openwashdata

a community effort to bring open data practices to the WASH sector

Lars Schöbitz

Global Health Engineering

January 23, 2024

openwashdata community

Vision

An active global community that applies FAIR principles (Wilkinson et al. 2016) to data generated in the great water, sanitation, and hygiene sector.

Mission

Empower WASH professionals to engage with tools and workflows for open data and code.

The Opportunity

Journal Articles

PDF reports

PDF reports + Dropbox

The Journey

The Product

What does final look like?

Screenshot of the wasteskipsblantyre R package documentation homepage at https://openwashdata.github.io/wasteskipsblantyre/.

Engage

Our channels

One-way communication

Website: openwashdata.org
Newsletter: buttondown.email/openwashdata
Email: ghe@mavt.ethz.ch

Two-way engagement

Instant messaging: Element based on Matrix Chat | openwashdata-lobby | ghe-open
Data donation ideas: github.com/openwashdata/data/issues
Social media: Global Health Engineering LinkedIn

course: data science for openwashdata

ds4owd-001.github.io/website/

Zoom for 10 modules over 2 months at the following times:

Start: 31st October 2023 - 2 pm to 4:30 pm CET
End: 20th February 2024 - 2 pm to 4:30 pm CET

Registration open for next course: https://forms.gle/AhhWpPfnbLwzp5Ai9

free
provides participants with a certificate
using exclusively tools that are free and open source
offers 1:1 coding support for a final project with own data

course: data science for openwashdata 001

200 registrations
110 show-ups
expected 40 graduates all with a reproducible data analysis report (paper)
motivating graduates to publish underlying data with openwashdata

open - misconceptions

Misconception 1: Publishing my data does not benefit anyone
Misconception 2: Others may criticize my code
Misconception 3: Publishing my content under CC-BY-NC will prevent people from exploiting my content commercially. (go for CC-BY)

The only way to write good code is to write tons of shitty code first. Feeling shame about bad code stops you from getting to good code. - Hadley Wickham (Chief Scientist, Posit PBC)

Misconception 1

You don’t know all the ways that somebody else could use to work with the data you collected
Publishing research data can benefit both the researcher and the scientific community. Sharing data increases the visibility of the research, potentially leading to more citations and recognition for the researcher. It promotes collaboration and enables other researchers to build upon the existing work, fostering innovation and scientific progress while avoiding inefficiencies.

Misconception 2

While it is true that open-source code may be subject to criticism, this can be a positive aspect of Open Science. Constructive criticism can help identify potential issues, improve the code, and contribute to the overall quality of the project.

Misconception 3

While the CC-BY-NC (Creative Commons Attribution-NonCommercial) license does restrict commercial use of the content, it may also inadvertently limit the potential impact and reach of your work.

By using a CC-BY-NC license, you prevent the following use cases:

Commercial use in academic research: Researchers working in collaboration with commercial entities or receiving funding from commercial sources may be unable to use your content, limiting the potential for interdisciplinary research and collaboration.
Commercial use in educational materials: Publishers of textbooks, online courses, and other educational materials that are sold for profit may be unable to include your content, reducing its potential reach and impact on students and educators.
Commercial use in software development: Companies developing software or applications that incorporate your content may be unable to do so under a CC-BY-NC license, limiting the potential for innovation and the development of new tools and technologies.
Commercial use in creative works: Artists, writers, and other creators who wish to incorporate your content into their commercial works may be unable to do so, limiting the potential for your work to inspire and influence others.

Thanks 🌻

This project was supported by the Open Research Data Program of the ETH Board.

The slides were created via revealjs and Quarto: https://quarto.org/docs/presentations/revealjs/

You can view source code of slides on GitHub

Or you can download slides in PDF format

This material is licensed under Creative Commons Attribution Share Alike 4.0 International.

References

Greene, Nicola, Sarah Hennessy, Tate W. Rogers, Jocelyn Tsai, and Francis L. de los Reyes III. 2021. “The Role of Emptying Services in Provision of Safely Managed Sanitation: A Classification and Quantification of the Needs of LMICs.” Journal of Environmental Management 290 (July): 112612. https://doi.org/10.1016/j.jenvman.2021.112612.

Mupinga, Ratidzaishe T, Tanaka M Chatema, Savanna R Perumal, Eva Mary, et al. 2021. “Addendum of Data Related to Drying of Faecal Sludge from on-Site Sanitation Facilities and Fresh Faeces.” Gates Open Res 4 (188): 188.

Soeters, S, P Mukheibir, and J Willetts. 2021. “Treatment Technologies in Practice: On-the-Ground Experiences of Faecal Sludge and Wastewater Treatment.”

Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (1). https://doi.org/10.1038/sdata.2016.18.

Yesaya, Mabvuto, Limbani Msuku, Elizabeth Tilley, and Sebastian Camilo Loos. 2023. “Wasteskipsblantyre: Locations of Public Waste Skips in Blantyre, Malawi.” https://doi.org/10.5281/zenodo.6470427.