Data is expanding at an unimaginable rate, and with this development comes the responsibility of the quality of data. Data Quality refers to the relevance of the information present and helps in various operations like decision making and planning in a particular organization. Mostly data quality is measured on an ad-hoc basis, and hence none of the developed concepts provide any practical application. The current empirical study was undertaken to formulate a concrete automated data quality platform to assess the quality of incoming dataset and generate a quality label, score and comprehensive report. We utilize various datasets from healthdata.gov, opendata.nhs and Demographics and Health Surveys (DHS) Program to observe the variations in the quality score and formulate a label using Principal Component Analysis(PCA). The results of the current empirical study revealed a metric that encompasses nine quality ingredients, namely provenance, dataset characteristics, uniformity, metadata coupling, percentage of missing cells and duplicate rows, skewness of data, the ratio of inconsistencies of categorical columns, and correlation between these attributes. The study also provides an illustrative case study and validation of the metric following Mutation Testing approaches. This research study provides an automated platform which takes an incoming dataset and metadata to provide the DQ score, report and label. The results of this study would be useful to data scientists as the value of this quality label would instill confidence before deploying the data for his/her respective practical application.
Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring
Aix-Marseille School of Economics (AMSE)（艾克斯-马赛经济学院（AMSE））；Amundi（法国东方汇理银行）；AXA Investment Managers（安联投资管理公司）；COFACE（法国对外贸易保险公司）；University of Orléans（奥尔良大学）；Institut Universitaire de France (IUF)（法国大学研究院（IUF））；Aix-Marseille University（艾克斯-马赛大学）
The Explabox: Model-Agnostic Machine Learning Transparency & Analysis
Utrecht University（乌得勒支大学）；National Police Lab AI（国家警察人工智能实验室）；Netherlands National Police（荷兰国家警察）；Tilburg University（蒂尔堡大学）；Tilburg Institute for Law, Technology and Society（蒂尔堡法律、技术与社会研究所）
AlloGen: Conformation-Selective Binder Generation with Differential State Scoring
Department of Computer Science and Engineering（计算机科学与工程系）；The Chinese University of Hong Kong（香港中文大学）；Department of Bioengineering（生物工程系）；University of Pennsylvania（宾夕法尼亚大学）；Department of Computer and Information Science（计算机与信息科学系）
