Hi, I'm Eura

I'm a compsci PhD student by day and... still a student by night. But on the weekends, you can find me at Rumney.

This blog is an exploration of rock climbing and data, two large enough parts of my life that their combination was somewhat inevitable. Here, you'll find a mix of posts that use different ML techniques to analyze MP data, provide code for visualizing your own, and so on...

Importantly, this is a living document with the goal of providing food for thought in the climbing community. If you notice unexpected trends / bugs, have constructive feedback, or just want to offer thoughts, drop me a note through the contact page, and you might see your thoughts reflected in the posts.

A major theme in this blog is exploring the extent to which gender differences in climbing are evident in the data. The goal of this exploration is two-fold. First, I want to offer a practical resource for shorter climbers out there to gain some data-driven insights. Second, I want to document and validate the experiences of woman climbers, with the goal of opening up discussions surrounding the current range of experiences in climbing and what we hope to accomplish as a community. You may notice that all of my analysis is exclusive to Rumney data, my local-ish sport crag. This is by intention; in running this blog, I try my best to follow the principle of Data Feminism by Catherine D'Ignazio and Lauren F. Klein, which they so graciously make available online.

Guiding Principles from Data Feminism

Data Feminism is about recognizing and challenging power differentials with data. This reflection goes both ways; it asks how the way that data is used can perperuate existing power differentials. For example, how might my choice to exclude presenting results from MP users who identify as genderqueer, due to a lack of data, reinforce the gender binary? It also asks how we can use data to challenge power differentials.

• Locality. I restrict my analysis to Rumney, an area that I climb often enough at to understand the local norms and ethics, to avoid being a stranger in the dataset.
• Focus on gender differentials. This focus is not because data feminism is restricted to gendered power differentials, but simply because it is a salient part of my experience as a woman climber. There are several more power differentials that exist in climbing which I don't expect to report on, but should still be acknowledged. For example, the relationship between those who develop, visit, and climb on the rock for recreation, and those who live in towns where the rock is located or have histories and relationships with the rock beyond climbing.
• This data (and analysis) is not objective. I really like the phrase that data is " cooked, not raw," possibly because in an alternate life I would have loved to go to culinary school, but also because it is a great reminder that my chalky little fingers have been all over the data. In processing, analyzing, and presenting the data, I made several design decisions that inevitably influence the presented results. For example, my decision form my dataset from the users who have climbed the list of "classic climbs" (a subset of the 606 sport climbs decided by Mountain Project) certainly influenced the sample of users included in my set. Throughout, I will try my best to make my design decisions transparent and provide warnings in interpreting the results.