Emotional and attitudinal responses to remote versus co-located usability testing

Current usability testing is often conducted via face-to-face interactions. This method can be costly, both in terms of timelines and budget. However, remote usability testing has been shown to be a viable alternative, in that performance scores have been shown to be quite similar to face-to-face methods. Although performance appears similar, remote usability testing may present challenges that threaten the validity and reliability of usability testing results. Rather than focusing on the performance of users in remote versus co-located conditions, the proposed study investigates the emotional and attitudinal responses of users engaged in software usability tests. The purpose of this study was to compare users’ anxiety and satisfaction with communication in remote and face-to-face usability tests. It was hypothesized that participants in the remote condition would exhibit a lower level of anxiety and be less satisfied with the communication method. Multiple usability tasks were administered and measures were recorded at three time intervals. Responses on the Social Anxiety Thoughts (SAT) questionnaire and the Communication Satisfaction Inventory (CSI) were collected. Although there were no significant differences between the groups in terms of anxiety and communication satisfaction, methodological limitations may have prevented the detection of differences and additional research is required to explore the strengths and weaknesses of remote usability testing

TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications

Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 ± 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of ∼ 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet

TermPicks: A century of Greenland glacier terminus data for use in machine learning applications

Abstract. Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of time scales. Numerous studies have digitized termini manually, but this process is labor-intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and SAR satellite sensors. These techniques rely on high quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39,060 individual terminus traces for 278 glaciers with a mean and median number of traces per glacier of 136 ± 190 and 93, respectively. Across all glaciers, 32,567 dates have been picked, of which 4,467 have traces from more than one author (duplication of 14 %). We find a median error of ∼100 m among manually-traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of The Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet. </jats:p