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Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus , an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus , although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance. [ABSTRACT FROM AUTHOR]

Background: Canadian specialist residency training programs are implementing a form of competency-based medical education (CBME) that requires the assessment of entrustable professional activities (EPAs). Dashboards could be used to track the completion of EPAs to support program evaluation., Methods: Using a design-based research process, we identified program evaluation needs related to CBME assessments and designed a dashboard containing elements (data, analytics, and visualizations) meeting these needs. We interviewed leaders from the emergency medicine program and postgraduate medical education office at the University of Saskatchewan. Two investigators thematically analyzed interview transcripts to identify program evaluation needs that were audited by two additional investigators. Identified needs were described using quotes, analytics, and visualizations., Results: Between July 1, 2019 and April 6, 2021 we conducted 17 interviews with six participants (two program leaders and four institutional leaders). Four needs emerged as themes: tracking changes in overall assessment metrics, comparing metrics to the assessment plan, evaluating rotation performance, and engagement with the assessment metrics. We addressed these needs by presenting analytics and visualizations within a dashboard., Conclusions: We identified program evaluation needs related to EPA assessments and designed dashboard elements to meet them. This work will inform the development of other CBME assessment dashboards designed to support program evaluation., Competing Interests: Dr. Yilmaz is the recipient of the TUBITAK Postdoctoral Fellowship grant. The remaining authors have no conflict of interest. There are no plans for the commercialization of the described dashboard. The dashboard code has been published under an open access license., (© 2022 Yilmaz, Carey, Chan, Bandi, Wang, Wood, Mondal, Thoma; licensee Synergies Partners.)

SynVisio and Accusyn ( genomevis.usask.ca ) are freely available web-based tools for visualizing genomic conservation that provide easy-to-access visualizations for researchers to interact with their datasets and change parameters in real time to carry out synteny exploration and analysis through multiple coordinated visual representations. The tools use standard file formats and outputs from existing synteny detection systems such as MCScanX or DAGChainer, and provide several features that are valuable for large-scale genomic analysis: a range of visualization scales from full genomes down to single collinearity blocks; single-level and multiple-level plots that enable the analysis of more than two genomic regions; annotation tracks that can be loaded using standard BedGraph files; several techniques for reducing visual clutter in visualizations; the ability to download high-quality images of the visualizations; and a snapshot panel for storing configurations of the interface for later revisitation., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Canadian specialist residency training programs are implementing a form of competency-based medical education (CBME) that requires frequent assessments of entrustable professional activities (EPAs). Faculty struggle to provide helpful feedback and assign appropriate entrustment scores. CBME faculty development initiatives rarely incorporate teaching metrics. Dashboards could be used to visualize faculty assessment data to support faculty development., Methods: Using a design-based research process, we identified faculty development needs related to CBME assessments and designed a dashboard containing elements (data, analytics, and visualizations) meeting these needs. Data was collected within the emergency medicine residency program at the University of Saskatchewan through interviews with program leaders, faculty development experts, and faculty participating in development sessions. Two investigators thematically analyzed interview transcripts to identify faculty needs that were audited by a third investigator. The needs were described using representative quotes and the dashboard elements designed to address them., Results: Between July 1, 2019 and December 11, 2020 we conducted 15 interviews with nine participants (two program leaders, three faculty development experts, and four faculty members). Three needs emerged as themes from the analysis: analysis of assessments, contextualization of assessments, and accessible reporting. We addressed these needs by designing an accessible dashboard to present contextualized quantitative and narrative assessment data for each faculty member., Conclusions: We identified faculty development needs related to EPA assessments and designed dashboard elements to meet them. The resulting dashboard was used for faculty development sessions. This work will inform the development of CBME assessment dashboards for faculty., Competing Interests: Conflicts of Interest: Dr. Yilmaz is the recipient of the TUBITAK Postdoctoral Fellowship grant. The remaining authors have no conflict of interest. There are no plans for the commercialization of the described dashboard. The dashboard code has been published under an open access license., (© 2021 Yilmaz, Carey, Chan, Bandi, Wang, Woods, Mondal, Thoma; licensee Synergies Partners.)

Background: Canadian specialty programs are implementing Competence By Design, a competency-based medical education (CBME) program which requires frequent assessments of entrustable professional activities. To be used for learning, the large amount of assessment data needs to be interpreted by residents, but little work has been done to determine how visualizing and interacting with this data can be supported. Within the University of Saskatchewan emergency medicine residency program, we sought to determine how our residents' CBME assessment data should be presented to support their learning and to develop a dashboard that meets our residents' needs., Methods: We utilized a design-based research process to identify and address resident needs surrounding the presentation of their assessment data. Data was collected within the emergency medicine residency program at the University of Saskatchewan via four resident focus groups held over 10 months. Focus group discussions were analyzed using a grounded theory approach to identify resident needs. This guided the development of a dashboard which contained elements (data, analytics, and visualizations) that support their interpretation of the data. The identified needs are described using quotes from the focus groups as well as visualizations of the dashboard elements., Results: Resident needs were classified under three themes: (1) Provide guidance through the assessment program, (2) Present workplace-based assessment data, and (3) Present other assessment data. Seventeen dashboard elements were designed to address these needs., Conclusions: Our design-based research process identified resident needs and developed dashboard elements to meet them. This work will inform the creation and evolution of CBME assessment dashboards designed to support resident learning., Competing Interests: Conflict of Interest: The authors have no conflict of interest. There are no plans for the commercialization of the described dashboard. The dashboard code has been published under an open access license., (© 2020 Carey, Wilson, Bandi, Mondal, Martin, Woods, Chan, Thoma; licensee Synergies Partners.)

Background: Competency-based programs are being adopted in medical education around the world. Competence Committees must visualize learner assessment data effectively to support their decision-making. Dashboards play an integral role in decision support systems in other fields. Design-based research allows the simultaneous development and study of educational environments., Methods: We utilized a design-based research process within the emergency medicine residency program at the University of Saskatchewan to identify the data, analytics, and visualizations needed by its Competence Committee, and developed a dashboard incorporating these elements. Narrative data were collected from two focus groups, five interviews, and the observation of two Competence Committee meetings. Data were qualitatively analyzed to develop a thematic framework outlining the needs of the Competence Committee and to inform the development of the dashboard., Results: The qualitative analysis identified four Competence Committee needs (Explore Workplace-Based Assessment Data, Explore Other Assessment Data, Understand the Data in Context, and Ensure the Security of the Data). These needs were described with narratives and represented through visualizations of the dashboard elements., Conclusions: This work addresses the practical challenges of supporting data-driven decision making by Competence Committees and will inform the development of dashboards for programs, institutions, and learner management systems., Competing Interests: Conflict of Interest: The authors have no conflict of interest. There are no plans for the commercialization of the described dashboard. The dashboard code has been published under an open access license., (© 2020 Thoma, Bandi, Carey, Mondal, Woods, Martin, Chan; licensee Synergies Partners.)