Our approach

We believe open practices can accelerate data-driven solutions and increase diversity, equity, inclusion, and belonging in science. These are critical to uncovering enduring science-based solutions faster, as well as for the well-being and resilience of research communities. We work towards kinder science.

We approach open science as a spectrum, as a behavior change, and as a movement. There are many ways to practice open science and to welcome others to participate. We see data analysis and stewardship as powerful entryways for open science, and we meet scientists where they are, helping them develop new skillsets and mindsets of immediate value while empowering them as leaders. We are influenced and inspired by many leaders and community organizers, particularly in climate justice and get out the vote movements.

We know that daily demands leave little time for researchers to transition to better data practices and open science, which can be lonely and overwhelming. Openscapes helps researchers reimagine data-intensive science, develop modern skills that are of immediate value to them, and cultivate collaborative and inclusive research teams. We do this through mentorship, coaching, teaching, and community organizing, leveraging existing resources from the open community along with our own. Our activities include:

  1. Openscapes Champions Program. A remote-by-design, cohort-based open data science mentorship program for research groups. Cohorts of research groups are mentored with their peers over several months following the Champions Lesson Series, and learn how to supercharge their research.

  2. Openscapes Framework. A scalable leadership and community building framework that supports researchers and staff transitioning to open data science. The framework involves developing a Mentor community, empowering science teams through the Champions Program, and scaling the Champions program with the Mentors in a way that is sustainable and supported. See our flywheel below, and an example: NASA Openscapes.

The Openscapes Flywheel: the orange hexagonal logo with 6 parts of the flywheel: Welcome; Create space and place; invest in learning and trust; work openly; leverage common workflows, skills, tools; inspire
Openscapes Flywheel (Robinson & Lowndes 2022; preprint). The Flywheel concept was developed by Jim Collins in the book Good to Great. No matter how dramatic the end result, good-to-great transformations never happen in one fell swoop. Rather, the process resembles relentlessly pushing a giant, heavy flywheel, turn upon turn, building momentum until a point of breakthrough, and beyond.

We share our most current thinking and showcase community efforts through talks and publications on our media page. Some recent highlights include:

We’re also developing the Openscapes Approach Guide openly as an attempt to codify our approach so that we can onboard others to the team. Aligned with our values for kinder, open science, we are developing it openly and designing it so that others can also use the Openscapes Approach on their own too.


Our theory of change is that by engaging, empowering, and amplifying researchers with open habits and mindsets for data-intensive science, they become leaders in the open science movement and have more enduring scientific impact while also creating a kinder, more inclusive scientific culture.

We are iterating our theory of change as we learn, and have also summarized it with this illustration:

Openscapes Theory of Change: As teams tackle research questions (blue), their approaches (grey) can be accelerated through
open data science practices (green), which are underpinned by existing open source tools (orange). Art by Allison Horst.

In mentoring research teams, we are interested in evaluating and understanding which open data science tools are adopted, who participates, and how questions and mindsets shift. Our hypothesis is that adoption of open data science tools will lead to robust, data-driven outcomes; that participation in open data science practices will lead to deeper inclusion and belonging; and that shifting mindsets and questions asked will lead to enduring partnerships and expanded questions. We define open data science as the tools and practices enabling reproducible, transparent, and inclusive data-driven research.

What is a Theory of Change?
A Theory of Change (TOC) is a way to think through and communicate the goals of a project to help track progress when it’s not possible to use more traditional metrics (i.e. revenue). The purpose is to articulate the overall aim/long-term impact, and the outcomes expected through the activities you do. Outcomes must be measurable (through appropriate indicators) so you can use the TOC to track progress.

Our Theory of Change is stylized after Mozilla’s AI TOC. This is a useful resource for creating your own from project-oracle (recommended by Mozilla).