We believe open practices can accelerate data-driven research 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 approach open science as a spectrum, as a behavior change, and as a movement. We see data analysis as an entryway to 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 the social justice, climate action, and get out the vote movements. We define open data science as the tools and practices enabling reproducible, transparent, and inclusive data-driven research.
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 analysis, develop modern skills that are of immediate value to them, and cultivate collaborative and inclusive research teams. We do this through mentorship, coaching, training, and community organizing, leveraging existing resources from the open community along with our own. Our activities include:
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.
Openscapes Framework. A skill-building and community engagement framework for research communities. This is a multi-year series of events (including Champions Cohorts) to build technical and leadership skills that is co-designed with partners to meet specific community needs.
We share our most current thinking and showcase community efforts through talks and publications on our media page. Some recent highlights include:
- How to empower researchers with open science through meeting analytical needs and supporting people. Lowndes 2020, invited talk at the National Academies of Sciences, Engineering, and Medicine (NASEM) Roundtable
- Entryways to open data science and the power of welcome. Lowndes 2020, invited plenary at the Earth Science Information Partners Conference
- Putting data to work. Robinson 2020, invited Leptoukh Lecture at the American Geophysical Union Conference
- Open software means kinder science. Lowndes 2019, Scientific American
OUR THEORY OF CHANGE
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:
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.
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.