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This closure provides a response to the discussions of “Indefinability of Effective Stress for Unsaturated Soils” by Xiong Zhang and Sandra Houston. https://doi.org/10.1061/JGGEFK.GTENG-12435. 

This paper presents the results of a community survey that was designed to better understand the effects of permafrost degradation and coastal erosion on civil infrastructure. Observations were collected from residents in four Arctic coastal communities: Point Lay, Wainwright, Utqiaġvik, and Kaktovik. All four communities are underlain by continuous ice-rich permafrost with varying degrees of degradation and coastal erosion. The types, locations, and periods of observed permafrost thaw and coastal erosion were elicited. Survey participants also reported the types of civil infrastructure being affected by permafrost degradation and coastal erosion and any damage to residential buildings. Most survey participants reported that coastal erosion has been occurring for a longer period than permafrost thaw. Surface water ponding, ground surface collapse, and differential ground settlement are the three types of changes in ground surface manifested by permafrost degradation that are most frequently reported by the participants, while houses are reported as the most affected type of infrastructure in the Arctic coastal communities. Wall cracking and house tilting are the most commonly reported types of residential building damage. The effects of permafrost degradation and coastal erosion on civil infrastructure vary between communities. Locations of observed permafrost degradation and coastal erosion collected from all survey participants in each community were stacked using heatmap data visualization. The heatmaps constructed using the community survey data are reasonably consistent with modeled data synthesized from the scientific literature. This study shows a useful approach to coproduce knowledge with Arctic residents to identify locations of permafrost thaw and coastal erosion at higher spatial resolution as well as the types of infrastructure damage of most concern to Arctic residents. 

This dataset presents the results of a community survey that was designed to better understand the effects of permafrost degradation and coastal erosion on civil infrastructure. Observations were collected from residents in four Arctic coastal communities: Point Lay, Wainwright, Utqiaġvik, and Kaktovik. There are three categories of questions in this survey: permafrost degradation, coastal erosion, and infrastructure damage and repair. The participants identified changes in ground surface manifested by permafrost degradation in and around their communities. The options provided in the questionnaire included surface water ponding, sinkholes, ground surface collapse, differential ground settlement along roads and gravel pads, and others. The periods during which these changes have been happening were also recorded; the options include less than 6 months, 0.5–1 year, 1–3 years, 3–5 years, 5–10 years, and greater than 10 years. Participants also indicated the infrastructure types affected by permafrost degradation. The options include houses, runways, schools, ice cellars, water and sewer lines, and others. Effects of permafrost degradation on residential buildings, buried pipelines, utilidors, and roads were reported in the survey. Detailed information such as damage type, damage location, repair method, and effectiveness of repair methods was also recorded. For the questions related to coastal erosion, participants identified events of coastal erosion, periods during which coastal erosion has been happening, types of civil infrastructure affected, and types of erosion control measures implemented and their effectiveness. Participants were able to provide their plans if permafrost degradation and coastal erosion continue to happen. They identified the locations of permafrost degradation and coastal erosion on provided maps with three different scales of approximately 600 km, 40 km, and 8 km. 

It is hard for experts to create good instructional resources due to a phenomenon known as the expert blind spot: They forget what it was like to be a novice, so they cannot pinpoint exactly where novices commonly struggle and how to best phrase their explanations. To help overcome these expert blind spots for computer programming topics, we created a learnersourcing system that elicits explanations of misconceptions directly from learners while they are coding. We have deployed this system for the past three years to the widely-used Python Tutor coding website (pythontutor.com) and collected 16,791 learner-written explanations. To our knowledge, this is the largest dataset of explanations for programming misconceptions. By inspecting this dataset, we found surprising insights that we did not originally think of due to our own expert blind spots as programming instructors. We are now using these insights to improve compiler and run-time error messages to explain common novice misconceptions. 

Data science courses and tutorials have grown popular in recent years, yet they are still taught using production-grade programming tools (e.g., R, MATLAB, and Python IDEs) within desktop computing environments. Although powerful, these tools present high barriers to entry for novices, forcing them to grapple with the extrinsic complexities of software installation and configuration, data file management, data parsing, and Unix-like command-line interfaces. To lower the barrier for novices to get started with learning data science, we created DS.js, a bookmarklet that embeds a data science programming environment directly into any existing webpage. By transforming any webpage into an examplecentric IDE, DS.js eliminates the aforementioned complexities of desktop-based environments and turns the entire web into a rich substrate for learning data science. DS.js automatically parses HTML tables and CSV/TSV data sets on the target webpage, attaches code editors to each data set, provides a data table manipulation and visualization API designed for novices, and gives instructional scaffolding in the form of bidirectional previews of how the user’s code and data relate.