The three petals of pyOpenSci
pyOpenSci was founded with the mission to build diverse community that supports free and open Python tools for processing scientific data. We also build the technical skills needed to contribute to open source and that support open science. While a diverse, inclusive community is at our core, radiating out from it are the three petals–how we accomplish our community goals–of pyOpenSci. Those are: open peer review, community partnerships, and training and open educational resources.
Peer review
The pyOpenSci open peer review process facilitates scientists getting credit and recognition for the work they’ve invested in developing scientific Python tools. The peer review process also supports scientists in finding vetted and maintained software, which drives their open science workflows.
Software peer review, similar to the review of scientific papers, is a process where scientists vet software code, documentation and infrastructure. pyOpenSci leads an open peer review process run by a community of dedicated volunteers. Reviews are supportive and fully transparent with the shared goal of improving the quality, usability and maintainability of the software that is driving open science.
Learn more about the peer review timeline and roles.
Community partnerships
pyOpenSci adds an extra layer of community-specific review to our established open peer review process. This allows domain-specific scientific Python communities to vet affiliated tools through our robust peer review process. Communities then don’t have to develop and maintain their own review processes and software guidelines.
Our catalog of vetted open source tools makes it easier for scientists to find the trusted tools that they need to develop their open science workflows.
View our growing list of accepted scientific Python packages.
Education
pyOpenSci creates resources to help you navigate the Python packaging ecosystem with ease. Our materials are community-developed and go through extensive technical and pedagogical review. Keep reading to learn more about our approach to education!
pyOpenSci believes in open education resources (OER)
When pyOpenSci publishes community-developed Python tutorials for scientists, it goes through a rigorous process of review and evaluation. All of our tutorials are created through a multi-stage community review process–where tutorials are first developed by the pyOpenSci team or community members–before being reviewed by tool maintainers to ensure ideas and concepts are accurate. Tutorials then go through several rounds of community review for accuracy, usability and accessibility before being published as part of pyOpenSci’s open education resources..
All of our written content is available on the Learn section of our website, where you’ll find resources such as our Python Packaging Tutorial, as well as in-depth guides on Python packaging, documentation, and testing.
Free, online, asynchronous training
Our vision for our free, online trainings is for the curriculum to be added to our library of open education resources, making them free, accessible, and published for anyone to use. And we intend to experiment with delivering this curriculum in an asynchronous, cohort-based manner that allows learners from all over the world to come together for a period of time to work through the curriculum together. If you were part of one of the original cohorts of the R for Data Science Online Learning Community created by our Community Manager, Jesse Mostipak, then you know that these asynchronous trainings will be thoughtfully crafted, a fantastic learning experience, and a ton of fun!
pyOpenSci is the recipient of the Better Software for Science Fellowship, which will help fund the creation of these open educational resources. You can stay up to date with more information on these types of lessons through our Discourse forum as well as our mailing list.
These asynchronous trainings will be free, online, and center around community building and collaborative learning with larger cohort sizes. Part of the intention of these trainings is to foster a vibrant learning community where participants are empowered to learn from one another.
Paid, online, real-time training
pyOpenSci is also developing paid trainings that will be offered online to a smaller cohort of learners, using a customized curriculum. While these will be online, we guarantee they won’t be your typical Zoom workshop! For us, it’s all about conversation, collaborative learning, and engagement. These instructor-led workshops will have a high instructor to student ratio to ensure that all participants receive personalized feedback and guidance on the workshop material. Although these will be paid trainings, we hope to offer scholarships where feasible.
Enroll in pyOpenSci’s upcoming workshop: From Python Code to Module
If you’re interested in participating in our first paid, online, real-time training, sign up for our upcoming workshop: “From Python Code to Module”. This three hour course is intended for individuals who have experience writing Python code and Python functions, and will be taught by pyOpenSci’s Executive Director and founder, Leah Wasser. Leah has over 20 years of experience teaching data-intensive science with an emphasis helping scientists work through the pain points of working with different types of data, and puts an incredible amount of care and attention into ensuring each learner is successful in their educational goals. This is definitely a workshop you don’t want to miss!
In this workshop, Leah will cover:
- How to identify and explain the use of the basic components of a Python package: (a specific directory structure, an init.py file, a pyroject.toml file and some code).
- Creating a basic python package that allows you to install your code into a local Python environment.
- Installing your package in editable mode into a Python environment.
She will also briefly discuss how LLM’s can be used to support tasks such as documenting and formatting your code to improve usability and maintainability. While also considering the ethical and logistical challenges, pitfalls and concerns associated with using AI-based tools in software development.
The course will take place on Thursday, April 25th, from 10AM–1PM Mountain time, and use the Spatial Chat platform. Tickets are $10, and can be purchased on the workshop’s Eventbrite page. The class size is capped at 35, and tickets are selling fast–we hope to see you there!
Connect with us!
You can stay up-to-date with all things pyOpenSci by following us on LinkedIn and Fosstodon, and you can connect with the broader pyOpenSci community on our Discourse forum. And if you’re interested in our weekly newsletter where we share news, blog posts, and monthly updates, subscribe on LinkedIn
Leave a comment