---
title: "Examples of Developing Pathways"
source: The Turing Way
source_url: https://github.com/the-turing-way/the-turing-way
licence: CC-BY-4.0
domain: data-and-analysis
subdomain: turing-way
date_added: 2026-04-25
---

(ch-pathways-example)=
# Examples of Developing Pathways

This subchapter provides examples, each demonstrating distinct approaches to curating learning pathways in the data science domain.
These examples emphasise the importance of community engagement and the strategic leveraging of resources in developing pathways for learning and referencing.

(ch-pathways-example-dswb)=
## Example 1: Data Science Without Borders (DSWB) - Community-Driven Curation

The [DSWB pathway](#pw-data-science-without-borders), developed for an international network in Africa, prioritised inclusivity and direct relevance to diverse project requirements.

### Methodology:

The curation process was facilitated by Malvika Sharan and Precious Onyewuchi, technical partners in DSWB. They applied an iterative and collaborative process as described below:

- Community Brainstorming and Needs Identification: DSWB community members reviewed _The Turing Way_ book to identify chapters most relevant to their ongoing work.
This was facilitated through coworking sessions and working group meetings, fostering a shared understanding of needs.
- Initial Chapter Selection and GitHub Integration: A preliminary list of potentially valuable chapters was compiled and documented on a dedicated GitHub page for transparent review and feedback.
- Community Review and Prioritisation: The curated list was then shared back with the DSWB community for a second evaluation.
Members specifically commented on each chapter, indicating whether it should be retained or removed based on its immediate applicability to current projects.
Chapters deemed potentially useful in the future but not immediately required were removed by consensus, with the possibility of later reintegration, to ensure the pathway remained focused.
- Feedback Integration and Finalisation: All feedback and suggestions received on GitHub were carefully considered.
Future content recommendations were noted. The finalised DSWB pathway was then submitted as a pull request to merge into the main _Turing Way_ book, solidifying community ownership and integration.

### Outcomes and Insights:

The DSWB pathway curation process yielded several key insights:

* Community as Experts: Empowering the community to identify their own learning needs resulted in a highly relevant and targeted pathway.
* Iterative Feedback: Multiple rounds of review and feedback ensured the final content directly addressed the community's requirements.
* Focus on what is needed: Prioritising currently needed content significantly enhanced the pathway's utility and prevented information overload.
* Transparent Process: Utilising GitHub facilitated collaborative review and fostered a strong sense of ownership within the community.

(ch-pathways-example-citation)=
## Example 2: Software Citation - Thematic Workshop for Focused Content Development

Curation of the [Software Citation pathway](#pw-software-citation) employed a workshop-based methodology to address a specific thematic challenge.

### Methodology:

In contrast to the broad, community-driven DSWB approach, the development of this pathway occurred during a dedicated workshop as part of the Research Software Funders Forum.
The initiative's proposal was developed collaboratively by experts and stakeholders, and the workshop attendees were invited to discuss solutions to the scattered knowledge base, leading to inconsistent guidance on research software sharing.

Researchers, publishers, and funders identified a critical challenge: existing guidance on research software sharing was fragmented across numerous websites, lacking a unified and clear set of instructions for different stakeholders.
This created confusion and hindered the adoption of best practices in software citation and preservation.

While a long-term solution involved establishing a central, community-driven website (proposed as Cite.Software), an intermediate solution was co-developed: the Software Citation Pathway in _The Turing Way_.
Key stakeholders, including Chris Erdmann (ScilifeLab Sweden), Malvika Sharan (_The Turing Way_), and Carlos Martinez (The Netherlands eScience Centre), facilitated the identification of best practices and curated relevant chapters from _The Turing Way_.
Gaps were identified and documented on GitHub issues for follow-up.

### Outcomes and Insights:

The workshop-based approach for the Software Citation pathway demonstrated the effectiveness of:

- Targeted Thematic Focus: Concentrating on a specific challenge allowed for in-depth discussion and the development of a highly focused solution.
- Convening Practitioners: Bringing together key individuals and organisations ensured diverse perspectives and strong buy-in.
- Leveraging Existing Resources: Building upon the content of _The Turing Way_ and FAIR software recommendations provided a robust foundation.
- Collaborative Development: The workshop format actively fostered collaboration and the co-creation of the proposed solution.

## Different Ways to Engage Different Groups

Both the DSWB and Software Citation pathways offer invaluable strategies for creating effective learning resources.
The DSWB pathway provides a good example for community-led initiatives in identifying and addressing diverse learning needs through an iterative and inclusive process. 

The Software Citation pathway highlights the efficacy of focused workshops in tackling specific challenges by engaging key stakeholders and strategically leveraging existing knowledge.

These examples highlight the role of community involvement and targeted engagement in curating learning pathways.
By empowering communities to define their needs and actively contribute to content development, and by strategically convening stakeholders to address specific thematic areas, we can create more relevant, engaging, and ultimately more effective learning experiences for the data science community.
The lessons learned from these initiatives can be applied to future pathway development efforts, fostering a more engaged and empowered data science community.
