diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d682a068..18b0f92c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -41,8 +41,8 @@ repos:
additional_dependencies:
- tomli
-# - repo: https://github.com/pre-commit/mirrors-prettier
-# rev: v3.1.0
-# hooks:
-# - id: prettier
-# types_or: [yaml]
+ - repo: https://github.com/pre-commit/mirrors-prettier
+ rev: v3.1.0
+ hooks:
+ - id: prettier
+ types_or: [yaml, html, css, scss, javascript, json, toml]
diff --git a/_posts/2019-10-26-pyos-min-mistakes-theme.md b/_posts/2019-10-26-pyos-min-mistakes-theme.md
index 2e30bb95..b9b23696 100644
--- a/_posts/2019-10-26-pyos-min-mistakes-theme.md
+++ b/_posts/2019-10-26-pyos-min-mistakes-theme.md
@@ -7,7 +7,7 @@ permalink: docs/pyos-website/
header:
overlay_filter: 0.6
overlay_color: "#333"
-last_modified: 2024-08-28
+last_modified: 2024-08-23
toc: true
categories:
- docs
@@ -117,16 +117,13 @@ You can also adjust the width inline:
### Figure -- align right
-Below is a right aligned image
+And now we're going to shift things to the **right align**. Again, there should be plenty of room above, below, and to the left of the image. Just look at him there --- Hey guy! Way to rock that right side. I don't care what the left aligned image says, you look great. Don't let anyone else tell you differently.
Growth of pyOpenSci from 2019 to 2024 Growth of pyOpenSci from 2019 to 2024 Growth of pyOpenSci from 2019 to 2024 Growth of pyOpenSci from 2019 to 2024
-
-And now we're going to shift things to the **right align**. Again, there should be plenty of room above, below, and to the left of the image. Just look at him there --- Hey guy! Way to rock that right side. I don't care what the left aligned image says, you look great. Don't let anyone else tell you differently.
-
The html looks like this:
@@ -187,53 +184,6 @@ The image below should extend outside of the parent container on right.
> meaningful progress.
{: .highlight-quote }
-
-### Quotes using include files
-
-You can also create blockquotes using include files.
-Below is a green and magenta version of the same quote
-
-````html
-{% raw %}
-{% include pyos-blockquote.html quote="[*I want to... *] Streamline the development of good quality, socially responsible, and easily shareable software." author="Anonymous" event="AGU 2019 Townhall" class="highlight magenta" %}
-{% endraw %}
-````
-
-{% include pyos-blockquote.html quote="[*I want to... *] Streamline the development of good quality, socially responsible, and easily shareable software." author="Anonymous" event="AGU 2019 Townhall" class="highlight magenta" %}
-
-```markdown
-{% raw %}
-{% include pyos-blockquote.html quote="[*I want to... *] Streamline the development of good quality, socially responsible, and easily shareable software." author="Anonymous" event="AGU 2019 Townhall" class="highlight purple" %}
-{% endraw %}
-```
-
-{% include pyos-blockquote.html quote="[*I want to... *] Streamline the development of good quality, socially responsible, and easily shareable software." author="Anonymous" event="AGU 2019 Townhall" class="highlight purple" %}
-
-The default color for the quotes is the pyos teal green.
-
-```
-{% raw %}
-{% include pyos-blockquote.html quote="[*I want to... *] Streamline the development of good quality, socially responsible, and easily shareable software." author="Anonymous" event="AGU 2019 Townhall" class="highlight" %}
-{% endraw %}
-```
-
-{% include pyos-blockquote.html quote="[*I want to... *] Streamline the development of good quality, socially responsible, and easily shareable software." author="Anonymous" event="AGU 2019 Townhall" class="highlight" %}
-
-### Quotes in notice blocks
-
-```
-
-{% raw %}
-{% include pyos-blockquote.html quote="[*I want to... *] Streamline the development of good quality, socially responsible, and easily shareable software." author="Anonymous" event="AGU 2019 Townhall" class="highlight" %}
-{% endraw %}
-
-```
-
-
-{% include pyos-blockquote.html quote="[*I want to... *] Streamline the development of good quality, socially responsible, and easily shareable software." author="Anonymous" event="AGU 2019 Townhall" class="highlight" %}
-
-
-
## Buttons
Make any link standout more when applying the `.btn .btn--primary` classes.
diff --git a/_posts/2024-08-19-how-pyopensci-was-started.md b/_posts/2024-08-19-how-pyopensci-was-started.md
new file mode 100644
index 00000000..088b520b
--- /dev/null
+++ b/_posts/2024-08-19-how-pyopensci-was-started.md
@@ -0,0 +1,296 @@
+---
+layout: single
+title: "Crafting pyOpenSci: creating a vibrant, supportive, and thoughtfully open (science) community"
+excerpt: "Dive into my journey of nurturing a vibrant, inclusive, pyOpenSci community. Learn about how things started, how the organization evolved, what we learned, and where we are today. Experience the evolution, challenges, and victories that have shaped **pyOpenSci** into today's thriving community."
+author: "Leah Wasser"
+permalink: /blog/how-pyopensci-got-started.html
+header:
+ overlay_image: images/headers/scipy-2024-workshop.png
+ overlay_filter: rgba(20, 13, 36, 0.8)
+categories:
+ - blog-post
+ - community
+classes: wide
+toc: true
+comments: true
+---
+
+
+
+## TL;DR
+
+*
+
+I founded pyOpenSci in 2018. The idea was born from a fusion of frustration and
+inspiration. As someone deeply involved in teaching open science with Python and
+who had taught R previously with many more successes, I was surprised by the
+challenges I experienced. At the same time, I clearly understood what a powerful
+language Python is and how valuable knowing it would be to students' careers.
+After all, it's consistently been a top 10 data science favorite (top 1!) language
+according to Stack Overflow (link) and other surveys.
+
+The cohesiveness that I had experienced in the R community wasn't as present in
+the scientific Python ecosystem. These experiences sparked a desire in me to
+create a more supportive and accessible community for scientists so that they
+could be empowered to fully embrace Python as a tool—especially given its use in
+the workplace.
+
+What began as a small initiative, with meetings of 3-4 people in 2018, has since
+evolved into a thriving, inclusive community that is:
+
+* x packages reviewed
+* editorial team of XX
+* over 250 contributors
+* formally partnered with Astropy and the Journal of Open Source Software (JOSS)
+
+pyOpenSci is a vibrant community that has grown through persistence, collaboration,
+and learning from successes and setbacks.
+
+This blog post will overview how we got to where we are today.
+
+## The why behind creating pyOpenSci -- Concept and history
+
+I've taught data science for a long time. When I built the [Data Skills program at NEON](TODO: LINK HERE), I focused on teaching R because it was the go-to language for ecologists. Later, when I created the Earth Analytics education program at CU Boulder,
+
+I initially taught R but quickly realized that a significant portion of the earth science community used Python. Data I collected from industry and academic sources confirmed that Python was the most in-demand language for scientists seeking professional jobs
+
+[TODO: add zenodo citation and link to the report and add results plot].
+
+### Transitioning from R to Python to teach earth and environmental data science
+
+Transitioning the Earth Analytics program to Python deepened my understanding of the challenges students face at all levels when learning Python, particularly for open science applications. Though necessary, this shift also revealed new obstacles for me as an educator teaching scientists new to coding and eager to learn open science best practices to overcome.
+
+### Navigating the Transition to Python
+
+Transitioning from teaching spatial earth data science with R to Python was difficult. The thousands of packages to select from to teach on PyPI felt overwhelming. I spent significant time deciding which ones to incorporate into our online, open curriculum. Once I found the right package, the next hurdle was learning:
+
+* how to use these packages effectively
+* how to effectively teach them to beginners.
+
+I selected packages carefully based on the package's:
+
+* ease of use
+* quality of documentation
+* maintenance
+
+### Python specific pain points that I experienced
+
+Unfortunately, I often found the package documentation either too technical or missing, leaving me to figure out how to use the packages on my own. This process usually led to extensive dialogues with package maintainers as I sought to understand and clear up bugs. Learning how to process spatial data in Python made me feel like a beginner again. I could process spatial data with my eyes closed using R, Matlab and even ArcGIS / QGIS.
+
+What stood out to me during this transition was that I had to write more code using Python to do the same things I could do using less code in R. This was largely because R had a lot of "Wrapper" packages that made normal plotting ana anlayiss operations straight forward.
+
+Combine the experience of writing more code with bugs and navigating the labyrinth of highly / not beginner-friendly technical package documentation left me frustrated.. This, in turn, resulted in my developing relationships with numerous package maintainers who sometimes even used our online lessons to support their documentation; this was an ideal synergy when it happened.
+
+Through these experiences, I realized that developing online lessons in Python for spatial earth data science was a continuous learning and adaptation journey. The challenges encountered along the way were not simply obstacles. I saw opportunities to engage with the scientific open-source Python community to enhance the learning experience for my students and any human trying to learn Python to develop open science workflows.
+
+### Creating my first Python package - EarthPy - to streamline spatial data processing
+
+To address some of these challenges, I created EarthPy, a package designed to simplify standard spatial data processing and visualization tasks. At the time, creating a Python package seemed amorphous - I didn't know what best practices were and I certainly didn't know how to create a package. It was easy to learn in R as there was one way to do it.
+
+I learned to build a Python package by mimicking the infrastructure used by GeoPandas, one of my favorite spatial libraries. The clip module from EarthPy was later upstreamed into GeoPandas (link to PR).
+
+EarthPy's marked the start of my deep dive into open-source software and the scientific Python ecosystem.
+
+## pyOpenSci's foundation - learning from other communities including rOpenSci, JOSS and the broader scientific Python community
+
+Based on my own experiences, I saw a demand for a community that could support scientists in both creating better packages with beginner-friendly and accessible documentation and finding high-quality packages that had maintainers committed to maintaining them and were well-built and documented. A modified version of the rOpenSci model would be invaluable to scientists in the vast Python ecosystem.
+
+### Learning from and leveraging the work rOpenSci has done
+
+I wanted pyOpenSci to learn from our friends at rOpenSci. After all, the true spirit of open science and open source is about building on each other's work.
+
+rOpenSci was the first to pioneer software peer review processes, allowing scientists to get academic credit for improving software. Their work inspired me. However, I knew we needed to tailor the rOpenSci processes to support the unique needs of the scientific Python community.
+
+This was particularly true when it came to packaging - Phew!
+
+A group even discussed the idea of pyOpenSci at the annual SciPy meeting one year. While I heard there was plenty of enthusiasm in the SciPy meeting room at the time, these ideas never gained traction. I suspect many people were unsure what a Python version of "OpenSci" would look like.
+
+
+### The R and Python communities are fundamentally different
+
+In my experience, having taught and used both R and Python, this is because the two communities are quite different. The Python community spans numerous applications from web development to xxxxx. Its science part includes many domain-specific sub-communities, with Astropy being the most developed in terms of age and governance / structure.
+
+Because of this:
+
+* There are many different ways to do one thing in Python. In fact, even installing Python presents many options (Conda vs python.org vs...). This choice is valued in the Python community, but it's also a challenge for beginners.
+* There are thousands of packages on PyPI (the packaging index from which you can get packages). It can be hard to figure out which one is the best, which is why, in part, we started a peer review of Sofwta (more on that below).
+* Python is used much more broadly for many different applications beyond
+ science.
+* Python has many more approaches and complexities associated with packaging.
+* Python is more often used as a "glue" language, meaning it's common for
+ scientists to have compiled C and other language extensions in their Python
+ code and packages.
+* The Python community is larger and generally more dispersed.
+* There is not a single core decision-making body within the Python ecosystem. Sure, there are core Python developers who work on making Python the awesome language it is, but...
+
+## Step 1: Early pyOpenSci community meetings - Building pyOpenSci
+
+To try to build momentum around pyOpenSci, I began by holding community meetings. The first few meetings in 2018 were small and comprised of myself and a few others. Chris Holdgraf, now the Executive Director of [2i2c](https://2i2c.org), was instrumental in the early days and helped me run two community events at meetings (more on that below). We'd also get incredible folks like Filipe Fernandes—a [conda-forge](https://conda-forge.org) maintainer who has since joined our editorial team—and folks from projects like [rOpenSci](https://ropensci.org), who reviews R packages, the [Journal of Open Source Software](https://joss.theoj.org), which reviews and publishes software providing academic credit for any software with a research application, and the [Software Sustainability Institute](https://software.ac.uk).
+
+As weeks passed and we continued to meet, friends told other friends, and our core crew began
+to grow ([link to early meeting notes]()).
+
+
+
+We slowly built up momentum.
+
+Meetings became increasingly productive, discretely focusing on developing a proof-of-concept scientific Python software peer review process.
+
+Discussions included:
+
+### 1. Community Building and Governance:
+The early discussions focused on establishing the structure of pyOpenSci and its governance. We discussed setting up community guidelines and a code of conduct to ensure a welcoming environment for contributors. Even in the early meetings, we emphasized the importance of inclusivity and diversity within the budding pyOpenSci community of practice.
+
+What is a community of practice? A community of practice (CoP) is a group of people
+with a shared interest who come together to achieve common goals. In
+the pyOpenSci community, our focus is on open science and open source. We engage in
+joint activities, help each other with challenges, and share information.
+{:: .notice }
+
+### 2. Developing a Python-specific software Peer Review Process:
+
+In many initial meetings, we discussed ways to create a truly Python-centric pyOpenSci peer review process to better support high-quality scientific Python software. The goal was to create a transparent, constructive, non-adversarial review system and guidelines around creating a package. A community-run peer review process would slowly elevate the quality and standards of Python packages over time while making it easier for scientists to find the quality tools they need. And, of course, it was important that the software that we reviewed would be maintained over time and documented in a way that helped beginners harness the full potential of each tool without too much struggle.
+
+### 3. Collaborating with Journal of Open Source Software (JOSS)
+
+Collaboration with the JOSS was a recurring theme too. We wanted to partner with JOSS to allow someone who went through our peer review process to get academic credit for their work.
+
+Why is academic credit so important?
+You see, in academic traditionally you can get credit for writing peer reviewed papers. While I'm a deep advocate of scientific research, there are a lot of problems with a system that only values highly technical papers that only other scientists can understand. Related to open source software, this means that the scientists don't get actual "credit" for high quality software that is needed to make data processing more efficient and more open.
+
+Frankly, this lack of credit for important work that is driving open science - system stinks.
+
+JOSS is a journal that reviews software and when accepted, provides a cross-ref enabled DOI. This is different than a DOI you'd get by linking your GitHub repo to a platform like Zenodo . A cross-ref DOi is connected to a scientist's OrcidID, which is their unique identifier. What does this mean?
+
+It means actual academic credit as a "publication" for the hard work of creating a research-focused open source software tool.
+
+However, pyOpenSci reviews software, our goals are different than those of JOSS.
+
+we care about the python specific packaging painpoints. As such we are developing review guideblines for what makes a sicentific Python software good
+we care about long term maintenance of software. While JOSS will publish a paper, they don't necessarily engage with the maintainer or support the maintainer after. As a community of practice, we do
+The partnership aimed to streamline the submission and review process across both organizations so pythonista could harness the python specific review process and also post review support of the community while also getting academic credit for their work.
+
+In short - You can submit a package to pyOpenSci; we will review it, and if it's in scope for JOSS< then you can become accepted by JOSS, too. They take our review and only review your software's paper.
+
+This partnership is a true win/win for JOSS and us. It provides a structured pathway for scientific software developers to earn academic credit for their work while also gaining visibility and community support from pyOpenSci.
+
+### 4. Challenges and Strategic Goals
+
+Some of the biggest challenges in peer review revolved around project maintenance. So many scientific Python tools are maintained by one person. We wanted to ensure that the pyOpenSci software ecosystem represented maintained tools that scientists could trust. This meant thinking hard about what happened to a package after it was reviewed.
+
+How could we support maintainers in updating their packages over time?
+
+We knew that this type of effort would require some funding support. Strategic goals included expanding the community's reach, increasing engagement through events, and securing sustainable funding.
+
+### 5. Holding pyOpenSci events and connecting with the community
+
+We also wanted to get the word out about pyOpenSci to encourage new package submissions, and to grow our editorial and reviewer base. After all, at the time, we were just a small group of scientists talking about this community. We discussed holding events at meetings such as SciPy and AGU . And we discussed holding maintainer sprints It's funny to look back at those early meetings, now that we are actually doing all of the things we talked about back then and more!
+
+
+Even then, we knew that pyOpenSci was about more than reviewing software. It was about supporting science and getting more scientists involved.
+
+We launched software peer review formally in 2019. We initially identified a package to review that was internal to our team—as a prototype. We even tested out the peer-mentor review approach, pairing someone newer to peer review with someone more seasoned to see how it would go.
+
+From there, things took off. Especially as I began to travel to scientific meetings and promote the work. More on that below
+
+## Understanding the community's needs and interests interest: outreach at conferences 2019-2021
+
+Before fully launching pyOpenSci, I trusted my professional instincts that others in the scientific Python ecosystem faced similar challenges to those my students and I encountered. However, I needed evidence from the community to justify pursuing funding for pyOpenSci. I needed the community to tell me what they needed so I strategically build pyOpenSci to file down those pain points.
+
+To do this, I held several community meetings between 2019 and 2021, including sessions at the SciPy and AGU conferences.
+
+By holding community events at SciPy and AGU, I was able to reach diverse communities to understand their needs. While both communities shared an interest in scientific Python, their focus areas differed
+
+* SciPy attendees are more software-centric
+* AGU attendees are generally more science focused with deep roots in the earth and environmental sciences.
+
+Getting input from both the scientific and the scientific Python communities was invaluable in shaping pyOpenSci's direction and ensuring that our efforts aligned with the needs of both software developers and scientists.
+
+### SciPy Meeting 2019 & 2021: A Filled Room and a Lively Discussion Around Peer Review of Scientific Software
+
+In 2019, we held our very first pyOpenSci event—a Birds of a Feather (BoF) session at the SciPy meeting in Austin, Texas. It was me, Chris Holdgraf and Jenny Palomino leading the session. The second event was virtual and co-hosted by the wonderful, David Nicholson.
+
+link - more about scipy - link to 2024 blog
+
+During these events, the goal was to engage the scientific Python community about pyOpenSci and our peer review process and better understand some of the pain points. We held a followup Bof in 2021 but this time in a virtual space given the COVID-19 pandemic was still in full swing.
+
+What is a BoF? A bof is an informal meeting where participants with similar interests gather to discuss a specific topic, often leading to valuable insights and collaborations. If you've been to PyCon before, the Open Space structure is similar to SciPy Bof sessions
+{: .notice }
+
+Both SciPy BoFs were well-attended with at least 30-40 people in the room each time. The discussions centered around pyOpenSci's peer review process and the community's pain points. These sessions helped us gauge the community's needs and interests, which were crucial in shaping our initiatives.
+
+## 2019 AGU (American Geophysical Union) Town Hall
+
+In 2019 and again in 2021, I held town halls at the massive AGU meeting. My friend and long time colleague [Chris Holdgraf](https://github.com/choldgraf)
+
+agreed to co-lead the very first in person town hall with me, which was a lot
+of fun! We had well over 60 people in the room. At that time, the incredible organization 2i2c was still just a
+vision, with efforts underway to make it a reality. And look where it is now.
+
+If you haven't been to AGU before, it's a huge (20,000-25.000 person) international conference. Given my background in earth and environmental science
+and remote sensing, this meeting had long been my absolute favorite. It was
+where "my people" were—where I could talk for days about sensors and remote
+sensing.
+
+The AGU meeting also has a strong bioinformatics track. The town hall
+resulted in a full room of geoscientists who cared about Python.
+
+
+
+This experience reinforced that pyOpenSci was not only needed but wanted
+within the scientific Python community. From there, our peer review process
+began to grow.
+
+
+In 2019, I also hosted a town hall at the massive AGU (American Geophysical Union) meeting. My long-time colleague and friend, [Chris Holdgraf](https://github.com/choldgraf), co-led the session with me, which was a lot of fun! At the time, 2i2c, the incredible organization Chris would go on to lead, was still in its early stages of becoming a reality.
+
+AGU is a huge international conference with a geoscience focus, aligning closely with my earth and environmental science and remote sensing background. The AGU meeting has always been my favorite—-I could connect with "my people" and talk for days about sensors and remote sensing. This town hall was important because the audience is quite different from that at sciPy. At SciPy you meet a lot of maintainers and tool developers. While there are beginning Pythonistas there, the audience tends to be more technically focused. AGU, on the other hand, is this lovely mixture of earth scientists, geosceintists and also some open source and bioinformatcs folks.
+
+This can be clearly seen in the data we collected
+at SciPy over 80% of bof attendees were maintainers or people who had contributed to open source before compared to XX % at agu.
+
+
+The AGU town hall attracted a full room of geoscientists who were passionate about Python, reinforcing that pyOpenSci was not only needed but also wanted within the scientific Python community. The feedback and insights from this event further fueled the growth of our peer review process.
+
+### Outcomes from the early meetings
+
+what did people need and want
+include quotes and a plto or two.
+
+
+> * clarify python packaging. Too many different mechanisms floating around...
+> * What are best practices of sharing the code?
+> * Training about developing tools
+> * Training for people who can code for themselves, but want to start developing software for others. Style, documentation, testing, got, etc.
+
+
+## Getting funding
+* applied to Sloan - etter software for science
+* got funding - it was a ahrd time - king soopers shooting...
+* decided to leave academia and pursue what makes me happy - community work.
+
+the rest is history....
+
+## **pyOpenSci Today**
+
+- **Joining pyOpenSci**: A primer on how individuals can contribute to and engage with pyOpenSci, including practical steps for getting involved.
+
+
+
+**Community Feedback and Needs**: - Collating a comprehensive summary of community expectations. - Insight into how these expectations steered the project towards its current approach.
+
+
+
+***************
+
+
+I've always trusted by professional gut when it comes to building new programs. Everything inside of me was telling me that if I and my students were encountering these challenges in the scientific python ecosystem - that others were experienceing them too. But i needed that evidence from the community before trying to get funding for pyOpenSci. So in an effort to get the word out and better understand community needs, I held severa
+
+
+
+* **Open Licensing Challenges**: These are particularly relevant for those working at agencies or corporations with licensing policies that do not fully embrace open source principles.
+* **packaging challenges**:
+* **Peer Review Partnership**: Astropy members were present, and they were developing their own peer review process at the time. We spent some time talking about what would happen after the review ends in terms of tracking package maintenance over time.
+
+
+
diff --git a/_posts/2024-08-21-pyopensci-monumental-growth-2024.md b/_posts/2024-08-21-pyopensci-monumental-growth-2024.md
new file mode 100644
index 00000000..37e66f6e
--- /dev/null
+++ b/_posts/2024-08-21-pyopensci-monumental-growth-2024.md
@@ -0,0 +1,418 @@
+---
+layout: single
+title: "It's Been a Long Short Road: The Monumental Past 2 Years of pyOpenSci"
+excerpt: "Learn about what pyOpenSci has accomplished in the last two years, including the evolution of our packaging guide, the expansion of our peer review process, and the vibrant community we've built."
+author: "Leah Wasser"
+permalink: /blog/what-pyopensci-accomplished-with-two-years-of-funding.html
+header:
+ overlay_image: images/headers/scipy-2024-workshop.png
+ overlay_filter: rgba(20, 13, 36, 0.8)
+categories:
+ - blog-post
+ - community
+classes: wide
+toc: true
+comments: true
+---
+
+## It's been two years since I started working full-time on pyOpenSci
+
+I have been working full-time building pyOpenSci for two years now, thanks to funding from the [Sloan Foundation](https://sloan.org/) and [CZI (Chan Zuckerberg Initiative)](https://chanzuckerberg.com/). pyOpenSci has come SO FAR in two years.
+
+It's time to take a breath and celebrate everything the pyOpenSci community has accomplished. Before we move on to the next big thing—our pyOpenSci Fall Festival (more on that below)—I want to take a moment to reflect on:
+
+* Where we've been,
+* What we've accomplished, and
+* The incredible community of practice that we've built.
+
+I'll wrap up by discussing what's next for pyOpenSci.
+
+## A brief history of pyOpenSci
+
+I founded pyOpenSci in 2018 because I saw the pain points that scientists were
+facing in creating open, reproducible workflows using Python. My experience
+inspired me to establish a vibrant, inclusive pyOpenSci community of practice
+fueled by making open science best practices more accessible to scientists.
+
+pyOpenSci makes open science more accessible by [developing education resources](/learn.html), [running training events](/events/index.html), running an [open software peer review process](/about-peer-review/index.html) and [partnering with other communities](/partners.html). From humble beginnings characterized by small community meetings, pyOpenSci has blossomed into a thriving community marked by:
+
+* a robust editorial team,
+* hundreds of contributors,
+* numerous valuable community partners and friends
+
+Our software peer review program has seen **over 50 packages**; we've [accepted 35 scientific Python packages into our growing library of trusted scientific Python packages](https://www.pyopensci.org/python-packages.html) and have **17 packages** in [active stages of review](https://github.com/orgs/pyOpenSci/projects/7) as I write this post.
+
+[ Learn more about how pyOpenSci started ](/blog/how-pyopensci-got-started.html)
+{: .button }
+
+
+
+
+
+ pyOpenSci supports scientists developing open-source software through three programs: 1) peer review of scientific software, 2) community partnerships, and 3) education & training. A diverse and inclusive community that cares about the open-source software that drives open science supports all three of these programs.
+
+
+
+### pyOpenSci and iterative, data-driven program design
+
+pyOpenSci's development and design is data-driven. I collected survey data
+from scientists and Pythonistas at meetings and conferences to identify community pain points and needs. I used this data to inform the iterative development of pyOpenSci programs, events, and resources and to define pyOpenSci's mission, vision, values, and structure.
+
+The survey data I've collected shows what I've seen in the classroom-- scientists face many challenges when
+processing, analyzing, and sharing their workflows. While reproducible
+science is critical to accelerating science, sharing and publishing code is hard.
+
+This blog post includes quotes and data I've collected over the past 5+ years; these data have shaped the vibrant community of practice that pyOpenSci is today.
+
+
+
+
+
+ Iterative data-driven program design is a fusion of evaluation and program development that utilizes community feedback to build a program using an iterative and community-responsive approach. You test approaches and collect data about the program's effectiveness. This type of development allows an organization to grow dynamically, following community needs.
+
+
+
+
+
+### Responses to the question: *How could pyOpenSci help you with your science, code, and software?*
+{: .not_toc}
+
+{% include pyos-blockquote.html quote="[*I want to know...*] What are the best practices for sharing the code?" author="Anonymous" event="AGU 2019 Townhall" class="highlight" event="2019 AGU Town Hall" %}
+
+{% include pyos-blockquote.html quote="[*I want to... *] Streamline the development of good quality, socially responsible, and easily shareable software." author="Anonymous" event="AGU 2019 Townhall" class="highlight magenta" %}
+
+{% include pyos-blockquote.html quote="[*I want more *] Bullet-proof, well-documented packages for Earth science." author="Anonymous" event="AGU 2019 Townhall" class="highlight" %}
+
+Many earth scientists attend AGU. It's a different crowd than who you meet
+at the SciPy meetings.
+
+
+### Why pyOpenSci tackled Python packaging
+
+pyOpenSci's design is also based on our experiences developing software peer review guidelines for Python packages. We [interact daily with scientific software maintainers](https://pyopensci.discourse.group/t/api-reference-building-locally-but-not-on-rth-missing-path/368/7). According to our surveys, 80% of our maintainers and reviewers identify strongly as scientists.
+
+As we developed our [peer review guide]((https://www.pyopensci.org/software-peer-review/)), it became clear that a beginner-friendly packaging guide was essential to support scientists in sharing their code because:
+
+1. [Our pre-review software checks](https://www.pyopensci.org/software-peer-review/how-to/editor-in-chief-guide.html#editor-checklist-template) require basic package infrastructure. Scientists must be clear about the core elements of a Python package.
+1. We want to help scientists make their packages more maintainable over time by adding tests and [continuous integration (CI)](https://www.pyopensci.org/python-package-guide/tests/tests-ci.html#run-tests-with-continuous-integration) checks that run when someone submits a suggested change (or a pull request). We want to set scientists up for success.
+1. We realize that Python packaging is a thorny ecosystem to navigate. I knew that pyOpenSci could help file down those thorns.
+
+
+### *"How could pyOpenSci help you with your science, code, and software?"*
+
+{% include pyos-blockquote.html quote="Training for people who can code for themselves but want to start developing software for others. Topics include style, documentation, testing, git, etc." author="Anonymous" event="AGU 2019 Townhall" class="highlight magenta" %}
+
+
+
+#### Helping scientists navigate a complex and difficult-to-understand Python packaging ecosystem
+
+The packaging ecosystem has evolved rapidly. Numerous tools and approaches are available to create Python packages. Further, recent changes to ecosystem standards have led to an explosion of packaging tools like [Hatch](https://hatch.pypa.io/latest/), [Flit](https://flit.pypa.io/en/stable/), [PDM](https://pdm.fming.dev/latest/), and [Poetry](https://python-poetry.org/); other tools like Pixi and Rye are also on the horizon. Scientists often feel overwhelmed by the sheer number of options and have begged for clear guidance for years.
+
+
+
+
QUESTION: "How could pyOpenSci help you with your science, code, and software?"
+
+
+{% include pyos-blockquote.html quote="[*I want pyOpenSci to*] clarify Python packaging. There are too many different mechanisms floating around..." author="" event="2021 SciPy BoF" class="highlight magenta" %}
+
+
+
+
+
+ This word cloud represents around 100 responses to the question: What Python packaging tools do you most often use? It demonstrates both 1) how many tools there are to choose from and 2) how divided the ecosystem is in terms of which tools they are using. Yes, a few tools there aren't explicitly Python packaging tools. :)
+
+
+There are some excellent, more advanced guides and tutorials available now, such as [PyPA's packaging tutorial](https://packaging.python.org/tutorials/) and the [scientific Python development guide](add link). However, our resources serve a different audience.
+
+1. The people using our resources are often folks who find the packaging ecosystem to be overwhelming.
+1. Scientists are also only sometimes familiar with various terms. For example, it's easy to confuse a build backend with a build frontend, especially when both have the word "hatch" in them (e.g., Hatch vs. Hatchling).
+1. Scientists often want to avoid deciding what tools to use. pyOpenSci intends to alleviate this burden and provide a robust and practical approach.
+
+From a beginner's perspective, every decision is a potential opportunity to go down the wrong path, adding cognitive load. Armed with a clear understanding of the packaging pain points, I knew that pyOpenSci could simplify the packaging journey for scientists.
+
+If you want to learn more, my [talk at PyCon dove into the Python packaging challenges of too many options and cognitive overload.](/blog/python-packaging-friends-dont-let-friends-package-alone.html)
+
+### pyOpenSci guided the community towards a single way to create a Python package in under a year
+
+In just under a year, pyOpenSci created a [comprehensive packaging guide](https://www.pyopensci.org/python-package-guide/) that includes an end-to-end Python packaging / share your code tutorial that walks scientists through creating a package and publishing it to both [PyPI](https://www.pyopensci.org/python-package-guide/tutorials/publish-pypi.html) and [Conda-Forge](https://www.pyopensci.org/python-package-guide/tutorials/publish-conda-forge.html) using [Hatch](https://www.pyopensci.org/python-package-guide/tutorials/get-to-know-hatch.html). Together, the pyOpenSci community built consensus around which packaging approaches should be adopted as best practices. Building consensus around packaging decisions was challenging, but we were successful because we focused on our users first. We decided on which tools we thought would create the best packaging experience for scientists creating pure Python packages.
+
+[Check out the GitHub pull requests for packaging guide pages](https://github.com/pyOpenSci/python-package-guide/pulls?q=is%3Apr+sort%3Acomments-desc+is%3Aclosed) if you're curious about the feedback and discussions that we had when writing the packaging guide. Notice the number of comments on the most popular PRs. The sheer volume of comments on some of the early PRs associated with packaging build tools speaks to the various issues, complexity, and decisions we needed to make.
+
+## **Building Python packaging consensus and developing the pyOpenSci Python packaging guide**
+
+The success of our Python packaging guide is due to extensive community input.
+We directed Python Packaging Guide contributors towards the objective of simplifying Python packaging for beginners. Focusing on a specific audience allowed contributors to make decisions about the content in the Guidebook more easily. It also ensured that the outcome text is easy to understand for beginners and for scientists who prefer to avoid delving into packaging nuances. In each review, we made sure to include a diverse group of reviewers, including:
+
+* **Packaging tool maintainers:** We asked each maintainer from Flit, PDM, Hatch, and Poetry to provide feedback on our overviews of their tools
+* **[PyPA](https://www.pypa.io/en/latest/) members** who have already developed packaging resources that serve the broader Python community
+* **Core scientific Python community members** who are maintaining tools like Matplotlib, Numpy, and Pandas that require more technical complex builds
+* **Scientists with varying levels of packaging expertise:** This group is most important as packaging beginners provide the lens of what is confusing and ensure that the technical content is more beginner-friendly.
+The truly collaborative effort of creating the Python packaging guide resulted in a beginner-friendly and accurate guide that the pyOpenSci community now maintains and continuously updates.
+
+It was essential to have a mix of content reviewers with a range of packaging expertise to make the guide accurate and accessible. Some were packaging experts building packaging tools, and others were beginners, the audience for whom we wrote the guide.
+
+### What's in the pyOpenSci Python packaging guide?
+
+The pyOpenSci packaging guide provides an overview of the Python packaging ecosystem to support users who want to understand the packaging landscape. These users may want to decide on their own which tools best serve their needs. The guide also defines core packaging terms that can make the ecosystem seem more complex than it is, such as:
+
+* [build backend](https://www.pyopensci.org/python-package-guide/package-structure-code/python-package-build-tools.html#build-back-ends)
+* [build frontend](https://www.pyopensci.org/python-package-guide/package-structure-code/python-package-build-tools.html#python-package-build-front-ends)
+* [wheel](https://www.pyopensci.org/python-package-guide/package-structure-code/python-package-distribution-files-sdist-wheel.html#wheel-whl-files)
+* [sDist](https://www.pyopensci.org/python-package-guide/package-structure-code/python-package-distribution-files-sdist-wheel.html#source-distribution-sdist)
+
+Our Python Packaging Guide aims to translate the technical jargon that gets in the way of new users having a successful packaging experience. Over time, we
+hope that this will make packaging less confusing and more accessible to more people.
+
+
+
+
+ Our opinionated Python packaging tutorials demonstrate one way to create a Python package using modern packaging best practices. Through these tutorials, pyOpenSci has reduced the cognitive load for scientists who want to share their code. This graphic represents the steps a user needs to understand to make their code installable and create a basic Python package.
+
+
+
+### Creating opinionated Python packaging tutorials
+
+
+
+ This response ratio of most people either struggling to create a package or never having created a Python package before is common when I ask this questions at events. More often than not, people want help learning packaging.
+
+
+In addition to the ecosystem overview provided in the guide, I knew that we needed to have opinionated, complete Python packaging tutorials that guided users through the entire packaging process from
+* [creating a package](https://www.pyopensci.org/python-package-guide/tutorials/installable-code.html) to
+* publishing it on [PyPI](https://www.pyopensci.org/python-package-guide/tutorials/publish-pypi.html) and
+* [conda-forge](https://www.pyopensci.org/python-package-guide/tutorials/publish-conda-forge.html)
+
+Most scientists (and packaging beginners) want to get the job done; they don't want to become packaging experts. A community presenter made this statement directly at the PyCon US 2023 Python packaging summit:
+
+> I just want to create a Python package. Where is the tutorial or
+> documentation that teaches me how to do that?
+{: .highlight-quote }
+
+Our Python packaging tutorials went through the same community development and review process as the rest of our Python packaging guide. We have also begun teaching the lessons; most workshop attendees have successfully created their first Python packages!
+
+
+
+ It's always hard to collect data at the end of a long workshop. Here, 11 out of 30+ people responded. Most people in our workshop were successful. We worked with the one person who responded "no" after the survey at the end of the workshop. They successfully made their first package with our support! This feedback was similar to the success rates at our first online workshop held in May.
+
+
+
+### Teaching online lessons is the best way to ensure they stay current
+
+I have created hundreds of [open online lessons](https://www.leahwasser.com/my-path-to-open-education-earth-environmental-data-science) both for ecologists [NEON](https://www.neonscience.org/resources/learning-hub/teaching-modules/quantifying-drivers-and-impacts-natural-disturbance-events) and [earth and environmental scientists](https://www/earthdatascience.org). One of the biggest challenges is that it's easy for data science lessons to [become dated quickly](https://www.leahwasser.com/open-education-data-science-posting-online-is-not-enough)--especially in the rapidly evolving data science space. By teaching the lessons, we can update them regularly as the ecosystem evolves. We also often have users review the lessons at our [annual sprint events](blog/pyopensci-pyconus-2024-sprints.html) to test them out; more on pyOpenSci beginner-friendly sprints below.
+
+
+
+> Hi Leah. Thanks for the course today. I really enjoyed it. I heard about it from your post here in the Python channel so I'm glad you shared it here. I'll keep my eye out for more coming up and will be referring to the tutorials and guides on your site. Hopefully you work out the spatial chat because that seemed to have a lot of potential!
+I also want to let you know that I got a ton of value out of your materials on the CU Open Earth data analytics site, and it's still my go-to resource to point people to when they ask me how to get started learning the open source spatial stack. So thank you!
+> workshop attendee -- Software underground community
+{: .highlight-quote }
+
+
+
+
+
+
+In addition to the valuable feedback we receive from our community, the data we collect through Matomo web analytics shows our guide's usage and growth.
+I will leave web and social media growth, which have also shown extraordinary growth for future posts!
+
+### The power of community: Translating the guide & tutorials to other languages
+
+The success of our packaging guide has been remarkable, thanks to the tremendous input and feedback from the community. What began as a simple guide has evolved into a collaborative project, with enthusiastic participation from contributors worldwide.
+
+> @leahawasser @pyOpenSci clicking through and eventually found myself looking at "what is a python package" [www.pyopensci.org] and involuntarily performed a standing ovation. bookmarked it as an example of great docs for an incredibly complex subject with many meanings in many different contexts
+{: .highlight-quote }
+
+[View toot on mastodon](https://circumstances.run/@hipsterelectron/112557545629456885)
+
+At our last PyCon sprint, a then-new contributor, [Felipe Moreno](https://www.github.com/flpm), set up translation infrastructure for our guide. The translation infrastructure uses a Sphinx extension that allows contributors to translate as much or as little of the guide as they wish. This process supports
+small iterative pull requests that any contributor with little time can make.
+
+```markdown
+#: ../../documentation/hosting-tools/intro.md:3
+msgid ""
+"The most common tool for building documentation in the Python ecosystem "
+"currently is Sphinx. However, some maintainers are using tools like "
+"[mkdocs](https://www.mkdocs.org/) for documentation. It is up to you to "
+"use the platform that you prefer for your documentation!"
+msgstr ""
+"Sphinx es la herramienta más común para construir documentación en el "
+"ecosistema de Python. Sin embargo, algunos mantenedores están usando "
+"herramientas como [mkdocs](https://www.mkdocs.org/) para generar la "
+"documentación. ¡Es tu decisión usar la herramienta que prefieras para tu "
+"documentación!"
+```
+
+Felipe's translation infrastructure contribution has sparked significant community momentum. As I write this, pyOpenSci contributors are actively translating our Python packaging guide into Spanish and Japanese; we have had dozens of pull requests. One contributor may even teach the pyOpenSci Python packaging lessons at [PyCon Japan](https://2024.pycon.jp/en) next year! This entire effort underscores the power of community when guided in the right direction.
+
+Our guide is becoming a global resource!
+
+You may be wondering where all of these new contributors are
+coming from. We get many new active contributors when we lead events at core meetings.
+
+I'll talk about that next.
+
+## pyOpenSci at PyCon and SciPy 2024 -- sprints, talks and new contributors
+
+We've held sprints at SciPy and PyCon US in the past two years.
+We had significantly more engagement at SciPy and PyCon in 2024 compared to 2023. What is most impressive is many of our sprint participants have continued to contribute to pyOpenSci after our in-person sprint events.
+
+While pyOpenSci operates mainly as an online community, I find there is no better way to build a core community than by holding in-person events at large meetings. While we get plenty of expert Pythonistas at our sprints, our sprint events are always beginner-friendly. Many first-time contributors help pyOpenSci with various open issues on our help-wanted GitHub project board while submitting their first pull requests and issues to GitHub.
+
+Beginner-friendly sprints represent a true win-win for both contributors and pyOpenSci.Contributors learn new skills, and pyOpenSci gets help with the vital work that they are doing.
+
+
+
+### What is a community sprint?
+{: .no-toc}
+
+Community sprints are collaborative coding and documentation update sessions where new and experienced contributors work together on open-source projects. These sprints provide a supportive environment with guidance from project maintainers or experienced developers, helping participants contribute effectively. They are an excellent opportunity for learning, networking, and making meaningful contributions to the open-source community. [You can read more about that here.](https://www.pyopensci.org/blog/pyopensci-pyconus-2024-sprints.html)
+
+
+
+
+| Meeting | total prs & issues submitted | total contributors |
+|----------|----------|----------|
+| PyCon 2023 | Item 2 | Item 3 |
+| PyCon 2024 | Item 2 | Item 3 |
+| Scipy 2023 | Item 5 | Item 6 |
+| Scipy 2024 | Item 8 | Item 9 |
+| pyCascades 2024 (community lead) | 19 | Item 9 |
+
+### My talks at SciPy and PyCon US 2024
+
+Last year, I gave a talk at PyCon US at the [Maintainers Summit](https://www.pyopensci.org/blog/pycon-2023-packaging-presentation-sprints-leah-wasser.html#python-packaging-packaging-packaging).
+This year, I gave talks about both [SciPy's Maintainers Summit](https://www.youtube.com/watch?v=uCLlshkTBM0) and at [PyCon US - in the main track](https://www.youtube.com/watch?v=uCLlshkTBM0)! At PyCon, I spoke about how pyOpenSci is leveraging the community and building consensus on the thorniest topic: Python packaging. At SciPy, I focused on bringing the scientific community together to help solve Python packaging. I also overviewed our community-run scientific Python software peer review program and the success that we've
+had collaborated with [other partner communities, such as Astropy](https://www.pyopensci.org/software-peer-review/partners/astropy.html).
+
+Here is my SciPy video if you want to check it out now. :)
+
+{% include video id="uCLlshkTBM0" provider="youtube" %}
+
+
+## pyOpenSci's software peer review program is growing, too
+
+Funding has propelled pyOpenSci's peer review process forward. While pyOpenSci has run peer review since 2019, funding allowed us to document, formalize, and make the peer review program more sustainable and scalable. The [peer review guide](https://www.pyopensci.org/software-peer-review/) was one of the first things I worked on in the Fall of 2022. The goal of this guide was to
+define each role in the software peer review process
+define policies around our review package scope, how we review, how we determine what is in scope, and more
+The guide also documents our [community partnership program](https://www.pyopensci.org/partners.html), that pyOpenSci in 2024 through our collaboration with Astropy.
+
+In four months, we published a shiny new peer review guide. In January 2023, we re-launched peer review again. Peer review submissions increased dramatically starting in January 2024 (see below).
+
+
+
+ It is clear the impact that 1) working on our peer review guide and governance plus 2) funding, which allowed me to devote all of my professional time to pyOpenSci, had on our peer review program. It has been incredibly successful in the past year! And true success lies in the hands of the community that leads the review process. Today, on average, pyOpenSci has 10-14 packages in our software review queue.
+
+
+
+
+
+You can see more of our [peer review metrics on the myst markdown metrics dashboard here](https://www.pyopensci.org/peer-review-metrics/pyopensci-peer-review-stats)
+{: .button }
+
+
+### Is pyOpenSci's peer review program diverse?
+
+Diversity, equity, inclusion, and accessibility (DEIA) are core to everything that pyOpenSci does. But how do we know that we are aligning with that core value?
+
+Data can help us here, too. Here are a few insights from 93 people who have participated in our peer review program survey over the past two years. Our peer review participants represent a healthy mix of students, industry professionals, and academics.
+
+Almost half of the participants identify as having some computer science application in their work; 80% self-identify as scientists (as mentioned above).
+
+About 50% of our peer review team self-identifies as white. The other help are from a mix of different backgrounds and ethnicities—about 60% of our audience self-identify as male.
+
+#### Open Source metrics
+
+Some of the open-source responses surprised me:
+
+* 60% of people maintainers have funding to do their work! (*This number surprised me.*)
+* 70% of maintainers report that outside contributors contribute to their projects through issues and pull requests.
+
+I'll dedicate another blog post to examining this data more thoroughly. Still, you can get the gist from the above summary: We have a strong representation of people from different backgrounds and career stages. However, we still need to do more to support increased gender diversity.
+
+I'm confident that we'll get there.
+
+## A thoughtful, kind, and supportive community is what makes pyOpenSci special
+
+Every morning, I wake up and am excited to begin my work. It doesn't matter if I'm working on budgets and other Executive Director-type tasks or developing educational content and teaching (my two favorite things); I can't wait to see what messages pop up in my inbox, be it Slack, email, Discord, or GitHub. I love my job because the pyOpenSci community is extraordinary.
+
+ People care about our organization's mission to help scientists make their science more open and collaborative. So they can solve the world's greatest challenges.
+
+ The community wants to help--be it each other or pyOpenSci as an organization.
+
+ The community is kind; the discussions are engaging and people help each other.
+
+{% include pyos-blockquote.html quote="I love the friendliness and positive energy around the combination of science+computing (in Python, too)! It's a lovely community of practice!" author="Titus Brown, Faculty - University of California, Davis, pyOpenSci community member" class="highlight magenta" %}
+
+The community also cares a lot about the scientific Python ecosystem and wants to see more robust software.
+
+{% include pyos-blockquote.html quote="As maintainers of the Scientific Python library, we see great potential in pyOpenSci. Test, code, documentation, and internationalization. These are things we have gradually accumulated know-how for in order to create a robust library on GitHub. Now, these are about to be integrated by the pyOpenSci project." author="Tetsuo Koyama - PyVista maintainer" class="highlight" %}
+
+They care about the packaging guidelines we have worked so hard to co-create and the impact those guidelines and online resources have on scientists who are just trying to get their jobs done.
+
+{% include pyos-blockquote.html quote="I'm hopeful that the standards established by pyOpenSci will help alleviate the reproducibility crisis, increasing public trust in science, accelerating progress, and making all our jobs a little bit easier!" author="Jackson Burns, MIT" class="highlight magenta" %}
+
+And, like me, the community cares deeply about education. The community wants to help
+eliminate the packaging and open science thorns. They want to make packaging (and using Python) more
+accessible to more people.
+
+{% include pyos-blockquote.html quote="I love the educational aspect of pyOpenSci and how it focuses on removing the technical friction that prevents people from contributing to open source and open science. The community makes a conscious effort to create a welcoming culture and provide a safe place to learn for beginners. It helps prepare people for even bigger future contributions to the open science and open source movements." author="Felipe Moreno - Tech Industry Professional" class="highlight" %}
+
+Carol below was more than just a participant! She also helped many students
+in their workshop learning experience.
+
+{% include pyos-blockquote.html quote="As a participant in the first packaging workshop, I found that individual attention and focus on success were outstanding. The organization and approach to teaching a scientist how to create a package helped build skills and ongoing productivity." author="Carol Willing - Open Source & Open Science Leader, Project Jupyter, Python Core Dev" class="highlight purple" %}
+
+## Building a sustainability model for pyOpenSci so we can continue to thrive - what's next
+
+There’s a strong demand for the work pyOpenSci is doing. Still, every nonprofit or fiscally sponsored project has to ask:
+
+*How do we keep this going long-term?*
+
+Writing grants to bring in funding is essential, but I need to do more than just write grants. Writing grants takes tremendous energy, with only a ~10-20% chance of success. Also, grant calls usually focus on innovation rather than keeping a project maintenance.
+
+To ensure that pyOpenSci is sustainable, we’re rolling out paid online training events. Paid events are a revenue model used by other nonprofits in our ecosystem:
+
+NumFocus runs PyData and SciPy meetings
+The Python Software Foundation runs PyCon as a significant fundraising event.
+These paid events will support the low-cost training, event scholarships, free online tutorials, and resources we will continue to create and publish online.
+
+## What's next for pyOpenSci - The Road Ahead
+Our next training event is the inaugural pyOpenSci Fall Festival - a week-long event that teaches skills needed to:
+
+* Write cleaner, more modular code,
+* Package and share code,
+* Publish and cite code,
+* Create reproducible reports that connect code, data, and outputs into a dynamically produced interactive publication
+
+We also will develop collaborative GitHub lessons following the BSSw fellowship I received this year.
+
+## Get involved with pyOpenSci
+
+Congratulations on reading my longest post ever. I hope you are as excited as I am about pyOpenSci's future.
+
+If you'd like to get involved with pyOpenSci, check out our [volunteer page](/volunteer.html).
+
+You can also:
+
+* Keep an eye on our [events page](/events.html) for upcoming training events.
+
+Follow us on social platforms:
+
+ [ Discourse](https://pyopensci.discourse.group/) ** [ Mastodon](https://fosstodon.org/@pyopensci)
+** [ Bluesky](https://bsky.app/profile/pyopensci.bsky.social)
+** [ LinkedIn](https://www.linkedin.com/company/pyopensci)
+** [ GitHub](https://github.com/pyOpenSci)
+
+If you are on LinkedIn, you should subscribe to our newsletter too.
+
+
+## About Me
+My name is Leah, and I'm the executive director and founder of pyOpenSci. I have over 20 years of experience in both academic and nonprofit spaces and have dedicated my career to helping scientists overcome the challenges of open science. I've built and led two successful data science programs:
+
+1. **NEON Data Skills Program** at NEON
+2. **Earth Analytics Program** at CU Boulder
+
+I'm now building **pyOpenSci** - a vibrant, active, and diverse community of practice that supports open science and the open-source software that drives that science.
+
+The programs I build have consistently stayed at the cutting edge of technology through continual evaluation and a data-driven approach. Throughout my career, I've observed significant gaps between the innovative tools being developed and the training scientists receive, which has driven my work in bridging these gaps.
+
+
+ 
+
+ 
+ 
+ 
+ 
+ 
+