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Introduction As mobile robots proliferate in communities, designers must consider the impacts these systems have on the users, onlookers, and places they encounter. It becomes increasingly necessary to study situations where humans and robots coexist in common spaces, even if they are not directly interacting. This dataset presents a multidisciplinary approach to study human-robot encounters in an indoor apartment-like setting between participants and two mobile robots. Participants take questionnaires, wear sensors for physiological measures, and take part in a focus group after experiments finish. This dataset contains raw time series data from sensors and robots, and qualitative results from focus groups. The data can be used to analyze measures of human physiological response to varied encounter conditions, and to gain insights into human preferences and comfort during community encounters with mobile robots. Dataset Contents A dictionary of terms found in the dataset can be found in the "Data-Dictionary.pdf" Synchronized XDF files from every trial with raw data from electrodermal activity (EDA), electrocardiography (ECG), photoplethysmography (PPG) and seismocardiography (SCG). These synchronized files also contain robot pose data and microphone data. Results from analysis of two important features found from heart rate variability (HRV) and EDA. Specifically, HRV_CMSEn and nsEDRfreq is computed for each participant over each trial. These results also include Robot Confidence, which is a classification score representing the confidence that the 80 physiological features considered originate from a subject in a robot encounter. The higher the score, the higher the confidence A vectormap of the environment used during testing ("AHG_vectormap.txt") and a csv with locations of participant seating within the map ("Participant-Seating-Coordinates.csv"). Each line of the vectormap represents two endpoints of a line: x1,y1,x2,y2. The coordinates of participant seating are x,y positions and rotation about the vertical axis in radians. Anonymized videos captured using two static cameras placed in the environment. They are located in the living room and small room, respectively. Animations visualized from XDF files that show participant location, robot behaviors and additional characteristics like participant-robot line-of-sight and relative audio volume. Quotes associated with themes taken from focus group data. These quotes demonstrate and justify the results of the thematic analysis. Raw text from focus groups is not included for privacy concerns. Quantitative results from focus groups associated with factors influencing perceived safety. These results demonstrate the findings from deductive content analysis. The deductive codebook is also included. Results from pre-experiment and between-trial questionnaires Copies of both questionnaires and the semi-structured focus group protocol. Human Subjects This dataset contain de-identified information for 24 total subjects over 13 experiment sessions. The population for the study is the students, faculty and staff at the University of Texas at Austin. Of the 24 participants, 18 are students and 6 are staff at the university. Ages range from 19-48 and there are 10 males and 14 females who participated. Published data has been de-identified in coordination with the university Internal Review Board. All participants signed informed consent to participate in the study and for the distribution of this data. Access Restrictions Transcripts from focus groups are not published due to privacy concerns. Videos including participants are de-identified with overlays on videos. All other data is labeled only by participant ID, which is not associated with any identifying characteristics. Experiment Design Robots This study considers indoor encounters with two quadruped mobile robots. Namely, the Boston Dynamics Spot and Unitree Go1. These mobile robots are capable of everyday movement tasks like inspection, search or mapping which may be common tasks for autonomous agents in university communities. The study focus on perceived safety of bystanders under encounters with these relevant platforms. Control Conditions and Experiment Session Layout We control three variables in this study: Participant seating social (together in the living room) v. isolated (one in living room, other in small room) Robots Together v. Separate Robot Navigation v. Search Behavior A visual representation of the three control variables are shown on the left in (a)-(d) including the robot behaviors and participant seating locations, shown as X's. Blue represent social seating and yellow represent isolated seating. (a) shows the single robot navigation path. (b) is the two robot navigation paths. In (c) is the single robot search path and (d) shows the two robot search paths. The order of behaviors and seating locations are randomized and then inserted into the experiment session as overviewed in (e). These experiments are designed to gain insights into human responses to encounters with robots. The first step is receiving consent from the followed by a pre-experiment questionnaire that documents demographics, baseline stress information and big 5 personality traits. The nature video is repeated before and after the experimental session to establish a relaxed baseline physiological state. Experiments take place over 8 individual trials, which are defined by a subject seat arrangement, search or navigation behavior, and robots together or separate. After each of the 8 trials, participants take the between trial questionnaire, which is a 7 point Likert scale questionnaire designed to assess perceived safety during the preceding trial. After experiments and sensor removal, participants take part in a focus group. Synchronized Data Acquisition Data is synchronized from physiological sensors, environment microphones and the robots using the architecture shown. These raw xdf files are named using the following file naming convention: Trials where participants sit together in the living room [Session number]-[trial number]-social-[robots together or separate]-[search or navigation behavior].xdf Trials where participants are isolated [Session number]-[trial number]-isolated-[subject ID living room]-[subject ID small room]-[robots together or separate]-[search or navigation behavior].xdf Qualitative Data Qualitative data is obtained from focus groups with participants after experiments. Typically, two participants take part however two sessions only included one participant. The semi-structured focus group protocol can be found in the dataset. Two different research methods are applied to focus group transcripts. Note: the full transcripts are not provided for privacy concerns. First, we performed a qualitative content analysis using deductive codes found from an existing model of perceived safety during HRI (Akalin et al. 2023). The quantitative results from this analysis are reported as frequencies of references to the various factors of perceived safety. The codebook describing these factors is included in the dataset. Second, an inductive thematic analysis was performed on the data to identify emergent themes. The resulting themes and associated quotes taken from focus groups are also included. Data Organization Data is organized in separate folders, namely: animation-videos anonymized-session-videos focus-group-results questionnaire-responses research-materials signal-analysis-results synchronized-xdf-data Data Quality Statement In limited trials, participant EDA or ECG signals or robot pose information may be missing due to connectivity issues during data acquisition. Additionally, the questionnaires for Participant ID0 and ID1 are incomplete due to an error in the implementation of the Qualtrics survey instrument used. 

In February 2021, severe winter weather in Texas caused widespread electrical blackouts, water outages, and boil water notices. Water systems faced extensive challenges due to cascading failures across multiple interde- pendent infrastructure systems. Water utilities have since made considerable progress in improving resilience to extreme events, but ongoing challenges remain. Through a qualitative analysis of semi-structured interviews with 20 large water utilities in Texas, this study tracks the evolution of water infrastructure resilience across three phases: the storm and immediate aftermath, the subsequent one-year period, and the “new normal” in the post-disaster environment. We consider five dimensions of resilience—economic, environmental, governance, infrastructure, and social—to identify where solutions have been implemented and where barriers remain. This study contributes to efforts throughout the United States to build more robust water systems by capturing lessons learned from Winter Storm Uri and providing recommendations to improve hazard preparedness, resilience, and public health. 

A severe winter storm in February 2021 impacted multiple infrastructure systems in Texas, leaving over 13 million people without electricity and/or water, potentially $100 billion in economic damages, and almost 250 lives lost. While the entire state was impacted by temperatures up to 10 °C colder than expected for this time of year, as well as levels of snow and ice accumulation not observed in decades, the responses and outcomes from communities were inconsistent and exacerbated prevailing social and infrastructure inequities that are still impacting those communities. In this contribution, we synthesize a subset of multiple documented inequities stemming from the interdependence of the water, housing, transportation, and communication sectors with the energy sector, and present a summary of actions to address the interdependency of infrastructure system inequities. 

Disasters can have devastating impacts on communities particularly when they are disproportionately impacted by flooding. Despite the presence of governmental programs implemented to increase community preparedness for flooding, communities may still struggle. Currently, we have limited holistic knowledge of barriers that stifle community preparedness. To address this gap, we conducted 32 in‐depth interviews with stakeholders including community members, leaders, and city employees in a community subject to flood risk. The findings suggest that preparedness is not overcome simply by providing knowledge, and people do not necessarily embrace preparedness after participating in training programs. Rather, community preparedness is entwined with addressing chronic stressors, increasing community participation, and attending to social justice and broken trust due to historical mistreatment of the community. We hereby introduce the notion of community preparedness efficacy—defined as the barriers needed to be overcome for communities to be able to prepare for disasters—that considers chronic stressors, community participation, social justice, and equity to move underserved communities forward.

 

Climate change poses a multifaceted, complex, and existential threat to human health and well-being, but efforts to communicate these threats to the public lag behind what we know how to do in communication research. Effective communication about climate change’s health risks can improve a wide variety of individual and population health-related outcomes by: (1) helping people better make the connection between climate change and health risks and (2) empowering them to act on that newfound knowledge and understanding. The aim of this manuscript is to highlight communication methods that have received empirical support for improving knowledge uptake and/or driving higher-quality decision making and healthier behaviors and to recommend how to apply them at the intersection of climate change and health. This expert consensus about effective communication methods can be used by healthcare professionals, decision makers, governments, the general public, and other stakeholders including sectors outside of health. In particular, we argue for the use of 11 theory-based, evidence-supported communication strategies and practices. These methods range from leveraging social networks to making careful choices about the use of language, narratives, emotions, visual images, and statistics. Message testing with appropriate groups is also key. When implemented properly, these approaches are likely to improve the outcomes of climate change and health communication efforts.

 

Prior research has established the feasibility of conducting online interviews and observations, yet there is limited guidance in how to interact with participants when conducting fully mediated research with screen-sharing and video. This study, conducted during early phases of COVID-19, included 15 volunteer tweet-annotators working with an emergency response organization. This method contribution uses cues-related and surveillance theories to reveal challenges and best practices when asking research participants to share their screen, be on video, and participate in a multiple-interview study. The findings suggest that researchers conducting online-mediated research should be prepared to provide technical support for the devices and interfaces participants use during the study, find ways to “see” beyond what is on the mediated screen, and consider ethical issues not often discussed. In addition to these findings, an output of this research is two brief training videos useful for other researchers embarking on conducting fully mediated research.