By Melissa Strong

Hello, world!

I’m Melissa Strong, and I’m thrilled to contribute to both this newsletter and the upcoming Open Visualization Academy. I wear a few hats these days: IT professional by day, graduate student by night, web developer for fun, and new puppy trainer in between. It is a full plate, but if you love what you are doing, is it really work?

Training a puppy has taught me a lot about patience and choosing the right approach. Some methods click right away, others take a few tries to work. I have found the same is true when exploring data visualization tools. Each one has its own personality, strengths, and quirks, and part of the fun is figuring out which tool fits the problem you are trying to solve.

Because I value transparency and community, and because OVA is a free resource, I lean on open source tools whenever I can. They are easy to share, easy to learn from, and they make it possible for more people to join in and build together.

In this first article of a series of six, I will walk through a few of the tools I use most often: what they are best for, where they overlap, and how they fit into different stages of the visualization process.

Imagine a world where anyone, regardless of background, can take raw data and turn it into something meaningful. That is the real power of open source, and it's a big part of how I approach data visualization. It’s not just about saving money. They open up space for creativity and teamwork, and they make information design something more people can try. Open communities make it easy to jump in, experiment, and share what you make. As a community, we turn data into stories that inform and connect.

In my projects, I focus on four things: design, visualization, collaboration, and reproducibility. Design is the structure and feel; layout, hierarchy, typography, color, and accessibility. I create mock-ups in Penpot and carry those choices into Bootstrap 5 so pages and charts hold together on any screen. Visualization is how we encode data so the meaning is clear. With Observable Plot, I choose marks, scales, and annotations that fit the question and add interaction where it helps. Collaboration is how work actually moves forward. Comments and shared files in Penpot help align on intent, and Git keeps code, chart specs, and copy in one place where reviews and history are easy. Reproducibility is the promise that work can be rebuilt and trusted later. Docker pins the environment, and Git tracks changes so results can be checked and repeated.

Open source removes a lot of barriers like expensive licenses and closed systems. It’s about putting capable software in the hands of anyone who wants to learn and build. Whether you are tinkering with your first dataset or leading a complex study, these tools give you room to test ideas and grow. There are times when paid tools make sense, especially when a client or institution requires them, but open source keeps the field open to everyone.

Trust matters in data visualization. People need to believe what they see. Open source makes that possible because you can look under the hood and see how things work. That openness encourages accountability and sparks new ideas. It lets people improve, share, and gradually shape better ways to communicate data responsibly.

Open source works best when people roll up their sleeves and build side by side. Teams share context through small pull requests and clear commit messages, leave concrete design feedback in comments, and document choices so newcomers can follow along. Reusable examples, style guides, and checklist-driven reviews keep quality steady. The result is work that moves faster, is easier to learn from, and stands up when someone else has to maintain it later.

In the next articles, I’ll take a closer look at these tools, starting with Penpot and its collaborative design workflow, then Observable Plot for data‑driven visuals, Bootstrap 5 for presentation, Docker for reproducibility, and Git for day‑to‑day collaboration. Together, they form a toolkit for building visual stories that are open, ethical, and built to last.

Silence is the worst company, in my opinion. I prefer to listen to mostly instrumental music while I am working, I find it focuses me. Today I will leave you with Teardrop by Massive Attack. See you next time with Part 2!