{"title":"Deployment as a Critical Business Data Science Discipline","authors":"T. Davenport, Katie Malone","doi":"10.1162/99608F92.90814C32","DOIUrl":"https://doi.org/10.1162/99608F92.90814C32","url":null,"abstract":"Column Editors’ Note: In this article, we focus on a key problem in industry: getting data science models deployed into production within organizations. The tasks and skills involved in deployment are often not considered as a key component of data science initiatives, but they are critical to data science success. We describe evidence of the deployment problem, the components of deployment, and how some campus-based business analytics degree programs attempt to inculcate deployment skills.","PeriodicalId":194618,"journal":{"name":"Issue 3.1, Winter 2021","volume":"753 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117005719","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}
{"title":"Blue Chips and White Collars: Whose Data Science Is It?","authors":"Seliem El-Sayed, B. Prainsack","doi":"10.1162/99608F92.EA29EE5A","DOIUrl":"https://doi.org/10.1162/99608F92.EA29EE5A","url":null,"abstract":"","PeriodicalId":194618,"journal":{"name":"Issue 3.1, Winter 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128403305","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}
{"title":"We Need a (Responsible!) Data Science Rapid Response Network","authors":"E. Kolaczyk, Meredith M. Lee, J. Liu, M. Parker","doi":"10.1162/99608F92.2794E78D","DOIUrl":"https://doi.org/10.1162/99608F92.2794E78D","url":null,"abstract":"","PeriodicalId":194618,"journal":{"name":"Issue 3.1, Winter 2021","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116863748","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}
{"title":"Bringing Every Tool to the COVID-19 Fight – What We Need Now","authors":"N. Louissaint","doi":"10.1162/99608F92.70F8CB98","DOIUrl":"https://doi.org/10.1162/99608F92.70F8CB98","url":null,"abstract":"","PeriodicalId":194618,"journal":{"name":"Issue 3.1, Winter 2021","volume":"28 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117231966","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}
{"title":"What Are the Values of Data, Data Science, or Data Scientists?","authors":"Xiaomin Meng","doi":"10.1162/99608F92.EE717CF7","DOIUrl":"https://doi.org/10.1162/99608F92.EE717CF7","url":null,"abstract":"","PeriodicalId":194618,"journal":{"name":"Issue 3.1, Winter 2021","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117150947","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}
{"title":"Data Scientists Should Be Value-Driven, Not Neutral","authors":"Lord Tim Clement-Jones","doi":"10.1162/99608F92.39876CEA","DOIUrl":"https://doi.org/10.1162/99608F92.39876CEA","url":null,"abstract":"","PeriodicalId":194618,"journal":{"name":"Issue 3.1, Winter 2021","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121447971","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}
Margaret V. Powers-Fletcher, Erin McCabe, Sally Luken, Danny T. Y. Wu, Philip A Hagedorn, E. Edgerton, A. Koshoffer, Dorcas Washington, Suraj Kannayyagari, Jason K. W. Lee, J. Latessa, Anita Shah, J. J. Lee
{"title":"Convergence in Viral Outbreak Research: Using Natural Language Processing to Define Network Bridges in the Bench-Bedside-Population Paradigm","authors":"Margaret V. Powers-Fletcher, Erin McCabe, Sally Luken, Danny T. Y. Wu, Philip A Hagedorn, E. Edgerton, A. Koshoffer, Dorcas Washington, Suraj Kannayyagari, Jason K. W. Lee, J. Latessa, Anita Shah, J. J. Lee","doi":"10.1162/99608F92.CC479D52","DOIUrl":"https://doi.org/10.1162/99608F92.CC479D52","url":null,"abstract":"Research on viral outbreaks at the pandemic scale responds to heightened social urgency and the need to expedite scientific discovery from the “bench” to the “bedside” to the wider population. We sought to better understand translational research within the context of pandemics, both historical and present day, by tracking publication trends in the immediate aftermath of virus outbreaks. We used a blend of natural language processing (NLP), social network analysis and human annotation approaches to analyze the 85,663 articles in the COVID-19 Open Research Dataset (CORD-19). We found stable and repeated characteristics throughout subsets of peer-reviewed published literature corresponding to seven different viral outbreaks over the last several decades. Three distinct groups or “neighborhoods” recurred across all of the model networks – (1) bench science, (2) clinical treatments, and (3) broader public health trends. Notably, in each historical virus model, small “bridge” nodes representing translational research connect the three otherwise disconnected neighborhoods. These bridging studies embody research convergence by both integrating the vocabulary and methods of different disciplines and bodies of previous work and by citing other papers beyond their narrow field. In the case of COVID-19, the literature continues to evolve apace along with the virus, and we can witness the phases of response unfold as the science progresses. This study demonstrates how the different sectors of biomedical research respond independently to public health emergencies and how translational research can facilitate greater information synthesis and exchange between disciplinary silos.","PeriodicalId":194618,"journal":{"name":"Issue 3.1, Winter 2021","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128077155","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}
{"title":"Combining Statistical, Physical, and Historical Evidence to Improve Historical Sea-Surface Temperature Records","authors":"Duo Chan","doi":"10.1162/99608F92.EDCEE38F","DOIUrl":"https://doi.org/10.1162/99608F92.EDCEE38F","url":null,"abstract":"Reconstructing past sea-surface temperatures (SSTs) from historical measurements containing more than 100 million ship-based observations taken by over 500,000 ships from more than 150 countries using a variety of methodologies creates a wide range of historical, scientific, and statistical challenges. The reconstruction of historical SSTs for studying climate change is particularly challenging because SST measurements are uncertain and contain systematic biases of order 0.1◦C to 1◦C—these systematic biases are in the range of the historical global warming signal of approximately 1◦C. The biases are complicated and have generally been addressed using simplified corrections. In this review, I introduce a history of SST observations, review a statistical method developed for quantifying SST biases, and illustrate scientific insights obtained from adjusted SSTs. This article also documents the scientific journey of my Ph.D. work. As a result, I report personal stories on both successes, difficulties, and setbacks along the way. The statistical method for correcting SSTs (i.e., a linear-mixed-effect intercomparison framework) depends on identifying systematic offsets between intercomparable groups of SST obser-vations. Combining estimated offsets with physical and historical evidence has allowed for correcting discrepancies associated with SSTs, including the North Atlantic warming twice as fast as the North Pacific in the early twentieth century and anomalously warm SSTs during World War II. Corrections also permit better hindcasting of Atlantic hurricanes. I conclude with some discussion on how the SST records might be further improved. Given the importance of SSTs for understanding historical changes in climate, I hope that this review can help others appreciate challenges that are present and spark some interest and ideas for further improvement.","PeriodicalId":194618,"journal":{"name":"Issue 3.1, Winter 2021","volume":"20 19-20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123594370","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}
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