Publish with Ease
A Low-Cost, Open-Source Solution for Research Data Sharing
Colin Walder
October 9, 2024
Introduction
- Data Steward @ Global Health Engineering (GHE), ETH Zürich
- “Addressing the determinants of health as a function of engineered interventions and systems”.
- One major component of health is WASH – water, sanitation and hygiene
One major problem
WASH practitioners often lack skills in computational data management
openwashdata
- Established in 2021, applying FAIR principles (Wilkinson et al. 2016) to data generated in the WASH sector
- Empower WASH professionals to engage with tools and workflows for open data and code
- Support other organizations with their data management, data events (e.g., hackathon), free data science course
- Core team: Two data stewards and one intern at GHE; many collaborators
openwashdata academy
- 10-week free data science course to empower WASH professionals to engage with tools and workflows for open data
- 200 registrations from 46 countries
- 27 datasets submitted as final projects
How can we streamline the data publishing procedure?
washr
- An R package designed to simplify WASH data publishing
- User-friendly functions to ensure that data adheres to FAIR principles
- Easy to use, with a detailed guide and workflow visualization
So far:
- Almost a dozen datasets published
- Requires minimal computational power
- Easily generalizable to benefit the wider community
Preparing the data
- Start a local version-controlled folder, connect it to GitHub
Preparing the data
setup_rawdata()- Creates
data-rawas suggested inusethisR Package1 - Creates
data_processing.Rfor data cleaning
- Creates
Documenting the data
- Create roxygen skeletons
- Create README
- Codebook describing each variable
- Website with
pkgdownR package1 - Add a license and author(s)
Publishing the data
FAIR principles from a data perspective
- Findable by having an ORCID
- Accessible by utilizing open-source software for both preparing and publishing data
- Interoperable by exporting harmonized data as tidy data
- Reproducible by ensuring transparency in the data cleaning process (see
data-raw/data-processing.R)
FAIR principles from a user perspective
- Findable by publishing tools on accessible platforms (e.g., CRAN, group wiki)
- Accessible by thoroughly documenting functions and use cases
- Interoperable by maximizing generalizability
- Reproducible by consistently updating and revising
What now?
- Continue expanding
washrR package (more functions, tests) - Publish
washron CRAN to make it more accessible - Create meta package to easily download data published on openwashdata.org
- Enhance the guide for building and preparing data packages using
washr - Expand the package’s functionality to be applicable in diverse contexts
Thanks!
Links and Downloads
washr source code: https://github.com/openwashdata-dev/washr
washr guide: https://global-health-engineering.github.io/ghedatapublishing
Slides: https://ghe-open.ch/slides
openwashdata: https://openwashdata.org
Global Health Engineering: https://ghe.ethz.ch/
This material is licensed under Creative Commons Attribution Share Alike 4.0 International.
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Publish with Ease A Low-Cost, Open-Source Solution for Research Data Sharing Colin Walder Global Health Engineering - ETH Zurich October 9, 2024