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.

This data has been used (1) to quantify the appropriateness of a set of sanitation technologies for a small town (Katarnyia) in Nepal and (2) to generate sanitation system options from the appropriate technologies as an input into strategic sanitation planning using a structured decision making process. For (1), the appropriateness is quantified based on a set of criteria, also called screening criteria. These criteria include technical, socio-demographic, climatic, and institutional aspects and are quantified using uncertainty functions in order to account for the quality and quantity of available input information. The data contains raw data as well as modelling results. The raw data is a compilation of information collected from literature, information collected through a household survey in the small town, field observations. They are all used to describe the screening criteria for the studied sanitation technologies and the small town. Results include: (1) the outcome of the technology appropriateness assessment (technology appropriateness scores); and (2) the sanitation system options (all possible sanitation systems built from the appropriate technologies, and a smaller set of divers and highly appropriate sanitation system options as an input into decision-making).

Anonymized data and R code needed to replicate the analysis presented in the study "Networks of Swiss water governance issues. Studying fit between media attention and organizational activity" to be published in Society & Natural Resources. The study looks at how relations between Swiss water governance issues are portrayed in the media as compared to the way organizations involved in water governance reflect these relations in their activity. This is a paper output of the SNF funded project "Overlapping subsystems". Access to the complete, non-anonymized dataset is restricted.

This package provides material that can be openly published for the paper "Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network". It consists in the code used to generate results and figures as well as the weights of the deep convolutional neural networks trained to segment water in the surveillance camera images.

Data sets on the percentage of virus transferred between fingers and water or saliva. Transfer with saliva is for both wet hands (where the virus inoculum was not allowed to appreciably dry before transfer) and for dry hands (where the virus inoculum was allowed to dry before transfer).

This data delineates New Jersey drought regions. Drought regions provide a regulatory basis for coordinating local responses to regional water-supply shortages. The six drought regions are based on watershed and water-supply considerations. Drought region boundaries coincide with municipal boundaries as referenced by the N.J. Dept of Environmental Protection (NJDEP). Each municipality in New Jersey is assigned to a drought region based on the watershed covering and supplying water to the municipality. Each data record of the file includes name of the drought region. This is the second version of the drought regions. It differs from the original delineation in two ways: 1) The number of drought regions were expanded from five to six in order to account for water-supply issues identified for the New Jersey coastal plain. The original version included the north part of the Coastal Plain in the central region. Version 2 separates a north coastal region, consisting of Monmouth and northern Ocean Counties, from the coastal region to the south. 2) Municipalities in Mercer County receiving water from the Trenton Water Dept. were moved into the southwest region from the northwest. Pennington and Hopewell boroughs were also moved to the southwest region. This is the third version of the drought regions and was released in May 2004. There are two changes from the second version. Roosevelt Boro in Monmouth County was moved to the southwest drought region (from the coastal north) and Berlin Twp in Camden County was moved to the coastal south drought region (from the southwest). Both changes were made in recognition of the location of actual water supply to these municipalities.

This package contains image data for conducting multi- and single-view sewer inlet detection in UAV image clouds. The package consists in: - individual UAV images, taken with high overlap and corrected for lens distortion - orthophoto of the case study area, clipped to road boundaries