JooYoung Seo, Sile O'Modhrain, Yilin Xia, Sanchita Kamath, Bongshin Lee, James M Coughlan
While recent years have seen a growing interest in accessible visualization tools and techniques for blind people, little attention is paid to the learning opportunities and teaching strategies of data science and visualization tailored for blind individuals. Whereas the former focuses on the accessibility and usability issues of data visualization tools, the latter is concerned with the learnability of concepts and skills for data science and visualization. In this paper, we present novel approaches to teaching data science and visualization to blind students in an online setting. Taught by blind instructors, nine blind learners having a wide range of professional backgrounds participated in a two-week summer course. We describe the course design, teaching strategies, and learning outcomes. We also discuss the challenges and opportunities of teaching data science and visualization to blind students. Our work contributes to the growing body of knowledge on accessible data science and visualization education, and provides insights into the design of online courses for blind students.
{"title":"Designing Born-Accessible Courses in Data Science and Visualization: Challenges and Opportunities of a Remote Curriculum Taught by Blind Instructors to Blind Students.","authors":"JooYoung Seo, Sile O'Modhrain, Yilin Xia, Sanchita Kamath, Bongshin Lee, James M Coughlan","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>While recent years have seen a growing interest in accessible visualization tools and techniques for blind people, little attention is paid to the learning opportunities and teaching strategies of data science and visualization tailored for blind individuals. Whereas the former focuses on the accessibility and usability issues of data visualization tools, the latter is concerned with the learnability of concepts and skills for data science and visualization. In this paper, we present novel approaches to teaching data science and visualization to blind students in an online setting. Taught by blind instructors, nine blind learners having a wide range of professional backgrounds participated in a two-week summer course. We describe the course design, teaching strategies, and learning outcomes. We also discuss the challenges and opportunities of teaching data science and visualization to blind students. Our work contributes to the growing body of knowledge on accessible data science and visualization education, and provides insights into the design of online courses for blind students.</p>","PeriodicalId":72959,"journal":{"name":"Eurographics/IEEE VGTC Symposium on Visualization : EUROVIS : [proceedings]. Eurographics/IEEE VGTC Symposium on Visualization","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Live-cell imaging is a common data acquisition technique used by biologists to analyze cell behavior. Since manually tracking cells in a video sequence is extremely time-consuming, many automatic algorithms have been developed in the last twenty years to accomplish the task. However, none of these algorithms can yet claim robust tracking performance at the varying of acquisition conditions (e.g., cell type, acquisition device, cell treatments). While many visualization tools exist to help with cell behavior analysis, there are no tools to help with the algorithm's validation. This paper proposes CellTrackVis, a new visualization tool for evaluating cell tracking algorithms. CellTrackVis allows comparing automatically generated cell tracks with ground truth data to help biologists select the best-suited algorithm for their experimented pipeline. Moreover, CellTackVis can be used as a debugging tool while developing a new cell tracking algorithm to investigate where, when, and why each tracking error occurred.
{"title":"CellTrackVis: analyzing the performance of cell tracking algorithms.","authors":"W Li, X Zhang, A Stern, M Birtwistle, F Iuricich","doi":"10.2312/evs.20221103","DOIUrl":"https://doi.org/10.2312/evs.20221103","url":null,"abstract":"<p><p>Live-cell imaging is a common data acquisition technique used by biologists to analyze cell behavior. Since manually tracking cells in a video sequence is extremely time-consuming, many automatic algorithms have been developed in the last twenty years to accomplish the task. However, none of these algorithms can yet claim robust tracking performance at the varying of acquisition conditions (e.g., cell type, acquisition device, cell treatments). While many visualization tools exist to help with cell behavior analysis, there are no tools to help with the algorithm's validation. This paper proposes CellTrackVis, a new visualization tool for evaluating cell tracking algorithms. CellTrackVis allows comparing automatically generated cell tracks with ground truth data to help biologists select the best-suited algorithm for their experimented pipeline. Moreover, CellTackVis can be used as a debugging tool while developing a new cell tracking algorithm to investigate where, when, and why each tracking error occurred.</p>","PeriodicalId":72959,"journal":{"name":"Eurographics/IEEE VGTC Symposium on Visualization : EUROVIS : [proceedings]. Eurographics/IEEE VGTC Symposium on Visualization","volume":"2022 ","pages":"115-119"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841471/pdf/nihms-1862810.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10604659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-06DOI: 10.2312/eurovisshort.20161158
B. Hollister, Gordon Duffley, C. Butson, Chris R. Johnson, P. Rosen
We have created the Neurostimulation Uncertainty Viewer (nuView or νView) tool for exploring data arising from deep brain stimulation (DBS). Simulated volume of tissue activated (VTA), using clinical electrode placements, are recorded along with patient outcomes in the Unified Parkinson's disease rating scale (UPDRS). The data is volumetric and sparse, with multi-value patient results for each activated voxel in the simulation. νView provides a collection of visual methods to explore the activated tissue to enhance understanding of electrode usage for improved therapy with DBS.
{"title":"Visualization for Understanding Uncertainty in Activation Volumes for Deep Brain Stimulation","authors":"B. Hollister, Gordon Duffley, C. Butson, Chris R. Johnson, P. Rosen","doi":"10.2312/eurovisshort.20161158","DOIUrl":"https://doi.org/10.2312/eurovisshort.20161158","url":null,"abstract":"We have created the Neurostimulation Uncertainty Viewer (nuView or νView) tool for exploring data arising from deep brain stimulation (DBS). Simulated volume of tissue activated (VTA), using clinical electrode placements, are recorded along with patient outcomes in the Unified Parkinson's disease rating scale (UPDRS). The data is volumetric and sparse, with multi-value patient results for each activated voxel in the simulation. νView provides a collection of visual methods to explore the activated tissue to enhance understanding of electrode usage for improved therapy with DBS.","PeriodicalId":72959,"journal":{"name":"Eurographics/IEEE VGTC Symposium on Visualization : EUROVIS : [proceedings]. Eurographics/IEEE VGTC Symposium on Visualization","volume":"60 1","pages":"37-41"},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83590702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-01-01DOI: 10.2312/PE/EuroVisShort/EuroVisShort2012/078-083
Yishi Guo, Yang Wang, Shiaofen Fang, Hongyang Chao, Andrew J Saykin, Li Shen
The human brain is a complex network with countless connected neurons, and can be described as a "connectome". Existing studies on analyzing human connectome data are primarily focused on characterizing the brain networks with a small number of easily computable measures that may be inadequate for revealing complex relationship between brain function and its structural substrate. To facilitate large-scale connectomic analysis, in this paper, we propose a powerful and flexible volume rendering scheme to effectively visualize and interactively explore thousands of network measures in the context of brain anatomy, and to aid pattern discovery. We demonstrate the effectiveness of the proposed scheme by applying it to a real connectome data set.
{"title":"Pattern Visualization of Human Connectome Data.","authors":"Yishi Guo, Yang Wang, Shiaofen Fang, Hongyang Chao, Andrew J Saykin, Li Shen","doi":"10.2312/PE/EuroVisShort/EuroVisShort2012/078-083","DOIUrl":"https://doi.org/10.2312/PE/EuroVisShort/EuroVisShort2012/078-083","url":null,"abstract":"<p><p>The human brain is a complex network with countless connected neurons, and can be described as a \"connectome\". Existing studies on analyzing human connectome data are primarily focused on characterizing the brain networks with a small number of easily computable measures that may be inadequate for revealing complex relationship between brain function and its structural substrate. To facilitate large-scale connectomic analysis, in this paper, we propose a powerful and flexible volume rendering scheme to effectively visualize and interactively explore thousands of network measures in the context of brain anatomy, and to aid pattern discovery. We demonstrate the effectiveness of the proposed scheme by applying it to a real connectome data set.</p>","PeriodicalId":72959,"journal":{"name":"Eurographics/IEEE VGTC Symposium on Visualization : EUROVIS : [proceedings]. Eurographics/IEEE VGTC Symposium on Visualization","volume":"2012 ","pages":"78-83"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4469287/pdf/nihms695796.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33403179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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