Adam Joseph Coscia, H. Sapers, Noah Deutsch, Malika Khurana, J. Magyar, Sergio A. Parra, Daniel R. Utter, R.L. Wipfler, D. Caress, Eric J. Martin, J. Paduan, M. Hendrie, S. Lombeyda, H. Mushkin, A. Endert, Scott Davidoff, V. Orphan
{"title":"DeepSee:多维可视化海底生态系统","authors":"Adam Joseph Coscia, H. Sapers, Noah Deutsch, Malika Khurana, J. Magyar, Sergio A. Parra, Daniel R. Utter, R.L. Wipfler, D. Caress, Eric J. Martin, J. Paduan, M. Hendrie, S. Lombeyda, H. Mushkin, A. Endert, Scott Davidoff, V. Orphan","doi":"10.1145/3613904.3642001","DOIUrl":null,"url":null,"abstract":"Scientists studying deep ocean microbial ecosystems use limited numbers of sediment samples collected from the seafloor to characterize important life-sustaining biogeochemical cycles in the environment. Yet conducting fieldwork to sample these extreme remote environments is both expensive and time consuming, requiring tools that enable scientists to explore the sampling history of field sites and predict where taking new samples is likely to maximize scientific return. We conducted a collaborative, user-centered design study with a team of scientific researchers to develop DeepSee, an interactive data workspace that visualizes 2D and 3D interpolations of biogeochemical and microbial processes in context together with sediment sampling history overlaid on 2D seafloor maps. Based on a field deployment and qualitative interviews, we found that DeepSee increased the scientific return from limited sample sizes, catalyzed new research workflows, reduced long-term costs of sharing data, and supported teamwork and communication between team members with diverse research goals.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"23 25","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DeepSee: Multidimensional Visualizations of Seabed Ecosystems\",\"authors\":\"Adam Joseph Coscia, H. Sapers, Noah Deutsch, Malika Khurana, J. Magyar, Sergio A. Parra, Daniel R. Utter, R.L. Wipfler, D. Caress, Eric J. Martin, J. Paduan, M. Hendrie, S. Lombeyda, H. Mushkin, A. Endert, Scott Davidoff, V. Orphan\",\"doi\":\"10.1145/3613904.3642001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Scientists studying deep ocean microbial ecosystems use limited numbers of sediment samples collected from the seafloor to characterize important life-sustaining biogeochemical cycles in the environment. Yet conducting fieldwork to sample these extreme remote environments is both expensive and time consuming, requiring tools that enable scientists to explore the sampling history of field sites and predict where taking new samples is likely to maximize scientific return. We conducted a collaborative, user-centered design study with a team of scientific researchers to develop DeepSee, an interactive data workspace that visualizes 2D and 3D interpolations of biogeochemical and microbial processes in context together with sediment sampling history overlaid on 2D seafloor maps. Based on a field deployment and qualitative interviews, we found that DeepSee increased the scientific return from limited sample sizes, catalyzed new research workflows, reduced long-term costs of sharing data, and supported teamwork and communication between team members with diverse research goals.\",\"PeriodicalId\":513202,\"journal\":{\"name\":\"ArXiv\",\"volume\":\"23 25\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ArXiv\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3613904.3642001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ArXiv","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3613904.3642001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DeepSee: Multidimensional Visualizations of Seabed Ecosystems
Scientists studying deep ocean microbial ecosystems use limited numbers of sediment samples collected from the seafloor to characterize important life-sustaining biogeochemical cycles in the environment. Yet conducting fieldwork to sample these extreme remote environments is both expensive and time consuming, requiring tools that enable scientists to explore the sampling history of field sites and predict where taking new samples is likely to maximize scientific return. We conducted a collaborative, user-centered design study with a team of scientific researchers to develop DeepSee, an interactive data workspace that visualizes 2D and 3D interpolations of biogeochemical and microbial processes in context together with sediment sampling history overlaid on 2D seafloor maps. Based on a field deployment and qualitative interviews, we found that DeepSee increased the scientific return from limited sample sizes, catalyzed new research workflows, reduced long-term costs of sharing data, and supported teamwork and communication between team members with diverse research goals.