Savic, Selena2020-12-042020-12-042020-08-18https://irf.fhnw.ch/handle/11654/31855https://doi.org/10.26041/fhnw-3546As we slowly accustom to thinking about planetary issues through the notion of ‘assemblage’ rather than that of the ‘system’, we get better at acknowledging complex entanglements between living and inert, between social and technical. This paper presents a critical reflection on the use of machine learning techniques to support reasoning about natural phenomena. It engages data/natures by focusing on data radio signals: a phenomenon that pertains to both culture (telecommunications) and nature (atmospheric lightning discharges). Signal Identification Guide Wiki, a rich archive of signals observed and documented by a community of radio enthusiasts is the starting point of this study. In order to articulate alternative ways to study and engage with radio signals, I develop 'digital observatories': new methods for organizing and navigating abundant digital information based on critical use of self-organising map algorithm. I present a study of distribution patterns and clustering of signal qualities, when signals are reduced to spectrograms (visual representation of signal frequency composition). This 'digital observatory' aims to facilitate speculation on the connection between signal representation and technical communication protocols, by enabling the observer to identify criteria of similarity, and intervene in this organised space by adding new (real or imaginary) data. The project contributes to the fields of STS and experimental design research with an interest in the digital, unsettling the dichotomies previously described and providing avenues for recognition of the entangled nature of matter and information, of human and other-than-human, beyond simple ontological distinctions.enAttribution-NonCommercial-ShareAlike 3.0 United Statesradiomachine learningnature/culturedata/naturedatadigital observatory06 - Präsentation