Journal of data science : JDS最新文献_第7页

Identifying Prerequisite Courses in Undergraduate Biology Using Machine Learning 利用机器学习确定本科生物学的必修课程

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1068

Youngjin Lee

{"title":"Identifying Prerequisite Courses in Undergraduate Biology Using Machine Learning","authors":"Youngjin Lee","doi":"10.6339/22-jds1068","DOIUrl":"https://doi.org/10.6339/22-jds1068","url":null,"abstract":"Many undergraduate students who matriculated in Science, Technology, Engineering and Mathematics (STEM) degree programs drop out or switch their major. Previous studies indicate that performance of students in prerequisite courses is important for attrition of students in STEM. This study analyzed demographic information, ACT/SAT score, and performance of students in freshman year courses to develop machine learning models predicting their success in earning a bachelor’s degree in biology. The predictive model based on Random Forest (RF) and Extreme Gradient Boosting (XGBoost) showed a better performance in terms of AUC (Area Under the Curve) with more balanced sensitivity and specificity than Logistic Regression (LR), K-Nearest Neighbor (KNN), and Neural Network (NN) models. An explainable machine learning approach called break-down was employed to identify important freshman year courses that could have a larger impact on student success at the biology degree program and student levels. More important courses identified at the program level can help program coordinators to prioritize their effort in addressing student attrition while more important courses identified at the student level can help academic advisors to provide more personalized, data-driven guidance to students.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"491 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320701","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}

引用次数: 0

‘You Draw It’: Implementation of Visually Fitted Trends with r2d3 “你画它”:使用r2d3实现视觉拟合趋势

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1083

Emily A. Robinson, Réka Howard, Susan Vanderplas

引用次数: 2

Addressing the Impact of the COVID-19 Pandemic on Survival Outcomes in Randomized Phase III Oncology Trials 解决COVID-19大流行对随机III期肿瘤试验生存结果的影响

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1079

Jiabu Ye, Binbing Yu, H. Mann, A. Sabin, Z. Szíjgyártó, David Wright, P. Mukhopadhyay, C. Massacesi, S. Ghiorghiu, R. Iacona

{"title":"Addressing the Impact of the COVID-19 Pandemic on Survival Outcomes in Randomized Phase III Oncology Trials","authors":"Jiabu Ye, Binbing Yu, H. Mann, A. Sabin, Z. Szíjgyártó, David Wright, P. Mukhopadhyay, C. Massacesi, S. Ghiorghiu, R. Iacona","doi":"10.6339/22-jds1079","DOIUrl":"https://doi.org/10.6339/22-jds1079","url":null,"abstract":"We assessed the impact of the coronavirus disease 2019 (COVID-19) pandemic on the statistical analysis of time-to-event outcomes in late-phase oncology trials. Using a simulated case study that mimics a Phase III ongoing trial during the pandemic, we evaluated the impact of COVID-19-related deaths, time off-treatment and missed clinical visits due to the pandemic, on overall survival and/or progression-free survival in terms of test size (also referred to as Type 1 error rate or alpha level), power, and hazard ratio (HR) estimates. We found that COVID-19-related deaths would impact both size and power, and lead to biased HR estimates; the impact would be more severe if there was an imbalance in COVID-19-related deaths between the study arms. Approaches censoring COVID-19-related deaths may mitigate the impact on power and HR estimation, especially if study data cut-off was extended to recover censoring-related event loss. The impact of COVID-19-related time off-treatment would be modest for power, and moderate for size and HR estimation. Different rules of censoring cancer progression times result in a slight difference in the power for the analysis of progression-free survival. The simulations provided valuable information for determining whether clinical-trial modifications should be required for ongoing trials during the COVID-19 pandemic.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320957","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}

引用次数: 0

Editorial: Large-Scale Spatial Data Science 社论:大规模空间数据科学

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds204edi

Sameh Abdulah, S. Castruccio, M. Genton, Ying Sun

{"title":"Editorial: Large-Scale Spatial Data Science","authors":"Sameh Abdulah, S. Castruccio, M. Genton, Ying Sun","doi":"10.6339/22-jds204edi","DOIUrl":"https://doi.org/10.6339/22-jds204edi","url":null,"abstract":"This special issue features eight articles on “Large-Scale Spatial Data Science.” Data science for complex and large-scale spatial and spatio-temporal data has become essential in many research ﬁelds, such as climate science and environmental applications. Due to the ever-increasing amounts of data collected, traditional statistical approaches tend to break down and computa-tionally eﬃcient methods and scalable algorithms that are suitable for large-scale spatial data have become crucial to cope with many challenges associated with big data. This special issue aims at highlighting some of the latest developments in the area of large-scale spatial data science. The research papers presented showcase advanced statistical methods and machine learn-ing approaches for solving complex and large-scale problems arising from modern data science applications. Abdulah et al. (2022) reported the results of the second competition on spatial statistics for large datasets organized by the King Abdullah University of Science and Technology (KAUST). Very large datasets (up to 1 million in size) were generated with the ExaGeoStat software to design the competition on large-scale predictions in challenging settings, including univariate nonstationary spatial processes, univariate stationary space-time processes, and bivariate stationary spatial processes. The authors described the data generation process in detail in each setting and made these valuable datasets publicly available. They reviewed the methods used by fourteen competing teams worldwide, analyzed the results of the competition, and assessed the performance of each team.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320563","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}

引用次数: 0

Data Science Applications and Implications in Legal Studies: A Perspective Through Topic Modelling 数据科学在法律研究中的应用和意义:通过主题建模的视角

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1058

Jinzhe Tan, Huan Wan, Ping Yan, Zheng Hua Zhu

{"title":"Data Science Applications and Implications in Legal Studies: A Perspective Through Topic Modelling","authors":"Jinzhe Tan, Huan Wan, Ping Yan, Zheng Hua Zhu","doi":"10.6339/22-jds1058","DOIUrl":"https://doi.org/10.6339/22-jds1058","url":null,"abstract":"Law and legal studies has been an exciting new field for data science applications whereas the technological advancement also has profound implications for legal practice. For example, the legal industry has accumulated a rich body of high quality texts, images and other digitised formats, which are ready to be further processed and analysed by data scientists. On the other hand, the increasing popularity of data science has been a genuine challenge to legal practitioners, regulators and even general public and has motivated a long-lasting debate in the academia focusing on issues such as privacy protection and algorithmic discrimination. This paper collects 1236 journal articles involving both law and data science from the platform Web of Science to understand the patterns and trends of this interdisciplinary research field in terms of English journal publications. We find a clear trend of increasing publication volume over time and a strong presence of high-impact law and political science journals. We then use the Latent Dirichlet Allocation (LDA) as a topic modelling method to classify the abstracts into four topics based on the coherence measure. The four topics identified confirm that both challenges and opportunities have been investigated in this interdisciplinary field and help offer directions for future research.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320450","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}

引用次数: 0

Creating a Census County Assessment Tool for Visualizing Census Data 创建一个用于可视化普查数据的普查县评估工具

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1082

Izzy Youngs, R. Prevost, Christopher Dick

引用次数: 2

Exploring Rural Shrink Smart Through Guided Discovery Dashboards 通过引导发现仪表板探索农村收缩智能

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1080

Denise Bradford, Susan Vanderplas

引用次数: 2

Improving the Science of Annotation for Natural Language Processing: The Use of the Single-Case Study for Piloting Annotation Projects 改进自然语言处理的标注科学:在试点标注项目中使用单案例研究

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1054

Kylie L. Anglin, Arielle Boguslav, Todd Hall

引用次数: 1

Motion Picture Editing as a Hawkes Process 作为霍克斯过程的电影剪辑

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1055

Nick Redfern

引用次数: 0

Modeling Dynamic Transport Network with Matrix Factor Models: an Application to International Trade Flow 基于矩阵因子模型的动态运输网络建模:在国际贸易流中的应用

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1065

Elynn Y. Chen, Rong Chen

{"title":"Modeling Dynamic Transport Network with Matrix Factor Models: an Application to International Trade Flow","authors":"Elynn Y. Chen, Rong Chen","doi":"10.6339/22-jds1065","DOIUrl":"https://doi.org/10.6339/22-jds1065","url":null,"abstract":"International trade research plays an important role to inform trade policy and shed light on wider economic issues. With recent advances in information technology, economic agencies distribute an enormous amount of internationally comparable trading data, providing a gold mine for empirical analysis of international trade. International trading data can be viewed as a dynamic transport network because it emphasizes the amount of goods moving across network edges. Most literature on dynamic network analysis concentrates on parametric modeling of the connectivity network that focuses on link formation or deformation rather than the transport moving across the network. We take a different non-parametric perspective from the pervasive node-and-edge-level modeling: the dynamic transport network is modeled as a time series of relational matrices; variants of the matrix factor model of Wang et al. (2019) are applied to provide a specific interpretation for the dynamic transport network. Under the model, the observed surface network is assumed to be driven by a latent dynamic transport network with lower dimensions. Our method is able to unveil the latent dynamic structure and achieves the goal of dimension reduction. We applied the proposed method to a dataset of monthly trading volumes among 24 countries (and regions) from 1982 to 2015. Our findings shed light on trading hubs, centrality, trends, and patterns of international trade and show matching change points to trading policies. The dataset also provides a fertile ground for future research on international trade.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320647","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}

引用次数: 1