Data can help makerspace staff and leadership understand the “pulse” of their space and inform strategic planning and decision making. Data can also be useful in crafting compelling narratives to stakeholders and funders. Multiple prior papers at ISAM have stressed the importance of collecting and analyzing data about makerspace usage. Prior work tends to follow one of two primary methodological approaches. First, descriptive statistics of automatically collected usage or sign-in data may reveal a fine-grained view of activity patterns. Second, surveys completed by makerspace users can unravel the motivations and reasons for makerspace use. In this paper, we describe how to gain additional insights from automatically collected makerspace data by applying aggregated time-series analytics over multiple semesters. We focus on some of the most pervasive and accessible data in makerspaces:(1) sign-in data when users enter a space or start using a particular machine;(2) reservation data (eg, calendar sign-ups) for popular machines; and (3) training records for individual machine types. Many makerspaces already collect such data for access control and scheduling purposes. We show how aggregated time-series analyses of such data over days, weeks, months, and semesters can yield a richer picture than instantaneous statistics.

Our dataset for these analyses is data collected over three semesters at the Jacobs Institute for Design Innovation and the Citris Invention Lab at UC Berkeley. The makerspace in Jacobs Hall is classified as S-3, A-4, U-3, F-4, M-3 [1], serving roughly 1,000 unique students per semester. A comprehensive introduction to this makerspace can be found in [2]. The Citris Invention Lab is a satellite makerspace which shares training and access control with Jacobs Hall, serving approximately 350 unique students per semester (S-3, A-4, U-2, F-1, M-3). While we analyze the data from these particular makerspaces, our goal is to show that our analyses may be replicated at other similar makerspaces.