
name: Paper Draft
steps:
- name: Checkout
uses: actions/checkout@v2
- name: Build draft PDF
uses: openjournals/openjournals-draft-action@master
with:
journal: joss
# This should be the path to the paper within your repo.
paper-path: paper/paper.md
- name: Upload
uses: actions/upload-artifact@v1
with:
name: paper
# This is the output path where Pandoc will write the compiled
# PDF. Note, this should be the same directory as the input
# paper.md
path: paper/paper.pdf
34 changes: 34 additions & 0 deletions paper/paper.bib
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@article{cwl,
Author = {Michael R. Crusoe and Sanne Abeln and Alexandru Iosup and Peter Amstutz and John Chilton and Nebojša Tijanić and Hervé Ménager and Stian Soiland-Reyes and Bogdan Gavrilovic and Carole Goble and The CWL Community},
Title = {Methods Included: Standardizing Computational Reuse and Portability with the Common Workflow Language},
Year = {2021},
Eprint = {arXiv:2105.07028},
Doi = {10.1145/3486897},
}

@misc{wdl,
author = {WDL Community},
title = {Workflow Description Language (WDL)},
year = {2017},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/openwdl/wdl}
}

@misc{miniwdl,
author = {miniwdl Community},
title = {miniwdl},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/chanzuckerberg/miniwdl/}
}

@misc{cwl_utils,
author = {CWL Community},
title = {cwl-utils},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/common-workflow-language/cwl-utils}
}
56 changes: 56 additions & 0 deletions paper/paper.md
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---
title: 'wdl2cwl: A Python tool for converting WDL workflows to CWL'
tags:
- Python
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- workflow
- workflow description language
- common workflow language
authors:
- name: Dinithi Wickramaratne
- name: Dennis Chukwunta
- name: Michael R. Crusoe
orcid: 0000-0002-2961-9670
- name: Bruno P. Kinoshita
orcid: 0000-0001-8250-4074
- name: Peter Amstutz
orcid: 0000-0003-3566-7705
date: August 2022
bibliography: paper.bib
---

# Summary

Computational workflows are used in data analysis, enabling innovation and
decision-making in various fields of science. There are several competing workflow description systems,
WDL (Workflow description Language) and CWL (Common Workflow Language) are two examples
of popularly used systems. WDL offers a way to specify data processiing workflows with
a human-readable and writeable syntax. WDL makes it straightforward to define
complex analysis tasks, chain them together in workflows, and parallelize their execution [@wdl].
CWL on the other hand, is a way to describe command line tools and connect
them together to create workflows. Because CWL is a specification and not a
specific piece of software, tools and workflows described using CWL are portable across
a variety of platforms that support the CWL standard [@cwl].

Groups using WDL or CWL can find it difficult to learn the other workflow definition
language if they need to run their workflows in other platforms. It becomes even harder
when the workflows contain domain specific knowledge mixed with the definitions, and
when analysts and scientists need to consider future collaboration of other groups on
the workflows definitions.

``wdl2cwl`` is a Python package for converting workflow written in WDL to workflows in CWL.
The ``wdl2cwl`` package uses the ``miniwdl`` package [@miniwdl], an open source, Python package,
developer toolkit and local runner for running WDL files. The ``wdl2cwl`` package was
designed to provide a class-based and developer-friendly interface for extracting
the various sections of a WDL file, dynamically create the various objects
needed to create a valid CWL file ( this is done using the ``cwl-utils`` package [@cwl_utils],
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an open source, Python package for parsing CWL files) and performing error checking
for valid imports, input declarations, expressions and other useful operations on CWL files.

``wdl2cwl`` was designed for users familiar with CWL or WDL, and can be used in different domains such as data analytics, bioinformatics, GIS, and more. Users that have CWL workflows, for instance, will not have to worry
about installing the dependencies for running a WDL workflow. It also eases the learning curve when one knows CWL and needs to use WDL and vice-versa.

# Acknowledgements

We acknowledge the work by the developers of miniwdl, CWL and WDL.
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# References