Anonymized data and R scripts needed to replicate the identification of subsystems in Swiss water governance described in the study "Bottom-up identification of subsystems in complex governance systems". Theories of policymaking often focus on subsystems within a larger, overarching governance system. However, subsystem identification is complicated by the complexity of governance systems, characterized by multiple, interrelated issues, multi‐level interactions, and a diverse set of organizations. This study suggests an empirical, bottom‐up methodology to identify subsystems. Subsystems are identified based on bundles of similar observed organizational activity. The study further suggests a set of three elementary criteria to classify individual subsystems. In order to prove the value of the methodology, subsystems are identified through cluster analysis, and subsequently classified in a study of Swiss water governance. Results suggest that Swiss water governance can be understood as a network of overlapping subsystems connected by boundary penetrating organizations, with high‐conflict and quiet politics subgroups. The study shows that a principled analysis of subsystems as the interconnected, constituent parts of complex governance systems offers insights into important contextual factors shaping outcomes. Such insights are prerequisite knowledge in order to understand and navigate complex systems for researchers and practitioners alike.

Biofilms serve essential ecosystem functions and are used in different technical applications. Studies from stream ecology and waste water treatment have shown that biofilm functionality depends to a great extent on community structure. Here we present a fast and easy-to-use method for individual cell-based analysis of stream biofilms, based on stain-free flow cytometry and visualization of the high-dimensional data by viSNE. The method allows the combined assessment of community structure, decay of phototrophic organisms and presence of abiotic particles. In laboratory experiments, it allows quantification of cellular decay and detection of survival of larger cells after temperature stress, while in the field it enables detection of community structure changes that correlate with known environmental drivers (flow conditions, dissolved organic carbonDOC, calcium) and detection of microplastic contamination. The method can potentially be applied to other biofilm types, e.g. for inferring community structure for environmental and industrial research and monitoring.

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fuid-fow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fuid-fow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes. This submission includes the published journal article detailing this work, the published 3D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using the GeoThermalCloud framework, which is part of SmartTensors (both are linked below).